diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 37a45f6e108..1033af0dfa7 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -1,28 +1,20 @@
 <!-- Thank you for contributing to LangChain!
 
-Replace this comment with:
+Replace this entire comment with:
   - Description: a description of the change, 
   - Issue: the issue # it fixes (if applicable),
   - Dependencies: any dependencies required for this change,
   - Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
   - Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
 
-Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
+Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
+
+See contribution guidelines for more information on how to write/run tests, lint, etc: 
+https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
 
 If you're adding a new integration, please include:
   1. a test for the integration, preferably unit tests that do not rely on network access,
-  2. an example notebook showing its use.
+  2. an example notebook showing its use. These live is docs/extras directory.
 
-Maintainer responsibilities:
-  - General / Misc / if you don't know who to tag: @baskaryan
-  - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
-  - Models / Prompts: @hwchase17, @baskaryan
-  - Memory: @hwchase17
-  - Agents / Tools / Toolkits: @hinthornw
-  - Tracing / Callbacks: @agola11
-  - Async: @agola11
-
-If no one reviews your PR within a few days, feel free to @-mention the same people again.
-
-See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
+If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
  -->
diff --git a/.github/actions/poetry_setup/action.yml b/.github/actions/poetry_setup/action.yml
index 79dff7cc276..1a7a992fed1 100644
--- a/.github/actions/poetry_setup/action.yml
+++ b/.github/actions/poetry_setup/action.yml
@@ -62,6 +62,19 @@ runs:
       run: |
         poetry lock --check
 
+    - name: Set proper Poetry.lock file
+      shell: bash
+      env:
+        WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
+      run: |
+        if [ -f "$WORKDIR/poetry.lock" ]; then
+          echo 'Using working directory poetry.lock in cache key'
+          cp "$WORKDIR/poetry.lock" poetry-lock.cache-key
+        else
+          echo 'Using the top-level poetry.lock in cache key'
+          cp poetry.lock poetry-lock.cache-key
+        fi
+
     - uses: actions/cache@v3
       id: cache-poetry
       env:
@@ -71,7 +84,8 @@ runs:
           ~/.cache/pypoetry/virtualenvs
           ~/.cache/pypoetry/cache
           ~/.cache/pypoetry/artifacts
-        key: poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles('poetry.lock') }}
+          ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}/.venv
+        key: poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles('poetry-lock.cache-key') }}
 
     - run: ${{ inputs.install-command }}
       working-directory: ${{ inputs.working-directory }}
diff --git a/.github/workflows/_lint.yml b/.github/workflows/_lint.yml
index 99af18a7ef7..b99cd79eb28 100644
--- a/.github/workflows/_lint.yml
+++ b/.github/workflows/_lint.yml
@@ -13,9 +13,6 @@ env:
 
 jobs:
   build:
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -26,21 +23,47 @@ jobs:
           - "3.11"
     steps:
       - uses: actions/checkout@v3
+      - uses: actions/cache@v3
+        id: cache-pip
+        name: Cache langchain editable pip install - ${{ matrix.python-version }}
+        env:
+          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
+        with:
+          path: |
+            ~/.cache/pip
+          key: pip-editable-langchain-deps-${{ runner.os }}-${{ runner.arch }}-py-${{ matrix.python-version }}
       - name: Install poetry
         run: |
           pipx install poetry==$POETRY_VERSION
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v4
+        env:
+          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
         with:
           python-version: ${{ matrix.python-version }}
           cache: poetry
+          cache-dependency-path: |
+            ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}/**/poetry.lock
       - name: Install dependencies
+        working-directory: ${{ inputs.working-directory }}
         run: |
           poetry install
       - name: Install langchain editable
+        working-directory: ${{ inputs.working-directory }}
         if: ${{ inputs.working-directory != 'langchain' }}
         run: |
           pip install -e ../langchain
+      - name: Get .mypy_cache to speed up mypy
+        uses: actions/cache@v3
+        env:
+          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
+          WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
+        with:
+          path: |
+            ${{ env.WORKDIR }}/.mypy_cache
+          key: mypy-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
+
       - name: Analysing the code with our lint
+        working-directory: ${{ inputs.working-directory }}
         run: |
           make lint
diff --git a/.github/workflows/_test.yml b/.github/workflows/_test.yml
index 116702804e6..2465aa1ace9 100644
--- a/.github/workflows/_test.yml
+++ b/.github/workflows/_test.yml
@@ -10,7 +10,7 @@ on:
       test_type:
         type: string
         description: "Test types to run"
-        default: '["core", "extended"]'
+        default: '["core", "extended", "core-pydantic-2"]'
 
 env:
   POETRY_VERSION: "1.4.2"
@@ -43,19 +43,42 @@ jobs:
               if [ "${{ matrix.test_type }}" == "core" ]; then
                 echo "Running core tests, installing dependencies with poetry..."
                 poetry install
+              elif [ "${{ matrix.test_type }}" == "core-pydantic-2" ]; then
+                echo "Running core-pydantic-v2 tests, installing dependencies with poetry..."
+                poetry install
+                poetry add pydantic@2.1
               else
                 echo "Running extended tests, installing dependencies with poetry..."
                 poetry install -E extended_testing
               fi
-      - name: Install langchain editable
-        if: ${{ inputs.working-directory != 'langchain' }}
+      - name: Verify pydantic version
         run: |
-          pip install -e ../langchain
+          if [ "${{ matrix.test_type }}" == "core-pydantic-2" ]; then
+            EXPECTED_VERSION=2
+          else
+            EXPECTED_VERSION=1
+          fi
+          echo "Checking pydantic version... Expecting ${EXPECTED_VERSION}"
+          
+          # Determine the major part of pydantic version
+          VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1)
+          
+          # Check that the major part of pydantic version is as expected, if not
+          # raise an error
+          if [[ "$VERSION" -ne $EXPECTED_VERSION ]]; then
+            echo "Error: pydantic version must be equal to ${EXPECTED_VERSION}; Found: ${VERSION}"
+            exit 1
+          fi
+          echo "Found pydantic version ${VERSION}, as expected"
+        shell: bash
       - name: Run ${{matrix.test_type}} tests
         run: |
-          if [ "${{ matrix.test_type }}" == "core" ]; then
-            make test
-          else
-            make extended_tests
-          fi
+          case "${{ matrix.test_type }}" in
+              core | core-pydantic-2)
+                  make test
+                  ;;
+              *)
+                  make extended_tests
+                  ;;
+          esac
         shell: bash
diff --git a/.github/workflows/langchain_ci.yml b/.github/workflows/langchain_ci.yml
index a3bf6157573..9dfbd8c3d6e 100644
--- a/.github/workflows/langchain_ci.yml
+++ b/.github/workflows/langchain_ci.yml
@@ -24,4 +24,5 @@ jobs:
       ./.github/workflows/_test.yml
     with:
       working-directory: libs/langchain
+      test_type: '["core", "extended", "core-pydantic-2"]'
     secrets: inherit
\ No newline at end of file
diff --git a/.github/workflows/scheduled_test.yml b/.github/workflows/scheduled_test.yml
index aad5f9e0e94..29ca8c119d5 100644
--- a/.github/workflows/scheduled_test.yml
+++ b/.github/workflows/scheduled_test.yml
@@ -1,6 +1,7 @@
 name: Scheduled tests
 
 on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
   schedule:
     - cron:  '0 13 * * *'
 
@@ -9,6 +10,9 @@ env:
 
 jobs:
   build:
+    defaults:
+      run:
+        working-directory: libs/langchain
     runs-on: ubuntu-latest
     environment: Scheduled testing
     strategy:
@@ -26,13 +30,13 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           poetry-version: "1.4.2"
+          working-directory: libs/langchain
           install-command: |
             echo "Running scheduled tests, installing dependencies with poetry..."
-            poetry install -E scheduled_testing
+            poetry install --with=test_integration
       - name: Run tests
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         run: |
           make scheduled_tests
         shell: bash
-    secrets: inherit
diff --git a/README.md b/README.md
index 1b2e823aa77..201903c6c2d 100644
--- a/README.md
+++ b/README.md
@@ -21,7 +21,7 @@ Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwcha
 **Production Support:** As you move your LangChains into production, we'd love to offer more hands-on support.
 Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to share more about what you're building, and our team will get in touch.
 
-## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28
+## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
 
 In an effort to make `langchain` leaner and safer, we are moving select chains to `langchain_experimental`.
 This migration has already started, but we are remaining backwards compatible until 7/28.
diff --git a/docs/api_reference/create_api_rst.py b/docs/api_reference/create_api_rst.py
index e49b89b6afd..23fa327af60 100644
--- a/docs/api_reference/create_api_rst.py
+++ b/docs/api_reference/create_api_rst.py
@@ -145,30 +145,37 @@ def _load_package_modules(
     package_name = package_path.name
 
     for file_path in package_path.rglob("*.py"):
-        if not file_path.name.startswith("__"):
-            relative_module_name = file_path.relative_to(package_path)
-            # Get the full namespace of the module
-            namespace = str(relative_module_name).replace(".py", "").replace("/", ".")
-            # Keep only the top level namespace
-            top_namespace = namespace.split(".")[0]
+        if file_path.name.startswith("_"):
+            continue
 
-            try:
-                module_members = _load_module_members(
-                    f"{package_name}.{namespace}", namespace
+        relative_module_name = file_path.relative_to(package_path)
+
+        # Skip if any module part starts with an underscore
+        if any(part.startswith("_") for part in relative_module_name.parts):
+            continue
+
+        # Get the full namespace of the module
+        namespace = str(relative_module_name).replace(".py", "").replace("/", ".")
+        # Keep only the top level namespace
+        top_namespace = namespace.split(".")[0]
+
+        try:
+            module_members = _load_module_members(
+                f"{package_name}.{namespace}", namespace
+            )
+            # Merge module members if the namespace already exists
+            if top_namespace in modules_by_namespace:
+                existing_module_members = modules_by_namespace[top_namespace]
+                _module_members = _merge_module_members(
+                    [existing_module_members, module_members]
                 )
-                # Merge module members if the namespace already exists
-                if top_namespace in modules_by_namespace:
-                    existing_module_members = modules_by_namespace[top_namespace]
-                    _module_members = _merge_module_members(
-                        [existing_module_members, module_members]
-                    )
-                else:
-                    _module_members = module_members
+            else:
+                _module_members = module_members
 
-                modules_by_namespace[top_namespace] = _module_members
+            modules_by_namespace[top_namespace] = _module_members
 
-            except ImportError as e:
-                print(f"Error: Unable to import module '{namespace}' with error: {e}")
+        except ImportError as e:
+            print(f"Error: Unable to import module '{namespace}' with error: {e}")
 
     return modules_by_namespace
 
@@ -222,9 +229,9 @@ Classes
 """
 
             for class_ in classes:
-                if not class_['is_public']:
+                if not class_["is_public"]:
                     continue
-                    
+
                 if class_["kind"] == "TypedDict":
                     template = "typeddict.rst"
                 elif class_["kind"] == "enum":
diff --git a/docs/api_reference/guide_imports.json b/docs/api_reference/guide_imports.json
index 9efc571c7e9..9c608a7328f 100644
--- a/docs/api_reference/guide_imports.json
+++ b/docs/api_reference/guide_imports.json
@@ -1 +1,3413 @@
-{"XinferenceEmbeddings": {"Xorbits inference (Xinference)": "https://python.langchain.com/docs/integrations/text_embedding/xinference"}, "DeepInfraEmbeddings": {"DeepInfra": "https://python.langchain.com/docs/integrations/text_embedding/deepinfra"}, "HuggingFaceEmbeddings": {"Hugging Face Hub": "https://python.langchain.com/docs/integrations/text_embedding/huggingfacehub", "Sentence Transformers Embeddings": "https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers", "LOTR (Merger Retriever)": "https://python.langchain.com/docs/integrations/retrievers/merger_retriever", "Hugging Face": "https://python.langchain.com/docs/integrations/providers/huggingface", "Annoy": "https://python.langchain.com/docs/integrations/vectorstores/annoy", "Pairwise Embedding Distance ": "https://python.langchain.com/docs/guides/evaluation/comparison/pairwise_embedding_distance", "Embedding Distance": "https://python.langchain.com/docs/guides/evaluation/string/embedding_distance", "Lost in the middle: The problem with long contexts": "https://python.langchain.com/docs/modules/data_connection/document_transformers/post_retrieval/long_context_reorder"}, "GPT4AllEmbeddings": {"GPT4All": "https://python.langchain.com/docs/integrations/text_embedding/gpt4all", "Running LLMs locally": "https://python.langchain.com/docs/use_cases/question_answering/local_retrieval_qa", "Use local LLMs": "https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa", "WebResearchRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/web_research"}, "MosaicMLInstructorEmbeddings": {"MosaicML embeddings": "https://python.langchain.com/docs/integrations/text_embedding/mosaicml"}, "OpenAIEmbeddings": {"OpenAI": "https://python.langchain.com/docs/integrations/providers/openai", "AzureOpenAI": "https://python.langchain.com/docs/integrations/text_embedding/azureopenai", "Cohere Reranker": "https://python.langchain.com/docs/integrations/retrievers/cohere-reranker", "kNN": "https://python.langchain.com/docs/integrations/retrievers/knn", "DocArray Retriever": "https://python.langchain.com/docs/integrations/retrievers/docarray_retriever", "SVM": "https://python.langchain.com/docs/integrations/retrievers/svm", "Pinecone Hybrid Search": "https://python.langchain.com/docs/integrations/retrievers/pinecone_hybrid_search", "LOTR (Merger Retriever)": "https://python.langchain.com/docs/integrations/retrievers/merger_retriever", "Azure OpenAI": "https://python.langchain.com/docs/integrations/providers/azure_openai", "Document Comparison": "https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit", "Vectorstore Agent": "https://python.langchain.com/docs/integrations/toolkits/vectorstore", "LanceDB": "https://python.langchain.com/docs/integrations/vectorstores/lancedb", "Weaviate": "https://python.langchain.com/docs/integrations/vectorstores/weaviate", "Activeloop's Deep Lake": "https://python.langchain.com/docs/integrations/vectorstores/deeplake", "Redis": "https://python.langchain.com/docs/integrations/vectorstores/redis", "PGVector": "https://python.langchain.com/docs/integrations/vectorstores/pgvector", "Rockset": "https://python.langchain.com/docs/integrations/vectorstores/rockset", "Zilliz": "https://python.langchain.com/docs/integrations/vectorstores/zilliz", "SingleStoreDB": "https://python.langchain.com/docs/integrations/vectorstores/singlestoredb", "Typesense": "https://python.langchain.com/docs/integrations/vectorstores/typesense", "Atlas": "https://python.langchain.com/docs/integrations/vectorstores/atlas", "Chroma": "https://python.langchain.com/docs/integrations/vectorstores/chroma", "Alibaba Cloud OpenSearch": "https://python.langchain.com/docs/integrations/vectorstores/alibabacloud_opensearch", "StarRocks": "https://python.langchain.com/docs/integrations/vectorstores/starrocks", "scikit-learn": "https://python.langchain.com/docs/integrations/vectorstores/sklearn", "DocArrayHnswSearch": "https://python.langchain.com/docs/integrations/vectorstores/docarray_hnsw", "MyScale": "https://python.langchain.com/docs/integrations/vectorstores/myscale", "ClickHouse Vector Search": "https://python.langchain.com/docs/integrations/vectorstores/clickhouse", "Qdrant": "https://python.langchain.com/docs/integrations/vectorstores/qdrant", "Tigris": "https://python.langchain.com/docs/integrations/vectorstores/tigris", "Supabase (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/supabase", "OpenSearch": "https://python.langchain.com/docs/integrations/vectorstores/opensearch", "Pinecone": "https://python.langchain.com/docs/integrations/vectorstores/pinecone", "Azure Cognitive Search": "https://python.langchain.com/docs/integrations/vectorstores/azuresearch", "Cassandra": "https://python.langchain.com/docs/integrations/vectorstores/cassandra", "Milvus": "https://python.langchain.com/docs/integrations/vectorstores/milvus", "ElasticSearch": "https://python.langchain.com/docs/integrations/vectorstores/elasticsearch", "DocArrayInMemorySearch": "https://python.langchain.com/docs/integrations/vectorstores/docarray_in_memory", "pg_embedding": "https://python.langchain.com/docs/integrations/vectorstores/pgembedding", "FAISS": "https://python.langchain.com/docs/integrations/vectorstores/faiss", "AnalyticDB": "https://python.langchain.com/docs/integrations/vectorstores/analyticdb", "Hologres": "https://python.langchain.com/docs/integrations/vectorstores/hologres", "MongoDB Atlas": "https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas", "Meilisearch": "https://python.langchain.com/docs/integrations/vectorstores/meilisearch", "Loading documents from a YouTube url": "https://python.langchain.com/docs/integrations/document_loaders/youtube_audio", "Psychic": "https://python.langchain.com/docs/integrations/document_loaders/psychic", "Docugami": "https://python.langchain.com/docs/integrations/document_loaders/docugami", "Caching integrations": "https://python.langchain.com/docs/integrations/llms/llm_caching", "Data Augmented Question Answering": "https://python.langchain.com/docs/guides/evaluation/examples/data_augmented_question_answering", "AutoGPT": "https://python.langchain.com/docs/use_cases/autonomous_agents/autogpt", "BabyAGI User Guide": "https://python.langchain.com/docs/use_cases/agents/baby_agi", "BabyAGI with Tools": "https://python.langchain.com/docs/use_cases/agents/baby_agi_with_agent", "!pip install bs4": "https://python.langchain.com/docs/use_cases/autonomous_agents/marathon_times", "Context aware text splitting and QA / Chat": "https://python.langchain.com/docs/use_cases/question_answering/document-context-aware-QA", "QA over Documents": "https://python.langchain.com/docs/use_cases/question_answering/index", "Question answering over a group chat messages using Activeloop's DeepLake": "https://python.langchain.com/docs/use_cases/question_answering/semantic-search-over-chat", "Perform context-aware text splitting": "https://python.langchain.com/docs/use_cases/question_answering/how_to/document-context-aware-QA", "Retrieve from vector stores directly": "https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_text_generation", "Retrieve as you generate with FLARE": "https://python.langchain.com/docs/use_cases/question_answering/how_to/flare", "Improve document indexing with HyDE": "https://python.langchain.com/docs/use_cases/question_answering/how_to/hyde", "Structure answers with OpenAI functions": "https://python.langchain.com/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa", "QA using Activeloop's DeepLake": "https://python.langchain.com/docs/use_cases/question_answering/integrations/semantic-search-over-chat", "Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep Lake": "https://python.langchain.com/docs/use_cases/code/twitter-the-algorithm-analysis-deeplake", "Use LangChain, GPT and Activeloop's Deep Lake to work with code base": "https://python.langchain.com/docs/use_cases/code/code-analysis-deeplake", "Plug-and-Plai": "https://python.langchain.com/docs/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai", "SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base": "https://python.langchain.com/docs/use_cases/agents/sales_agent_with_context", "Custom Agent with PlugIn Retrieval": "https://python.langchain.com/docs/use_cases/agents/custom_agent_with_plugin_retrieval", "Generative Agents in LangChain": "https://python.langchain.com/docs/use_cases/agent_simulations/characters", "MultiQueryRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever", "WebResearchRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/web_research", "Weaviate self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/weaviate_self_query", "Chroma self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query", "DeepLake self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/deeplake_self_query", "Self-querying with Pinecone": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone", "Self-querying with MyScale": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/myscale_self_query", "Qdrant self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query", "How to add memory to a Multi-Input Chain": "https://python.langchain.com/docs/modules/memory/adding_memory_chain_multiple_inputs", "Combine agents and vector stores": "https://python.langchain.com/docs/modules/agents/how_to/agent_vectorstore", "Custom agent with tool retrieval": "https://python.langchain.com/docs/modules/agents/how_to/custom_agent_with_tool_retrieval", "Select by maximal marginal relevance (MMR)": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr", "Few shot examples for chat models": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat", "Loading from LangChainHub": "https://python.langchain.com/docs/modules/chains/how_to/from_hub", "Retrieval QA using OpenAI functions": "https://python.langchain.com/docs/modules/chains/additional/openai_functions_retrieval_qa", "Vector store-augmented text generation": "https://python.langchain.com/docs/modules/chains/additional/vector_db_text_generation", "FLARE": "https://python.langchain.com/docs/modules/chains/additional/flare", "Hypothetical Document Embeddings": 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"https://python.langchain.com/docs/modules/model_io/output_parsers/retry"}, "RetryOutputParser": {"Retry parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry"}, "RetryWithErrorOutputParser": {"Retry parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry"}, "EnumOutputParser": {"Enum parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/enum"}, "DatetimeOutputParser": {"Datetime parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/datetime"}, "MaxMarginalRelevanceExampleSelector": {"Select by maximal marginal relevance (MMR)": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr"}, "SemanticSimilarityExampleSelector": {"Select by maximal marginal relevance (MMR)": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr", "Few shot examples for chat models": 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"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization"}, "ChatMessagePromptTemplate": {"Types of `MessagePromptTemplate`": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates"}, "MultiPromptChain": {"Router": "https://python.langchain.com/docs/modules/chains/foundational/router"}, "LLMRouterChain": {"Router": "https://python.langchain.com/docs/modules/chains/foundational/router"}, "RouterOutputParser": {"Router": "https://python.langchain.com/docs/modules/chains/foundational/router"}, "EmbeddingRouterChain": {"Router": "https://python.langchain.com/docs/modules/chains/foundational/router"}, "BaseLanguageModel": {"Custom chain": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain"}, "AsyncCallbackManagerForChainRun": {"Custom chain": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain"}, "CallbackManagerForChainRun": {"Custom chain": 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\ No newline at end of file
+{
+    "HuggingFaceBgeEmbeddings": {
+        "BGE Hugging Face Embeddings": "https://python.langchain.com/docs/integrations/text_embedding/bge_huggingface"
+    },
+    "XinferenceEmbeddings": {
+        "Xorbits inference (Xinference)": "https://python.langchain.com/docs/integrations/text_embedding/xinference"
+    },
+    "DeepInfraEmbeddings": {
+        "DeepInfra": "https://python.langchain.com/docs/integrations/text_embedding/deepinfra"
+    },
+    "HuggingFaceEmbeddings": {
+        "Hugging Face Hub": "https://python.langchain.com/docs/integrations/text_embedding/huggingfacehub",
+        "Sentence Transformers Embeddings": "https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers",
+        "LOTR (Merger Retriever)": "https://python.langchain.com/docs/integrations/retrievers/merger_retriever",
+        "Hugging Face": "https://python.langchain.com/docs/integrations/providers/huggingface",
+        "ScaNN": "https://python.langchain.com/docs/integrations/vectorstores/scann",
+        "Annoy": "https://python.langchain.com/docs/integrations/vectorstores/annoy",
+        "Pairwise Embedding Distance ": "https://python.langchain.com/docs/guides/evaluation/comparison/pairwise_embedding_distance",
+        "Embedding Distance": "https://python.langchain.com/docs/guides/evaluation/string/embedding_distance",
+        "Lost in the middle: The problem with long contexts": "https://python.langchain.com/docs/modules/data_connection/document_transformers/post_retrieval/long_context_reorder"
+    },
+    "GPT4AllEmbeddings": {
+        "GPT4All": "https://python.langchain.com/docs/integrations/text_embedding/gpt4all",
+        "Ollama": "https://python.langchain.com/docs/integrations/llms/ollama",
+        "Use local LLMs": "https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa",
+        "WebResearchRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/web_research"
+    },
+    "MosaicMLInstructorEmbeddings": {
+        "MosaicML embeddings": "https://python.langchain.com/docs/integrations/text_embedding/mosaicml"
+    },
+    "OpenAIEmbeddings": {
+        "OpenAI": "https://python.langchain.com/docs/integrations/providers/openai",
+        "AzureOpenAI": "https://python.langchain.com/docs/integrations/text_embedding/azureopenai",
+        "RePhraseQueryRetriever": "https://python.langchain.com/docs/integrations/retrievers/re_phrase",
+        "Cohere Reranker": "https://python.langchain.com/docs/integrations/retrievers/cohere-reranker",
+        "kNN": "https://python.langchain.com/docs/integrations/retrievers/knn",
+        "DocArray Retriever": "https://python.langchain.com/docs/integrations/retrievers/docarray_retriever",
+        "SVM": "https://python.langchain.com/docs/integrations/retrievers/svm",
+        "Pinecone Hybrid Search": "https://python.langchain.com/docs/integrations/retrievers/pinecone_hybrid_search",
+        "LOTR (Merger Retriever)": "https://python.langchain.com/docs/integrations/retrievers/merger_retriever",
+        "Azure OpenAI": "https://python.langchain.com/docs/integrations/providers/azure_openai",
+        "Document Comparison": "https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit",
+        "Vectorstore Agent": "https://python.langchain.com/docs/integrations/toolkits/vectorstore",
+        "LanceDB": "https://python.langchain.com/docs/integrations/vectorstores/lancedb",
+        "Weaviate": "https://python.langchain.com/docs/integrations/vectorstores/weaviate",
+        "Xata": "https://python.langchain.com/docs/integrations/vectorstores/xata",
+        "Redis": "https://python.langchain.com/docs/integrations/vectorstores/redis",
+        "PGVector": "https://python.langchain.com/docs/integrations/vectorstores/pgvector",
+        "Rockset": "https://python.langchain.com/docs/integrations/vectorstores/rockset",
+        "Dingo": "https://python.langchain.com/docs/integrations/vectorstores/dingo",
+        "Zilliz": "https://python.langchain.com/docs/integrations/vectorstores/zilliz",
+        "SingleStoreDB": "https://python.langchain.com/docs/integrations/vectorstores/singlestoredb",
+        "Typesense": "https://python.langchain.com/docs/integrations/vectorstores/typesense",
+        "Atlas": "https://python.langchain.com/docs/integrations/vectorstores/atlas",
+        "Activeloop's Deep Lake": "https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake",
+        "Chroma": "https://python.langchain.com/docs/integrations/vectorstores/chroma",
+        "Alibaba Cloud OpenSearch": "https://python.langchain.com/docs/integrations/vectorstores/alibabacloud_opensearch",
+        "StarRocks": "https://python.langchain.com/docs/integrations/vectorstores/starrocks",
+        "scikit-learn": "https://python.langchain.com/docs/integrations/vectorstores/sklearn",
+        "DocArrayHnswSearch": "https://python.langchain.com/docs/integrations/vectorstores/docarray_hnsw",
+        "MyScale": "https://python.langchain.com/docs/integrations/vectorstores/myscale",
+        "ClickHouse Vector Search": "https://python.langchain.com/docs/integrations/vectorstores/clickhouse",
+        "Qdrant": "https://python.langchain.com/docs/integrations/vectorstores/qdrant",
+        "Tigris": "https://python.langchain.com/docs/integrations/vectorstores/tigris",
+        "Supabase (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/supabase",
+        "OpenSearch": "https://python.langchain.com/docs/integrations/vectorstores/opensearch",
+        "Pinecone": "https://python.langchain.com/docs/integrations/vectorstores/pinecone",
+        "Azure Cognitive Search": "https://python.langchain.com/docs/integrations/vectorstores/azuresearch",
+        "Cassandra": "https://python.langchain.com/docs/integrations/vectorstores/cassandra",
+        "USearch": "https://python.langchain.com/docs/integrations/vectorstores/usearch",
+        "Milvus": "https://python.langchain.com/docs/integrations/vectorstores/milvus",
+        "Elasticsearch": "https://python.langchain.com/docs/integrations/vectorstores/elasticsearch",
+        "DocArrayInMemorySearch": "https://python.langchain.com/docs/integrations/vectorstores/docarray_in_memory",
+        "pg_embedding": "https://python.langchain.com/docs/integrations/vectorstores/pgembedding",
+        "FAISS": "https://python.langchain.com/docs/integrations/vectorstores/faiss",
+        "AnalyticDB": "https://python.langchain.com/docs/integrations/vectorstores/analyticdb",
+        "Hologres": "https://python.langchain.com/docs/integrations/vectorstores/hologres",
+        "MongoDB Atlas": "https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas",
+        "Meilisearch": "https://python.langchain.com/docs/integrations/vectorstores/meilisearch",
+        "Loading documents from a YouTube url": "https://python.langchain.com/docs/integrations/document_loaders/youtube_audio",
+        "Psychic": "https://python.langchain.com/docs/integrations/document_loaders/psychic",
+        "Docugami": "https://python.langchain.com/docs/integrations/document_loaders/docugami",
+        "Caching integrations": "https://python.langchain.com/docs/integrations/llms/llm_caching",
+        "Cookbook": "https://python.langchain.com/docs/guides/expression_language/cookbook",
+        "Web Scraping": "https://python.langchain.com/docs/use_cases/web_scraping",
+        "Chatbots": "https://python.langchain.com/docs/use_cases/chatbots",
+        "Code Understanding": "https://python.langchain.com/docs/use_cases/code_understanding",
+        "AutoGPT": "https://python.langchain.com/docs/use_cases/autonomous_agents/autogpt",
+        "BabyAGI User Guide": "https://python.langchain.com/docs/use_cases/agents/baby_agi",
+        "BabyAGI with Tools": "https://python.langchain.com/docs/use_cases/agents/baby_agi_with_agent",
+        "!pip install bs4": "https://python.langchain.com/docs/use_cases/autonomous_agents/marathon_times",
+        "QA over Documents": "https://python.langchain.com/docs/use_cases/question_answering/index",
+        "Perform context-aware text splitting": "https://python.langchain.com/docs/use_cases/question_answering/how_to/document-context-aware-QA",
+        "Conversational Retrieval Agent": "https://python.langchain.com/docs/use_cases/question_answering/how_to/conversational_retrieval_agents",
+        "Retrieve from vector stores directly": "https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_text_generation",
+        "Retrieve as you generate with FLARE": "https://python.langchain.com/docs/use_cases/question_answering/how_to/flare",
+        "Improve document indexing with HyDE": "https://python.langchain.com/docs/use_cases/question_answering/how_to/hyde",
+        "Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep Lake": "https://python.langchain.com/docs/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake",
+        "Use LangChain, GPT and Activeloop's Deep Lake to work with code base": "https://python.langchain.com/docs/use_cases/question_answering/how_to/code/code-analysis-deeplake",
+        "Structure answers with OpenAI functions": "https://python.langchain.com/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa",
+        "QA using Activeloop's DeepLake": "https://python.langchain.com/docs/use_cases/question_answering/integrations/semantic-search-over-chat",
+        "Plug-and-Plai": "https://python.langchain.com/docs/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai",
+        "SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base": "https://python.langchain.com/docs/use_cases/agents/sales_agent_with_context",
+        "Custom Agent with PlugIn Retrieval": "https://python.langchain.com/docs/use_cases/agents/custom_agent_with_plugin_retrieval",
+        "Generative Agents in LangChain": "https://python.langchain.com/docs/use_cases/agent_simulations/characters",
+        "Caching Embeddings": "https://python.langchain.com/docs/modules/data_connection/caching_embeddings",
+        "MultiQueryRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever",
+        "Parent Document Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/parent_document_retriever",
+        "WebResearchRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/web_research",
+        "Weaviate self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/weaviate_self_query",
+        "Elasticsearch self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/elasticsearch_self_query",
+        "Chroma self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query",
+        "Self-querying with Pinecone": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone",
+        "Self-querying with MyScale": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/myscale_self_query",
+        "Deep Lake self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query",
+        "Qdrant self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query",
+        "How to add memory to a Multi-Input Chain": "https://python.langchain.com/docs/modules/memory/adding_memory_chain_multiple_inputs",
+        "Combine agents and vector stores": "https://python.langchain.com/docs/modules/agents/how_to/agent_vectorstore",
+        "Custom agent with tool retrieval": "https://python.langchain.com/docs/modules/agents/how_to/custom_agent_with_tool_retrieval",
+        "Select by maximal marginal relevance (MMR)": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr",
+        "Few shot examples for chat models": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat",
+        "Loading from LangChainHub": "https://python.langchain.com/docs/modules/chains/how_to/from_hub"
+    },
+    "VertexAIEmbeddings": {
+        "Google Cloud Platform Vertex AI PaLM ": "https://python.langchain.com/docs/integrations/text_embedding/google_vertex_ai_palm"
+    },
+    "BedrockEmbeddings": {
+        "Bedrock Embeddings": "https://python.langchain.com/docs/integrations/text_embedding/bedrock",
+        "Bedrock": "https://python.langchain.com/docs/integrations/providers/bedrock"
+    },
+    "LlamaCppEmbeddings": {
+        "Llama-cpp": "https://python.langchain.com/docs/integrations/text_embedding/llamacpp",
+        "Llama.cpp": "https://python.langchain.com/docs/integrations/providers/llamacpp"
+    },
+    "NLPCloudEmbeddings": {
+        "NLP Cloud": "https://python.langchain.com/docs/integrations/text_embedding/nlp_cloud"
+    },
+    "SpacyEmbeddings": {
+        "Spacy Embedding": "https://python.langchain.com/docs/integrations/text_embedding/spacy_embedding"
+    },
+    "HuggingFaceInstructEmbeddings": {
+        "InstructEmbeddings": "https://python.langchain.com/docs/integrations/text_embedding/instruct_embeddings"
+    },
+    "CohereEmbeddings": {
+        "Cohere": "https://python.langchain.com/docs/integrations/providers/cohere",
+        "How to add memory to a Multi-Input Chain": "https://python.langchain.com/docs/modules/memory/adding_memory_chain_multiple_inputs",
+        "Router": "https://python.langchain.com/docs/modules/chains/foundational/router"
+    },
+    "EdenAiEmbeddings": {
+        "EDEN AI": "https://python.langchain.com/docs/integrations/text_embedding/edenai"
+    },
+    "SentenceTransformerEmbeddings": {
+        "Sentence Transformers Embeddings": "https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers",
+        "Chroma": "https://python.langchain.com/docs/integrations/vectorstores/chroma"
+    },
+    "ClarifaiEmbeddings": {
+        "Clarifai": "https://python.langchain.com/docs/integrations/providers/clarifai"
+    },
+    "AwaEmbeddings": {
+        "AwaEmbedding": "https://python.langchain.com/docs/integrations/text_embedding/Awa"
+    },
+    "MiniMaxEmbeddings": {
+        "MiniMax": "https://python.langchain.com/docs/integrations/text_embedding/minimax",
+        "Minimax": "https://python.langchain.com/docs/integrations/providers/minimax"
+    },
+    "FakeEmbeddings": {
+        "Fake Embeddings": "https://python.langchain.com/docs/integrations/text_embedding/fake",
+        "DocArray Retriever": "https://python.langchain.com/docs/integrations/retrievers/docarray_retriever",
+        "Vectara": "https://python.langchain.com/docs/integrations/vectorstores/vectara",
+        "Tair": "https://python.langchain.com/docs/integrations/vectorstores/tair"
+    },
+    "ElasticsearchEmbeddings": {
+        "Elasticsearch": "https://python.langchain.com/docs/integrations/text_embedding/elasticsearch"
+    },
+    "SelfHostedEmbeddings": {
+        "Self Hosted Embeddings": "https://python.langchain.com/docs/integrations/text_embedding/self-hosted"
+    },
+    "SelfHostedHuggingFaceEmbeddings": {
+        "Self Hosted Embeddings": "https://python.langchain.com/docs/integrations/text_embedding/self-hosted"
+    },
+    "SelfHostedHuggingFaceInstructEmbeddings": {
+        "Self Hosted Embeddings": "https://python.langchain.com/docs/integrations/text_embedding/self-hosted"
+    },
+    "EmbaasEmbeddings": {
+        "Embaas": "https://python.langchain.com/docs/integrations/text_embedding/embaas"
+    },
+    "JinaEmbeddings": {
+        "Jina": "https://python.langchain.com/docs/integrations/providers/jina"
+    },
+    "AlephAlphaAsymmetricSemanticEmbedding": {
+        "Aleph Alpha": "https://python.langchain.com/docs/integrations/providers/aleph_alpha"
+    },
+    "AlephAlphaSymmetricSemanticEmbedding": {
+        "Aleph Alpha": "https://python.langchain.com/docs/integrations/providers/aleph_alpha"
+    },
+    "DashScopeEmbeddings": {
+        "DashScope": "https://python.langchain.com/docs/integrations/text_embedding/dashscope",
+        "DashVector": "https://python.langchain.com/docs/integrations/vectorstores/dashvector"
+    },
+    "TensorflowHubEmbeddings": {
+        "TensorflowHub": "https://python.langchain.com/docs/integrations/text_embedding/tensorflowhub",
+        "ScaNN": "https://python.langchain.com/docs/integrations/vectorstores/scann"
+    },
+    "ModelScopeEmbeddings": {
+        "ModelScope": "https://python.langchain.com/docs/integrations/providers/modelscope"
+    },
+    "SagemakerEndpointEmbeddings": {
+        "SageMaker Endpoint Embeddings": "https://python.langchain.com/docs/integrations/text_embedding/sagemaker-endpoint",
+        "SageMaker Endpoint": "https://python.langchain.com/docs/integrations/providers/sagemaker_endpoint"
+    },
+    "EmbeddingsContentHandler": {
+        "SageMaker Endpoint Embeddings": "https://python.langchain.com/docs/integrations/text_embedding/sagemaker-endpoint"
+    },
+    "LocalAIEmbeddings": {
+        "LocalAI": "https://python.langchain.com/docs/integrations/text_embedding/localai"
+    },
+    "WebBaseLoader": {
+        "RePhraseQueryRetriever": "https://python.langchain.com/docs/integrations/retrievers/re_phrase",
+        "Vectorstore Agent": "https://python.langchain.com/docs/integrations/toolkits/vectorstore",
+        "WebBaseLoader": "https://python.langchain.com/docs/integrations/document_loaders/web_base",
+        "MergeDocLoader": "https://python.langchain.com/docs/integrations/document_loaders/merge_doc_loader",
+        "Ollama": "https://python.langchain.com/docs/integrations/llms/ollama",
+        "Chatbots": "https://python.langchain.com/docs/use_cases/chatbots",
+        "Summarization": "https://python.langchain.com/docs/use_cases/summarization",
+        "QA over Documents": "https://python.langchain.com/docs/use_cases/question_answering/index",
+        "Use local LLMs": "https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa",
+        "MultiQueryRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever",
+        "Combine agents and vector stores": "https://python.langchain.com/docs/modules/agents/how_to/agent_vectorstore"
+    },
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+        "Loading documents from a YouTube url": "https://python.langchain.com/docs/integrations/document_loaders/youtube_audio",
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+        "Loading from LangChainHub": "https://python.langchain.com/docs/modules/chains/how_to/from_hub"
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+        "How to add Memory to an LLMChain": "https://python.langchain.com/docs/modules/memory/adding_memory",
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+        "Zep Memory": "https://python.langchain.com/docs/integrations/memory/zep_memory"
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+    "HumanMessage": {
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+        "Anthropic": "https://python.langchain.com/docs/integrations/chat/anthropic",
+        "\ud83d\ude85 LiteLLM": "https://python.langchain.com/docs/integrations/chat/litellm",
+        "OpenAI": "https://python.langchain.com/docs/integrations/chat/openai",
+        "Google Cloud Platform Vertex AI PaLM ": "https://python.langchain.com/docs/integrations/chat/google_vertex_ai_palm",
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+        "Multi-Agent Simulated Environment: Petting Zoo": "https://python.langchain.com/docs/use_cases/agent_simulations/petting_zoo",
+        "Multi-agent decentralized speaker selection": "https://python.langchain.com/docs/use_cases/agent_simulations/multiagent_bidding",
+        "Multi-agent authoritarian speaker selection": "https://python.langchain.com/docs/use_cases/agent_simulations/multiagent_authoritarian",
+        "Two-Player Dungeons & Dragons": "https://python.langchain.com/docs/use_cases/agent_simulations/two_player_dnd",
+        "Multi-Player Dungeons & Dragons": "https://python.langchain.com/docs/use_cases/agent_simulations/multi_player_dnd",
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+        "Agent Debates with Tools": "https://python.langchain.com/docs/use_cases/agent_simulations/two_agent_debate_tools",
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+        "Tools as OpenAI Functions": "https://python.langchain.com/docs/modules/agents/tools/tools_as_openai_functions",
+        "Prompt Pipelining": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining",
+        "Using OpenAI functions": "https://python.langchain.com/docs/modules/chains/how_to/openai_functions"
+    },
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+        "Zep": "https://python.langchain.com/docs/integrations/retrievers/zep_memorystore",
+        "Zep Memory": "https://python.langchain.com/docs/integrations/memory/zep_memory",
+        "Anthropic": "https://python.langchain.com/docs/integrations/chat/anthropic",
+        "\ud83d\ude85 LiteLLM": "https://python.langchain.com/docs/integrations/chat/litellm",
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+        "JinaChat": "https://python.langchain.com/docs/integrations/chat/jinachat",
+        "ERNIE-Bot Chat": "https://python.langchain.com/docs/integrations/chat/ernie",
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+        "Multi-agent decentralized speaker selection": "https://python.langchain.com/docs/use_cases/agent_simulations/multiagent_bidding",
+        "Multi-agent authoritarian speaker selection": "https://python.langchain.com/docs/use_cases/agent_simulations/multiagent_authoritarian",
+        "Multi-Player Dungeons & Dragons": "https://python.langchain.com/docs/use_cases/agent_simulations/multi_player_dnd",
+        "Simulated Environment: Gymnasium": "https://python.langchain.com/docs/use_cases/agent_simulations/gymnasium",
+        "Agent Debates with Tools": "https://python.langchain.com/docs/use_cases/agent_simulations/two_agent_debate_tools",
+        "Prompt Pipelining": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining"
+    },
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+        "Zep": "https://python.langchain.com/docs/integrations/providers/zep",
+        "Zep Memory": "https://python.langchain.com/docs/integrations/memory/zep_memory"
+    },
+    "VespaRetriever": {
+        "Vespa": "https://python.langchain.com/docs/integrations/providers/vespa"
+    },
+    "AmazonKendraRetriever": {
+        "Amazon Kendra": "https://python.langchain.com/docs/integrations/retrievers/amazon_kendra_retriever"
+    },
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+        "Cohere Reranker": "https://python.langchain.com/docs/integrations/retrievers/cohere-reranker",
+        "Elasticsearch": "https://python.langchain.com/docs/integrations/vectorstores/elasticsearch",
+        "Chat Over Documents with Vectara": "https://python.langchain.com/docs/integrations/providers/vectara/vectara_chat",
+        "Vectorstore Agent": "https://python.langchain.com/docs/integrations/toolkits/vectorstore",
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+        "Weaviate": "https://python.langchain.com/docs/integrations/vectorstores/weaviate",
+        "DashVector": "https://python.langchain.com/docs/integrations/vectorstores/dashvector",
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+        "Vectara": "https://python.langchain.com/docs/integrations/vectorstores/vectara",
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+        "PGVector": "https://python.langchain.com/docs/integrations/vectorstores/pgvector",
+        "Rockset": "https://python.langchain.com/docs/integrations/vectorstores/rockset",
+        "Dingo": "https://python.langchain.com/docs/integrations/vectorstores/dingo",
+        "Zilliz": "https://python.langchain.com/docs/integrations/vectorstores/zilliz",
+        "SingleStoreDB": "https://python.langchain.com/docs/integrations/vectorstores/singlestoredb",
+        "Annoy": "https://python.langchain.com/docs/integrations/vectorstores/annoy",
+        "Typesense": "https://python.langchain.com/docs/integrations/vectorstores/typesense",
+        "Atlas": "https://python.langchain.com/docs/integrations/vectorstores/atlas",
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+        "Tair": "https://python.langchain.com/docs/integrations/vectorstores/tair",
+        "Chroma": "https://python.langchain.com/docs/integrations/vectorstores/chroma",
+        "Alibaba Cloud OpenSearch": "https://python.langchain.com/docs/integrations/vectorstores/alibabacloud_opensearch",
+        "StarRocks": "https://python.langchain.com/docs/integrations/vectorstores/starrocks",
+        "Clarifai": "https://python.langchain.com/docs/integrations/vectorstores/clarifai",
+        "scikit-learn": "https://python.langchain.com/docs/integrations/vectorstores/sklearn",
+        "DocArrayHnswSearch": "https://python.langchain.com/docs/integrations/vectorstores/docarray_hnsw",
+        "MyScale": "https://python.langchain.com/docs/integrations/vectorstores/myscale",
+        "ClickHouse Vector Search": "https://python.langchain.com/docs/integrations/vectorstores/clickhouse",
+        "Qdrant": "https://python.langchain.com/docs/integrations/vectorstores/qdrant",
+        "Tigris": "https://python.langchain.com/docs/integrations/vectorstores/tigris",
+        "AwaDB": "https://python.langchain.com/docs/integrations/vectorstores/awadb",
+        "Supabase (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/supabase",
+        "OpenSearch": "https://python.langchain.com/docs/integrations/vectorstores/opensearch",
+        "Pinecone": "https://python.langchain.com/docs/integrations/vectorstores/pinecone",
+        "BagelDB": "https://python.langchain.com/docs/integrations/vectorstores/bageldb",
+        "Azure Cognitive Search": "https://python.langchain.com/docs/integrations/vectorstores/azuresearch",
+        "Cassandra": "https://python.langchain.com/docs/integrations/vectorstores/cassandra",
+        "USearch": "https://python.langchain.com/docs/integrations/vectorstores/usearch",
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+        "Marqo": "https://python.langchain.com/docs/integrations/vectorstores/marqo",
+        "DocArrayInMemorySearch": "https://python.langchain.com/docs/integrations/vectorstores/docarray_in_memory",
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+        "AnalyticDB": "https://python.langchain.com/docs/integrations/vectorstores/analyticdb",
+        "Hologres": "https://python.langchain.com/docs/integrations/vectorstores/hologres",
+        "MongoDB Atlas": "https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas",
+        "Meilisearch": "https://python.langchain.com/docs/integrations/vectorstores/meilisearch",
+        "Conversational Retrieval Agent": "https://python.langchain.com/docs/use_cases/question_answering/how_to/conversational_retrieval_agents",
+        "Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep Lake": "https://python.langchain.com/docs/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake",
+        "Use LangChain, GPT and Activeloop's Deep Lake to work with code base": "https://python.langchain.com/docs/use_cases/question_answering/how_to/code/code-analysis-deeplake",
+        "Structure answers with OpenAI functions": "https://python.langchain.com/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa",
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+        "Parent Document Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/parent_document_retriever",
+        "Combine agents and vector stores": "https://python.langchain.com/docs/modules/agents/how_to/agent_vectorstore",
+        "Loading from LangChainHub": "https://python.langchain.com/docs/modules/chains/how_to/from_hub"
+    },
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+        "Document Comparison": "https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit",
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+        "BabyAGI User Guide": "https://python.langchain.com/docs/use_cases/agents/baby_agi",
+        "BabyAGI with Tools": "https://python.langchain.com/docs/use_cases/agents/baby_agi_with_agent",
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+        "Plug-and-Plai": "https://python.langchain.com/docs/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai",
+        "Custom Agent with PlugIn Retrieval": "https://python.langchain.com/docs/use_cases/agents/custom_agent_with_plugin_retrieval",
+        "Generative Agents in LangChain": "https://python.langchain.com/docs/use_cases/agent_simulations/characters",
+        "Caching Embeddings": "https://python.langchain.com/docs/modules/data_connection/caching_embeddings",
+        "Ensemble Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/ensemble",
+        "Custom agent with tool retrieval": "https://python.langchain.com/docs/modules/agents/how_to/custom_agent_with_tool_retrieval",
+        "Select by maximal marginal relevance (MMR)": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr"
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+        "SceneXplain": "https://python.langchain.com/docs/integrations/tools/sceneXplain",
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+        "Entity Memory with SQLite storage": "https://python.langchain.com/docs/integrations/memory/entity_memory_with_sqlite",
+        "Streamlit Chat Message History": "https://python.langchain.com/docs/integrations/memory/streamlit_chat_message_history",
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+        "Weights & Biases": "https://python.langchain.com/docs/integrations/providers/wandb_tracking",
+        "SageMaker Tracking": "https://python.langchain.com/docs/integrations/providers/sagemaker_tracking",
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+        "Shale Protocol": "https://python.langchain.com/docs/integrations/providers/shaleprotocol",
+        "WhyLabs": "https://python.langchain.com/docs/integrations/providers/whylabs_profiling",
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+        "ClearML": "https://python.langchain.com/docs/integrations/providers/clearml_tracking",
+        "Ray Serve": "https://python.langchain.com/docs/integrations/providers/ray_serve",
+        "Log, Trace, and Monitor Langchain LLM Calls": "https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey",
+        "Portkey": "https://python.langchain.com/docs/integrations/providers/portkey/index",
+        "Chat Over Documents with Vectara": "https://python.langchain.com/docs/integrations/providers/vectara/vectara_chat",
+        "Vectara Text Generation": "https://python.langchain.com/docs/integrations/providers/vectara/vectara_text_generation",
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+        "Xorbits Agent": "https://python.langchain.com/docs/integrations/toolkits/xorbits",
+        "Jira": "https://python.langchain.com/docs/integrations/toolkits/jira",
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+        "Python Agent": "https://python.langchain.com/docs/integrations/toolkits/python",
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+        "Interacting with APIs": "https://python.langchain.com/docs/use_cases/apis",
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+        "Perform context-aware text splitting": "https://python.langchain.com/docs/use_cases/question_answering/how_to/document-context-aware-QA",
+        "Retrieve from vector stores directly": "https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_text_generation",
+        "Retrieve as you generate with FLARE": "https://python.langchain.com/docs/use_cases/question_answering/how_to/flare",
+        "Improve document indexing with HyDE": "https://python.langchain.com/docs/use_cases/question_answering/how_to/hyde",
+        "QA using Activeloop's DeepLake": "https://python.langchain.com/docs/use_cases/question_answering/integrations/semantic-search-over-chat",
+        "SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base": "https://python.langchain.com/docs/use_cases/agents/sales_agent_with_context",
+        "Graph QA": "https://python.langchain.com/docs/use_cases/more/graph/graph_qa",
+        "Tree of Thought (ToT) example": "https://python.langchain.com/docs/use_cases/more/graph/tot",
+        "Bash chain": "https://python.langchain.com/docs/use_cases/more/code_writing/llm_bash",
+        "LLM Symbolic Math ": "https://python.langchain.com/docs/use_cases/more/code_writing/llm_symbolic_math",
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+        "Weaviate self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/weaviate_self_query",
+        "Elasticsearch self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/elasticsearch_self_query",
+        "Chroma self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query",
+        "Self-querying with Pinecone": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone",
+        "Self-querying with MyScale": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/myscale_self_query",
+        "Deep Lake self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query",
+        "Qdrant self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query",
+        "Lost in the middle: The problem with long contexts": "https://python.langchain.com/docs/modules/data_connection/document_transformers/post_retrieval/long_context_reorder",
+        "How to add memory to a Multi-Input Chain": "https://python.langchain.com/docs/modules/memory/adding_memory_chain_multiple_inputs",
+        "How to add Memory to an LLMChain": "https://python.langchain.com/docs/modules/memory/adding_memory",
+        "How to use multiple memory classes in the same chain": "https://python.langchain.com/docs/modules/memory/multiple_memory",
+        "How to customize conversational memory": "https://python.langchain.com/docs/modules/memory/conversational_customization",
+        "Conversation Knowledge Graph Memory": "https://python.langchain.com/docs/modules/memory/types/kg",
+        "ConversationTokenBufferMemory": "https://python.langchain.com/docs/modules/memory/types/token_buffer",
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+        "Tool Input Schema": "https://python.langchain.com/docs/modules/agents/tools/tool_input_validation",
+        "Human-in-the-loop Tool Validation": "https://python.langchain.com/docs/modules/agents/tools/human_approval",
+        "Combine agents and vector stores": "https://python.langchain.com/docs/modules/agents/how_to/agent_vectorstore",
+        "Access intermediate steps": "https://python.langchain.com/docs/modules/agents/how_to/intermediate_steps",
+        "Timeouts for agents": "https://python.langchain.com/docs/modules/agents/how_to/max_time_limit",
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+        "Cap the max number of iterations": "https://python.langchain.com/docs/modules/agents/how_to/max_iterations",
+        "Async API": "https://python.langchain.com/docs/modules/chains/how_to/async_chain",
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+        "Serialization": "https://python.langchain.com/docs/modules/model_io/models/llms/llm_serialization",
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+        "Datetime parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/datetime",
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+        "Router": "https://python.langchain.com/docs/modules/chains/foundational/router",
+        "Transformation": "https://python.langchain.com/docs/modules/chains/foundational/transformation"
+    },
+    "ContextualCompressionRetriever": {
+        "Cohere Reranker": "https://python.langchain.com/docs/integrations/retrievers/cohere-reranker",
+        "LOTR (Merger Retriever)": "https://python.langchain.com/docs/integrations/retrievers/merger_retriever"
+    },
+    "CohereRerank": {
+        "Cohere Reranker": "https://python.langchain.com/docs/integrations/retrievers/cohere-reranker",
+        "Cohere": "https://python.langchain.com/docs/integrations/providers/cohere"
+    },
+    "RetrievalQA": {
+        "Cohere Reranker": "https://python.langchain.com/docs/integrations/retrievers/cohere-reranker",
+        "Document Comparison": "https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit",
+        "ScaNN": "https://python.langchain.com/docs/integrations/vectorstores/scann",
+        "Activeloop's Deep Lake": "https://python.langchain.com/docs/integrations/vectorstores/activeloop_deeplake",
+        "StarRocks": "https://python.langchain.com/docs/integrations/vectorstores/starrocks",
+        "Loading documents from a YouTube url": "https://python.langchain.com/docs/integrations/document_loaders/youtube_audio",
+        "Docugami": "https://python.langchain.com/docs/integrations/document_loaders/docugami",
+        "Ollama": "https://python.langchain.com/docs/integrations/llms/ollama",
+        "QA over Documents": "https://python.langchain.com/docs/use_cases/question_answering/index",
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+        "Use local LLMs": "https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa",
+        "Structure answers with OpenAI functions": "https://python.langchain.com/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa",
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+        "SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base": "https://python.langchain.com/docs/use_cases/agents/sales_agent_with_context",
+        "Combine agents and vector stores": "https://python.langchain.com/docs/modules/agents/how_to/agent_vectorstore"
+    },
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+        "kNN": "https://python.langchain.com/docs/integrations/retrievers/knn"
+    },
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+        "Wikipedia": "https://python.langchain.com/docs/integrations/providers/wikipedia"
+    },
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+        "Wikipedia": "https://python.langchain.com/docs/integrations/retrievers/wikipedia",
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+        "Chatbots": "https://python.langchain.com/docs/use_cases/chatbots",
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+        "QA over Documents": "https://python.langchain.com/docs/use_cases/question_answering/index",
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+        "Use LangChain, GPT and Activeloop's Deep Lake to work with code base": "https://python.langchain.com/docs/use_cases/question_answering/how_to/code/code-analysis-deeplake",
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+    },
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+        "Metal": "https://python.langchain.com/docs/integrations/providers/metal"
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+        "CSV": "https://python.langchain.com/docs/integrations/document_loaders/csv"
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+        "Doctran Interrogate Documents": "https://python.langchain.com/docs/integrations/document_transformers/doctran_interrogate_document",
+        "Doctran Translate Documents": "https://python.langchain.com/docs/integrations/document_transformers/doctran_translate_document",
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+        "Airbyte Salesforce": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_salesforce",
+        "Airbyte CDK": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_cdk",
+        "Airbyte Stripe": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_stripe",
+        "Copy Paste": "https://python.langchain.com/docs/integrations/document_loaders/copypaste",
+        "Airbyte Typeform": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_typeform",
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+        "Airbyte Hubspot": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_hubspot",
+        "Airbyte Gong": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_gong",
+        "Airbyte Shopify": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_shopify",
+        "Airbyte Zendesk Support": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_zendesk_support",
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+        "!pip install bs4": "https://python.langchain.com/docs/use_cases/autonomous_agents/marathon_times",
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+        "Retrieve as you generate with FLARE": "https://python.langchain.com/docs/use_cases/question_answering/how_to/flare",
+        "Plug-and-Plai": "https://python.langchain.com/docs/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai",
+        "Custom Agent with PlugIn Retrieval": "https://python.langchain.com/docs/use_cases/agents/custom_agent_with_plugin_retrieval",
+        "Weaviate self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/weaviate_self_query",
+        "Elasticsearch self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/elasticsearch_self_query",
+        "Chroma self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query",
+        "Self-querying with Pinecone": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/pinecone",
+        "Self-querying with MyScale": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/myscale_self_query",
+        "Deep Lake self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query",
+        "Qdrant self-querying ": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/qdrant_self_query",
+        "How to add memory to a Multi-Input Chain": "https://python.langchain.com/docs/modules/memory/adding_memory_chain_multiple_inputs",
+        "Custom agent with tool retrieval": "https://python.langchain.com/docs/modules/agents/how_to/custom_agent_with_tool_retrieval"
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+        "ChatGPT Plugin": "https://python.langchain.com/docs/integrations/retrievers/chatgpt-plugin",
+        "OpenAI": "https://python.langchain.com/docs/integrations/providers/openai"
+    },
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+        "Google Cloud Enterprise Search": "https://python.langchain.com/docs/integrations/retrievers/google_cloud_enterprise_search"
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+        "DocArray Retriever": "https://python.langchain.com/docs/integrations/retrievers/docarray_retriever"
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+        "QA over Documents": "https://python.langchain.com/docs/use_cases/question_answering/index"
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+        "Pinecone Hybrid Search": "https://python.langchain.com/docs/integrations/retrievers/pinecone_hybrid_search"
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+        "PubMed": "https://python.langchain.com/docs/integrations/providers/pubmed"
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+        "Weaviate Hybrid Search": "https://python.langchain.com/docs/integrations/retrievers/weaviate-hybrid"
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+        "BM25": "https://python.langchain.com/docs/integrations/retrievers/bm25",
+        "Ensemble Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/ensemble"
+    },
+    "AzureCognitiveSearchRetriever": {
+        "Azure Cognitive Search": "https://python.langchain.com/docs/integrations/providers/azure_cognitive_search_"
+    },
+    "ChaindeskRetriever": {
+        "Chaindesk": "https://python.langchain.com/docs/integrations/providers/chaindesk"
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+    "MergerRetriever": {
+        "LOTR (Merger Retriever)": "https://python.langchain.com/docs/integrations/retrievers/merger_retriever"
+    },
+    "EmbeddingsRedundantFilter": {
+        "LOTR (Merger Retriever)": "https://python.langchain.com/docs/integrations/retrievers/merger_retriever"
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+        "LOTR (Merger Retriever)": "https://python.langchain.com/docs/integrations/retrievers/merger_retriever"
+    },
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+        "LOTR (Merger Retriever)": "https://python.langchain.com/docs/integrations/retrievers/merger_retriever"
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+        "LOTR (Merger Retriever)": "https://python.langchain.com/docs/integrations/retrievers/merger_retriever",
+        "Lost in the middle: The problem with long contexts": "https://python.langchain.com/docs/modules/data_connection/document_transformers/post_retrieval/long_context_reorder"
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+        "Human as a tool": "https://python.langchain.com/docs/integrations/tools/human_tools",
+        "AWS Lambda API": "https://python.langchain.com/docs/integrations/tools/awslambda",
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+        "ArXiv API Tool": "https://python.langchain.com/docs/integrations/tools/arxiv",
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+        "SceneXplain": "https://python.langchain.com/docs/integrations/tools/sceneXplain",
+        "Dall-E Image Generator": "https://python.langchain.com/docs/integrations/tools/dalle_image_generator",
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+        "Weights & Biases": "https://python.langchain.com/docs/integrations/providers/wandb_tracking",
+        "SageMaker Tracking": "https://python.langchain.com/docs/integrations/providers/sagemaker_tracking",
+        "Wolfram Alpha": "https://python.langchain.com/docs/integrations/providers/wolfram_alpha",
+        "MLflow": "https://python.langchain.com/docs/integrations/providers/mlflow_tracking",
+        "DataForSEO": "https://python.langchain.com/docs/integrations/providers/dataforseo",
+        "SearxNG Search API": "https://python.langchain.com/docs/integrations/providers/searx",
+        "Google Serper": "https://python.langchain.com/docs/integrations/providers/google_serper",
+        "OpenWeatherMap": "https://python.langchain.com/docs/integrations/providers/openweathermap",
+        "Flyte": "https://python.langchain.com/docs/integrations/providers/flyte",
+        "WandB Tracing": "https://python.langchain.com/docs/integrations/providers/wandb_tracing",
+        "ClearML": "https://python.langchain.com/docs/integrations/providers/clearml_tracking",
+        "Google Search": "https://python.langchain.com/docs/integrations/providers/google_search",
+        "Log, Trace, and Monitor Langchain LLM Calls": "https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey",
+        "Portkey": "https://python.langchain.com/docs/integrations/providers/portkey/index",
+        "NIBittensorLLM": "https://python.langchain.com/docs/integrations/llms/bittensor",
+        "Amazon API Gateway": "https://python.langchain.com/docs/integrations/llms/amazon_api_gateway_example",
+        "Debugging": "https://python.langchain.com/docs/guides/debugging",
+        "LangSmith Walkthrough": "https://python.langchain.com/docs/guides/langsmith/walkthrough",
+        "Agent Debates with Tools": "https://python.langchain.com/docs/use_cases/agent_simulations/two_agent_debate_tools",
+        "Multiple callback handlers": "https://python.langchain.com/docs/modules/callbacks/multiple_callbacks",
+        "Defining Custom Tools": "https://python.langchain.com/docs/modules/agents/tools/custom_tools",
+        "Human-in-the-loop Tool Validation": "https://python.langchain.com/docs/modules/agents/tools/human_approval",
+        "Access intermediate steps": "https://python.langchain.com/docs/modules/agents/how_to/intermediate_steps",
+        "Timeouts for agents": "https://python.langchain.com/docs/modules/agents/how_to/max_time_limit",
+        "Streaming final agent output": "https://python.langchain.com/docs/modules/agents/how_to/streaming_stdout_final_only",
+        "Cap the max number of iterations": "https://python.langchain.com/docs/modules/agents/how_to/max_iterations",
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+        "Human input Chat Model": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model",
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+        "Human input LLM": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"
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+        "Google Serper API": "https://python.langchain.com/docs/integrations/tools/google_serper",
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+        "OpenWeatherMap API": "https://python.langchain.com/docs/integrations/tools/openweathermap",
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+        "Zapier Natural Language Actions API": "https://python.langchain.com/docs/integrations/tools/zapier",
+        "ArXiv API Tool": "https://python.langchain.com/docs/integrations/tools/arxiv",
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+        "Dynamodb Chat Message History": "https://python.langchain.com/docs/integrations/memory/dynamodb_chat_message_history",
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+        "Comet": "https://python.langchain.com/docs/integrations/providers/comet_tracking",
+        "Aim": "https://python.langchain.com/docs/integrations/providers/aim_tracking",
+        "Weights & Biases": "https://python.langchain.com/docs/integrations/providers/wandb_tracking",
+        "SageMaker Tracking": "https://python.langchain.com/docs/integrations/providers/sagemaker_tracking",
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+        "Flyte": "https://python.langchain.com/docs/integrations/providers/flyte",
+        "WandB Tracing": "https://python.langchain.com/docs/integrations/providers/wandb_tracing",
+        "ClearML": "https://python.langchain.com/docs/integrations/providers/clearml_tracking",
+        "Log, Trace, and Monitor Langchain LLM Calls": "https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey",
+        "Portkey": "https://python.langchain.com/docs/integrations/providers/portkey/index",
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+        "Github Toolkit": "https://python.langchain.com/docs/integrations/toolkits/github",
+        "PlayWright Browser Toolkit": "https://python.langchain.com/docs/integrations/toolkits/playwright",
+        "Office365 Toolkit": "https://python.langchain.com/docs/integrations/toolkits/office365",
+        "MultiOn Toolkit": "https://python.langchain.com/docs/integrations/toolkits/multion",
+        "Amadeus Toolkit": "https://python.langchain.com/docs/integrations/toolkits/amadeus",
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+        "Debugging": "https://python.langchain.com/docs/guides/debugging",
+        "LangSmith Walkthrough": "https://python.langchain.com/docs/guides/langsmith/walkthrough",
+        "Comparing Chain Outputs": "https://python.langchain.com/docs/guides/evaluation/examples/comparisons",
+        "Agent Trajectory": "https://python.langchain.com/docs/guides/evaluation/trajectory/trajectory_eval",
+        "Multi-modal outputs: Image & Text": "https://python.langchain.com/docs/use_cases/multi_modal/image_agent",
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+        "Multi-Input Tools": "https://python.langchain.com/docs/modules/agents/tools/multi_input_tool",
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+        "Tool Input Schema": "https://python.langchain.com/docs/modules/agents/tools/tool_input_validation",
+        "Human-in-the-loop Tool Validation": "https://python.langchain.com/docs/modules/agents/tools/human_approval",
+        "Self ask with search": "https://python.langchain.com/docs/modules/agents/agent_types/self_ask_with_search",
+        "ReAct document store": "https://python.langchain.com/docs/modules/agents/agent_types/react_docstore",
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+        "Combine agents and vector stores": "https://python.langchain.com/docs/modules/agents/how_to/agent_vectorstore",
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+        "Handle parsing errors": "https://python.langchain.com/docs/modules/agents/how_to/handle_parsing_errors",
+        "Running Agent as an Iterator": "https://python.langchain.com/docs/modules/agents/how_to/agent_iter",
+        "Timeouts for agents": "https://python.langchain.com/docs/modules/agents/how_to/max_time_limit",
+        "Streaming final agent output": "https://python.langchain.com/docs/modules/agents/how_to/streaming_stdout_final_only",
+        "Add Memory to OpenAI Functions Agent": "https://python.langchain.com/docs/modules/agents/how_to/add_memory_openai_functions",
+        "Cap the max number of iterations": "https://python.langchain.com/docs/modules/agents/how_to/max_iterations",
+        "Custom functions with OpenAI Functions Agent": "https://python.langchain.com/docs/modules/agents/how_to/custom-functions-with-openai-functions-agent",
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+        "Use ToolKits with OpenAI Functions": "https://python.langchain.com/docs/modules/agents/how_to/use_toolkits_with_openai_functions",
+        "Human input Chat Model": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model",
+        "Fake LLM": "https://python.langchain.com/docs/modules/model_io/models/llms/fake_llm",
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+        "Human input LLM": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"
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+        "Google Serper API": "https://python.langchain.com/docs/integrations/tools/google_serper",
+        "Human as a tool": "https://python.langchain.com/docs/integrations/tools/human_tools",
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+        "ArXiv API Tool": "https://python.langchain.com/docs/integrations/tools/arxiv",
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+        "GraphQL tool": "https://python.langchain.com/docs/integrations/tools/graphql",
+        "Shell Tool": "https://python.langchain.com/docs/integrations/tools/bash",
+        "Zep Memory": "https://python.langchain.com/docs/integrations/memory/zep_memory",
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+    "RdfGraph": {
+        "GraphSparqlQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_sparql_qa"
+    },
+    "ArangoGraph": {
+        "ArangoDB QA chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_arangodb_qa"
+    },
+    "ArangoGraphQAChain": {
+        "ArangoDB QA chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_arangodb_qa"
+    },
+    "GraphIndexCreator": {
+        "Graph QA": "https://python.langchain.com/docs/use_cases/more/graph/graph_qa"
+    },
+    "GraphQAChain": {
+        "Graph QA": "https://python.langchain.com/docs/use_cases/more/graph/graph_qa"
+    },
+    "NetworkxEntityGraph": {
+        "Graph QA": "https://python.langchain.com/docs/use_cases/more/graph/graph_qa"
+    },
+    "GraphCypherQAChain": {
+        "Graph DB QA chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_cypher_qa"
+    },
+    "Neo4jGraph": {
+        "Graph DB QA chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_cypher_qa"
+    },
+    "LLMBashChain": {
+        "Bash chain": "https://python.langchain.com/docs/use_cases/more/code_writing/llm_bash"
+    },
+    "BashOutputParser": {
+        "Bash chain": "https://python.langchain.com/docs/use_cases/more/code_writing/llm_bash"
+    },
+    "BashProcess": {
+        "Bash chain": "https://python.langchain.com/docs/use_cases/more/code_writing/llm_bash"
+    },
+    "LLMSymbolicMathChain": {
+        "LLM Symbolic Math ": "https://python.langchain.com/docs/use_cases/more/code_writing/llm_symbolic_math"
+    },
+    "LLMSummarizationCheckerChain": {
+        "Summarization checker chain": "https://python.langchain.com/docs/use_cases/more/self_check/llm_summarization_checker"
+    },
+    "LLMCheckerChain": {
+        "Self-checking chain": "https://python.langchain.com/docs/use_cases/more/self_check/llm_checker"
+    },
+    "RegexParser": {
+        "Multi-Agent Simulated Environment: Petting Zoo": "https://python.langchain.com/docs/use_cases/agent_simulations/petting_zoo",
+        "Multi-agent decentralized speaker selection": "https://python.langchain.com/docs/use_cases/agent_simulations/multiagent_bidding",
+        "Multi-agent authoritarian speaker selection": "https://python.langchain.com/docs/use_cases/agent_simulations/multiagent_authoritarian",
+        "Simulated Environment: Gymnasium": "https://python.langchain.com/docs/use_cases/agent_simulations/gymnasium"
+    },
+    "TimeWeightedVectorStoreRetriever": {
+        "Generative Agents in LangChain": "https://python.langchain.com/docs/use_cases/agent_simulations/characters"
+    },
+    "InMemoryStore": {
+        "Caching Embeddings": "https://python.langchain.com/docs/modules/data_connection/caching_embeddings",
+        "Parent Document Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/parent_document_retriever"
+    },
+    "LocalFileStore": {
+        "Caching Embeddings": "https://python.langchain.com/docs/modules/data_connection/caching_embeddings"
+    },
+    "RedisStore": {
+        "Caching Embeddings": "https://python.langchain.com/docs/modules/data_connection/caching_embeddings"
+    },
+    "CacheBackedEmbeddings": {
+        "Caching Embeddings": "https://python.langchain.com/docs/modules/data_connection/caching_embeddings"
+    },
+    "EnsembleRetriever": {
+        "Ensemble Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/ensemble"
+    },
+    "ParentDocumentRetriever": {
+        "Parent Document Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/parent_document_retriever"
+    },
+    "SentenceTransformersTokenTextSplitter": {
+        "Split by tokens ": "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/split_by_token"
+    },
+    "NLTKTextSplitter": {
+        "Split by tokens ": "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/split_by_token"
+    },
+    "ChatMessageHistory": {
+        "Adding Message Memory backed by a database to an Agent": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"
+    },
+    "BaseMemory": {
+        "How to create a custom Memory class": "https://python.langchain.com/docs/modules/memory/custom_memory"
+    },
+    "ConversationKGMemory": {
+        "Conversation Knowledge Graph Memory": "https://python.langchain.com/docs/modules/memory/types/kg"
+    },
+    "ConversationTokenBufferMemory": {
+        "ConversationTokenBufferMemory": "https://python.langchain.com/docs/modules/memory/types/token_buffer"
+    },
+    "tracing_enabled": {
+        "Multiple callback handlers": "https://python.langchain.com/docs/modules/callbacks/multiple_callbacks"
+    },
+    "FileCallbackHandler": {
+        "Logging to file": "https://python.langchain.com/docs/modules/callbacks/filecallbackhandler"
+    },
+    "AsyncCallbackHandler": {
+        "Async callbacks": "https://python.langchain.com/docs/modules/callbacks/async_callbacks"
+    },
+    "StructuredTool": {
+        "Multi-Input Tools": "https://python.langchain.com/docs/modules/agents/tools/multi_input_tool",
+        "Defining Custom Tools": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"
+    },
+    "AsyncCallbackManagerForToolRun": {
+        "Defining Custom Tools": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"
+    },
+    "CallbackManagerForToolRun": {
+        "Defining Custom Tools": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"
+    },
+    "ToolException": {
+        "Defining Custom Tools": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"
+    },
+    "format_tool_to_openai_function": {
+        "Tools as OpenAI Functions": "https://python.langchain.com/docs/modules/agents/tools/tools_as_openai_functions"
+    },
+    "RequestsGetTool": {
+        "Tool Input Schema": "https://python.langchain.com/docs/modules/agents/tools/tool_input_validation"
+    },
+    "HumanApprovalCallbackHandler": {
+        "Human-in-the-loop Tool Validation": "https://python.langchain.com/docs/modules/agents/tools/human_approval"
+    },
+    "XMLAgent": {
+        "XML Agent": "https://python.langchain.com/docs/modules/agents/agent_types/xml_agent"
+    },
+    "DocstoreExplorer": {
+        "ReAct document store": "https://python.langchain.com/docs/modules/agents/agent_types/react_docstore"
+    },
+    "ReadOnlySharedMemory": {
+        "Shared memory across agents and tools": "https://python.langchain.com/docs/modules/agents/how_to/sharedmemory_for_tools"
+    },
+    "BaseMultiActionAgent": {
+        "Custom multi-action agent": "https://python.langchain.com/docs/modules/agents/how_to/custom_multi_action_agent"
+    },
+    "FinalStreamingStdOutCallbackHandler": {
+        "Streaming final agent output": "https://python.langchain.com/docs/modules/agents/how_to/streaming_stdout_final_only"
+    },
+    "LangChainTracer": {
+        "Async API": "https://python.langchain.com/docs/modules/agents/how_to/async_agent"
+    },
+    "BaseSingleActionAgent": {
+        "Custom agent": "https://python.langchain.com/docs/modules/agents/how_to/custom_agent"
+    },
+    "HumanInputChatModel": {
+        "Human input Chat Model": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model"
+    },
+    "FakeListLLM": {
+        "Fake LLM": "https://python.langchain.com/docs/modules/model_io/models/llms/fake_llm"
+    },
+    "CallbackManagerForLLMRun": {
+        "Custom LLM": "https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm"
+    },
+    "LLM": {
+        "Custom LLM": "https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm"
+    },
+    "HumanInputLLM": {
+        "Human input LLM": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"
+    },
+    "OutputFixingParser": {
+        "Retry parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry"
+    },
+    "RetryOutputParser": {
+        "Retry parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry"
+    },
+    "RetryWithErrorOutputParser": {
+        "Retry parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry"
+    },
+    "EnumOutputParser": {
+        "Enum parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/enum"
+    },
+    "MaxMarginalRelevanceExampleSelector": {
+        "Select by maximal marginal relevance (MMR)": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr"
+    },
+    "SemanticSimilarityExampleSelector": {
+        "Select by maximal marginal relevance (MMR)": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr",
+        "Few shot examples for chat models": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat"
+    },
+    "FewShotPromptTemplate": {
+        "Select by maximal marginal relevance (MMR)": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/mmr",
+        "Select by n-gram overlap": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/ngram_overlap"
+    },
+    "BaseExampleSelector": {
+        "Custom example selector": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/custom_example_selector"
+    },
+    "NGramOverlapExampleSelector": {
+        "Select by n-gram overlap": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/ngram_overlap"
+    },
+    "FewShotChatMessagePromptTemplate": {
+        "Few shot examples for chat models": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat"
+    },
+    "load_prompt": {
+        "Serialization": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompt_serialization"
+    },
+    "ChatMessagePromptTemplate": {
+        "Types of `MessagePromptTemplate`": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates"
+    },
+    "MultiPromptChain": {
+        "Router": "https://python.langchain.com/docs/modules/chains/foundational/router"
+    },
+    "LLMRouterChain": {
+        "Router": "https://python.langchain.com/docs/modules/chains/foundational/router"
+    },
+    "RouterOutputParser": {
+        "Router": "https://python.langchain.com/docs/modules/chains/foundational/router"
+    },
+    "EmbeddingRouterChain": {
+        "Router": "https://python.langchain.com/docs/modules/chains/foundational/router"
+    },
+    "BaseLanguageModel": {
+        "Custom chain": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain"
+    },
+    "AsyncCallbackManagerForChainRun": {
+        "Custom chain": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain"
+    },
+    "CallbackManagerForChainRun": {
+        "Custom chain": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain"
+    },
+    "BasePromptTemplate": {
+        "Custom chain": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain"
+    },
+    "load_chain": {
+        "Serialization": "https://python.langchain.com/docs/modules/chains/how_to/serialization",
+        "Loading from LangChainHub": "https://python.langchain.com/docs/modules/chains/how_to/from_hub"
+    },
+    "create_openai_fn_chain": {
+        "Using OpenAI functions": "https://python.langchain.com/docs/modules/chains/how_to/openai_functions"
+    },
+    "create_structured_output_chain": {
+        "Using OpenAI functions": "https://python.langchain.com/docs/modules/chains/how_to/openai_functions"
+    }
+}
\ No newline at end of file
diff --git a/docs/docs_skeleton/docs/community.md b/docs/docs_skeleton/docs/community.md
new file mode 100644
index 00000000000..c3c7031505c
--- /dev/null
+++ b/docs/docs_skeleton/docs/community.md
@@ -0,0 +1,54 @@
+# Community navigator
+
+Hi! Thanks for being here. We’re lucky to have a community of so many passionate developers building with LangChain–we have so much to teach and learn from each other. Community members contribute code, host meetups, write blog posts, amplify each other’s work, become each other's customers and collaborators, and so much more.
+
+Whether you’re new to LangChain, looking to go deeper, or just want to get more exposure to the world of building with LLMs, this page can point you in the right direction. 
+
+- **🦜 Contribute to LangChain**
+
+- **🌍 Meetups, Events, and Hackathons**
+
+- **📣 Help Us Amplify Your Work**
+
+- **💬 Stay in the loop**
+
+
+# 🦜 Contribute to LangChain
+
+LangChain is the product of over 5,000+ contributions by 1,500+ contributors, and there is ******still****** so much to do together. Here are some ways to get involved:
+
+- **[Open a pull request](https://github.com/langchain-ai/langchain/issues):** we’d appreciate all forms of contributions–new features, infrastructure improvements, better documentation, bug fixes, etc. If you have an improvement or an idea, we’d love to work on it with you.
+- **[Read our contributor guidelines:](https://github.com/langchain-ai/langchain/blob/bbd22b9b761389a5e40fc45b0570e1830aabb707/.github/CONTRIBUTING.md)** We ask contributors to follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow, run a few local checks for formatting, linting, and testing before submitting, and follow certain documentation and testing conventions.
+    - **First time contributor?** [Try one of these PRs with the “good first issue” tag](https://github.com/langchain-ai/langchain/contribute).
+- **Become an expert:** our experts help the community by answering product questions in Discord. If that’s a role you’d like to play, we’d be so grateful! (And we have some special experts-only goodies/perks we can tell you more about). Send us an email to introduce yourself at hello@langchain.dev and we’ll take it from there!
+- **Integrate with LangChain:** if your product integrates with LangChain–or aspires to–we want to help make sure the experience is as smooth as possible for you and end users. Send us an email at hello@langchain.dev and tell us what you’re working on.
+    - **Become an Integration Maintainer:** Partner with our team to ensure your integration stays up-to-date and talk directly with users (and answer their inquiries) in our Discord. Introduce yourself at hello@langchain.dev if you’d like to explore this role.
+
+
+# 🌍 Meetups, Events, and Hackathons
+
+One of our favorite things about working in AI is how much enthusiasm there is for building together. We want to help make that as easy and impactful for you as possible! 
+- **Find a meetup, hackathon, or webinar:** you can find the one for you on our [global events calendar](https://mirror-feeling-d80.notion.site/0bc81da76a184297b86ca8fc782ee9a3?v=0d80342540df465396546976a50cfb3f).  
+    - **Submit an event to our calendar:** email us at events@langchain.dev with a link to your event page! We can also help you spread the word with our local communities.
+- **Host a meetup:** If you want to bring a group of builders together, we want to help! We can publicize your event on our event calendar/Twitter, share with our local communities in Discord, send swag, or potentially hook you up with a sponsor. Email us at events@langchain.dev to tell us about your event!
+- **Become a meetup sponsor:** we often hear from groups of builders that want to get together, but are blocked or limited on some dimension (space to host, budget for snacks, prizes to distribute, etc.). If you’d like to help, send us an email to events@langchain.dev we can share more about how it works!
+- **Speak at an event:** meetup hosts are always looking for great speakers, presenters, and panelists. If you’d like to do that at an event, send us an email to hello@langchain.dev with more information about yourself, what you want to talk about, and what city you’re based in and we’ll try to match you with an upcoming event!
+- **Tell us about your LLM community:** If you host or participate in a community that would welcome support from LangChain and/or our team, send us an email at hello@langchain.dev and let us know how we can help.
+
+# 📣 Help Us Amplify Your Work
+
+If you’re working on something you’re proud of, and think the LangChain community would benefit from knowing about it, we want to help you show it off.
+
+- **Post about your work and mention us:** we love hanging out on Twitter to see what people in the space are talking about and working on. If you tag [@langchainai](https://twitter.com/LangChainAI), we’ll almost certainly see it and can show you some love.
+- **Publish something on our blog:** if you’re writing about your experience building with LangChain, we’d love to post (or crosspost) it on our blog! E-mail hello@langchain.dev with a draft of your post! Or even an idea for something you want to write about.
+- **Get your product onto our [integrations hub](https://integrations.langchain.com/):** Many developers take advantage of our seamless integrations with other products, and come to our integrations hub to find out who those are. If you want to get your product up there, tell us about it (and how it works with LangChain) at hello@langchain.dev.
+
+# ☀️ Stay in the loop
+
+Here’s where our team hangs out, talks shop, spotlights cool work, and shares what we’re up to. We’d love to see you there too.
+
+- **[Twitter](https://twitter.com/LangChainAI):** we post about what we’re working on and what cool things we’re seeing in the space. If you tag @langchainai in your post, we’ll almost certainly see it, and can snow you some love!
+- **[Discord](https://discord.gg/6adMQxSpJS):** connect with with >30k developers who are building with LangChain
+- **[GitHub](https://github.com/langchain-ai/langchain):** open pull requests, contribute to a discussion, and/or contribute
+- **[Subscribe to our bi-weekly Release Notes](https://6w1pwbss0py.typeform.com/to/KjZB1auB):** a twice/month email roundup of the coolest things going on in our orbit
+- **Slack:** if you’re building an application in production at your company, we’d love to get into a Slack channel together. Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) and we’ll get in touch about setting one up.
diff --git a/docs/docs_skeleton/docs/guides/evaluation/index.mdx b/docs/docs_skeleton/docs/guides/evaluation/index.mdx
index a608527bed0..e2d04b12284 100644
--- a/docs/docs_skeleton/docs/guides/evaluation/index.mdx
+++ b/docs/docs_skeleton/docs/guides/evaluation/index.mdx
@@ -8,9 +8,9 @@ import DocCardList from "@theme/DocCardList";
 
 Building applications with language models involves many moving parts. One of the most critical components is ensuring that the outcomes produced by your models are reliable and useful across a broad array of inputs, and that they work well with your application's other software components. Ensuring reliability usually boils down to some combination of application design, testing & evaluation, and runtime checks. 
 
-The guides in this section review the APIs and functionality LangChain provides to help yous better evaluate your applications. Evaluation and testing are both critical when thinking about deploying LLM applications, since production environments require repeatable and useful outcomes.
+The guides in this section review the APIs and functionality LangChain provides to help you better evaluate your applications. Evaluation and testing are both critical when thinking about deploying LLM applications, since production environments require repeatable and useful outcomes.
 
-LangChain offers various types of evaluators to help you measure performance and integrity on diverse data, and we hope to encourage the the community to create and share other useful evaluators so everyone can improve. These docs will introduce the evaluator types, how to use them, and provide some examples of their use in real-world scenarios.
+LangChain offers various types of evaluators to help you measure performance and integrity on diverse data, and we hope to encourage the community to create and share other useful evaluators so everyone can improve. These docs will introduce the evaluator types, how to use them, and provide some examples of their use in real-world scenarios.
 
 Each evaluator type in LangChain comes with ready-to-use implementations and an extensible API that allows for customization according to your unique requirements. Here are some of the types of evaluators we offer:
 
diff --git a/docs/docs_skeleton/docs/guides/langsmith/index.md b/docs/docs_skeleton/docs/guides/langsmith/index.md
index 42d7e937ddc..9915e70c5fb 100644
--- a/docs/docs_skeleton/docs/guides/langsmith/index.md
+++ b/docs/docs_skeleton/docs/guides/langsmith/index.md
@@ -5,8 +5,8 @@ import DocCardList from "@theme/DocCardList";
 LangSmith helps you trace and evaluate your language model applications and intelligent agents to help you
 move from prototype to production.
 
-Check out the [interactive walkthrough](walkthrough) below to get started.
+Check out the [interactive walkthrough](/docs/guides/langsmith/walkthrough) below to get started.
 
 For more information, please refer to the [LangSmith documentation](https://docs.smith.langchain.com/)
 
-<DocCardList />
\ No newline at end of file
+<DocCardList />
diff --git a/docs/docs_skeleton/docs/use_cases/apis/api.mdx b/docs/docs_skeleton/docs/use_cases/apis/api.mdx
deleted file mode 100644
index 7760ab04af6..00000000000
--- a/docs/docs_skeleton/docs/use_cases/apis/api.mdx
+++ /dev/null
@@ -1,9 +0,0 @@
----
-sidebar_position: 0
----
-# API chains
-APIChain enables using LLMs to interact with APIs to retrieve relevant information. Construct the chain by providing a question relevant to the provided API documentation.
-
-import Example from "@snippets/modules/chains/popular/api.mdx"
-
-<Example/>
diff --git a/docs/docs_skeleton/docs/use_cases/web_scraping/index.mdx b/docs/docs_skeleton/docs/use_cases/web_scraping/index.mdx
new file mode 100644
index 00000000000..c62c8e68214
--- /dev/null
+++ b/docs/docs_skeleton/docs/use_cases/web_scraping/index.mdx
@@ -0,0 +1,9 @@
+---
+sidebar_position: 3
+---
+
+# Web Scraping
+
+Web scraping has historically been a challenging endeavor due to the ever-changing nature of website structures, making it tedious for developers to maintain their scraping scripts. Traditional methods often rely on specific HTML tags and patterns which, when altered, can disrupt data extraction processes.
+
+Enter the LLM-based method for parsing HTML: By leveraging the capabilities of LLMs, and especially OpenAI Functions in LangChain's extraction chain, developers can instruct the model to extract only the desired data in a specified format. This method not only streamlines the extraction process but also significantly reduces the time spent on manual debugging and script modifications. Its adaptability means that even if websites undergo significant design changes, the extraction remains consistent and robust. This level of resilience translates to reduced maintenance efforts, cost savings, and ensures a higher quality of extracted data. Compared to its predecessors, LLM-based approach wins out the web scraping domain by transforming a historically cumbersome task into a more automated and efficient process.
diff --git a/docs/docs_skeleton/package-lock.json b/docs/docs_skeleton/package-lock.json
index 9a9c91f907d..db966d261fb 100644
--- a/docs/docs_skeleton/package-lock.json
+++ b/docs/docs_skeleton/package-lock.json
@@ -12,7 +12,7 @@
         "@docusaurus/preset-classic": "2.4.0",
         "@docusaurus/remark-plugin-npm2yarn": "^2.4.0",
         "@mdx-js/react": "^1.6.22",
-        "@mendable/search": "^0.0.137",
+        "@mendable/search": "^0.0.150",
         "clsx": "^1.2.1",
         "json-loader": "^0.5.7",
         "process": "^0.11.10",
@@ -3212,9 +3212,9 @@
       }
     },
     "node_modules/@mendable/search": {
-      "version": "0.0.137",
-      "resolved": "https://registry.npmjs.org/@mendable/search/-/search-0.0.137.tgz",
-      "integrity": "sha512-2J2fd5eqToK+mLzrSDA6NAr4F1kfql7QRiHpD7AUJJX0nqpvInhr/mMJKBCUSCv2z76UKCmF5wLuPSw+C90Qdg==",
+      "version": "0.0.150",
+      "resolved": "https://registry.npmjs.org/@mendable/search/-/search-0.0.150.tgz",
+      "integrity": "sha512-Eb5SeAWlMxzEim/8eJ/Ysn01Pyh39xlPBzRBw/5OyOBhti0HVLXk4wd1Fq2TKgJC2ppQIvhEKO98PUcj9dNDFw==",
       "dependencies": {
         "html-react-parser": "^4.2.0",
         "posthog-js": "^1.45.1"
diff --git a/docs/docs_skeleton/package.json b/docs/docs_skeleton/package.json
index b280212e83c..8256b3a334f 100644
--- a/docs/docs_skeleton/package.json
+++ b/docs/docs_skeleton/package.json
@@ -23,7 +23,7 @@
     "@docusaurus/preset-classic": "2.4.0",
     "@docusaurus/remark-plugin-npm2yarn": "^2.4.0",
     "@mdx-js/react": "^1.6.22",
-    "@mendable/search": "^0.0.137",
+    "@mendable/search": "^0.0.150",
     "clsx": "^1.2.1",
     "json-loader": "^0.5.7",
     "process": "^0.11.10",
diff --git a/docs/docs_skeleton/sidebars.js b/docs/docs_skeleton/sidebars.js
index 308782e49fc..90950592be3 100644
--- a/docs/docs_skeleton/sidebars.js
+++ b/docs/docs_skeleton/sidebars.js
@@ -75,6 +75,7 @@ module.exports = {
       slug: "additional_resources",
       },
     },
+    'community'
   ],
   integrations: [
     {
diff --git a/docs/docs_skeleton/static/img/OSS_LLM_overview.png b/docs/docs_skeleton/static/img/OSS_LLM_overview.png
new file mode 100644
index 00000000000..33bf4b914b6
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diff --git a/docs/docs_skeleton/static/img/api_chain.png b/docs/docs_skeleton/static/img/api_chain.png
new file mode 100644
index 00000000000..f296056498e
Binary files /dev/null and b/docs/docs_skeleton/static/img/api_chain.png differ
diff --git a/docs/docs_skeleton/static/img/api_chain_response.png b/docs/docs_skeleton/static/img/api_chain_response.png
new file mode 100644
index 00000000000..9b3103a9e35
Binary files /dev/null and b/docs/docs_skeleton/static/img/api_chain_response.png differ
diff --git a/docs/docs_skeleton/static/img/api_function_call.png b/docs/docs_skeleton/static/img/api_function_call.png
new file mode 100644
index 00000000000..82f151afe1c
Binary files /dev/null and b/docs/docs_skeleton/static/img/api_function_call.png differ
diff --git a/docs/docs_skeleton/static/img/api_use_case.png b/docs/docs_skeleton/static/img/api_use_case.png
new file mode 100644
index 00000000000..f2fa0cdb7ac
Binary files /dev/null and b/docs/docs_skeleton/static/img/api_use_case.png differ
diff --git a/docs/docs_skeleton/static/img/code_retrieval.png b/docs/docs_skeleton/static/img/code_retrieval.png
new file mode 100644
index 00000000000..4a9b607600a
Binary files /dev/null and b/docs/docs_skeleton/static/img/code_retrieval.png differ
diff --git a/docs/docs_skeleton/static/img/code_understanding.png b/docs/docs_skeleton/static/img/code_understanding.png
new file mode 100644
index 00000000000..f9face6add3
Binary files /dev/null and b/docs/docs_skeleton/static/img/code_understanding.png differ
diff --git a/docs/docs_skeleton/static/img/llama-memory-weights.png b/docs/docs_skeleton/static/img/llama-memory-weights.png
new file mode 100644
index 00000000000..f1b80c29f32
Binary files /dev/null and b/docs/docs_skeleton/static/img/llama-memory-weights.png differ
diff --git a/docs/docs_skeleton/static/img/llama_t_put.png b/docs/docs_skeleton/static/img/llama_t_put.png
new file mode 100644
index 00000000000..f448b1aafa3
Binary files /dev/null and b/docs/docs_skeleton/static/img/llama_t_put.png differ
diff --git a/docs/docs_skeleton/static/img/tagging.png b/docs/docs_skeleton/static/img/tagging.png
new file mode 100644
index 00000000000..cd4443be2b0
Binary files /dev/null and b/docs/docs_skeleton/static/img/tagging.png differ
diff --git a/docs/docs_skeleton/static/img/tagging_trace.png b/docs/docs_skeleton/static/img/tagging_trace.png
new file mode 100644
index 00000000000..3cc1231d865
Binary files /dev/null and b/docs/docs_skeleton/static/img/tagging_trace.png differ
diff --git a/docs/docs_skeleton/static/img/web_research.png b/docs/docs_skeleton/static/img/web_research.png
new file mode 100644
index 00000000000..3192f7570e5
Binary files /dev/null and b/docs/docs_skeleton/static/img/web_research.png differ
diff --git a/docs/docs_skeleton/static/img/web_scraping.png b/docs/docs_skeleton/static/img/web_scraping.png
new file mode 100644
index 00000000000..738fbdd449b
Binary files /dev/null and b/docs/docs_skeleton/static/img/web_scraping.png differ
diff --git a/docs/docs_skeleton/static/img/wsj_page.png b/docs/docs_skeleton/static/img/wsj_page.png
new file mode 100644
index 00000000000..65644746ae1
Binary files /dev/null and b/docs/docs_skeleton/static/img/wsj_page.png differ
diff --git a/docs/docs_skeleton/vercel.json b/docs/docs_skeleton/vercel.json
index 224afa7ba48..6472c045301 100644
--- a/docs/docs_skeleton/vercel.json
+++ b/docs/docs_skeleton/vercel.json
@@ -4,154 +4,78 @@
       "source": "/en/latest/additional_resources/youtube.html",
       "destination": "/docs/additional_resources/youtube"
     },
-    {
-      "source": "/en/latest/integrations/agent_with_wandb_tracing.html",
-      "destination": "/docs/integrations/providers/agent_with_wandb_tracing"
-    },
     {
       "source": "/docs/integrations/agent_with_wandb_tracing",
       "destination": "/docs/integrations/providers/agent_with_wandb_tracing"
     },
-    {
-      "source": "/en/latest/integrations/ai21.html",
-      "destination": "/docs/integrations/providers/ai21"
-    },
     {
       "source": "/docs/integrations/ai21",
       "destination": "/docs/integrations/providers/ai21"
     },
-    {
-      "source": "/en/latest/integrations/aim_tracking.html",
-      "destination": "/docs/integrations/providers/aim_tracking"
-    },
     {
       "source": "/docs/integrations/aim_tracking",
       "destination": "/docs/integrations/providers/aim_tracking"
     },
-    {
-      "source": "/en/latest/integrations/airbyte.html",
-      "destination": "/docs/integrations/providers/airbyte"
-    },
     {
       "source": "/docs/integrations/airbyte",
       "destination": "/docs/integrations/providers/airbyte"
     },
-    {
-      "source": "/en/latest/integrations/aleph_alpha.html",
-      "destination": "/docs/integrations/providers/aleph_alpha"
-    },
     {
       "source": "/docs/integrations/aleph_alpha",
       "destination": "/docs/integrations/providers/aleph_alpha"
     },
-    {
-      "source": "/en/latest/integrations/analyticdb.html",
-      "destination": "/docs/integrations/providers/analyticdb"
-    },
     {
       "source": "/docs/integrations/analyticdb",
       "destination": "/docs/integrations/providers/analyticdb"
     },
-    {
-      "source": "/en/latest/integrations/annoy.html",
-      "destination": "/docs/integrations/providers/annoy"
-    },
     {
       "source": "/docs/integrations/annoy",
       "destination": "/docs/integrations/providers/annoy"
     },
-    {
-      "source": "/en/latest/integrations/anyscale.html",
-      "destination": "/docs/integrations/providers/anyscale"
-    },
     {
       "source": "/docs/integrations/anyscale",
       "destination": "/docs/integrations/providers/anyscale"
     },
-    {
-      "source": "/en/latest/integrations/apify.html",
-      "destination": "/docs/integrations/providers/apify"
-    },
     {
       "source": "/docs/integrations/apify",
       "destination": "/docs/integrations/providers/apify"
     },
-    {
-      "source": "/en/latest/integrations/argilla.html",
-      "destination": "/docs/integrations/providers/argilla"
-    },
     {
       "source": "/docs/integrations/argilla",
       "destination": "/docs/integrations/providers/argilla"
     },
-    {
-      "source": "/en/latest/integrations/arxiv.html",
-      "destination": "/docs/integrations/providers/arxiv"
-    },
     {
       "source": "/docs/integrations/arxiv",
       "destination": "/docs/integrations/providers/arxiv"
     },
-    {
-      "source": "/en/latest/integrations/atlas.html",
-      "destination": "/docs/integrations/providers/atlas"
-    },
     {
       "source": "/docs/integrations/atlas",
       "destination": "/docs/integrations/providers/atlas"
     },
-    {
-      "source": "/en/latest/integrations/awadb.html",
-      "destination": "/docs/integrations/providers/awadb"
-    },
     {
       "source": "/docs/integrations/awadb",
       "destination": "/docs/integrations/providers/awadb"
     },
-    {
-      "source": "/en/latest/integrations/aws_s3.html",
-      "destination": "/docs/integrations/providers/aws_s3"
-    },
     {
       "source": "/docs/integrations/aws_s3",
       "destination": "/docs/integrations/providers/aws_s3"
     },
-    {
-      "source": "/en/latest/integrations/azlyrics.html",
-      "destination": "/docs/integrations/providers/azlyrics"
-    },
     {
       "source": "/docs/integrations/azlyrics",
       "destination": "/docs/integrations/providers/azlyrics"
     },
-    {
-      "source": "/en/latest/integrations/azure_blob_storage.html",
-      "destination": "/docs/integrations/providers/azure_blob_storage"
-    },
     {
       "source": "/docs/integrations/azure_blob_storage",
       "destination": "/docs/integrations/providers/azure_blob_storage"
     },
-    {
-      "source": "/en/latest/integrations/azure_cognitive_search_.html",
-      "destination": "/docs/integrations/providers/azure_cognitive_search_"
-    },
     {
       "source": "/docs/integrations/azure_cognitive_search_",
       "destination": "/docs/integrations/providers/azure_cognitive_search_"
     },
-    {
-      "source": "/en/latest/integrations/azure_openai.html",
-      "destination": "/docs/integrations/providers/azure_openai"
-    },
     {
       "source": "/docs/integrations/azure_openai",
       "destination": "/docs/integrations/providers/azure_openai"
     },
-    {
-      "source": "/en/latest/integrations/bananadev.html",
-      "destination": "/docs/integrations/providers/bananadev"
-    },
     {
       "source": "/docs/integrations/bananadev",
       "destination": "/docs/integrations/providers/bananadev"
@@ -164,114 +88,58 @@
       "source": "/docs/integrations/baseten",
       "destination": "/docs/integrations/providers/baseten"
     },
-    {
-      "source": "/en/latest/integrations/beam.html",
-      "destination": "/docs/integrations/providers/beam"
-    },
     {
       "source": "/docs/integrations/beam",
       "destination": "/docs/integrations/providers/beam"
     },
-    {
-      "source": "/en/latest/integrations/amazon_bedrock.html",
-      "destination": "/docs/integrations/providers/bedrock"
-    },
     {
       "source": "/docs/integrations/bedrock",
       "destination": "/docs/integrations/providers/bedrock"
     },
-    {
-      "source": "/en/latest/integrations/bilibili.html",
-      "destination": "/docs/integrations/providers/bilibili"
-    },
     {
       "source": "/docs/integrations/bilibili",
       "destination": "/docs/integrations/providers/bilibili"
     },
-    {
-      "source": "/en/latest/integrations/blackboard.html",
-      "destination": "/docs/integrations/providers/blackboard"
-    },
     {
       "source": "/docs/integrations/blackboard",
       "destination": "/docs/integrations/providers/blackboard"
     },
-    {
-      "source": "/en/latest/integrations/cassandra.html",
-      "destination": "/docs/integrations/providers/cassandra"
-    },
     {
       "source": "/docs/integrations/cassandra",
       "destination": "/docs/integrations/providers/cassandra"
     },
-    {
-      "source": "/en/latest/integrations/cerebriumai.html",
-      "destination": "/docs/integrations/providers/cerebriumai"
-    },
     {
       "source": "/docs/integrations/cerebriumai",
       "destination": "/docs/integrations/providers/cerebriumai"
     },
-    {
-      "source": "/en/latest/integrations/chroma.html",
-      "destination": "/docs/integrations/providers/chroma"
-    },
     {
       "source": "/docs/integrations/chroma",
       "destination": "/docs/integrations/providers/chroma"
     },
-    {
-      "source": "/en/latest/integrations/clearml_tracking.html",
-      "destination": "/docs/integrations/providers/clearml_tracking"
-    },
     {
       "source": "/docs/integrations/clearml_tracking",
       "destination": "/docs/integrations/providers/clearml_tracking"
     },
-    {
-      "source": "/en/latest/integrations/cohere.html",
-      "destination": "/docs/integrations/providers/cohere"
-    },
     {
       "source": "/docs/integrations/cohere",
       "destination": "/docs/integrations/providers/cohere"
     },
-    {
-      "source": "/en/latest/integrations/college_confidential.html",
-      "destination": "/docs/integrations/providers/college_confidential"
-    },
     {
       "source": "/docs/integrations/college_confidential",
       "destination": "/docs/integrations/providers/college_confidential"
     },
-    {
-      "source": "/en/latest/integrations/comet_tracking.html",
-      "destination": "/docs/integrations/providers/comet_tracking"
-    },
     {
       "source": "/docs/integrations/comet_tracking",
       "destination": "/docs/integrations/providers/comet_tracking"
     },
-    {
-      "source": "/en/latest/integrations/confluence.html",
-      "destination": "/docs/integrations/providers/confluence"
-    },
     {
       "source": "/docs/integrations/confluence",
       "destination": "/docs/integrations/providers/confluence"
     },
-    {
-      "source": "/en/latest/integrations/ctransformers.html",
-      "destination": "/docs/integrations/providers/ctransformers"
-    },
     {
       "source": "/docs/integrations/ctransformers",
       "destination": "/docs/integrations/providers/ctransformers"
     },
-    {
-      "source": "/en/latest/integrations/databerry.html",
-      "destination": "/docs/integrations/providers/chaindesk"
-    },
     {
       "source": "/docs/integrations/chaindesk",
       "destination": "/docs/integrations/providers/chaindesk"
@@ -285,345 +153,173 @@
       "destination": "/docs/integrations/providers/chaindesk"
     },
     {
-      "source": "/en/latest/integrations/databricks/databricks.html",
+      "source": "/docs/integrations/databricks",
       "destination": "/docs/integrations/providers/databricks"
     },
     {
       "source": "/docs/integrations/databricks",
       "destination": "/docs/integrations/providers/databricks"
     },
-    {
-      "source": "/en/latest/integrations/databricks.html",
-      "destination": "/docs/integrations/providers/databricks"
-    },
-    {
-      "source": "/docs/integrations/databricks",
-      "destination": "/docs/integrations/providers/databricks"
-    },
-    {
-      "source": "/en/latest/integrations/deepinfra.html",
-      "destination": "/docs/integrations/providers/deepinfra"
-    },
     {
       "source": "/docs/integrations/deepinfra",
       "destination": "/docs/integrations/providers/deepinfra"
     },
-    {
-      "source": "/en/latest/integrations/deeplake.html",
-      "destination": "/docs/integrations/providers/deeplake"
-    },
     {
       "source": "/docs/integrations/deeplake",
       "destination": "/docs/integrations/providers/deeplake"
     },
-    {
-      "source": "/en/latest/integrations/diffbot.html",
-      "destination": "/docs/integrations/providers/diffbot"
-    },
     {
       "source": "/docs/integrations/diffbot",
       "destination": "/docs/integrations/providers/diffbot"
     },
-    {
-      "source": "/en/latest/integrations/discord.html",
-      "destination": "/docs/integrations/providers/discord"
-    },
     {
       "source": "/docs/integrations/discord",
       "destination": "/docs/integrations/providers/discord"
     },
-    {
-      "source": "/en/latest/integrations/docugami.html",
-      "destination": "/docs/integrations/providers/docugami"
-    },
     {
       "source": "/docs/integrations/docugami",
       "destination": "/docs/integrations/providers/docugami"
     },
-    {
-      "source": "/en/latest/integrations/duckdb.html",
-      "destination": "/docs/integrations/providers/duckdb"
-    },
     {
       "source": "/docs/integrations/duckdb",
       "destination": "/docs/integrations/providers/duckdb"
     },
-    {
-      "source": "/en/latest/integrations/elasticsearch.html",
-      "destination": "/docs/integrations/providers/elasticsearch"
-    },
     {
       "source": "/docs/integrations/elasticsearch",
       "destination": "/docs/integrations/providers/elasticsearch"
     },
-    {
-      "source": "/en/latest/integrations/evernote.html",
-      "destination": "/docs/integrations/providers/evernote"
-    },
     {
       "source": "/docs/integrations/evernote",
       "destination": "/docs/integrations/providers/evernote"
     },
-    {
-      "source": "/en/latest/integrations/facebook_chat.html",
-      "destination": "/docs/integrations/providers/facebook_chat"
-    },
     {
       "source": "/docs/integrations/facebook_chat",
       "destination": "/docs/integrations/providers/facebook_chat"
     },
-    {
-      "source": "/en/latest/integrations/figma.html",
-      "destination": "/docs/integrations/providers/figma"
-    },
     {
       "source": "/docs/integrations/figma",
       "destination": "/docs/integrations/providers/figma"
     },
-    {
-      "source": "/en/latest/integrations/forefrontai.html",
-      "destination": "/docs/integrations/providers/forefrontai"
-    },
     {
       "source": "/docs/integrations/forefrontai",
       "destination": "/docs/integrations/providers/forefrontai"
     },
-    {
-      "source": "/en/latest/integrations/git.html",
-      "destination": "/docs/integrations/providers/git"
-    },
     {
       "source": "/docs/integrations/git",
       "destination": "/docs/integrations/providers/git"
     },
-    {
-      "source": "/en/latest/integrations/gitbook.html",
-      "destination": "/docs/integrations/providers/gitbook"
-    },
     {
       "source": "/docs/integrations/gitbook",
       "destination": "/docs/integrations/providers/gitbook"
     },
-    {
-      "source": "/en/latest/integrations/google_bigquery.html",
-      "destination": "/docs/integrations/providers/google_bigquery"
-    },
     {
       "source": "/docs/integrations/google_bigquery",
       "destination": "/docs/integrations/providers/google_bigquery"
     },
-    {
-      "source": "/en/latest/integrations/google_cloud_storage.html",
-      "destination": "/docs/integrations/providers/google_cloud_storage"
-    },
     {
       "source": "/docs/integrations/google_cloud_storage",
       "destination": "/docs/integrations/providers/google_cloud_storage"
     },
-    {
-      "source": "/en/latest/integrations/google_drive.html",
-      "destination": "/docs/integrations/providers/google_drive"
-    },
     {
       "source": "/docs/integrations/google_drive",
       "destination": "/docs/integrations/providers/google_drive"
     },
-    {
-      "source": "/en/latest/integrations/google_search.html",
-      "destination": "/docs/integrations/providers/google_search"
-    },
     {
       "source": "/docs/integrations/google_search",
       "destination": "/docs/integrations/providers/google_search"
     },
-    {
-      "source": "/en/latest/integrations/google_serper.html",
-      "destination": "/docs/integrations/providers/google_serper"
-    },
     {
       "source": "/docs/integrations/google_serper",
       "destination": "/docs/integrations/providers/google_serper"
     },
-    {
-      "source": "/en/latest/integrations/gooseai.html",
-      "destination": "/docs/integrations/providers/gooseai"
-    },
     {
       "source": "/docs/integrations/gooseai",
       "destination": "/docs/integrations/providers/gooseai"
     },
-    {
-      "source": "/en/latest/integrations/gpt4all.html",
-      "destination": "/docs/integrations/providers/gpt4all"
-    },
     {
       "source": "/docs/integrations/gpt4all",
       "destination": "/docs/integrations/providers/gpt4all"
     },
-    {
-      "source": "/en/latest/integrations/graphsignal.html",
-      "destination": "/docs/integrations/providers/graphsignal"
-    },
     {
       "source": "/docs/integrations/graphsignal",
       "destination": "/docs/integrations/providers/graphsignal"
     },
-    {
-      "source": "/en/latest/integrations/gutenberg.html",
-      "destination": "/docs/integrations/providers/gutenberg"
-    },
     {
       "source": "/docs/integrations/gutenberg",
       "destination": "/docs/integrations/providers/gutenberg"
     },
-    {
-      "source": "/en/latest/integrations/hacker_news.html",
-      "destination": "/docs/integrations/providers/hacker_news"
-    },
     {
       "source": "/docs/integrations/hacker_news",
       "destination": "/docs/integrations/providers/hacker_news"
     },
-    {
-      "source": "/en/latest/integrations/hazy_research.html",
-      "destination": "/docs/integrations/providers/hazy_research"
-    },
     {
       "source": "/docs/integrations/hazy_research",
       "destination": "/docs/integrations/providers/hazy_research"
     },
-    {
-      "source": "/en/latest/integrations/helicone.html",
-      "destination": "/docs/integrations/providers/helicone"
-    },
     {
       "source": "/docs/integrations/helicone",
       "destination": "/docs/integrations/providers/helicone"
     },
-    {
-      "source": "/en/latest/integrations/huggingface.html",
-      "destination": "/docs/integrations/providers/huggingface"
-    },
     {
       "source": "/docs/integrations/huggingface",
       "destination": "/docs/integrations/providers/huggingface"
     },
-    {
-      "source": "/en/latest/integrations/ifixit.html",
-      "destination": "/docs/integrations/providers/ifixit"
-    },
     {
       "source": "/docs/integrations/ifixit",
       "destination": "/docs/integrations/providers/ifixit"
     },
-    {
-      "source": "/en/latest/integrations/imsdb.html",
-      "destination": "/docs/integrations/providers/imsdb"
-    },
     {
       "source": "/docs/integrations/imsdb",
       "destination": "/docs/integrations/providers/imsdb"
     },
-    {
-      "source": "/en/latest/integrations/jina.html",
-      "destination": "/docs/integrations/providers/jina"
-    },
     {
       "source": "/docs/integrations/jina",
       "destination": "/docs/integrations/providers/jina"
     },
-    {
-      "source": "/en/latest/integrations/lancedb.html",
-      "destination": "/docs/integrations/providers/lancedb"
-    },
     {
       "source": "/docs/integrations/lancedb",
       "destination": "/docs/integrations/providers/lancedb"
     },
-    {
-      "source": "/en/latest/integrations/langchain_decorators.html",
-      "destination": "/docs/integrations/providers/langchain_decorators"
-    },
     {
       "source": "/docs/integrations/langchain_decorators",
       "destination": "/docs/integrations/providers/langchain_decorators"
     },
-    {
-      "source": "/en/latest/integrations/llamacpp.html",
-      "destination": "/docs/integrations/providers/llamacpp"
-    },
     {
       "source": "/docs/integrations/llamacpp",
       "destination": "/docs/integrations/providers/llamacpp"
     },
-    {
-      "source": "/en/latest/integrations/log10.html",
-      "destination": "/docs/integrations/providers/log10"
-    },
     {
       "source": "/docs/integrations/log10",
       "destination": "/docs/integrations/providers/log10"
     },
-    {
-      "source": "/en/latest/integrations/mediawikidump.html",
-      "destination": "/docs/integrations/providers/mediawikidump"
-    },
     {
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@@ -636,450 +332,226 @@
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@@ -1620,17 +1092,29 @@
       "source": "/en/latest/modules/chains/examples/flare.html",
       "destination": "/docs/use_cases/question_answering/how_to/flare"
     },
+    {
+      "source": "/docs/use_cases/graph/graph_cypher_qa",
+      "destination": "/docs/use_cases/more/graph/graph_cypher_qa"
+    },
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-      "destination": "/docs/use_cases/graph/graph_cypher_qa"
+      "destination": "/docs/use_cases/more/graph/graph_cypher_qa"
+    },
+    {
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+      "destination": "/docs/use_cases/more/graph/graph_nebula_qa"
     },
     {
       "source": "/en/latest/modules/chains/examples/graph_nebula_qa.html",
-      "destination": "/docs/use_cases/graph/graph_nebula_qa"
+      "destination": "/docs/use_cases/more/graph/graph_nebula_qa"
+    },
+    {
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+      "destination": "/docs/use_cases/more/graph/graph_qa"
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     {
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-      "destination": "/docs/use_cases/graph/graph_qa"
+      "destination": "/docs/use_cases/more/graph/graph_qa"
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@@ -1642,11 +1126,11 @@
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-      "destination": "/docs/use_cases/self_check/llm_checker"
+      "destination": "/docs/use_cases/more/self_check/llm_checker"
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     {
       "source": "/en/latest/modules/chains/examples/llm_math.html",
-      "destination": "/docs/use_cases/code_writing/llm_math"
+      "destination": "/docs/use_cases/more/code_writing/llm_math"
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@@ -1654,7 +1138,7 @@
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-      "destination": "/docs/use_cases/self_check/llm_summarization_checker"
+      "destination": "/docs/use_cases/more/self_check/llm_summarization_checker"
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@@ -1662,7 +1146,7 @@
     },
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-      "destination": "/docs/use_cases/code_writing/pal"
+      "destination": "/docs/use_cases/more/code_writing/pal"
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     {
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@@ -2778,11 +2262,19 @@
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     {
       "source": "/en/latest/modules/indexes/vectorstores/examples/deeplake.html",
-      "destination": "/docs/integrations/vectorstores/deeplake"
+      "destination": "/docs/integrations/vectorstores/activeloop_deeplake"
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     {
       "source": "/docs/modules/data_connection/vectorstores/integrations/deeplake",
-      "destination": "/docs/integrations/vectorstores/deeplake"
+      "destination": "/docs/integrations/vectorstores/activeloop_deeplake"
+    },
+    {
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+      "destination": "/docs/integrations/vectorstores/activeloop_deeplake"
+    },
+    {
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+      "destination": "/docs/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query"
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@@ -3858,59 +3350,127 @@
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       "source": "/docs/modules/chains/additional/cpal",
-      "destination": "/docs/use_cases/code_writing/cpal"
+      "destination": "/docs/use_cases/more/code_writing/cpal"
+    },
+    {
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+      "destination": "/docs/use_cases/more/code_writing/cpal"
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     {
       "source": "/docs/modules/chains/additional/llm_bash",
-      "destination": "/docs/use_cases/code_writing/llm_bash"
+      "destination": "/docs/use_cases/more/code_writing/llm_bash"
+    },
+    {
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+      "destination": "/docs/use_cases/more/code_writing/llm_bash"
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     {
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-      "destination": "/docs/use_cases/code_writing/llm_math"
+      "destination": "/docs/use_cases/more/code_writing/llm_math"
+    },
+    {
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+      "destination": "/docs/use_cases/more/code_writing/llm_math"
     },
     {
       "source": "/docs/modules/chains/additional/llm_symbolic_math",
-      "destination": "/docs/use_cases/code_writing/llm_symbolic_math"
+      "destination": "/docs/use_cases/more/code_writing/llm_symbolic_math"
+    },
+    {
+      "source": "/docs/use_cases/code_writing/llm_symbolic_math",
+      "destination": "/docs/use_cases/more/code_writing/llm_symbolic_math"
     },
     {
       "source": "/docs/modules/chains/additional/pal",
-      "destination": "/docs/use_cases/code_writing/pal"
+      "destination": "/docs/use_cases/more/code_writing/pal"
+    },
+    {
+      "source": "/docs/use_cases/code_writing/pal",
+      "destination": "/docs/use_cases/more/code_writing/pal"
+    },
+    {
+      "source": "/docs/use_cases/code_writing/",
+      "destination": "/docs/use_cases/more/code_writing/"
+    },
+    {
+      "source": "/docs/use_cases/code_writing(/?)",
+      "destination": "/docs/use_cases/more/code_writing/"
+    },
+    {
+      "source": "/docs/use_cases/graph(/?)",
+      "destination": "/docs/use_cases/more/graph/"
+    },
+    {
+      "source": "/docs/use_cases/graph/graph_arangodb_qa",
+      "destination": "/docs/use_cases/more/graph/graph_arangodb_qa"
     },
     {
       "source": "/docs/modules/chains/additional/graph_arangodb_qa",
-      "destination": "/docs/use_cases/graph/graph_arangodb_qa"
+      "destination": "/docs/use_cases/more/graph/graph_arangodb_qa"
+    },
+    {
+      "source": "/docs/use_cases/graph/graph_cypher_qa",
+      "destination": "/docs/use_cases/more/graph/graph_cypher_qa"
     },
     {
       "source": "/docs/modules/chains/additional/graph_cypher_qa",
-      "destination": "/docs/use_cases/graph/graph_cypher_qa"
+      "destination": "/docs/use_cases/more/graph/graph_cypher_qa"
+    },
+    {
+      "source": "/docs/use_cases/graph/graph_hugegraph_qa",
+      "destination": "/docs/use_cases/more/graph/graph_hugegraph_qa"
     },
     {
       "source": "/docs/modules/chains/additional/graph_hugegraph_qa",
-      "destination": "/docs/use_cases/graph/graph_hugegraph_qa"
+      "destination": "/docs/use_cases/more/graph/graph_hugegraph_qa"
+    },
+    {
+      "source": "/docs/use_cases/graph/graph_kuzu_qa",
+      "destination": "/docs/use_cases/more/graph/graph_kuzu_qa"
     },
     {
       "source": "/docs/modules/chains/additional/graph_kuzu_qa",
-      "destination": "/docs/use_cases/graph/graph_kuzu_qa"
+      "destination": "/docs/use_cases/more/graph/graph_kuzu_qa"
+    },
+    {
+      "source": "/docs/use_cases/graph/graph_nebula_qa",
+      "destination": "/docs/use_cases/more/graph/graph_nebula_qa"
     },
     {
       "source": "/docs/modules/chains/additional/graph_nebula_qa",
-      "destination": "/docs/use_cases/graph/graph_nebula_qa"
+      "destination": "/docs/use_cases/more/graph/graph_nebula_qa"
+    },
+    {
+      "source": "/docs/use_cases/graph/graph_qa",
+      "destination": "/docs/use_cases/more/graph/graph_qa"
     },
     {
       "source": "/docs/modules/chains/additional/graph_qa",
-      "destination": "/docs/use_cases/graph/graph_qa"
+      "destination": "/docs/use_cases/more/graph/graph_qa"
+    },
+    {
+      "source": "/docs/use_cases/graph/graph_sparql_qa",
+      "destination": "/docs/use_cases/more/graph/graph_sparql_qa"
     },
     {
       "source": "/docs/modules/chains/additional/graph_sparql_qa",
-      "destination": "/docs/use_cases/graph/graph_sparql_qa"
+      "destination": "/docs/use_cases/more/graph/graph_sparql_qa"
+    },
+    {
+      "source": "/docs/use_cases/graph/neptune_cypher_qa",
+      "destination": "/docs/use_cases/more/graph/neptune_cypher_qa"
     },
     {
       "source": "/docs/modules/chains/additional/neptune_cypher_qa",
-      "destination": "/docs/use_cases/graph/neptune_cypher_qa"
+      "destination": "/docs/use_cases/more/graph/neptune_cypher_qa"
+    },
+    {
+      "source": "/docs/use_cases/graph/tot",
+      "destination": "/docs/use_cases/more/graph/tot"
     },
     {
       "source": "/docs/modules/chains/additional/tot",
-      "destination": "/docs/use_cases/graph/tot"
+      "destination": "/docs/use_cases/more/graph/tot"
     },
     {
       "source": "/docs/use_cases/question_answering//document-context-aware-QA",
@@ -3946,11 +3506,27 @@
     },
     {
       "source": "/docs/modules/chains/additional/llm_checker",
-      "destination": "/docs/use_cases/self_check/llm_checker"
+      "destination": "/docs/use_cases/more/self_check/llm_checker"
+    },
+    {
+      "source": "/docs/use_cases/self_check/llm_checker",
+      "destination": "/docs/use_cases/more/self_check/llm_checker"
     },
     {
       "source": "/docs/modules/chains/additional/llm_summarization_checker",
-      "destination": "/docs/use_cases/self_check/llm_summarization_checker"
+      "destination": "/docs/use_cases/more/self_check/llm_summarization_checker"
+    },
+    {
+      "source": "/docs/use_cases/self_check/llm_summarization_checker",
+      "destination": "/docs/use_cases/more/self_check/llm_summarization_checker"
+    },
+    {
+      "source": "/docs/use_cases/self_check/smart_llm",
+      "destination": "/docs/use_cases/more/self_check/smart_llm"
+    },
+    {
+      "source": "/docs/use_cases/self_check(/?)",
+      "destination": "/docs/use_cases/more/self_check/"
     },
     {
       "source": "/docs/modules/chains/additional/elasticsearch_database",
@@ -3963,6 +3539,10 @@
     {
       "source": "docs/integrations/providers/agent_with_wandb_tracing",
       "destination": "docs/integrations/providers/wandb_tracing"
+    },
+    {
+      "source": "/en/latest/integrations/:path*",
+      "destination": "/docs/integrations/providers/:path*"
     }
   ]
 }
\ No newline at end of file
diff --git a/docs/extras/additional_resources/tutorials.mdx b/docs/extras/additional_resources/tutorials.mdx
index 3a56b1cd770..d8128aeff13 100644
--- a/docs/extras/additional_resources/tutorials.mdx
+++ b/docs/extras/additional_resources/tutorials.mdx
@@ -42,8 +42,8 @@ Below are links to video tutorials and courses on LangChain. For written guides
 ### [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Greg Kamradt (Data Indy)](https://www.youtube.com/@DataIndependent)
 - [What Is LangChain? - LangChain + `ChatGPT` Overview](https://youtu.be/_v_fgW2SkkQ)
 - [Quickstart Guide](https://youtu.be/kYRB-vJFy38)
-- [Beginner Guide To 7 Essential Concepts](https://youtu.be/2xxziIWmaSA)
-- [Beginner Guide To 9 Use Cases](https://youtu.be/vGP4pQdCocw)
+- [Beginner's Guide To 7 Essential Concepts](https://youtu.be/2xxziIWmaSA)
+- [Beginner's Guide To 9 Use Cases](https://youtu.be/vGP4pQdCocw)
 - [Agents Overview + Google Searches](https://youtu.be/Jq9Sf68ozk0)
 - [`OpenAI` + `Wolfram Alpha`](https://youtu.be/UijbzCIJ99g)
 - [Ask Questions On Your Custom (or Private) Files](https://youtu.be/EnT-ZTrcPrg)
@@ -73,7 +73,7 @@ Below are links to video tutorials and courses on LangChain. For written guides
 - [Conversations with Memory (explanation & code walkthrough)](https://youtu.be/X550Zbz_ROE)
 - [Chat with `Flan20B`](https://youtu.be/VW5LBavIfY4)
 - [Using `Hugging Face Models` locally (code walkthrough)](https://youtu.be/Kn7SX2Mx_Jk)
-- [`PAL` : Program-aided Language Models with LangChain code](https://youtu.be/dy7-LvDu-3s)
+- [`PAL`: Program-aided Language Models with LangChain code](https://youtu.be/dy7-LvDu-3s)
 - [Building a Summarization System with LangChain and `GPT-3` - Part 1](https://youtu.be/LNq_2s_H01Y)
 - [Building a Summarization System with LangChain and `GPT-3` - Part 2](https://youtu.be/d-yeHDLgKHw)
 - [Microsoft's `Visual ChatGPT` using LangChain](https://youtu.be/7YEiEyfPF5U)
@@ -85,7 +85,7 @@ Below are links to video tutorials and courses on LangChain. For written guides
 - [`BabyAGI`: Discover the Power of Task-Driven Autonomous Agents!](https://youtu.be/QBcDLSE2ERA)
 - [Improve your `BabyAGI` with LangChain](https://youtu.be/DRgPyOXZ-oE)
 - [Master `PDF` Chat with LangChain - Your essential guide to queries on documents](https://youtu.be/ZzgUqFtxgXI)
-- [Using LangChain with `DuckDuckGO` `Wikipedia` & `PythonREPL` Tools](https://youtu.be/KerHlb8nuVc)
+- [Using LangChain with `DuckDuckGO`, `Wikipedia` & `PythonREPL` Tools](https://youtu.be/KerHlb8nuVc)
 - [Building Custom Tools and Agents with LangChain (gpt-3.5-turbo)](https://youtu.be/biS8G8x8DdA)
 - [LangChain Retrieval QA Over Multiple Files with `ChromaDB`](https://youtu.be/3yPBVii7Ct0)
 - [LangChain Retrieval QA with Instructor Embeddings & `ChromaDB` for PDFs](https://youtu.be/cFCGUjc33aU)
@@ -107,7 +107,7 @@ Below are links to video tutorials and courses on LangChain. For written guides
 - [Working with MULTIPLE `PDF` Files in LangChain: `ChatGPT` for your Data](https://youtu.be/s5LhRdh5fu4)
 - [`ChatGPT` for YOUR OWN `PDF` files with LangChain](https://youtu.be/TLf90ipMzfE)
 - [Talk to YOUR DATA without OpenAI APIs: LangChain](https://youtu.be/wrD-fZvT6UI)
-- [Langchain: PDF Chat App (GUI) | ChatGPT for Your PDF FILES](https://youtu.be/RIWbalZ7sTo)
+- [LangChain: PDF Chat App (GUI) | ChatGPT for Your PDF FILES](https://youtu.be/RIWbalZ7sTo)
 - [LangFlow: Build Chatbots without Writing Code](https://youtu.be/KJ-ux3hre4s)
 - [LangChain: Giving Memory to LLMs](https://youtu.be/dxO6pzlgJiY)
 - [BEST OPEN Alternative to `OPENAI's EMBEDDINGs` for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY)
@@ -122,4 +122,4 @@ Below are links to video tutorials and courses on LangChain. For written guides
 
 
 ---------------------
-⛓ icon marks a new addition [last update 2023-07-05]
\ No newline at end of file
+⛓ icon marks a new addition [last update 2023-07-05]
diff --git a/docs/extras/guides/adapters/_category_.yml b/docs/extras/guides/adapters/_category_.yml
new file mode 100644
index 00000000000..f2009d077dc
--- /dev/null
+++ b/docs/extras/guides/adapters/_category_.yml
@@ -0,0 +1 @@
+label: 'Adapters'
diff --git a/docs/extras/guides/adapters/openai.ipynb b/docs/extras/guides/adapters/openai.ipynb
new file mode 100644
index 00000000000..e00241d74f3
--- /dev/null
+++ b/docs/extras/guides/adapters/openai.ipynb
@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "700a516b",
+   "metadata": {},
+   "source": [
+    "# OpenAI Adapter\n",
+    "\n",
+    "A lot of people get started with OpenAI but want to explore other models. LangChain's integrations with many model providers make this easy to do so. While LangChain has it's own message and model APIs, we've also made it as easy as possible to explore other models by exposing an adapter to adapt LangChain models to the OpenAI api.\n",
+    "\n",
+    "At the moment this only deals with output and does not return other information (token counts, stop reasons, etc)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6017f26a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "from langchain.adapters import openai as lc_openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b522ceda",
+   "metadata": {},
+   "source": [
+    "## ChatCompletion.create"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "1d22eb61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [{\"role\": \"user\", \"content\": \"hi\"}]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d550d3ad",
+   "metadata": {},
+   "source": [
+    "Original OpenAI call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e1d27dfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = openai.ChatCompletion.create(\n",
+    "    messages=messages, \n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "012d81ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result[\"choices\"][0]['message'].to_dict_recursive()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db5b5500",
+   "metadata": {},
+   "source": [
+    "LangChain OpenAI wrapper call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "87c2d515",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lc_result = lc_openai.ChatCompletion.create(\n",
+    "    messages=messages, \n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c67a5ac8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lc_result[\"choices\"][0]['message']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "034ba845",
+   "metadata": {},
+   "source": [
+    "Swapping out model providers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7a2c011c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lc_result = lc_openai.ChatCompletion.create(\n",
+    "    messages=messages, \n",
+    "    model=\"claude-2\", \n",
+    "    temperature=0, \n",
+    "    provider=\"ChatAnthropic\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f7c94827",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': ' Hello!'}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lc_result[\"choices\"][0]['message']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb3f181d",
+   "metadata": {},
+   "source": [
+    "## ChatCompletion.stream"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7b8cd18",
+   "metadata": {},
+   "source": [
+    "Original OpenAI call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "fd8cb1ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ''}\n",
+      "{'content': 'Hello'}\n",
+      "{'content': '!'}\n",
+      "{'content': ' How'}\n",
+      "{'content': ' can'}\n",
+      "{'content': ' I'}\n",
+      "{'content': ' assist'}\n",
+      "{'content': ' you'}\n",
+      "{'content': ' today'}\n",
+      "{'content': '?'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in openai.ChatCompletion.create(\n",
+    "    messages = messages,\n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0,\n",
+    "    stream=True\n",
+    "):\n",
+    "    print(c[\"choices\"][0]['delta'].to_dict_recursive())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b2a076b",
+   "metadata": {},
+   "source": [
+    "LangChain OpenAI wrapper call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "9521218c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ''}\n",
+      "{'content': 'Hello'}\n",
+      "{'content': '!'}\n",
+      "{'content': ' How'}\n",
+      "{'content': ' can'}\n",
+      "{'content': ' I'}\n",
+      "{'content': ' assist'}\n",
+      "{'content': ' you'}\n",
+      "{'content': ' today'}\n",
+      "{'content': '?'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in lc_openai.ChatCompletion.create(\n",
+    "    messages = messages,\n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0,\n",
+    "    stream=True\n",
+    "):\n",
+    "    print(c[\"choices\"][0]['delta'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fc39750",
+   "metadata": {},
+   "source": [
+    "Swapping out model providers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "68f0214e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ' Hello'}\n",
+      "{'content': '!'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in lc_openai.ChatCompletion.create(\n",
+    "    messages = messages,\n",
+    "    model=\"claude-2\", \n",
+    "    temperature=0,\n",
+    "    stream=True,\n",
+    "    provider=\"ChatAnthropic\",\n",
+    "):\n",
+    "    print(c[\"choices\"][0]['delta'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/guides/expression_language/cookbook.ipynb b/docs/extras/guides/expression_language/cookbook.ipynb
index 3375260b55e..d7aad42915e 100644
--- a/docs/extras/guides/expression_language/cookbook.ipynb
+++ b/docs/extras/guides/expression_language/cookbook.ipynb
@@ -1638,196 +1638,6 @@
    "source": [
     "moderated_chain.invoke({\"input\": \"you are stupid\"})"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a0a85ba4-f782-47b8-b16f-8b7a61d6dab7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Conversational Retrieval With Memory"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "92c87dd8-bb6f-4f32-a30d-8f5459ce6265",
-   "metadata": {},
-   "source": [
-    "## Fallbacks\n",
-    "\n",
-    "With LCEL you can easily introduce fallbacks for any Runnable component, like an LLM."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "1b1cb744-31fc-4261-ab25-65fe1fcad559",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='To get to the other side.', additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "\n",
-    "bad_llm = ChatOpenAI(model_name=\"gpt-fake\")\n",
-    "good_llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
-    "llm = bad_llm.with_fallbacks([good_llm])\n",
-    "\n",
-    "llm.invoke(\"Why did the the chicken cross the road?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8cf3982-03f6-49b3-8ff5-7cd12444f19c",
-   "metadata": {},
-   "source": [
-    "Looking at the trace, we can see that the first model failed but the second succeeded, so we still got an output: https://smith.langchain.com/public/dfaf0bf6-d86d-43e9-b084-dd16a56df15c/r\n",
-    "\n",
-    "We can add an arbitrary sequence of fallbacks, which will be executed in order:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "31819be0-7f40-4e67-b5ab-61340027b948",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='To get to the other side.', additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm = bad_llm.with_fallbacks([bad_llm, bad_llm, good_llm])\n",
-    "\n",
-    "llm.invoke(\"Why did the the chicken cross the road?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acad6e88-8046-450e-b005-db7e50f33b80",
-   "metadata": {},
-   "source": [
-    "Trace: https://smith.langchain.com/public/c09efd01-3184-4369-a225-c9da8efcaf47/r\n",
-    "\n",
-    "We can continue to use our Runnable with fallbacks the same way we use any Runnable, mean we can include it in sequences:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "bab114a1-bb93-4b7e-a639-e7e00f21aebc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='To show off its incredible jumping skills! Kangaroos are truly amazing creatures.', additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You're a nice assistant who always includes a compliment in your response\"),\n",
-    "        (\"human\", \"Why did the {animal} cross the road\"),\n",
-    "    ]\n",
-    ")\n",
-    "chain = prompt | llm\n",
-    "chain.invoke({\"animal\": \"kangaroo\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "58340afa-8187-4ffe-9bd2-7912fb733a15",
-   "metadata": {},
-   "source": [
-    "Trace: https://smith.langchain.com/public/ba03895f-f8bd-4c70-81b7-8b930353eabd/r\n",
-    "\n",
-    "Note, since every sequence of Runnables is itself a Runnable, we can create fallbacks for whole Sequences. We can also continue using the full interface, including asynchronous calls, batched calls, and streams:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "45aa3170-b2e6-430d-887b-bd879048060a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\"\\n\\nAnswer: The rabbit crossed the road to get to the other side. That's quite clever of him!\",\n",
-       " '\\n\\nAnswer: The turtle crossed the road to get to the other side. You must be pretty clever to come up with that riddle!']"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "\n",
-    "chat_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You're a nice assistant who always includes a compliment in your response\"),\n",
-    "        (\"human\", \"Why did the {animal} cross the road\"),\n",
-    "    ]\n",
-    ")\n",
-    "chat_model = ChatOpenAI(model_name=\"gpt-fake\")\n",
-    "\n",
-    "prompt_template = \"\"\"Instructions: You should always include a compliment in your response.\n",
-    "\n",
-    "Question: Why did the {animal} cross the road?\"\"\"\n",
-    "prompt = PromptTemplate.from_template(prompt_template)\n",
-    "llm = OpenAI()\n",
-    "\n",
-    "bad_chain = chat_prompt | chat_model\n",
-    "good_chain = prompt | llm\n",
-    "chain = bad_chain.with_fallbacks([good_chain])\n",
-    "await chain.abatch([{\"animal\": \"rabbit\"}, {\"animal\": \"turtle\"}])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af6731c6-0c73-4b1d-a433-6e8f6ecce2bb",
-   "metadata": {},
-   "source": [
-    "Traces: \n",
-    "1. https://smith.langchain.com/public/ccd73236-9ae5-48a6-94b5-41210be18a46/r\n",
-    "2. https://smith.langchain.com/public/f43f608e-075c-45c7-bf73-b64e4d3f3082/r"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3d2fe1fe-506b-4ee5-8056-8b9df801765f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1846,7 +1656,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/extras/guides/expression_language/interface.ipynb b/docs/extras/guides/expression_language/interface.ipynb
index d263805bd9f..98a1a38600d 100644
--- a/docs/extras/guides/expression_language/interface.ipynb
+++ b/docs/extras/guides/expression_language/interface.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "id": "466b65b3",
    "metadata": {},
    "outputs": [],
@@ -256,6 +256,131 @@
    "source": [
     "await chain.abatch([{\"topic\": \"bears\"}])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a1c409d",
+   "metadata": {},
+   "source": [
+    "## Parallelism\n",
+    "\n",
+    "Let's take a look at how LangChain Expression Language support parralel requests as much as possible. For example, when using a RunnableMapping (often written as a dictionary) it executes each element in parralel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e3014c7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema.runnable import RunnableMap\n",
+    "chain1 = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\") | model\n",
+    "chain2 = ChatPromptTemplate.from_template(\"write a short (2 line) poem about {topic}\") | model\n",
+    "combined = RunnableMap({\n",
+    "    \"joke\": chain1,\n",
+    "    \"poem\": chain2,\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "08044c0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 31.7 ms, sys: 8.59 ms, total: 40.3 ms\n",
+      "Wall time: 1.05 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Why don't bears like fast food?\\n\\nBecause they can't catch it!\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "chain1.invoke({\"topic\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "22c56804",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 42.9 ms, sys: 10.2 ms, total: 53 ms\n",
+      "Wall time: 1.93 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"In forest's embrace, bears roam free,\\nSilent strength, nature's majesty.\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "chain2.invoke({\"topic\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4fff4cbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 96.3 ms, sys: 20.4 ms, total: 117 ms\n",
+      "Wall time: 1.1 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'joke': AIMessage(content=\"Why don't bears wear socks?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False),\n",
+       " 'poem': AIMessage(content=\"In forest's embrace,\\nMajestic bears leave their trace.\", additional_kwargs={}, example=False)}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "combined.invoke({\"topic\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fab75d1d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -274,7 +399,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/extras/guides/fallbacks.ipynb b/docs/extras/guides/fallbacks.ipynb
new file mode 100644
index 00000000000..2dd7c6f5949
--- /dev/null
+++ b/docs/extras/guides/fallbacks.ipynb
@@ -0,0 +1,430 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "19c9cbd6",
+   "metadata": {},
+   "source": [
+    "# Fallbacks\n",
+    "\n",
+    "When working with language models, you may often encounter issues from the underlying APIs, whether these be rate limiting or downtime. Therefore, as you go to move your LLM applications into production it becomes more and more important to safe guard against these. That's why we've introduced the concept of fallbacks.\n",
+    "\n",
+    "Crucially, fallbacks can be applied not only on the LLM level but on the whole runnable level. This is important because often times different models require different prompts. So if your call to OpenAI fails, you don't just want to send the same prompt to Anthropic - you probably want want to use a different prompt template and send a different version there."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6bb9ba9",
+   "metadata": {},
+   "source": [
+    "## Handling LLM API Errors\n",
+    "\n",
+    "This is maybe the most common use case for fallbacks. A request to an LLM API can fail for a variety of reasons - the API could be down, you could have hit rate limits, any number of things. Therefor, using fallbacks can help protect against these types of things.\n",
+    "\n",
+    "IMPORTANT: By default, a lot of the LLM wrappers catch errors and retry. You will most likely want to turn those off when working with fallbacks. Otherwise the first wrapper will keep on retying and not failing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d3e893bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI, ChatAnthropic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4847c82d",
+   "metadata": {},
+   "source": [
+    "First, let's mock out what happens if we hit a RateLimitError from OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "dfdd8bf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from unittest.mock import patch\n",
+    "from openai.error import RateLimitError"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e6fdffc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note that we set max_retries = 0 to avoid retrying on RateLimits, etc\n",
+    "openai_llm = ChatOpenAI(max_retries=0)\n",
+    "anthropic_llm = ChatAnthropic()\n",
+    "llm = openai_llm.with_fallbacks([anthropic_llm])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "584461ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hit error\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's use just the OpenAI LLm first, to show that we run into an error\n",
+    "with patch('openai.ChatCompletion.create', side_effect=RateLimitError()):\n",
+    "    try:\n",
+    "         print(openai_llm.invoke(\"Why did the chicken cross the road?\"))\n",
+    "    except:\n",
+    "        print(\"Hit error\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "4fc1e673",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content=' I don\\'t actually know why the chicken crossed the road, but here are some possible humorous answers:\\n\\n- To get to the other side!\\n\\n- It was too chicken to just stand there. \\n\\n- It wanted a change of scenery.\\n\\n- It wanted to show the possum it could be done.\\n\\n- It was on its way to a poultry farmers\\' convention.\\n\\nThe joke plays on the double meaning of \"the other side\" - literally crossing the road to the other side, or the \"other side\" meaning the afterlife. So it\\'s an anti-joke, with a silly or unexpected pun as the answer.' additional_kwargs={} example=False\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now let's try with fallbacks to Anthropic\n",
+    "with patch('openai.ChatCompletion.create', side_effect=RateLimitError()):\n",
+    "    try:\n",
+    "         print(llm.invoke(\"Why did the the chicken cross the road?\"))\n",
+    "    except:\n",
+    "        print(\"Hit error\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f00bea25",
+   "metadata": {},
+   "source": [
+    "We can use our \"LLM with Fallbacks\" as we would a normal LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4f8eaaa0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content=\" I don't actually know why the kangaroo crossed the road, but I can take a guess! Here are some possible reasons:\\n\\n- To get to the other side (the classic joke answer!)\\n\\n- It was trying to find some food or water \\n\\n- It was trying to find a mate during mating season\\n\\n- It was fleeing from a predator or perceived threat\\n\\n- It was disoriented and crossed accidentally \\n\\n- It was following a herd of other kangaroos who were crossing\\n\\n- It wanted a change of scenery or environment \\n\\n- It was trying to reach a new habitat or territory\\n\\nThe real reason is unknown without more context, but hopefully one of those potential explanations does the joke justice! Let me know if you have any other animal jokes I can try to decipher.\" additional_kwargs={} example=False\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You're a nice assistant who always includes a compliment in your response\"),\n",
+    "        (\"human\", \"Why did the {animal} cross the road\"),\n",
+    "    ]\n",
+    ")\n",
+    "chain = prompt | llm\n",
+    "with patch('openai.ChatCompletion.create', side_effect=RateLimitError()):\n",
+    "    try:\n",
+    "         print(chain.invoke({\"animal\": \"kangaroo\"}))\n",
+    "    except:\n",
+    "        print(\"Hit error\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d62241b",
+   "metadata": {},
+   "source": [
+    "## Fallbacks for Sequences\n",
+    "\n",
+    "We can also create fallbacks for sequences, that are sequences themselves. Here we do that with two different models: ChatOpenAI and then normal OpenAI (which does not use a chat model). Because OpenAI is NOT a chat model, you likely want a different prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "6d0b8056",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First let's create a chain with a ChatModel\n",
+    "# We add in a string output parser here so the outputs between the two are the same type\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "\n",
+    "chat_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You're a nice assistant who always includes a compliment in your response\"),\n",
+    "        (\"human\", \"Why did the {animal} cross the road\"),\n",
+    "    ]\n",
+    ")\n",
+    "# Here we're going to use a bad model name to easily create a chain that will error\n",
+    "chat_model = ChatOpenAI(model_name=\"gpt-fake\")\n",
+    "bad_chain = chat_prompt | chat_model | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "8d1fc2a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now lets create a chain with the normal OpenAI model\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "prompt_template = \"\"\"Instructions: You should always include a compliment in your response.\n",
+    "\n",
+    "Question: Why did the {animal} cross the road?\"\"\"\n",
+    "prompt = PromptTemplate.from_template(prompt_template)\n",
+    "llm = OpenAI()\n",
+    "good_chain = prompt | llm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "283bfa44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nAnswer: The turtle crossed the road to get to the other side, and I have to say he had some impressive determination.'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can now create a final chain which combines the two\n",
+    "chain = bad_chain.with_fallbacks([good_chain])\n",
+    "chain.invoke({\"animal\": \"turtle\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec4685b4",
+   "metadata": {},
+   "source": [
+    "## Handling Long Inputs\n",
+    "\n",
+    "One of the big limiting factors of LLMs in their context window. Usually you can count and track the length of prompts before sending them to an LLM, but in situations where that is hard/complicated you can fallback to a model with longer context length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "564b84c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "short_llm = ChatOpenAI()\n",
+    "long_llm = ChatOpenAI(model=\"gpt-3.5-turbo-16k\")\n",
+    "llm = short_llm.with_fallbacks([long_llm])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "5e27a775",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = \"What is the next number: \" + \", \".join([\"one\", \"two\"] * 3000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "0a502731",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This model's maximum context length is 4097 tokens. However, your messages resulted in 12012 tokens. Please reduce the length of the messages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print(short_llm.invoke(inputs))\n",
+    "except Exception as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d91ba5d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content='The next number in the sequence is two.' additional_kwargs={} example=False\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print(llm.invoke(inputs))\n",
+    "except Exception as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a6735df",
+   "metadata": {},
+   "source": [
+    "## Fallback to Better Model\n",
+    "\n",
+    "Often times we ask models to output format in a specific format (like JSON). Models like GPT-3.5 can do this okay, but sometimes struggle. This naturally points to fallbacks - we can try with GPT-3.5 (faster, cheaper), but then if parsing fails we can use GPT-4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "867a3793",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.output_parsers import DatetimeOutputParser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "b8d9959d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = ChatPromptTemplate.from_template(\n",
+    "    \"what time was {event} (in %Y-%m-%dT%H:%M:%S.%fZ format - only return this value)\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "98087a76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In this case we are going to do the fallbacks on the LLM + output parser level\n",
+    "# Because the error will get raised in the OutputParser\n",
+    "openai_35 = ChatOpenAI() | DatetimeOutputParser()\n",
+    "openai_4 = ChatOpenAI(model=\"gpt-4\")| DatetimeOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "17ec9e8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "only_35 = prompt | openai_35 \n",
+    "fallback_4 = prompt | openai_35.with_fallbacks([openai_4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "7e536f0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: Could not parse datetime string: The Super Bowl in 1994 took place on January 30th at 3:30 PM local time. Converting this to the specified format (%Y-%m-%dT%H:%M:%S.%fZ) results in: 1994-01-30T15:30:00.000Z\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print(only_35.invoke({\"event\": \"the superbowl in 1994\"}))\n",
+    "except Exception as e:\n",
+    "    print(f\"Error: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "01355c5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1994-01-30 15:30:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print(fallback_4.invoke({\"event\": \"the superbowl in 1994\"}))\n",
+    "except Exception as e:\n",
+    "    print(f\"Error: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c537f9d0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/guides/local_llms.ipynb b/docs/extras/guides/local_llms.ipynb
new file mode 100644
index 00000000000..5f399df3567
--- /dev/null
+++ b/docs/extras/guides/local_llms.ipynb
@@ -0,0 +1,807 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b8982428",
+   "metadata": {},
+   "source": [
+    "# Private, local, open source LLMs\n",
+    "\n",
+    "## Use case\n",
+    "\n",
+    "The popularity of projects like [PrivateGPT](https://github.com/imartinez/privateGPT), [llama.cpp](https://github.com/ggerganov/llama.cpp), and [GPT4All](https://github.com/nomic-ai/gpt4all) underscore the demand to run LLMs locally (on your own device).\n",
+    "\n",
+    "This has at least two important benefits:\n",
+    "\n",
+    "1. `Privacy`: Your data is not sent to a third party, and it is not subject to the terms of service of a commercial service\n",
+    "2. `Cost`: There is no inference fee, which is important for token-intensive applications (e.g., [long-running simulations](https://twitter.com/RLanceMartin/status/1691097659262820352?s=20), summarization)\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "Running an LLM locally requires a few things:\n",
+    "\n",
+    "1. `Open source LLM`: An open source LLM that can be freely modified and shared \n",
+    "2. `Inference`: Ability to run this LLM on your device w/ acceptable latency\n",
+    "\n",
+    "### Open Source LLMs\n",
+    "\n",
+    "Users can now gain access to a rapidly growing set of [open source LLMs](https://cameronrwolfe.substack.com/p/the-history-of-open-source-llms-better). \n",
+    "\n",
+    "These LLMs can be assessed across at least two dimentions (see figure):\n",
+    " \n",
+    "1. `Base model`: What is the base-model and how was it trained?\n",
+    "2. `Fine-tuning approach`: Was the base-model fine-tuned and, if so, what [set of instructions](https://cameronrwolfe.substack.com/p/beyond-llama-the-power-of-open-llms#%C2%A7alpaca-an-instruction-following-llama-model) was used?\n",
+    "\n",
+    "![Image description](/img/OSS_LLM_overview.png)\n",
+    "\n",
+    "The relative performance of these models can be assessed using several leaderboards, including:\n",
+    "\n",
+    "1. [LmSys](https://chat.lmsys.org/?arena)\n",
+    "2. [GPT4All](https://gpt4all.io/index.html)\n",
+    "3. [HuggingFace](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)\n",
+    "\n",
+    "### Inference\n",
+    "\n",
+    "A few frameworks for this have emerged to support inference of open source LLMs on various devices:\n",
+    "\n",
+    "1. [`llama.cpp`](https://github.com/ggerganov/llama.cpp): C++ implementation of llama inference code with [weight optimization / quantization](https://finbarr.ca/how-is-llama-cpp-possible/)\n",
+    "2. [`gpt4all`](https://docs.gpt4all.io/index.html): Optimized C backend for inference\n",
+    "3. [`Ollama`](https://ollama.ai/): Bundles model weights and environment into an app that runs on device and serves the LLM \n",
+    "\n",
+    "In general, these frameworks will do a few things:\n",
+    "\n",
+    "1. `Quantization`: Reduce the memory footprint of the raw model weights\n",
+    "2. `Efficient implementation for inference`: Support inference on consumer hardware (e.g., CPU or laptop GPU)\n",
+    "\n",
+    "In particular, see [this excellent post](https://finbarr.ca/how-is-llama-cpp-possible/) on the importance of quantization.\n",
+    "\n",
+    "![Image description](/img/llama-memory-weights.png)\n",
+    "\n",
+    "With less precision, we radically decrease the memory needed to store the LLM in memory.\n",
+    "\n",
+    "In addition, we can see the importance of GPU memory bandwidth [sheet](https://docs.google.com/spreadsheets/d/1OehfHHNSn66BP2h3Bxp2NJTVX97icU0GmCXF6pK23H8/edit#gid=0)!\n",
+    "\n",
+    "A Mac M2 Max is 5-6x faster than a M1 for inference due to the larger GPU memory bandwidth.\n",
+    "\n",
+    "![Image description](/img/llama_t_put.png)\n",
+    "\n",
+    "## Quickstart\n",
+    "\n",
+    "[`Ollama`](https://ollama.ai/) is one way to easily run inference on macOS.\n",
+    " \n",
+    "The instructions [here](docs/integrations/llms/ollama) provide details, which we summarize:\n",
+    " \n",
+    "* [Download and run](https://ollama.ai/download) the app\n",
+    "* From command line, fetch a model from this [list of options](https://github.com/jmorganca/ollama): e.g., `ollama pull llama2`\n",
+    "* When the app is running, all models are automatically served on `localhost:11434`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "86178adb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' The first man on the moon was Neil Armstrong, who landed on the moon on July 20, 1969 as part of the Apollo 11 mission. obviously.'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.llms import Ollama\n",
+    "llm = Ollama(model=\"llama2\")\n",
+    "llm(\"The first man on the moon was ...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "343ab645",
+   "metadata": {},
+   "source": [
+    "Stream tokens as they are being generated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "9cd83603",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " The first man to walk on the moon was Neil Armstrong, an American astronaut who was part of the Apollo 11 mission in 1969. февруари 20, 1969, Armstrong stepped out of the lunar module Eagle and onto the moon's surface, famously declaring \"That's one small step for man, one giant leap for mankind\" as he took his first steps. He was followed by fellow astronaut Edwin \"Buzz\" Aldrin, who also walked on the moon during the mission."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' The first man to walk on the moon was Neil Armstrong, an American astronaut who was part of the Apollo 11 mission in 1969. февруари 20, 1969, Armstrong stepped out of the lunar module Eagle and onto the moon\\'s surface, famously declaring \"That\\'s one small step for man, one giant leap for mankind\" as he took his first steps. He was followed by fellow astronaut Edwin \"Buzz\" Aldrin, who also walked on the moon during the mission.'"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler \n",
+    "llm = Ollama(model=\"llama2\", \n",
+    "             callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]))\n",
+    "llm(\"The first man on the moon was ...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cb27414",
+   "metadata": {},
+   "source": [
+    "## Environment\n",
+    "\n",
+    "Inference speed is a chllenge when running models locally (see above).\n",
+    "\n",
+    "To minimize latency, it is desiable to run models locally on GPU, which ships with many consumer laptops [e.g., Apple devices](https://www.apple.com/newsroom/2022/06/apple-unveils-m2-with-breakthrough-performance-and-capabilities/).\n",
+    "\n",
+    "And even with GPU, the available GPU memory bandwidth (as noted above) is important.\n",
+    "\n",
+    "### Running Apple silicon GPU\n",
+    "\n",
+    "`Ollama` will automatically utilize the GPU on Apple devices.\n",
+    " \n",
+    "Other frameworks require the user to set up the environment to utilize the Apple GPU.\n",
+    "\n",
+    "For example, `llama.cpp` python bindings can be configured to use the GPU via [Metal](https://developer.apple.com/metal/).\n",
+    "\n",
+    "Metal is a graphics and compute API created by Apple providing near-direct access to the GPU. \n",
+    "\n",
+    "See the [`llama.cpp`](docs/integrations/llms/llamacpp) setup [here](https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md) to enable this.\n",
+    "\n",
+    "In particular, ensure that conda is using the correct virtual enviorment that you created (`miniforge3`).\n",
+    "\n",
+    "E.g., for me:\n",
+    "\n",
+    "```\n",
+    "conda activate /Users/rlm/miniforge3/envs/llama\n",
+    "```\n",
+    "\n",
+    "With the above confirmed, then:\n",
+    "\n",
+    "```\n",
+    "CMAKE_ARGS=\"-DLLAMA_METAL=on\" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c382e79a",
+   "metadata": {},
+   "source": [
+    "## LLMs\n",
+    "\n",
+    "There are various ways to gain access to quantized model weights.\n",
+    "\n",
+    "1. [`HuggingFace`](https://huggingface.co/TheBloke) - Many quantized model are available for download and can be run with framework such as [`llama.cpp`](https://github.com/ggerganov/llama.cpp)\n",
+    "2. [`gpt4all`](https://gpt4all.io/index.html) - The model explorer offers a leaderboard of metrics and associated quantized models available for download \n",
+    "3. [`Ollama`](https://github.com/jmorganca/ollama) - Several models can be accessed directly via `pull`\n",
+    "\n",
+    "### Ollama\n",
+    "\n",
+    "With [Ollama](docs/integrations/llms/ollama), fetch a model via `ollama pull <model family>:<tag>`:\n",
+    "\n",
+    "* E.g., for Llama-7b: `ollama pull llama2` will download the most basic version of the model (e.g., smallest # parameters and 4 bit quantization)\n",
+    "* We can also specify a particular version from the [model list](https://github.com/jmorganca/ollama), e.g., `ollama pull llama2:13b`\n",
+    "* See the full set of parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain.llms.ollama.Ollama.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "8ecd2f78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' Sure! Here\\'s the answer, broken down step by step:\\n\\nThe first man on the moon was... Neil Armstrong.\\n\\nHere\\'s how I arrived at that answer:\\n\\n1. The first manned mission to land on the moon was Apollo 11.\\n2. The mission included three astronauts: Neil Armstrong, Edwin \"Buzz\" Aldrin, and Michael Collins.\\n3. Neil Armstrong was the mission commander and the first person to set foot on the moon.\\n4. On July 20, 1969, Armstrong stepped out of the lunar module Eagle and onto the moon\\'s surface, famously declaring \"That\\'s one small step for man, one giant leap for mankind.\"\\n\\nSo, the first man on the moon was Neil Armstrong!'"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.llms import Ollama\n",
+    "llm = Ollama(model=\"llama2:13b\")\n",
+    "llm(\"The first man on the moon was ... think step by step\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07c8c0d1",
+   "metadata": {},
+   "source": [
+    "### Llama.cpp\n",
+    "\n",
+    "Llama.cpp is compatible with a [broad set of models](https://github.com/ggerganov/llama.cpp).\n",
+    "\n",
+    "For example, below we run inference on `llama2-13b` with 4 bit quantization downloaded from [HuggingFace](https://huggingface.co/TheBloke/Llama-2-13B-GGML/tree/main).\n",
+    "\n",
+    "As noted above, see the [API reference](https://api.python.langchain.com/en/latest/llms/langchain.llms.llamacpp.LlamaCpp.html?highlight=llamacpp#langchain.llms.llamacpp.LlamaCpp) for the full set of parameters. \n",
+    "\n",
+    "From the [llama.cpp docs](https://python.langchain.com/docs/integrations/llms/llamacpp), a few are worth commenting on:\n",
+    "\n",
+    "`n_gpu_layers`: number of layers to be loaded into GPU memory\n",
+    "\n",
+    "* Value: 1\n",
+    "* Meaning: Only one layer of the model will be loaded into GPU memory (1 is often sufficient).\n",
+    "\n",
+    "`n_batch`: number of tokens the model should process in parallel \n",
+    "* Value: n_batch\n",
+    "* Meaning: It's recommended to choose a value between 1 and n_ctx (which in this case is set to 2048)\n",
+    "\n",
+    "`n_ctx`: Token context window .\n",
+    "* Value: 2048\n",
+    "* Meaning: The model will consider a window of 2048 tokens at a time\n",
+    "\n",
+    "`f16_kv`: whether the model should use half-precision for the key/value cache\n",
+    "* Value: True\n",
+    "* Meaning: The model will use half-precision, which can be more memory efficient; Metal only support True."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5eba38dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install llama-cpp-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "9d5f94b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[10142]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x2a0c4c208) and /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/libllama.dylib (0x2c28bc208). One of the two will be used. Which one is undefined.\n",
+      "llama.cpp: loading model from /Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\n",
+      "llama_model_load_internal: format     = ggjt v3 (latest)\n",
+      "llama_model_load_internal: n_vocab    = 32000\n",
+      "llama_model_load_internal: n_ctx      = 2048\n",
+      "llama_model_load_internal: n_embd     = 5120\n",
+      "llama_model_load_internal: n_mult     = 256\n",
+      "llama_model_load_internal: n_head     = 40\n",
+      "llama_model_load_internal: n_layer    = 40\n",
+      "llama_model_load_internal: n_rot      = 128\n",
+      "llama_model_load_internal: freq_base  = 10000.0\n",
+      "llama_model_load_internal: freq_scale = 1\n",
+      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
+      "llama_model_load_internal: n_ff       = 13824\n",
+      "llama_model_load_internal: model size = 13B\n",
+      "llama_model_load_internal: ggml ctx size =    0.09 MB\n",
+      "llama_model_load_internal: mem required  = 8953.71 MB (+ 1608.00 MB per state)\n",
+      "llama_new_context_with_model: kv self size  = 1600.00 MB\n",
+      "ggml_metal_init: allocating\n",
+      "ggml_metal_init: using MPS\n",
+      "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n",
+      "ggml_metal_init: loaded kernel_add                            0x47774af60\n",
+      "ggml_metal_init: loaded kernel_mul                            0x47774bc00\n",
+      "ggml_metal_init: loaded kernel_mul_row                        0x47774c230\n",
+      "ggml_metal_init: loaded kernel_scale                          0x47774c890\n",
+      "ggml_metal_init: loaded kernel_silu                           0x47774cef0\n",
+      "ggml_metal_init: loaded kernel_relu                           0x10e33e500\n",
+      "ggml_metal_init: loaded kernel_gelu                           0x47774b2f0\n",
+      "ggml_metal_init: loaded kernel_soft_max                       0x47771a580\n",
+      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x47774dab0\n",
+      "ggml_metal_init: loaded kernel_get_rows_f16                   0x47774e110\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x47774e7d0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x13efd7170\n",
+      "ggml_metal_init: loaded kernel_get_rows_q2_K                  0x13efd73d0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q3_K                  0x13efd7630\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_K                  0x13efd7890\n",
+      "ggml_metal_init: loaded kernel_get_rows_q5_K                  0x4744c9740\n",
+      "ggml_metal_init: loaded kernel_get_rows_q6_K                  0x4744ca6b0\n",
+      "ggml_metal_init: loaded kernel_rms_norm                       0x4744cb250\n",
+      "ggml_metal_init: loaded kernel_norm                           0x4744cb970\n",
+      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x10e33f700\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x10e33fcd0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x4744cc2d0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q2_K_f32               0x4744cc6f0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q3_K_f32               0x4744cd6b0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_K_f32               0x4744cde20\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q5_K_f32               0x10e33ff30\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q6_K_f32               0x10e340190\n",
+      "ggml_metal_init: loaded kernel_rope                           0x10e3403f0\n",
+      "ggml_metal_init: loaded kernel_alibi_f32                      0x10e340de0\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x10e3416d0\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x10e342080\n",
+      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x10e342ca0\n",
+      "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
+      "ggml_metal_init: hasUnifiedMemory             = true\n",
+      "ggml_metal_init: maxTransferRate              = built-in GPU\n",
+      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  6984.06 MB, ( 6986.19 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =  1032.00 MB, ( 8018.19 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1602.00 MB, ( 9620.19 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr0            ' buffer, size =   426.00 MB, (10046.19 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr1            ' buffer, size =   512.00 MB, (10558.19 / 21845.34)\n",
+      "AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.llms import LlamaCpp\n",
+    "llm = LlamaCpp(\n",
+    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\",\n",
+    "    n_gpu_layers=1,\n",
+    "    n_batch=512,\n",
+    "    n_ctx=2048,\n",
+    "    f16_kv=True,  \n",
+    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f56f5168",
+   "metadata": {},
+   "source": [
+    "The console log will show the the below to indicate Metal was enabled properly from steps above:\n",
+    "```\n",
+    "ggml_metal_init: allocating\n",
+    "ggml_metal_init: using MPS\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "7890a077",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Llama.generate: prefix-match hit\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " and use logical reasoning to figure out who the first man on the moon was.\n",
+      "\n",
+      "Here are some clues:\n",
+      "\n",
+      "1. The first man on the moon was an American.\n",
+      "2. He was part of the Apollo 11 mission.\n",
+      "3. He stepped out of the lunar module and became the first person to set foot on the moon's surface.\n",
+      "4. His last name is Armstrong.\n",
+      "\n",
+      "Now, let's use our reasoning skills to figure out who the first man on the moon was. Based on clue #1, we know that the first man on the moon was an American. Clue #2 tells us that he was part of the Apollo 11 mission. Clue #3 reveals that he was the first person to set foot on the moon's surface. And finally, clue #4 gives us his last name: Armstrong.\n",
+      "Therefore, the first man on the moon was Neil Armstrong!"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "llama_print_timings:        load time =  9623.21 ms\n",
+      "llama_print_timings:      sample time =   143.77 ms /   203 runs   (    0.71 ms per token,  1412.01 tokens per second)\n",
+      "llama_print_timings: prompt eval time =   485.94 ms /     7 tokens (   69.42 ms per token,    14.40 tokens per second)\n",
+      "llama_print_timings:        eval time =  6385.16 ms /   202 runs   (   31.61 ms per token,    31.64 tokens per second)\n",
+      "llama_print_timings:       total time =  7279.28 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\" and use logical reasoning to figure out who the first man on the moon was.\\n\\nHere are some clues:\\n\\n1. The first man on the moon was an American.\\n2. He was part of the Apollo 11 mission.\\n3. He stepped out of the lunar module and became the first person to set foot on the moon's surface.\\n4. His last name is Armstrong.\\n\\nNow, let's use our reasoning skills to figure out who the first man on the moon was. Based on clue #1, we know that the first man on the moon was an American. Clue #2 tells us that he was part of the Apollo 11 mission. Clue #3 reveals that he was the first person to set foot on the moon's surface. And finally, clue #4 gives us his last name: Armstrong.\\nTherefore, the first man on the moon was Neil Armstrong!\""
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm(\"The first man on the moon was ... Let's think step by step\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "831ddf7c",
+   "metadata": {},
+   "source": [
+    "### GPT4All\n",
+    "\n",
+    "We can use model weights downloaded from [GPT4All](https://python.langchain.com/docs/integrations/llms/gpt4all) model explorer.\n",
+    "\n",
+    "Similar to what is shown above, we can run inference and use [the API reference](https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html?highlight=gpt4all#langchain.llms.gpt4all.GPT4All) to set parameters of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e27baf6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install gpt4all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "b55a2147",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found model file at  /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n",
+      "llama_new_context_with_model: max tensor size =    87.89 MB\n",
+      "llama_new_context_with_model: max tensor size =    87.89 MB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "llama.cpp: using Metal\n",
+      "llama.cpp: loading model from /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n",
+      "llama_model_load_internal: format     = ggjt v3 (latest)\n",
+      "llama_model_load_internal: n_vocab    = 32001\n",
+      "llama_model_load_internal: n_ctx      = 2048\n",
+      "llama_model_load_internal: n_embd     = 5120\n",
+      "llama_model_load_internal: n_mult     = 256\n",
+      "llama_model_load_internal: n_head     = 40\n",
+      "llama_model_load_internal: n_layer    = 40\n",
+      "llama_model_load_internal: n_rot      = 128\n",
+      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
+      "llama_model_load_internal: n_ff       = 13824\n",
+      "llama_model_load_internal: n_parts    = 1\n",
+      "llama_model_load_internal: model size = 13B\n",
+      "llama_model_load_internal: ggml ctx size =    0.09 MB\n",
+      "llama_model_load_internal: mem required  = 9031.71 MB (+ 1608.00 MB per state)\n",
+      "llama_new_context_with_model: kv self size  = 1600.00 MB\n",
+      "ggml_metal_init: allocating\n",
+      "ggml_metal_init: using MPS\n",
+      "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/ggml-metal.metal'\n",
+      "ggml_metal_init: loaded kernel_add                            0x37944d850\n",
+      "ggml_metal_init: loaded kernel_mul                            0x37944f350\n",
+      "ggml_metal_init: loaded kernel_mul_row                        0x37944fdd0\n",
+      "ggml_metal_init: loaded kernel_scale                          0x3794505a0\n",
+      "ggml_metal_init: loaded kernel_silu                           0x379450800\n",
+      "ggml_metal_init: loaded kernel_relu                           0x379450a60\n",
+      "ggml_metal_init: loaded kernel_gelu                           0x379450cc0\n",
+      "ggml_metal_init: loaded kernel_soft_max                       0x379450ff0\n",
+      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x379451250\n",
+      "ggml_metal_init: loaded kernel_get_rows_f16                   0x3794514b0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x379451710\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x379451970\n",
+      "ggml_metal_init: loaded kernel_get_rows_q2_k                  0x379451bd0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q3_k                  0x379451e30\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_k                  0x379452090\n",
+      "ggml_metal_init: loaded kernel_get_rows_q5_k                  0x3794522f0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q6_k                  0x379452550\n",
+      "ggml_metal_init: loaded kernel_rms_norm                       0x3794527b0\n",
+      "ggml_metal_init: loaded kernel_norm                           0x379452a10\n",
+      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x379452c70\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x379452ed0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x379453130\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q2_k_f32               0x379453390\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q3_k_f32               0x3794535f0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_k_f32               0x379453850\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q5_k_f32               0x379453ab0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q6_k_f32               0x379453d10\n",
+      "ggml_metal_init: loaded kernel_rope                           0x379453f70\n",
+      "ggml_metal_init: loaded kernel_alibi_f32                      0x3794541d0\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x379454430\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x379454690\n",
+      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x3794548f0\n",
+      "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
+      "ggml_metal_init: hasUnifiedMemory             = true\n",
+      "ggml_metal_init: maxTransferRate              = built-in GPU\n",
+      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  6984.06 MB, (17542.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =  1024.00 MB, (18566.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1602.00 MB, (20168.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr0            ' buffer, size =   512.00 MB, (20680.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr1            ' buffer, size =   512.00 MB, (21192.94 / 21845.34)\n",
+      "ggml_metal_free: deallocating\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.llms import GPT4All\n",
+    "llm = GPT4All(model=\"/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "e3d4526f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\".\\n1) The United States decides to send a manned mission to the moon.2) They choose their best astronauts and train them for this specific mission.3) They build a spacecraft that can take humans to the moon, called the Lunar Module (LM).4) They also create a larger spacecraft, called the Saturn V rocket, which will launch both the LM and the Command Service Module (CSM), which will carry the astronauts into orbit.5) The mission is planned down to the smallest detail: from the trajectory of the rockets to the exact movements of the astronauts during their moon landing.6) On July 16, 1969, the Saturn V rocket launches from Kennedy Space Center in Florida, carrying the Apollo 11 mission crew into space.7) After one and a half orbits around the Earth, the LM separates from the CSM and begins its descent to the moon's surface.8) On July 20, 1969, at 2:56 pm EDT (GMT-4), Neil Armstrong becomes the first man on the moon. He speaks these\""
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm(\"The first man on the moon was ... Let's think step by step\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b84e543",
+   "metadata": {},
+   "source": [
+    "## Prompts\n",
+    "\n",
+    "Some LLMs will benefit from specific prompts.\n",
+    "\n",
+    "For example, llama2 can use [special tokens](https://twitter.com/RLanceMartin/status/1681879318493003776?s=20).\n",
+    "\n",
+    "We can use `ConditionalPromptSelector` to set prompt based on the model type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "d082b10a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "llama.cpp: loading model from /Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\n",
+      "llama_model_load_internal: format     = ggjt v3 (latest)\n",
+      "llama_model_load_internal: n_vocab    = 32000\n",
+      "llama_model_load_internal: n_ctx      = 2048\n",
+      "llama_model_load_internal: n_embd     = 5120\n",
+      "llama_model_load_internal: n_mult     = 256\n",
+      "llama_model_load_internal: n_head     = 40\n",
+      "llama_model_load_internal: n_layer    = 40\n",
+      "llama_model_load_internal: n_rot      = 128\n",
+      "llama_model_load_internal: freq_base  = 10000.0\n",
+      "llama_model_load_internal: freq_scale = 1\n",
+      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
+      "llama_model_load_internal: n_ff       = 13824\n",
+      "llama_model_load_internal: model size = 13B\n",
+      "llama_model_load_internal: ggml ctx size =    0.09 MB\n",
+      "llama_model_load_internal: mem required  = 8953.71 MB (+ 1608.00 MB per state)\n",
+      "llama_new_context_with_model: kv self size  = 1600.00 MB\n",
+      "ggml_metal_init: allocating\n",
+      "ggml_metal_init: using MPS\n",
+      "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n",
+      "ggml_metal_init: loaded kernel_add                            0x4744d09d0\n",
+      "ggml_metal_init: loaded kernel_mul                            0x3781cb3d0\n",
+      "ggml_metal_init: loaded kernel_mul_row                        0x37813bb60\n",
+      "ggml_metal_init: loaded kernel_scale                          0x474481080\n",
+      "ggml_metal_init: loaded kernel_silu                           0x4744d29f0\n",
+      "ggml_metal_init: loaded kernel_relu                           0x3781254c0\n",
+      "ggml_metal_init: loaded kernel_gelu                           0x47447f280\n",
+      "ggml_metal_init: loaded kernel_soft_max                       0x4744cf470\n",
+      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x4744cf6d0\n",
+      "ggml_metal_init: loaded kernel_get_rows_f16                   0x4744cf930\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x4744cfb90\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x4744cfdf0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q2_K                  0x4744d0050\n",
+      "ggml_metal_init: loaded kernel_get_rows_q3_K                  0x4744ce980\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_K                  0x4744cebe0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q5_K                  0x4744cee40\n",
+      "ggml_metal_init: loaded kernel_get_rows_q6_K                  0x4744cf0a0\n",
+      "ggml_metal_init: loaded kernel_rms_norm                       0x474482450\n",
+      "ggml_metal_init: loaded kernel_norm                           0x4744826b0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x474482910\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x474482b70\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x474482dd0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q2_K_f32               0x474483030\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q3_K_f32               0x474483290\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_K_f32               0x4744834f0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q5_K_f32               0x474483750\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q6_K_f32               0x4744839b0\n",
+      "ggml_metal_init: loaded kernel_rope                           0x474483c10\n",
+      "ggml_metal_init: loaded kernel_alibi_f32                      0x474483e70\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x4744840d0\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x474484330\n",
+      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x474484590\n",
+      "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
+      "ggml_metal_init: hasUnifiedMemory             = true\n",
+      "ggml_metal_init: maxTransferRate              = built-in GPU\n",
+      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  6984.06 MB, ( 6986.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =  1032.00 MB, ( 8018.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1602.00 MB, ( 9620.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr0            ' buffer, size =   426.00 MB, (10046.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr1            ' buffer, size =   512.00 MB, (10558.94 / 21845.34)\n",
+      "AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set our LLM\n",
+    "llm = LlamaCpp(\n",
+    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\",\n",
+    "    n_gpu_layers=1,\n",
+    "    n_batch=512,\n",
+    "    n_ctx=2048,\n",
+    "    f16_kv=True,  \n",
+    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66656084",
+   "metadata": {},
+   "source": [
+    "Set the associated prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "8555f5bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='<<SYS>> \\n You are an assistant tasked with improving Google search results. \\n <</SYS>> \\n\\n [INST] Generate THREE Google search queries that are similar to this question. The output should be a numbered list of questions and each should have a question mark at the end: \\n\\n {question} [/INST]', template_format='f-string', validate_template=True)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import PromptTemplate, LLMChain\n",
+    "from langchain.chains.prompt_selector import ConditionalPromptSelector\n",
+    "\n",
+    "DEFAULT_LLAMA_SEARCH_PROMPT = PromptTemplate(\n",
+    "    input_variables=[\"question\"],\n",
+    "    template=\"\"\"<<SYS>> \\n You are an assistant tasked with improving Google search \\\n",
+    "results. \\n <</SYS>> \\n\\n [INST] Generate THREE Google search queries that \\\n",
+    "are similar to this question. The output should be a numbered list of questions \\\n",
+    "and each should have a question mark at the end: \\n\\n {question} [/INST]\"\"\",\n",
+    ")\n",
+    "\n",
+    "DEFAULT_SEARCH_PROMPT = PromptTemplate(\n",
+    "    input_variables=[\"question\"],\n",
+    "    template=\"\"\"You are an assistant tasked with improving Google search \\\n",
+    "results. Generate THREE Google search queries that are similar to \\\n",
+    "this question. The output should be a numbered list of questions and each \\\n",
+    "should have a question mark at the end: {question}\"\"\",\n",
+    ")\n",
+    "\n",
+    "QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(\n",
+    "                default_prompt=DEFAULT_SEARCH_PROMPT,\n",
+    "                conditionals=[\n",
+    "                    (lambda llm: isinstance(llm, LlamaCpp), DEFAULT_LLAMA_SEARCH_PROMPT)\n",
+    "                ],\n",
+    "            )\n",
+    "\n",
+    "prompt = QUESTION_PROMPT_SELECTOR.get_prompt(llm)\n",
+    "prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "d0aedfd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Sure! Here are three similar search queries with a question mark at the end:\n",
+      "\n",
+      "1. Which NBA team did LeBron James lead to a championship in the year he was drafted?\n",
+      "2. Who won the Grammy Awards for Best New Artist and Best Female Pop Vocal Performance in the same year that Lady Gaga was born?\n",
+      "3. What MLB team did Babe Ruth play for when he hit 60 home runs in a single season?"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "llama_print_timings:        load time = 14943.19 ms\n",
+      "llama_print_timings:      sample time =    72.93 ms /   101 runs   (    0.72 ms per token,  1384.87 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 14942.95 ms /    93 tokens (  160.68 ms per token,     6.22 tokens per second)\n",
+      "llama_print_timings:        eval time =  3430.85 ms /   100 runs   (   34.31 ms per token,    29.15 tokens per second)\n",
+      "llama_print_timings:       total time = 18578.26 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'  Sure! Here are three similar search queries with a question mark at the end:\\n\\n1. Which NBA team did LeBron James lead to a championship in the year he was drafted?\\n2. Who won the Grammy Awards for Best New Artist and Best Female Pop Vocal Performance in the same year that Lady Gaga was born?\\n3. What MLB team did Babe Ruth play for when he hit 60 home runs in a single season?'"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Chain\n",
+    "llm_chain = LLMChain(prompt=prompt,llm=llm)\n",
+    "question = \"What NFL team won the Super Bowl in the year that Justin Bieber was born?\"\n",
+    "llm_chain.run({\"question\":question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ba66260",
+   "metadata": {},
+   "source": [
+    "## Use cases\n",
+    "\n",
+    "Given an `llm` created from one of the models above, you can use it for [many use cases](docs/use_cases).\n",
+    "\n",
+    "For example, here is a guide to [RAG](docs/use_cases/question_answering/how_to/local_retrieval_qa) with local LLMs.\n",
+    "\n",
+    "In general, use cases for local model can be driven by at least two factors:\n",
+    "\n",
+    "* `Privacy`: private data (e.g., journals, etc) that a user does not want to share \n",
+    "* `Cost`: text preprocessing (extraction/tagging), summarization, and agent simulations are token-use-intensive tasks\n",
+    "\n",
+    "There are a few approach to support specific use-cases: \n",
+    "\n",
+    "* Fine-tuning (e.g., [gpt-llm-trainer](https://github.com/mshumer/gpt-llm-trainer), [Anyscale](https://www.anyscale.com/blog/fine-tuning-llama-2-a-comprehensive-case-study-for-tailoring-models-to-unique-applications)) \n",
+    "* [Function-calling](https://github.com/MeetKai/functionary/tree/main) for use-cases like extraction or tagging\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/guides/pydantic_compatibility.md b/docs/extras/guides/pydantic_compatibility.md
new file mode 100644
index 00000000000..15518c5e23e
--- /dev/null
+++ b/docs/extras/guides/pydantic_compatibility.md
@@ -0,0 +1,121 @@
+# Pydantic Compatibility
+
+- Pydantic v2 was released in June, 2023 (https://docs.pydantic.dev/2.0/blog/pydantic-v2-final/)
+- v2 contains has a number of breaking changes (https://docs.pydantic.dev/2.0/migration/)
+- Pydantic v2 and v1 are under the same package name, so both versions cannot be installed at the same time
+
+
+## LangChain Pydantic Migration Plan
+
+Langchain will carry out the migration to pydantic v2 in two steps:
+
+1. 2023-08-17: LangChain will allow users to install either Pydantic V1 or V2. 
+   * Internally LangChain will continue to [use V1](https://docs.pydantic.dev/latest/migration/#continue-using-pydantic-v1-features).
+   * During this time, users can pin their pydantic version to v1 to avoid breaking changes, or start a partial
+   migration using pydantic v2 throughout their code, but avoiding mixing v1 and v2 code for LangChain (see below).
+
+2. 2023-08-25: Langchain will migrate internally to using V2 code. 
+  * Users will have to upgrade to V2 as well to use LangChain.
+  * Users should stop using the `pydantic.v1` namespace when using LangChain.
+  * See the [bump-pydantic package](https://github.com/pydantic/bump-pydantic) to help with the upgrade process.
+
+## Between 2023-08-17 and 2023-08-25 releases
+
+User can either pin to pydantic v1, and upgrade their code in one go once LangChain has migrated to v2 internally, or they can start a partial migration to v2, but must avoid mixing v1 and v2 code for LangChain.
+
+Below are two examples of showing how to avoid mixing pydantic v1 and v2 code in
+the case of inheritance and in the case of passing objects to LangChain.
+
+**Example 1: Extending via inheritance**
+
+**YES** 
+
+```python
+from pydantic.v1 import root_validator, validator
+
+class CustomTool(BaseTool): # BaseTool is v1 code
+    x: int = Field(default=1)
+
+    def _run(*args, **kwargs):
+        return "hello"
+
+    @validator('x') # v1 code
+    @classmethod
+    def validate_x(cls, x: int) -> int:
+        return 1
+    
+
+CustomTool(
+    name='custom_tool',
+    description="hello",
+    x=1,
+)
+```
+
+Mixing Pydantic v2 primitives with Pydantic v1 primitives can raise cryptic errors
+
+**NO** 
+
+```python
+from pydantic import Field, field_validator # pydantic v2
+
+class CustomTool(BaseTool): # BaseTool is v1 code
+    x: int = Field(default=1)
+
+    def _run(*args, **kwargs):
+        return "hello"
+
+    @field_validator('x') # v2 code
+    @classmethod
+    def validate_x(cls, x: int) -> int:
+        return 1
+    
+
+CustomTool( 
+    name='custom_tool',
+    description="hello",
+    x=1,
+)
+```
+
+**Example 2: Passing objects to LangChain**
+
+**YES**
+
+```python
+from langchain.tools.base import Tool
+from pydantic.v1 import BaseModel, Field # <-- Uses v1 namespace
+
+class CalculatorInput(BaseModel):
+    question: str = Field()
+
+Tool.from_function( # <-- tool uses v1 namespace
+    func=lambda question: 'hello',
+    name="Calculator",
+    description="useful for when you need to answer questions about math",
+    args_schema=CalculatorInput
+)
+```
+
+**NO**
+
+```python
+from langchain.tools.base import Tool
+from pydantic import BaseModel, Field # <-- Uses v2 namespace
+
+class CalculatorInput(BaseModel):
+    question: str = Field()
+
+Tool.from_function( # <-- tool uses v1 namespace
+    func=lambda question: 'hello',
+    name="Calculator",
+    description="useful for when you need to answer questions about math",
+    args_schema=CalculatorInput
+)
+```
+
+## After 2023-08-25 release
+
+* Users must upgrade to v2
+* Users should not pass `pydantic.v1` derived objects to LangChain or rely on `pydantic.v1` when extending functionality
+
diff --git a/docs/extras/integrations/callbacks/argilla.ipynb b/docs/extras/integrations/callbacks/argilla.ipynb
index 7a78b3198c6..ff46507151a 100644
--- a/docs/extras/integrations/callbacks/argilla.ipynb
+++ b/docs/extras/integrations/callbacks/argilla.ipynb
@@ -147,7 +147,7 @@
     "    api_key=os.environ[\"ARGILLA_API_KEY\"],\n",
     ")\n",
     "\n",
-    "dataset.push_to_argilla(\"langchain-dataset\")"
+    "dataset.push_to_argilla(\"langchain-dataset\");"
    ]
   },
   {
diff --git a/docs/extras/integrations/callbacks/context.ipynb b/docs/extras/integrations/callbacks/context.ipynb
index c1ad8cb10db..50e422562e5 100644
--- a/docs/extras/integrations/callbacks/context.ipynb
+++ b/docs/extras/integrations/callbacks/context.ipynb
@@ -7,12 +7,12 @@
    "source": [
     "# Context\n",
     "\n",
-    "![Context - Product Analytics for AI Chatbots](https://go.getcontext.ai/langchain.png)\n",
+    "![Context - User Analytics for LLM Powered Products](https://with.context.ai/langchain.png)\n",
     "\n",
-    "[Context](https://getcontext.ai/) provides product analytics for AI chatbots.\n",
+    "[Context](https://context.ai/) provides user analytics for LLM powered products and features.\n",
     "\n",
-    "Context helps you understand how users are interacting with your AI chat products.\n",
-    "Gain critical insights, optimise poor experiences, and minimise brand risks.\n"
+    "With Context, you can start understanding your users and improving their experiences in less than 30 minutes.\n",
+    "\n"
    ]
   },
   {
@@ -55,7 +55,7 @@
     "\n",
     "To get your Context API token:\n",
     "\n",
-    "1. Go to the settings page within your Context account (https://go.getcontext.ai/settings).\n",
+    "1. Go to the settings page within your Context account (https://with.context.ai/settings).\n",
     "2. Generate a new API Token.\n",
     "3. Store this token somewhere secure."
    ]
@@ -207,7 +207,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/extras/integrations/callbacks/labelstudio.ipynb b/docs/extras/integrations/callbacks/labelstudio.ipynb
new file mode 100644
index 00000000000..927db2d639a
--- /dev/null
+++ b/docs/extras/integrations/callbacks/labelstudio.ipynb
@@ -0,0 +1,382 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Label Studio\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"https://labelstudio-pub.s3.amazonaws.com/lc/open-source-data-labeling-platform.png\" width=\"400\"/>\n",
+    "</div>\n",
+    "\n",
+    "Label Studio is an open-source data labeling platform that provides LangChain with flexibility when it comes to labeling data for fine-tuning large language models (LLMs). It also enables the preparation of custom training data and the collection and evaluation of responses through human feedback.\n",
+    "\n",
+    "In this guide, you will learn how to connect a LangChain pipeline to Label Studio to:\n",
+    "\n",
+    "- Aggregate all input prompts, conversations, and responses in a single LabelStudio project. This consolidates all the data in one place for easier labeling and analysis.\n",
+    "- Refine prompts and responses to create a dataset for supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) scenarios. The labeled data can be used to further train the LLM to improve its performance.\n",
+    "- Evaluate model responses through human feedback. LabelStudio provides an interface for humans to review and provide feedback on model responses, allowing evaluation and iteration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Installation and setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "First install latest versions of Label Studio and Label Studio API client:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -U label-studio label-studio-sdk openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Next, run `label-studio` on the command line to start the local LabelStudio instance at `http://localhost:8080`. See the [Label Studio installation guide](https://labelstud.io/guide/install) for more options."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "You'll need a token to make API calls.\n",
+    "\n",
+    "Open your LabelStudio instance in your browser, go to `Account & Settings > Access Token` and copy the key.\n",
+    "\n",
+    "Set environment variables with your LabelStudio URL, API key and OpenAI API key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ['LABEL_STUDIO_URL'] = '<YOUR-LABEL-STUDIO-URL>'  # e.g. http://localhost:8080\n",
+    "os.environ['LABEL_STUDIO_API_KEY'] = '<YOUR-LABEL-STUDIO-API-KEY>'\n",
+    "os.environ['OPENAI_API_KEY'] = '<YOUR-OPENAI-API-KEY>'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Collecting LLMs prompts and responses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data used for labeling is stored in projects within Label Studio. Every project is identified by an XML configuration that details the specifications for input and output data. \n",
+    "\n",
+    "Create a project that takes human input in text format and outputs an editable LLM response in a text area:\n",
+    "\n",
+    "```xml\n",
+    "<View>\n",
+    "<Style>\n",
+    "    .prompt-box {\n",
+    "        background-color: white;\n",
+    "        border-radius: 10px;\n",
+    "        box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1);\n",
+    "        padding: 20px;\n",
+    "    }\n",
+    "</Style>\n",
+    "<View className=\"root\">\n",
+    "    <View className=\"prompt-box\">\n",
+    "        <Text name=\"prompt\" value=\"$prompt\"/>\n",
+    "    </View>\n",
+    "    <TextArea name=\"response\" toName=\"prompt\"\n",
+    "              maxSubmissions=\"1\" editable=\"true\"\n",
+    "              required=\"true\"/>\n",
+    "</View>\n",
+    "<Header value=\"Rate the response:\"/>\n",
+    "<Rating name=\"rating\" toName=\"prompt\"/>\n",
+    "</View>\n",
+    "```\n",
+    "\n",
+    "1. To create a project in Label Studio, click on the \"Create\" button. \n",
+    "2. Enter a name for your project in the \"Project Name\" field, such as `My Project`.\n",
+    "3. Navigate to `Labeling Setup > Custom Template` and paste the XML configuration provided above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "You can collect input LLM prompts and output responses in a LabelStudio project, connecting it via `LabelStudioCallbackHandler`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.callbacks import LabelStudioCallbackHandler\n",
+    "\n",
+    "llm = OpenAI(\n",
+    "    temperature=0,\n",
+    "    callbacks=[\n",
+    "        LabelStudioCallbackHandler(\n",
+    "            project_name=\"My Project\"\n",
+    "    )]\n",
+    ")\n",
+    "print(llm(\"Tell me a joke\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "In the Label Studio, open `My Project`. You will see the prompts, responses, and metadata like the model name. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Collecting Chat model Dialogues"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also track and display full chat dialogues in LabelStudio, with the ability to rate and modify the last response:\n",
+    "\n",
+    "1. Open Label Studio and click on the \"Create\" button.\n",
+    "2. Enter a name for your project in the \"Project Name\" field, such as `New Project with Chat`.\n",
+    "3. Navigate to Labeling Setup > Custom Template and paste the following XML configuration:\n",
+    "\n",
+    "```xml\n",
+    "<View>\n",
+    "<View className=\"root\">\n",
+    "     <Paragraphs name=\"dialogue\"\n",
+    "               value=\"$prompt\"\n",
+    "               layout=\"dialogue\"\n",
+    "               textKey=\"content\"\n",
+    "               nameKey=\"role\"\n",
+    "               granularity=\"sentence\"/>\n",
+    "  <Header value=\"Final response:\"/>\n",
+    "    <TextArea name=\"response\" toName=\"dialogue\"\n",
+    "              maxSubmissions=\"1\" editable=\"true\"\n",
+    "              required=\"true\"/>\n",
+    "</View>\n",
+    "<Header value=\"Rate the response:\"/>\n",
+    "<Rating name=\"rating\" toName=\"dialogue\"/>\n",
+    "</View>\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "from langchain.callbacks import LabelStudioCallbackHandler\n",
+    "\n",
+    "chat_llm = ChatOpenAI(callbacks=[\n",
+    "    LabelStudioCallbackHandler(\n",
+    "        mode=\"chat\",\n",
+    "        project_name=\"New Project with Chat\",\n",
+    "    )\n",
+    "])\n",
+    "llm_results = chat_llm([\n",
+    "    SystemMessage(content=\"Always use a lot of emojis\"),\n",
+    "    HumanMessage(content=\"Tell me a joke\")\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In Label Studio, open \"New Project with Chat\". Click on a created task to view dialog history and edit/annotate responses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Custom Labeling Configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "You can modify the default labeling configuration in LabelStudio to add more target labels like response sentiment, relevance, and many [other types annotator's feedback](https://labelstud.io/tags/).\n",
+    "\n",
+    "New labeling configuration can be added from UI: go to `Settings > Labeling Interface` and set up a custom configuration with additional tags like `Choices` for sentiment or `Rating` for relevance. Keep in mind that [`TextArea` tag](https://labelstud.io/tags/textarea) should be presented in any configuration to display the LLM responses.\n",
+    "\n",
+    "Alternatively, you can specify the labeling configuration on the initial call before project creation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ls = LabelStudioCallbackHandler(project_config='''\n",
+    "<View>\n",
+    "<Text name=\"prompt\" value=\"$prompt\"/>\n",
+    "<TextArea name=\"response\" toName=\"prompt\"/>\n",
+    "<TextArea name=\"user_feedback\" toName=\"prompt\"/>\n",
+    "<Rating name=\"rating\" toName=\"prompt\"/>\n",
+    "<Choices name=\"sentiment\" toName=\"prompt\">\n",
+    "    <Choice value=\"Positive\"/>\n",
+    "    <Choice value=\"Negative\"/>\n",
+    "</Choices>\n",
+    "</View>\n",
+    "''')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that if the project doesn't exist, it will be created with the specified labeling configuration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Other parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "The `LabelStudioCallbackHandler` accepts several optional parameters:\n",
+    "\n",
+    "- **api_key** - Label Studio API key. Overrides environmental variable `LABEL_STUDIO_API_KEY`.\n",
+    "- **url** - Label Studio URL. Overrides `LABEL_STUDIO_URL`, default `http://localhost:8080`.\n",
+    "- **project_id** - Existing Label Studio project ID. Overrides `LABEL_STUDIO_PROJECT_ID`. Stores data in this project.\n",
+    "- **project_name** - Project name if project ID not specified. Creates a new project. Default is `\"LangChain-%Y-%m-%d\"` formatted with the current date.\n",
+    "- **project_config** - [custom labeling configuration](#custom-labeling-configuration)\n",
+    "- **mode**: use this shortcut to create target configuration from scratch:\n",
+    "   - `\"prompt\"` - Single prompt, single response. Default.\n",
+    "   - `\"chat\"` - Multi-turn chat mode.\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "labelops",
+   "language": "python",
+   "name": "labelops"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/extras/integrations/chat/ernie.ipynb b/docs/extras/integrations/chat/ernie.ipynb
new file mode 100644
index 00000000000..b887991f576
--- /dev/null
+++ b/docs/extras/integrations/chat/ernie.ipynb
@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ERNIE-Bot Chat\n",
+    "\n",
+    "[ERNIE-Bot](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/jlil56u11) is a large language model developed by Baidu, covering a huge amount of Chinese data.\n",
+    "This notebook covers how to get started with ErnieBot chat models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ErnieBotChat\n",
+    "from langchain.schema import HumanMessage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat = ErnieBotChat(ernie_client_id='YOUR_CLIENT_ID', ernie_client_secret='YOUR_CLIENT_SECRET')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "or you can set `client_id` and `client_secret` in your environment variables\n",
+    "```bash\n",
+    "export ERNIE_CLIENT_ID=YOUR_CLIENT_ID\n",
+    "export ERNIE_CLIENT_SECRET=YOUR_CLIENT_SECRET\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Hello, I am an artificial intelligence language model. My purpose is to help users answer questions or provide information. What can I do for you?', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat([\n",
+    "    HumanMessage(content='hello there, who are you?')\n",
+    "])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/extras/integrations/chat/litellm.ipynb b/docs/extras/integrations/chat/litellm.ipynb
new file mode 100644
index 00000000000..977f5f15546
--- /dev/null
+++ b/docs/extras/integrations/chat/litellm.ipynb
@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "bf733a38-db84-4363-89e2-de6735c37230",
+   "metadata": {},
+   "source": [
+    "# 🚅 LiteLLM\n",
+    "\n",
+    "[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc. \n",
+    "\n",
+    "This notebook covers how to get started with using Langchain + the LiteLLM I/O library. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatLiteLLM\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    AIMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "from langchain.schema import AIMessage, HumanMessage, SystemMessage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat = ChatLiteLLM(model=\"gpt-3.5-turbo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    HumanMessage(\n",
+    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
+    "    )\n",
+    "]\n",
+    "chat(messages)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
+   "metadata": {},
+   "source": [
+    "## `ChatLiteLLM` also supports async and streaming functionality:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LLMResult(generations=[[ChatGeneration(text=\" J'aime programmer.\", generation_info=None, message=AIMessage(content=\" J'aime programmer.\", additional_kwargs={}, example=False))]], llm_output={}, run=[RunInfo(run_id=UUID('8cc8fb68-1c35-439c-96a0-695036a93652'))])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await chat.agenerate([messages])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " J'aime la programmation."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat = ChatLiteLLM(\n",
+    "    streaming=True,\n",
+    "    verbose=True,\n",
+    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
+    ")\n",
+    "chat(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c253883f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/integrations/document_loaders/arcgis.ipynb b/docs/extras/integrations/document_loaders/arcgis.ipynb
new file mode 100644
index 00000000000..9420d4c4e0c
--- /dev/null
+++ b/docs/extras/integrations/document_loaders/arcgis.ipynb
@@ -0,0 +1,309 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "62359e08-cf80-4210-a30c-f450000e65b9",
+   "metadata": {},
+   "source": [
+    "# ArcGIS\n",
+    "\n",
+    "This notebook demonstrates the use of the `langchain.document_loaders.ArcGISLoader` class.\n",
+    "\n",
+    "You will need to install the ArcGIS API for Python `arcgis` and, optionally, `bs4.BeautifulSoup`.\n",
+    "\n",
+    "You can use an `arcgis.gis.GIS` object for authenticated data loading, or leave it blank to access public data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b782cab5-0584-4e2a-9073-009fb8dc93a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import ArcGISLoader\n",
+    "\n",
+    "\n",
+    "url = \"https://maps1.vcgov.org/arcgis/rest/services/Beaches/MapServer/7\"\n",
+    "\n",
+    "loader = ArcGISLoader(url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aa3053cf-4127-43ea-bf56-e378b348091f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.86 ms, sys: 0 ns, total: 7.86 ms\n",
+      "Wall time: 802 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a2444519-9117-4feb-8bb9-8931ce286fa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'accessed': '2023-08-15T04:30:41.689270+00:00Z',\n",
+       " 'name': 'Beach Ramps',\n",
+       " 'url': 'https://maps1.vcgov.org/arcgis/rest/services/Beaches/MapServer/7',\n",
+       " 'layer_description': '(Not Provided)',\n",
+       " 'item_description': '(Not Provided)',\n",
+       " 'layer_properties': {\n",
+       "   \"currentVersion\": 10.81,\n",
+       "   \"id\": 7,\n",
+       "   \"name\": \"Beach Ramps\",\n",
+       "   \"type\": \"Feature Layer\",\n",
+       "   \"description\": \"\",\n",
+       "   \"geometryType\": \"esriGeometryPoint\",\n",
+       "   \"sourceSpatialReference\": {\n",
+       "     \"wkid\": 2881,\n",
+       "     \"latestWkid\": 2881\n",
+       "   },\n",
+       "   \"copyrightText\": \"\",\n",
+       "   \"parentLayer\": null,\n",
+       "   \"subLayers\": [],\n",
+       "   \"minScale\": 750000,\n",
+       "   \"maxScale\": 0,\n",
+       "   \"drawingInfo\": {\n",
+       "     \"renderer\": {\n",
+       "       \"type\": \"simple\",\n",
+       "       \"symbol\": {\n",
+       "         \"type\": \"esriPMS\",\n",
+       "         \"url\": \"9bb2e5ca499bb68aa3ee0d4e1ecc3849\",\n",
+       "         \"imageData\": \"iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAJJJREFUOI3NkDEKg0AQRZ9kkSnSGBshR7DJqdJYeg7BMpcS0uQWQsqoCLExkcUJzGqT38zw2fcY1rEzbp7vjXz0EXC7gBxs1ABcG/8CYkCcDqwyLqsV+RlV0I/w7PzuJBArr1VB20H58Ls6h+xoFITkTwWpQJX7XSIBAnFwVj7MLAjJV/AC6G3QoAmK+74Lom04THTBEp/HCSc6AAAAAElFTkSuQmCC\",\n",
+       "         \"contentType\": \"image/png\",\n",
+       "         \"width\": 12,\n",
+       "         \"height\": 12,\n",
+       "         \"angle\": 0,\n",
+       "         \"xoffset\": 0,\n",
+       "         \"yoffset\": 0\n",
+       "       },\n",
+       "       \"label\": \"\",\n",
+       "       \"description\": \"\"\n",
+       "     },\n",
+       "     \"transparency\": 0,\n",
+       "     \"labelingInfo\": null\n",
+       "   },\n",
+       "   \"defaultVisibility\": true,\n",
+       "   \"extent\": {\n",
+       "     \"xmin\": -81.09480168806815,\n",
+       "     \"ymin\": 28.858349245353473,\n",
+       "     \"xmax\": -80.77512908572814,\n",
+       "     \"ymax\": 29.41078388840041,\n",
+       "     \"spatialReference\": {\n",
+       "       \"wkid\": 4326,\n",
+       "       \"latestWkid\": 4326\n",
+       "     }\n",
+       "   },\n",
+       "   \"hasAttachments\": false,\n",
+       "   \"htmlPopupType\": \"esriServerHTMLPopupTypeNone\",\n",
+       "   \"displayField\": \"AccessName\",\n",
+       "   \"typeIdField\": null,\n",
+       "   \"subtypeFieldName\": null,\n",
+       "   \"subtypeField\": null,\n",
+       "   \"defaultSubtypeCode\": null,\n",
+       "   \"fields\": [\n",
+       "     {\n",
+       "       \"name\": \"OBJECTID\",\n",
+       "       \"type\": \"esriFieldTypeOID\",\n",
+       "       \"alias\": \"OBJECTID\",\n",
+       "       \"domain\": null\n",
+       "     },\n",
+       "     {\n",
+       "       \"name\": \"Shape\",\n",
+       "       \"type\": \"esriFieldTypeGeometry\",\n",
+       "       \"alias\": \"Shape\",\n",
+       "       \"domain\": null\n",
+       "     },\n",
+       "     {\n",
+       "       \"name\": \"AccessName\",\n",
+       "       \"type\": \"esriFieldTypeString\",\n",
+       "       \"alias\": \"AccessName\",\n",
+       "       \"length\": 40,\n",
+       "       \"domain\": null\n",
+       "     },\n",
+       "     {\n",
+       "       \"name\": \"AccessID\",\n",
+       "       \"type\": \"esriFieldTypeString\",\n",
+       "       \"alias\": \"AccessID\",\n",
+       "       \"length\": 50,\n",
+       "       \"domain\": null\n",
+       "     },\n",
+       "     {\n",
+       "       \"name\": \"AccessType\",\n",
+       "       \"type\": \"esriFieldTypeString\",\n",
+       "       \"alias\": \"AccessType\",\n",
+       "       \"length\": 25,\n",
+       "       \"domain\": null\n",
+       "     },\n",
+       "     {\n",
+       "       \"name\": \"GeneralLoc\",\n",
+       "       \"type\": \"esriFieldTypeString\",\n",
+       "       \"alias\": \"GeneralLoc\",\n",
+       "       \"length\": 100,\n",
+       "       \"domain\": null\n",
+       "     },\n",
+       "     {\n",
+       "       \"name\": \"MilePost\",\n",
+       "       \"type\": \"esriFieldTypeDouble\",\n",
+       "       \"alias\": \"MilePost\",\n",
+       "       \"domain\": null\n",
+       "     },\n",
+       "     {\n",
+       "       \"name\": \"City\",\n",
+       "       \"type\": \"esriFieldTypeString\",\n",
+       "       \"alias\": \"City\",\n",
+       "       \"length\": 50,\n",
+       "       \"domain\": null\n",
+       "     },\n",
+       "     {\n",
+       "       \"name\": \"AccessStatus\",\n",
+       "       \"type\": \"esriFieldTypeString\",\n",
+       "       \"alias\": \"AccessStatus\",\n",
+       "       \"length\": 50,\n",
+       "       \"domain\": null\n",
+       "     },\n",
+       "     {\n",
+       "       \"name\": \"Entry_Date_Time\",\n",
+       "       \"type\": \"esriFieldTypeDate\",\n",
+       "       \"alias\": \"Entry_Date_Time\",\n",
+       "       \"length\": 8,\n",
+       "       \"domain\": null\n",
+       "     },\n",
+       "     {\n",
+       "       \"name\": \"DrivingZone\",\n",
+       "       \"type\": \"esriFieldTypeString\",\n",
+       "       \"alias\": \"DrivingZone\",\n",
+       "       \"length\": 50,\n",
+       "       \"domain\": null\n",
+       "     }\n",
+       "   ],\n",
+       "   \"geometryField\": {\n",
+       "     \"name\": \"Shape\",\n",
+       "     \"type\": \"esriFieldTypeGeometry\",\n",
+       "     \"alias\": \"Shape\"\n",
+       "   },\n",
+       "   \"indexes\": null,\n",
+       "   \"subtypes\": [],\n",
+       "   \"relationships\": [],\n",
+       "   \"canModifyLayer\": true,\n",
+       "   \"canScaleSymbols\": false,\n",
+       "   \"hasLabels\": false,\n",
+       "   \"capabilities\": \"Map,Query,Data\",\n",
+       "   \"maxRecordCount\": 1000,\n",
+       "   \"supportsStatistics\": true,\n",
+       "   \"supportsAdvancedQueries\": true,\n",
+       "   \"supportedQueryFormats\": \"JSON, geoJSON\",\n",
+       "   \"isDataVersioned\": false,\n",
+       "   \"ownershipBasedAccessControlForFeatures\": {\n",
+       "     \"allowOthersToQuery\": true\n",
+       "   },\n",
+       "   \"useStandardizedQueries\": true,\n",
+       "   \"advancedQueryCapabilities\": {\n",
+       "     \"useStandardizedQueries\": true,\n",
+       "     \"supportsStatistics\": true,\n",
+       "     \"supportsHavingClause\": true,\n",
+       "     \"supportsCountDistinct\": true,\n",
+       "     \"supportsOrderBy\": true,\n",
+       "     \"supportsDistinct\": true,\n",
+       "     \"supportsPagination\": true,\n",
+       "     \"supportsTrueCurve\": true,\n",
+       "     \"supportsReturningQueryExtent\": true,\n",
+       "     \"supportsQueryWithDistance\": true,\n",
+       "     \"supportsSqlExpression\": true\n",
+       "   },\n",
+       "   \"supportsDatumTransformation\": true,\n",
+       "   \"dateFieldsTimeReference\": null,\n",
+       "   \"supportsCoordinatesQuantization\": true\n",
+       " }}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs[0].metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1d132b7d-5a13-4d66-98e8-785ffdf87af0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"OBJECTID\": 4, \"AccessName\": \"BEACHWAY AV\", \"AccessID\": \"NS-106\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"1400 N ATLANTIC AV\", \"MilePost\": 1.57, \"City\": \"NEW SMYRNA BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 5, \"AccessName\": \"SEABREEZE BLVD\", \"AccessID\": \"DB-051\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"500 BLK N ATLANTIC AV\", \"MilePost\": 14.24, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 6, \"AccessName\": \"27TH AV\", \"AccessID\": \"NS-141\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"3600 BLK S ATLANTIC AV\", \"MilePost\": 4.83, \"City\": \"NEW SMYRNA BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 11, \"AccessName\": \"INTERNATIONAL SPEEDWAY BLVD\", \"AccessID\": \"DB-059\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"300 BLK S ATLANTIC AV\", \"MilePost\": 15.27, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 14, \"AccessName\": \"GRANADA BLVD\", \"AccessID\": \"OB-030\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"20 BLK OCEAN SHORE BLVD\", \"MilePost\": 10.02, \"City\": \"ORMOND BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 27, \"AccessName\": \"UNIVERSITY BLVD\", \"AccessID\": \"DB-048\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"900 BLK N ATLANTIC AV\", \"MilePost\": 13.74, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 38, \"AccessName\": \"BEACH ST\", \"AccessID\": \"PI-097\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"4890 BLK S ATLANTIC AV\", \"MilePost\": 25.85, \"City\": \"PONCE INLET\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 42, \"AccessName\": \"BOTEFUHR AV\", \"AccessID\": \"DBS-067\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"1900 BLK S ATLANTIC AV\", \"MilePost\": 16.68, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 43, \"AccessName\": \"SILVER BEACH AV\", \"AccessID\": \"DB-064\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"1000 BLK S ATLANTIC AV\", \"MilePost\": 15.98, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 45, \"AccessName\": \"MILSAP RD\", \"AccessID\": \"OB-037\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"700 BLK S ATLANTIC AV\", \"MilePost\": 11.52, \"City\": \"ORMOND BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 56, \"AccessName\": \"3RD AV\", \"AccessID\": \"NS-118\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"1200 BLK HILL ST\", \"MilePost\": 3.25, \"City\": \"NEW SMYRNA BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 64, \"AccessName\": \"DUNLAWTON BLVD\", \"AccessID\": \"DBS-078\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"3400 BLK S ATLANTIC AV\", \"MilePost\": 20.61, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 69, \"AccessName\": \"EMILIA AV\", \"AccessID\": \"DBS-082\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"3790 BLK S ATLANTIC AV\", \"MilePost\": 21.38, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"BOTH\"}\n",
+      "{\"OBJECTID\": 94, \"AccessName\": \"FLAGLER AV\", \"AccessID\": \"NS-110\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"500 BLK FLAGLER AV\", \"MilePost\": 2.57, \"City\": \"NEW SMYRNA BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 96, \"AccessName\": \"CRAWFORD RD\", \"AccessID\": \"NS-108\", \"AccessType\": \"OPEN VEHICLE RAMP - PASS\", \"GeneralLoc\": \"800 BLK N ATLANTIC AV\", \"MilePost\": 2.19, \"City\": \"NEW SMYRNA BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 124, \"AccessName\": \"HARTFORD AV\", \"AccessID\": \"DB-043\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"1890 BLK N ATLANTIC AV\", \"MilePost\": 12.76, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 127, \"AccessName\": \"WILLIAMS AV\", \"AccessID\": \"DB-042\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"2200 BLK N ATLANTIC AV\", \"MilePost\": 12.5, \"City\": \"DAYTONA BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 136, \"AccessName\": \"CARDINAL DR\", \"AccessID\": \"OB-036\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"600 BLK S ATLANTIC AV\", \"MilePost\": 11.27, \"City\": \"ORMOND BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 229, \"AccessName\": \"EL PORTAL ST\", \"AccessID\": \"DBS-076\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"3200 BLK S ATLANTIC AV\", \"MilePost\": 20.04, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 230, \"AccessName\": \"HARVARD DR\", \"AccessID\": \"OB-038\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"900 BLK S ATLANTIC AV\", \"MilePost\": 11.72, \"City\": \"ORMOND BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 232, \"AccessName\": \"VAN AV\", \"AccessID\": \"DBS-075\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"3100 BLK S ATLANTIC AV\", \"MilePost\": 19.6, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 234, \"AccessName\": \"ROCKEFELLER DR\", \"AccessID\": \"OB-034\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"400 BLK S ATLANTIC AV\", \"MilePost\": 10.9, \"City\": \"ORMOND BEACH\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n",
+      "{\"OBJECTID\": 235, \"AccessName\": \"MINERVA RD\", \"AccessID\": \"DBS-069\", \"AccessType\": \"OPEN VEHICLE RAMP\", \"GeneralLoc\": \"2300 BLK S ATLANTIC AV\", \"MilePost\": 17.52, \"City\": \"DAYTONA BEACH SHORES\", \"AccessStatus\": \"CLOSED\", \"Entry_Date_Time\": 1692039947000, \"DrivingZone\": \"YES\"}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for doc in docs:\n",
+    "    print(doc.page_content)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/integrations/document_loaders/async_chromium.ipynb b/docs/extras/integrations/document_loaders/async_chromium.ipynb
new file mode 100644
index 00000000000..ddb220913ca
--- /dev/null
+++ b/docs/extras/integrations/document_loaders/async_chromium.ipynb
@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ad553e51",
+   "metadata": {},
+   "source": [
+    "# Async Chromium\n",
+    "\n",
+    "Chromium is one of the browsers supported by Playwright, a library used to control browser automation. \n",
+    "\n",
+    "By running `p.chromium.launch(headless=True)`, we are launching a headless instance of Chromium. \n",
+    "\n",
+    "Headless mode means that the browser is running without a graphical user interface.\n",
+    "\n",
+    "`AsyncChromiumLoader` load the page, and then we use `Html2TextTransformer` to trasnform to text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c3a4c19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install -q playwright beautifulsoup4\n",
+    "! playwright install"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dd2cdea7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'<!DOCTYPE html><html lang=\"en\"><head><script src=\"https://s0.2mdn.net/instream/video/client.js\" asyn'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.document_loaders import AsyncChromiumLoader\n",
+    "urls = [\"https://www.wsj.com\"]\n",
+    "loader = AsyncChromiumLoader(urls)\n",
+    "docs = loader.load()\n",
+    "docs[0].page_content[0:100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "013caa7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Skip to Main ContentSkip to SearchSkip to... Select * Top News * What's News *\\nFeatured Stories * Retirement * Life & Arts * Hip-Hop * Sports * Video *\\nEconomy * Real Estate * Sports * CMO * CIO * CFO * Risk & Compliance *\\nLogistics Report * Sustainable Business * Heard on the Street * Barron’s *\\nMarketWatch * Mansion Global * Penta * Opinion * Journal Reports * Sponsored\\nOffers Explore Our Brands * WSJ * * * * * Barron's * * * * * MarketWatch * * *\\n* * IBD # The Wall Street Journal SubscribeSig\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.document_transformers import Html2TextTransformer\n",
+    "html2text = Html2TextTransformer()\n",
+    "docs_transformed = html2text.transform_documents(docs)\n",
+    "docs_transformed[0].page_content[0:500]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/integrations/document_loaders/grobid.ipynb b/docs/extras/integrations/document_loaders/grobid.ipynb
index 96bf6b8ddb4..83ffffcc29b 100644
--- a/docs/extras/integrations/document_loaders/grobid.ipynb
+++ b/docs/extras/integrations/document_loaders/grobid.ipynb
@@ -9,66 +9,16 @@
     "\n",
     "GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents.\n",
     "\n",
-    "It is particularly good for sturctured PDFs, like academic papers.\n",
+    "It is designed and expected to be used to parse academic papers, where it works particularly well. Note: if the articles supplied to Grobid are large documents (e.g. dissertations) exceeding a certain number of elements, they might not be processed. \n",
     "\n",
-    "This loader uses GROBIB to parse PDFs into `Documents` that retain metadata associated with the section of text.\n",
+    "This loader uses Grobid to parse PDFs into `Documents` that retain metadata associated with the section of text.\n",
     "\n",
     "---\n",
+    "The best approach is to install Grobid via docker, see https://grobid.readthedocs.io/en/latest/Grobid-docker/. \n",
     "\n",
-    "For users on `Mac` - \n",
+    "(Note: additional instructions can be found [here](https://python.langchain.com/docs/extras/integrations/providers/grobid.mdx).)\n",
     "\n",
-    "(Note: additional instructions can be found [here](https://python.langchain.com/docs/ecosystem/integrations/grobid.mdx).)\n",
-    "\n",
-    "Install Java (Apple Silicon):\n",
-    "```\n",
-    "$ arch -arm64 brew install openjdk@11\n",
-    "$ brew --prefix openjdk@11\n",
-    "/opt/homebrew/opt/openjdk@ 11\n",
-    "```\n",
-    "\n",
-    "In `~/.zshrc`:\n",
-    "```\n",
-    "export JAVA_HOME=/opt/homebrew/opt/openjdk@11\n",
-    "export PATH=$JAVA_HOME/bin:$PATH\n",
-    "```\n",
-    "\n",
-    "Then, in Terminal:\n",
-    "```\n",
-    "$ source ~/.zshrc\n",
-    "```\n",
-    "\n",
-    "Confirm install:\n",
-    "```\n",
-    "$ which java\n",
-    "/opt/homebrew/opt/openjdk@11/bin/java\n",
-    "$ java -version \n",
-    "openjdk version \"11.0.19\" 2023-04-18\n",
-    "OpenJDK Runtime Environment Homebrew (build 11.0.19+0)\n",
-    "OpenJDK 64-Bit Server VM Homebrew (build 11.0.19+0, mixed mode)\n",
-    "```\n",
-    "\n",
-    "Then, get [Grobid](https://grobid.readthedocs.io/en/latest/Install-Grobid/#getting-grobid):\n",
-    "```\n",
-    "$ curl -LO https://github.com/kermitt2/grobid/archive/0.7.3.zip\n",
-    "$ unzip 0.7.3.zip\n",
-    "```\n",
-    "                   \n",
-    "Build\n",
-    "```\n",
-    "$ ./gradlew clean install\n",
-    "```\n",
-    "\n",
-    "Then, run the server:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2d8992fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')"
+    "Once grobid is up-and-running you can interact as described below. \n"
    ]
   },
   {
diff --git a/docs/extras/integrations/document_transformers/beautiful_soup.ipynb b/docs/extras/integrations/document_transformers/beautiful_soup.ipynb
new file mode 100644
index 00000000000..43e4af40dda
--- /dev/null
+++ b/docs/extras/integrations/document_transformers/beautiful_soup.ipynb
@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2ed9a4c2",
+   "metadata": {},
+   "source": [
+    "# Beautiful Soup\n",
+    "\n",
+    "Beautiful Soup offers fine-grained control over HTML content, enabling specific tag extraction, removal, and content cleaning. \n",
+    "\n",
+    "It's suited for cases where you want to extract specific information and clean up the HTML content according to your needs.\n",
+    "\n",
+    "For example, we can scrape text content within `<p>, <li>, <div>, and <a>` tags from the HTML content:\n",
+    "\n",
+    "* `<p>`: The paragraph tag. It defines a paragraph in HTML and is used to group together related sentences and/or phrases.\n",
+    " \n",
+    "* `<li>`: The list item tag. It is used within ordered (`<ol>`) and unordered (`<ul>`) lists to define individual items within the list.\n",
+    " \n",
+    "* `<div>`: The division tag. It is a block-level element used to group other inline or block-level elements.\n",
+    " \n",
+    "* `<a>`: The anchor tag. It is used to define hyperlinks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dd710e5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import AsyncChromiumLoader\n",
+    "from langchain.document_transformers import BeautifulSoupTransformer\n",
+    "\n",
+    "# Load HTML\n",
+    "loader = AsyncChromiumLoader([\"https://www.wsj.com\"])\n",
+    "html = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "052b64dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Transform\n",
+    "bs_transformer = BeautifulSoupTransformer()\n",
+    "docs_transformed = bs_transformer.transform_documents(html,tags_to_extract=[\"p\", \"li\", \"div\", \"a\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b53a5307",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Conservative legal activists are challenging Amazon, Comcast and others using many of the same tools that helped kill affirmative-action programs in colleges.1,2099 min read U.S. stock indexes fell and government-bond prices climbed, after Moody’s lowered credit ratings for 10 smaller U.S. banks and said it was reviewing ratings for six larger ones. The Dow industrials dropped more than 150 points.3 min read Penn Entertainment’s Barstool Sportsbook app will be rebranded as ESPN Bet this fall as '"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs_transformed[0].page_content[0:500]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/integrations/llms/Fireworks.ipynb b/docs/extras/integrations/llms/Fireworks.ipynb
index 3f30ab19239..4da49230bfa 100644
--- a/docs/extras/integrations/llms/Fireworks.ipynb
+++ b/docs/extras/integrations/llms/Fireworks.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 1,
    "id": "60b6dbb2",
    "metadata": {},
    "outputs": [],
@@ -42,25 +42,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "9ca87a2e",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Initialize a Fireworks LLM\n",
-    "os.environ['FIREWORKS_API_KEY'] = \"\" #change this to your own API KEY\n",
-    "llm = Fireworks(model_id=\"accounts/fireworks/models/fireworks-llama-v2-13b-chat\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "43a11ba8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create LLM chain\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+    "os.environ['FIREWORKS_API_KEY'] = \"<YOUR_API_KEY>\"  # Change this to your own API key\n",
+    "llm = Fireworks(model_id=\"accounts/fireworks/models/llama-v2-13b-chat\")"
    ]
   },
   {
@@ -72,16 +61,33 @@
     "\n",
     "You can use the LLMs to call the model for specified prompt(s). \n",
     "\n",
-    "Current Specified Models: \n",
-    "* accounts/fireworks/models/fireworks-falcon-7b, accounts/fireworks/models/fireworks-falcon-40b-w8a16\n",
-    "* accounts/fireworks/models/fireworks-starcoder-1b-w8a16-1gpu, accounts/fireworks/models/fireworks-starcoder-3b-w8a16-1gpu, accounts/fireworks/models/fireworks-starcoder-7b-w8a16-1gpu, accounts/fireworks/models/fireworks-starcoder-16b-w8a16 \n",
-    "* accounts/fireworks/models/fireworks-llama-v2-13b, accounts/fireworks/models/fireworks-llama-v2-13b-chat, accounts/fireworks/models/fireworks-llama-v2-13b-w8a16, accounts/fireworks/models/fireworks-llama-v2-13b-chat-w8a16\n",
-    "* accounts/fireworks/models/fireworks-llama-v2-7b, accounts/fireworks/models/fireworks-llama-v2-7b-chat, accounts/fireworks/models/fireworks-llama-v2-7b-w8a16, accounts/fireworks/models/fireworks-llama-v2-7b-chat-w8a16, accounts/fireworks/models/fireworks-llama-v2-70b-chat-4gpu"
+    "Currently supported models: \n",
+    "\n",
+    "*   Falcon\n",
+    "    *   `accounts/fireworks/models/falcon-7b`\n",
+    "    *   `accounts/fireworks/models/falcon-40b-w8a16`\n",
+    "*   Llama 2\n",
+    "    *   `accounts/fireworks/models/llama-v2-7b`\n",
+    "    *   `accounts/fireworks/models/llama-v2-7b-w8a16`\n",
+    "    *   `accounts/fireworks/models/llama-v2-7b-chat`\n",
+    "    *   `accounts/fireworks/models/llama-v2-7b-chat-w8a16`\n",
+    "    *   `accounts/fireworks/models/llama-v2-13b`\n",
+    "    *   `accounts/fireworks/models/llama-v2-13b-w8a16`\n",
+    "    *   `accounts/fireworks/models/llama-v2-13b-chat`\n",
+    "    *   `accounts/fireworks/models/llama-v2-13b-chat-w8a16`\n",
+    "    *   `accounts/fireworks/models/llama-v2-70b-chat-4gpu`\n",
+    "*   StarCoder\n",
+    "    *   `accounts/fireworks/models/starcoder-1b-w8a16-1gpu`\n",
+    "    *   `accounts/fireworks/models/starcoder-3b-w8a16-1gpu`\n",
+    "    *   `accounts/fireworks/models/starcoder-7b-w8a16-1gpu`\n",
+    "    *   `accounts/fireworks/models/starcoder-16b-w8a16`\n",
+    "\n",
+    "See the full, most up-to-date list on [app.fireworks.ai](https://app.fireworks.ai)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "id": "bf0a425c",
    "metadata": {},
    "outputs": [
@@ -89,29 +95,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Is it Tom Brady, Aaron Rodgers, or someone else? It's a tough question to answer, and there are strong arguments for each of these quarterbacks. Here are some of the reasons why each of these quarterbacks could be considered the best:\n",
       "\n",
+      "Tom Brady:\n",
       "\n",
-      "It's a question that has been debated for years, with different analysts and fans making their cases for various signal-callers. Here are some of the top contenders for the title of best quarterback in the NFL:\n",
+      "* He has the most Super Bowl wins (6) of any quarterback in NFL history.\n",
+      "* He has been named Super Bowl MVP four times, more than any other player.\n",
+      "* He has led the New England Patriots to 18 playoff victories, the most in NFL history.\n",
+      "* He has thrown for over 70,000 yards in his career, the most of any quarterback in NFL history.\n",
+      "* He has thrown for 50 or more touchdowns in a season four times, the most of any quarterback in NFL history.\n",
       "\n",
-      "1. Tom Brady: The New England Patriots legend has won six Super Bowls and has been named Super Bowl MVP four times. He's known for his precision passing, pocket presence, and ability to read defenses.\n",
-      "2. Aaron Rodgers: The Green Bay Packers quarterback has won two Super Bowls and has been named NFL MVP twice. He's known for his quick release, accuracy, and ability to extend plays with his feet.\n",
-      "3. Drew Brees: The New Orleans Saints quarterback has won a Super Bowl and has been named NFL MVP once. He's known for his accuracy, pocket presence, and ability to read defenses.\n",
-      "4. Patrick Mahomes: The Kansas City Chiefs quarterback has won a Super Bowl and has been named NFL MVP twice. He's known for his arm strength, athleticism, and ability to make plays outside of the pocket.\n",
-      "5. Russell Wilson: The Seattle Seahawks quarterback has won a Super Bowl and has been named NFL MVP once. He's known for his mobility, accuracy, and ability to extend plays with his feet.\n",
+      "Aaron Rodgers:\n",
       "\n",
-      "Of course, there are other talented quarterbacks in the league, such as Lamar Jackson, Deshaun Watson, and Carson Wentz, who could also be considered among the best. Ultimately, the answer to the question of who's the best quarterback in the NFL is subjective and can vary depending on individual perspectives and criteria.\n"
+      "* He has led the Green Bay Packers to a Super Bowl victory in 2010.\n",
+      "* He has been named Super Bowl MVP once.\n",
+      "* He has thrown for over 40,000 yards in his career, the most of any quarterback in NFL history.\n",
+      "* He has thrown for 40 or more touchdowns in a season three times, the most of any quarterback in NFL history.\n",
+      "* He has a career passer rating of 103.1, the highest of any quarterback in NFL history.\n",
+      "\n",
+      "So, who's the best quarterback in the NFL? It's a tough call, but here's my opinion:\n",
+      "\n",
+      "I think Aaron Rodgers is the best quarterback in the NFL right now. He has led the Packers to a Super Bowl victory and has had some incredible seasons, including the 2011 season when he threw for 45 touchdowns and just 6 interceptions. He has a strong arm, great accuracy, and is incredibly mobile for a quarterback of his size. He also has a great sense of timing and knows when to take risks and when to play it safe.\n",
+      "\n",
+      "Tom Brady is a close second, though. He has an incredible track record of success, including six Super Bowl victories, and has been one of the most consistent quarterbacks in the league for the past two decades. He has a strong arm and is incredibly accurate\n"
      ]
     }
    ],
    "source": [
-    "#single prompt\n",
+    "# Single prompt\n",
     "output = llm(\"Who's the best quarterback in the NFL?\")\n",
     "print(output)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "id": "afc7de6f",
    "metadata": {},
    "outputs": [
@@ -119,19 +137,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "generations=[[Generation(text=\"\\nWho is the best cricket player in the world in 2016?\\nThe best cricket player in the world in 2016 is Virat Kohli. The Indian captain has had a fabulous year, scoring heavily in all formats of the game, leading India to several victories, and breaking several records. In Test cricket, Kohli has scored 1215 runs at an average of 75.33 with 6 centuries and 4 fifties, which is the highest number of runs scored by any player in a calendar year. In ODI cricket, he has scored 1143 runs at an average of 83.42 with 7 centuries and 6 fifties, which is also the highest number of runs scored by any player in a calendar year. Additionally, Kohli has led India to the number one ranking in Test cricket, and has been named the ICC Test Player of the Year and the ICC ODI Player of the Year.\\nVirat Kohli has been in incredible form in 2016, and his performances have made him the standout player of the year. Other players who have had a great year include Steve Smith, Joe Root, and Kane Williamson, but Kohli's consistency and dominance in all formats of the game make him the best cricket player in the world in 2016.\", generation_info=None)], [Generation(text=\"\\n\\nA: LeBron James.\\n\\nB: Kevin Durant.\\n\\nC: Steph Curry.\\n\\nD: James Harden.\\n\\nE: Other (please specify).\\n\\nWhat's your answer?\", generation_info=None)]] llm_output={'token_usage': {}, 'model_id': 'fireworks-llama-v2-13b-chat'} run=[RunInfo(run_id=UUID('d14b6bee-7692-46ad-8798-acb6f72fc7fb')), RunInfo(run_id=UUID('b9f5b3b5-9e62-4eaf-b269-ecf0cbbcfb82'))]\n"
+      "[[Generation(text='\\nThe best cricket player in 2016 is a matter of opinion, but some of the top contenders for the title include:\\n\\n1. Virat Kohli (India): Kohli had a phenomenal year in 2016, scoring over 1,000 runs in Test cricket, including four centuries, and averaging over 70. He also scored heavily in ODI cricket, with an average of over 80.\\n2. Steve Smith (Australia): Smith had a remarkable year in 2016, leading Australia to a Test series victory in India and scoring over 1,000 runs in the format, including five centuries. He also averaged over 60 in ODI cricket.\\n3. KL Rahul (India): Rahul had a breakout year in 2016, scoring over 1,000 runs in Test cricket, including four centuries, and averaging over 60. He also scored heavily in ODI cricket, with an average of over 70.\\n4. Joe Root (England): Root had a solid year in 2016, scoring over 1,000 runs in Test cricket, including four centuries, and averaging over 50. He also scored heavily in ODI cricket, with an average of over 80.\\n5. Quinton de Kock (South Africa): De Kock had a remarkable year in 2016, scoring over 1,000 runs in ODI cricket, including six centuries, and averaging over 80. He also scored heavily in Test cricket, with an average of over 50.\\n\\nThese are just a few of the top contenders for the title of best cricket player in 2016, but there were many other talented players who also had impressive years. Ultimately, the answer to this question is subjective and depends on individual opinions and criteria for evaluation.', generation_info=None)], [Generation(text=\"\\nThis is a tough one, as there are so many great players in the league right now. But if I had to choose one, I'd say LeBron James is the best basketball player in the league. He's a once-in-a-generation talent who can dominate the game in so many ways. He's got incredible speed, strength, and court vision, and he's always finding new ways to improve his game. Plus, he's been doing it at an elite level for over a decade now, which is just amazing.\\n\\nBut don't just take my word for it - there are plenty of other great players in the league who could make a strong case for being the best. Guys like Kevin Durant, Steph Curry, James Harden, and Giannis Antetokounmpo are all having incredible seasons, and they've all got their own unique skills and strengths that make them special. So ultimately, it's up to you to decide who you think is the best basketball player in the league.\", generation_info=None)]]\n"
      ]
     }
    ],
    "source": [
-    "#calling multiple prompts\n",
-    "output = llm.generate([\"Who's the best cricket player in 2016?\", \"Who's the best basketball player in the league?\"])\n",
-    "print(output)"
+    "# Calling multiple prompts\n",
+    "output = llm.generate([\n",
+    "    \"Who's the best cricket player in 2016?\",\n",
+    "    \"Who's the best basketball player in the league?\",\n",
+    "])\n",
+    "print(output.generations)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "id": "b801c20d",
    "metadata": {},
    "outputs": [
@@ -140,13 +161,13 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Kansas City in December can be quite chilly, with average high\n"
+      "Kansas City in December is quite cold, with temperatures typically r\n"
      ]
     }
    ],
    "source": [
-    "#setting parameters: model_id, temperature, max_tokens, top_p\n",
-    "llm = Fireworks(model_id=\"accounts/fireworks/models/fireworks-llama-v2-13b-chat\", temperature=0.7, max_tokens=15, top_p=1.0)\n",
+    "# Setting additional parameters: temperature, max_tokens, top_p\n",
+    "llm = Fireworks(model_id=\"accounts/fireworks/models/llama-v2-13b-chat\", temperature=0.7, max_tokens=15, top_p=1.0)\n",
     "print(llm(\"What's the weather like in Kansas City in December?\"))"
    ]
   },
@@ -162,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 6,
    "id": "fd2c6bc1",
    "metadata": {},
    "outputs": [
@@ -171,34 +192,25 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "(Note: I'm just an AI and not a branding expert, so take this as a starting point for your own research and brainstorming.)\n",
-      "A good name for a company that makes football helmets could be:\n",
+      "Naming a company can be a fun and creative process! Here are a few name ideas for a company that makes football helmets:\n",
       "\n",
-      "1. Helix Pro: This name plays off the idea of a helix, or spiral, shape that is commonly associated with football helmets. \"Pro\" implies a professional-level product.\n",
-      "2. Gridiron Gear: \"Gridiron\" is a term used to describe a football field, and \"gear\" highlights the company's focus on producing high-quality football helmets.\n",
-      "3. Linebacker Lab: \"Linebacker\" is a position on the football field, and \"Lab\" suggests a focus on research and development.\n",
-      "4. Helmet Hut: This name is simple and easy to remember, and it immediately conveys the company's focus on football helmets.\n",
-      "5. Tackle Tech: \"Tackle\" is a term used in football to describe a hit or collision, and \"Tech\" implies a focus on advanced technology and innovation.\n",
-      "6. Victory Vest: \"Victory\" implies a focus on winning and success, and \"Vest\" could suggest a protective or armored design.\n",
-      "7. Pigskin Pro: \"Pigskin\" is a term used to describe a football, and \"Pro\" implies a professional-level product.\n",
-      "8. Football Fusion: This name could suggest a combination of different materials or technologies to create a high-quality football helmet.\n",
-      "9. Endzone Edge: \"Endzone\" is the area of the football field where a team scores a touchdown, and \"Edge\" implies a competitive advantage.\n",
-      "10. MVP Masks: \"MVP\" stands for \"Most Valuable Player,\" and \"Masks\" highlights the protective nature of the company's football helmets.\n",
+      "1. Helix Headgear: This name plays off the idea of the helix shape of a football helmet and could be a memorable and catchy name for a company.\n",
+      "2. Gridiron Gear: \"Gridiron\" is a term used to describe a football field, and \"gear\" refers to the products the company sells. This name is straightforward and easy to understand.\n",
+      "3. Cushion Crusaders: This name emphasizes the protective qualities of football helmets and could appeal to customers looking for safety-conscious products.\n",
+      "4. Helmet Heroes: This name has a fun, heroic tone and could appeal to customers looking for high-quality products.\n",
+      "5. Tackle Tech: \"Tackle\" is a term used in football to describe a player's attempt to stop an opponent, and \"tech\" refers to the technology used in the helmets. This name could appeal to customers interested in innovative products.\n",
+      "6. Padded Protection: This name emphasizes the protective qualities of football helmets and could appeal to customers looking for products that prioritize safety.\n",
+      "7. Gridiron Gear Co.: This name is simple and straightforward, and it clearly conveys the company's focus on football-related products.\n",
+      "8. Helmet Haven: This name has a soothing, protective tone and could appeal to customers looking for a reliable brand.\n",
       "\n",
-      "Remember, the name you choose for your company should be memorable, easy to pronounce and spell, and convey a sense of quality and professionalism. It's also important to check that the name isn't already in use by another company, and to consider any potential trademark issues.\n"
+      "Remember to choose a name that reflects your company's values and mission, and that resonates with your target market. Good luck with your company!\n"
      ]
     }
    ],
    "source": [
-    "human_message_prompt = HumanMessagePromptTemplate(\n",
-    "    prompt=PromptTemplate(\n",
-    "        template=\"What is a good name for a company that makes {product}?\",\n",
-    "        input_variables=[\"product\"],\n",
-    "    )\n",
-    ")\n",
-    "\n",
+    "human_message_prompt = HumanMessagePromptTemplate.from_template(\"What is a good name for a company that makes {product}?\")\n",
     "chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])\n",
-    "chat = Fireworks()\n",
+    "chat = FireworksChat()\n",
     "chain = LLMChain(llm=chat, prompt=chat_prompt_template)\n",
     "output = chain.run(\"football helmets\")\n",
     "\n",
@@ -222,7 +234,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/extras/integrations/llms/bedrock.ipynb b/docs/extras/integrations/llms/bedrock.ipynb
index 56847a00fd4..06ae9f4cee1 100644
--- a/docs/extras/integrations/llms/bedrock.ipynb
+++ b/docs/extras/integrations/llms/bedrock.ipynb
@@ -31,12 +31,11 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.llms.bedrock import Bedrock\n",
+    "from langchain.llms import Bedrock\n",
     "\n",
     "llm = Bedrock(\n",
     "    credentials_profile_name=\"bedrock-admin\",\n",
-    "    model_id=\"amazon.titan-tg1-large\",\n",
-    "    endpoint_url=\"custom_endpoint_url\",\n",
+    "    model_id=\"amazon.titan-tg1-large\"\n",
     ")"
    ]
   },
diff --git a/docs/extras/integrations/llms/bittensor.ipynb b/docs/extras/integrations/llms/bittensor.ipynb
new file mode 100644
index 00000000000..6b92e31d1b5
--- /dev/null
+++ b/docs/extras/integrations/llms/bittensor.ipynb
@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NIBittensorLLM\n",
+    "\n",
+    "NIBittensorLLM is developed by [Neural Internet](https://neuralinternet.ai/), powered by [Bittensor](https://bittensor.com/).\n",
+    "\n",
+    "This LLM showcases true potential of decentralized AI by giving you the best response(s) from the Bittensor protocol, which consist of various AI models such as OpenAI, LLaMA2 etc.\n",
+    "\n",
+    "Users can view their logs, requests, and API keys on the [Validator Endpoint Frontend](https://api.neuralinterent.ai/). However, changes to the configuration are currently prohibited; otherwise, the user's queries will be blocked.\n",
+    "\n",
+    "If you encounter any difficulties or have any questions, please feel free to reach out to our developer on [GitHub](https://github.com/Kunj-2206), [Discord](https://discordapp.com/users/683542109248159777) or join our discord server for latest update and queries [Neural Internet](https://discord.gg/neuralinternet).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Different Parameter and response handling for NIBittensorLLM "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import langchain\n",
+    "from langchain.llms import NIBittensorLLM\n",
+    "import json\n",
+    "from pprint import pprint\n",
+    "\n",
+    "langchain.debug = True\n",
+    "\n",
+    "# System parameter in NIBittensorLLM is optional but you can set whatever you want to perform with model\n",
+    "llm_sys = NIBittensorLLM(\n",
+    "    system_prompt=\"Your task is to determine response based on user prompt.Explain me like I am technical lead of a project\"\n",
+    ")\n",
+    "sys_resp = llm_sys(\n",
+    "    \"What is bittensor and What are the potential benifits of decentralized AI?\"\n",
+    ")\n",
+    "print(f\"Response provided by LLM with system prompt set is : {sys_resp}\")\n",
+    "\n",
+    "# The top_responses parameter can give multiple responses based on its parameter value\n",
+    "# This below code retrive top 10 miner's response all the response are in format of json\n",
+    "\n",
+    "# Json response structure is\n",
+    "\"\"\" {\n",
+    "    \"choices\":  [\n",
+    "                    {\"index\": Bittensor's Metagraph index number,\n",
+    "                    \"uid\": Unique Identifier of a miner,\n",
+    "                    \"responder_hotkey\": Hotkey of a miner,\n",
+    "                    \"message\":{\"role\":\"assistant\",\"content\": Contains actual response},\n",
+    "                    \"response_ms\": Time in millisecond required to fetch response from a miner} \n",
+    "                ]\n",
+    "    } \"\"\"\n",
+    "\n",
+    "multi_response_llm = NIBittensorLLM(top_responses=10)\n",
+    "multi_resp = multi_response_llm(\"What is Neural Network Feeding Mechanism?\")\n",
+    "json_multi_resp = json.loads(multi_resp)\n",
+    "pprint(json_multi_resp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Using NIBittensorLLM with LLMChain and PromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import langchain\n",
+    "from langchain import PromptTemplate, LLMChain\n",
+    "from langchain.llms import NIBittensorLLM\n",
+    "\n",
+    "langchain.debug = True\n",
+    "\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "\n",
+    "# System parameter in NIBittensorLLM is optional but you can set whatever you want to perform with model\n",
+    "llm = NIBittensorLLM(system_prompt=\"Your task is to determine response based on user prompt.\")\n",
+    "\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "question = \"What is bittensor?\"\n",
+    "\n",
+    "llm_chain.run(question)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Using NIBittensorLLM with Conversational Agent and Google Search Tool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import (\n",
+    "    AgentType,\n",
+    "    initialize_agent,\n",
+    "    load_tools,\n",
+    "    ZeroShotAgent,\n",
+    "    Tool,\n",
+    "    AgentExecutor,\n",
+    ")\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain import LLMChain, PromptTemplate\n",
+    "from langchain.utilities import GoogleSearchAPIWrapper, SerpAPIWrapper\n",
+    "from langchain.llms import NIBittensorLLM\n",
+    "\n",
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
+    "\n",
+    "\n",
+    "prefix = \"\"\"Answer prompt based on LLM if there is need to search something then use internet and observe internet result and give accurate reply of user questions also try to use authenticated sources\"\"\"\n",
+    "suffix = \"\"\"Begin!\n",
+    "            {chat_history}\n",
+    "            Question: {input}\n",
+    "            {agent_scratchpad}\"\"\"\n",
+    "\n",
+    "prompt = ZeroShotAgent.create_prompt(\n",
+    "    tools,\n",
+    "    prefix=prefix,\n",
+    "    suffix=suffix,\n",
+    "    input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"],\n",
+    ")\n",
+    "\n",
+    "llm = NIBittensorLLM(system_prompt=\"Your task is to determine response based on user prompt\")\n",
+    "\n",
+    "llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
+    "\n",
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
+    "\n",
+    "agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
+    "agent_chain = AgentExecutor.from_agent_and_tools(\n",
+    "    agent=agent, tools=tools, verbose=True, memory=memory\n",
+    ")\n",
+    "\n",
+    "response = agent_chain.run(input=prompt)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/extras/integrations/llms/deepsparse.ipynb b/docs/extras/integrations/llms/deepsparse.ipynb
new file mode 100644
index 00000000000..05ac4be7160
--- /dev/null
+++ b/docs/extras/integrations/llms/deepsparse.ipynb
@@ -0,0 +1,78 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "15d7ce70-8879-42a0-86d9-a3d604a3ec83",
+   "metadata": {},
+   "source": [
+    "# DeepSparse\n",
+    "\n",
+    "This page covers how to use the [DeepSparse](https://github.com/neuralmagic/deepsparse) inference runtime within LangChain.\n",
+    "It is broken into two parts: installation and setup, and then examples of DeepSparse usage.\n",
+    "\n",
+    "## Installation and Setup\n",
+    "\n",
+    "- Install the Python package with `pip install deepsparse`\n",
+    "- Choose a [SparseZoo model](https://sparsezoo.neuralmagic.com/?useCase=text_generation) or export a support model to ONNX [using Optimum](https://github.com/neuralmagic/notebooks/blob/main/notebooks/opt-text-generation-deepsparse-quickstart/OPT_Text_Generation_DeepSparse_Quickstart.ipynb)\n",
+    "\n",
+    "\n",
+    "There exists a DeepSparse LLM wrapper, that provides a unified interface for all models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79d24d37-737a-428c-b6c5-84c1633070d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import DeepSparse\n",
+    "\n",
+    "llm = DeepSparse(model='zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none')\n",
+    "\n",
+    "print(llm('def fib():'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea7ea674-d6b0-49d9-9c2b-014032973be6",
+   "metadata": {},
+   "source": [
+    "Additional parameters can be passed using the `config` parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff61b845-41e6-4457-8625-6e21a11bfe7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = {'max_generated_tokens': 256}\n",
+    "\n",
+    "llm = DeepSparse(model='zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none', config=config)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/integrations/llms/huggingface_textgen_inference.ipynb b/docs/extras/integrations/llms/huggingface_textgen_inference.ipynb
index 6aacfc8a315..9b934806b5b 100644
--- a/docs/extras/integrations/llms/huggingface_textgen_inference.ipynb
+++ b/docs/extras/integrations/llms/huggingface_textgen_inference.ipynb
@@ -74,7 +74,7 @@
     "    typical_p=0.95,\n",
     "    temperature=0.01,\n",
     "    repetition_penalty=1.03,\n",
-    "    stream=True\n",
+    "    streaming=True\n",
     ")\n",
     "llm(\"What did foo say about bar?\", callbacks=[StreamingStdOutCallbackHandler()])"
    ]
diff --git a/docs/extras/integrations/llms/llamacpp.ipynb b/docs/extras/integrations/llms/llamacpp.ipynb
index 2ec6de39c94..68c8680b050 100644
--- a/docs/extras/integrations/llms/llamacpp.ipynb
+++ b/docs/extras/integrations/llms/llamacpp.ipynb
@@ -241,7 +241,9 @@
     "# Make sure the model path is correct for your system!\n",
     "llm = LlamaCpp(\n",
     "    model_path=\"/Users/rlm/Desktop/Code/llama/llama-2-7b-ggml/llama-2-7b-chat.ggmlv3.q4_0.bin\",\n",
-    "    input={\"temperature\": 0.75, \"max_length\": 2000, \"top_p\": 1},\n",
+    "    temperature=0.75,\n",
+    "    max_tokens=2000,\n",
+    "    top_p=1,\n",
     "    callback_manager=callback_manager,\n",
     "    verbose=True,\n",
     ")"
diff --git a/docs/extras/integrations/llms/ollama.ipynb b/docs/extras/integrations/llms/ollama.ipynb
index 41df0b49aa3..8cfd71cbb3e 100644
--- a/docs/extras/integrations/llms/ollama.ipynb
+++ b/docs/extras/integrations/llms/ollama.ipynb
@@ -19,8 +19,29 @@
     "First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
     "\n",
     "* [Download](https://ollama.ai/download)\n",
-    "* Fetch a model, e.g., `Llama-7b`: `ollama pull llama2`\n",
-    "* Run `ollama run llama2`\n",
+    "* Fetch a model via `ollama pull <model family>`\n",
+    "* e.g., for `Llama-7b`: `ollama pull llama2` (see full list [here](https://github.com/jmorganca/ollama))\n",
+    "* This will download the most basic version of the model typically (e.g., smallest # parameters and `q4_0`)\n",
+    "* On Mac, it will download to \n",
+    "\n",
+    "`~/.ollama/models/manifests/registry.ollama.ai/library/<model family>/latest`\n",
+    "\n",
+    "* And we specify a particular version, e.g., for `ollama pull vicuna:13b-v1.5-16k-q4_0`\n",
+    "* The file is here with the model version in place of `latest`\n",
+    "\n",
+    "`~/.ollama/models/manifests/registry.ollama.ai/library/vicuna/13b-v1.5-16k-q4_0`\n",
+    "\n",
+    "You can easily access models in a few ways:\n",
+    "\n",
+    "1/ if the app is running:\n",
+    "* All of your local models are automatically served on `localhost:11434`\n",
+    "* Select your model when setting `llm = Ollama(..., model=\"<model family>:<version>\")`\n",
+    "* If you set `llm = Ollama(..., model=\"<model family\")` withoout a version it will simply look for `latest`\n",
+    "\n",
+    "2/ if building from source or just running the binary: \n",
+    "* Then you must run `ollama serve`\n",
+    "* All of your local models are automatically served on `localhost:11434`\n",
+    "* Then, select as shown above\n",
     "\n",
     "\n",
     "## Usage\n",
diff --git a/docs/extras/integrations/llms/symblai_nebula.ipynb b/docs/extras/integrations/llms/symblai_nebula.ipynb
index 1ca58697e0e..304917a5cf4 100644
--- a/docs/extras/integrations/llms/symblai_nebula.ipynb
+++ b/docs/extras/integrations/llms/symblai_nebula.ipynb
@@ -1,80 +1,83 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
-   "id": "9597802c",
-   "metadata": {},
    "source": [
-    "# Nebula\n",
+    "# Nebula (Symbl.ai)\n",
+    "[Nebula](https://symbl.ai/nebula/) is a large language model (LLM) built by [Symbl.ai](https://symbl.ai). It is trained to perform generative tasks on human conversations. Nebula excels at modeling the nuanced details of a conversation and performing tasks on the conversation.\n",
     "\n",
-    "[Nebula](https://symbl.ai/nebula/) is a fully-managed Conversation platform, on which you can build, deploy, and manage scalable AI applications.\n",
+    "Nebula documentation: https://docs.symbl.ai/docs/nebula-llm\n",
     "\n",
-    "This example goes over how to use LangChain to interact with the [Nebula platform](https://docs.symbl.ai/docs/nebula-llm-overview). \n",
-    "\n",
-    "It will send the requests to Nebula Service endpoint, which concatenates `SYMBLAI_NEBULA_SERVICE_URL` and `SYMBLAI_NEBULA_SERVICE_PATH`, with a token defined in `SYMBLAI_NEBULA_SERVICE_TOKEN`"
-   ]
+    "This example goes over how to use LangChain to interact with the [Nebula platform](https://docs.symbl.ai/docs/nebula-llm)."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "bb8cd830db4a004e"
   },
   {
    "cell_type": "markdown",
-   "id": "f15ebe0d",
-   "metadata": {},
    "source": [
-    "### Integrate with a LLMChain"
-   ]
+    "Make sure you have API Key with you. If you don't have one please [request one](https://info.symbl.ai/Nebula_Private_Beta.html)."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "519570b6539aa18c"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5472a7cd-af26-48ca-ae9b-5f6ae73c74d2",
-   "metadata": {
-    "tags": []
-   },
    "outputs": [],
    "source": [
-    "import os\n",
+    "from langchain.llms.symblai_nebula import Nebula\n",
     "\n",
-    "os.environ[\"NEBULA_SERVICE_URL\"] = NEBULA_SERVICE_URL\n",
-    "os.environ[\"NEBULA_SERVICE_PATH\"] = NEBULA_SERVICE_PATH\n",
-    "os.environ[\"NEBULA_SERVICE_API_KEY\"] = NEBULA_SERVICE_API_KEY"
-   ]
+    "llm = Nebula(nebula_api_key='<your_api_key>')"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "9f47bef45880aece"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6fb585dd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "cell_type": "markdown",
    "source": [
-    "from langchain.llms import OpenLLM\n",
-    "\n",
-    "llm = OpenLLM(\n",
-    "    conversation=\"<Drop your text conversation that you want to ask Nebula to analyze here>\",\n",
-    ")"
-   ]
+    "Use a conversation transcript and instruction to construct a prompt."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "88c6a516ef51c74b"
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "035dea0f",
-   "metadata": {
-    "tags": []
-   },
    "outputs": [],
    "source": [
     "from langchain import PromptTemplate, LLMChain\n",
     "\n",
-    "template = \"Identify the {count} main objectives or goals mentioned in this context concisely in less points. Emphasize on key intents.\"\n",
+    "conversation = \"\"\"Sam: Good morning, team! Let's keep this standup concise. We'll go in the usual order: what you did yesterday, what you plan to do today, and any blockers. Alex, kick us off.\n",
+    "Alex: Morning! Yesterday, I wrapped up the UI for the user dashboard. The new charts and widgets are now responsive. I also had a sync with the design team to ensure the final touchups are in line with the brand guidelines. Today, I'll start integrating the frontend with the new API endpoints Rhea was working on. The only blocker is waiting for some final API documentation, but I guess Rhea can update on that.\n",
+    "Rhea: Hey, all! Yep, about the API documentation - I completed the majority of the backend work for user data retrieval yesterday. The endpoints are mostly set up, but I need to do a bit more testing today. I'll finalize the API documentation by noon, so that should unblock Alex. After that, I’ll be working on optimizing the database queries for faster data fetching. No other blockers on my end.\n",
+    "Sam: Great, thanks Rhea. Do reach out if you need any testing assistance or if there are any hitches with the database. Now, my update: Yesterday, I coordinated with the client to get clarity on some feature requirements. Today, I'll be updating our project roadmap and timelines based on their feedback. Additionally, I'll be sitting with the QA team in the afternoon for preliminary testing. Blocker: I might need both of you to be available for a quick call in case the client wants to discuss the changes live.\n",
+    "Alex: Sounds good, Sam. Just let us know a little in advance for the call.\n",
+    "Rhea: Agreed. We can make time for that.\n",
+    "Sam: Perfect! Let's keep the momentum going. Reach out if there are any sudden issues or support needed. Have a productive day!\n",
+    "Alex: You too.\n",
+    "Rhea: Thanks, bye!\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"count\"])\n",
+    "instruction = \"Identify the main objectives mentioned in this conversation.\"\n",
+    "\n",
+    "prompt = PromptTemplate.from_template(\"{instruction}\\n{conversation}\")\n",
     "\n",
     "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
     "\n",
-    "generated = llm_chain.run(count=\"five\")\n",
-    "print(generated)"
-   ]
+    "llm_chain.run(instruction=instruction, conversation=conversation)"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "5977ccc2d4432624"
   }
  ],
  "metadata": {
diff --git a/docs/extras/integrations/llms/titan_takeoff.ipynb b/docs/extras/integrations/llms/titan_takeoff.ipynb
new file mode 100644
index 00000000000..b7150168d86
--- /dev/null
+++ b/docs/extras/integrations/llms/titan_takeoff.ipynb
@@ -0,0 +1,169 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titan Takeoff\n",
+    "\n",
+    "TitanML helps businesses build and deploy better, smaller, cheaper, and faster NLP models through our training, compression, and inference optimization platform. \n",
+    "\n",
+    "Our inference server, [Titan Takeoff](https://docs.titanml.co/docs/titan-takeoff/getting-started) enables deployment of LLMs locally on your hardware in a single command. Most generative model architectures are supported, such as Falcon, Llama 2, GPT2, T5 and many more."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation\n",
+    "\n",
+    "To get started with Iris Takeoff, all you need is to have docker and python installed on your local system. If you wish to use the server with gpu suport, then you will need to install docker with cuda support.\n",
+    "\n",
+    "For Mac and Windows users, make sure you have the docker daemon running! You can check this by running docker ps in your terminal. To start the daemon, open the docker desktop app.\n",
+    "\n",
+    "Run the following command to install the Iris CLI that will enable you to run the takeoff server:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pip install titan-iris"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Choose a Model\n",
+    "Iris Takeoff supports many of the most powerful generative text models, such as Falcon, MPT, and Llama. See the [supported models](https://docs.titanml.co/docs/titan-takeoff/supported-models) for more information. For information about using your own models, see the [custom models](https://docs.titanml.co/docs/titan-takeoff/Advanced/custom-models).\n",
+    "\n",
+    "Going forward in this demo we will be using the falcon 7B instruct model. This is a good open source model that is trained to follow instructions, and is small enough to easily inference even on CPUs.\n",
+    "\n",
+    "## Taking off\n",
+    "Models are referred to by their model id on HuggingFace. Takeoff uses port 8000 by default, but can be configured to use another port. There is also support to use a Nvidia GPU by specifing cuda for the device flag.\n",
+    "\n",
+    "To start the takeoff server, run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu\n",
+    "iris takeoff --model tiiuae/falcon-7b-instruct --device cuda # Nvidia GPU required\n",
+    "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu --port 5000 # run on port 5000 (default: 8000)\n",
+    "```"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You will then be directed to a login page, where you will need to create an account to proceed.\n",
+    "After logging in, run the command onscreen to check whether the server is ready. When it is ready, you can start using the Takeoff integration\n",
+    "\n",
+    "## Inferencing your model\n",
+    "To access your LLM, use the TitanTakeoff LLM wrapper:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import TitanTakeoff\n",
+    "\n",
+    "llm = TitanTakeoff(\n",
+    "    port=8000,\n",
+    "    generate_max_length=128,\n",
+    "    temperature=1.0\n",
+    ")\n",
+    "\n",
+    "prompt = \"What is the largest planet in the solar system?\"\n",
+    "\n",
+    "llm(prompt)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No parameters are needed by default, but a port can be specified and [generation parameters](https://docs.titanml.co/docs/titan-takeoff/Advanced/generation-parameters) can be supplied.\n",
+    "\n",
+    "### Streaming\n",
+    "Streaming is also supported via the streaming flag:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "\n",
+    "llm = TitanTakeoff(port=8000, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), streaming=True)\n",
+    "\n",
+    "prompt = \"What is the capital of France?\"\n",
+    "\n",
+    "llm(prompt)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Integration with LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import PromptTemplate, LLMChain\n",
+    "\n",
+    "llm = TitanTakeoff()\n",
+    "\n",
+    "template = \"What is the capital of {country}\"\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"country\"])\n",
+    "\n",
+    "llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
+    "\n",
+    "generated = llm_chain.run(country=\"Belgium\")\n",
+    "print(generated)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/extras/integrations/llms/vllm.ipynb b/docs/extras/integrations/llms/vllm.ipynb
index 7e3c5068c6c..8a9ff01df13 100644
--- a/docs/extras/integrations/llms/vllm.ipynb
+++ b/docs/extras/integrations/llms/vllm.ipynb
@@ -170,6 +170,51 @@
     "\n",
     "llm(\"What is the future of AI?\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64e89be0-6ad7-43a8-9dac-1324dcd4e851",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## OpenAI-Compatible Server\n",
+    "\n",
+    "vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API.\n",
+    "\n",
+    "This server can be queried in the same format as OpenAI API.\n",
+    "\n",
+    "### OpenAI-Compatible Completion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c3cbc428-0bb8-422a-913e-1c6fef8b89d4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " a city that is filled with history, ancient buildings, and art around every corner\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.llms import VLLMOpenAI\n",
+    "\n",
+    "\n",
+    "llm = VLLMOpenAI(\n",
+    "    openai_api_key=\"EMPTY\",\n",
+    "    openai_api_base=\"http://localhost:8000/v1\",\n",
+    "    model_name=\"tiiuae/falcon-7b\",\n",
+    "    model_kwargs={\"stop\": [\".\"]}\n",
+    ")\n",
+    "print(llm(\"Rome is\"))"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/extras/integrations/providers/arangodb.mdx b/docs/extras/integrations/providers/arangodb.mdx
index 5866dc92311..dcf0378a122 100644
--- a/docs/extras/integrations/providers/arangodb.mdx
+++ b/docs/extras/integrations/providers/arangodb.mdx
@@ -13,7 +13,7 @@ pip install python-arango
 
 Connect your ArangoDB Database with a Chat Model to get insights on your data. 
 
-See the notebook example [here](/docs/use_cases/graph/graph_arangodb_qa.html).
+See the notebook example [here](/docs/use_cases/more/graph/graph_arangodb_qa.html).
 
 ```python
 from arango import ArangoClient
diff --git a/docs/extras/integrations/providers/bageldb.mdx b/docs/extras/integrations/providers/bageldb.mdx
new file mode 100644
index 00000000000..ec05493169d
--- /dev/null
+++ b/docs/extras/integrations/providers/bageldb.mdx
@@ -0,0 +1,21 @@
+# BagelDB
+
+> [BagelDB](https://www.bageldb.ai/) (`Open Vector Database for AI`), is like GitHub for AI data.
+It is a collaborative platform where users can create,
+share, and manage vector datasets. It can support private projects for independent developers,
+internal collaborations for enterprises, and public contributions for data DAOs.
+
+## Installation and Setup
+
+```bash
+pip install betabageldb
+```
+
+
+## VectorStore
+
+See a [usage example](/docs/integrations/vectorstores/bageldb).
+
+```python
+from langchain.vectorstores import Bagel
+```
diff --git a/docs/extras/integrations/providers/bittensor.mdx b/docs/extras/integrations/providers/bittensor.mdx
new file mode 100644
index 00000000000..531a1844181
--- /dev/null
+++ b/docs/extras/integrations/providers/bittensor.mdx
@@ -0,0 +1,37 @@
+# NIBittensor
+
+This page covers how to use the BittensorLLM inference runtime within LangChain.
+It is broken into two parts: installation and setup, and then examples of NIBittensorLLM usage.
+
+## Installation and Setup
+
+- Install the Python package with `pip install langchain`
+
+## Wrappers
+
+### LLM
+
+There exists a NIBittensor LLM wrapper, which you can access with:
+
+```python
+from langchain.llms import NIBittensorLLM
+```
+
+It provides a unified interface for all models:
+
+```python
+llm = NIBittensorLLM(system_prompt="Your task is to provide consice and accurate response based on user prompt")
+
+print(llm('Write a fibonacci function in python with golder ratio'))
+```
+
+Multiple responses from top miners can be accessible using the `top_responses` parameter:
+
+```python
+multi_response_llm = NIBittensorLLM(top_responses=10)
+multi_resp = multi_response_llm("What is Neural Network Feeding Mechanism?")
+json_multi_resp = json.loads(multi_resp)
+
+print(json_multi_resp)
+```
+
diff --git a/docs/extras/integrations/providers/dashvector.mdx b/docs/extras/integrations/providers/dashvector.mdx
new file mode 100644
index 00000000000..d1ab24a0d0b
--- /dev/null
+++ b/docs/extras/integrations/providers/dashvector.mdx
@@ -0,0 +1,24 @@
+# DashVector
+
+> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.  
+
+This document demonstrates to leverage DashVector within the LangChain ecosystem. In particular, it shows how to install DashVector, and how to use it as a VectorStore plugin in LangChain.
+It is broken into two parts: installation and setup, and then references to specific DashVector wrappers.
+
+## Installation and Setup
+Install the Python SDK:
+```bash
+pip install dashvector
+```
+
+## VectorStore
+
+A DashVector Collection is wrapped as a familiar VectorStore for native usage within LangChain, 
+which allows it to be readily used for various scenarios, such as semantic search or example selection.
+
+You may import the vectorstore by:
+```python
+from langchain.vectorstores import DashVector
+```
+
+For a detailed walkthrough of the DashVector wrapper, please refer to [this notebook](/docs/integrations/vectorstores/dashvector.html)
diff --git a/docs/extras/integrations/providers/deepsparse.mdx b/docs/extras/integrations/providers/deepsparse.mdx
new file mode 100644
index 00000000000..1682f757fe0
--- /dev/null
+++ b/docs/extras/integrations/providers/deepsparse.mdx
@@ -0,0 +1,35 @@
+# DeepSparse
+
+This page covers how to use the [DeepSparse](https://github.com/neuralmagic/deepsparse) inference runtime within LangChain.
+It is broken into two parts: installation and setup, and then examples of DeepSparse usage.
+
+## Installation and Setup
+
+- Install the Python package with `pip install deepsparse`
+- Choose a [SparseZoo model](https://sparsezoo.neuralmagic.com/?useCase=text_generation) or export a support model to ONNX [using Optimum](https://github.com/neuralmagic/notebooks/blob/main/notebooks/opt-text-generation-deepsparse-quickstart/OPT_Text_Generation_DeepSparse_Quickstart.ipynb)
+
+## Wrappers
+
+### LLM
+
+There exists a DeepSparse LLM wrapper, which you can access with:
+
+```python
+from langchain.llms import DeepSparse
+```
+
+It provides a unified interface for all models:
+
+```python
+llm = DeepSparse(model='zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none')
+
+print(llm('def fib():'))
+```
+
+Additional parameters can be passed using the `config` parameter:
+
+```python
+config = {'max_generated_tokens': 256}
+
+llm = DeepSparse(model='zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none', config=config)
+```
diff --git a/docs/extras/integrations/providers/dingo.mdx b/docs/extras/integrations/providers/dingo.mdx
new file mode 100644
index 00000000000..5fd59675b15
--- /dev/null
+++ b/docs/extras/integrations/providers/dingo.mdx
@@ -0,0 +1,19 @@
+# Dingo
+
+This page covers how to use the Dingo ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Dingo wrappers.
+
+## Installation and Setup
+- Install the Python SDK with `pip install dingodb`
+
+## VectorStore
+
+There exists a wrapper around Dingo indexes, allowing you to use it as a vectorstore,
+whether for semantic search or example selection.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import Dingo
+```
+
+For a more detailed walkthrough of the Dingo wrapper, see [this notebook](/docs/integrations/vectorstores/dingo.html)
diff --git a/docs/extras/integrations/providers/elasticsearch.mdx b/docs/extras/integrations/providers/elasticsearch.mdx
index 8df323aa138..93e93055db1 100644
--- a/docs/extras/integrations/providers/elasticsearch.mdx
+++ b/docs/extras/integrations/providers/elasticsearch.mdx
@@ -1,24 +1,54 @@
 # Elasticsearch
 
->[Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine. 
-> It provides a distributed, multi-tenant-capable full-text search engine with an HTTP web interface and schema-free 
+> [Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine.
+> It provides a distributed, multi-tenant-capable full-text search engine with an HTTP web interface and schema-free
 > JSON documents.
 
-
 ## Installation and Setup
 
+There are two ways to get started with Elasticsearch:
+
+#### Install Elasticsearch on your local machine via docker
+
+Example: Run a single-node Elasticsearch instance with security disabled. This is not recommended for production use.
+
+```bash
+    docker run -p 9200:9200 -e "discovery.type=single-node" -e "xpack.security.enabled=false" -e "xpack.security.http.ssl.enabled=false" docker.elastic.co/elasticsearch/elasticsearch:8.9.0
+```
+
+#### Deploy Elasticsearch on Elastic Cloud
+
+Elastic Cloud is a managed Elasticsearch service. Signup for a [free trial](https://cloud.elastic.co/registration?storm=langchain-notebook).
+
+### Install Client
+
 ```bash
 pip install elasticsearch
 ```
 
-## Retriever
+## Vector Store
 
->In information retrieval, [Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25) (BM is an abbreviation of best matching) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson, Karen Spärck Jones, and others.
-
->The name of the actual ranking function is BM25. The fuller name, Okapi BM25, includes the name of the first system to use it, which was the Okapi information retrieval system, implemented at London's City University in the 1980s and 1990s. BM25 and its newer variants, e.g. BM25F (a version of BM25 that can take document structure and anchor text into account), represent TF-IDF-like retrieval functions used in document retrieval.
-
-See a [usage example](/docs/integrations/retrievers/elastic_search_bm25).
+The vector store is a simple wrapper around Elasticsearch. It provides a simple interface to store and retrieve vectors.
 
 ```python
-from langchain.retrievers import ElasticSearchBM25Retriever
+from langchain.vectorstores import ElasticsearchStore
+
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+
+loader = TextLoader("./state_of_the_union.txt")
+documents = loader.load()
+text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+docs = text_splitter.split_documents(documents)
+
+embeddings = OpenAIEmbeddings()
+
+db = ElasticsearchStore.from_documents(
+    docs, embeddings, es_url="http://localhost:9200", index_name="test-basic",
+)
+
+db.client.indices.refresh(index="test-basic")
+
+query = "What did the president say about Ketanji Brown Jackson"
+results = db.similarity_search(query)
 ```
diff --git a/docs/extras/integrations/providers/fireworks.md b/docs/extras/integrations/providers/fireworks.md
index 42c8436c8e6..aeb4d75b438 100644
--- a/docs/extras/integrations/providers/fireworks.md
+++ b/docs/extras/integrations/providers/fireworks.md
@@ -4,14 +4,15 @@ This page covers how to use the Fireworks models within Langchain.
 
 ## Installation and Setup
 
-- To use the Fireworks model, you need to have a Fireworks API key. To generate one, sign up at platform.fireworks.ai
+- To use the Fireworks model, you need to have a Fireworks API key. To generate one, sign up at [app.fireworks.ai](https://app.fireworks.ai).
 - Authenticate by setting the FIREWORKS_API_KEY environment variable.
 
 ## LLM
 
 Fireworks integrates with Langchain through the LLM module, which allows for standardized usage of any models deployed on the Fireworks models.
 
-In this example, we'll work the llama-v2-13b. 
+In this example, we'll work the llama-v2-13b-chat model. 
+
 ```python
 from langchain.llms.fireworks import Fireworks 
 
diff --git a/docs/extras/integrations/providers/grobid.mdx b/docs/extras/integrations/providers/grobid.mdx
index 6a24e68baa2..4fd52abe234 100644
--- a/docs/extras/integrations/providers/grobid.mdx
+++ b/docs/extras/integrations/providers/grobid.mdx
@@ -1,22 +1,23 @@
 # Grobid
 
+GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents.
+
+It is designed and expected to be used to parse academic papers, where it works particularly well.
+
+*Note*: if the articles supplied to Grobid are large documents (e.g. dissertations) exceeding a certain number
+of elements, they might not be processed.
+
 This page covers how to use the Grobid to parse articles for LangChain.
-It is separated into two parts: installation and running the server
 
-## Installation and Setup
-#Ensure You have Java installed
-!apt-get install -y openjdk-11-jdk -q
-!update-alternatives --set java /usr/lib/jvm/java-11-openjdk-amd64/bin/java
+## Installation
+The grobid installation is described in details in https://grobid.readthedocs.io/en/latest/Install-Grobid/.
+However, it is probably easier and less troublesome to run grobid through a docker container,
+as documented [here](https://grobid.readthedocs.io/en/latest/Grobid-docker/).
 
-#Clone and install the Grobid Repo
-import os
-!git clone https://github.com/kermitt2/grobid.git
-os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-11-openjdk-amd64"
-os.chdir('grobid')
-!./gradlew clean install
+## Use Grobid with LangChain
 
-#Run the server, 
-get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')
+Once grobid is installed and up and running (you can check by accessing it http://localhost:8070),
+you're ready to go.
 
 You can now use the GrobidParser to produce documents
 ```python
@@ -41,4 +42,5 @@ loader = GenericLoader.from_filesystem(
 )
 docs = loader.load()
 ```
-Chunk metadata will include bboxes although these are a bit funky to parse, see https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/
+Chunk metadata will include Bounding Boxes. Although these are a bit funky to parse,
+they are explained in https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/
\ No newline at end of file
diff --git a/docs/extras/integrations/providers/symblai_nebula.mdx b/docs/extras/integrations/providers/symblai_nebula.mdx
index 24ecd76a0ed..b716af6ff0c 100644
--- a/docs/extras/integrations/providers/symblai_nebula.mdx
+++ b/docs/extras/integrations/providers/symblai_nebula.mdx
@@ -5,16 +5,14 @@ It is broken into two parts: installation and setup, and then references to spec
 
 ## Installation and Setup
 
-- Get an Nebula API Key and set as environment variables (`SYMBLAI_NEBULA_SERVICE_URL`, `SYMBLAI_NEBULA_SERVICE_PATH`, `SYMBLAI_NEBULA_SERVICE_TOKEN`)
-  - Sign up for a FREE Symbl.ai/Nebula Account: [https://nebula.symbl.ai/playground/](https://nebula.symbl.ai/playground/)
-- Please see the [Nebula documentation](https://docs.symbl.ai/docs/nebula-llm-overview) for more details.
-  - No time? Visit the [Nebula Quickstart Guide](https://docs.symbl.ai/docs/nebula-quickstart).
-
-## Wrappers
+- Get an [Nebula API Key](https://info.symbl.ai/Nebula_Private_Beta.html) and set as environment variable `NEBULA_API_KEY`
+- Please see the [Nebula documentation](https://docs.symbl.ai/docs/nebula-llm) for more details.
+- No time? Visit the [Nebula Quickstart Guide](https://docs.symbl.ai/docs/nebula-quickstart).
 
 ### LLM
 
-There exists an Nebula LLM wrapper, which you can access with 
+There exists an Nebula LLM wrapper, which you can access with
 ```python
 from langchain.llms import Nebula
+llm = Nebula()
 ```
diff --git a/docs/extras/integrations/providers/vectara/index.mdx b/docs/extras/integrations/providers/vectara/index.mdx
index 627a234a3b4..0e30735f0b7 100644
--- a/docs/extras/integrations/providers/vectara/index.mdx
+++ b/docs/extras/integrations/providers/vectara/index.mdx
@@ -63,7 +63,7 @@ results = vectara.similarity_score("what is LangChain?")
 - `k`: number of results to return (defaults to 5)
 - `lambda_val`: the [lexical matching](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) factor for hybrid search (defaults to 0.025)
 - `filter`: a [filter](https://docs.vectara.com/docs/common-use-cases/filtering-by-metadata/filter-overview) to apply to the results (default None)
-- `n_sentence_context`: number of sentences to include before/after the actual matching segment when returning results. This defaults to 0 so as to return the exact text segment that matches, but can be used with other values e.g. 2 or 3 to return adjacent text segments.
+- `n_sentence_context`: number of sentences to include before/after the actual matching segment when returning results. This defaults to 2.
 
 The results are returned as a list of relevant documents, and a relevance score of each document.
 
diff --git a/docs/extras/integrations/providers/vectara/vectara_chat.ipynb b/docs/extras/integrations/providers/vectara/vectara_chat.ipynb
index 758bef9fb5a..2f5dfdfc0fc 100644
--- a/docs/extras/integrations/providers/vectara/vectara_chat.ipynb
+++ b/docs/extras/integrations/providers/vectara/vectara_chat.ipynb
@@ -45,7 +45,7 @@
    "source": [
     "from langchain.document_loaders import TextLoader\n",
     "\n",
-    "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
+    "loader = TextLoader(\"../../../modules/state_of_the_union.txt\")\n",
     "documents = loader.load()"
    ]
   },
@@ -136,7 +136,7 @@
     {
      "data": {
       "text/plain": [
-       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
+       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds and a former top litigator in private practice, and that she will continue Justice Breyer's legacy of excellence.\""
       ]
      },
      "execution_count": 7,
@@ -168,7 +168,7 @@
     {
      "data": {
       "text/plain": [
-       "' Justice Stephen Breyer'"
+       "' Ketanji Brown Jackson succeeded Justice Breyer.'"
       ]
      },
      "execution_count": 9,
@@ -237,7 +237,7 @@
     {
      "data": {
       "text/plain": [
-       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
+       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds and a former top litigator in private practice, and that she will continue Justice Breyer's legacy of excellence.\""
       ]
      },
      "execution_count": 12,
@@ -282,7 +282,7 @@
     {
      "data": {
       "text/plain": [
-       "' Justice Stephen Breyer'"
+       "' Ketanji Brown Jackson succeeded Justice Breyer.'"
       ]
      },
      "execution_count": 14,
@@ -342,7 +342,7 @@
     {
      "data": {
       "text/plain": [
-       "Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})"
+       "Document(page_content='Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice.', metadata={'source': '../../../modules/state_of_the_union.txt'})"
       ]
      },
      "execution_count": 17,
@@ -396,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 20,
    "id": "24ebdaec",
    "metadata": {},
    "outputs": [
@@ -404,7 +404,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " The president said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\n"
+      " The president said that Ketanji Brown Jackson is one of the nation's top legal minds and a former top litigator in private practice, and that she will continue Justice Breyer's legacy of excellence.\n"
      ]
     }
    ],
@@ -423,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "e53a9d66",
    "metadata": {
     "tags": []
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "bf205e35",
    "metadata": {
     "tags": []
@@ -456,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "78155887",
    "metadata": {
     "tags": []
@@ -470,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "e54b5fa2",
    "metadata": {
     "tags": []
@@ -479,10 +479,10 @@
     {
      "data": {
       "text/plain": [
-       "\" The president said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, who he described as one of the nation's top legal minds, to continue Justice Breyer's legacy of excellence.\""
+       "\" The president said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, who is one of the nation's top legal minds and a former top litigator in private practice.\""
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -503,7 +503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "d1058fd2",
    "metadata": {
     "tags": []
@@ -515,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "a6594482",
    "metadata": {
     "tags": []
@@ -534,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "e2badd21",
    "metadata": {
     "tags": []
@@ -548,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "edb31fe5",
    "metadata": {
     "tags": []
@@ -557,10 +557,10 @@
     {
      "data": {
       "text/plain": [
-       "\" The president said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, who he described as one of the nation's top legal minds, and that she will continue Justice Breyer's legacy of excellence.\\nSOURCES: ../../../state_of_the_union.txt\""
+       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds and a former top litigator in private practice.\\nSOURCES: ../../../modules/state_of_the_union.txt\""
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -581,7 +581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "id": "2efacec3-2690-4b05-8de3-a32fd2ac3911",
    "metadata": {
     "tags": []
@@ -618,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "id": "fd6d43f4-7428-44a4-81bc-26fe88a98762",
    "metadata": {
     "tags": []
@@ -628,7 +628,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " The president said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence."
+      " The president said that Ketanji Brown Jackson is one of the nation's top legal minds and a former top litigator in private practice, and that she will continue Justice Breyer's legacy of excellence."
      ]
     }
    ],
@@ -640,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "id": "5ab38978-f3e8-4fa7-808c-c79dec48379a",
    "metadata": {
     "tags": []
@@ -650,7 +650,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " Justice Stephen Breyer"
+      " Justice Breyer"
      ]
     }
    ],
@@ -671,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "id": "a7ba9d8c",
    "metadata": {
     "tags": []
@@ -692,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "id": "a3e33c0d",
    "metadata": {
     "tags": []
@@ -706,7 +706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "id": "936dc62f",
    "metadata": {
     "tags": []
@@ -715,10 +715,10 @@
     {
      "data": {
       "text/plain": [
-       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
+       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds and a former top litigator in private practice, and that she will continue Justice Breyer's legacy of excellence.\""
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/docs/extras/integrations/providers/vectara/vectara_text_generation.ipynb b/docs/extras/integrations/providers/vectara/vectara_text_generation.ipynb
index e5e908e815c..e9e6d765d8a 100644
--- a/docs/extras/integrations/providers/vectara/vectara_text_generation.ipynb
+++ b/docs/extras/integrations/providers/vectara/vectara_text_generation.ipynb
@@ -97,8 +97,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
     "search_index = Vectara.from_texts(source_chunks, embedding=None)"
    ]
   },
@@ -161,7 +159,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[{'text': '\\n\\nEnvironment variables are a powerful tool for managing configuration settings in your applications. They allow you to store and access values from anywhere in your code, making it easier to keep your codebase organized and maintainable.\\n\\nHowever, there are times when you may want to use environment variables specifically for a single command. This is where shell variables come in. Shell variables are similar to environment variables, but they won\\'t be exported to spawned commands. They are defined with the following syntax:\\n\\n```sh\\nVAR_NAME=value\\n```\\n\\nFor example, if you wanted to use a shell variable instead of an environment variable in a command, you could do something like this:\\n\\n```sh\\nVAR=hello && echo $VAR && deno eval \"console.log(\\'Deno: \\' + Deno.env.get(\\'VAR\\'))\"\\n```\\n\\nThis would output the following:\\n\\n```\\nhello\\nDeno: undefined\\n```\\n\\nShell variables can be useful when you want to re-use a value, but don\\'t want it available in any spawned processes.\\n\\nAnother way to use environment variables is through pipelines. Pipelines provide a way to pipe the'}, {'text': '\\n\\nEnvironment variables are a great way to store and access sensitive information in your applications. They are also useful for configuring applications and managing different environments. In Deno, there are two ways to use environment variables: the built-in `Deno.env` and the `.env` file.\\n\\nThe `Deno.env` is a built-in feature of the Deno runtime that allows you to set and get environment variables. It has getter and setter methods that you can use to access and set environment variables. For example, you can set the `FIREBASE_API_KEY` and `FIREBASE_AUTH_DOMAIN` environment variables like this:\\n\\n```ts\\nDeno.env.set(\"FIREBASE_API_KEY\", \"examplekey123\");\\nDeno.env.set(\"FIREBASE_AUTH_DOMAIN\", \"firebasedomain.com\");\\n\\nconsole.log(Deno.env.get(\"FIREBASE_API_KEY\")); // examplekey123\\nconsole.log(Deno.env.get(\"FIREBASE_AUTH_DOMAIN\")); // firebasedomain'}, {'text': \"\\n\\nEnvironment variables are a powerful tool for managing configuration and settings in your applications. They allow you to store and access values that can be used in your code, and they can be set and changed without having to modify your code.\\n\\nIn Deno, environment variables are defined using the `export` command. For example, to set a variable called `VAR_NAME` to the value `value`, you would use the following command:\\n\\n```sh\\nexport VAR_NAME=value\\n```\\n\\nYou can then access the value of the environment variable in your code using the `Deno.env.get()` method. For example, if you wanted to log the value of the `VAR_NAME` variable, you could use the following code:\\n\\n```js\\nconsole.log(Deno.env.get('VAR_NAME'));\\n```\\n\\nYou can also set environment variables for a single command. To do this, you can list the environment variables before the command, like so:\\n\\n```\\nVAR=hello VAR2=bye deno run main.ts\\n```\\n\\nThis will set the environment variables `VAR` and `V\"}, {'text': \"\\n\\nEnvironment variables are a powerful tool for managing settings and configuration in your applications. They can be used to store information such as user preferences, application settings, and even passwords. In this blog post, we'll discuss how to make Deno scripts executable with a hashbang (shebang).\\n\\nA hashbang is a line of code that is placed at the beginning of a script. It tells the system which interpreter to use when running the script. In the case of Deno, the hashbang should be `#!/usr/bin/env -S deno run --allow-env`. This tells the system to use the Deno interpreter and to allow the script to access environment variables.\\n\\nOnce the hashbang is in place, you may need to give the script execution permissions. On Linux, this can be done with the command `sudo chmod +x hashbang.ts`. After that, you can execute the script by calling it like any other command: `./hashbang.ts`.\\n\\nIn the example program, we give the context permission to access the environment variables and print the Deno installation path. This is done by using the `Deno.env.get()` function, which returns the value of the specified environment\"}]\n"
+      "[{'text': '\\n\\nWhen it comes to running Deno CLI tasks, environment variables can be a powerful tool for customizing the behavior of your tasks. With the Deno Task Definition interface, you can easily configure environment variables to be set when executing your tasks.\\n\\nThe Deno Task Definition interface is configured in a `tasks.json` within your workspace. It includes a `env` field, which allows you to specify any environment variables that should be set when executing the task. For example, if you wanted to set the `NODE_ENV` environment variable to `production` when running a Deno task, you could add the following to your `tasks.json`:\\n\\n```json\\n{\\n \"version\": \"2.0.0\",\\n \"tasks\": [\\n {\\n \"type\": \"deno\",\\n \"command\": \"run\",\\n \"args\": [\\n \"mod.ts\"\\n ],\\n \"env\": {\\n \"NODE_ENV\": \"production\"\\n },\\n \"problemMatcher\": [\\n \"$deno\"\\n ],\\n \"label\": \"deno: run\"\\n }\\n ]\\n}\\n```\\n\\nThe Deno language server and this extension also'}, {'text': '\\n\\nEnvironment variables are a great way to store and access data in your applications. They are especially useful when you need to store sensitive information such as API keys, passwords, and other credentials.\\n\\nDeno.env is a library that provides getter and setter methods for environment variables. This makes it easy to store and retrieve data from environment variables. For example, you can use the setter method to set a variable like this:\\n\\n```ts\\nDeno.env.set(\"FIREBASE_API_KEY\", \"examplekey123\");\\nDeno.env.set(\"FIREBASE_AUTH_DOMAIN\", \"firebasedomain.com\");\\n```\\n\\nAnd then you can use the getter method to retrieve the data like this:\\n\\n```ts\\nconsole.log(Deno.env.get(\"FIREBASE_API_KEY\")); // examplekey123\\nconsole.log(Deno.env.get(\"FIREBASE_AUTH_DOMAIN\")); // firebasedomain.com\\n```\\n\\nYou can also store environment variables in a `.env` file and retrieve them using `dotenv` in the standard'}, {'text': '\\n\\nEnvironment variables are a powerful tool for developers, allowing them to store and access data without hard-coding it into their applications. Deno, the secure JavaScript and TypeScript runtime, offers built-in support for environment variables with the `Deno.env` API.\\n\\nUsing `Deno.env` is simple. It has getter and setter methods that allow you to easily set and retrieve environment variables. For example, you can set the `FIREBASE_API_KEY` and `FIREBASE_AUTH_DOMAIN` environment variables like this:\\n\\n```ts\\nDeno.env.set(\"FIREBASE_API_KEY\", \"examplekey123\");\\nDeno.env.set(\"FIREBASE_AUTH_DOMAIN\", \"firebasedomain.com\");\\n```\\n\\nAnd then you can retrieve them like this:\\n\\n```ts\\nconsole.log(Deno.env.get(\"FIREBASE_API_KEY\")); // examplekey123\\nconsole.log(Deno.env.get(\"FIREBASE_AUTH_DOMAIN\")); // firebasedomain.com\\n```'}, {'text': '\\n\\nEnvironment variables are an important part of any programming language, and Deno is no exception. Environment variables are used to store information about the environment in which a program is running, such as the operating system, user preferences, and other settings. In Deno, environment variables are used to set up proxies, control the output of colors, and more.\\n\\nThe `NO_PROXY` environment variable is a de facto standard in Deno that indicates which hosts should bypass the proxy set in other environment variables. This is useful for developers who want to access certain resources without having to go through a proxy. For more information on this standard, you can check out the website no-color.org.\\n\\nThe `Deno.noColor` environment variable is another important environment variable in Deno. This variable is used to control the output of colors in the Deno terminal. By setting this variable to true, you can disable the output of colors in the terminal. This can be useful for developers who want to focus on the output of their code without being distracted by the colors.\\n\\nFinally, the `Deno.env` environment variable is used to access the environment variables set in the Deno runtime. This variable is useful for developers who want'}]\n"
      ]
     }
    ],
diff --git a/docs/extras/integrations/retrievers/zep_memorystore.ipynb b/docs/extras/integrations/retrievers/zep_memorystore.ipynb
index 5e77711f509..85b753709cc 100644
--- a/docs/extras/integrations/retrievers/zep_memorystore.ipynb
+++ b/docs/extras/integrations/retrievers/zep_memorystore.ipynb
@@ -44,21 +44,20 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-25T15:03:27.863217Z",
-     "start_time": "2023-05-25T15:03:25.690273Z"
-    },
     "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
+    "ExecuteTime": {
+     "end_time": "2023-08-11T20:31:12.231459Z",
+     "start_time": "2023-08-11T20:31:11.211176Z"
     }
    },
    "outputs": [],
    "source": [
-    "from langchain.memory.chat_message_histories import ZepChatMessageHistory\n",
-    "from langchain.schema import HumanMessage, AIMessage\n",
-    "from uuid import uuid4\n",
     "import getpass\n",
+    "import time\n",
+    "from uuid import uuid4\n",
+    "\n",
+    "from langchain.memory import ZepMemory, CombinedMemory, VectorStoreRetrieverMemory\n",
+    "from langchain.schema import HumanMessage, AIMessage\n",
     "\n",
     "# Set this to your Zep server URL\n",
     "ZEP_API_URL = \"http://localhost:8000\""
@@ -76,56 +75,50 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-11T20:31:12.237545Z",
+     "start_time": "2023-08-11T20:31:12.232416Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# Provide your Zep API key. Note that this is optional. See https://docs.getzep.com/deployment/auth\n",
+    "AUTHENTICATE = False\n",
     "\n",
-    "zep_api_key = getpass.getpass()"
+    "zep_api_key = None\n",
+    "if AUTHENTICATE:\n",
+    "    zep_api_key = getpass.getpass()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-25T15:03:29.118416Z",
-     "start_time": "2023-05-25T15:03:29.022464Z"
-    },
     "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
+    "ExecuteTime": {
+     "end_time": "2023-08-11T20:31:12.342790Z",
+     "start_time": "2023-08-11T20:31:12.235291Z"
     }
    },
    "outputs": [],
    "source": [
     "session_id = str(uuid4())  # This is a unique identifier for the user/session\n",
     "\n",
-    "# Set up Zep Chat History. We'll use this to add chat histories to the memory store\n",
-    "zep_chat_history = ZepChatMessageHistory(\n",
+    "# Initialize the Zep Memory Class\n",
+    "zep_memory = ZepMemory(\n",
     "    session_id=session_id, url=ZEP_API_URL, api_key=zep_api_key\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-25T15:03:30.271181Z",
-     "start_time": "2023-05-25T15:03:30.180442Z"
-    },
     "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
+    "ExecuteTime": {
+     "end_time": "2023-08-11T20:31:14.455269Z",
+     "start_time": "2023-08-11T20:31:12.345635Z"
     }
    },
    "outputs": [],
@@ -191,11 +184,13 @@
     "]\n",
     "\n",
     "for msg in test_history:\n",
-    "    zep_chat_history.add_message(\n",
+    "    zep_memory.chat_memory.add_message(\n",
     "        HumanMessage(content=msg[\"content\"])\n",
     "        if msg[\"role\"] == \"human\"\n",
     "        else AIMessage(content=msg[\"content\"])\n",
-    "    )"
+    "    )\n",
+    "    \n",
+    "time.sleep(2)  # Wait for the messages to be embedded"
    ]
   },
   {
@@ -211,29 +206,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-25T15:03:32.979155Z",
-     "start_time": "2023-05-25T15:03:32.590310Z"
-    },
     "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
+    "ExecuteTime": {
+     "end_time": "2023-08-11T20:31:14.758738Z",
+     "start_time": "2023-08-11T20:31:14.458850Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[Document(page_content='Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', metadata={'score': 0.8897116216176073, 'uuid': 'db60ff57-f259-4ec4-8a81-178ed4c6e54f', 'created_at': '2023-06-26T23:40:22.816214Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}]}}, 'token_count': 56}),\n",
-       " Document(page_content=\"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", metadata={'score': 0.8856661080361157, 'uuid': 'f1a5981a-8f6d-4168-a548-6e9c32f35fa1', 'created_at': '2023-06-26T23:40:22.809621Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}]}}, 'token_count': 23}),\n",
-       " Document(page_content='Who was Octavia Butler?', metadata={'score': 0.7757595298492976, 'uuid': '361d0043-1009-4e13-a7f0-8aea8b1ee869', 'created_at': '2023-06-26T23:40:22.709886Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}], 'intent': 'The subject wants to know about the identity or background of an individual named Octavia Butler.'}}, 'token_count': 8}),\n",
-       " Document(page_content=\"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", metadata={'score': 0.7601242516059306, 'uuid': '56c45e8a-0f65-45f0-bc46-d9e65164b563', 'created_at': '2023-06-26T23:40:22.778836Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': \"Octavia Butler's\"}], 'Name': \"Octavia Butler's\"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': \"The subject is providing information about Octavia Butler's contemporaries.\"}}, 'token_count': 27}),\n",
-       " Document(page_content='You might want to read Ursula K. Le Guin or Joanna Russ.', metadata={'score': 0.7594731095320668, 'uuid': '6951f2fd-dfa4-4e05-9380-f322ef8f72f8', 'created_at': '2023-06-26T23:40:22.80464Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 18})]"
-      ]
+      "text/plain": "[Document(page_content='Who was Octavia Butler?', metadata={'score': 0.7758688965570713, 'uuid': 'b3322d28-f589-48c7-9daf-5eb092d65976', 'created_at': '2023-08-11T20:31:12.3856Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}]}}, 'token_count': 8}),\n Document(page_content=\"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", metadata={'score': 0.7602672137411663, 'uuid': '756b7136-0b4c-4664-ad33-c4431670356c', 'created_at': '2023-08-11T20:31:12.420717Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': \"Octavia Butler's\"}], 'Name': \"Octavia Butler's\"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 27}),\n Document(page_content='You might want to read Ursula K. Le Guin or Joanna Russ.', metadata={'score': 0.7596040989115522, 'uuid': '166d9556-2d48-4237-8a84-5d8a1024d5f4', 'created_at': '2023-08-11T20:31:12.434522Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 18}),\n Document(page_content='Who were her contemporaries?', metadata={'score': 0.7575531381951208, 'uuid': 'c6a16691-4012-439f-b223-84fd4e79c4cf', 'created_at': '2023-08-11T20:31:12.410336Z', 'role': 'human', 'token_count': 8}),\n Document(page_content='Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American science fiction author.', metadata={'score': 0.7546476914454683, 'uuid': '7c093a2a-0099-415a-95c5-615a8026a894', 'created_at': '2023-08-11T20:31:12.399979Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 0, 'Text': 'Octavia Estelle Butler'}], 'Name': 'Octavia Estelle Butler'}, {'Label': 'DATE', 'Matches': [{'End': 37, 'Start': 24, 'Text': 'June 22, 1947'}], 'Name': 'June 22, 1947'}, {'Label': 'DATE', 'Matches': [{'End': 57, 'Start': 40, 'Text': 'February 24, 2006'}], 'Name': 'February 24, 2006'}, {'Label': 'NORP', 'Matches': [{'End': 74, 'Start': 66, 'Text': 'American'}], 'Name': 'American'}]}}, 'token_count': 31})]"
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -260,29 +246,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-25T15:03:34.713354Z",
-     "start_time": "2023-05-25T15:03:34.577974Z"
-    },
     "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
+    "ExecuteTime": {
+     "end_time": "2023-08-11T20:31:14.922838Z",
+     "start_time": "2023-08-11T20:31:14.751737Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[Document(page_content='Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', metadata={'score': 0.889661105796371, 'uuid': 'db60ff57-f259-4ec4-8a81-178ed4c6e54f', 'created_at': '2023-06-26T23:40:22.816214Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}]}}, 'token_count': 56}),\n",
-       " Document(page_content=\"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", metadata={'score': 0.885754241595424, 'uuid': 'f1a5981a-8f6d-4168-a548-6e9c32f35fa1', 'created_at': '2023-06-26T23:40:22.809621Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}]}}, 'token_count': 23}),\n",
-       " Document(page_content='Who was Octavia Butler?', metadata={'score': 0.7758688965570713, 'uuid': '361d0043-1009-4e13-a7f0-8aea8b1ee869', 'created_at': '2023-06-26T23:40:22.709886Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}], 'intent': 'The subject wants to know about the identity or background of an individual named Octavia Butler.'}}, 'token_count': 8}),\n",
-       " Document(page_content=\"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", metadata={'score': 0.7602672137411663, 'uuid': '56c45e8a-0f65-45f0-bc46-d9e65164b563', 'created_at': '2023-06-26T23:40:22.778836Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': \"Octavia Butler's\"}], 'Name': \"Octavia Butler's\"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': \"The subject is providing information about Octavia Butler's contemporaries.\"}}, 'token_count': 27}),\n",
-       " Document(page_content='You might want to read Ursula K. Le Guin or Joanna Russ.', metadata={'score': 0.7596040989115522, 'uuid': '6951f2fd-dfa4-4e05-9380-f322ef8f72f8', 'created_at': '2023-06-26T23:40:22.80464Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 18})]"
-      ]
+      "text/plain": "[Document(page_content=\"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", metadata={'score': 0.8857504413268114, 'uuid': '82f07ab5-9d4b-4db6-aaae-6028e6fd836b', 'created_at': '2023-08-11T20:31:12.437365Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}]}}, 'token_count': 23}),\n Document(page_content='Who was Octavia Butler?', metadata={'score': 0.7758688965570713, 'uuid': 'b3322d28-f589-48c7-9daf-5eb092d65976', 'created_at': '2023-08-11T20:31:12.3856Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}]}}, 'token_count': 8}),\n Document(page_content=\"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", metadata={'score': 0.7602672137411663, 'uuid': '756b7136-0b4c-4664-ad33-c4431670356c', 'created_at': '2023-08-11T20:31:12.420717Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': \"Octavia Butler's\"}], 'Name': \"Octavia Butler's\"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 27}),\n Document(page_content='You might want to read Ursula K. Le Guin or Joanna Russ.', metadata={'score': 0.7596040989115522, 'uuid': '166d9556-2d48-4237-8a84-5d8a1024d5f4', 'created_at': '2023-08-11T20:31:12.434522Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}]}}, 'token_count': 18}),\n Document(page_content='Who were her contemporaries?', metadata={'score': 0.7575531381951208, 'uuid': 'c6a16691-4012-439f-b223-84fd4e79c4cf', 'created_at': '2023-08-11T20:31:12.410336Z', 'role': 'human', 'token_count': 8})]"
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,19 +270,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-05-18T20:09:21.298710Z",
-     "start_time": "2023-05-18T20:09:21.297169Z"
-    },
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
+   "execution_count": 6,
    "outputs": [],
-   "source": []
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-11T20:31:14.923032Z",
+     "start_time": "2023-08-11T20:31:14.918181Z"
+    }
+   }
   }
  ],
  "metadata": {
diff --git a/docs/extras/integrations/text_embedding/bedrock.ipynb b/docs/extras/integrations/text_embedding/bedrock.ipynb
index a69c99de1dd..7c16cb8ead4 100644
--- a/docs/extras/integrations/text_embedding/bedrock.ipynb
+++ b/docs/extras/integrations/text_embedding/bedrock.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "282239c8-e03a-4abc-86c1-ca6120231a20",
    "metadata": {},
    "outputs": [],
@@ -28,7 +28,7 @@
     "from langchain.embeddings import BedrockEmbeddings\n",
     "\n",
     "embeddings = BedrockEmbeddings(\n",
-    "    credentials_profile_name=\"bedrock-admin\", endpoint_url=\"custom_endpoint_url\"\n",
+    "    credentials_profile_name=\"bedrock-admin\", region_name=\"us-east-1\"\n",
     ")"
    ]
   },
@@ -49,7 +49,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings.embed_documents([\"This is a content of the document\"])"
+    "embeddings.embed_documents([\"This is a content of the document\", \"This is another document\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f6b364d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# async embed query\n",
+    "await embeddings.aembed_query(\"This is a content of the document\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9240a5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# async embed documents\n",
+    "await embeddings.aembed_documents([\"This is a content of the document\", \"This is another document\"])"
    ]
   }
  ],
@@ -69,7 +91,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,
diff --git a/docs/extras/integrations/text_embedding/openai.ipynb b/docs/extras/integrations/text_embedding/openai.ipynb
index 9cb9c625023..7dbf174ceb9 100644
--- a/docs/extras/integrations/text_embedding/openai.ipynb
+++ b/docs/extras/integrations/text_embedding/openai.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "id": "0be1af71",
    "metadata": {},
    "outputs": [],
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 29,
    "id": "2c66e5da",
    "metadata": {},
    "outputs": [],
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 30,
    "id": "01370375",
    "metadata": {},
    "outputs": [],
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 31,
    "id": "bfb6142c",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 32,
+   "id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.003186025367556387,\n",
+       " 0.011071979803637493,\n",
+       " -0.004020420763285827,\n",
+       " -0.011658221276953042,\n",
+       " -0.0010534035786864363]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_result[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
    "id": "0356c3b7",
    "metadata": {},
    "outputs": [],
@@ -60,6 +85,31 @@
     "doc_result = embeddings.embed_documents([text])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.003186025367556387,\n",
+       " 0.011071979803637493,\n",
+       " -0.004020420763285827,\n",
+       " -0.011658221276953042,\n",
+       " -0.0010534035786864363]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "doc_result[0][:5]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bb61bbeb",
@@ -70,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "c0b072cc",
    "metadata": {},
    "outputs": [],
@@ -80,17 +130,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "a56b70f5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings = OpenAIEmbeddings()"
+    "embeddings = OpenAIEmbeddings(model=\"text-search-ada-doc-001\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "14aefb64",
    "metadata": {},
    "outputs": [],
@@ -100,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "3c39ed33",
    "metadata": {},
    "outputs": [],
@@ -110,7 +160,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
+   "id": "2ee7ce9f-d506-4810-8897-e44334412714",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.004452846988523035,\n",
+       " 0.034550655976098514,\n",
+       " -0.015029939040690051,\n",
+       " 0.03827273883655212,\n",
+       " 0.005785414075152477]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_result[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
    "id": "e3221db6",
    "metadata": {},
    "outputs": [],
@@ -118,6 +193,31 @@
     "doc_result = embeddings.embed_documents([text])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a0865409-3a6d-468f-939f-abde17c7cac3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.004452846988523035,\n",
+       " 0.034550655976098514,\n",
+       " -0.015029939040690051,\n",
+       " 0.03827273883655212,\n",
+       " 0.005785414075152477]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "doc_result[0][:5]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -132,7 +232,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.11.1 64-bit",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -146,7 +246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/extras/integrations/tools/alpha_vantage.ipynb b/docs/extras/integrations/tools/alpha_vantage.ipynb
new file mode 100644
index 00000000000..08cd1e7ad99
--- /dev/null
+++ b/docs/extras/integrations/tools/alpha_vantage.ipynb
@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "245a954a",
+   "metadata": {
+    "id": "245a954a"
+   },
+   "source": [
+    "# Alpha Vantage\n",
+    "\n",
+    ">[Alpha Vantage](https://www.alphavantage.co) Alpha Vantage provides realtime and historical financial market data through a set of powerful and developer-friendly data APIs and spreadsheets. \n",
+    "\n",
+    "Use the ``AlphaVantageAPIWrapper`` to get currency exchange rates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "34bb5968",
+   "metadata": {
+    "id": "34bb5968"
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"ALPHAVANTAGE_API_KEY\"] = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ac4910f8",
+   "metadata": {
+    "id": "ac4910f8"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.utilities.alpha_vantage import AlphaVantageAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "84b8f773",
+   "metadata": {
+    "id": "84b8f773"
+   },
+   "outputs": [],
+   "source": [
+    "alpha_vantage = AlphaVantageAPIWrapper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "068991a6",
+   "metadata": {
+    "id": "068991a6",
+    "outputId": "c5cdc6ec-03cf-4084-cc6f-6ae792d91d39"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'1. From_Currency Code': 'USD',\n",
+       " '2. From_Currency Name': 'United States Dollar',\n",
+       " '3. To_Currency Code': 'JPY',\n",
+       " '4. To_Currency Name': 'Japanese Yen',\n",
+       " '5. Exchange Rate': '144.93000000',\n",
+       " '6. Last Refreshed': '2023-08-11 21:31:01',\n",
+       " '7. Time Zone': 'UTC',\n",
+       " '8. Bid Price': '144.92600000',\n",
+       " '9. Ask Price': '144.93400000'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alpha_vantage.run(\"USD\", \"JPY\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84fc2b66-c08f-4cd3-ae13-494c54789c09",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "53f3bc57609c7a84333bb558594977aa5b4026b1d6070b93987956689e367341"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/integrations/tools/dalle_image_generator.ipynb b/docs/extras/integrations/tools/dalle_image_generator.ipynb
index 83f424b15e8..ba01e52df33 100644
--- a/docs/extras/integrations/tools/dalle_image_generator.ipynb
+++ b/docs/extras/integrations/tools/dalle_image_generator.ipynb
@@ -1,181 +1,194 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Dall-E Image Generator\n",
-        "\n",
-        "This notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Needed if you would like to display images in the notebook\n",
-        "!pip install opencv-python scikit-image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "q-k8wmp0zquh"
-      },
-      "outputs": [],
-      "source": [
-        "from langchain.llms import OpenAI\n",
-        "import os\n",
-        "os.environ[\"OPENAI_API_KEY\"] = \"<your-key-here>\""
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Run as a chain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain.utilities.dalle_image_generator import DallEAPIWrapper\n",
-        "from langchain.prompts import PromptTemplate\n",
-        "from langchain.chains import LLMChain\n",
-        "\n",
-        "llm = OpenAI(temperature=0.9)\n",
-        "prompt = PromptTemplate(\n",
-        "    input_variables=[\"image_desc\"],\n",
-        "    template=\"Generate a detailed prompt to generate an image based on the following description: {image_desc}\",\n",
-        ")\n",
-        "chain = LLMChain(llm=llm, prompt=prompt)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "https://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-mg1OWiziXxQN1aR2XRsLNndg.png?st=2023-01-31T07%3A34%3A15Z&se=2023-01-31T09%3A34%3A15Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A44Z&ske=2023-01-31T22%3A19%3A44Z&sks=b&skv=2021-08-06&sig=XDPee5aEng%2BcbXq2mqhh39uHGZTBmJgGAerSd0g%2BMEs%3D\n"
-          ]
-        }
-      ],
-      "source": [
-        "image_url = DallEAPIWrapper().run(chain.run(\"halloween night at a haunted museum\"))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# You can click on the link above to display the image for\n",
-        "# Or you can try the options below to display the image inline in this notebook\n",
-        "\n",
-        "try:\n",
-        "    import google.colab\n",
-        "    IN_COLAB = True\n",
-        "except:\n",
-        "    IN_COLAB = False\n",
-        "\n",
-        "if IN_COLAB:\n",
-        "    from google.colab.patches import cv2_imshow # for image display\n",
-        "    from skimage import io\n",
-        "\n",
-        "    image = io.imread(image_url) \n",
-        "    cv2_imshow(image)\n",
-        "else:\n",
-        "    import cv2\n",
-        "    from skimage import io\n",
-        "\n",
-        "    image = io.imread(image_url) \n",
-        "    cv2.imshow('image', image)\n",
-        "    cv2.waitKey(0)   #wait for a keyboard input\n",
-        "    cv2.destroyAllWindows()\n"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Run as a tool with an agent"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "\n",
-            "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-            "\u001b[32;1m\u001b[1;3m What is the best way to turn this description into an image?\n",
-            "Action: Dall-E Image Generator\n",
-            "Action Input: A spooky Halloween night at a haunted museum\u001b[0mhttps://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D\n",
-            "\n",
-            "Observation: \u001b[36;1m\u001b[1;3mhttps://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D\u001b[0m\n",
-            "Thought:\u001b[32;1m\u001b[1;3m With the image generated, I can now make my final answer.\n",
-            "Final Answer: An image of a Halloween night at a haunted museum can be seen here: https://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22\u001b[0m\n",
-            "\n",
-            "\u001b[1m> Finished chain.\u001b[0m\n"
-          ]
-        }
-      ],
-      "source": [
-        "from langchain.agents import load_tools\n",
-        "from langchain.agents import initialize_agent\n",
-        "\n",
-        "tools = load_tools(['dalle-image-generator'])\n",
-        "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)\n",
-        "output = agent.run(\"Create an image of a halloween night at a haunted museum\")"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "langchain",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.16"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "3570c8892273ffbeee7ead61dc7c022b73551d9f55fb2584ac0e8e8920b18a89"
-      }
-    }
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dall-E Image Generator\n",
+    "\n",
+    "This notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Needed if you would like to display images in the notebook\n",
+    "!pip install opencv-python scikit-image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "q-k8wmp0zquh"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "import os\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"<your-key-here>\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run as a chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.utilities.dalle_image_generator import DallEAPIWrapper\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
+    "\n",
+    "llm = OpenAI(temperature=0.9)\n",
+    "prompt = PromptTemplate(\n",
+    "    input_variables=[\"image_desc\"],\n",
+    "    template=\"Generate a detailed prompt to generate an image based on the following description: {image_desc}\",\n",
+    ")\n",
+    "chain = LLMChain(llm=llm, prompt=prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_url = DallEAPIWrapper().run(chain.run(\"halloween night at a haunted museum\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://oaidalleapiprodscus.blob.core.windows.net/private/org-i0zjYONU3PemzJ222esBaAzZ/user-f6uEIOFxoiUZivy567cDSWni/img-i7Z2ZxvJ4IbbdAiO6OXJgS3v.png?st=2023-08-11T14%3A03%3A14Z&se=2023-08-11T16%3A03%3A14Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-08-10T20%3A58%3A32Z&ske=2023-08-11T20%3A58%3A32Z&sks=b&skv=2021-08-06&sig=/sECe7C0EAq37ssgBm7g7JkVIM/Q1W3xOstd0Go6slA%3D'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image_url"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can click on the link above to display the image \n",
+    "# Or you can try the options below to display the image inline in this notebook\n",
+    "\n",
+    "try:\n",
+    "    import google.colab\n",
+    "    IN_COLAB = True\n",
+    "except:\n",
+    "    IN_COLAB = False\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    from google.colab.patches import cv2_imshow # for image display\n",
+    "    from skimage import io\n",
+    "\n",
+    "    image = io.imread(image_url) \n",
+    "    cv2_imshow(image)\n",
+    "else:\n",
+    "    import cv2\n",
+    "    from skimage import io\n",
+    "\n",
+    "    image = io.imread(image_url) \n",
+    "    cv2.imshow('image', image)\n",
+    "    cv2.waitKey(0)   #wait for a keyboard input\n",
+    "    cv2.destroyAllWindows()\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run as a tool with an agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m What is the best way to turn this description into an image?\n",
+      "Action: Dall-E Image Generator\n",
+      "Action Input: A spooky Halloween night at a haunted museum\u001b[0mhttps://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D\n",
+      "\n",
+      "Observation: \u001b[36;1m\u001b[1;3mhttps://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m With the image generated, I can now make my final answer.\n",
+      "Final Answer: An image of a Halloween night at a haunted museum can be seen here: https://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.agents import load_tools\n",
+    "from langchain.agents import initialize_agent\n",
+    "\n",
+    "tools = load_tools(['dalle-image-generator'])\n",
+    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)\n",
+    "output = agent.run(\"Create an image of a halloween night at a haunted museum\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "poetry-venv",
+   "language": "python",
+   "name": "poetry-venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3570c8892273ffbeee7ead61dc7c022b73551d9f55fb2584ac0e8e8920b18a89"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
diff --git a/docs/extras/integrations/vectorstores/activeloop_deeplake.ipynb b/docs/extras/integrations/vectorstores/activeloop_deeplake.ipynb
new file mode 100644
index 00000000000..fb644fcee46
--- /dev/null
+++ b/docs/extras/integrations/vectorstores/activeloop_deeplake.ipynb
@@ -0,0 +1,980 @@
+{
+   "cells": [
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "#  Activeloop's Deep Lake\n",
+            "\n",
+            ">[Activeloop's Deep Lake](https://docs.activeloop.ai/) as a Multi-Modal Vector Store that stores embeddings and their metadata including text, jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.\n",
+            "\n",
+            "This notebook showcases basic functionality related to `Activeloop's Deep Lake`. While `Deep Lake` can store embeddings, it is capable of storing any type of data. It is a serverless data lake with version control, query engine and streaming dataloaders to deep learning frameworks.  \n",
+            "\n",
+            "For more information, please see the Deep Lake [documentation](https://docs.activeloop.ai) or [api reference](https://docs.deeplake.ai)"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": null,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "!pip install openai 'deeplake[enterprise]' tiktoken"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 2,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+            "from langchain.text_splitter import CharacterTextSplitter\n",
+            "from langchain.vectorstores import DeepLake"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 3,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "import os\n",
+            "import getpass\n",
+            "\n",
+            "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
+            "activeloop_token = getpass.getpass(\"activeloop token:\")\n",
+            "embeddings = OpenAIEmbeddings()"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 4,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "from langchain.document_loaders import TextLoader\n",
+            "\n",
+            "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
+            "documents = loader.load()\n",
+            "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+            "docs = text_splitter.split_documents(documents)\n",
+            "\n",
+            "embeddings = OpenAIEmbeddings()"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "Create a dataset locally at `./deeplake/`, then run similarity search. The Deeplake+LangChain integration uses Deep Lake datasets under the hood, so `dataset` and `vector store` are used interchangeably. To create a dataset in your own cloud, or in the Deep Lake storage, [adjust the path accordingly](https://docs.activeloop.ai/storage-and-credentials/storage-options)."
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 5,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": []
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text'])\n",
+                  "\n",
+                  "  tensor      htype      shape      dtype  compression\n",
+                  "  -------    -------    -------    -------  ------- \n",
+                  " embedding  embedding  (42, 1536)  float32   None   \n",
+                  "    id        text      (42, 1)      str     None   \n",
+                  " metadata     json      (42, 1)      str     None   \n",
+                  "   text       text      (42, 1)      str     None   \n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": []
+            }
+         ],
+         "source": [
+            "db = DeepLake(\n",
+            "    dataset_path=\"./my_deeplake/\", embedding=embeddings, overwrite=True\n",
+            ")\n",
+            "db.add_documents(docs)\n",
+            "# or shorter\n",
+            "# db = DeepLake.from_documents(docs, dataset_path=\"./my_deeplake/\", embedding=embeddings, overwrite=True)\n",
+            "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+            "docs = db.similarity_search(query)"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "To disable dataset summary printings all the time, you can specify verbose=False during VectorStore initialization."
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 6,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+                  "\n",
+                  "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+                  "\n",
+                  "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+                  "\n",
+                  "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
+               ]
+            }
+         ],
+         "source": [
+            "print(docs[0].page_content)"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "Later, you can reload the dataset without recomputing embeddings"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 7,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Deep Lake Dataset in ./my_deeplake/ already exists, loading from the storage\n"
+               ]
+            }
+         ],
+         "source": [
+            "db = DeepLake(\n",
+            "    dataset_path=\"./my_deeplake/\", embedding=embeddings, read_only=True\n",
+            ")\n",
+            "docs = db.similarity_search(query)"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "Deep Lake, for now, is single writer and multiple reader. Setting `read_only=True` helps to avoid acquiring the writer lock."
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "### Retrieval Question/Answering"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 8,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  "/home/ubuntu/langchain_activeloop/langchain/libs/langchain/langchain/llms/openai.py:786: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
+                  "  warnings.warn(\n"
+               ]
+            }
+         ],
+         "source": [
+            "from langchain.chains import RetrievalQA\n",
+            "from langchain.llms import OpenAIChat\n",
+            "\n",
+            "qa = RetrievalQA.from_chain_type(\n",
+            "    llm=OpenAIChat(model=\"gpt-3.5-turbo\"),\n",
+            "    chain_type=\"stuff\",\n",
+            "    retriever=db.as_retriever(),\n",
+            ")"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 9,
+         "metadata": {},
+         "outputs": [
+            {
+               "data": {
+                  "text/plain": [
+                     "'The president said that Ketanji Brown Jackson is a former top litigator in private practice and a former federal public defender. She comes from a family of public school educators and police officers. She is a consensus builder and has received a broad range of support since being nominated.'"
+                  ]
+               },
+               "execution_count": 9,
+               "metadata": {},
+               "output_type": "execute_result"
+            }
+         ],
+         "source": [
+            "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+            "qa.run(query)"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "### Attribute based filtering in metadata"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "Let's create another vector store containing metadata with the year the documents were created."
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 10,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": []
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text'])\n",
+                  "\n",
+                  "  tensor      htype      shape     dtype  compression\n",
+                  "  -------    -------    -------   -------  ------- \n",
+                  " embedding  embedding  (4, 1536)  float32   None   \n",
+                  "    id        text      (4, 1)      str     None   \n",
+                  " metadata     json      (4, 1)      str     None   \n",
+                  "   text       text      (4, 1)      str     None   \n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": []
+            }
+         ],
+         "source": [
+            "import random\n",
+            "\n",
+            "for d in docs:\n",
+            "    d.metadata[\"year\"] = random.randint(2012, 2014)\n",
+            "\n",
+            "db = DeepLake.from_documents(\n",
+            "    docs, embeddings, dataset_path=\"./my_deeplake/\", overwrite=True\n",
+            ")"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 11,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  "100%|██████████| 4/4 [00:00<00:00, 2936.16it/s]\n"
+               ]
+            },
+            {
+               "data": {
+                  "text/plain": [
+                     "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n",
+                     " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n",
+                     " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013})]"
+                  ]
+               },
+               "execution_count": 11,
+               "metadata": {},
+               "output_type": "execute_result"
+            }
+         ],
+         "source": [
+            "db.similarity_search(\n",
+            "    \"What did the president say about Ketanji Brown Jackson\",\n",
+            "    filter={\"metadata\": {\"year\": 2013}},\n",
+            ")"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "### Choosing distance function\n",
+            "Distance function `L2` for Euclidean, `L1` for Nuclear, `Max` l-infinity distance, `cos` for cosine similarity, `dot` for dot product "
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 12,
+         "metadata": {},
+         "outputs": [
+            {
+               "data": {
+                  "text/plain": [
+                     "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n",
+                     " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n",
+                     " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n",
+                     " Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]"
+                  ]
+               },
+               "execution_count": 12,
+               "metadata": {},
+               "output_type": "execute_result"
+            }
+         ],
+         "source": [
+            "db.similarity_search(\n",
+            "    \"What did the president say about Ketanji Brown Jackson?\", distance_metric=\"cos\"\n",
+            ")"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "### Maximal Marginal relevance\n",
+            "Using maximal marginal relevance"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 13,
+         "metadata": {},
+         "outputs": [
+            {
+               "data": {
+                  "text/plain": [
+                     "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n",
+                     " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n",
+                     " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n",
+                     " Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]"
+                  ]
+               },
+               "execution_count": 13,
+               "metadata": {},
+               "output_type": "execute_result"
+            }
+         ],
+         "source": [
+            "db.max_marginal_relevance_search(\n",
+            "    \"What did the president say about Ketanji Brown Jackson?\"\n",
+            ")"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "### Delete dataset"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 14,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": []
+            }
+         ],
+         "source": [
+            "db.delete_dataset()"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "and if delete fails you can also force delete"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 15,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": []
+            }
+         ],
+         "source": [
+            "DeepLake.force_delete_by_path(\"./my_deeplake\")"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "## Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory\n",
+            "By default, Deep Lake datasets are stored locally. To store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the [corresponding path and credentials when creating the vector store](https://docs.activeloop.ai/storage-and-credentials/storage-options). Some paths require registration with Activeloop and creation of an API token that can be [retrieved here](https://app.activeloop.ai/)"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 16,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 26,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Your Deep Lake dataset has been successfully created!\n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": []
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Dataset(path='hub://adilkhan/langchain_testing_python', tensors=['embedding', 'id', 'metadata', 'text'])\n",
+                  "\n",
+                  "  tensor      htype      shape      dtype  compression\n",
+                  "  -------    -------    -------    -------  ------- \n",
+                  " embedding  embedding  (42, 1536)  float32   None   \n",
+                  "    id        text      (42, 1)      str     None   \n",
+                  " metadata     json      (42, 1)      str     None   \n",
+                  "   text       text      (42, 1)      str     None   \n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": []
+            }
+         ],
+         "source": [
+            "# Embed and store the texts\n",
+            "username = \"<USERNAME_OR_ORG>\"  # your username on app.activeloop.ai\n",
+            "dataset_path = f\"hub://{username}/langchain_testing_python\"  # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
+            "\n",
+            "docs = text_splitter.split_documents(documents)\n",
+            "\n",
+            "embedding = OpenAIEmbeddings()\n",
+            "db = DeepLake(dataset_path=dataset_path, embedding=embeddings, overwrite=True)\n",
+            "ids = db.add_documents(docs)"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 18,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+                  "\n",
+                  "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+                  "\n",
+                  "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+                  "\n",
+                  "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
+               ]
+            }
+         ],
+         "source": [
+            "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+            "docs = db.similarity_search(query)\n",
+            "print(docs[0].page_content)"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "#### `tensor_db` execution option "
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "In order to utilize Deep Lake's Managed Tensor Database, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 27,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Your Deep Lake dataset has been successfully created!\n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  "|"
+               ]
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n",
+                  "\n",
+                  "  tensor      htype      shape      dtype  compression\n",
+                  "  -------    -------    -------    -------  ------- \n",
+                  " embedding  embedding  (42, 1536)  float32   None   \n",
+                  "    id        text      (42, 1)      str     None   \n",
+                  " metadata     json      (42, 1)      str     None   \n",
+                  "   text       text      (42, 1)      str     None   \n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  " \r"
+               ]
+            }
+         ],
+         "source": [
+            "# Embed and store the texts\n",
+            "username = \"<USERNAME_OR_ORG>\"  # your username on app.activeloop.ai\n",
+            "dataset_path = f\"hub://{username}/langchain_testing\"\n",
+            "\n",
+            "docs = text_splitter.split_documents(documents)\n",
+            "\n",
+            "embedding = OpenAIEmbeddings()\n",
+            "db = DeepLake(\n",
+            "    dataset_path=dataset_path,\n",
+            "    embedding=embeddings,\n",
+            "    overwrite=True,\n",
+            "    runtime={\"tensor_db\": True},\n",
+            ")\n",
+            "ids = db.add_documents(docs)"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "### TQL Search"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "Furthermore, the execution of queries is also supported within the similarity_search method, whereby the query can be specified utilizing Deep Lake's Tensor Query Language (TQL)."
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 21,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "search_id = db.vectorstore.dataset.id[0].numpy()"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 22,
+         "metadata": {},
+         "outputs": [
+            {
+               "data": {
+                  "text/plain": [
+                     "'8a6ff326-3a85-11ee-b840-13905694aaaf'"
+                  ]
+               },
+               "execution_count": 22,
+               "metadata": {},
+               "output_type": "execute_result"
+            }
+         ],
+         "source": [
+            "search_id[0]"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 23,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "docs = db.similarity_search(\n",
+            "    query=None,\n",
+            "    tql=f\"SELECT * WHERE id == '{search_id[0]}'\",\n",
+            ")"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 25,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n",
+                  "\n",
+                  "  tensor      htype      shape      dtype  compression\n",
+                  "  -------    -------    -------    -------  ------- \n",
+                  " embedding  embedding  (42, 1536)  float32   None   \n",
+                  "    id        text      (42, 1)      str     None   \n",
+                  " metadata     json      (42, 1)      str     None   \n",
+                  "   text       text      (42, 1)      str     None   \n"
+               ]
+            }
+         ],
+         "source": [
+            "db.vectorstore.summary()"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "### Creating vector stores on AWS S3"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 82,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "s3://hub-2.0-datasets-n/langchain_test loaded successfully.\n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  "Evaluating ingest: 100%|██████████| 1/1 [00:10<00:00\n",
+                  "\\"
+               ]
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+                  "\n",
+                  "  tensor     htype     shape     dtype  compression\n",
+                  "  -------   -------   -------   -------  ------- \n",
+                  " embedding  generic  (4, 1536)  float32   None   \n",
+                  "    ids      text     (4, 1)      str     None   \n",
+                  " metadata    json     (4, 1)      str     None   \n",
+                  "   text      text     (4, 1)      str     None   \n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  " \r"
+               ]
+            }
+         ],
+         "source": [
+            "dataset_path = f\"s3://BUCKET/langchain_test\"  # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
+            "\n",
+            "embedding = OpenAIEmbeddings()\n",
+            "db = DeepLake.from_documents(\n",
+            "    docs,\n",
+            "    dataset_path=dataset_path,\n",
+            "    embedding=embeddings,\n",
+            "    overwrite=True,\n",
+            "    creds={\n",
+            "        \"aws_access_key_id\": os.environ[\"AWS_ACCESS_KEY_ID\"],\n",
+            "        \"aws_secret_access_key\": os.environ[\"AWS_SECRET_ACCESS_KEY\"],\n",
+            "        \"aws_session_token\": os.environ[\"AWS_SESSION_TOKEN\"],  # Optional\n",
+            "    },\n",
+            ")"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "## Deep Lake API\n",
+            "you can access the Deep Lake  dataset at `db.vectorstore`"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 26,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n",
+                  "\n",
+                  "  tensor      htype      shape      dtype  compression\n",
+                  "  -------    -------    -------    -------  ------- \n",
+                  " embedding  embedding  (42, 1536)  float32   None   \n",
+                  "    id        text      (42, 1)      str     None   \n",
+                  " metadata     json      (42, 1)      str     None   \n",
+                  "   text       text      (42, 1)      str     None   \n"
+               ]
+            }
+         ],
+         "source": [
+            "# get structure of the dataset\n",
+            "db.vectorstore.summary()"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 27,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "# get embeddings numpy array\n",
+            "embeds = db.vectorstore.dataset.embedding.numpy()"
+         ]
+      },
+      {
+         "attachments": {},
+         "cell_type": "markdown",
+         "metadata": {},
+         "source": [
+            "### Transfer local dataset to cloud\n",
+            "Copy already created dataset to the cloud. You can also transfer from cloud to local."
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 73,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  "Copying dataset: 100%|██████████| 56/56 [00:38<00:00\n"
+               ]
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n",
+                  "Your Deep Lake dataset has been successfully created!\n",
+                  "The dataset is private so make sure you are logged in!\n"
+               ]
+            },
+            {
+               "data": {
+                  "text/plain": [
+                     "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])"
+                  ]
+               },
+               "execution_count": 73,
+               "metadata": {},
+               "output_type": "execute_result"
+            }
+         ],
+         "source": [
+            "import deeplake\n",
+            "\n",
+            "username = \"davitbun\"  # your username on app.activeloop.ai\n",
+            "source = f\"hub://{username}/langchain_testing\"  # could be local, s3, gcs, etc.\n",
+            "destination = f\"hub://{username}/langchain_test_copy\"  # could be local, s3, gcs, etc.\n",
+            "\n",
+            "deeplake.deepcopy(src=source, dest=destination, overwrite=True)"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 76,
+         "metadata": {},
+         "outputs": [
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  " \r"
+               ]
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n",
+                  "\n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  "/"
+               ]
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "hub://davitbun/langchain_test_copy loaded successfully.\n",
+                  "\n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  "Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage\n"
+               ]
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+                  "\n",
+                  "  tensor     htype     shape     dtype  compression\n",
+                  "  -------   -------   -------   -------  ------- \n",
+                  " embedding  generic  (4, 1536)  float32   None   \n",
+                  "    ids      text     (4, 1)      str     None   \n",
+                  " metadata    json     (4, 1)      str     None   \n",
+                  "   text      text     (4, 1)      str     None   \n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  "Evaluating ingest: 100%|██████████| 1/1 [00:31<00:00\n",
+                  "-"
+               ]
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+                  "\n",
+                  "  tensor     htype     shape     dtype  compression\n",
+                  "  -------   -------   -------   -------  ------- \n",
+                  " embedding  generic  (8, 1536)  float32   None   \n",
+                  "    ids      text     (8, 1)      str     None   \n",
+                  " metadata    json     (8, 1)      str     None   \n",
+                  "   text      text     (8, 1)      str     None   \n"
+               ]
+            },
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  " \r"
+               ]
+            },
+            {
+               "data": {
+                  "text/plain": [
+                     "['ad42f3fe-e188-11ed-b66d-41c5f7b85421',\n",
+                     " 'ad42f3ff-e188-11ed-b66d-41c5f7b85421',\n",
+                     " 'ad42f400-e188-11ed-b66d-41c5f7b85421',\n",
+                     " 'ad42f401-e188-11ed-b66d-41c5f7b85421']"
+                  ]
+               },
+               "execution_count": 76,
+               "metadata": {},
+               "output_type": "execute_result"
+            }
+         ],
+         "source": [
+            "db = DeepLake(dataset_path=destination, embedding=embeddings)\n",
+            "db.add_documents(docs)"
+         ]
+      }
+   ],
+   "metadata": {
+      "kernelspec": {
+         "display_name": "Python 3.9.6 ('langchain_venv': venv)",
+         "language": "python",
+         "name": "python3"
+      },
+      "language_info": {
+         "codemirror_mode": {
+            "name": "ipython",
+            "version": 3
+         },
+         "file_extension": ".py",
+         "mimetype": "text/x-python",
+         "name": "python",
+         "nbconvert_exporter": "python",
+         "pygments_lexer": "ipython3",
+         "version": "3.11.4"
+      },
+      "vscode": {
+         "interpreter": {
+            "hash": "0b0bacaffd430edc3085253ee7ee1bcda9f76a5e66b369dda8ba68baa6d14ba7"
+         }
+      }
+   },
+   "nbformat": 4,
+   "nbformat_minor": 4
+}
diff --git a/docs/extras/integrations/vectorstores/bageldb.ipynb b/docs/extras/integrations/vectorstores/bageldb.ipynb
new file mode 100644
index 00000000000..7f65486569f
--- /dev/null
+++ b/docs/extras/integrations/vectorstores/bageldb.ipynb
@@ -0,0 +1,300 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BagelDB\n",
+    "\n",
+    "> [BagelDB](https://www.bageldb.ai/) (`Open Vector Database for AI`), is like GitHub for AI data.\n",
+    "It is a collaborative platform where users can create,\n",
+    "share, and manage vector datasets. It can support private projects for independent developers,\n",
+    "internal collaborations for enterprises, and public contributions for data DAOs.\n",
+    "\n",
+    "### Installation and Setup\n",
+    "\n",
+    "```bash\n",
+    "pip install betabageldb\n",
+    "```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create VectorStore from texts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.vectorstores import Bagel\n",
+    "\n",
+    "texts = [\"hello bagel\", \"hello langchain\", \"I love salad\", \"my car\", \"a dog\"]\n",
+    "# create cluster and add texts\n",
+    "cluster = Bagel.from_texts(cluster_name=\"testing\", texts=texts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='hello bagel', metadata={}),\n",
+       " Document(page_content='my car', metadata={}),\n",
+       " Document(page_content='I love salad', metadata={})]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# similarity search\n",
+    "cluster.similarity_search(\"bagel\", k=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(Document(page_content='hello bagel', metadata={}), 0.27392977476119995),\n",
+       " (Document(page_content='my car', metadata={}), 1.4783176183700562),\n",
+       " (Document(page_content='I love salad', metadata={}), 1.5342965126037598)]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# the score is a distance metric, so lower is better\n",
+    "cluster.similarity_search_with_score(\"bagel\", k=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# delete the cluster\n",
+    "cluster.delete_cluster()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create VectorStore from docs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "\n",
+    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create cluster with docs\n",
+    "cluster = Bagel.from_documents(cluster_name=\"testing_with_docs\", documents=docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the \n"
+     ]
+    }
+   ],
+   "source": [
+    "# similarity search\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = cluster.similarity_search(query)\n",
+    "print(docs[0].page_content[:102])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get all text/doc from Cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts = [\"hello bagel\", \"this is langchain\"]\n",
+    "cluster = Bagel.from_texts(cluster_name=\"testing\", texts=texts)\n",
+    "cluster_data = cluster.get()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['ids', 'embeddings', 'metadatas', 'documents'])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# all keys\n",
+    "cluster_data.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ids': ['578c6d24-3763-11ee-a8ab-b7b7b34f99ba',\n",
+       "  '578c6d25-3763-11ee-a8ab-b7b7b34f99ba',\n",
+       "  'fb2fc7d8-3762-11ee-a8ab-b7b7b34f99ba',\n",
+       "  'fb2fc7d9-3762-11ee-a8ab-b7b7b34f99ba',\n",
+       "  '6b40881a-3762-11ee-a8ab-b7b7b34f99ba',\n",
+       "  '6b40881b-3762-11ee-a8ab-b7b7b34f99ba',\n",
+       "  '581e691e-3762-11ee-a8ab-b7b7b34f99ba',\n",
+       "  '581e691f-3762-11ee-a8ab-b7b7b34f99ba'],\n",
+       " 'embeddings': None,\n",
+       " 'metadatas': [{}, {}, {}, {}, {}, {}, {}, {}],\n",
+       " 'documents': ['hello bagel',\n",
+       "  'this is langchain',\n",
+       "  'hello bagel',\n",
+       "  'this is langchain',\n",
+       "  'hello bagel',\n",
+       "  'this is langchain',\n",
+       "  'hello bagel',\n",
+       "  'this is langchain']}"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# all values and keys\n",
+    "cluster_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster.delete_cluster()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create cluster with metadata & filter using metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(Document(page_content='hello bagel', metadata={'source': 'notion'}), 0.0)]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "texts = [\"hello bagel\", \"this is langchain\"]\n",
+    "metadatas = [{\"source\": \"notion\"}, {\"source\": \"google\"}]\n",
+    "\n",
+    "cluster = Bagel.from_texts(cluster_name=\"testing\", texts=texts, metadatas=metadatas)\n",
+    "cluster.similarity_search_with_score(\"hello bagel\", where={\"source\": \"notion\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# delete the cluster\n",
+    "cluster.delete_cluster()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/extras/integrations/vectorstores/dashvector.ipynb b/docs/extras/integrations/vectorstores/dashvector.ipynb
new file mode 100644
index 00000000000..2352cf6f275
--- /dev/null
+++ b/docs/extras/integrations/vectorstores/dashvector.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# DashVector\n",
+    "\n",
+    "> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `DashVector` vector database.\n",
+    "\n",
+    "To use DashVector, you must have an API key.\n",
+    "Here are the [installation instructions](https://help.aliyun.com/document_detail/2510223.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Install"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install dashvector dashscope"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "We want to use `DashScopeEmbeddings` so we also have to get the Dashscope API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n",
+     "is_executing": true
+    },
+    "ExecuteTime": {
+     "end_time": "2023-08-11T10:37:15.091585Z",
+     "start_time": "2023-08-11T10:36:51.859753Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ[\"DASHVECTOR_API_KEY\"] = getpass.getpass(\"DashVector API Key:\")\n",
+    "os.environ[\"DASHSCOPE_API_KEY\"] = getpass.getpass(\"DashScope API Key:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n",
+     "is_executing": true
+    },
+    "ExecuteTime": {
+     "end_time": "2023-08-11T10:42:30.243460Z",
+     "start_time": "2023-08-11T10:42:27.783785Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.dashscope import DashScopeEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import DashVector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2023-08-11T10:42:30.391580Z",
+     "start_time": "2023-08-11T10:42:30.249021Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "\n",
+    "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = DashScopeEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "We can create DashVector from documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dashvector = DashVector.from_documents(docs, embeddings)\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = dashvector.similarity_search(query)\n",
+    "print(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "We can add texts with meta datas and ids, and search with meta filter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2023-08-11T10:42:51.641309Z",
+     "start_time": "2023-08-11T10:42:51.132109Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Document(page_content='baz', metadata={'key': 2})]\n"
+     ]
+    }
+   ],
+   "source": [
+    "texts = [\"foo\", \"bar\", \"baz\"]\n",
+    "metadatas = [{\"key\": i} for i in range(len(texts))]\n",
+    "ids = [\"0\", \"1\", \"2\"]\n",
+    "\n",
+    "dashvector.add_texts(texts, metadatas=metadatas, ids=ids)\n",
+    "\n",
+    "docs = dashvector.similarity_search(\"foo\", filter=\"key = 2\")\n",
+    "print(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/extras/integrations/vectorstores/deeplake.ipynb b/docs/extras/integrations/vectorstores/deeplake.ipynb
deleted file mode 100644
index 5ec10647174..00000000000
--- a/docs/extras/integrations/vectorstores/deeplake.ipynb
+++ /dev/null
@@ -1,719 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#  Activeloop's Deep Lake\n",
-    "\n",
-    ">[Activeloop's Deep Lake](https://docs.activeloop.ai/) as a Multi-Modal Vector Store that stores embeddings and their metadata including text, jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.\n",
-    "\n",
-    "This notebook showcases basic functionality related to `Activeloop's Deep Lake`. While `Deep Lake` can store embeddings, it is capable of storing any type of data. It is a serverless data lake with version control, query engine and streaming dataloaders to deep learning frameworks.  \n",
-    "\n",
-    "For more information, please see the Deep Lake [documentation](https://docs.activeloop.ai) or [api reference](https://docs.deeplake.ai)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install openai 'deeplake[enterprise]' tiktoken"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import DeepLake"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import getpass\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
-    "activeloop_token = getpass.getpass(\"activeloop token:\")\n",
-    "embeddings = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "\n",
-    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "docs = text_splitter.split_documents(documents)\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a dataset locally at `./deeplake/`, then run similarity search. The Deeplake+LangChain integration uses Deep Lake datasets under the hood, so `dataset` and `vector store` are used interchangeably. To create a dataset in your own cloud, or in the Deep Lake storage, [adjust the path accordingly](https://docs.activeloop.ai/storage-and-credentials/storage-options)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "db = DeepLake(\n",
-    "    dataset_path=\"./my_deeplake/\", embedding_function=embeddings, overwrite=True\n",
-    ")\n",
-    "db.add_documents(docs)\n",
-    "# or shorter\n",
-    "# db = DeepLake.from_documents(docs, dataset_path=\"./my_deeplake/\", embedding=embeddings, overwrite=True)\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = db.similarity_search(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(docs[0].page_content)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Later, you can reload the dataset without recomputing embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "db = DeepLake(\n",
-    "    dataset_path=\"./my_deeplake/\", embedding_function=embeddings, read_only=True\n",
-    ")\n",
-    "docs = db.similarity_search(query)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Deep Lake, for now, is single writer and multiple reader. Setting `read_only=True` helps to avoid acquiring the writer lock."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieval Question/Answering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import RetrievalQA\n",
-    "from langchain.llms import OpenAIChat\n",
-    "\n",
-    "qa = RetrievalQA.from_chain_type(\n",
-    "    llm=OpenAIChat(model=\"gpt-3.5-turbo\"),\n",
-    "    chain_type=\"stuff\",\n",
-    "    retriever=db.as_retriever(),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "qa.run(query)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Attribute based filtering in metadata"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's create another vector store containing metadata with the year the documents were created."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "\n",
-    "for d in docs:\n",
-    "    d.metadata[\"year\"] = random.randint(2012, 2014)\n",
-    "\n",
-    "db = DeepLake.from_documents(\n",
-    "    docs, embeddings, dataset_path=\"./my_deeplake/\", overwrite=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "db.similarity_search(\n",
-    "    \"What did the president say about Ketanji Brown Jackson\",\n",
-    "    filter={\"metadata\": {\"year\": 2013}},\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Choosing distance function\n",
-    "Distance function `L2` for Euclidean, `L1` for Nuclear, `Max` l-infinity distance, `cos` for cosine similarity, `dot` for dot product "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "db.similarity_search(\n",
-    "    \"What did the president say about Ketanji Brown Jackson?\", distance_metric=\"cos\"\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Maximal Marginal relevance\n",
-    "Using maximal marginal relevance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "db.max_marginal_relevance_search(\n",
-    "    \"What did the president say about Ketanji Brown Jackson?\"\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Delete dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    }
-   ],
-   "source": [
-    "db.delete_dataset()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "and if delete fails you can also force delete"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    }
-   ],
-   "source": [
-    "DeepLake.force_delete_by_path(\"./my_deeplake\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory\n",
-    "By default, Deep Lake datasets are stored locally. To store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the [corresponding path and credentials when creating the vector store](https://docs.activeloop.ai/storage-and-credentials/storage-options). Some paths require registration with Activeloop and creation of an API token that can be [retrieved here](https://app.activeloop.ai/)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Embed and store the texts\n",
-    "username = \"<username>\"  # your username on app.activeloop.ai\n",
-    "dataset_path = f\"hub://{username}/langchain_testing_python\"  # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
-    "\n",
-    "docs = text_splitter.split_documents(documents)\n",
-    "\n",
-    "embedding = OpenAIEmbeddings()\n",
-    "db = DeepLake(dataset_path=dataset_path, embedding_function=embeddings, overwrite=True)\n",
-    "db.add_documents(docs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = db.similarity_search(query)\n",
-    "print(docs[0].page_content)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `tensor_db` execution option "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order to utilize Deep Lake's Managed Tensor Database, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Embed and store the texts\n",
-    "username = \"adilkhan\"  # your username on app.activeloop.ai\n",
-    "dataset_path = f\"hub://{username}/langchain_testing\"\n",
-    "\n",
-    "docs = text_splitter.split_documents(documents)\n",
-    "\n",
-    "embedding = OpenAIEmbeddings()\n",
-    "db = DeepLake(\n",
-    "    dataset_path=dataset_path,\n",
-    "    embedding_function=embeddings,\n",
-    "    overwrite=True,\n",
-    "    runtime={\"tensor_db\": True},\n",
-    ")\n",
-    "db.add_documents(docs)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### TQL Search"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Furthermore, the execution of queries is also supported within the similarity_search method, whereby the query can be specified utilizing Deep Lake's Tensor Query Language (TQL)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search_id = db.vectorstore.dataset.id[0].numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs = db.similarity_search(\n",
-    "    query=None,\n",
-    "    tql_query=f\"SELECT * WHERE id == '{search_id[0]}'\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating vector stores on AWS S3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "s3://hub-2.0-datasets-n/langchain_test loaded successfully.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Evaluating ingest: 100%|██████████| 1/1 [00:10<00:00\n",
-      "\\"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (4, 1536)  float32   None   \n",
-      "    ids      text     (4, 1)      str     None   \n",
-      " metadata    json     (4, 1)      str     None   \n",
-      "   text      text     (4, 1)      str     None   \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " \r"
-     ]
-    }
-   ],
-   "source": [
-    "dataset_path = f\"s3://BUCKET/langchain_test\"  # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
-    "\n",
-    "embedding = OpenAIEmbeddings()\n",
-    "db = DeepLake.from_documents(\n",
-    "    docs,\n",
-    "    dataset_path=dataset_path,\n",
-    "    embedding=embeddings,\n",
-    "    overwrite=True,\n",
-    "    creds={\n",
-    "        \"aws_access_key_id\": os.environ[\"AWS_ACCESS_KEY_ID\"],\n",
-    "        \"aws_secret_access_key\": os.environ[\"AWS_SECRET_ACCESS_KEY\"],\n",
-    "        \"aws_session_token\": os.environ[\"AWS_SESSION_TOKEN\"],  # Optional\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deep Lake API\n",
-    "you can access the Deep Lake  dataset at `db.vectorstore`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor      htype      shape      dtype  compression\n",
-      "  -------    -------    -------    -------  ------- \n",
-      " embedding  embedding  (42, 1536)  float32   None   \n",
-      "    id        text      (42, 1)      str     None   \n",
-      " metadata     json      (42, 1)      str     None   \n",
-      "   text       text      (42, 1)      str     None   \n"
-     ]
-    }
-   ],
-   "source": [
-    "# get structure of the dataset\n",
-    "db.vectorstore.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get embeddings numpy array\n",
-    "embeds = db.vectorstore.dataset.embedding.numpy()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Transfer local dataset to cloud\n",
-    "Copy already created dataset to the cloud. You can also transfer from cloud to local."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Copying dataset: 100%|██████████| 56/56 [00:38<00:00\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n",
-      "Your Deep Lake dataset has been successfully created!\n",
-      "The dataset is private so make sure you are logged in!\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import deeplake\n",
-    "\n",
-    "username = \"davitbun\"  # your username on app.activeloop.ai\n",
-    "source = f\"hub://{username}/langchain_test\"  # could be local, s3, gcs, etc.\n",
-    "destination = f\"hub://{username}/langchain_test_copy\"  # could be local, s3, gcs, etc.\n",
-    "\n",
-    "deeplake.deepcopy(src=source, dest=destination, overwrite=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hub://davitbun/langchain_test_copy loaded successfully.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (4, 1536)  float32   None   \n",
-      "    ids      text     (4, 1)      str     None   \n",
-      " metadata    json     (4, 1)      str     None   \n",
-      "   text      text     (4, 1)      str     None   \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Evaluating ingest: 100%|██████████| 1/1 [00:31<00:00\n",
-      "-"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor     htype     shape     dtype  compression\n",
-      "  -------   -------   -------   -------  ------- \n",
-      " embedding  generic  (8, 1536)  float32   None   \n",
-      "    ids      text     (8, 1)      str     None   \n",
-      " metadata    json     (8, 1)      str     None   \n",
-      "   text      text     (8, 1)      str     None   \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " \r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['ad42f3fe-e188-11ed-b66d-41c5f7b85421',\n",
-       " 'ad42f3ff-e188-11ed-b66d-41c5f7b85421',\n",
-       " 'ad42f400-e188-11ed-b66d-41c5f7b85421',\n",
-       " 'ad42f401-e188-11ed-b66d-41c5f7b85421']"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db = DeepLake(dataset_path=destination, embedding_function=embeddings)\n",
-    "db.add_documents(docs)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.6 ('langchain_venv': venv)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "0b0bacaffd430edc3085253ee7ee1bcda9f76a5e66b369dda8ba68baa6d14ba7"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/extras/integrations/vectorstores/dingo.ipynb b/docs/extras/integrations/vectorstores/dingo.ipynb
new file mode 100644
index 00000000000..0369f1ecb02
--- /dev/null
+++ b/docs/extras/integrations/vectorstores/dingo.ipynb
@@ -0,0 +1,246 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "683953b3",
+   "metadata": {},
+   "source": [
+    "# Dingo\n",
+    "\n",
+    ">[Dingo](https://dingodb.readthedocs.io/en/latest/) is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases, and can store data of any type and size (Key-Value, PDF, audio, video, etc.). It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the DingoDB vector database.\n",
+    "\n",
+    "To run, you should have a [DingoDB instance up and running](https://github.com/dingodb/dingo-deploy/blob/main/README.md)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a62cff8a-bcf7-4e33-bbbc-76999c2e3e20",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install dingodb\n",
+    "or install latest:\n",
+    "!pip install git+https://git@github.com/dingodb/pydingo.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a0f9e02-8eb0-4aef-b11f-8861360472ee",
+   "metadata": {},
+   "source": [
+    "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8b6ed9cd-81b9-46e5-9c20-5aafca2844d0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key:········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aac9563e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Dingo\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a3c3999a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "\n",
+    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dcf88bdf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from dingodb import DingoDB\n",
+    "\n",
+    "index_name = \"langchain-demo\"\n",
+    "\n",
+    "dingo_client = DingoDB(user=\"\", password=\"\", host=[\"127.0.0.1:13000\"])\n",
+    "# First, check if our index already exists. If it doesn't, we create it\n",
+    "if index_name not in dingo_client.get_index():\n",
+    "    # we create a new index, modify to your own\n",
+    "    dingo_client.create_index(\n",
+    "      index_name=index_name,\n",
+    "      dimension=1536,\n",
+    "      metric_type='cosine',\n",
+    "      auto_id=False\n",
+    ")\n",
+    "\n",
+    "# The OpenAI embedding model `text-embedding-ada-002 uses 1536 dimensions`\n",
+    "docsearch = Dingo.from_documents(docs, embeddings, client=dingo_client, index_name=index_name)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3aae49e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Dingo\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a8c513ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fc516993",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1eca81e4",
+   "metadata": {},
+   "source": [
+    "### Adding More Text to an Existing Index\n",
+    "\n",
+    "More text can embedded and upserted to an existing Dingo index using the `add_texts` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e40d558b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorstore = Dingo(embeddings, \"text\", client=dingo_client, index_name=index_name)\n",
+    "\n",
+    "vectorstore.add_texts([\"More text!\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcb858a8",
+   "metadata": {},
+   "source": [
+    "### Maximal Marginal Relevance Searches\n",
+    "\n",
+    "In addition to using similarity search in the retriever object, you can also use `mmr` as retriever."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "649083ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = docsearch.as_retriever(search_type=\"mmr\")\n",
+    "matched_docs = retriever.get_relevant_documents(query)\n",
+    "for i, d in enumerate(matched_docs):\n",
+    "    print(f\"\\n## Document {i}\\n\")\n",
+    "    print(d.page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d3831ad",
+   "metadata": {},
+   "source": [
+    "Or use `max_marginal_relevance_search` directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "732f58b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "found_docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10)\n",
+    "for i, doc in enumerate(found_docs):\n",
+    "    print(f\"{i + 1}.\", doc.page_content, \"\\n\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/integrations/vectorstores/elasticsearch.ipynb b/docs/extras/integrations/vectorstores/elasticsearch.ipynb
index 188b9cd2402..6164b76da13 100644
--- a/docs/extras/integrations/vectorstores/elasticsearch.ipynb
+++ b/docs/extras/integrations/vectorstores/elasticsearch.ipynb
@@ -7,9 +7,9 @@
     "id": "683953b3"
    },
    "source": [
-    "# ElasticSearch\n",
+    "# Elasticsearch\n",
     "\n",
-    ">[Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.\n",
+    ">[Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine, capable of performing both vector and lexical search. It is built on top of the Apache Lucene library. \n",
     "\n",
     "This notebook shows how to use functionality related to the `Elasticsearch` database."
    ]
@@ -22,7 +22,7 @@
     "tags": []
    },
    "source": [
-    "## Installation"
+    "## Running and connecting to Elasticsearch"
    ]
   },
   {
@@ -32,35 +32,57 @@
     "id": "81f43794-f002-477c-9b68-4975df30e718"
    },
    "source": [
-    "Check out [Elasticsearch installation instructions](https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html).\n",
+    "There are two main ways to setup an Elasticsearch instance for use with:\n",
+    "\n",
+    "1. Elastic Cloud: Elastic Cloud is a managed Elasticsearch service. Signup for a [free trial](https://cloud.elastic.co/registration?storm=langchain-notebook).\n",
     "\n",
     "To connect to an Elasticsearch instance that does not require\n",
-    "login credentials, pass the Elasticsearch URL and index name along with the\n",
+    "login credentials (starting the docker instance with security enabled), pass the Elasticsearch URL and index name along with the\n",
     "embedding object to the constructor.\n",
     "\n",
+    "2. Local Install Elasticsearch: Get started with Elasticsearch by running it locally. The easiest way is to use the official Elasticsearch Docker image. See the [Elasticsearch Docker documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html) for more information.\n",
+    "\n",
+    "\n",
+    "### Running Elasticsearch via Docker \n",
+    "Example: Run a single-node Elasticsearch instance with security disabled. This is not recommended for production use.\n",
+    "\n",
+    "```bash\n",
+    "    docker run -p 9200:9200 -e \"discovery.type=single-node\" -e \"xpack.security.enabled=false\" -e \"xpack.security.http.ssl.enabled=false\" docker.elastic.co/elasticsearch/elasticsearch:8.9.0\n",
+    "```\n",
+    "\n",
+    "Once the Elasticsearch instance is running, you can connect to it using the Elasticsearch URL and index name along with the embedding object to the constructor.\n",
+    "\n",
     "Example:\n",
     "```python\n",
-    "        from langchain import ElasticVectorSearch\n",
-    "        from langchain.embeddings import OpenAIEmbeddings\n",
+    "        from langchain.vectorstores.elasticsearch import ElasticsearchStore\n",
+    "        from langchain.embeddings.openai import OpenAIEmbeddings\n",
     "\n",
     "        embedding = OpenAIEmbeddings()\n",
-    "        elastic_vector_search = ElasticVectorSearch(\n",
-    "            elasticsearch_url=\"http://localhost:9200\",\n",
+    "        elastic_vector_search = ElasticsearchStore(\n",
+    "            es_url=\"http://localhost:9200\",\n",
     "            index_name=\"test_index\",\n",
     "            embedding=embedding\n",
     "        )\n",
     "```\n",
+    "### Authentication\n",
+    "For production, we recommend you run with security enabled. To connect with login credentials, you can use the parameters `api_key` or `es_user` and `es_password`.\n",
     "\n",
-    "To connect to an Elasticsearch instance that requires login credentials,\n",
-    "including Elastic Cloud, use the Elasticsearch URL format\n",
-    "https://username:password@es_host:9243. For example, to connect to Elastic\n",
-    "Cloud, create the Elasticsearch URL with the required authentication details and\n",
-    "pass it to the ElasticVectorSearch constructor as the named parameter\n",
-    "elasticsearch_url.\n",
+    "Example:\n",
+    "```python\n",
+    "        from langchain.vectorstores import ElasticsearchStore\n",
+    "        from langchain.embeddings import OpenAIEmbeddings\n",
     "\n",
-    "You can obtain your Elastic Cloud URL and login credentials by logging in to the\n",
-    "Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and\n",
-    "navigating to the \"Deployments\" page.\n",
+    "        embedding = OpenAIEmbeddings()\n",
+    "        elastic_vector_search = ElasticsearchStore(\n",
+    "            es_url=\"http://localhost:9200\",\n",
+    "            index_name=\"test_index\",\n",
+    "            embedding=embedding,\n",
+    "            es_user=\"elastic\",\n",
+    "            es_password=\"changeme\"\n",
+    "        )\n",
+    "```\n",
+    "\n",
+    "#### How to obtain a password for the default \"elastic\" user?\n",
     "\n",
     "To obtain your Elastic Cloud password for the default \"elastic\" user:\n",
     "1. Log in to the Elastic Cloud console at https://cloud.elastic.co\n",
@@ -69,57 +91,79 @@
     "4. Click \"Reset password\"\n",
     "5. Follow the prompts to reset the password\n",
     "\n",
-    "Format for Elastic Cloud URLs is\n",
-    "https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.\n",
+    "#### How to obtain an API key?\n",
+    "\n",
+    "To obtain an API key:\n",
+    "1. Log in to the Elastic Cloud console at https://cloud.elastic.co\n",
+    "2. Open Kibana and go to Stack Management > API Keys\n",
+    "3. Click \"Create API key\"\n",
+    "4. Enter a name for the API key and click \"Create\"\n",
+    "5. Copy the API key and paste it into the `api_key` parameter\n",
+    "\n",
+    "### Elastic Cloud\n",
+    "To connect to an Elasticsearch instance on Elastic Cloud, you can use either the `es_cloud_id` parameter or `es_url`.\n",
     "\n",
     "Example:\n",
     "```python\n",
-    "        from langchain import ElasticVectorSearch\n",
+    "        from langchain.vectorstores.elasticsearch import ElasticsearchStore\n",
     "        from langchain.embeddings import OpenAIEmbeddings\n",
     "\n",
     "        embedding = OpenAIEmbeddings()\n",
-    "\n",
-    "        elastic_host = \"cluster_id.region_id.gcp.cloud.es.io\"\n",
-    "        elasticsearch_url = f\"https://username:password@{elastic_host}:9243\"\n",
-    "        elastic_vector_search = ElasticVectorSearch(\n",
-    "            elasticsearch_url=elasticsearch_url,\n",
+    "        elastic_vector_search = ElasticsearchStore(\n",
+    "            es_cloud_id=\"<cloud_id>\",\n",
     "            index_name=\"test_index\",\n",
-    "            embedding=embedding\n",
+    "            embedding=embedding,\n",
+    "            es_user=\"elastic\",\n",
+    "            es_password=\"changeme\"\n",
     "        )\n",
     "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d6197931-cbe5-460c-a5e6-b5eedb83887c",
    "metadata": {
     "id": "d6197931-cbe5-460c-a5e6-b5eedb83887c",
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: elasticsearch in /Users/joe/Library/Caches/pypoetry/virtualenvs/langchain-monorepo-ln7dNLl5-py3.10/lib/python3.10/site-packages (8.9.0)\n",
+      "Requirement already satisfied: elastic-transport<9,>=8 in /Users/joe/Library/Caches/pypoetry/virtualenvs/langchain-monorepo-ln7dNLl5-py3.10/lib/python3.10/site-packages (from elasticsearch) (8.4.0)\n",
+      "Requirement already satisfied: urllib3<2,>=1.26.2 in /Users/joe/Library/Caches/pypoetry/virtualenvs/langchain-monorepo-ln7dNLl5-py3.10/lib/python3.10/site-packages (from elastic-transport<9,>=8->elasticsearch) (1.26.16)\n",
+      "Requirement already satisfied: certifi in /Users/joe/Library/Caches/pypoetry/virtualenvs/langchain-monorepo-ln7dNLl5-py3.10/lib/python3.10/site-packages (from elastic-transport<9,>=8->elasticsearch) (2023.7.22)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
     "!pip install elasticsearch"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ea167a29",
+   "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "67ab8afa-f7c6-4fbf-b596-cb512da949da",
    "metadata": {
     "id": "67ab8afa-f7c6-4fbf-b596-cb512da949da",
     "outputId": "fd16b37f-cb76-40a9-b83f-eab58dd0d912",
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "OpenAI API Key: ········\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import getpass\n",
@@ -135,7 +179,12 @@
     "tags": []
    },
    "source": [
-    "## Example"
+    "## Basic Example\n",
+    "This example we are going to load \"state_of_the_union.txt\" via the TextLoader, chunk the text into 500 word chunks, and then index each chunk into Elasticsearch.\n",
+    "\n",
+    "Once the data is indexed, we perform a simple query to find the top 4 chunks that similar to the query \"What did the president say about Ketanji Brown Jackson\".\n",
+    "\n",
+    "Elasticsearch is running locally on localhost:9200 with [docker](#running-elasticsearch-via-docker). For more details on how to connect to Elasticsearch from Elastic Cloud, see [connecting with authentication](#authentication) above.\n"
    ]
   },
   {
@@ -149,14 +198,12 @@
    "outputs": [],
    "source": [
     "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import ElasticVectorSearch\n",
-    "from langchain.document_loaders import TextLoader"
+    "from langchain.vectorstores import ElasticsearchStore"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "a3c3999a",
    "metadata": {
     "id": "a3c3999a",
@@ -165,10 +212,11 @@
    "outputs": [],
    "source": [
     "from langchain.document_loaders import TextLoader\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
     "\n",
-    "loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
+    "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
     "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
     "docs = text_splitter.split_documents(documents)\n",
     "\n",
     "embeddings = OpenAIEmbeddings()"
@@ -176,392 +224,552 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "12eb86d8",
    "metadata": {
     "id": "12eb86d8",
     "tags": []
    },
-   "outputs": [],
-   "source": [
-    "db = ElasticVectorSearch.from_documents(\n",
-    "    docs, embeddings, elasticsearch_url=\"http://localhost:9200\"\n",
-    ")\n",
-    "\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = db.similarity_search(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4b172de8",
-   "metadata": {
-    "id": "4b172de8",
-    "outputId": "ca05a209-4514-4b5c-f6cb-2348f58c19a2"
-   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n",
-      "\n",
-      "We cannot let this happen. \n",
-      "\n",
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
+      "[Document(page_content='One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt'}), Document(page_content='One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'date': '2016-01-01', 'rating': 2, 'author': 'John Doe'}), Document(page_content='One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'date': '2010-01-01', 'rating': 1, 'author': 'John Doe'}), Document(page_content='As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.', metadata={'source': '../../modules/state_of_the_union.txt'})]\n"
      ]
     }
    ],
    "source": [
-    "print(docs[0].page_content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "FheGPztJsrRB",
-   "metadata": {
-    "id": "FheGPztJsrRB"
-   },
-   "source": [
-    "# ElasticKnnSearch Class\n",
-    "The `ElasticKnnSearch` implements features allowing storing vectors and documents in Elasticsearch for use with approximate [kNN search](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "gRVcbh5zqCJQ",
-   "metadata": {
-    "id": "gRVcbh5zqCJQ"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install langchain elasticsearch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "TJtqiw5AqBp8",
-   "metadata": {
-    "id": "TJtqiw5AqBp8"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch\n",
-    "from langchain.embeddings import ElasticsearchEmbeddings\n",
-    "import elasticsearch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "XHfC0As6qN3T",
-   "metadata": {
-    "id": "XHfC0As6qN3T"
-   },
-   "outputs": [],
-   "source": [
-    "# Initialize ElasticsearchEmbeddings\n",
-    "model_id = \"<model_id_from_es>\"\n",
-    "dims = dim_count\n",
-    "es_cloud_id = \"ESS_CLOUD_ID\"\n",
-    "es_user = \"es_user\"\n",
-    "es_password = \"es_pass\"\n",
-    "test_index = \"<index_name>\"\n",
-    "# input_field = \"your_input_field\" # if different from 'text_field'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "UkTipx1lqc3h",
-   "metadata": {
-    "id": "UkTipx1lqc3h"
-   },
-   "outputs": [],
-   "source": [
-    "# Generate embedding object\n",
-    "embeddings = ElasticsearchEmbeddings.from_credentials(\n",
-    "    model_id,\n",
-    "    # input_field=input_field,\n",
-    "    es_cloud_id=es_cloud_id,\n",
-    "    es_user=es_user,\n",
-    "    es_password=es_password,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "74psgD0oqjYK",
-   "metadata": {
-    "id": "74psgD0oqjYK"
-   },
-   "outputs": [],
-   "source": [
-    "# Initialize ElasticKnnSearch\n",
-    "knn_search = ElasticKnnSearch(\n",
-    "    es_cloud_id=es_cloud_id,\n",
-    "    es_user=es_user,\n",
-    "    es_password=es_password,\n",
-    "    index_name=test_index,\n",
-    "    embedding=embeddings,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7AfgIKLWqnQl",
-   "metadata": {
-    "id": "7AfgIKLWqnQl"
-   },
-   "source": [
-    "## Test adding vectors"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "yNUUIaL9qmze",
-   "metadata": {
-    "id": "yNUUIaL9qmze"
-   },
-   "outputs": [],
-   "source": [
-    "# Test `add_texts` method\n",
-    "texts = [\"Hello, world!\", \"Machine learning is fun.\", \"I love Python.\"]\n",
-    "knn_search.add_texts(texts)\n",
     "\n",
-    "# Test `from_texts` method\n",
-    "new_texts = [\n",
-    "    \"This is a new text.\",\n",
-    "    \"Elasticsearch is powerful.\",\n",
-    "    \"Python is great for data analysis.\",\n",
-    "]\n",
-    "knn_search.from_texts(new_texts, dims=dims)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0zdR-Iubquov",
-   "metadata": {
-    "id": "0zdR-Iubquov"
-   },
-   "source": [
-    "## Test knn search using query vector builder "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bwR4jYvqqxTo",
-   "metadata": {
-    "id": "bwR4jYvqqxTo"
-   },
-   "outputs": [],
-   "source": [
-    "# Test `knn_search` method with model_id and query_text\n",
-    "query = \"Hello\"\n",
-    "knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2)\n",
-    "print(f\"kNN search results for query '{query}': {knn_result}\")\n",
-    "print(\n",
-    "    f\"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'\"\n",
+    "db = ElasticsearchStore.from_documents(\n",
+    "    docs, embeddings, es_url=\"http://localhost:9200\", index_name=\"test-basic\", \n",
     ")\n",
     "\n",
-    "# Test `hybrid_search` method\n",
-    "query = \"Hello\"\n",
-    "hybrid_result = knn_search.knn_hybrid_search(query=query, model_id=model_id, k=2)\n",
-    "print(f\"Hybrid search results for query '{query}': {hybrid_result}\")\n",
-    "print(\n",
-    "    f\"The 'text' field value from the top hit is: '{hybrid_result['hits']['hits'][0]['_source']['text']}'\"\n",
-    ")"
+    "db.client.indices.refresh(index=\"test-basic\")\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "results = db.similarity_search(query)\n",
+    "print(results)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ltXYqp0qqz7R",
-   "metadata": {
-    "id": "ltXYqp0qqz7R"
-   },
+   "id": "f86ec452",
+   "metadata": {},
    "source": [
-    "## Test knn search using pre generated vector \n"
+    "# Metadata\n",
+    "\n",
+    "`ElasticsearchStore` supports metadata to stored along with the document. This metadata dict object is stored in a metadata object field in the Elasticsearch document. Based on the metadata value, Elasticsearch will automatically setup the mapping by infering the data type of the metadata value. For example, if the metadata value is a string, Elasticsearch will setup the mapping for the metadata object field as a string type."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "O5COtpTqq23t",
-   "metadata": {
-    "id": "O5COtpTqq23t"
-   },
-   "outputs": [],
+   "execution_count": 8,
+   "id": "5d076412",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': '../../modules/state_of_the_union.txt', 'date': '2016-01-01', 'rating': 2, 'author': 'John Doe'}\n"
+     ]
+    }
+   ],
    "source": [
-    "# Generate embedding for tests\n",
-    "query_text = \"Hello\"\n",
-    "query_embedding = embeddings.embed_query(query_text)\n",
-    "print(\n",
-    "    f\"Length of embedding: {len(query_embedding)}\\nFirst two items in embedding: {query_embedding[:2]}\"\n",
+    "\n",
+    "# Adding metadata to documents\n",
+    "for i, doc in enumerate(docs):\n",
+    "    doc.metadata[\"date\"] = f\"{range(2010, 2020)[i % 10]}-01-01\"\n",
+    "    doc.metadata[\"rating\"] = range(1, 6)[i % 5] \n",
+    "    doc.metadata[\"author\"] = [\"John Doe\", \"Jane Doe\"][i % 2]\n",
+    "\n",
+    "db = ElasticsearchStore.from_documents(\n",
+    "    docs, embeddings, es_url=\"http://localhost:9200\", index_name=\"test-metadata\"\n",
     ")\n",
     "\n",
-    "# Test knn Search\n",
-    "knn_result = knn_search.knn_search(query_vector=query_embedding, k=2)\n",
-    "print(\n",
-    "    f\"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'\"\n",
-    ")\n",
-    "\n",
-    "# Test hybrid search - Requires both query_text and query_vector\n",
-    "knn_result = knn_search.knn_hybrid_search(\n",
-    "    query_vector=query_embedding, query=query_text, k=2\n",
-    ")\n",
-    "print(\n",
-    "    f\"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'\"\n",
-    ")"
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = db.similarity_search(query)\n",
+    "print(docs[0].metadata)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0dnmimcJq42C",
-   "metadata": {
-    "id": "0dnmimcJq42C"
-   },
+   "id": "3befa9e0",
+   "metadata": {},
    "source": [
-    "## Test source option"
+    "## Filtering Metadata\n",
+    "With metadata added to the documents, you can add metadata filtering at query time. \n",
+    "\n",
+    "### Example: Filter by keyword"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "v4_B72nHq7g1",
-   "metadata": {
-    "id": "v4_B72nHq7g1"
-   },
-   "outputs": [],
+   "execution_count": 57,
+   "id": "b2a4bd1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': '../../modules/state_of_the_union.txt', 'date': '2010-01-01', 'rating': 1, 'author': 'John Doe', 'geo_location': {'lat': 40.12, 'lon': -71.34}}\n"
+     ]
+    }
+   ],
    "source": [
-    "# Test `knn_search` method with model_id and query_text\n",
-    "query = \"Hello\"\n",
-    "knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2, source=False)\n",
-    "assert not \"_source\" in knn_result[\"hits\"][\"hits\"][0].keys()\n",
-    "\n",
-    "# Test `hybrid_search` method\n",
-    "query = \"Hello\"\n",
-    "hybrid_result = knn_search.knn_hybrid_search(\n",
-    "    query=query, model_id=model_id, k=2, source=False\n",
-    ")\n",
-    "assert not \"_source\" in hybrid_result[\"hits\"][\"hits\"][0].keys()"
+    "docs = db.similarity_search(query, filter=[{ \"match\": { \"metadata.author\": \"John Doe\"}}])\n",
+    "print(docs[0].metadata)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "teHgJgrlq-Jb",
-   "metadata": {
-    "id": "teHgJgrlq-Jb"
-   },
+   "id": "647d26ea",
+   "metadata": {},
    "source": [
-    "## Test fields option "
+    "### Example: Filter by Date Range"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "utNBbpZYrAYW",
-   "metadata": {
-    "id": "utNBbpZYrAYW"
-   },
-   "outputs": [],
+   "execution_count": 59,
+   "id": "55b63a61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': '../../modules/state_of_the_union.txt', 'date': '2012-01-01', 'rating': 3, 'author': 'John Doe', 'geo_location': {'lat': 40.12, 'lon': -71.34}}\n"
+     ]
+    }
+   ],
    "source": [
-    "# Test `knn_search` method with model_id and query_text\n",
-    "query = \"Hello\"\n",
-    "knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2, fields=[\"text\"])\n",
-    "assert \"text\" in knn_result[\"hits\"][\"hits\"][0][\"fields\"].keys()\n",
-    "\n",
-    "# Test `hybrid_search` method\n",
-    "query = \"Hello\"\n",
-    "hybrid_result = knn_search.knn_hybrid_search(\n",
-    "    query=query, model_id=model_id, k=2, fields=[\"text\"]\n",
-    ")\n",
-    "assert \"text\" in hybrid_result[\"hits\"][\"hits\"][0][\"fields\"].keys()"
+    "docs = db.similarity_search(\"Any mention about Fred?\", filter=[{ \"range\": { \"metadata.date\": { \"gte\": \"2010-01-01\" }}}])\n",
+    "print(docs[0].metadata)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "hddsIFferBy1",
-   "metadata": {
-    "id": "hddsIFferBy1"
-   },
+   "id": "08f57936",
+   "metadata": {},
    "source": [
-    "### Test with es client connection rather than cloud_id "
+    "### Example: Filter by Numeric Range"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "9b831b3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': '../../modules/state_of_the_union.txt', 'date': '2012-01-01', 'rating': 3, 'author': 'John Doe', 'geo_location': {'lat': 40.12, 'lon': -71.34}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "docs = db.similarity_search(\"Any mention about Fred?\", filter=[{ \"range\": { \"metadata.rating\": { \"gte\": 2 }}}])\n",
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05e7ec40",
+   "metadata": {},
+   "source": [
+    "### Example: Filter by Geo Distance\n",
+    "Requires an index with a geo_point mapping to be declared for `metadata.geo_location`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bXqrUnoirFia",
-   "metadata": {
-    "id": "bXqrUnoirFia"
-   },
+   "id": "fb1482e7",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Create Elasticsearch connection\n",
-    "es_connection = Elasticsearch(\n",
-    "    hosts=[\"https://es_cluster_url:port\"], basic_auth=(\"user\", \"password\")\n",
-    ")"
+    "docs = db.similarity_search(\"Any mention about Fred?\", filter=[{ \"geo_distance\": { \"distance\": \"200km\", \"metadata.geo_location\": { \"lat\": 40, \"lon\": -70 } } }])\n",
+    "print(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "729eb73b",
+   "metadata": {},
+   "source": [
+    "Filter supports many more types of queries than above. \n",
+    "\n",
+    "Read more about them in the [documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d54068c",
+   "metadata": {},
+   "source": [
+    "# Distance Similarity Algorithm\n",
+    "Elasticsearch supports the following vector distance similarity algorithms:\n",
+    "\n",
+    "- cosine\n",
+    "- euclidean\n",
+    "- dot_product\n",
+    "\n",
+    "The cosine similarity algorithm is the default.\n",
+    "\n",
+    "You can specify the similarity Algorithm needed via the similarity parameter.\n",
+    "\n",
+    "**NOTE**\n",
+    "Depending on the retrieval strategy, the similarity algorithm cannot be changed at query time. It is needed to be set when creating the index mapping for field. If you need to change the similarity algorithm, you need to delete the index and recreate it with the correct distance_strategy.\n",
+    "\n",
+    "```python\n",
+    "\n",
+    "db = ElasticsearchStore.from_documents(\n",
+    "    docs, \n",
+    "    embeddings, \n",
+    "    es_url=\"http://localhost:9200\", \n",
+    "    index_name=\"test\",\n",
+    "    distance_strategy=\"COSINE\"\n",
+    "    # distance_strategy=\"EUCLIDEAN_DISTANCE\"\n",
+    "    # distance_strategy=\"DOT_PRODUCT\"\n",
+    ")\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b06da4d7",
+   "metadata": {},
+   "source": [
+    "# Retrieval Strategies\n",
+    "Elasticsearch has big advantages over other vector only databases from its ability to support a wide range of retrieval strategies.  In this notebook we will configure `ElasticsearchStore` to support some of the most common retrieval strategies. \n",
+    "\n",
+    "By default, `ElasticsearchStore` uses the `ApproxRetrievalStrategy`.\n",
+    "\n",
+    "## ApproxRetrievalStrategy\n",
+    "This will return the top `k` most similar vectors to the query vector.  The `k` parameter is set when the `ElasticsearchStore` is initialized.  The default value is `10`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "TIM__Hm8rSEW",
-   "metadata": {
-    "id": "TIM__Hm8rSEW"
-   },
+   "id": "999b5ef5",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Instantiate ElasticsearchEmbeddings using es_connection\n",
-    "embeddings = ElasticsearchEmbeddings.from_es_connection(\n",
-    "    model_id,\n",
-    "    es_connection,\n",
-    ")"
+    "db = ElasticsearchStore.from_documents(\n",
+    "    docs, \n",
+    "    embeddings, \n",
+    "    es_url=\"http://localhost:9200\", \n",
+    "    index_name=\"test\",\n",
+    "    strategy=ElasticsearchStore.ApproxRetrievalStrategy()\n",
+    ")\n",
+    "\n",
+    "docs = db.similarity_search(query=\"What did the president say about Ketanji Brown Jackson?\", k=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b651be5",
+   "metadata": {},
+   "source": [
+    "### Example: Approx with hybrid\n",
+    "This example will show how to configure `ElasticsearchStore` to perform a hybrid retrieval, using a combination of approximate semantic search and keyword based search. \n",
+    "\n",
+    "We use RRF to balance the two scores from different retrieval methods.\n",
+    "\n",
+    "To enable hybrid retrieval, we need to set `hybrid=True` in `ElasticsearchStore` `ApproxRetrievalStrategy` constructor.\n",
+    "\n",
+    "```python\n",
+    "\n",
+    "db = ElasticsearchStore.from_documents(\n",
+    "    docs, \n",
+    "    embeddings, \n",
+    "    es_url=\"http://localhost:9200\", \n",
+    "    index_name=\"test\",\n",
+    "    strategy=ElasticsearchStore.ApproxRetrievalStrategy(\n",
+    "        hybrid=True,\n",
+    "    )\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "When `hybrid` is enabled, the query performed will be a combination of approximate semantic search and keyword based search. \n",
+    "\n",
+    "It will use `rrf` (Reciprocal Rank Fusion) to balance the two scores from different retrieval methods.\n",
+    "\n",
+    "**Note** RRF requires Elasticsearch 8.9.0 or above.\n",
+    "\n",
+    "```json\n",
+    "{\n",
+    "    \"knn\": {\n",
+    "        \"field\": \"vector\",\n",
+    "        \"filter\": [],\n",
+    "        \"k\": 1,\n",
+    "        \"num_candidates\": 50,\n",
+    "        \"query_vector\": [1.0, ..., 0.0],\n",
+    "    },\n",
+    "    \"query\": {\n",
+    "        \"bool\": {\n",
+    "            \"filter\": [],\n",
+    "            \"must\": [{\"match\": {\"text\": {\"query\": \"foo\"}}}],\n",
+    "        }\n",
+    "    },\n",
+    "    \"rank\": {\"rrf\": {}},\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "### Example: Approx with Embedding Model in Elasticsearch\n",
+    "This example will show how to configure `ElasticsearchStore` to use the embedding model deployed in Elasticsearch for approximate retrieval. \n",
+    "\n",
+    "To use this, specify the model_id in `ElasticsearchStore` `ApproxRetrievalStrategy` constructor via the `query_model_id` argument.\n",
+    "\n",
+    "**NOTE** This requires the model to be deployed and running in Elasticsearch ml node. \n",
+    "\n",
+    "```python\n",
+    "\n",
+    "# Note: This does not have an embedding function specified\n",
+    "# Instead, we will use the embedding model deployed in Elasticsearch\n",
+    "db = ElasticsearchStore( \n",
+    "    es_url=\"http://localhost:9200\", \n",
+    "    index_name=\"test\",\n",
+    "    strategy=ElasticsearchStore.ApproxRetrievalStrategy(\n",
+    "      query_model_id=\"sentence-transformers__all-minilm-l6-v2\"\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Perform search\n",
+    "db.similarity_search(\"hello world\", k=10)\n",
+    "```\n",
+    "\n",
+    "## SparseVectorRetrievalStrategy (ELSER)\n",
+    "This strategy uses Elasticsearch's sparse vector retrieval to retrieve the top-k results. We only support our own \"ELSER\" embedding model for now.\n",
+    "\n",
+    "**NOTE** This requires the ELSER model to be deployed and running in Elasticsearch ml node. \n",
+    "\n",
+    "To use this, specify `SparseVectorRetrievalStrategy` in `ElasticsearchStore` constructor."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1-CdnOrArVc_",
-   "metadata": {
-    "id": "1-CdnOrArVc_"
-   },
-   "outputs": [],
+   "execution_count": 5,
+   "id": "38a63256",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.' metadata={'source': '../../modules/state_of_the_union.txt'}\n"
+     ]
+    }
+   ],
    "source": [
-    "# Initialize ElasticKnnSearch\n",
-    "knn_search = ElasticKnnSearch(\n",
-    "    es_connection=es_connection, index_name=test_index, embedding=embeddings\n",
-    ")"
+    "\n",
+    "# Note that this example doesn't have an embedding function. This is because we infer the tokens at index time and at query time within Elasticsearch. \n",
+    "# This requires the ELSER model to be loaded and running in Elasticsearch.\n",
+    "db = ElasticsearchStore.from_documents(\n",
+    "    docs, \n",
+    "    es_cloud_id=\"My_deployment:dXMtY2VudHJhbDEuZ2NwLmNsb3VkLmVzLmlvOjQ0MyQ2OGJhMjhmNDc1M2Y0MWVjYTk2NzI2ZWNkMmE5YzRkNyQ3NWI4ODRjNWQ2OTU0MTYzODFjOTkxNmQ1YzYxMGI1Mw==\",\n",
+    "    es_user=\"elastic\",\n",
+    "    es_password=\"GgUPiWKwEzgHIYdHdgPk1Lwi\",\n",
+    "    index_name=\"test-elser\",\n",
+    "    strategy=ElasticsearchStore.SparseVectorRetrievalStrategy()\n",
+    ")\n",
+    "\n",
+    "db.client.indices.refresh(index=\"test-elser\")\n",
+    "\n",
+    "results = db.similarity_search(\"What did the president say about Ketanji Brown Jackson\", k=4)\n",
+    "print(results[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edf3a093",
+   "metadata": {},
+   "source": [
+    "## ExactRetrievalStrategy\n",
+    "This strategy uses Elasticsearch's exact retrieval (also known as brute force) to retrieve the top-k results.\n",
+    "\n",
+    "To use this, specify `ExactRetrievalStrategy` in `ElasticsearchStore` constructor.\n",
+    "\n",
+    "```python\n",
+    "\n",
+    "db = ElasticsearchStore.from_documents(\n",
+    "    docs, \n",
+    "    embeddings, \n",
+    "    es_url=\"http://localhost:9200\", \n",
+    "    index_name=\"test\",\n",
+    "    strategy=ElasticsearchStore.ExactRetrievalStrategy()\n",
+    ")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0960fa0a",
+   "metadata": {},
+   "source": [
+    "# Customise the Query\n",
+    "With `custom_query` parameter at search, you are able to adjust the query that is used to retrieve documents from Elasticsearch. This is useful if you want to want to use a more complex query, to support linear boosting of fields."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "0kgyaL6QrYVF",
-   "metadata": {
-    "id": "0kgyaL6QrYVF"
-   },
-   "outputs": [],
+   "execution_count": 11,
+   "id": "b926a606",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query Retriever created by the retrieval strategy:\n",
+      "{'query': {'bool': {'must': [{'text_expansion': {'vector.tokens': {'model_id': '.elser_model_1', 'model_text': 'What did the president say about Ketanji Brown Jackson'}}}], 'filter': []}}}\n",
+      "\n",
+      "Query thats actually used in Elasticsearch:\n",
+      "{'query': {'match': {'text': 'What did the president say about Ketanji Brown Jackson'}}}\n",
+      "\n",
+      "Results:\n",
+      "page_content='One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.' metadata={'source': '../../modules/state_of_the_union.txt'}\n"
+     ]
+    }
+   ],
    "source": [
-    "# Test `knn_search` method with model_id and query_text\n",
-    "query = \"Hello\"\n",
-    "knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2)\n",
-    "print(f\"kNN search results for query '{query}': {knn_result}\")\n",
-    "print(\n",
-    "    f\"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'\"\n",
-    ")"
+    "# Example of a custom query thats just doing a BM25 search on the text field.\n",
+    "def custom_query(query_body: dict, query: str):\n",
+    "    \"\"\"Custom query to be used in Elasticsearch.\n",
+    "    Args:\n",
+    "        query_body (dict): Elasticsearch query body.\n",
+    "        query (str): Query string.\n",
+    "    Returns:\n",
+    "        dict: Elasticsearch query body.\n",
+    "    \"\"\"\n",
+    "    print(\"Query Retriever created by the retrieval strategy:\")\n",
+    "    print(query_body)\n",
+    "    print()\n",
+    "\n",
+    "    new_query_body = {\n",
+    "        \"query\": {\n",
+    "            \"match\": {\n",
+    "                \"text\": query\n",
+    "            }\n",
+    "        }\n",
+    "    }\n",
+    "\n",
+    "    print(\"Query thats actually used in Elasticsearch:\")\n",
+    "    print(new_query_body)\n",
+    "    print()\n",
+    "\n",
+    "    return new_query_body\n",
+    "\n",
+    "results = db.similarity_search(\"What did the president say about Ketanji Brown Jackson\", k=4, custom_query=custom_query)\n",
+    "print(\"Results:\")\n",
+    "print(results[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "604c66ea",
+   "metadata": {},
+   "source": [
+    "# Upgrading to ElasticsearchStore\n",
+    "\n",
+    "If you're already using Elasticsearch in your langchain based project, you may be using the old implementations: `ElasticVectorSearch` and `ElasticKNNSearch` which are now deprecated. We've introduced a new implementation called `ElasticsearchStore` which is more flexible and easier to use. This notebook will guide you through the process of upgrading to the new implementation.\n",
+    "\n",
+    "## What's new?\n",
+    "\n",
+    "The new implementation is now one class called `ElasticsearchStore` which can be used for approx, exact, and ELSER search retrieval, via strategies.\n",
+    "\n",
+    "## Im using ElasticKNNSearch\n",
+    "\n",
+    "Old implementation:\n",
+    "\n",
+    "```python\n",
+    "\n",
+    "from langchain.vectorstores.elastic_vector_search import ElasticKNNSearch\n",
+    "\n",
+    "db = ElasticKNNSearch(\n",
+    "  elasticsearch_url=\"http://localhost:9200\",\n",
+    "  index_name=\"test_index\",\n",
+    "  embedding=embedding\n",
+    ")\n",
+    "\n",
+    "```\n",
+    "\n",
+    "New implementation:\n",
+    "\n",
+    "```python\n",
+    "\n",
+    "from langchain.vectorstores.elasticsearch import ElasticsearchStore\n",
+    "\n",
+    "db = ElasticsearchStore(\n",
+    "  es_url=\"http://localhost:9200\",\n",
+    "  index_name=\"test_index\",\n",
+    "  embedding=embedding,\n",
+    "  # if you use the model_id\n",
+    "  # strategy=ElasticsearchStore.ApproxRetrievalStrategy( query_model_id=\"test_model\" )\n",
+    "  # if you use hybrid search\n",
+    "  # strategy=ElasticsearchStore.ApproxRetrievalStrategy( hybrid=True )\n",
+    ")\n",
+    "\n",
+    "```\n",
+    "\n",
+    "## Im using ElasticVectorSearch\n",
+    "\n",
+    "Old implementation:\n",
+    "\n",
+    "```python\n",
+    "\n",
+    "from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
+    "\n",
+    "db = ElasticVectorSearch(\n",
+    "  elasticsearch_url=\"http://localhost:9200\",\n",
+    "  index_name=\"test_index\",\n",
+    "  embedding=embedding\n",
+    ")\n",
+    "\n",
+    "```\n",
+    "\n",
+    "New implementation:\n",
+    "\n",
+    "```python\n",
+    "\n",
+    "from langchain.vectorstores.elasticsearch import ElasticsearchStore\n",
+    "\n",
+    "db = ElasticsearchStore(\n",
+    "  es_url=\"http://localhost:9200\",\n",
+    "  index_name=\"test_index\",\n",
+    "  embedding=embedding,\n",
+    "  strategy=ElasticsearchStore.ExactRetrievalStrategy()\n",
+    ")\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3cff8421",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ObjectApiResponse({'acknowledged': True})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db.client.indices.delete(index='test-metadata, test-elser, test-basic', ignore_unavailable=True, allow_no_indices=True)"
    ]
   }
  ],
@@ -584,7 +792,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/extras/integrations/vectorstores/supabase.ipynb b/docs/extras/integrations/vectorstores/supabase.ipynb
index 3f012948a1a..19dbc3d3661 100644
--- a/docs/extras/integrations/vectorstores/supabase.ipynb
+++ b/docs/extras/integrations/vectorstores/supabase.ipynb
@@ -35,7 +35,7 @@
     "\n",
     "       -- Create a table to store your documents\n",
     "       create table documents (\n",
-    "       id bigserial primary key,\n",
+    "       id uuid primary key,\n",
     "       content text, -- corresponds to Document.pageContent\n",
     "       metadata jsonb, -- corresponds to Document.metadata\n",
     "       embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed\n",
diff --git a/docs/extras/integrations/vectorstores/zep.ipynb b/docs/extras/integrations/vectorstores/zep.ipynb
new file mode 100644
index 00000000000..0354da32105
--- /dev/null
+++ b/docs/extras/integrations/vectorstores/zep.ipynb
@@ -0,0 +1,494 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Zep\n",
+    "\n",
+    "Zep is an open source long-term memory store for LLM applications. Zep makes it easy to add relevant documents,\n",
+    "chat history memory & rich user data to your LLM app's prompts.\n",
+    "\n",
+    "**Note:** The `ZepVectorStore` works with `Documents` and is intended to be used as a `Retriever`.\n",
+    "It offers separate functionality to Zep's `ZepMemory` class, which is designed for persisting, enriching\n",
+    "and searching your user's chat history.\n",
+    "\n",
+    "## Why Zep's VectorStore? 🤖🚀\n",
+    "Zep automatically embeds documents added to the Zep Vector Store using low-latency models local to the Zep server.\n",
+    "The Zep client also offers async interfaces for all document operations. These two together with Zep's chat memory\n",
+    " functionality make Zep ideal for building conversational LLM apps where latency and performance are important.\n",
+    "\n",
+    "## Installation\n",
+    "Follow the [Zep Quickstart Guide](https://docs.getzep.com/deployment/quickstart/) to install and get started with Zep.\n",
+    "\n",
+    "## Usage\n",
+    "\n",
+    "You'll need your Zep API URL and optionally an API key to use the Zep VectorStore. \n",
+    "See the [Zep docs](https://docs.getzep.com) for more information.\n",
+    "\n",
+    "In the examples below, we're using Zep's auto-embedding feature which automatically embed documents on the Zep server \n",
+    "using low-latency embedding models.\n",
+    "\n",
+    "## Note\n",
+    "- These examples use Zep's async interfaces. Call sync interfaces by removing the `a` prefix from the method names.\n",
+    "- If you pass in an `Embeddings` instance Zep will use this to embed documents rather than auto-embed them.\n",
+    "You must also set your document collection to `isAutoEmbedded === false`. \n",
+    "- If you set your collection to `isAutoEmbedded === false`, you must pass in an `Embeddings` instance."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "9eb8dfa6fdb71ef5"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Load or create a Collection from documents"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "9a3a11aab1412d98"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "from uuid import uuid4\n",
+    "\n",
+    "from langchain.document_loaders import WebBaseLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.vectorstores import ZepVectorStore\n",
+    "from langchain.vectorstores.zep import CollectionConfig\n",
+    "\n",
+    "ZEP_API_URL = \"http://localhost:8000\"  # this is the API url of your Zep instance\n",
+    "ZEP_API_KEY = \"<optional_key>\"  # optional API Key for your Zep instance\n",
+    "collection_name = f\"babbage{uuid4().hex}\"  # a unique collection name. alphanum only\n",
+    "\n",
+    "# Collection config is needed if we're creating a new Zep Collection\n",
+    "config = CollectionConfig(\n",
+    "    name=collection_name,\n",
+    "    description=\"<optional description>\",\n",
+    "    metadata={\"optional_metadata\": \"associated with the collection\"},\n",
+    "    is_auto_embedded=True,  # we'll have Zep embed our documents using its low-latency embedder\n",
+    "    embedding_dimensions=1536  # this should match the model you've configured Zep to use.\n",
+    ")\n",
+    "\n",
+    "# load the document\n",
+    "article_url = \"https://www.gutenberg.org/cache/epub/71292/pg71292.txt\"\n",
+    "loader = WebBaseLoader(article_url)\n",
+    "documents = loader.load()\n",
+    "\n",
+    "# split it into chunks\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "# Instantiate the VectorStore. Since the collection does not already exist in Zep,\n",
+    "# it will be created and populated with the documents we pass in.\n",
+    "vs = ZepVectorStore.from_documents(docs,\n",
+    "                                   collection_name=collection_name,\n",
+    "                                   config=config,\n",
+    "                                   api_url=ZEP_API_URL,\n",
+    "                                   api_key=ZEP_API_KEY\n",
+    "                                   )"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-13T01:07:50.672390Z",
+     "start_time": "2023-08-13T01:07:48.777799Z"
+    }
+   },
+   "id": "519418421a32e4d"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Embedding status: 0/402 documents embedded\n",
+      "Embedding status: 0/402 documents embedded\n",
+      "Embedding status: 402/402 documents embedded\n"
+     ]
+    }
+   ],
+   "source": [
+    "# wait for the collection embedding to complete\n",
+    "\n",
+    "async def wait_for_ready(collection_name: str) -> None:\n",
+    "    from zep_python import ZepClient\n",
+    "    import time\n",
+    "\n",
+    "    client = ZepClient(ZEP_API_URL, ZEP_API_KEY)\n",
+    "\n",
+    "    while True:\n",
+    "        c = await client.document.aget_collection(collection_name)\n",
+    "        print(\n",
+    "            \"Embedding status: \"\n",
+    "            f\"{c.document_embedded_count}/{c.document_count} documents embedded\"\n",
+    "        )\n",
+    "        time.sleep(1)\n",
+    "        if c.status == \"ready\":\n",
+    "            break\n",
+    "\n",
+    "\n",
+    "await wait_for_ready(collection_name)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-13T01:07:53.807663Z",
+     "start_time": "2023-08-13T01:07:50.671241Z"
+    }
+   },
+   "id": "201dc57b124cb6d7"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Simarility Search Query over the Collection"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "94ca9dfa7d0ecaa5"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tables necessary to determine the places of the planets are not less\r\n",
+      "necessary than those for the sun, moon, and stars. Some notion of the\r\n",
+      "number and complexity of these tables may be formed, when we state that\r\n",
+      "the positions of the two principal planets, (and these the most\r\n",
+      "necessary for the navigator,) Jupiter and Saturn, require each not less\r\n",
+      "than one hundred and sixteen tables. Yet it is not only necessary to\r\n",
+      "predict the position of these bodies, but it is likewise expedient to  ->  0.8998482592744614 \n",
+      "====\n",
+      "\n",
+      "tabulate the motions of the four satellites of Jupiter, to predict the\r\n",
+      "exact times at which they enter his shadow, and at which their shadows\r\n",
+      "cross his disc, as well as the times at which they are interposed\r\n",
+      "between him and the Earth, and he between them and the Earth.\r\n",
+      "\r\n",
+      "Among the extensive classes of tables here enumerated, there are several\r\n",
+      "which are in their nature permanent and unalterable, and would never\r\n",
+      "require to be recomputed, if they could once be computed with perfect  ->  0.8976143854195493 \n",
+      "====\n",
+      "\n",
+      "the scheme of notation thus applied, immediately suggested the\r\n",
+      "advantages which must attend it as an instrument for expressing the\r\n",
+      "structure, operation, and circulation of the animal system; and we\r\n",
+      "entertain no doubt of its adequacy for that purpose. Not only the\r\n",
+      "mechanical connexion of the solid members of the bodies of men and\r\n",
+      "animals, but likewise the structure and operation of the softer parts,\r\n",
+      "including the muscles, integuments, membranes, &c. the nature, motion,  ->  0.889982614061763 \n",
+      "====\n"
+     ]
+    }
+   ],
+   "source": [
+    "# query it\n",
+    "query = \"what is the structure of our solar system?\"\n",
+    "docs_scores = await vs.asimilarity_search_with_relevance_scores(query, k=3)\n",
+    "\n",
+    "# print results\n",
+    "for d, s in docs_scores:\n",
+    "    print(d.page_content, \" -> \", s, \"\\n====\\n\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-13T01:07:54.195988Z",
+     "start_time": "2023-08-13T01:07:53.808550Z"
+    }
+   },
+   "id": "1998de0a96fe89c3"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Search over Collection Re-ranked by MMR"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "e02b61a9af0b2c80"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tables necessary to determine the places of the planets are not less\r\n",
+      "necessary than those for the sun, moon, and stars. Some notion of the\r\n",
+      "number and complexity of these tables may be formed, when we state that\r\n",
+      "the positions of the two principal planets, (and these the most\r\n",
+      "necessary for the navigator,) Jupiter and Saturn, require each not less\r\n",
+      "than one hundred and sixteen tables. Yet it is not only necessary to\r\n",
+      "predict the position of these bodies, but it is likewise expedient to \n",
+      "====\n",
+      "\n",
+      "the scheme of notation thus applied, immediately suggested the\r\n",
+      "advantages which must attend it as an instrument for expressing the\r\n",
+      "structure, operation, and circulation of the animal system; and we\r\n",
+      "entertain no doubt of its adequacy for that purpose. Not only the\r\n",
+      "mechanical connexion of the solid members of the bodies of men and\r\n",
+      "animals, but likewise the structure and operation of the softer parts,\r\n",
+      "including the muscles, integuments, membranes, &c. the nature, motion, \n",
+      "====\n",
+      "\n",
+      "tabulate the motions of the four satellites of Jupiter, to predict the\r\n",
+      "exact times at which they enter his shadow, and at which their shadows\r\n",
+      "cross his disc, as well as the times at which they are interposed\r\n",
+      "between him and the Earth, and he between them and the Earth.\r\n",
+      "\r\n",
+      "Among the extensive classes of tables here enumerated, there are several\r\n",
+      "which are in their nature permanent and unalterable, and would never\r\n",
+      "require to be recomputed, if they could once be computed with perfect \n",
+      "====\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"what is the structure of our solar system?\"\n",
+    "docs = await vs.asearch(query, search_type=\"mmr\", k=3)\n",
+    "\n",
+    "for d in docs:\n",
+    "    print(d.page_content, \"\\n====\\n\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-13T01:07:54.394873Z",
+     "start_time": "2023-08-13T01:07:54.180901Z"
+    }
+   },
+   "id": "488112da752b1d58"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Filter by Metadata\n",
+    "\n",
+    "Use a metadata filter to narrow down results. First, load another book: \"Adventures of Sherlock Holmes\""
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "42455e31d4ab0d68"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Embedding status: 402/1692 documents embedded\n",
+      "Embedding status: 402/1692 documents embedded\n",
+      "Embedding status: 552/1692 documents embedded\n",
+      "Embedding status: 702/1692 documents embedded\n",
+      "Embedding status: 1002/1692 documents embedded\n",
+      "Embedding status: 1002/1692 documents embedded\n",
+      "Embedding status: 1152/1692 documents embedded\n",
+      "Embedding status: 1302/1692 documents embedded\n",
+      "Embedding status: 1452/1692 documents embedded\n",
+      "Embedding status: 1602/1692 documents embedded\n",
+      "Embedding status: 1692/1692 documents embedded\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's add more content to the existing Collection\n",
+    "article_url = \"https://www.gutenberg.org/files/48320/48320-0.txt\"\n",
+    "loader = WebBaseLoader(article_url)\n",
+    "documents = loader.load()\n",
+    "\n",
+    "# split it into chunks\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "await vs.aadd_documents(docs)\n",
+    "\n",
+    "await wait_for_ready(collection_name)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-13T01:08:06.323569Z",
+     "start_time": "2023-08-13T01:07:54.381822Z"
+    }
+   },
+   "id": "146c8a96201c0ab9"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### We see results from both books. Note the `source` metadata"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "5b225f3ae1e61de8"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "by that body to Mr Babbage:--'In no department of science, or of the\r\n",
+      "arts, does this discovery promise to be so eminently useful as in that\r\n",
+      "of astronomy, and its kindred sciences, with the various arts dependent\r\n",
+      "on them. In none are computations more operose than those which\r\n",
+      "astronomy in particular requires;--in none are preparatory facilities\r\n",
+      "more needful;--in none is error more detrimental. The practical\r\n",
+      "astronomer is interrupted in his pursuit, and diverted from his task of  ->  {'source': 'https://www.gutenberg.org/cache/epub/71292/pg71292.txt'} \n",
+      "====\n",
+      "\n",
+      "possess all knowledge which is likely to be useful to him in his work,\r\n",
+      "and this I have endeavored in my case to do. If I remember rightly, you\r\n",
+      "on one occasion, in the early days of our friendship, defined my limits\r\n",
+      "in a very precise fashion.”\r\n",
+      "\r\n",
+      "“Yes,” I answered, laughing. “It was a singular document. Philosophy,\r\n",
+      "astronomy, and politics were marked at zero, I remember. Botany\r\n",
+      "variable, geology profound as regards the mud-stains from any region  ->  {'source': 'https://www.gutenberg.org/files/48320/48320-0.txt'} \n",
+      "====\n",
+      "\n",
+      "in all its relations; but above all, with Astronomy and Navigation. So\r\n",
+      "important have they been considered, that in many instances large sums\r\n",
+      "have been appropriated by the most enlightened nations in the production\r\n",
+      "of them; and yet so numerous and insurmountable have been the\r\n",
+      "difficulties attending the attainment of this end, that after all, even\r\n",
+      "navigators, putting aside every other department of art and science,\r\n",
+      "have, until very recently, been scantily and imperfectly supplied with  ->  {'source': 'https://www.gutenberg.org/cache/epub/71292/pg71292.txt'} \n",
+      "====\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"Was he interested in astronomy?\"\n",
+    "docs = await vs.asearch(query, search_type=\"similarity\", k=3)\n",
+    "\n",
+    "for d in docs:\n",
+    "    print(d.page_content, \" -> \", d.metadata, \"\\n====\\n\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-13T01:08:06.504769Z",
+     "start_time": "2023-08-13T01:08:06.325435Z"
+    }
+   },
+   "id": "53700a9cd817cde4"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Let's try again using a filter for only the Sherlock Holmes document."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "7b81d7cae351a1ec"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "possess all knowledge which is likely to be useful to him in his work,\r\n",
+      "and this I have endeavored in my case to do. If I remember rightly, you\r\n",
+      "on one occasion, in the early days of our friendship, defined my limits\r\n",
+      "in a very precise fashion.”\r\n",
+      "\r\n",
+      "“Yes,” I answered, laughing. “It was a singular document. Philosophy,\r\n",
+      "astronomy, and politics were marked at zero, I remember. Botany\r\n",
+      "variable, geology profound as regards the mud-stains from any region  ->  {'source': 'https://www.gutenberg.org/files/48320/48320-0.txt'} \n",
+      "====\n",
+      "\n",
+      "the light shining upon his strong-set aquiline features. So he sat as I\r\n",
+      "dropped off to sleep, and so he sat when a sudden ejaculation caused me\r\n",
+      "to wake up, and I found the summer sun shining into the apartment. The\r\n",
+      "pipe was still between his lips, the smoke still curled upward, and the\r\n",
+      "room was full of a dense tobacco haze, but nothing remained of the heap\r\n",
+      "of shag which I had seen upon the previous night.\r\n",
+      "\r\n",
+      "“Awake, Watson?” he asked.\r\n",
+      "\r\n",
+      "“Yes.”\r\n",
+      "\r\n",
+      "“Game for a morning drive?”  ->  {'source': 'https://www.gutenberg.org/files/48320/48320-0.txt'} \n",
+      "====\n",
+      "\n",
+      "“I glanced at the books upon the table, and in spite of my ignorance\r\n",
+      "of German I could see that two of them were treatises on science, the\r\n",
+      "others being volumes of poetry. Then I walked across to the window,\r\n",
+      "hoping that I might catch some glimpse of the country-side, but an oak\r\n",
+      "shutter, heavily barred, was folded across it. It was a wonderfully\r\n",
+      "silent house. There was an old clock ticking loudly somewhere in the\r\n",
+      "passage, but otherwise everything was deadly still. A vague feeling of  ->  {'source': 'https://www.gutenberg.org/files/48320/48320-0.txt'} \n",
+      "====\n"
+     ]
+    }
+   ],
+   "source": [
+    "filter = {\n",
+    "    \"where\": {\"jsonpath\": \"$[*] ? (@.source == 'https://www.gutenberg.org/files/48320/48320-0.txt')\"},\n",
+    "}\n",
+    "\n",
+    "docs = await vs.asearch(query, search_type=\"similarity\", metadata=filter, k=3)\n",
+    "\n",
+    "for d in docs:\n",
+    "    print(d.page_content, \" -> \", d.metadata, \"\\n====\\n\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2023-08-13T01:08:06.672836Z",
+     "start_time": "2023-08-13T01:08:06.505944Z"
+    }
+   },
+   "id": "8f1bdcba03979d22"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/modules/chains/foundational/router.ipynb b/docs/extras/modules/chains/foundational/router.ipynb
index 88f502fbfe8..c95e613d74d 100644
--- a/docs/extras/modules/chains/foundational/router.ipynb
+++ b/docs/extras/modules/chains/foundational/router.ipynb
@@ -15,7 +15,7 @@
     "- destination_chains: chains that the router chain can route to\n",
     "\n",
     "\n",
-    "In this notebook we will focus on the different types of routing chains. We will show these routing chains used in a `MultiPromptChain` to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt."
+    "In this notebook, we will focus on the different types of routing chains. We will show these routing chains used in a `MultiPromptChain` to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt."
    ]
   },
   {
@@ -231,7 +231,7 @@
     }
    ],
    "source": [
-    "print(chain.run(\"What is the name of the type of cloud that rins\"))"
+    "print(chain.run(\"What is the name of the type of cloud that rains?\"))"
    ]
   },
   {
diff --git a/docs/extras/modules/chains/how_to/openai_functions.ipynb b/docs/extras/modules/chains/how_to/openai_functions.ipynb
index 62e9067beb3..16b2df12910 100644
--- a/docs/extras/modules/chains/how_to/openai_functions.ipynb
+++ b/docs/extras/modules/chains/how_to/openai_functions.ipynb
@@ -83,10 +83,8 @@
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for extracting information in structured formats.\n",
-      "Human: Use the given format to extract information from the following input:\n",
-      "Human: Sally is 13\n",
-      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
-      " {'function_call': {'name': '_OutputFormatter', 'arguments': '{\\n  \"output\": {\\n    \"name\": \"Sally\",\\n    \"age\": 13,\\n    \"fav_food\": \"Unknown\"\\n  }\\n}'}}\n",
+      "Human: Use the given format to extract information from the following input: Sally is 13\n",
+      "Human: Tip: Make sure to answer in the correct format\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -105,18 +103,13 @@
    "source": [
     "# If we pass in a model explicitly, we need to make sure it supports the OpenAI function-calling API.\n",
     "llm = ChatOpenAI(model=\"gpt-4\", temperature=0)\n",
-    "\n",
-    "prompt_msgs = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a world class algorithm for extracting information in structured formats.\"\n",
-    "    ),\n",
-    "    HumanMessage(\n",
-    "        content=\"Use the given format to extract information from the following input:\"\n",
-    "    ),\n",
-    "    HumanMessagePromptTemplate.from_template(\"{input}\"),\n",
-    "    HumanMessage(content=\"Tips: Make sure to answer in the correct format\"),\n",
-    "]\n",
-    "prompt = ChatPromptTemplate(messages=prompt_msgs)\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You are a world class algorithm for extracting information in structured formats.\"),\n",
+    "        (\"human\", \"Use the given format to extract information from the following input: {input}\"),\n",
+    "        (\"human\", \"Tip: Make sure to answer in the correct format\"),\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "chain = create_structured_output_chain(Person, llm, prompt, verbose=True)\n",
     "chain.run(\"Sally is 13\")"
@@ -132,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "4d8ea815",
    "metadata": {},
    "outputs": [
@@ -145,10 +138,8 @@
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for extracting information in structured formats.\n",
-      "Human: Use the given format to extract information from the following input:\n",
-      "Human: Sally is 13, Joey just turned 12 and loves spinach. Caroline is 10 years older than Sally, so she's 23.\n",
-      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
-      " {'function_call': {'name': '_OutputFormatter', 'arguments': '{\\n  \"output\": {\\n    \"people\": [\\n      {\\n        \"name\": \"Sally\",\\n        \"age\": 13,\\n        \"fav_food\": \"\"\\n      },\\n      {\\n        \"name\": \"Joey\",\\n        \"age\": 12,\\n        \"fav_food\": \"spinach\"\\n      },\\n      {\\n        \"name\": \"Caroline\",\\n        \"age\": 23,\\n        \"fav_food\": \"\"\\n      }\\n    ]\\n  }\\n}'}}\n",
+      "Human: Use the given format to extract information from the following input: Sally is 13, Joey just turned 12 and loves spinach. Caroline is 10 years older than Sally.\n",
+      "Human: Tip: Make sure to answer in the correct format\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -159,7 +150,7 @@
        "People(people=[Person(name='Sally', age=13, fav_food=''), Person(name='Joey', age=12, fav_food='spinach'), Person(name='Caroline', age=23, fav_food='')])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -176,7 +167,7 @@
     "\n",
     "chain = create_structured_output_chain(People, llm, prompt, verbose=True)\n",
     "chain.run(\n",
-    "    \"Sally is 13, Joey just turned 12 and loves spinach. Caroline is 10 years older than Sally, so she's 23.\"\n",
+    "    \"Sally is 13, Joey just turned 12 and loves spinach. Caroline is 10 years older than Sally.\"\n",
     ")"
    ]
   },
@@ -229,10 +220,8 @@
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
       "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for extracting information in structured formats.\n",
-      "Human: Use the given format to extract information from the following input:\n",
-      "Human: Sally is 13\n",
-      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
-      " {'function_call': {'name': 'output_formatter', 'arguments': '{\\n  \"name\": \"Sally\",\\n  \"age\": 13\\n}'}}\n",
+      "Human: Use the given format to extract information from the following input: Sally is 13\n",
+      "Human: Tip: Make sure to answer in the correct format\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -279,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "17f52508",
    "metadata": {},
    "outputs": [],
@@ -302,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "a4658ad8",
    "metadata": {},
    "outputs": [
@@ -314,11 +303,9 @@
       "\n",
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities\n",
-      "Human: Make calls to the relevant function to record the entities in the following input:\n",
-      "Human: Harry was a chubby brown beagle who loved chicken\n",
-      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
-      " {'function_call': {'name': 'RecordDog', 'arguments': '{\\n  \"name\": \"Harry\",\\n  \"color\": \"brown\",\\n  \"fav_food\": \"chicken\"\\n}'}}\n",
+      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities.\n",
+      "Human: Make calls to the relevant function to record the entities in the following input: Harry was a chubby brown beagle who loved chicken\n",
+      "Human: Tip: Make sure to answer in the correct format\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -329,21 +316,19 @@
        "RecordDog(name='Harry', color='brown', fav_food='chicken')"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "prompt_msgs = [\n",
-    "    SystemMessage(content=\"You are a world class algorithm for recording entities\"),\n",
-    "    HumanMessage(\n",
-    "        content=\"Make calls to the relevant function to record the entities in the following input:\"\n",
-    "    ),\n",
-    "    HumanMessagePromptTemplate.from_template(\"{input}\"),\n",
-    "    HumanMessage(content=\"Tips: Make sure to answer in the correct format\"),\n",
-    "]\n",
-    "prompt = ChatPromptTemplate(messages=prompt_msgs)\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You are a world class algorithm for recording entities.\"),\n",
+    "        (\"human\", \"Make calls to the relevant function to record the entities in the following input: {input}\"),\n",
+    "        (\"human\", \"Tip: Make sure to answer in the correct format\"),\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "chain = create_openai_fn_chain([RecordPerson, RecordDog], llm, prompt, verbose=True)\n",
     "chain.run(\"Harry was a chubby brown beagle who loved chicken\")"
@@ -362,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "95ac5825",
    "metadata": {},
    "outputs": [
@@ -374,11 +359,9 @@
       "\n",
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities\n",
-      "Human: Make calls to the relevant function to record the entities in the following input:\n",
-      "Human: The most important thing to remember about Tommy, my 12 year old, is that he'll do anything for apple pie.\n",
-      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
-      " {'function_call': {'name': 'record_person', 'arguments': '{\\n  \"name\": \"Tommy\",\\n  \"age\": 12,\\n  \"fav_food\": {\\n    \"food\": \"apple pie\"\\n  }\\n}'}}\n",
+      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities.\n",
+      "Human: Make calls to the relevant function to record the entities in the following input: The most important thing to remember about Tommy, my 12 year old, is that he'll do anything for apple pie.\n",
+      "Human: Tip: Make sure to answer in the correct format\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -389,7 +372,7 @@
        "{'name': 'Tommy', 'age': 12, 'fav_food': {'food': 'apple pie'}}"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -434,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "8b0d11de",
    "metadata": {},
    "outputs": [
@@ -446,11 +429,9 @@
       "\n",
       "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
       "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities\n",
-      "Human: Make calls to the relevant function to record the entities in the following input:\n",
-      "Human: I can't find my dog Henry anywhere, he's a small brown beagle. Could you send a message about him?\n",
-      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
-      " {'function_call': {'name': 'record_dog', 'arguments': '{\\n  \"name\": \"Henry\",\\n  \"color\": \"brown\",\\n  \"fav_food\": {\\n    \"food\": null\\n  }\\n}'}}\n",
+      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities.\n",
+      "Human: Make calls to the relevant function to record the entities in the following input: I can't find my dog Henry anywhere, he's a small brown beagle. Could you send a message about him?\n",
+      "Human: Tip: Make sure to answer in the correct format\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -462,7 +443,7 @@
        " 'arguments': {'name': 'Henry', 'color': 'brown', 'fav_food': {'food': None}}}"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -502,9 +483,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "venv",
+   "display_name": "poetry-venv",
    "language": "python",
-   "name": "venv"
+   "name": "poetry-venv"
   },
   "language_info": {
    "codemirror_mode": {
@@ -516,7 +497,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/extras/modules/data_connection/caching_embeddings.ipynb b/docs/extras/modules/data_connection/caching_embeddings.ipynb
new file mode 100644
index 00000000000..bf87f9e0523
--- /dev/null
+++ b/docs/extras/modules/data_connection/caching_embeddings.ipynb
@@ -0,0 +1,594 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bf4061ce",
+   "metadata": {},
+   "source": [
+    "# Caching Embeddings\n",
+    "\n",
+    "Embeddings can be stored or temporarily cached to avoid needing to recompute them.\n",
+    "\n",
+    "Caching embeddings can be done using a `CacheBackedEmbeddings`.\n",
+    "\n",
+    "The cache backed embedder is a wrapper around an embedder that caches\n",
+    "embeddings in a key-value store. \n",
+    "\n",
+    "The text is hashed and the hash is used as the key in the cache.\n",
+    "\n",
+    "\n",
+    "The main supported way to initialized a `CacheBackedEmbeddings` is `from_bytes_store`. This takes in the following parameters:\n",
+    "\n",
+    "- underlying_embedder: The embedder to use for embedding.\n",
+    "- document_embedding_cache: The cache to use for storing document embeddings.\n",
+    "- namespace: (optional, defaults to `\"\"`) The namespace to use for document cache. This namespace is used to avoid collisions with other caches. For example, set it to the name of the embedding model used.\n",
+    "\n",
+    "**Attention**: Be sure to set the `namespace` parameter to avoid collisions of the same text embedded using different embeddings models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a463c3c2-749b-40d1-a433-84f68a1cd1c7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.storage import InMemoryStore, LocalFileStore, RedisStore\n",
+    "from langchain.embeddings import OpenAIEmbeddings, CacheBackedEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ddf07dd-3e72-41de-99d4-78e9521e272f",
+   "metadata": {},
+   "source": [
+    "## Using with a vectorstore\n",
+    "\n",
+    "First, let's see an example that uses the local file system for storing embeddings and uses FAISS vectorstore for retrieval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9e4314d8-88ef-4f52-81ae-0be771168bb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import FAISS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3e751f26-9b5b-4c10-843a-d784b5ea8538",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "underlying_embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "30743664-38f5-425d-8216-772b64e7f348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fs = LocalFileStore(\"./cache/\")\n",
+    "\n",
+    "cached_embedder = CacheBackedEmbeddings.from_bytes_store(\n",
+    "    underlying_embeddings, fs, namespace=underlying_embeddings.model\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8cdf33c-321d-4d2c-b76b-d6f5f8b42a92",
+   "metadata": {},
+   "source": [
+    "The cache is empty prior to embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f9ad627f-ced2-4277-b336-2434f22f2c8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(fs.yield_keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4effe04-b40f-42f8-a449-72fe6991cf20",
+   "metadata": {},
+   "source": [
+    "Load the document, split it into chunks, embed each chunk and load it into the vector store."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cf958ac2-e60e-4668-b32c-8bb2d78b3c61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_documents = TextLoader(\"../state_of_the_union.txt\").load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "documents = text_splitter.split_documents(raw_documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f526444b-93f8-423f-b6d1-dab539450921",
+   "metadata": {},
+   "source": [
+    "create the vectorstore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a1d7bb8-3b72-4bb5-9013-cf7729caca61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 608 ms, sys: 58.9 ms, total: 667 ms\n",
+      "Wall time: 1.3 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "db = FAISS.from_documents(documents, cached_embedder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64fc53f5-d559-467f-bf62-5daef32ffbc0",
+   "metadata": {},
+   "source": [
+    "If we try to create the vectostore again, it'll be much faster since it does not need to re-compute any embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "714cb2e2-77ba-41a8-bb83-84e75342af2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 33.6 ms, sys: 3.96 ms, total: 37.6 ms\n",
+      "Wall time: 36.8 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "db2 = FAISS.from_documents(documents, cached_embedder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1acc76b9-9c70-4160-b593-5f932c75e2b6",
+   "metadata": {},
+   "source": [
+    "And here are some of the embeddings that got created:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f2ca32dd-3712-4093-942b-4122f3dc8a8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['text-embedding-ada-002614d7cf6-46f1-52fa-9d3a-740c39e7a20e',\n",
+       " 'text-embedding-ada-0020fc1ede2-407a-5e14-8f8f-5642214263f5',\n",
+       " 'text-embedding-ada-002e4ad20ef-dfaa-5916-9459-f90c6d8e8159',\n",
+       " 'text-embedding-ada-002a5ef11e4-0474-5725-8d80-81c91943b37f',\n",
+       " 'text-embedding-ada-00281426526-23fe-58be-9e84-6c7c72c8ca9a']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(fs.yield_keys())[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "564c9801-29f0-4452-aeac-527382e2c0e8",
+   "metadata": {},
+   "source": [
+    "## In Memory\n",
+    "\n",
+    "This section shows how to set up an in memory cache for embeddings. This type of cache is primarily \n",
+    "useful for unit tests or prototyping. Do **not** use this cache if you need to actually store the embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "13bd1c5b-b7ba-4394-957c-7d5b5a841972",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "store = InMemoryStore()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9d99885f-99e1-498c-904d-6db539ac9466",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "underlying_embeddings = OpenAIEmbeddings()\n",
+    "embedder = CacheBackedEmbeddings.from_bytes_store(\n",
+    "    underlying_embeddings, store, namespace=underlying_embeddings.model\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "682eb5d4-0b7a-4dac-b8fb-3de4ca6e421c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 10.9 ms, sys: 916 µs, total: 11.8 ms\n",
+      "Wall time: 159 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "embeddings = embedder.embed_documents([\"hello\", \"goodbye\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95233026-147f-49d1-bd87-e1e8b88ebdbc",
+   "metadata": {},
+   "source": [
+    "The second time we try to embed the embedding time is only 2 ms because the embeddings are looked up in the cache."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f819c3ff-a212-4d06-a5f7-5eb1435c1feb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.67 ms, sys: 342 µs, total: 2.01 ms\n",
+      "Wall time: 2.01 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "embeddings_from_cache = embedder.embed_documents([\"hello\", \"goodbye\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ec38fb72-90a9-4687-a483-c62c87d1f4dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embeddings == embeddings_from_cache"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6cbe100-8587-4830-b207-fb8b524a9854",
+   "metadata": {},
+   "source": [
+    "## File system\n",
+    "\n",
+    "This section covers how to use a file system store."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a0070271-0809-4528-97e0-2a88216846f3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "fs = LocalFileStore(\"./test_cache/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0b20e9fe-f57f-4d7c-9f81-105c5f8726f4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "embedder2 = CacheBackedEmbeddings.from_bytes_store(\n",
+    "    underlying_embeddings, fs, namespace=underlying_embeddings.model\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "630515fd-bf5c-4d9c-a404-9705308f3a2c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6.89 ms, sys: 4.89 ms, total: 11.8 ms\n",
+      "Wall time: 184 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "embeddings = embedder2.embed_documents([\"hello\", \"goodbye\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "30e6bb87-42c9-4d08-88ac-0d22c9c449a1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 0 ns, sys: 3.24 ms, total: 3.24 ms\n",
+      "Wall time: 2.84 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "embeddings = embedder2.embed_documents([\"hello\", \"goodbye\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12ed5a45-8352-4e0f-8583-5537397f53c0",
+   "metadata": {},
+   "source": [
+    "Here are the embeddings that have been persisted to the directory `./test_cache`. \n",
+    "\n",
+    "Notice that the embedder takes a namespace parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "658e2914-05e9-44a3-a8fe-3fe17ca84039",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['text-embedding-ada-002e885db5b-c0bd-5fbc-88b1-4d1da6020aa5',\n",
+       " 'text-embedding-ada-0026ba52e44-59c9-5cc9-a084-284061b13c80']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(fs.yield_keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd5f5a96-6ffa-429d-aa82-00b3f6532871",
+   "metadata": {},
+   "source": [
+    "## Redis Store\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "4879c134-141f-48a0-acfe-7d6f30253af0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.storage import RedisStore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8b2bb9a0-6549-4487-8532-29ab4ab7336f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For cache isolation can use a separate DB\n",
+    "# Or additional namepace\n",
+    "store = RedisStore(redis_url=\"redis://localhost:6379\", client_kwargs={'db': 2}, namespace='embedding_caches')\n",
+    "\n",
+    "underlying_embeddings = OpenAIEmbeddings()\n",
+    "embedder = CacheBackedEmbeddings.from_bytes_store(\n",
+    "    underlying_embeddings, store, namespace=underlying_embeddings.model\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "eca3cb99-2bb3-49d5-81f9-1dee03da4b8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3.99 ms, sys: 0 ns, total: 3.99 ms\n",
+      "Wall time: 3.5 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "embeddings = embedder.embed_documents([\"hello\", \"goodbye\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "317ba5d8-89f9-462c-b807-ad4ef26e518b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2.47 ms, sys: 767 µs, total: 3.24 ms\n",
+      "Wall time: 2.75 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "embeddings = embedder.embed_documents([\"hello\", \"goodbye\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8a540317-5142-4491-9062-a097932b56e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['text-embedding-ada-002e885db5b-c0bd-5fbc-88b1-4d1da6020aa5',\n",
+       " 'text-embedding-ada-0026ba52e44-59c9-5cc9-a084-284061b13c80']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(store.yield_keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cd9b0d4a-f816-4dce-9dde-cde1ad9a65fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[b'embedding_caches/text-embedding-ada-002e885db5b-c0bd-5fbc-88b1-4d1da6020aa5',\n",
+       " b'embedding_caches/text-embedding-ada-0026ba52e44-59c9-5cc9-a084-284061b13c80']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(store.client.scan_iter())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/modules/data_connection/retrievers/self_query/deeplake_self_query.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query.ipynb
similarity index 93%
rename from docs/extras/modules/data_connection/retrievers/self_query/deeplake_self_query.ipynb
rename to docs/extras/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query.ipynb
index 1592f4693bd..b284889d07c 100644
--- a/docs/extras/modules/data_connection/retrievers/self_query/deeplake_self_query.ipynb
+++ b/docs/extras/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query.ipynb
@@ -6,7 +6,7 @@
    "id": "13afcae7",
    "metadata": {},
    "source": [
-    "# DeepLake self-querying \n",
+    "# Deep Lake self-querying \n",
     "\n",
     ">[DeepLake](https://www.activeloop.ai) is a multimodal database for building AI applications.\n",
     "\n",
@@ -19,7 +19,7 @@
    "id": "68e75fb9",
    "metadata": {},
    "source": [
-    "## Creating a DeepLake vectorstore\n",
+    "## Creating a Deep Lake vectorstore\n",
     "First we'll want to create a DeepLake VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
     "\n",
     "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `deeplake` package."
@@ -27,26 +27,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "63a8af5b",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": null,
+   "id": "a798fe66",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install lark"
+    "# !pip install lark"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "22431060-52c4-48a7-a97b-9f542b8b0928",
-   "metadata": {
-    "tags": []
-   },
+   "id": "43821a8e",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install 'deeplake[enterprise]'"
+    "# in case if some queries fail consider installing libdeeplake manually\n",
+    "# !pip install libdeeplake"
    ]
   },
   {
@@ -60,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "dd01b61b-7d32-4a55-85d6-b2d2d4f18840",
    "metadata": {
     "tags": []
@@ -70,12 +67,13 @@
     "import os\n",
     "import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
+    "os.environ[\"ACTIVELOOP_TOKEN\"] = getpass.getpass(\"Activeloop token:\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "cb4a5787",
    "metadata": {
     "tags": []
@@ -108,7 +106,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "-"
+      "/"
      ]
     },
     {
@@ -166,9 +164,9 @@
     "        },\n",
     "    ),\n",
     "]\n",
-    "username_or_org = \"<USER_NAME_OR_ORG>\"\n",
+    "username_or_org = \"<USERNAME_OR_ORG>\"\n",
     "vectorstore = DeepLake.from_documents(\n",
-    "    docs, embeddings, dataset_path=f\"hub://{username_or_org}/self_queery\"\n",
+    "    docs, embeddings, dataset_path=f\"hub://{username_or_org}/self_queery\", overwrite=True,\n",
     ")"
    ]
   },
@@ -185,10 +183,8 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "86e34dbf",
-   "metadata": {
-    "tags": []
-   },
+   "id": "c90b0b40",
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.llms import OpenAI\n",
@@ -242,7 +238,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/adilkhansarsen/Documents/work/LangChain/langchain/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n",
+      "/home/ubuntu/langchain_activeloop/langchain/libs/langchain/langchain/chains/llm.py:279: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -299,7 +295,9 @@
    ],
    "source": [
     "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
+    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")\n",
+    "\n",
+    "# in case if this example errored out, consider installing libdeeplake manually: `pip install libdeeplake`, and then restart notebook."
    ]
   },
   {
@@ -456,14 +454,6 @@
     "# This example only specifies a relevant query\n",
     "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c93f0847-cbd9-4c25-aed1-91588e856b5c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -482,7 +472,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/extras/modules/data_connection/retrievers/self_query/elasticsearch_self_query.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/elasticsearch_self_query.ipynb
new file mode 100644
index 00000000000..8a24fb1a9bf
--- /dev/null
+++ b/docs/extras/modules/data_connection/retrievers/self_query/elasticsearch_self_query.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "13afcae7",
+   "metadata": {},
+   "source": [
+    "# Elasticsearch self-querying "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68e75fb9",
+   "metadata": {},
+   "source": [
+    "## Creating a Elasticsearch vectorstore\n",
+    "First we'll want to create a Elasticsearch VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
+    "\n",
+    "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `elasticsearch` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "63a8af5b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install lark elasticsearch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb4a5787",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.schema import Document\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import ElasticsearchStore\n",
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bcbe04d9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "docs = [\n",
+    "    Document(\n",
+    "        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
+    "        metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
+    "        metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
+    "        metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
+    "        metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Toys come alive and have a blast doing so\",\n",
+    "        metadata={\"year\": 1995, \"genre\": \"animated\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
+    "        metadata={\n",
+    "            \"year\": 1979,\n",
+    "            \"rating\": 9.9,\n",
+    "            \"director\": \"Andrei Tarkovsky\",\n",
+    "            \"genre\": \"science fiction\",\n",
+    "            \"rating\": 9.9,\n",
+    "        },\n",
+    "    ),\n",
+    "]\n",
+    "vectorstore = ElasticsearchStore.from_documents(\n",
+    "    docs, embeddings, index_name=\"elasticsearch-self-query-demo\", es_url=\"http://localhost:9200\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ecaab6d",
+   "metadata": {},
+   "source": [
+    "## Creating our self-querying retriever\n",
+    "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "86e34dbf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
+    "from langchain.chains.query_constructor.base import AttributeInfo\n",
+    "\n",
+    "metadata_field_info = [\n",
+    "    AttributeInfo(\n",
+    "        name=\"genre\",\n",
+    "        description=\"The genre of the movie\",\n",
+    "        type=\"string or list[string]\",\n",
+    "    ),\n",
+    "    AttributeInfo(\n",
+    "        name=\"year\",\n",
+    "        description=\"The year the movie was released\",\n",
+    "        type=\"integer\",\n",
+    "    ),\n",
+    "    AttributeInfo(\n",
+    "        name=\"director\",\n",
+    "        description=\"The name of the movie director\",\n",
+    "        type=\"string\",\n",
+    "    ),\n",
+    "    AttributeInfo(\n",
+    "        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
+    "    ),\n",
+    "]\n",
+    "document_content_description = \"Brief summary of a movie\"\n",
+    "llm = OpenAI(temperature=0)\n",
+    "retriever = SelfQueryRetriever.from_llm(\n",
+    "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea9df8d4",
+   "metadata": {},
+   "source": [
+    "## Testing it out\n",
+    "And now we can try actually using our retriever!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "38a126e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='dinosaur' filter=None limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),\n",
+       " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n",
+       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'}),\n",
+       " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6})]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example only specifies a relevant query\n",
+    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b19d4da0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3})]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example specifies a query and a filter\n",
+    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
+   "metadata": {},
+   "source": [
+    "## Filter k\n",
+    "\n",
+    "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
+    "\n",
+    "We can do this by passing `enable_limit=True` to the constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bff36b88-b506-4877-9c63-e5a1a8d78e64",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "retriever = SelfQueryRetriever.from_llm(\n",
+    "    llm,\n",
+    "    vectorstore,\n",
+    "    document_content_description,\n",
+    "    metadata_field_info,\n",
+    "    enable_limit=True,\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2758d229-4f97-499c-819f-888acaf8ee10",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='dinosaur' filter=None limit=2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),\n",
+       " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example only specifies a relevant query\n",
+    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61a10294",
+   "metadata": {},
+   "source": [
+    "## Complex queries in Action!\n",
+    "We've tried out some simple queries, but what about more complex ones? Let's try out a few more complex queries that utilize the full power of Elasticsearch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e460da93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='animated toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Operation(operator=<Operator.OR: 'or'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated'), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='comedy')]), Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='year', value=1990)]) limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retriever.get_relevant_documents(\"what animated or comedy movies have been released in the last 30 years about animated toys?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0851fc42",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ObjectApiResponse({'acknowledged': True})"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vectorstore.client.indices.delete(index=\"elasticsearch-self-query-demo\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/use_cases/agent_simulations/index.mdx b/docs/extras/use_cases/agent_simulations/index.mdx
index c6af033e53f..4232231b85d 100644
--- a/docs/extras/use_cases/agent_simulations/index.mdx
+++ b/docs/extras/use_cases/agent_simulations/index.mdx
@@ -1,3 +1,7 @@
+---
+sidebar_position: 0
+---
+
 # Agent simulations
 
 Agent simulations involve interacting one of more agents with each other.
diff --git a/docs/extras/use_cases/agents/index.mdx b/docs/extras/use_cases/agents/index.mdx
index 7a6b4a36a28..6c4da5f0a7a 100644
--- a/docs/extras/use_cases/agents/index.mdx
+++ b/docs/extras/use_cases/agents/index.mdx
@@ -1,3 +1,7 @@
+---
+sidebar_position: 0
+---
+
 # Agents
 
 Agents can be used for a variety of tasks.
diff --git a/docs/extras/use_cases/agents/wikibase_agent.ipynb b/docs/extras/use_cases/agents/wikibase_agent.ipynb
index 86fdca69de0..2b3fe21d9f4 100644
--- a/docs/extras/use_cases/agents/wikibase_agent.ipynb
+++ b/docs/extras/use_cases/agents/wikibase_agent.ipynb
@@ -123,7 +123,7 @@
     "wikidata_user_agent_header = (\n",
     "    None\n",
     "    if not config.has_section(\"WIKIDATA\")\n",
-    "    else config[\"WIKIDATA\"][\"WIKIDAtA_USER_AGENT_HEADER\"]\n",
+    "    else config[\"WIKIDATA\"][\"WIKIDATA_USER_AGENT_HEADER\"]\n",
     ")"
    ]
   },
diff --git a/docs/extras/use_cases/apis.ipynb b/docs/extras/use_cases/apis.ipynb
new file mode 100644
index 00000000000..dba17c1c01c
--- /dev/null
+++ b/docs/extras/use_cases/apis.ipynb
@@ -0,0 +1,423 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a15e6a18",
+   "metadata": {},
+   "source": [
+    "# Interacting with APIs\n",
+    "\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/apis.ipynb)\n",
+    "\n",
+    "## Use case \n",
+    "\n",
+    "Suppose you want an LLM to interact with external APIs.\n",
+    "\n",
+    "This can be very useful for retrieving context for the LLM to utilize.\n",
+    "\n",
+    "And, more generally, it allows us to interact with APIs using natural langugage! \n",
+    " \n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "There are two primary ways to interface LLMs with external APIs:\n",
+    " \n",
+    "* `Functions`: For example, [OpenAI functions](https://platform.openai.com/docs/guides/gpt/function-calling) is one popular means of doing this.\n",
+    "* `LLM-generated interface`: Use an LLM with access to API documentation to create an interface.\n",
+    "\n",
+    "![Image description](/img/api_use_case.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abbd82f0",
+   "metadata": {},
+   "source": [
+    "## Quickstart \n",
+    "\n",
+    "Many APIs are already compatible with OpenAI function calling.\n",
+    "\n",
+    "For example, [Klarna](https://www.klarna.com/international/press/klarna-brings-smoooth-shopping-to-chatgpt/) has a YAML file that describes its API and allows OpenAI to interact with it:\n",
+    "\n",
+    "```\n",
+    "https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\n",
+    "```\n",
+    "\n",
+    "Other options include:\n",
+    "\n",
+    "* [Speak](https://api.speak.com/openapi.yaml) for translation\n",
+    "* [XKCD](https://gist.githubusercontent.com/roaldnefs/053e505b2b7a807290908fe9aa3e1f00/raw/0a212622ebfef501163f91e23803552411ed00e4/openapi.yaml) for comics\n",
+    "\n",
+    "We can supply the specification to `get_openapi_chain` directly in order to query the API with OpenAI functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a218fcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install langchain openai \n",
+    "\n",
+    "# Set env var OPENAI_API_KEY or load from a .env file:\n",
+    "# import dotenv\n",
+    "# dotenv.load_env()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "30b780e3",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'query': \"What are some options for a men's large blue button down shirt\",\n",
+       " 'response': {'products': [{'name': 'Cubavera Four Pocket Guayabera Shirt',\n",
+       "    'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202055522/Clothing/Cubavera-Four-Pocket-Guayabera-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
+       "    'price': '$13.50',\n",
+       "    'attributes': ['Material:Polyester,Cotton',\n",
+       "     'Target Group:Man',\n",
+       "     'Color:Red,White,Blue,Black',\n",
+       "     'Properties:Pockets',\n",
+       "     'Pattern:Solid Color',\n",
+       "     'Size (Small-Large):S,XL,L,M,XXL']},\n",
+       "   {'name': 'Polo Ralph Lauren Plaid Short Sleeve Button-down Oxford Shirt',\n",
+       "    'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3207163438/Clothing/Polo-Ralph-Lauren-Plaid-Short-Sleeve-Button-down-Oxford-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
+       "    'price': '$52.20',\n",
+       "    'attributes': ['Material:Cotton',\n",
+       "     'Target Group:Man',\n",
+       "     'Color:Red,Blue,Multicolor',\n",
+       "     'Size (Small-Large):S,XL,L,M,XXL']},\n",
+       "   {'name': 'Brixton Bowery Flannel Shirt',\n",
+       "    'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202331096/Clothing/Brixton-Bowery-Flannel-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
+       "    'price': '$27.48',\n",
+       "    'attributes': ['Material:Cotton',\n",
+       "     'Target Group:Man',\n",
+       "     'Color:Gray,Blue,Black,Orange',\n",
+       "     'Properties:Pockets',\n",
+       "     'Pattern:Checkered',\n",
+       "     'Size (Small-Large):XL,3XL,4XL,5XL,L,M,XXL']},\n",
+       "   {'name': 'Vineyard Vines Gingham On-The-Go brrr Classic Fit Shirt Crystal',\n",
+       "    'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3201938510/Clothing/Vineyard-Vines-Gingham-On-The-Go-brrr-Classic-Fit-Shirt-Crystal/?utm_source=openai&ref-site=openai_plugin',\n",
+       "    'price': '$80.64',\n",
+       "    'attributes': ['Material:Cotton',\n",
+       "     'Target Group:Man',\n",
+       "     'Color:Blue',\n",
+       "     'Size (Small-Large):XL,XS,L,M']},\n",
+       "   {'name': \"Carhartt Men's Loose Fit Midweight Short Sleeve Plaid Shirt\",\n",
+       "    'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3201826024/Clothing/Carhartt-Men-s-Loose-Fit-Midweight-Short-Sleeve-Plaid-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
+       "    'price': '$17.99',\n",
+       "    'attributes': ['Material:Cotton',\n",
+       "     'Target Group:Man',\n",
+       "     'Color:Red,Brown,Blue,Green',\n",
+       "     'Properties:Pockets',\n",
+       "     'Pattern:Checkered',\n",
+       "     'Size (Small-Large):S,XL,L,M']}]}}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.chains.openai_functions.openapi import get_openapi_chain\n",
+    "chain = get_openapi_chain(\"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\")\n",
+    "chain(\"What are some options for a men's large blue button down shirt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9162c91c",
+   "metadata": {},
+   "source": [
+    "## Functions \n",
+    "\n",
+    "We can unpack what is hapening when we use the funtions to calls external APIs.\n",
+    "\n",
+    "Let's look at the [LangSmith trace](https://smith.langchain.com/public/76a58b85-193f-4eb7-ba40-747f0d5dd56e/r):\n",
+    "\n",
+    "* See [here](https://github.com/langchain-ai/langchain/blob/7fc07ba5df99b9fa8bef837b0fafa220bc5c932c/libs/langchain/langchain/chains/openai_functions/openapi.py#L279C9-L279C19) that we call the OpenAI LLM with the provided API spec:\n",
+    "\n",
+    "```\n",
+    "https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\n",
+    "```\n",
+    "\n",
+    "* The prompt then tells the LLM to use the API spec wiith input question:\n",
+    "\n",
+    "```\n",
+    "Use the provided API's to respond to this user query:\n",
+    "What are some options for a men's large blue button down shirt\n",
+    "```\n",
+    "\n",
+    "* The LLM returns the parameters for the function call `productsUsingGET`, which is [specified in the provided API spec](https://www.klarna.com/us/shopping/public/openai/v0/api-docs/):\n",
+    "```\n",
+    "function_call:\n",
+    "  name: productsUsingGET\n",
+    "  arguments: |-\n",
+    "    {\n",
+    "      \"params\": {\n",
+    "        \"countryCode\": \"US\",\n",
+    "        \"q\": \"men's large blue button down shirt\",\n",
+    "        \"size\": 5,\n",
+    "        \"min_price\": 0,\n",
+    "        \"max_price\": 100\n",
+    "      }\n",
+    "    }\n",
+    " ```\n",
+    " \n",
+    "![Image description](/img/api_function_call.png)\n",
+    " \n",
+    "* This `Dict` above split and the [API is called here](https://github.com/langchain-ai/langchain/blob/7fc07ba5df99b9fa8bef837b0fafa220bc5c932c/libs/langchain/langchain/chains/openai_functions/openapi.py#L215)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fe49a0d",
+   "metadata": {},
+   "source": [
+    "## API Chain \n",
+    "\n",
+    "We can also build our own interface to external APIs using the `APIChain` and provided API documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4ef0c3d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new APIChain chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mhttps://api.open-meteo.com/v1/forecast?latitude=48.1351&longitude=11.5820&hourly=temperature_2m&temperature_unit=fahrenheit&current_weather=true\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3m{\"latitude\":48.14,\"longitude\":11.58,\"generationtime_ms\":1.0769367218017578,\"utc_offset_seconds\":0,\"timezone\":\"GMT\",\"timezone_abbreviation\":\"GMT\",\"elevation\":521.0,\"current_weather\":{\"temperature\":52.9,\"windspeed\":12.6,\"winddirection\":239.0,\"weathercode\":3,\"is_day\":0,\"time\":\"2023-08-07T22:00\"},\"hourly_units\":{\"time\":\"iso8601\",\"temperature_2m\":\"°F\"},\"hourly\":{\"time\":[\"2023-08-07T00:00\",\"2023-08-07T01:00\",\"2023-08-07T02:00\",\"2023-08-07T03:00\",\"2023-08-07T04:00\",\"2023-08-07T05:00\",\"2023-08-07T06:00\",\"2023-08-07T07:00\",\"2023-08-07T08:00\",\"2023-08-07T09:00\",\"2023-08-07T10:00\",\"2023-08-07T11:00\",\"2023-08-07T12:00\",\"2023-08-07T13:00\",\"2023-08-07T14:00\",\"2023-08-07T15:00\",\"2023-08-07T16:00\",\"2023-08-07T17:00\",\"2023-08-07T18:00\",\"2023-08-07T19:00\",\"2023-08-07T20:00\",\"2023-08-07T21:00\",\"2023-08-07T22:00\",\"2023-08-07T23:00\",\"2023-08-08T00:00\",\"2023-08-08T01:00\",\"2023-08-08T02:00\",\"2023-08-08T03:00\",\"2023-08-08T04:00\",\"2023-08-08T05:00\",\"2023-08-08T06:00\",\"2023-08-08T07:00\",\"2023-08-08T08:00\",\"2023-08-08T09:00\",\"2023-08-08T10:00\",\"2023-08-08T11:00\",\"2023-08-08T12:00\",\"2023-08-08T13:00\",\"2023-08-08T14:00\",\"2023-08-08T15:00\",\"2023-08-08T16:00\",\"2023-08-08T17:00\",\"2023-08-08T18:00\",\"2023-08-08T19:00\",\"2023-08-08T20:00\",\"2023-08-08T21:00\",\"2023-08-08T22:00\",\"2023-08-08T23:00\",\"2023-08-09T00:00\",\"2023-08-09T01:00\",\"2023-08-09T02:00\",\"2023-08-09T03:00\",\"2023-08-09T04:00\",\"2023-08-09T05:00\",\"2023-08-09T06:00\",\"2023-08-09T07:00\",\"2023-08-09T08:00\",\"2023-08-09T09:00\",\"2023-08-09T10:00\",\"2023-08-09T11:00\",\"2023-08-09T12:00\",\"2023-08-09T13:00\",\"2023-08-09T14:00\",\"2023-08-09T15:00\",\"2023-08-09T16:00\",\"2023-08-09T17:00\",\"2023-08-09T18:00\",\"2023-08-09T19:00\",\"2023-08-09T20:00\",\"2023-08-09T21:00\",\"2023-08-09T22:00\",\"2023-08-09T23:00\",\"2023-08-10T00:00\",\"2023-08-10T01:00\",\"2023-08-10T02:00\",\"2023-08-10T03:00\",\"2023-08-10T04:00\",\"2023-08-10T05:00\",\"2023-08-10T06:00\",\"2023-08-10T07:00\",\"2023-08-10T08:00\",\"2023-08-10T09:00\",\"2023-08-10T10:00\",\"2023-08-10T11:00\",\"2023-08-10T12:00\",\"2023-08-10T13:00\",\"2023-08-10T14:00\",\"2023-08-10T15:00\",\"2023-08-10T16:00\",\"2023-08-10T17:00\",\"2023-08-10T18:00\",\"2023-08-10T19:00\",\"2023-08-10T20:00\",\"2023-08-10T21:00\",\"2023-08-10T22:00\",\"2023-08-10T23:00\",\"2023-08-11T00:00\",\"2023-08-11T01:00\",\"2023-08-11T02:00\",\"2023-08-11T03:00\",\"2023-08-11T04:00\",\"2023-08-11T05:00\",\"2023-08-11T06:00\",\"2023-08-11T07:00\",\"2023-08-11T08:00\",\"2023-08-11T09:00\",\"2023-08-11T10:00\",\"2023-08-11T11:00\",\"2023-08-11T12:00\",\"2023-08-11T13:00\",\"2023-08-11T14:00\",\"2023-08-11T15:00\",\"2023-08-11T16:00\",\"2023-08-11T17:00\",\"2023-08-11T18:00\",\"2023-08-11T19:00\",\"2023-08-11T20:00\",\"2023-08-11T21:00\",\"2023-08-11T22:00\",\"2023-08-11T23:00\",\"2023-08-12T00:00\",\"2023-08-12T01:00\",\"2023-08-12T02:00\",\"2023-08-12T03:00\",\"2023-08-12T04:00\",\"2023-08-12T05:00\",\"2023-08-12T06:00\",\"2023-08-12T07:00\",\"2023-08-12T08:00\",\"2023-08-12T09:00\",\"2023-08-12T10:00\",\"2023-08-12T11:00\",\"2023-08-12T12:00\",\"2023-08-12T13:00\",\"2023-08-12T14:00\",\"2023-08-12T15:00\",\"2023-08-12T16:00\",\"2023-08-12T17:00\",\"2023-08-12T18:00\",\"2023-08-12T19:00\",\"2023-08-12T20:00\",\"2023-08-12T21:00\",\"2023-08-12T22:00\",\"2023-08-12T23:00\",\"2023-08-13T00:00\",\"2023-08-13T01:00\",\"2023-08-13T02:00\",\"2023-08-13T03:00\",\"2023-08-13T04:00\",\"2023-08-13T05:00\",\"2023-08-13T06:00\",\"2023-08-13T07:00\",\"2023-08-13T08:00\",\"2023-08-13T09:00\",\"2023-08-13T10:00\",\"2023-08-13T11:00\",\"2023-08-13T12:00\",\"2023-08-13T13:00\",\"2023-08-13T14:00\",\"2023-08-13T15:00\",\"2023-08-13T16:00\",\"2023-08-13T17:00\",\"2023-08-13T18:00\",\"2023-08-13T19:00\",\"2023-08-13T20:00\",\"2023-08-13T21:00\",\"2023-08-13T22:00\",\"2023-08-13T23:00\"],\"temperature_2m\":[53.0,51.2,50.9,50.4,50.7,51.3,51.7,52.9,54.3,56.1,57.4,59.3,59.1,60.7,59.7,58.8,58.8,57.8,56.6,55.3,53.9,52.7,52.9,53.2,52.0,51.8,51.3,50.7,50.8,51.5,53.9,57.7,61.2,63.2,64.7,66.6,67.5,67.0,68.7,68.7,67.9,66.2,64.4,61.4,59.8,58.9,57.9,56.3,55.7,55.3,55.5,55.4,55.7,56.5,57.6,58.8,59.7,59.1,58.9,60.6,59.9,59.8,59.9,61.7,63.2,63.6,62.3,58.9,57.3,57.1,57.0,56.5,56.2,56.0,55.3,54.7,54.4,55.2,57.8,60.7,63.0,65.3,66.9,68.2,70.1,72.1,72.6,71.4,69.7,68.6,66.2,63.6,61.8,60.6,59.6,58.9,58.0,57.1,56.3,56.2,56.7,57.9,59.9,63.7,68.4,72.4,75.0,76.8,78.0,78.7,78.9,78.4,76.9,74.8,72.5,70.1,67.6,65.6,64.4,63.9,63.4,62.7,62.2,62.1,62.5,63.4,65.1,68.0,71.7,74.8,76.8,78.2,79.1,79.6,79.7,79.2,77.6,75.3,73.7,68.6,66.8,65.3,64.2,63.4,62.6,61.7,60.9,60.6,60.9,61.6,63.2,65.9,69.3,72.2,74.4,76.2,77.6,78.8,79.6,79.6,78.4,76.4,74.3,72.3,70.4,68.7,67.6,66.8]}}\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' The current temperature in Munich, Germany is 52.9°F.'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.chains import APIChain\n",
+    "from langchain.chains.api import open_meteo_docs\n",
+    "llm = OpenAI(temperature=0)\n",
+    "chain = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True)\n",
+    "chain.run('What is the weather like right now in Munich, Germany in degrees Fahrenheit?')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b179318",
+   "metadata": {},
+   "source": [
+    "Note that we supply information about the API:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a9e03cc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'BASE URL: https://api.open-meteo.com/\\n\\nAPI Documentation\\nThe API endpoint /v1/forecast accepts a geographical coordinate, a list of weather variables and responds with a JSON hourly weather forecast for 7 days. Time always starts at 0:00 today and contains 168 hours. All URL parameters are listed below:\\n\\nParameter\\tFormat\\tRequired\\tDefault\\tDescription\\nlatitude, longitude\\tFloating point\\tYes\\t\\tGeographical WGS84 coordinate of the location\\nhourly\\tString array\\tNo\\t\\tA list of weather variables which shou'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "open_meteo_docs.OPEN_METEO_DOCS[0:500]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fab7930",
+   "metadata": {},
+   "source": [
+    "Under the hood, we do two things:\n",
+    "    \n",
+    "* `api_request_chain`: Generate an API URL based on the input question and the api_docs\n",
+    "* `api_answer_chain`: generate a final answer based on the API response\n",
+    "\n",
+    "We can look at the [LangSmith trace](https://smith.langchain.com/public/1e0d18ca-0d76-444c-97df-a939a6a815a7/r) to inspect this:\n",
+    "\n",
+    "* The `api_request_chain` produces the API url from our question and the API documentation:\n",
+    "\n",
+    "![Image description](/img/api_chain.png)\n",
+    "\n",
+    "* [Here](https://github.com/langchain-ai/langchain/blob/bbd22b9b761389a5e40fc45b0570e1830aabb707/libs/langchain/langchain/chains/api/base.py#L82) we make the API request with the API url.\n",
+    "* The `api_answer_chain` takes the response from the API and provides us with a natural langugae response:\n",
+    "\n",
+    "![Image description](/img/api_chain_response.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2511f446",
+   "metadata": {},
+   "source": [
+    "### Going deeper\n",
+    "\n",
+    "**Test with other APIs**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e1cf418",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['TMDB_BEARER_TOKEN'] = \"\"\n",
+    "from langchain.chains.api import tmdb_docs\n",
+    "headers = {\"Authorization\": f\"Bearer {os.environ['TMDB_BEARER_TOKEN']}\"}\n",
+    "chain = APIChain.from_llm_and_api_docs(llm, tmdb_docs.TMDB_DOCS, headers=headers, verbose=True)\n",
+    "chain.run(\"Search for 'Avatar'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd80a717",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.chains.api import podcast_docs\n",
+    "from langchain.chains import APIChain\n",
+    " \n",
+    "listen_api_key = 'xxx' # Get api key here: https://www.listennotes.com/api/pricing/\n",
+    "llm = OpenAI(temperature=0)\n",
+    "headers = {\"X-ListenAPI-Key\": listen_api_key}\n",
+    "chain = APIChain.from_llm_and_api_docs(llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True)\n",
+    "chain.run(\"Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5939be5",
+   "metadata": {},
+   "source": [
+    "**Web requests**\n",
+    "\n",
+    "URL requets are such a common use-case that we have the `LLMRequestsChain`, which makes a HTTP GET request. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "0b158296",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import LLMRequestsChain, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "d49c33e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Between >>> and <<< are the raw search result text from google.\n",
+    "Extract the answer to the question '{query}' or say \"not found\" if the information is not contained.\n",
+    "Use the format\n",
+    "Extracted:<answer or \"not found\">\n",
+    ">>> {requests_result} <<<\n",
+    "Extracted:\"\"\"\n",
+    "\n",
+    "PROMPT = PromptTemplate(\n",
+    "    input_variables=[\"query\", \"requests_result\"],\n",
+    "    template=template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "d0fd4aab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'query': 'What are the Three (3) biggest countries, and their respective sizes?',\n",
+       " 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',\n",
+       " 'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), China (9,706,961 km²)'}"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain = LLMRequestsChain(llm_chain=LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))\n",
+    "question = \"What are the Three (3) biggest countries, and their respective sizes?\"\n",
+    "inputs = {\n",
+    "    \"query\": question,\n",
+    "    \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\"),\n",
+    "}\n",
+    "chain(inputs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/use_cases/apis/index.mdx b/docs/extras/use_cases/apis/index.mdx
deleted file mode 100644
index c5f3c12932e..00000000000
--- a/docs/extras/use_cases/apis/index.mdx
+++ /dev/null
@@ -1,24 +0,0 @@
----
-sidebar_position: 3
----
-
-# Interacting with APIs
-
-Lots of data and information is stored behind APIs.
-This page covers all resources available in LangChain for working with APIs.
-
-## Chains
-
-If you are just getting started, and you have relatively simple apis, you should get started with chains.
-Chains are a sequence of predetermined steps, so they are good to get started with as they give you more control and let you 
-understand what is happening better.
-
-- [API Chain](/docs/use_cases/apis/api.html)
-
-## Agents
-
-Agents are more complex, and involve multiple queries to the LLM to understand what to do.
-The downside of agents are that you have less control. The upside is that they are more powerful,
-which allows you to use them on larger and more complex schemas. 
-
-- [OpenAPI Agent](/docs/integrations/toolkits/openapi.html)
diff --git a/docs/extras/use_cases/apis/llm_requests.ipynb b/docs/extras/use_cases/apis/llm_requests.ipynb
deleted file mode 100644
index a5bbe64ce39..00000000000
--- a/docs/extras/use_cases/apis/llm_requests.ipynb
+++ /dev/null
@@ -1,123 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "dd7ec7af",
-   "metadata": {},
-   "source": [
-    "# HTTP request chain\n",
-    "\n",
-    "Using the request library to get HTML results from a URL and then an LLM to parse results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "dd8eae75",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.chains import LLMRequestsChain, LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "65bf324e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "\n",
-    "template = \"\"\"Between >>> and <<< are the raw search result text from google.\n",
-    "Extract the answer to the question '{query}' or say \"not found\" if the information is not contained.\n",
-    "Use the format\n",
-    "Extracted:<answer or \"not found\">\n",
-    ">>> {requests_result} <<<\n",
-    "Extracted:\"\"\"\n",
-    "\n",
-    "PROMPT = PromptTemplate(\n",
-    "    input_variables=[\"query\", \"requests_result\"],\n",
-    "    template=template,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f36ae0d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = LLMRequestsChain(llm_chain=LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b5d22d9d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"What are the Three (3) biggest countries, and their respective sizes?\"\n",
-    "inputs = {\n",
-    "    \"query\": question,\n",
-    "    \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\"),\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2ea81168",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'query': 'What are the Three (3) biggest countries, and their respective sizes?',\n",
-       " 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',\n",
-       " 'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), United States (9,826,675 km²)'}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db8f2b6d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/extras/use_cases/apis/openai_openapi.yaml b/docs/extras/use_cases/apis/openai_openapi.yaml
deleted file mode 100644
index 8962cccc777..00000000000
--- a/docs/extras/use_cases/apis/openai_openapi.yaml
+++ /dev/null
@@ -1,3650 +0,0 @@
-openapi: 3.0.0
-info:
-  title: OpenAI API
-  description: APIs for sampling from and fine-tuning language models
-  version: '1.2.0'
-servers:
-  - url: https://api.openai.com/v1
-tags:
-- name: OpenAI
-  description: The OpenAI REST API
-paths:
-  /engines:
-    get:
-      operationId: listEngines
-      deprecated: true
-      tags:
-      - OpenAI
-      summary: Lists the currently available (non-finetuned) models, and provides basic information about each one such as the owner and availability.
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/ListEnginesResponse'
-      x-oaiMeta:
-        name: List engines
-        group: engines
-        path: list
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/engines \
-              -H 'Authorization: Bearer YOUR_API_KEY'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Engine.list()
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.listEngines();
-        response: |
-          {
-            "data": [
-              {
-                "id": "engine-id-0",
-                "object": "engine",
-                "owner": "organization-owner",
-                "ready": true
-              },
-              {
-                "id": "engine-id-2",
-                "object": "engine",
-                "owner": "organization-owner",
-                "ready": true
-              },
-              {
-                "id": "engine-id-3",
-                "object": "engine",
-                "owner": "openai",
-                "ready": false
-              },
-            ],
-            "object": "list"
-          }
-
-  /engines/{engine_id}:
-    get:
-      operationId: retrieveEngine
-      deprecated: true
-      tags:
-      - OpenAI
-      summary: Retrieves a model instance, providing basic information about it such as the owner and availability.
-      parameters:
-        - in: path
-          name: engine_id
-          required: true
-          schema:
-            type: string
-            # ideally this will be an actual ID, so this will always work from browser
-            example:
-              davinci
-          description: &engine_id_description >
-            The ID of the engine to use for this request
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/Engine'
-      x-oaiMeta:
-        name: Retrieve engine
-        group: engines
-        path: retrieve
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/engines/VAR_model_id \
-              -H 'Authorization: Bearer YOUR_API_KEY'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Engine.retrieve("VAR_model_id")
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.retrieveEngine("VAR_model_id");
-        response: |
-          {
-            "id": "VAR_model_id",
-            "object": "engine",
-            "owner": "openai",
-            "ready": true
-          }
-
-  /completions:
-    post:
-      operationId: createCompletion
-      tags:
-      - OpenAI
-      summary: Creates a completion for the provided prompt and parameters
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: '#/components/schemas/CreateCompletionRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/CreateCompletionResponse'
-      x-oaiMeta:
-        name: Create completion
-        group: completions
-        path: create
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/completions \
-              -H 'Content-Type: application/json' \
-              -H 'Authorization: Bearer YOUR_API_KEY' \
-              -d '{
-              "model": "VAR_model_id",
-              "prompt": "Say this is a test",
-              "max_tokens": 7,
-              "temperature": 0
-            }'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Completion.create(
-              model="VAR_model_id",
-              prompt="Say this is a test",
-              max_tokens=7,
-              temperature=0
-            )
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createCompletion({
-              model: "VAR_model_id",
-              prompt: "Say this is a test",
-              max_tokens: 7,
-              temperature: 0,
-            });
-        parameters: |
-          {
-            "model": "VAR_model_id",
-            "prompt": "Say this is a test",
-            "max_tokens": 7,
-            "temperature": 0,
-            "top_p": 1,
-            "n": 1,
-            "stream": false,
-            "logprobs": null,
-            "stop": "\n"
-          }
-        response: |
-          {
-            "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7",
-            "object": "text_completion",
-            "created": 1589478378,
-            "model": "VAR_model_id",
-            "choices": [
-              {
-                "text": "\n\nThis is indeed a test",
-                "index": 0,
-                "logprobs": null,
-                "finish_reason": "length"
-              }
-            ],
-            "usage": {
-              "prompt_tokens": 5,
-              "completion_tokens": 7,
-              "total_tokens": 12
-            }
-          }
-  /chat/completions:
-    post:
-      operationId: createChatCompletion
-      tags:
-      - OpenAI
-      summary: Creates a completion for the chat message
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: '#/components/schemas/CreateChatCompletionRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/CreateChatCompletionResponse'
-
-      x-oaiMeta:
-        name: Create chat completion
-        group: chat
-        path: create
-        beta: true
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/chat/completions \
-              -H 'Content-Type: application/json' \
-              -H 'Authorization: Bearer YOUR_API_KEY' \
-              -d '{
-              "model": "gpt-3.5-turbo",
-              "messages": [{"role": "user", "content": "Hello!"}]
-            }'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-
-            completion = openai.ChatCompletion.create(
-              model="gpt-3.5-turbo",
-              messages=[
-                {"role": "user", "content": "Hello!"}
-              ]
-            )
-
-            print(completion.choices[0].message)
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-
-            const completion = await openai.createChatCompletion({
-              model: "gpt-3.5-turbo",
-              messages: [{role: "user", content: "Hello world"}],
-            });
-            console.log(completion.data.choices[0].message);
-        parameters: |
-          {
-            "model": "gpt-3.5-turbo",
-            "messages": [{"role": "user", "content": "Hello!"}]
-          }
-        response: |
-          {
-            "id": "chatcmpl-123",
-            "object": "chat.completion",
-            "created": 1677652288,
-            "choices": [{
-              "index": 0,
-              "message": {
-                "role": "assistant",
-                "content": "\n\nHello there, how may I assist you today?",
-              },
-              "finish_reason": "stop"
-            }],
-            "usage": {
-              "prompt_tokens": 9,
-              "completion_tokens": 12,
-              "total_tokens": 21
-            }
-          }
-
-  /edits:
-    post:
-      operationId: createEdit
-      tags:
-        - OpenAI
-      summary: Creates a new edit for the provided input, instruction, and parameters.
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: '#/components/schemas/CreateEditRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/CreateEditResponse'
-      x-oaiMeta:
-        name: Create edit
-        group: edits
-        path: create
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/edits \
-              -H 'Content-Type: application/json' \
-              -H 'Authorization: Bearer YOUR_API_KEY' \
-              -d '{
-              "model": "VAR_model_id",
-              "input": "What day of the wek is it?",
-              "instruction": "Fix the spelling mistakes"
-            }'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Edit.create(
-              model="VAR_model_id",
-              input="What day of the wek is it?",
-              instruction="Fix the spelling mistakes"
-            )
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createEdit({
-              model: "VAR_model_id",
-              input: "What day of the wek is it?",
-              instruction: "Fix the spelling mistakes",
-            });
-        parameters: |
-          {
-            "model": "VAR_model_id",
-            "input": "What day of the wek is it?",
-            "instruction": "Fix the spelling mistakes",
-          }
-        response: |
-          {
-            "object": "edit",
-            "created": 1589478378,
-            "choices": [
-              {
-                "text": "What day of the week is it?",
-                "index": 0,
-              }
-            ],
-            "usage": {
-              "prompt_tokens": 25,
-              "completion_tokens": 32,
-              "total_tokens": 57
-            }
-          }
-
-  /images/generations:
-    post:
-      operationId: createImage
-      tags:
-      - OpenAI
-      summary: Creates an image given a prompt.
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: '#/components/schemas/CreateImageRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/ImagesResponse'
-      x-oaiMeta:
-        name: Create image
-        group: images
-        path: create
-        beta: true
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/images/generations \
-              -H 'Content-Type: application/json' \
-              -H 'Authorization: Bearer YOUR_API_KEY' \
-              -d '{
-              "prompt": "A cute baby sea otter",
-              "n": 2,
-              "size": "1024x1024"
-            }'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Image.create(
-              prompt="A cute baby sea otter",
-              n=2,
-              size="1024x1024"
-            )
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createImage({
-              prompt: "A cute baby sea otter",
-              n: 2,
-              size: "1024x1024",
-            });
-        parameters: |
-          {
-            "prompt": "A cute baby sea otter",
-            "n": 2,
-            "size": "1024x1024"
-          }
-        response: |
-          {
-            "created": 1589478378,
-            "data": [
-              {
-                "url": "https://..."
-              },
-              {
-                "url": "https://..."
-              }
-            ]
-          }
-
-  /images/edits:
-    post:
-      operationId: createImageEdit
-      tags:
-      - OpenAI
-      summary: Creates an edited or extended image given an original image and a prompt.
-      requestBody:
-        required: true
-        content:
-          multipart/form-data:
-            schema:
-              $ref: '#/components/schemas/CreateImageEditRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/ImagesResponse'
-      x-oaiMeta:
-        name: Create image edit
-        group: images
-        path: create-edit
-        beta: true
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/images/edits \
-              -H 'Authorization: Bearer YOUR_API_KEY' \
-              -F image='@otter.png' \
-              -F mask='@mask.png' \
-              -F prompt="A cute baby sea otter wearing a beret" \
-              -F n=2 \
-              -F size="1024x1024"
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Image.create_edit(
-              image=open("otter.png", "rb"),
-              mask=open("mask.png", "rb"),
-              prompt="A cute baby sea otter wearing a beret",
-              n=2,
-              size="1024x1024"
-            )
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createImageEdit(
-              fs.createReadStream("otter.png"),
-              fs.createReadStream("mask.png"),
-              "A cute baby sea otter wearing a beret",
-              2,
-              "1024x1024"
-            );
-        response: |
-          {
-            "created": 1589478378,
-            "data": [
-              {
-                "url": "https://..."
-              },
-              {
-                "url": "https://..."
-              }
-            ]
-          }
-
-  /images/variations:
-    post:
-      operationId: createImageVariation
-      tags:
-      - OpenAI
-      summary: Creates a variation of a given image.
-      requestBody:
-        required: true
-        content:
-          multipart/form-data:
-            schema:
-              $ref: '#/components/schemas/CreateImageVariationRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/ImagesResponse'
-      x-oaiMeta:
-        name: Create image variation
-        group: images
-        path: create-variation
-        beta: true
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/images/variations \
-              -H 'Authorization: Bearer YOUR_API_KEY' \
-              -F image='@otter.png' \
-              -F n=2 \
-              -F size="1024x1024"
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Image.create_variation(
-              image=open("otter.png", "rb"),
-              n=2,
-              size="1024x1024"
-            )
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createImageVariation(
-              fs.createReadStream("otter.png"),
-              2,
-              "1024x1024"
-            );
-        response: |
-          {
-            "created": 1589478378,
-            "data": [
-              {
-                "url": "https://..."
-              },
-              {
-                "url": "https://..."
-              }
-            ]
-          }
-
-  /embeddings:
-    post:
-      operationId: createEmbedding
-      tags:
-        - OpenAI
-      summary: Creates an embedding vector representing the input text.
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: '#/components/schemas/CreateEmbeddingRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/CreateEmbeddingResponse'
-      x-oaiMeta:
-        name: Create embeddings
-        group: embeddings
-        path: create
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/embeddings \
-              -X POST \
-              -H "Authorization: Bearer YOUR_API_KEY" \
-              -H "Content-Type: application/json" \
-              -d '{"input": "The food was delicious and the waiter...",
-                   "model": "text-embedding-ada-002"}'
-
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Embedding.create(
-              model="text-embedding-ada-002",
-              input="The food was delicious and the waiter..."
-            )
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createEmbedding({
-              model: "text-embedding-ada-002",
-              input: "The food was delicious and the waiter...",
-            });
-        parameters: |
-          {
-            "model": "text-embedding-ada-002",
-            "input": "The food was delicious and the waiter..."
-          }
-        response: |
-          {
-            "object": "list",
-            "data": [
-              {
-                "object": "embedding",
-                "embedding": [
-                  0.0023064255,
-                  -0.009327292,
-                  .... (1536 floats total for ada-002)
-                  -0.0028842222,
-                ],
-                "index": 0
-              }
-            ],
-            "model": "text-embedding-ada-002",
-            "usage": {
-              "prompt_tokens": 8,
-              "total_tokens": 8
-            }
-          }
-
-  /audio/transcriptions:
-    post:
-      operationId: createTranscription
-      tags:
-        - OpenAI
-      summary: Transcribes audio into the input language.
-      requestBody:
-        required: true
-        content:
-          multipart/form-data:
-            schema:
-              $ref: '#/components/schemas/CreateTranscriptionRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/CreateTranscriptionResponse'
-      x-oaiMeta:
-        name: Create transcription
-        group: audio
-        path: create
-        beta: true
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/audio/transcriptions \
-              -X POST \
-              -H 'Authorization: Bearer TOKEN' \
-              -H 'Content-Type: multipart/form-data' \
-              -F file=@/path/to/file/audio.mp3 \
-              -F model=whisper-1
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            audio_file = open("audio.mp3", "rb")
-            transcript = openai.Audio.transcribe("whisper-1", audio_file)
-          node: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const resp = await openai.createTranscription(
-              fs.createReadStream("audio.mp3"),
-              "whisper-1"
-            );
-        parameters: |
-          {
-            "file": "audio.mp3",
-            "model": "whisper-1"
-          }
-        response: |
-          {
-            "text": "Imagine the wildest idea that you've ever had, and you're curious about how it might scale to something that's a 100, a 1,000 times bigger. This is a place where you can get to do that."
-          }
-
-  /audio/translations:
-    post:
-      operationId: createTranslation
-      tags:
-        - OpenAI
-      summary: Translates audio into into English.
-      requestBody:
-        required: true
-        content:
-          multipart/form-data:
-            schema:
-              $ref: '#/components/schemas/CreateTranslationRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/CreateTranslationResponse'
-      x-oaiMeta:
-        name: Create translation
-        group: audio
-        path: create
-        beta: true
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/audio/translations \
-                -X POST \
-                -H 'Authorization: Bearer TOKEN' \
-                -H 'Content-Type: multipart/form-data' \
-                -F file=@/path/to/file/german.m4a \
-                -F model=whisper-1
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            audio_file = open("german.m4a", "rb")
-            transcript = openai.Audio.translate("whisper-1", audio_file)
-          node: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const resp = await openai.createTranslation(
-              fs.createReadStream("audio.mp3"),
-              "whisper-1"
-            );
-        parameters: |
-          {
-            "file": "german.m4a",
-            "model": "whisper-1"
-          }
-        response: |
-          {
-            "text": "Hello, my name is Wolfgang and I come from Germany. Where are you heading today?"
-          }
-
-  /engines/{engine_id}/search:
-    post:
-      operationId: createSearch
-      deprecated: true
-      tags:
-      - OpenAI
-      summary: |
-        The search endpoint computes similarity scores between provided query and documents. Documents can be passed directly to the API if there are no more than 200 of them.
-
-        To go beyond the 200 document limit, documents can be processed offline and then used for efficient retrieval at query time. When `file` is set, the search endpoint searches over all the documents in the given file and returns up to the `max_rerank` number of documents. These documents will be returned along with their search scores.
-
-        The similarity score is a positive score that usually ranges from 0 to 300 (but can sometimes go higher), where a score above 200 usually means the document is semantically similar to the query.
-      parameters:
-        - in: path
-          name: engine_id
-          required: true
-          schema:
-            type: string
-            example: davinci
-          description: The ID of the engine to use for this request.  You can select one of `ada`, `babbage`, `curie`, or `davinci`.
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: '#/components/schemas/CreateSearchRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/CreateSearchResponse'
-      x-oaiMeta:
-        name: Create search
-        group: searches
-        path: create
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/engines/davinci/search \
-              -H "Content-Type: application/json" \
-              -H 'Authorization: Bearer YOUR_API_KEY' \
-              -d '{
-              "documents": ["White House", "hospital", "school"],
-              "query": "the president"
-            }'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Engine("davinci").search(
-              documents=["White House", "hospital", "school"],
-              query="the president"
-            )
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createSearch("davinci", {
-              documents: ["White House", "hospital", "school"],
-              query: "the president",
-            });
-        parameters: |
-          {
-            "documents": [
-              "White House",
-              "hospital",
-              "school"
-            ],
-            "query": "the president"
-          }
-        response: |
-          {
-            "data": [
-              {
-                "document": 0,
-                "object": "search_result",
-                "score": 215.412
-              },
-              {
-                "document": 1,
-                "object": "search_result",
-                "score": 40.316
-              },
-              {
-                "document": 2,
-                "object": "search_result",
-                "score":  55.226
-              }
-            ],
-            "object": "list"
-          }
-
-  /files:
-    get:
-      operationId: listFiles
-      tags:
-      - OpenAI
-      summary: Returns a list of files that belong to the user's organization.
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/ListFilesResponse'
-      x-oaiMeta:
-        name: List files
-        group: files
-        path: list
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/files \
-              -H 'Authorization: Bearer YOUR_API_KEY'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.File.list()
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.listFiles();
-        response: |
-          {
-            "data": [
-              {
-                "id": "file-ccdDZrC3iZVNiQVeEA6Z66wf",
-                "object": "file",
-                "bytes": 175,
-                "created_at": 1613677385,
-                "filename": "train.jsonl",
-                "purpose": "search"
-              },
-              {
-                "id": "file-XjGxS3KTG0uNmNOK362iJua3",
-                "object": "file",
-                "bytes": 140,
-                "created_at": 1613779121,
-                "filename": "puppy.jsonl",
-                "purpose": "search"
-              }
-            ],
-            "object": "list"
-          }
-    post:
-      operationId: createFile
-      tags:
-      - OpenAI
-      summary: |
-        Upload a file that contains document(s) to be used across various endpoints/features. Currently, the size of all the files uploaded by one organization can be up to 1 GB. Please contact us if you need to increase the storage limit.
-
-      requestBody:
-        required: true
-        content:
-          multipart/form-data:
-            schema:
-              $ref: '#/components/schemas/CreateFileRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/OpenAIFile'
-      x-oaiMeta:
-        name: Upload file
-        group: files
-        path: upload
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/files \
-              -H "Authorization: Bearer YOUR_API_KEY" \
-              -F purpose="fine-tune" \
-              -F file='@mydata.jsonl'
-
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.File.create(
-              file=open("mydata.jsonl", "rb"),
-              purpose='fine-tune'
-            )
-          node.js: |
-            const fs = require("fs");
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createFile(
-              fs.createReadStream("mydata.jsonl"),
-              "fine-tune"
-            );
-        response: |
-          {
-            "id": "file-XjGxS3KTG0uNmNOK362iJua3",
-            "object": "file",
-            "bytes": 140,
-            "created_at": 1613779121,
-            "filename": "mydata.jsonl",
-            "purpose": "fine-tune"
-          }
-
-  /files/{file_id}:
-    delete:
-      operationId: deleteFile
-      tags:
-      - OpenAI
-      summary: Delete a file.
-      parameters:
-        - in: path
-          name: file_id
-          required: true
-          schema:
-            type: string
-          description: The ID of the file to use for this request
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/DeleteFileResponse'
-      x-oaiMeta:
-        name: Delete file
-        group: files
-        path: delete
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3 \
-              -X DELETE \
-              -H 'Authorization: Bearer YOUR_API_KEY'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.File.delete("file-XjGxS3KTG0uNmNOK362iJua3")
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.deleteFile("file-XjGxS3KTG0uNmNOK362iJua3");
-        response: |
-          {
-            "id": "file-XjGxS3KTG0uNmNOK362iJua3",
-            "object": "file",
-            "deleted": true
-          }
-    get:
-      operationId: retrieveFile
-      tags:
-      - OpenAI
-      summary: Returns information about a specific file.
-      parameters:
-        - in: path
-          name: file_id
-          required: true
-          schema:
-            type: string
-          description: The ID of the file to use for this request
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/OpenAIFile'
-      x-oaiMeta:
-        name: Retrieve file
-        group: files
-        path: retrieve
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3 \
-              -H 'Authorization: Bearer YOUR_API_KEY'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.File.retrieve("file-XjGxS3KTG0uNmNOK362iJua3")
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.retrieveFile("file-XjGxS3KTG0uNmNOK362iJua3");
-        response: |
-          {
-            "id": "file-XjGxS3KTG0uNmNOK362iJua3",
-            "object": "file",
-            "bytes": 140,
-            "created_at": 1613779657,
-            "filename": "mydata.jsonl",
-            "purpose": "fine-tune"
-          }
-
-  /files/{file_id}/content:
-    get:
-      operationId: downloadFile
-      tags:
-      - OpenAI
-      summary: Returns the contents of the specified file
-      parameters:
-        - in: path
-          name: file_id
-          required: true
-          schema:
-            type: string
-          description: The ID of the file to use for this request
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                type: string
-      x-oaiMeta:
-        name: Retrieve file content
-        group: files
-        path: retrieve-content
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/files/file-XjGxS3KTG0uNmNOK362iJua3/content \
-              -H 'Authorization: Bearer YOUR_API_KEY' > file.jsonl
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            content = openai.File.download("file-XjGxS3KTG0uNmNOK362iJua3")
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.downloadFile("file-XjGxS3KTG0uNmNOK362iJua3");
-
-  /answers:
-    post:
-      operationId: createAnswer
-      deprecated: true
-      tags:
-      - OpenAI
-      summary: |
-        Answers the specified question using the provided documents and examples.
-
-        The endpoint first [searches](/docs/api-reference/searches) over provided documents or files to find relevant context. The relevant context is combined with the provided examples and question to create the prompt for [completion](/docs/api-reference/completions).
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: '#/components/schemas/CreateAnswerRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/CreateAnswerResponse'
-      x-oaiMeta:
-        name: Create answer
-        group: answers
-        path: create
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/answers \
-              -X POST \
-              -H "Authorization: Bearer YOUR_API_KEY" \
-              -H 'Content-Type: application/json' \
-              -d '{
-                "documents": ["Puppy A is happy.", "Puppy B is sad."],
-                "question": "which puppy is happy?",
-                "search_model": "ada",
-                "model": "curie",
-                "examples_context": "In 2017, U.S. life expectancy was 78.6 years.",
-                "examples": [["What is human life expectancy in the United States?","78 years."]],
-                "max_tokens": 5,
-                "stop": ["\n", "<|endoftext|>"]
-              }'
-
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Answer.create(
-              search_model="ada",
-              model="curie",
-              question="which puppy is happy?",
-              documents=["Puppy A is happy.", "Puppy B is sad."],
-              examples_context="In 2017, U.S. life expectancy was 78.6 years.",
-              examples=[["What is human life expectancy in the United States?","78 years."]],
-              max_tokens=5,
-              stop=["\n", "<|endoftext|>"],
-            )
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createAnswer({
-              search_model: "ada",
-              model: "curie",
-              question: "which puppy is happy?",
-              documents: ["Puppy A is happy.", "Puppy B is sad."],
-              examples_context: "In 2017, U.S. life expectancy was 78.6 years.",
-              examples: [["What is human life expectancy in the United States?","78 years."]],
-              max_tokens: 5,
-              stop: ["\n", "<|endoftext|>"],
-            });
-        parameters: |
-          {
-            "documents": ["Puppy A is happy.", "Puppy B is sad."],
-            "question": "which puppy is happy?",
-            "search_model": "ada",
-            "model": "curie",
-            "examples_context": "In 2017, U.S. life expectancy was 78.6 years.",
-            "examples": [["What is human life expectancy in the United States?","78 years."]],
-            "max_tokens": 5,
-            "stop": ["\n", "<|endoftext|>"]
-          }
-        response: |
-          {
-            "answers": [
-              "puppy A."
-            ],
-            "completion": "cmpl-2euVa1kmKUuLpSX600M41125Mo9NI",
-            "model": "curie:2020-05-03",
-            "object": "answer",
-            "search_model": "ada",
-            "selected_documents": [
-              {
-                "document": 0,
-                "text": "Puppy A is happy. "
-              },
-              {
-                "document": 1,
-                "text": "Puppy B is sad. "
-              }
-            ]
-          }
-
-  /classifications:
-    post:
-      operationId: createClassification
-      deprecated: true
-      tags:
-      - OpenAI
-      summary: |
-        Classifies the specified `query` using provided examples.
-
-        The endpoint first [searches](/docs/api-reference/searches) over the labeled examples
-        to select the ones most relevant for the particular query. Then, the relevant examples
-        are combined with the query to construct a prompt to produce the final label via the
-        [completions](/docs/api-reference/completions) endpoint.
-
-        Labeled examples can be provided via an uploaded `file`, or explicitly listed in the
-        request using the `examples` parameter for quick tests and small scale use cases.
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: '#/components/schemas/CreateClassificationRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/CreateClassificationResponse'
-      x-oaiMeta:
-        name: Create classification
-        group: classifications
-        path: create
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/classifications \
-              -X POST \
-              -H "Authorization: Bearer YOUR_API_KEY" \
-              -H 'Content-Type: application/json' \
-              -d '{
-                "examples": [
-                  ["A happy moment", "Positive"],
-                  ["I am sad.", "Negative"],
-                  ["I am feeling awesome", "Positive"]],
-                "query": "It is a raining day :(",
-                "search_model": "ada",
-                "model": "curie",
-                "labels":["Positive", "Negative", "Neutral"]
-              }'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Classification.create(
-              search_model="ada",
-              model="curie",
-              examples=[
-                ["A happy moment", "Positive"],
-                ["I am sad.", "Negative"],
-                ["I am feeling awesome", "Positive"]
-              ],
-              query="It is a raining day :(",
-              labels=["Positive", "Negative", "Neutral"],
-            )
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createClassification({
-              search_model: "ada",
-              model: "curie",
-              examples: [
-                ["A happy moment", "Positive"],
-                ["I am sad.", "Negative"],
-                ["I am feeling awesome", "Positive"]
-              ],
-              query:"It is a raining day :(",
-              labels: ["Positive", "Negative", "Neutral"],
-            });
-        parameters: |
-          {
-            "examples": [
-              ["A happy moment", "Positive"],
-              ["I am sad.", "Negative"],
-              ["I am feeling awesome", "Positive"]
-            ],
-            "labels": ["Positive", "Negative", "Neutral"],
-            "query": "It is a raining day :(",
-            "search_model": "ada",
-            "model": "curie"
-          }
-        response: |
-          {
-            "completion": "cmpl-2euN7lUVZ0d4RKbQqRV79IiiE6M1f",
-            "label": "Negative",
-            "model": "curie:2020-05-03",
-            "object": "classification",
-            "search_model": "ada",
-            "selected_examples": [
-              {
-                "document": 1,
-                "label": "Negative",
-                "text": "I am sad."
-              },
-              {
-                "document": 0,
-                "label": "Positive",
-                "text": "A happy moment"
-              },
-              {
-                "document": 2,
-                "label": "Positive",
-                "text": "I am feeling awesome"
-              }
-            ]
-          }
-
-  /fine-tunes:
-    post:
-      operationId: createFineTune
-      tags:
-      - OpenAI
-      summary: |
-        Creates a job that fine-tunes a specified model from a given dataset.
-
-        Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.
-
-        [Learn more about Fine-tuning](/docs/guides/fine-tuning)
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: '#/components/schemas/CreateFineTuneRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/FineTune'
-      x-oaiMeta:
-        name: Create fine-tune
-        group: fine-tunes
-        path: create
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/fine-tunes \
-              -X POST \
-              -H "Content-Type: application/json" \
-              -H "Authorization: Bearer YOUR_API_KEY" \
-              -d '{
-              "training_file": "file-XGinujblHPwGLSztz8cPS8XY"
-            }'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.FineTune.create(training_file="file-XGinujblHPwGLSztz8cPS8XY")
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createFineTune({
-              training_file: "file-XGinujblHPwGLSztz8cPS8XY",
-            });
-        response: |
-          {
-            "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
-            "object": "fine-tune",
-            "model": "curie",
-            "created_at": 1614807352,
-            "events": [
-              {
-                "object": "fine-tune-event",
-                "created_at": 1614807352,
-                "level": "info",
-                "message": "Job enqueued. Waiting for jobs ahead to complete. Queue number: 0."
-              }
-            ],
-            "fine_tuned_model": null,
-            "hyperparams": {
-              "batch_size": 4,
-              "learning_rate_multiplier": 0.1,
-              "n_epochs": 4,
-              "prompt_loss_weight": 0.1,
-            },
-            "organization_id": "org-...",
-            "result_files": [],
-            "status": "pending",
-            "validation_files": [],
-            "training_files": [
-              {
-                "id": "file-XGinujblHPwGLSztz8cPS8XY",
-                "object": "file",
-                "bytes": 1547276,
-                "created_at": 1610062281,
-                "filename": "my-data-train.jsonl",
-                "purpose": "fine-tune-train"
-              }
-            ],
-            "updated_at": 1614807352,
-          }
-    get:
-      operationId: listFineTunes
-      tags:
-      - OpenAI
-      summary: |
-        List your organization's fine-tuning jobs
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/ListFineTunesResponse'
-      x-oaiMeta:
-        name: List fine-tunes
-        group: fine-tunes
-        path: list
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/fine-tunes \
-              -H 'Authorization: Bearer YOUR_API_KEY'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.FineTune.list()
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.listFineTunes();
-        response: |
-          {
-            "object": "list",
-            "data": [
-              {
-                "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
-                "object": "fine-tune",
-                "model": "curie",
-                "created_at": 1614807352,
-                "fine_tuned_model": null,
-                "hyperparams": { ... },
-                "organization_id": "org-...",
-                "result_files": [],
-                "status": "pending",
-                "validation_files": [],
-                "training_files": [ { ... } ],
-                "updated_at": 1614807352,
-              },
-              { ... },
-              { ... }
-            ]
-          }
-
-  /fine-tunes/{fine_tune_id}:
-    get:
-      operationId: retrieveFineTune
-      tags:
-      - OpenAI
-      summary: |
-        Gets info about the fine-tune job.
-
-        [Learn more about Fine-tuning](/docs/guides/fine-tuning)
-      parameters:
-        - in: path
-          name: fine_tune_id
-          required: true
-          schema:
-            type: string
-            example:
-              ft-AF1WoRqd3aJAHsqc9NY7iL8F
-          description: |
-            The ID of the fine-tune job
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/FineTune'
-      x-oaiMeta:
-        name: Retrieve fine-tune
-        group: fine-tunes
-        path: retrieve
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F \
-              -H "Authorization: Bearer YOUR_API_KEY"
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.FineTune.retrieve(id="ft-AF1WoRqd3aJAHsqc9NY7iL8F")
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.retrieveFineTune("ft-AF1WoRqd3aJAHsqc9NY7iL8F");
-        response: |
-          {
-            "id": "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
-            "object": "fine-tune",
-            "model": "curie",
-            "created_at": 1614807352,
-            "events": [
-              {
-                "object": "fine-tune-event",
-                "created_at": 1614807352,
-                "level": "info",
-                "message": "Job enqueued. Waiting for jobs ahead to complete. Queue number: 0."
-              },
-              {
-                "object": "fine-tune-event",
-                "created_at": 1614807356,
-                "level": "info",
-                "message": "Job started."
-              },
-              {
-                "object": "fine-tune-event",
-                "created_at": 1614807861,
-                "level": "info",
-                "message": "Uploaded snapshot: curie:ft-acmeco-2021-03-03-21-44-20."
-              },
-              {
-                "object": "fine-tune-event",
-                "created_at": 1614807864,
-                "level": "info",
-                "message": "Uploaded result files: file-QQm6ZpqdNwAaVC3aSz5sWwLT."
-              },
-              {
-                "object": "fine-tune-event",
-                "created_at": 1614807864,
-                "level": "info",
-                "message": "Job succeeded."
-              }
-            ],
-            "fine_tuned_model": "curie:ft-acmeco-2021-03-03-21-44-20",
-            "hyperparams": {
-              "batch_size": 4,
-              "learning_rate_multiplier": 0.1,
-              "n_epochs": 4,
-              "prompt_loss_weight": 0.1,
-            },
-            "organization_id": "org-...",
-            "result_files": [
-              {
-                "id": "file-QQm6ZpqdNwAaVC3aSz5sWwLT",
-                "object": "file",
-                "bytes": 81509,
-                "created_at": 1614807863,
-                "filename": "compiled_results.csv",
-                "purpose": "fine-tune-results"
-              }
-            ],
-            "status": "succeeded",
-            "validation_files": [],
-            "training_files": [
-              {
-                "id": "file-XGinujblHPwGLSztz8cPS8XY",
-                "object": "file",
-                "bytes": 1547276,
-                "created_at": 1610062281,
-                "filename": "my-data-train.jsonl",
-                "purpose": "fine-tune-train"
-              }
-            ],
-            "updated_at": 1614807865,
-          }
-
-  /fine-tunes/{fine_tune_id}/cancel:
-    post:
-      operationId: cancelFineTune
-      tags:
-      - OpenAI
-      summary: |
-        Immediately cancel a fine-tune job.
-      parameters:
-        - in: path
-          name: fine_tune_id
-          required: true
-          schema:
-            type: string
-            example:
-              ft-AF1WoRqd3aJAHsqc9NY7iL8F
-          description: |
-            The ID of the fine-tune job to cancel
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/FineTune'
-      x-oaiMeta:
-        name: Cancel fine-tune
-        group: fine-tunes
-        path: cancel
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F/cancel \
-              -X POST \
-              -H "Authorization: Bearer YOUR_API_KEY"
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.FineTune.cancel(id="ft-AF1WoRqd3aJAHsqc9NY7iL8F")
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.cancelFineTune("ft-AF1WoRqd3aJAHsqc9NY7iL8F");
-        response: |
-          {
-            "id": "ft-xhrpBbvVUzYGo8oUO1FY4nI7",
-            "object": "fine-tune",
-            "model": "curie",
-            "created_at": 1614807770,
-            "events": [ { ... } ],
-            "fine_tuned_model": null,
-            "hyperparams": { ... },
-            "organization_id": "org-...",
-            "result_files": [],
-            "status": "cancelled",
-            "validation_files": [],
-            "training_files": [
-              {
-                "id": "file-XGinujblHPwGLSztz8cPS8XY",
-                "object": "file",
-                "bytes": 1547276,
-                "created_at": 1610062281,
-                "filename": "my-data-train.jsonl",
-                "purpose": "fine-tune-train"
-              }
-            ],
-            "updated_at": 1614807789,
-          }
-
-  /fine-tunes/{fine_tune_id}/events:
-    get:
-      operationId: listFineTuneEvents
-      tags:
-      - OpenAI
-      summary: |
-        Get fine-grained status updates for a fine-tune job.
-      parameters:
-        - in: path
-          name: fine_tune_id
-          required: true
-          schema:
-            type: string
-            example:
-              ft-AF1WoRqd3aJAHsqc9NY7iL8F
-          description: |
-            The ID of the fine-tune job to get events for.
-        - in: query
-          name: stream
-          required: false
-          schema:
-            type: boolean
-            default: false
-          description: |
-            Whether to stream events for the fine-tune job. If set to true,
-            events will be sent as data-only
-            [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
-            as they become available. The stream will terminate with a
-            `data: [DONE]` message when the job is finished (succeeded, cancelled,
-            or failed).
-
-            If set to false, only events generated so far will be returned.
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/ListFineTuneEventsResponse'
-      x-oaiMeta:
-        name: List fine-tune events
-        group: fine-tunes
-        path: events
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/fine-tunes/ft-AF1WoRqd3aJAHsqc9NY7iL8F/events \
-              -H "Authorization: Bearer YOUR_API_KEY"
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.FineTune.list_events(id="ft-AF1WoRqd3aJAHsqc9NY7iL8F")
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.listFineTuneEvents("ft-AF1WoRqd3aJAHsqc9NY7iL8F");
-        response: |
-          {
-            "object": "list",
-            "data": [
-              {
-                "object": "fine-tune-event",
-                "created_at": 1614807352,
-                "level": "info",
-                "message": "Job enqueued. Waiting for jobs ahead to complete. Queue number: 0."
-              },
-              {
-                "object": "fine-tune-event",
-                "created_at": 1614807356,
-                "level": "info",
-                "message": "Job started."
-              },
-              {
-                "object": "fine-tune-event",
-                "created_at": 1614807861,
-                "level": "info",
-                "message": "Uploaded snapshot: curie:ft-acmeco-2021-03-03-21-44-20."
-              },
-              {
-                "object": "fine-tune-event",
-                "created_at": 1614807864,
-                "level": "info",
-                "message": "Uploaded result files: file-QQm6ZpqdNwAaVC3aSz5sWwLT."
-              },
-              {
-                "object": "fine-tune-event",
-                "created_at": 1614807864,
-                "level": "info",
-                "message": "Job succeeded."
-              }
-            ]
-          }
-
-  /models:
-    get:
-      operationId: listModels
-      tags:
-        - OpenAI
-      summary: Lists the currently available models, and provides basic information about each one such as the owner and availability.
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/ListModelsResponse'
-      x-oaiMeta:
-        name: List models
-        group: models
-        path: list
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/models \
-              -H 'Authorization: Bearer YOUR_API_KEY'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Model.list()
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.listModels();
-        response: |
-          {
-            "data": [
-              {
-                "id": "model-id-0",
-                "object": "model",
-                "owned_by": "organization-owner",
-                "permission": [...]
-              },
-              {
-                "id": "model-id-1",
-                "object": "model",
-                "owned_by": "organization-owner",
-                "permission": [...]
-              },
-              {
-                "id": "model-id-2",
-                "object": "model",
-                "owned_by": "openai",
-                "permission": [...]
-              },
-            ],
-            "object": "list"
-          }
-
-  /models/{model}:
-    get:
-      operationId: retrieveModel
-      tags:
-        - OpenAI
-      summary: Retrieves a model instance, providing basic information about the model such as the owner and permissioning.
-      parameters:
-        - in: path
-          name: model
-          required: true
-          schema:
-            type: string
-            # ideally this will be an actual ID, so this will always work from browser
-            example:
-              text-davinci-001
-          description:
-            The ID of the model to use for this request
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/Model'
-      x-oaiMeta:
-        name: Retrieve model
-        group: models
-        path: retrieve
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/models/VAR_model_id \
-              -H 'Authorization: Bearer YOUR_API_KEY'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Model.retrieve("VAR_model_id")
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.retrieveModel("VAR_model_id");
-        response: |
-          {
-            "id": "VAR_model_id",
-            "object": "model",
-            "owned_by": "openai",
-            "permission": [...]
-          }
-    delete:
-      operationId: deleteModel
-      tags:
-      - OpenAI
-      summary: Delete a fine-tuned model. You must have the Owner role in your organization.
-      parameters:
-        - in: path
-          name: model
-          required: true
-          schema:
-            type: string
-            example: curie:ft-acmeco-2021-03-03-21-44-20
-          description: The model to delete
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/DeleteModelResponse'
-      x-oaiMeta:
-        name: Delete fine-tune model
-        group: fine-tunes
-        path: delete-model
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/models/curie:ft-acmeco-2021-03-03-21-44-20 \
-              -X DELETE \
-              -H "Authorization: Bearer YOUR_API_KEY"
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Model.delete("curie:ft-acmeco-2021-03-03-21-44-20")
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.deleteModel('curie:ft-acmeco-2021-03-03-21-44-20');
-        response: |
-          {
-            "id": "curie:ft-acmeco-2021-03-03-21-44-20",
-            "object": "model",
-            "deleted": true
-          }
-
-  /moderations:
-    post:
-      operationId: createModeration
-      tags:
-        - OpenAI
-      summary: Classifies if text violates OpenAI's Content Policy
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: '#/components/schemas/CreateModerationRequest'
-      responses:
-        "200":
-          description: OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/CreateModerationResponse'
-      x-oaiMeta:
-        name: Create moderation
-        group: moderations
-        path: create
-        examples:
-          curl: |
-            curl https://api.openai.com/v1/moderations \
-              -H 'Content-Type: application/json' \
-              -H 'Authorization: Bearer YOUR_API_KEY' \
-              -d '{
-              "input": "I want to kill them."
-            }'
-          python: |
-            import os
-            import openai
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            openai.Moderation.create(
-              input="I want to kill them.",
-            )
-          node.js: |
-            const { Configuration, OpenAIApi } = require("openai");
-            const configuration = new Configuration({
-              apiKey: process.env.OPENAI_API_KEY,
-            });
-            const openai = new OpenAIApi(configuration);
-            const response = await openai.createModeration({
-              input: "I want to kill them.",
-            });
-        parameters: |
-          {
-            "input": "I want to kill them."
-          }
-        response: |
-          {
-            "id": "modr-5MWoLO",
-            "model": "text-moderation-001",
-            "results": [
-              {
-                "categories": {
-                  "hate": false,
-                  "hate/threatening": true,
-                  "self-harm": false,
-                  "sexual": false,
-                  "sexual/minors": false,
-                  "violence": true,
-                  "violence/graphic": false
-                },
-                "category_scores": {
-                  "hate": 0.22714105248451233,
-                  "hate/threatening": 0.4132447838783264,
-                  "self-harm": 0.005232391878962517,
-                  "sexual": 0.01407341007143259,
-                  "sexual/minors": 0.0038522258400917053,
-                  "violence": 0.9223177433013916,
-                  "violence/graphic": 0.036865197122097015
-                },
-                "flagged": true
-              }
-            ]
-          }
-
-components:
-  schemas:
-    ListEnginesResponse:
-      type: object
-      properties:
-        object:
-          type: string
-        data:
-          type: array
-          items:
-            $ref: '#/components/schemas/Engine'
-      required:
-        - object
-        - data
-
-    ListModelsResponse:
-      type: object
-      properties:
-        object:
-          type: string
-        data:
-          type: array
-          items:
-            $ref: '#/components/schemas/Model'
-      required:
-        - object
-        - data
-
-    DeleteModelResponse:
-      type: object
-      properties:
-        id:
-          type: string
-        object:
-          type: string
-        deleted:
-          type: boolean
-      required:
-        - id
-        - object
-        - deleted
-
-    CreateCompletionRequest:
-      type: object
-      properties:
-        model: &model_configuration
-          description: ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.
-          type: string
-        prompt:
-          description: &completions_prompt_description |
-            The prompt(s) to generate completions for, encoded as a string, array of strings, array of tokens, or array of token arrays.
-
-            Note that <|endoftext|> is the document separator that the model sees during training, so if a prompt is not specified the model will generate as if from the beginning of a new document.
-          default: '<|endoftext|>'
-          nullable: true
-          oneOf:
-            - type: string
-              default: ''
-              example: "This is a test."
-            - type: array
-              items:
-                type: string
-                default: ''
-                example: "This is a test."
-            - type: array
-              minItems: 1
-              items:
-                type: integer
-              example: "[1212, 318, 257, 1332, 13]"
-            - type: array
-              minItems: 1
-              items:
-                type: array
-                minItems: 1
-                items:
-                  type: integer
-              example: "[[1212, 318, 257, 1332, 13]]"
-        suffix:
-          description:
-            The suffix that comes after a completion of inserted text.
-          default: null
-          nullable: true
-          type: string
-          example: "test."
-        max_tokens:
-          type: integer
-          minimum: 0
-          default: 16
-          example: 16
-          nullable: true
-          description: &completions_max_tokens_description |
-            The maximum number of [tokens](/tokenizer) to generate in the completion.
-
-            The token count of your prompt plus `max_tokens` cannot exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096).
-        temperature:
-          type: number
-          minimum: 0
-          maximum: 2
-          default: 1
-          example: 1
-          nullable: true
-          description: &completions_temperature_description |
-            What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
-
-            We generally recommend altering this or `top_p` but not both.
-        top_p:
-          type: number
-          minimum: 0
-          maximum: 1
-          default: 1
-          example: 1
-          nullable: true
-          description: &completions_top_p_description |
-            An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
-
-            We generally recommend altering this or `temperature` but not both.
-        n:
-          type: integer
-          minimum: 1
-          maximum: 128
-          default: 1
-          example: 1
-          nullable: true
-          description: &completions_completions_description |
-            How many completions to generate for each prompt.
-
-            **Note:** Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for `max_tokens` and `stop`.
-        stream:
-          description: >
-            Whether to stream back partial progress. If set, tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
-            as they become available, with the stream terminated by a `data: [DONE]` message.
-          type: boolean
-          nullable: true
-          default: false
-        logprobs: &completions_logprobs_configuration
-          type: integer
-          minimum: 0
-          maximum: 5
-          default: null
-          nullable: true
-          description: &completions_logprobs_description |
-            Include the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. For example, if `logprobs` is 5, the API will return a list of the 5 most likely tokens. The API will always return the `logprob` of the sampled token, so there may be up to `logprobs+1` elements in the response.
-
-            The maximum value for `logprobs` is 5. If you need more than this, please contact us through our [Help center](https://help.openai.com) and describe your use case.
-        echo:
-          type: boolean
-          default: false
-          nullable: true
-          description: &completions_echo_description >
-            Echo back the prompt in addition to the completion
-        stop:
-          description: &completions_stop_description >
-            Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
-          default: null
-          nullable: true
-          oneOf:
-            - type: string
-              default: <|endoftext|>
-              example: "\n"
-              nullable: true
-            - type: array
-              minItems: 1
-              maxItems: 4
-              items:
-                type: string
-                example: '["\n"]'
-        presence_penalty:
-          type: number
-          default: 0
-          minimum: -2
-          maximum: 2
-          nullable: true
-          description: &completions_presence_penalty_description |
-            Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
-
-            [See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)
-        frequency_penalty:
-          type: number
-          default: 0
-          minimum: -2
-          maximum: 2
-          nullable: true
-          description: &completions_frequency_penalty_description |
-            Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
-
-            [See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)
-        best_of:
-          type: integer
-          default: 1
-          minimum: 0
-          maximum: 20
-          nullable: true
-          description: &completions_best_of_description |
-            Generates `best_of` completions server-side and returns the "best" (the one with the highest log probability per token). Results cannot be streamed.
-
-            When used with `n`, `best_of` controls the number of candidate completions and `n` specifies how many to return – `best_of` must be greater than `n`.
-
-            **Note:** Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for `max_tokens` and `stop`.
-        logit_bias: &completions_logit_bias
-          type: object
-          x-oaiTypeLabel: map
-          default: null
-          nullable: true
-          description: &completions_logit_bias_description |
-            Modify the likelihood of specified tokens appearing in the completion.
-
-            Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this [tokenizer tool](/tokenizer?view=bpe) (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
-
-            As an example, you can pass `{"50256": -100}` to prevent the <|endoftext|> token from being generated.
-        user: &end_user_param_configuration
-          type: string
-          example: user-1234
-          description: |
-            A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](/docs/guides/safety-best-practices/end-user-ids).
-      required:
-        - model
-    
-    CreateCompletionResponse:
-      type: object
-      properties:
-        id:
-          type: string
-        object:
-          type: string
-        created:
-          type: integer
-        model:
-          type: string
-        choices:
-          type: array
-          items:
-            type: object
-            properties:
-              text:
-                type: string
-              index:
-                type: integer
-              logprobs:
-                type: object
-                nullable: true
-                properties:
-                  tokens:
-                    type: array
-                    items:
-                      type: string
-                  token_logprobs:
-                    type: array
-                    items:
-                      type: number
-                  top_logprobs:
-                    type: array
-                    items:
-                      type: object
-                  text_offset:
-                    type: array
-                    items:
-                      type: integer
-              finish_reason:
-                type: string
-        usage:
-          type: object
-          properties:
-            prompt_tokens:
-              type: integer
-            completion_tokens:
-              type: integer
-            total_tokens:
-              type: integer
-          required: 
-            - prompt_tokens
-            - completion_tokens
-            - total_tokens
-      required: 
-        - id
-        - object
-        - created
-        - model
-        - choices
-
-    ChatCompletionRequestMessage:
-      type: object
-      properties:
-        role:
-          type: string
-          enum: ["system", "user", "assistant"]
-          description: The role of the author of this message.
-        content:
-          type: string
-          description: The contents of the message
-        name:
-          type: string
-          description: The name of the user in a multi-user chat
-      required: 
-        - role
-        - content
-
-    ChatCompletionResponseMessage:
-      type: object
-      properties:
-        role:
-          type: string
-          enum: ["system", "user", "assistant"]
-          description: The role of the author of this message.
-        content:
-          type: string
-          description: The contents of the message
-      required: 
-        - role
-        - content
-
-    CreateChatCompletionRequest:
-      type: object
-      properties:
-        model:
-          description: ID of the model to use. Currently, only `gpt-3.5-turbo` and `gpt-3.5-turbo-0301` are supported.
-          type: string
-        messages:
-          description: The messages to generate chat completions for, in the [chat format](/docs/guides/chat/introduction).
-          type: array
-          minItems: 1
-          items:
-            $ref: '#/components/schemas/ChatCompletionRequestMessage'
-        temperature:
-          type: number
-          minimum: 0
-          maximum: 2
-          default: 1
-          example: 1
-          nullable: true
-          description: *completions_temperature_description
-        top_p:
-          type: number
-          minimum: 0
-          maximum: 1
-          default: 1
-          example: 1
-          nullable: true
-          description: *completions_top_p_description
-        n:
-          type: integer
-          minimum: 1
-          maximum: 128
-          default: 1
-          example: 1
-          nullable: true
-          description: How many chat completion choices to generate for each input message.
-        stream:
-          description: >
-            If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
-            as they become available, with the stream terminated by a `data: [DONE]` message.
-          type: boolean
-          nullable: true
-          default: false
-        stop:
-          description: |
-            Up to 4 sequences where the API will stop generating further tokens.
-          default: null
-          oneOf:
-            - type: string
-              nullable: true
-            - type: array
-              minItems: 1
-              maxItems: 4
-              items:
-                type: string
-        max_tokens:
-          description: |
-            The maximum number of tokens allowed for the generated answer. By default, the number of tokens the model can return will be (4096 - prompt tokens).
-          default: inf
-          type: integer
-        presence_penalty:
-          type: number
-          default: 0
-          minimum: -2
-          maximum: 2
-          nullable: true
-          description: *completions_presence_penalty_description
-        frequency_penalty:
-          type: number
-          default: 0
-          minimum: -2
-          maximum: 2
-          nullable: true
-          description: *completions_frequency_penalty_description
-        logit_bias:
-          type: object
-          x-oaiTypeLabel: map
-          default: null
-          nullable: true
-          description: |
-            Modify the likelihood of specified tokens appearing in the completion.
-
-            Accepts a json object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
-        user: *end_user_param_configuration
-      required:
-        - model
-        - messages
-
-    CreateChatCompletionResponse:
-      type: object
-      properties:
-        id:
-          type: string
-        object:
-          type: string
-        created:
-          type: integer
-        model:
-          type: string
-        choices:
-          type: array
-          items:
-            type: object
-            properties:
-              index:
-                type: integer
-              message:
-                $ref: '#/components/schemas/ChatCompletionResponseMessage'
-              finish_reason:
-                type: string
-        usage:
-          type: object
-          properties:
-            prompt_tokens:
-              type: integer
-            completion_tokens:
-              type: integer
-            total_tokens:
-              type: integer
-          required: 
-            - prompt_tokens
-            - completion_tokens
-            - total_tokens
-      required: 
-        - id
-        - object
-        - created
-        - model
-        - choices
-
-    CreateEditRequest:
-      type: object
-      properties:
-        model:
-          description: ID of the model to use. You can use the `text-davinci-edit-001` or `code-davinci-edit-001` model with this endpoint.
-          type: string
-        input:
-          description:
-            The input text to use as a starting point for the edit.
-          type: string
-          default: ''
-          nullable: true
-          example: "What day of the wek is it?"
-        instruction:
-          description:
-            The instruction that tells the model how to edit the prompt.
-          type: string
-          example: "Fix the spelling mistakes."
-        n:
-          type: integer
-          minimum: 1
-          maximum: 20
-          default: 1
-          example: 1
-          nullable: true
-          description:
-            How many edits to generate for the input and instruction.
-        temperature:
-          type: number
-          minimum: 0
-          maximum: 2
-          default: 1
-          example: 1
-          nullable: true
-          description: *completions_temperature_description
-        top_p:
-          type: number
-          minimum: 0
-          maximum: 1
-          default: 1
-          example: 1
-          nullable: true
-          description: *completions_top_p_description
-      required:
-        - model
-        - instruction
-
-    CreateEditResponse:
-      type: object
-      properties:
-        object:
-          type: string
-        created:
-          type: integer
-        choices:
-          type: array
-          items:
-            type: object
-            properties:
-              text:
-                type: string
-              index:
-                type: integer
-              logprobs:
-                type: object
-                nullable: true
-                properties:
-                  tokens:
-                    type: array
-                    items:
-                      type: string
-                  token_logprobs:
-                    type: array
-                    items:
-                      type: number
-                  top_logprobs:
-                    type: array
-                    items:
-                      type: object
-                  text_offset:
-                    type: array
-                    items:
-                      type: integer
-              finish_reason:
-                type: string
-        usage:
-          type: object
-          properties:
-            prompt_tokens:
-              type: integer
-            completion_tokens:
-              type: integer
-            total_tokens:
-              type: integer
-          required: 
-            - prompt_tokens
-            - completion_tokens
-            - total_tokens
-      required: 
-        - object
-        - created
-        - choices
-        - usage
-
-    CreateImageRequest:
-      type: object
-      properties:
-        prompt:
-          description: A text description of the desired image(s). The maximum length is 1000 characters.
-          type: string
-          example: "A cute baby sea otter"
-        n: &images_n
-          type: integer
-          minimum: 1
-          maximum: 10
-          default: 1
-          example: 1
-          nullable: true
-          description: The number of images to generate. Must be between 1 and 10.
-        size: &images_size
-          type: string
-          enum: ["256x256", "512x512", "1024x1024"]
-          default: "1024x1024"
-          example: "1024x1024"
-          nullable: true
-          description: The size of the generated images. Must be one of `256x256`, `512x512`, or `1024x1024`.
-        response_format: &images_response_format
-          type: string
-          enum: ["url", "b64_json"]
-          default: "url"
-          example: "url"
-          nullable: true
-          description: The format in which the generated images are returned. Must be one of `url` or `b64_json`.
-        user: *end_user_param_configuration
-      required:
-        - prompt
-
-    ImagesResponse:
-      properties:
-        created:
-          type: integer
-        data:
-          type: array
-          items:
-            type: object
-            properties:
-              url:
-                type: string
-              b64_json:
-                type: string
-      required:
-        - created
-        - data
-
-    CreateImageEditRequest:
-      type: object
-      properties:
-        image:
-          description: The image to edit. Must be a valid PNG file, less than 4MB, and square. If mask is not provided, image must have transparency, which will be used as the mask.
-          type: string
-          format: binary
-        mask:
-          description: An additional image whose fully transparent areas (e.g. where alpha is zero) indicate where `image` should be edited. Must be a valid PNG file, less than 4MB, and have the same dimensions as `image`.
-          type: string
-          format: binary
-        prompt:
-          description: A text description of the desired image(s). The maximum length is 1000 characters.
-          type: string
-          example: "A cute baby sea otter wearing a beret"
-        n: *images_n
-        size: *images_size
-        response_format: *images_response_format
-        user: *end_user_param_configuration
-      required:
-        - prompt
-        - image
-
-    CreateImageVariationRequest:
-      type: object
-      properties:
-        image:
-          description: The image to use as the basis for the variation(s). Must be a valid PNG file, less than 4MB, and square.
-          type: string
-          format: binary
-        n: *images_n
-        size: *images_size
-        response_format: *images_response_format
-        user: *end_user_param_configuration
-      required:
-        - image
-
-    CreateModerationRequest:
-      type: object
-      properties:
-        input:
-          description: The input text to classify
-          oneOf:
-            - type: string
-              default: ''
-              example: "I want to kill them."
-            - type: array
-              items:
-                type: string
-                default: ''
-                example: "I want to kill them."
-        model:
-          description: |
-            Two content moderations models are available: `text-moderation-stable` and `text-moderation-latest`.
-
-            The default is `text-moderation-latest` which will be automatically upgraded over time. This ensures you are always using our most accurate model. If you use `text-moderation-stable`, we will provide advanced notice before updating the model. Accuracy of `text-moderation-stable` may be slightly lower than for `text-moderation-latest`.
-          type: string
-          nullable: false
-          default: "text-moderation-latest"
-          example: "text-moderation-stable"
-      required:
-        - input
-
-    CreateModerationResponse:
-      type: object
-      properties:
-        id:
-          type: string
-        model:
-          type: string
-        results:
-          type: array
-          items:
-            type: object
-            properties:
-              flagged:
-                type: boolean
-              categories:
-                type: object
-                properties:
-                  hate:
-                    type: boolean
-                  hate/threatening:
-                    type: boolean
-                  self-harm:
-                    type: boolean
-                  sexual:
-                    type: boolean
-                  sexual/minors:
-                    type: boolean
-                  violence:
-                    type: boolean
-                  violence/graphic:
-                    type: boolean
-                required: 
-                  - hate
-                  - hate/threatening
-                  - self-harm
-                  - sexual
-                  - sexual/minors
-                  - violence
-                  - violence/graphic
-              category_scores:
-                type: object
-                properties:
-                  hate:
-                    type: number
-                  hate/threatening:
-                    type: number
-                  self-harm:
-                    type: number
-                  sexual:
-                    type: number
-                  sexual/minors:
-                    type: number
-                  violence:
-                    type: number
-                  violence/graphic:
-                    type: number
-                required: 
-                  - hate
-                  - hate/threatening
-                  - self-harm
-                  - sexual
-                  - sexual/minors
-                  - violence
-                  - violence/graphic
-            required: 
-              - flagged
-              - categories
-              - category_scores
-      required: 
-        - id
-        - model
-        - results
-
-    CreateSearchRequest:
-      type: object
-      properties:
-        query:
-          description: Query to search against the documents.
-          type: string
-          example: "the president"
-          minLength: 1
-        documents:
-          description: |
-            Up to 200 documents to search over, provided as a list of strings.
-
-            The maximum document length (in tokens) is 2034 minus the number of tokens in the query.
-
-            You should specify either `documents` or a `file`, but not both.
-          type: array
-          minItems: 1
-          maxItems: 200
-          items:
-            type: string
-          nullable: true
-          example: "['White House', 'hospital', 'school']"
-        file:
-          description: |
-            The ID of an uploaded file that contains documents to search over.
-
-            You should specify either `documents` or a `file`, but not both.
-          type: string
-          nullable: true
-        max_rerank:
-          description: |
-            The maximum number of documents to be re-ranked and returned by search.
-
-            This flag only takes effect when `file` is set.
-          type: integer
-          minimum: 1
-          default: 200
-          nullable: true
-        return_metadata: &return_metadata_configuration
-          description: |
-            A special boolean flag for showing metadata. If set to `true`, each document entry in the returned JSON will contain a "metadata" field.
-
-            This flag only takes effect when `file` is set.
-          type: boolean
-          default: false
-          nullable: true
-        user: *end_user_param_configuration
-      required:
-        - query
-
-    CreateSearchResponse:
-      type: object
-      properties:
-        object:
-          type: string
-        model:
-          type: string
-        data:
-          type: array
-          items:
-            type: object
-            properties:
-              object:
-                type: string
-              document:
-                type: integer
-              score:
-                type: number
-
-    ListFilesResponse:
-      type: object
-      properties:
-        object:
-          type: string
-        data:
-          type: array
-          items:
-            $ref: '#/components/schemas/OpenAIFile'
-      required: 
-        - object
-        - data
-
-    CreateFileRequest:
-      type: object
-      additionalProperties: false
-      properties:
-        file:
-          description: |
-            Name of the [JSON Lines](https://jsonlines.readthedocs.io/en/latest/) file to be uploaded.
-
-            If the `purpose` is set to "fine-tune", each line is a JSON record with "prompt" and "completion" fields representing your [training examples](/docs/guides/fine-tuning/prepare-training-data).
-          type: string
-          format: binary
-        purpose:
-          description: |
-            The intended purpose of the uploaded documents.
-
-            Use "fine-tune" for [Fine-tuning](/docs/api-reference/fine-tunes). This allows us to validate the format of the uploaded file.
-
-          type: string
-      required:
-        - file
-        - purpose
-
-    DeleteFileResponse:
-      type: object
-      properties:
-        id:
-          type: string
-        object:
-          type: string
-        deleted:
-          type: boolean
-      required: 
-        - id
-        - object
-        - deleted
-
-    CreateAnswerRequest:
-      type: object
-      additionalProperties: false
-      properties:
-        model:
-          description: ID of the model to use for completion. You can select one of `ada`, `babbage`, `curie`, or `davinci`.
-          type: string
-        question:
-          description: Question to get answered.
-          type: string
-          minLength: 1
-          example: "What is the capital of Japan?"
-        examples:
-          description: List of (question, answer) pairs that will help steer the model towards the tone and answer format you'd like. We recommend adding 2 to 3 examples.
-          type: array
-          minItems: 1
-          maxItems: 200
-          items:
-            type: array
-            minItems: 2
-            maxItems: 2
-            items:
-              type: string
-              minLength: 1
-          example: "[['What is the capital of Canada?', 'Ottawa'], ['Which province is Ottawa in?', 'Ontario']]"
-        examples_context:
-          description: A text snippet containing the contextual information used to generate the answers for the `examples` you provide.
-          type: string
-          example: "Ottawa, Canada's capital, is located in the east of southern Ontario, near the city of Montréal and the U.S. border."
-        documents:
-          description: |
-            List of documents from which the answer for the input `question` should be derived. If this is an empty list, the question will be answered based on the question-answer examples.
-
-            You should specify either `documents` or a `file`, but not both.
-          type: array
-          maxItems: 200
-          items:
-            type: string
-          example: "['Japan is an island country in East Asia, located in the northwest Pacific Ocean.', 'Tokyo is the capital and most populous prefecture of Japan.']"
-          nullable: true
-        file:
-          description: |
-            The ID of an uploaded file that contains documents to search over. See [upload file](/docs/api-reference/files/upload) for how to upload a file of the desired format and purpose.
-
-            You should specify either `documents` or a `file`, but not both.
-          type: string
-          nullable: true
-        search_model: &search_model_configuration
-          description: ID of the model to use for [Search](/docs/api-reference/searches/create). You can select one of `ada`, `babbage`, `curie`, or `davinci`.
-          type: string
-          default: ada
-          nullable: true
-        max_rerank:
-          description: The maximum number of documents to be ranked by [Search](/docs/api-reference/searches/create) when using `file`. Setting it to a higher value leads to improved accuracy but with increased latency and cost.
-          type: integer
-          default: 200
-          nullable: true
-        temperature:
-          description: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
-          type: number
-          default: 0
-          nullable: true
-        logprobs: &context_completions_logprobs_configuration
-          type: integer
-          minimum: 0
-          maximum: 5
-          default: null
-          nullable: true
-          description: |
-            Include the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. For example, if `logprobs` is 5, the API will return a list of the 5 most likely tokens. The API will always return the `logprob` of the sampled token, so there may be up to `logprobs+1` elements in the response.
-
-            The maximum value for `logprobs` is 5. If you need more than this, please contact us through our [Help center](https://help.openai.com) and describe your use case.
-
-            When `logprobs` is set, `completion` will be automatically added into `expand` to get the logprobs.
-        max_tokens:
-          description: The maximum number of tokens allowed for the generated answer
-          type: integer
-          default: 16
-          nullable: true
-        stop:
-          description: *completions_stop_description
-          default: null
-          oneOf:
-            - type: string
-              default: <|endoftext|>
-              example: "\n"
-            - type: array
-              minItems: 1
-              maxItems: 4
-              items:
-                type: string
-                example: '["\n"]'
-          nullable: true
-        n:
-          description: How many answers to generate for each question.
-          type: integer
-          minimum: 1
-          maximum: 10
-          default: 1
-          nullable: true
-        logit_bias: *completions_logit_bias
-        return_metadata: *return_metadata_configuration
-        return_prompt: &return_prompt_configuration
-          description: If set to `true`, the returned JSON will include a "prompt" field containing the final prompt that was used to request a completion. This is mainly useful for debugging purposes.
-          type: boolean
-          default: false
-          nullable: true
-        expand: &expand_configuration
-          description: If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID. Currently we support `completion` and `file` objects for expansion.
-          type: array
-          items: {}
-          nullable: true
-          default: []
-        user: *end_user_param_configuration
-      required:
-        - model
-        - question
-        - examples
-        - examples_context
-
-    CreateAnswerResponse:
-      type: object
-      properties:
-        object:
-          type: string
-        model:
-          type: string
-        search_model:
-          type: string
-        completion:
-          type: string
-        answers:
-          type: array
-          items:
-            type: string
-        selected_documents:
-          type: array
-          items:
-            type: object
-            properties:
-              document:
-                type: integer
-              text:
-                type: string
-
-    CreateClassificationRequest:
-      type: object
-      additionalProperties: false
-      properties:
-        model: *model_configuration
-        query:
-          description: Query to be classified.
-          type: string
-          minLength: 1
-          example: "The plot is not very attractive."
-        examples:
-          description: |
-            A list of examples with labels, in the following format:
-
-            `[["The movie is so interesting.", "Positive"], ["It is quite boring.", "Negative"], ...]`
-
-            All the label strings will be normalized to be capitalized.
-
-            You should specify either `examples` or `file`, but not both.
-          type: array
-          minItems: 2
-          maxItems: 200
-          items:
-            type: array
-            minItems: 2
-            maxItems: 2
-            items:
-              type: string
-              minLength: 1
-          example: "[['Do not see this film.', 'Negative'], ['Smart, provocative and blisteringly funny.', 'Positive']]"
-          nullable: true
-        file:
-          description: |
-            The ID of the uploaded file that contains training examples. See [upload file](/docs/api-reference/files/upload) for how to upload a file of the desired format and purpose.
-
-            You should specify either `examples` or `file`, but not both.
-          type: string
-          nullable: true
-        labels:
-          description: The set of categories being classified. If not specified, candidate labels will be automatically collected from the examples you provide. All the label strings will be normalized to be capitalized.
-          type: array
-          minItems: 2
-          maxItems: 200
-          default: null
-          items:
-            type: string
-          example: ["Positive", "Negative"]
-          nullable: true
-        search_model: *search_model_configuration
-        temperature:
-          description:
-            What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
-          type: number
-          minimum: 0
-          maximum: 2
-          default: 0
-          nullable: true
-          example: 0
-        logprobs: *context_completions_logprobs_configuration
-        max_examples:
-          description: The maximum number of examples to be ranked by [Search](/docs/api-reference/searches/create) when using `file`. Setting it to a higher value leads to improved accuracy but with increased latency and cost.
-          type: integer
-          default: 200
-          nullable: true
-        logit_bias: *completions_logit_bias
-        return_prompt: *return_prompt_configuration
-        return_metadata: *return_metadata_configuration
-        expand: *expand_configuration
-        user: *end_user_param_configuration
-      required:
-        - model
-        - query
-
-    CreateClassificationResponse:
-      type: object
-      properties:
-        object:
-          type: string
-        model:
-          type: string
-        search_model:
-          type: string
-        completion:
-          type: string
-        label:
-          type: string
-        selected_examples:
-          type: array
-          items:
-            type: object
-            properties:
-              document:
-                type: integer
-              text:
-                type: string
-              label:
-                type: string
-
-    CreateFineTuneRequest:
-      type: object
-      properties:
-        training_file:
-          description: |
-            The ID of an uploaded file that contains training data.
-
-            See [upload file](/docs/api-reference/files/upload) for how to upload a file.
-
-            Your dataset must be formatted as a JSONL file, where each training
-            example is a JSON object with the keys "prompt" and "completion".
-            Additionally, you must upload your file with the purpose `fine-tune`.
-
-            See the [fine-tuning guide](/docs/guides/fine-tuning/creating-training-data) for more details.
-          type: string
-          example: "file-ajSREls59WBbvgSzJSVWxMCB"
-        validation_file:
-          description: |
-            The ID of an uploaded file that contains validation data.
-
-            If you provide this file, the data is used to generate validation
-            metrics periodically during fine-tuning. These metrics can be viewed in
-            the [fine-tuning results file](/docs/guides/fine-tuning/analyzing-your-fine-tuned-model).
-            Your train and validation data should be mutually exclusive.
-
-            Your dataset must be formatted as a JSONL file, where each validation
-            example is a JSON object with the keys "prompt" and "completion".
-            Additionally, you must upload your file with the purpose `fine-tune`.
-
-            See the [fine-tuning guide](/docs/guides/fine-tuning/creating-training-data) for more details.
-          type: string
-          nullable: true
-          example: "file-XjSREls59WBbvgSzJSVWxMCa"
-        model:
-          description: |
-            The name of the base model to fine-tune. You can select one of "ada",
-            "babbage", "curie", "davinci", or a fine-tuned model created after 2022-04-21.
-            To learn more about these models, see the
-            [Models](https://platform.openai.com/docs/models) documentation.
-          default: "curie"
-          type: string
-          nullable: true
-        n_epochs:
-          description: |
-            The number of epochs to train the model for. An epoch refers to one
-            full cycle through the training dataset.
-          default: 4
-          type: integer
-          nullable: true
-        batch_size:
-          description: |
-            The batch size to use for training. The batch size is the number of
-            training examples used to train a single forward and backward pass.
-
-            By default, the batch size will be dynamically configured to be
-            ~0.2% of the number of examples in the training set, capped at 256 -
-            in general, we've found that larger batch sizes tend to work better
-            for larger datasets.
-          default: null
-          type: integer
-          nullable: true
-        learning_rate_multiplier:
-          description: |
-            The learning rate multiplier to use for training.
-            The fine-tuning learning rate is the original learning rate used for
-            pretraining multiplied by this value.
-
-            By default, the learning rate multiplier is the 0.05, 0.1, or 0.2
-            depending on final `batch_size` (larger learning rates tend to
-            perform better with larger batch sizes). We recommend experimenting
-            with values in the range 0.02 to 0.2 to see what produces the best
-            results.
-          default: null
-          type: number
-          nullable: true
-        prompt_loss_weight:
-          description: |
-            The weight to use for loss on the prompt tokens. This controls how
-            much the model tries to learn to generate the prompt (as compared
-            to the completion which always has a weight of 1.0), and can add
-            a stabilizing effect to training when completions are short.
-
-            If prompts are extremely long (relative to completions), it may make
-            sense to reduce this weight so as to avoid over-prioritizing
-            learning the prompt.
-          default: 0.01
-          type: number
-          nullable: true
-        compute_classification_metrics:
-          description: |
-            If set, we calculate classification-specific metrics such as accuracy
-            and F-1 score using the validation set at the end of every epoch.
-            These metrics can be viewed in the [results file](/docs/guides/fine-tuning/analyzing-your-fine-tuned-model).
-
-            In order to compute classification metrics, you must provide a
-            `validation_file`. Additionally, you must
-            specify `classification_n_classes` for multiclass classification or
-            `classification_positive_class` for binary classification.
-          type: boolean
-          default: false
-          nullable: true
-        classification_n_classes:
-          description: |
-            The number of classes in a classification task.
-
-            This parameter is required for multiclass classification.
-          type: integer
-          default: null
-          nullable: true
-        classification_positive_class:
-          description: |
-            The positive class in binary classification.
-
-            This parameter is needed to generate precision, recall, and F1
-            metrics when doing binary classification.
-          type: string
-          default: null
-          nullable: true
-        classification_betas:
-          description: |
-            If this is provided, we calculate F-beta scores at the specified
-            beta values. The F-beta score is a generalization of F-1 score.
-            This is only used for binary classification.
-
-            With a beta of 1 (i.e. the F-1 score), precision and recall are
-            given the same weight. A larger beta score puts more weight on
-            recall and less on precision. A smaller beta score puts more weight
-            on precision and less on recall.
-          type: array
-          items:
-            type: number
-          example: [0.6, 1, 1.5, 2]
-          default: null
-          nullable: true
-        suffix:
-          description: |
-            A string of up to 40 characters that will be added to your fine-tuned model name.
-
-            For example, a `suffix` of "custom-model-name" would produce a model name like `ada:ft-your-org:custom-model-name-2022-02-15-04-21-04`.
-          type: string
-          minLength: 1
-          maxLength: 40
-          default: null
-          nullable: true
-      required:
-        - training_file
-
-    ListFineTunesResponse:
-      type: object
-      properties:
-        object:
-          type: string
-        data:
-          type: array
-          items:
-            $ref: '#/components/schemas/FineTune'
-      required: 
-        - object
-        - data
-
-    ListFineTuneEventsResponse:
-      type: object
-      properties:
-        object:
-          type: string
-        data:
-          type: array
-          items:
-            $ref: '#/components/schemas/FineTuneEvent'
-      required: 
-        - object
-        - data
-
-    CreateEmbeddingRequest:
-      type: object
-      additionalProperties: false
-      properties:
-        model: *model_configuration
-        input:
-          description: |
-            Input text to get embeddings for, encoded as a string or array of tokens. To get embeddings for multiple inputs in a single request, pass an array of strings or array of token arrays. Each input must not exceed 8192 tokens in length.
-          example: "The quick brown fox jumped over the lazy dog"
-          oneOf:
-            - type: string
-              default: ''
-              example: "This is a test."
-            - type: array
-              items:
-                type: string
-                default: ''
-                example: "This is a test."
-            - type: array
-              minItems: 1
-              items:
-                type: integer
-              example: "[1212, 318, 257, 1332, 13]"
-            - type: array
-              minItems: 1
-              items:
-                type: array
-                minItems: 1
-                items:
-                  type: integer
-              example: "[[1212, 318, 257, 1332, 13]]"
-        user: *end_user_param_configuration
-      required:
-        - model
-        - input
-
-    CreateEmbeddingResponse:
-      type: object
-      properties:
-        object:
-          type: string
-        model:
-          type: string
-        data:
-          type: array
-          items:
-            type: object
-            properties:
-              index:
-                type: integer
-              object:
-                type: string
-              embedding:
-                type: array
-                items:
-                  type: number
-            required: 
-              - index
-              - object
-              - embedding
-        usage:
-          type: object
-          properties:
-            prompt_tokens:
-              type: integer
-            total_tokens:
-              type: integer
-          required: 
-            - prompt_tokens
-            - total_tokens
-      required: 
-        - object
-        - model
-        - data
-        - usage
-
-    CreateTranscriptionRequest:
-      type: object
-      additionalProperties: false
-      properties:
-        file: 
-          description: |
-            The audio file to transcribe, in one of these formats: mp3, mp4, mpeg, mpga, m4a, wav, or webm.
-          type: string
-          format: binary
-        model: 
-          description: |
-            ID of the model to use. Only `whisper-1` is currently available.
-          type: string
-        prompt:
-          description: |
-            An optional text to guide the model's style or continue a previous audio segment. The [prompt](/docs/guides/speech-to-text/prompting) should match the audio language.
-          type: string
-        response_format:
-          description: |
-            The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.
-          type: string
-          default: json
-        temperature:
-          description: |
-            The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use [log probability](https://en.wikipedia.org/wiki/Log_probability) to automatically increase the temperature until certain thresholds are hit.
-          type: number
-          default: 0
-        language:
-          description: |
-            The language of the input audio. Supplying the input language in [ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) format will improve accuracy and latency.
-          type: string
-      required:
-        - file
-        - model
-
-    # Note: This does not currently support the non-default response format types. 
-    CreateTranscriptionResponse:
-      type: object
-      properties:
-        text:
-          type: string
-      required: 
-        - text
-
-    CreateTranslationRequest:
-      type: object
-      additionalProperties: false
-      properties:
-        file: 
-          description: |
-            The audio file to translate, in one of these formats: mp3, mp4, mpeg, mpga, m4a, wav, or webm.
-          type: string
-          format: binary
-        model: 
-          description: |
-            ID of the model to use. Only `whisper-1` is currently available.
-          type: string
-        prompt:
-          description: |
-            An optional text to guide the model's style or continue a previous audio segment. The [prompt](/docs/guides/speech-to-text/prompting) should be in English.
-          type: string
-        response_format:
-          description: |
-            The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.
-          type: string
-          default: json
-        temperature:
-          description: |
-            The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use [log probability](https://en.wikipedia.org/wiki/Log_probability) to automatically increase the temperature until certain thresholds are hit.
-          type: number
-          default: 0
-      required:
-        - file
-        - model
-
-    # Note: This does not currently support the non-default response format types. 
-    CreateTranslationResponse:
-      type: object
-      properties:
-        text:
-          type: string
-      required: 
-        - text
-
-    Engine:
-      title: Engine
-      properties:
-        id:
-          type: string
-        object:
-          type: string
-        created:
-          type: integer
-          nullable: true
-        ready:
-          type: boolean
-      required: 
-        - id
-        - object
-        - created
-        - ready
-
-    Model:
-      title: Model
-      properties:
-        id:
-          type: string
-        object:
-          type: string
-        created:
-          type: integer
-        owned_by:
-          type: string
-      required: 
-        - id
-        - object
-        - created
-        - owned_by
-
-    OpenAIFile:
-      title: OpenAIFile
-      properties:
-        id:
-          type: string
-        object:
-          type: string
-        bytes:
-          type: integer
-        created_at:
-          type: integer
-        filename:
-          type: string
-        purpose:
-          type: string
-        status:
-          type: string
-        status_details:
-          type: object
-          nullable: true
-      required: 
-        - id
-        - object
-        - bytes
-        - created_at
-        - filename
-        - purpose
-
-    FineTune:
-      title: FineTune
-      properties:
-        id:
-          type: string
-        object:
-          type: string
-        created_at:
-          type: integer
-        updated_at:
-          type: integer
-        model:
-          type: string
-        fine_tuned_model:
-          type: string
-          nullable: true
-        organization_id:
-          type: string
-        status:
-          type: string
-        hyperparams:
-          type: object
-        training_files:
-          type: array
-          items:
-            $ref: '#/components/schemas/OpenAIFile'
-        validation_files:
-          type: array
-          items:
-            $ref: '#/components/schemas/OpenAIFile'
-        result_files:
-          type: array
-          items:
-            $ref: '#/components/schemas/OpenAIFile'
-        events:
-          type: array
-          items:
-            $ref: '#/components/schemas/FineTuneEvent'
-      required: 
-        - id
-        - object
-        - created_at
-        - updated_at
-        - model
-        - fine_tuned_model
-        - organization_id
-        - status
-        - hyperparams
-        - training_files
-        - validation_files
-        - result_files
-
-    FineTuneEvent:
-      title: FineTuneEvent
-      properties:
-        object:
-          type: string
-        created_at:
-          type: integer
-        level:
-          type: string
-        message:
-          type: string
-      required: 
-        - object
-        - created_at
-        - level
-        - message
-
-x-oaiMeta:
-  groups:
-    - id: models
-      title: Models
-      description: |
-        List and describe the various models available in the API. You can refer to the [Models](/docs/models) documentation to understand what models are available and the differences between them.
-    - id: completions
-      title: Completions
-      description: |
-        Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position.
-    - id: chat
-      title: Chat
-      description: |
-        Given a chat conversation, the model will return a chat completion response.
-    - id: edits
-      title: Edits
-      description: |
-        Given a prompt and an instruction, the model will return an edited version of the prompt.
-    - id: images
-      title: Images
-      description: |
-        Given a prompt and/or an input image, the model will generate a new image.
-
-        Related guide: [Image generation](/docs/guides/images)
-    - id: embeddings
-      title: Embeddings
-      description: |
-        Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
-
-        Related guide: [Embeddings](/docs/guides/embeddings)
-    - id: audio
-      title: Audio
-      description: |
-        Learn how to turn audio into text.
-
-        Related guide: [Speech to text](/docs/guides/speech-to-text)
-    - id: files
-      title: Files
-      description: |
-        Files are used to upload documents that can be used with features like [Fine-tuning](/docs/api-reference/fine-tunes).
-    - id: fine-tunes
-      title: Fine-tunes
-      description: |
-        Manage fine-tuning jobs to tailor a model to your specific training data.
-
-        Related guide: [Fine-tune models](/docs/guides/fine-tuning)
-    - id: moderations
-      title: Moderations
-      description: |
-        Given a input text, outputs if the model classifies it as violating OpenAI's content policy.
-
-        Related guide: [Moderations](/docs/guides/moderation)
-    - id: searches
-      title: Searches
-      warning:
-        title: This endpoint is deprecated and will be removed on December 3rd, 2022
-        message: We’ve developed new methods with better performance. [Learn more](https://help.openai.com/en/articles/6272952-search-transition-guide).
-      description: |
-        Given a query and a set of documents or labels, the model ranks each document based on its semantic similarity to the provided query.
-
-        Related guide: [Search](/docs/guides/search)
-    - id: classifications
-      title: Classifications
-      warning:
-        title: This endpoint is deprecated and will be removed on December 3rd, 2022
-        message: We’ve developed new methods with better performance. [Learn more](https://help.openai.com/en/articles/6272941-classifications-transition-guide).
-      description: |
-        Given a query and a set of labeled examples, the model will predict the most likely label for the query. Useful as a drop-in replacement for any ML classification or text-to-label task.
-
-        Related guide: [Classification](/docs/guides/classifications)
-    - id: answers
-      title: Answers
-      warning:
-        title: This endpoint is deprecated and will be removed on December 3rd, 2022
-        message: We’ve developed new methods with better performance. [Learn more](https://help.openai.com/en/articles/6233728-answers-transition-guide).
-      description: |
-        Given a question, a set of documents, and some examples, the API generates an answer to the question based on the information in the set of documents. This is useful for question-answering applications on sources of truth, like company documentation or a knowledge base.
-
-        Related guide: [Question answering](/docs/guides/answers)
-    - id: engines
-      title: Engines
-      description: These endpoints describe and provide access to the various engines available in the API.
-      warning:
-        title: The Engines endpoints are deprecated.
-        message: Please use their replacement, [Models](/docs/api-reference/models), instead. [Learn more](https://help.openai.com/TODO).
diff --git a/docs/extras/use_cases/apis/openapi.ipynb b/docs/extras/use_cases/apis/openapi.ipynb
deleted file mode 100644
index 625a5f24140..00000000000
--- a/docs/extras/use_cases/apis/openapi.ipynb
+++ /dev/null
@@ -1,583 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9fcaa37f",
-   "metadata": {},
-   "source": [
-    "# OpenAPI chain\n",
-    "\n",
-    "This notebook shows an example of using an OpenAPI chain to call an endpoint in natural language, and get back a response in natural language."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "efa6909f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.tools import OpenAPISpec, APIOperation\n",
-    "from langchain.chains import OpenAPIEndpointChain\n",
-    "from langchain.requests import Requests\n",
-    "from langchain.llms import OpenAI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71e38c6c",
-   "metadata": {},
-   "source": [
-    "## Load the spec\n",
-    "\n",
-    "Load a wrapper of the spec (so we can work with it more easily). You can load from a url or from a local file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0831271b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n"
-     ]
-    }
-   ],
-   "source": [
-    "spec = OpenAPISpec.from_url(\n",
-    "    \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "189dd506",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Alternative loading from file\n",
-    "# spec = OpenAPISpec.from_file(\"openai_openapi.yaml\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7093582",
-   "metadata": {},
-   "source": [
-    "## Select the Operation\n",
-    "\n",
-    "In order to provide a focused on modular chain, we create a chain specifically only for one of the endpoints. Here we get an API operation from a specified endpoint and method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "157494b9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "operation = APIOperation.from_openapi_spec(spec, \"/public/openai/v0/products\", \"get\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3ab1c5c",
-   "metadata": {},
-   "source": [
-    "## Construct the chain\n",
-    "\n",
-    "We can now construct a chain to interact with it. In order to construct such a chain, we will pass in:\n",
-    "\n",
-    "1. The operation endpoint\n",
-    "2. A requests wrapper (can be used to handle authentication, etc)\n",
-    "3. The LLM to use to interact with it"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "788a7cef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = OpenAI()  # Load a Language Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "c5f27406",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = OpenAPIEndpointChain.from_api_operation(\n",
-    "    operation,\n",
-    "    llm,\n",
-    "    requests=Requests(),\n",
-    "    verbose=True,\n",
-    "    return_intermediate_steps=True,  # Return request and response text\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "23652053",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new OpenAPIEndpointChain chain...\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new APIRequesterChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mYou are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.\n",
-      "\n",
-      "API_SCHEMA: ```typescript\n",
-      "/* API for fetching Klarna product information */\n",
-      "type productsUsingGET = (_: {\n",
-      "/* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */\n",
-      "\t\tq: string,\n",
-      "/* number of products returned */\n",
-      "\t\tsize?: number,\n",
-      "/* (Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */\n",
-      "\t\tmin_price?: number,\n",
-      "/* (Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */\n",
-      "\t\tmax_price?: number,\n",
-      "}) => any;\n",
-      "```\n",
-      "\n",
-      "USER_INSTRUCTIONS: \"whats the most expensive shirt?\"\n",
-      "\n",
-      "Your arguments must be plain json provided in a markdown block:\n",
-      "\n",
-      "ARGS: ```json\n",
-      "{valid json conforming to API_SCHEMA}\n",
-      "```\n",
-      "\n",
-      "Example\n",
-      "-----\n",
-      "\n",
-      "ARGS: ```json\n",
-      "{\"foo\": \"bar\", \"baz\": {\"qux\": \"quux\"}}\n",
-      "```\n",
-      "\n",
-      "The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.\n",
-      "You MUST strictly comply to the types indicated by the provided schema, including all required args.\n",
-      "\n",
-      "If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:\n",
-      "\n",
-      "Message: ```text\n",
-      "Concise response requesting the additional information that would make calling the function successful.\n",
-      "```\n",
-      "\n",
-      "Begin\n",
-      "-----\n",
-      "ARGS:\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m{\"q\": \"shirt\", \"size\": 1, \"max_price\": null}\u001b[0m\n",
-      "\u001b[36;1m\u001b[1;3m{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new APIResponderChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mYou are a helpful AI assistant trained to answer user queries from API responses.\n",
-      "You attempted to call an API, which resulted in:\n",
-      "API_RESPONSE: {\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}\n",
-      "\n",
-      "USER_COMMENT: \"whats the most expensive shirt?\"\n",
-      "\n",
-      "\n",
-      "If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:\n",
-      "Response: ```json\n",
-      "{\"response\": \"Human-understandable synthesis of the API_RESPONSE\"}\n",
-      "```\n",
-      "\n",
-      "Otherwise respond with the following markdown json block:\n",
-      "Response Error: ```json\n",
-      "{\"response\": \"What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion.\"}\n",
-      "```\n",
-      "\n",
-      "You MUST respond as a markdown json code block. The person you are responding to CANNOT see the API_RESPONSE, so if there is any relevant information there you must include it in your response.\n",
-      "\n",
-      "Begin:\n",
-      "---\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mThe most expensive shirt in the API response is the Burberry Check Poplin Shirt, which costs $360.00.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "output = chain(\"whats the most expensive shirt?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c000295e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'request_args': '{\"q\": \"shirt\", \"size\": 1, \"max_price\": null}',\n",
-       " 'response_text': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]}]}'}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# View intermediate steps\n",
-    "output[\"intermediate_steps\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "092bdb4d",
-   "metadata": {},
-   "source": [
-    "## Return raw response\n",
-    "\n",
-    "We can also run this chain without synthesizing the response. This will have the effect of just returning the raw API output."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "4dff3849",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = OpenAPIEndpointChain.from_api_operation(\n",
-    "    operation,\n",
-    "    llm,\n",
-    "    requests=Requests(),\n",
-    "    verbose=True,\n",
-    "    return_intermediate_steps=True,  # Return request and response text\n",
-    "    raw_response=True,  # Return raw response\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "762499a9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new OpenAPIEndpointChain chain...\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new APIRequesterChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mYou are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.\n",
-      "\n",
-      "API_SCHEMA: ```typescript\n",
-      "/* API for fetching Klarna product information */\n",
-      "type productsUsingGET = (_: {\n",
-      "/* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */\n",
-      "\t\tq: string,\n",
-      "/* number of products returned */\n",
-      "\t\tsize?: number,\n",
-      "/* (Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */\n",
-      "\t\tmin_price?: number,\n",
-      "/* (Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */\n",
-      "\t\tmax_price?: number,\n",
-      "}) => any;\n",
-      "```\n",
-      "\n",
-      "USER_INSTRUCTIONS: \"whats the most expensive shirt?\"\n",
-      "\n",
-      "Your arguments must be plain json provided in a markdown block:\n",
-      "\n",
-      "ARGS: ```json\n",
-      "{valid json conforming to API_SCHEMA}\n",
-      "```\n",
-      "\n",
-      "Example\n",
-      "-----\n",
-      "\n",
-      "ARGS: ```json\n",
-      "{\"foo\": \"bar\", \"baz\": {\"qux\": \"quux\"}}\n",
-      "```\n",
-      "\n",
-      "The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.\n",
-      "You MUST strictly comply to the types indicated by the provided schema, including all required args.\n",
-      "\n",
-      "If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:\n",
-      "\n",
-      "Message: ```text\n",
-      "Concise response requesting the additional information that would make calling the function successful.\n",
-      "```\n",
-      "\n",
-      "Begin\n",
-      "-----\n",
-      "ARGS:\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m{\"q\": \"shirt\", \"max_price\": null}\u001b[0m\n",
-      "\u001b[36;1m\u001b[1;3m{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "output = chain(\"whats the most expensive shirt?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "4afc021a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'instructions': 'whats the most expensive shirt?',\n",
-       " 'output': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}',\n",
-       " 'intermediate_steps': {'request_args': '{\"q\": \"shirt\", \"max_price\": null}',\n",
-       "  'response_text': '{\"products\":[{\"name\":\"Burberry Check Poplin Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$360.00\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:Gray,Blue,Beige\",\"Properties:Pockets\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Cotton Shirt - Beige\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$229.02\",\"attributes\":[\"Material:Cotton,Elastane\",\"Color:Beige\",\"Model:Boy\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Vintage Check Stretch Cotton Twill Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$309.99\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Woman\",\"Color:Beige\",\"Properties:Stretch\",\"Pattern:Checkered\"]},{\"name\":\"Burberry Somerton Check Shirt - Camel\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$450.00\",\"attributes\":[\"Material:Elastane/Lycra/Spandex,Cotton\",\"Target Group:Man\",\"Color:Beige\"]},{\"name\":\"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin\",\"price\":\"$19.99\",\"attributes\":[\"Material:Polyester,Nylon\",\"Target Group:Man\",\"Color:Red,Pink,White,Blue,Purple,Beige,Black,Green\",\"Properties:Pockets\",\"Pattern:Solid Color\"]}]}'}}"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8d7924e4",
-   "metadata": {},
-   "source": [
-    "## Example POST message\n",
-    "\n",
-    "For this demo, we will interact with the speak API."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c56b1a04",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
-      "Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n"
-     ]
-    }
-   ],
-   "source": [
-    "spec = OpenAPISpec.from_url(\"https://api.speak.com/openapi.yaml\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "177d8275",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "operation = APIOperation.from_openapi_spec(\n",
-    "    spec, \"/v1/public/openai/explain-task\", \"post\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "835c5ddc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = OpenAI()\n",
-    "chain = OpenAPIEndpointChain.from_api_operation(\n",
-    "    operation, llm, requests=Requests(), verbose=True, return_intermediate_steps=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "59855d60",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new OpenAPIEndpointChain chain...\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new APIRequesterChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mYou are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.\n",
-      "\n",
-      "API_SCHEMA: ```typescript\n",
-      "type explainTask = (_: {\n",
-      "/* Description of the task that the user wants to accomplish or do. For example, \"tell the waiter they messed up my order\" or \"compliment someone on their shirt\" */\n",
-      "  task_description?: string,\n",
-      "/* The foreign language that the user is learning and asking about. The value can be inferred from question - for example, if the user asks \"how do i ask a girl out in mexico city\", the value should be \"Spanish\" because of Mexico City. Always use the full name of the language (e.g. Spanish, French). */\n",
-      "  learning_language?: string,\n",
-      "/* The user's native language. Infer this value from the language the user asked their question in. Always use the full name of the language (e.g. Spanish, French). */\n",
-      "  native_language?: string,\n",
-      "/* A description of any additional context in the user's question that could affect the explanation - e.g. setting, scenario, situation, tone, speaking style and formality, usage notes, or any other qualifiers. */\n",
-      "  additional_context?: string,\n",
-      "/* Full text of the user's question. */\n",
-      "  full_query?: string,\n",
-      "}) => any;\n",
-      "```\n",
-      "\n",
-      "USER_INSTRUCTIONS: \"How would ask for more tea in Delhi?\"\n",
-      "\n",
-      "Your arguments must be plain json provided in a markdown block:\n",
-      "\n",
-      "ARGS: ```json\n",
-      "{valid json conforming to API_SCHEMA}\n",
-      "```\n",
-      "\n",
-      "Example\n",
-      "-----\n",
-      "\n",
-      "ARGS: ```json\n",
-      "{\"foo\": \"bar\", \"baz\": {\"qux\": \"quux\"}}\n",
-      "```\n",
-      "\n",
-      "The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.\n",
-      "You MUST strictly comply to the types indicated by the provided schema, including all required args.\n",
-      "\n",
-      "If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:\n",
-      "\n",
-      "Message: ```text\n",
-      "Concise response requesting the additional information that would make calling the function successful.\n",
-      "```\n",
-      "\n",
-      "Begin\n",
-      "-----\n",
-      "ARGS:\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m{\"task_description\": \"ask for more tea\", \"learning_language\": \"Hindi\", \"native_language\": \"English\", \"full_query\": \"How would I ask for more tea in Delhi?\"}\u001b[0m\n",
-      "\u001b[36;1m\u001b[1;3m{\"explanation\":\"<what-to-say language=\\\"Hindi\\\" context=\\\"None\\\">\\nऔर चाय लाओ। (Aur chai lao.) \\n</what-to-say>\\n\\n<alternatives context=\\\"None\\\">\\n1. \\\"चाय थोड़ी ज्यादा मिल सकती है?\\\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\\n2. \\\"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\\\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\\n3. \\\"क्या मुझे or cup में milk/tea powder मिल सकता है?\\\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of milk or tea powder)*\\n</alternatives>\\n\\n<usage-notes>\\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\\n</usage-notes>\\n\\n<example-convo language=\\\"Hindi\\\">\\n<context>At home during breakfast.</context>\\nPreeti: सर, क्या main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\\nRahul: हां,बिल्कुल। और चाय की मात्रा में भी थोड़ा सा इजाफा करना। (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as well.)\\n</example-convo>\\n\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*\",\"extra_response_instructions\":\"Use all information in the API response and fully render all Markdown.\\nAlways end your response with a link to report an issue or leave feedback on the plugin.\"}\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new APIResponderChain chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mYou are a helpful AI assistant trained to answer user queries from API responses.\n",
-      "You attempted to call an API, which resulted in:\n",
-      "API_RESPONSE: {\"explanation\":\"<what-to-say language=\\\"Hindi\\\" context=\\\"None\\\">\\nऔर चाय लाओ। (Aur chai lao.) \\n</what-to-say>\\n\\n<alternatives context=\\\"None\\\">\\n1. \\\"चाय थोड़ी ज्यादा मिल सकती है?\\\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\\n2. \\\"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\\\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\\n3. \\\"क्या मुझे or cup में milk/tea powder मिल सकता है?\\\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of milk or tea powder)*\\n</alternatives>\\n\\n<usage-notes>\\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\\n</usage-notes>\\n\\n<example-convo language=\\\"Hindi\\\">\\n<context>At home during breakfast.</context>\\nPreeti: सर, क्या main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\\nRahul: हां,बिल्कुल। और चाय की मात्रा में भी थोड़ा सा इजाफा करना। (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as well.)\\n</example-convo>\\n\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*\",\"extra_response_instructions\":\"Use all information in the API response and fully render all Markdown.\\nAlways end your response with a link to report an issue or leave feedback on the plugin.\"}\n",
-      "\n",
-      "USER_COMMENT: \"How would ask for more tea in Delhi?\"\n",
-      "\n",
-      "\n",
-      "If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:\n",
-      "Response: ```json\n",
-      "{\"response\": \"Concise response to USER_COMMENT based on API_RESPONSE.\"}\n",
-      "```\n",
-      "\n",
-      "Otherwise respond with the following markdown json block:\n",
-      "Response Error: ```json\n",
-      "{\"response\": \"What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion.\"}\n",
-      "```\n",
-      "\n",
-      "You MUST respond as a markdown json code block.\n",
-      "\n",
-      "Begin:\n",
-      "---\n",
-      "\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mIn Delhi you can ask for more tea by saying 'Chai thodi zyada mil sakti hai?'\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "output = chain(\"How would ask for more tea in Delhi?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "91bddb18",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['{\"task_description\": \"ask for more tea\", \"learning_language\": \"Hindi\", \"native_language\": \"English\", \"full_query\": \"How would I ask for more tea in Delhi?\"}',\n",
-       " '{\"explanation\":\"<what-to-say language=\\\\\"Hindi\\\\\" context=\\\\\"None\\\\\">\\\\nऔर चाय लाओ। (Aur chai lao.) \\\\n</what-to-say>\\\\n\\\\n<alternatives context=\\\\\"None\\\\\">\\\\n1. \\\\\"चाय थोड़ी ज्यादा मिल सकती है?\\\\\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\\\\n2. \\\\\"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\\\\\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\\\\n3. \\\\\"क्या मुझे or cup में milk/tea powder मिल सकता है?\\\\\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of milk or tea powder)*\\\\n</alternatives>\\\\n\\\\n<usage-notes>\\\\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\\\\n</usage-notes>\\\\n\\\\n<example-convo language=\\\\\"Hindi\\\\\">\\\\n<context>At home during breakfast.</context>\\\\nPreeti: सर, क्या main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\\\\nRahul: हां,बिल्कुल। और चाय की मात्रा में भी थोड़ा सा इजाफा करना। (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as well.)\\\\n</example-convo>\\\\n\\\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*\",\"extra_response_instructions\":\"Use all information in the API response and fully render all Markdown.\\\\nAlways end your response with a link to report an issue or leave feedback on the plugin.\"}']"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Show the API chain's intermediate steps\n",
-    "output[\"intermediate_steps\"]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/extras/use_cases/apis/openapi_openai.ipynb b/docs/extras/use_cases/apis/openapi_openai.ipynb
deleted file mode 100644
index bb1cbce5935..00000000000
--- a/docs/extras/use_cases/apis/openapi_openai.ipynb
+++ /dev/null
@@ -1,249 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e734b314",
-   "metadata": {},
-   "source": [
-    "# OpenAPI calls with OpenAI functions\n",
-    "\n",
-    "In this notebook we'll show how to create a chain that automatically makes calls to an API based only on an OpenAPI  spec. Under the hood, we're parsing the OpenAPI spec into a JSON schema that the OpenAI functions API can handle. This allows ChatGPT to automatically select and populate the relevant API call to make for any user input. Using the output of ChatGPT we then make the actual API call, and return the result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "555661b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains.openai_functions.openapi import get_openapi_chain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a95f510a",
-   "metadata": {},
-   "source": [
-    "## Query Klarna"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "08e19b64",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = get_openapi_chain(\n",
-    "    \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3959f866",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'products': [{'name': \"Tommy Hilfiger Men's Short Sleeve Button-Down Shirt\",\n",
-       "   'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3204878580/Clothing/Tommy-Hilfiger-Men-s-Short-Sleeve-Button-Down-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
-       "   'price': '$26.78',\n",
-       "   'attributes': ['Material:Linen,Cotton',\n",
-       "    'Target Group:Man',\n",
-       "    'Color:Gray,Pink,White,Blue,Beige,Black,Turquoise',\n",
-       "    'Size:S,XL,M,XXL']},\n",
-       "  {'name': \"Van Heusen Men's Long Sleeve Button-Down Shirt\",\n",
-       "   'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3201809514/Clothing/Van-Heusen-Men-s-Long-Sleeve-Button-Down-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
-       "   'price': '$18.89',\n",
-       "   'attributes': ['Material:Cotton',\n",
-       "    'Target Group:Man',\n",
-       "    'Color:Red,Gray,White,Blue',\n",
-       "    'Size:XL,XXL']},\n",
-       "  {'name': 'Brixton Bowery Flannel Shirt',\n",
-       "   'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202331096/Clothing/Brixton-Bowery-Flannel-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
-       "   'price': '$34.48',\n",
-       "   'attributes': ['Material:Cotton',\n",
-       "    'Target Group:Man',\n",
-       "    'Color:Gray,Blue,Black,Orange',\n",
-       "    'Size:XL,3XL,4XL,5XL,L,M,XXL']},\n",
-       "  {'name': 'Cubavera Four Pocket Guayabera Shirt',\n",
-       "   'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202055522/Clothing/Cubavera-Four-Pocket-Guayabera-Shirt/?utm_source=openai&ref-site=openai_plugin',\n",
-       "   'price': '$23.22',\n",
-       "   'attributes': ['Material:Polyester,Cotton',\n",
-       "    'Target Group:Man',\n",
-       "    'Color:Red,White,Blue,Black',\n",
-       "    'Size:S,XL,L,M,XXL']},\n",
-       "  {'name': 'Theory Sylvain Shirt - Eclipse',\n",
-       "   'url': 'https://www.klarna.com/us/shopping/pl/cl10001/3202028254/Clothing/Theory-Sylvain-Shirt-Eclipse/?utm_source=openai&ref-site=openai_plugin',\n",
-       "   'price': '$86.01',\n",
-       "   'attributes': ['Material:Polyester,Cotton',\n",
-       "    'Target Group:Man',\n",
-       "    'Color:Blue',\n",
-       "    'Size:S,XL,XS,L,M,XXL']}]}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.run(\"What are some options for a men's large blue button down shirt\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6f648c77",
-   "metadata": {},
-   "source": [
-    "## Query a translation service\n",
-    "\n",
-    "Additionally, see the request payload by setting `verbose=True`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bf6cd695",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = get_openapi_chain(\"https://api.speak.com/openapi.yaml\", verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "1ba51609",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new  chain...\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new  chain...\u001b[0m\n",
-      "Prompt after formatting:\n",
-      "\u001b[32;1m\u001b[1;3mHuman: Use the provided API's to respond to this user query:\n",
-      "\n",
-      "How would you say no thanks in Russian\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new  chain...\u001b[0m\n",
-      "Calling endpoint \u001b[32;1m\u001b[1;3mtranslate\u001b[0m with arguments:\n",
-      "\u001b[32;1m\u001b[1;3m{\n",
-      "  \"json\": {\n",
-      "    \"phrase_to_translate\": \"no thanks\",\n",
-      "    \"learning_language\": \"russian\",\n",
-      "    \"native_language\": \"english\",\n",
-      "    \"additional_context\": \"\",\n",
-      "    \"full_query\": \"How would you say no thanks in Russian\"\n",
-      "  }\n",
-      "}\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'explanation': '<translation language=\"Russian\">\\nНет, спасибо. (Net, spasibo)\\n</translation>\\n\\n<alternatives>\\n1. \"Нет, я в порядке\" *(Neutral/Formal - Can be used in professional settings or formal situations.)*\\n2. \"Нет, спасибо, я откажусь\" *(Formal - Can be used in polite settings, such as a fancy dinner with colleagues or acquaintances.)*\\n3. \"Не надо\" *(Informal - Can be used in informal situations, such as declining an offer from a friend.)*\\n</alternatives>\\n\\n<example-convo language=\"Russian\">\\n<context>Max is being offered a cigarette at a party.</context>\\n* Sasha: \"Хочешь покурить?\"\\n* Max: \"Нет, спасибо. Я бросил.\"\\n* Sasha: \"Окей, понятно.\"\\n</example-convo>\\n\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=noczaa460do8yqs8xjun6zdm})*',\n",
-       " 'extra_response_instructions': 'Use all information in the API response and fully render all Markdown.\\nAlways end your response with a link to report an issue or leave feedback on the plugin.'}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.run(\"How would you say no thanks in Russian\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4923a291",
-   "metadata": {},
-   "source": [
-    "## Query XKCD"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a9198f62",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "chain = get_openapi_chain(\n",
-    "    \"https://gist.githubusercontent.com/roaldnefs/053e505b2b7a807290908fe9aa3e1f00/raw/0a212622ebfef501163f91e23803552411ed00e4/openapi.yaml\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3110c398",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'month': '6',\n",
-       " 'num': 2793,\n",
-       " 'link': '',\n",
-       " 'year': '2023',\n",
-       " 'news': '',\n",
-       " 'safe_title': 'Garden Path Sentence',\n",
-       " 'transcript': '',\n",
-       " 'alt': 'Arboretum Owner Denied Standing in Garden Path Suit on Grounds Grounds Appealing Appealing',\n",
-       " 'img': 'https://imgs.xkcd.com/comics/garden_path_sentence.png',\n",
-       " 'title': 'Garden Path Sentence',\n",
-       " 'day': '23'}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.run(\"What's today's comic?\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv",
-   "language": "python",
-   "name": "venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/extras/use_cases/autonomous_agents/index.mdx b/docs/extras/use_cases/autonomous_agents/index.mdx
index e930a97dbfb..bc2bb81af08 100644
--- a/docs/extras/use_cases/autonomous_agents/index.mdx
+++ b/docs/extras/use_cases/autonomous_agents/index.mdx
@@ -1,3 +1,7 @@
+---
+sidebar_position: 0
+---
+
 # Autonomous (long-running) agents
 
 Autonomous Agents are agents that designed to be more long running.
diff --git a/docs/extras/use_cases/code/code-analysis-deeplake.ipynb b/docs/extras/use_cases/code/code-analysis-deeplake.ipynb
deleted file mode 100644
index baf2cf4f58e..00000000000
--- a/docs/extras/use_cases/code/code-analysis-deeplake.ipynb
+++ /dev/null
@@ -1,442 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Use LangChain, GPT and Activeloop's Deep Lake to work with code base\n",
-    "In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT to analyze the code base of the LangChain itself. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Design"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1. Prepare data:\n",
-    "   1. Upload all python project files using the `langchain.document_loaders.TextLoader`. We will call these files the **documents**.\n",
-    "   2. Split all documents to chunks using the `langchain.text_splitter.CharacterTextSplitter`.\n",
-    "   3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain.vectorstores.DeepLake`\n",
-    "2. Question-Answering:\n",
-    "   1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`\n",
-    "   2. Prepare questions.\n",
-    "   3. Get answers running the chain.\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Implementation"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Integration preparations"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We need to set up keys for external services and install necessary python libraries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#!python3 -m pip install --upgrade langchain deeplake openai"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set up OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. \n",
-    "\n",
-    "For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
-    "# Please manually enter OpenAI Key"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the platform at [app.activeloop.ai](https://app.activeloop.ai)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "activeloop_token = getpass(\"Activeloop Token:\")\n",
-    "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Prepare data "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load all repository files. Here we assume this notebook is downloaded as the part of the langchain fork and we work with the python files of the `langchain` repo.\n",
-    "\n",
-    "If you want to use files from different repo, change `root_dir` to the root dir of your repo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!ls \"../../../..\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import TextLoader\n",
-    "\n",
-    "root_dir = \"../../../..\"\n",
-    "\n",
-    "docs = []\n",
-    "for dirpath, dirnames, filenames in os.walk(root_dir):\n",
-    "    for file in filenames:\n",
-    "        if file.endswith(\".py\") and \"/.venv/\" not in dirpath:\n",
-    "            try:\n",
-    "                loader = TextLoader(os.path.join(dirpath, file), encoding=\"utf-8\")\n",
-    "                docs.extend(loader.load_and_split())\n",
-    "            except Exception as e:\n",
-    "                pass\n",
-    "print(f\"{len(docs)}\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then, chunk the files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "texts = text_splitter.split_documents(docs)\n",
-    "print(f\"{len(texts)}\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then embed chunks and upload them to the DeepLake.\n",
-    "\n",
-    "This can take several minutes. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.vectorstores import DeepLake\n",
-    "\n",
-    "db = DeepLake.from_documents(\n",
-    "    texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\"\n",
-    ")\n",
-    "db"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from langchain.vectorstores import DeepLake\n",
-    "\n",
-    "# db = DeepLake.from_documents(\n",
-    "#     texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\", runtime={\"tensor_db\": True}\n",
-    "# )\n",
-    "# db"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question Answering\n",
-    "First load the dataset, construct the retriever, then construct the Conversational Chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "db = DeepLake(\n",
-    "    dataset_path=f\"hub://{<org_id>}/langchain-code\",\n",
-    "    read_only=True,\n",
-    "    embedding_function=embeddings,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "retriever = db.as_retriever()\n",
-    "retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
-    "retriever.search_kwargs[\"fetch_k\"] = 20\n",
-    "retriever.search_kwargs[\"maximal_marginal_relevance\"] = True\n",
-    "retriever.search_kwargs[\"k\"] = 20"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also specify user defined functions using [Deep Lake filters](https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def filter(x):\n",
-    "    # filter based on source code\n",
-    "    if \"something\" in x[\"text\"].data()[\"value\"]:\n",
-    "        return False\n",
-    "\n",
-    "    # filter based on path e.g. extension\n",
-    "    metadata = x[\"metadata\"].data()[\"value\"]\n",
-    "    return \"only_this\" in metadata[\"source\"] or \"also_that\" in metadata[\"source\"]\n",
-    "\n",
-    "\n",
-    "### turn on below for custom filtering\n",
-    "# retriever.search_kwargs['filter'] = filter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.chains import ConversationalRetrievalChain\n",
-    "\n",
-    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")  # 'ada' 'gpt-3.5-turbo' 'gpt-4',\n",
-    "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "questions = [\n",
-    "    \"What is the class hierarchy?\",\n",
-    "    # \"What classes are derived from the Chain class?\",\n",
-    "    # \"What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests?\",\n",
-    "    # \"What one improvement do you propose in code in relation to the class herarchy for the Chain class?\",\n",
-    "]\n",
-    "chat_history = []\n",
-    "\n",
-    "for question in questions:\n",
-    "    result = qa({\"question\": question, \"chat_history\": chat_history})\n",
-    "    chat_history.append((question, result[\"answer\"]))\n",
-    "    print(f\"-> **Question**: {question} \\n\")\n",
-    "    print(f\"**Answer**: {result['answer']} \\n\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "-> **Question**: What is the class hierarchy? \n",
-    "\n",
-    "**Answer**: There are several class hierarchies in the provided code, so I'll list a few:\n",
-    "\n",
-    "1. `BaseModel` -> `ConstitutionalPrinciple`: `ConstitutionalPrinciple` is a subclass of `BaseModel`.\n",
-    "2. `BasePromptTemplate` -> `StringPromptTemplate`, `AIMessagePromptTemplate`, `BaseChatPromptTemplate`, `ChatMessagePromptTemplate`, `ChatPromptTemplate`, `HumanMessagePromptTemplate`, `MessagesPlaceholder`, `SystemMessagePromptTemplate`, `FewShotPromptTemplate`, `FewShotPromptWithTemplates`, `Prompt`, `PromptTemplate`: All of these classes are subclasses of `BasePromptTemplate`.\n",
-    "3. `APIChain`, `Chain`, `MapReduceDocumentsChain`, `MapRerankDocumentsChain`, `RefineDocumentsChain`, `StuffDocumentsChain`, `HypotheticalDocumentEmbedder`, `LLMChain`, `LLMBashChain`, `LLMCheckerChain`, `LLMMathChain`, `LLMRequestsChain`, `PALChain`, `QAWithSourcesChain`, `VectorDBQAWithSourcesChain`, `VectorDBQA`, `SQLDatabaseChain`: All of these classes are subclasses of `Chain`.\n",
-    "4. `BaseLoader`: `BaseLoader` is a subclass of `ABC`.\n",
-    "5. `BaseTracer` -> `ChainRun`, `LLMRun`, `SharedTracer`, `ToolRun`, `Tracer`, `TracerException`, `TracerSession`: All of these classes are subclasses of `BaseTracer`.\n",
-    "6. `OpenAIEmbeddings`, `HuggingFaceEmbeddings`, `CohereEmbeddings`, `JinaEmbeddings`, `LlamaCppEmbeddings`, `HuggingFaceHubEmbeddings`, `TensorflowHubEmbeddings`, `SagemakerEndpointEmbeddings`, `HuggingFaceInstructEmbeddings`, `SelfHostedEmbeddings`, `SelfHostedHuggingFaceEmbeddings`, `SelfHostedHuggingFaceInstructEmbeddings`, `FakeEmbeddings`, `AlephAlphaAsymmetricSemanticEmbedding`, `AlephAlphaSymmetricSemanticEmbedding`: All of these classes are subclasses of `BaseLLM`. \n",
-    "\n",
-    "\n",
-    "-> **Question**: What classes are derived from the Chain class? \n",
-    "\n",
-    "**Answer**: There are multiple classes that are derived from the Chain class. Some of them are:\n",
-    "- APIChain\n",
-    "- AnalyzeDocumentChain\n",
-    "- ChatVectorDBChain\n",
-    "- CombineDocumentsChain\n",
-    "- ConstitutionalChain\n",
-    "- ConversationChain\n",
-    "- GraphQAChain\n",
-    "- HypotheticalDocumentEmbedder\n",
-    "- LLMChain\n",
-    "- LLMCheckerChain\n",
-    "- LLMRequestsChain\n",
-    "- LLMSummarizationCheckerChain\n",
-    "- MapReduceChain\n",
-    "- OpenAPIEndpointChain\n",
-    "- PALChain\n",
-    "- QAWithSourcesChain\n",
-    "- RetrievalQA\n",
-    "- RetrievalQAWithSourcesChain\n",
-    "- SequentialChain\n",
-    "- SQLDatabaseChain\n",
-    "- TransformChain\n",
-    "- VectorDBQA\n",
-    "- VectorDBQAWithSourcesChain\n",
-    "\n",
-    "There might be more classes that are derived from the Chain class as it is possible to create custom classes that extend the Chain class.\n",
-    "\n",
-    "\n",
-    "-> **Question**: What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests? \n",
-    "\n",
-    "**Answer**: All classes and functions in the `./langchain/utilities/` folder seem to have unit tests written for them. \n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/extras/use_cases/code/twitter-the-algorithm-analysis-deeplake.ipynb b/docs/extras/use_cases/code/twitter-the-algorithm-analysis-deeplake.ipynb
deleted file mode 100644
index 548780ef1e8..00000000000
--- a/docs/extras/use_cases/code/twitter-the-algorithm-analysis-deeplake.ipynb
+++ /dev/null
@@ -1,459 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep Lake\n",
-    "In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT4 to analyze the code base of the twitter algorithm. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. For full documentation of Deep Lake please follow [docs](https://docs.activeloop.ai/) and [API reference](https://docs.deeplake.ai/en/latest/).\n",
-    "\n",
-    "Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the [platform](https://app.activeloop.ai)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import getpass\n",
-    "\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import DeepLake\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
-    "activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",
-    "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings = OpenAIEmbeddings(disallowed_special=())"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "disallowed_special=() is required to avoid `Exception: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte` from tiktoken for some repositories"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. Index the code base (optional)\n",
-    "You can directly skip this part and directly jump into using already indexed dataset. To begin with, first we will clone the repository, then parse and chunk the code base and use OpenAI indexing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git clone https://github.com/twitter/the-algorithm # replace any repository of your choice"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load all files inside the repository"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from langchain.document_loaders import TextLoader\n",
-    "\n",
-    "root_dir = \"./the-algorithm\"\n",
-    "docs = []\n",
-    "for dirpath, dirnames, filenames in os.walk(root_dir):\n",
-    "    for file in filenames:\n",
-    "        try:\n",
-    "            loader = TextLoader(os.path.join(dirpath, file), encoding=\"utf-8\")\n",
-    "            docs.extend(loader.load_and_split())\n",
-    "        except Exception as e:\n",
-    "            pass"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then, chunk the files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "texts = text_splitter.split_documents(docs)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Execute the indexing. This will take about ~4 mins to compute embeddings and upload to Activeloop. You can then publish the dataset to be public."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "username = \"davitbun\"  # replace with your username from app.activeloop.ai\n",
-    "db = DeepLake(\n",
-    "    dataset_path=f\"hub://{username}/twitter-algorithm\",\n",
-    "    embedding_function=embeddings,\n",
-    ")\n",
-    "db.add_documents(texts)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# username = \"davitbun\"  # replace with your username from app.activeloop.ai\n",
-    "# db = DeepLake(\n",
-    "#     dataset_path=f\"hub://{username}/twitter-algorithm\",\n",
-    "#     embedding_function=embeddings,\n",
-    "#     runtime={\"tensor_db\": True}\n",
-    "# )\n",
-    "# db.add_documents(texts)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Question Answering on Twitter algorithm codebase\n",
-    "First load the dataset, construct the retriever, then construct the Conversational Chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Deep Lake Dataset in hub://davitbun/twitter-algorithm already exists, loading from the storage\n"
-     ]
-    }
-   ],
-   "source": [
-    "db = DeepLake(\n",
-    "    dataset_path=\"hub://davitbun/twitter-algorithm\",\n",
-    "    read_only=True,\n",
-    "    embedding_function=embeddings,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever = db.as_retriever()\n",
-    "retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
-    "retriever.search_kwargs[\"fetch_k\"] = 100\n",
-    "retriever.search_kwargs[\"maximal_marginal_relevance\"] = True\n",
-    "retriever.search_kwargs[\"k\"] = 10"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also specify user defined functions using [Deep Lake filters](https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter(x):\n",
-    "    # filter based on source code\n",
-    "    if \"com.google\" in x[\"text\"].data()[\"value\"]:\n",
-    "        return False\n",
-    "\n",
-    "    # filter based on path e.g. extension\n",
-    "    metadata = x[\"metadata\"].data()[\"value\"]\n",
-    "    return \"scala\" in metadata[\"source\"] or \"py\" in metadata[\"source\"]\n",
-    "\n",
-    "\n",
-    "### turn on below for custom filtering\n",
-    "# retriever.search_kwargs['filter'] = filter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.chains import ConversationalRetrievalChain\n",
-    "\n",
-    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")  # switch to 'gpt-4'\n",
-    "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "questions = [\n",
-    "    \"What does favCountParams do?\",\n",
-    "    \"is it Likes + Bookmarks, or not clear from the code?\",\n",
-    "    \"What are the major negative modifiers that lower your linear ranking parameters?\",\n",
-    "    \"How do you get assigned to SimClusters?\",\n",
-    "    \"What is needed to migrate from one SimClusters to another SimClusters?\",\n",
-    "    \"How much do I get boosted within my cluster?\",\n",
-    "    \"How does Heavy ranker work. what are it’s main inputs?\",\n",
-    "    \"How can one influence Heavy ranker?\",\n",
-    "    \"why threads and long tweets do so well on the platform?\",\n",
-    "    \"Are thread and long tweet creators building a following that reacts to only threads?\",\n",
-    "    \"Do you need to follow different strategies to get most followers vs to get most likes and bookmarks per tweet?\",\n",
-    "    \"Content meta data and how it impacts virality (e.g. ALT in images).\",\n",
-    "    \"What are some unexpected fingerprints for spam factors?\",\n",
-    "    \"Is there any difference between company verified checkmarks and blue verified individual checkmarks?\",\n",
-    "]\n",
-    "chat_history = []\n",
-    "\n",
-    "for question in questions:\n",
-    "    result = qa({\"question\": question, \"chat_history\": chat_history})\n",
-    "    chat_history.append((question, result[\"answer\"]))\n",
-    "    print(f\"-> **Question**: {question} \\n\")\n",
-    "    print(f\"**Answer**: {result['answer']} \\n\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-> **Question**: What does favCountParams do? \n",
-    "\n",
-    "**Answer**: `favCountParams` is an optional ThriftLinearFeatureRankingParams instance that represents the parameters related to the \"favorite count\" feature in the ranking process. It is used to control the weight of the favorite count feature while ranking tweets. The favorite count is the number of times a tweet has been marked as a favorite by users, and it is considered an important signal in the ranking of tweets. By using `favCountParams`, the system can adjust the importance of the favorite count while calculating the final ranking score of a tweet. \n",
-    "\n",
-    "-> **Question**: is it Likes + Bookmarks, or not clear from the code?\n",
-    "\n",
-    "**Answer**: From the provided code, it is not clear if the favorite count metric is determined by the sum of likes and bookmarks. The favorite count is mentioned in the code, but there is no explicit reference to how it is calculated in terms of likes and bookmarks. \n",
-    "\n",
-    "-> **Question**: What are the major negative modifiers that lower your linear ranking parameters?\n",
-    "\n",
-    "**Answer**: In the given code, major negative modifiers that lower the linear ranking parameters are:\n",
-    "\n",
-    "1. `scoringData.querySpecificScore`: This score adjustment is based on the query-specific information. If its value is negative, it will lower the linear ranking parameters.\n",
-    "\n",
-    "2. `scoringData.authorSpecificScore`: This score adjustment is based on the author-specific information. If its value is negative, it will also lower the linear ranking parameters.\n",
-    "\n",
-    "Please note that I cannot provide more information on the exact calculations of these negative modifiers, as the code for their determination is not provided. \n",
-    "\n",
-    "-> **Question**: How do you get assigned to SimClusters?\n",
-    "\n",
-    "**Answer**: The assignment to SimClusters occurs through a Metropolis-Hastings sampling-based community detection algorithm that is run on the Producer-Producer similarity graph. This graph is created by computing the cosine similarity scores between the users who follow each producer. The algorithm identifies communities or clusters of Producers with similar followers, and takes a parameter *k* for specifying the number of communities to be detected.\n",
-    "\n",
-    "After the community detection, different users and content are represented as sparse, interpretable vectors within these identified communities (SimClusters). The resulting SimClusters embeddings can be used for various recommendation tasks. \n",
-    "\n",
-    "-> **Question**: What is needed to migrate from one SimClusters to another SimClusters?\n",
-    "\n",
-    "**Answer**: To migrate from one SimClusters representation to another, you can follow these general steps:\n",
-    "\n",
-    "1. **Prepare the new representation**: Create the new SimClusters representation using any necessary updates or changes in the clustering algorithm, similarity measures, or other model parameters. Ensure that this new representation is properly stored and indexed as needed.\n",
-    "\n",
-    "2. **Update the relevant code and configurations**: Modify the relevant code and configuration files to reference the new SimClusters representation. This may involve updating paths or dataset names to point to the new representation, as well as changing code to use the new clustering method or similarity functions if applicable.\n",
-    "\n",
-    "3. **Test the new representation**: Before deploying the changes to production, thoroughly test the new SimClusters representation to ensure its effectiveness and stability. This may involve running offline jobs like candidate generation and label candidates, validating the output, as well as testing the new representation in the evaluation environment using evaluation tools like TweetSimilarityEvaluationAdhocApp.\n",
-    "\n",
-    "4. **Deploy the changes**: Once the new representation has been tested and validated, deploy the changes to production. This may involve creating a zip file, uploading it to the packer, and then scheduling it with Aurora. Be sure to monitor the system to ensure a smooth transition between representations and verify that the new representation is being used in recommendations as expected.\n",
-    "\n",
-    "5. **Monitor and assess the new representation**: After the new representation has been deployed, continue to monitor its performance and impact on recommendations. Take note of any improvements or issues that arise and be prepared to iterate on the new representation if needed. Always ensure that the results and performance metrics align with the system's goals and objectives. \n",
-    "\n",
-    "-> **Question**: How much do I get boosted within my cluster?\n",
-    "\n",
-    "**Answer**: It's not possible to determine the exact amount your content is boosted within your cluster in the SimClusters representation without specific data about your content and its engagement metrics. However, a combination of factors, such as the favorite score and follow score, alongside other engagement signals and SimCluster calculations, influence the boosting of content. \n",
-    "\n",
-    "-> **Question**: How does Heavy ranker work. what are it’s main inputs?\n",
-    "\n",
-    "**Answer**: The Heavy Ranker is a machine learning model that plays a crucial role in ranking and scoring candidates within the recommendation algorithm. Its primary purpose is to predict the likelihood of a user engaging with a tweet or connecting with another user on the platform.\n",
-    "\n",
-    "Main inputs to the Heavy Ranker consist of:\n",
-    "\n",
-    "1. Static Features: These are features that can be computed directly from a tweet at the time it's created, such as whether it has a URL, has cards, has quotes, etc. These features are produced by the Index Ingester as the tweets are generated and stored in the index.\n",
-    "\n",
-    "2. Real-time Features: These per-tweet features can change after the tweet has been indexed. They mostly consist of social engagements like retweet count, favorite count, reply count, and some spam signals that are computed with later activities. The Signal Ingester, which is part of a Heron topology, processes multiple event streams to collect and compute these real-time features.\n",
-    "\n",
-    "3. User Table Features: These per-user features are obtained from the User Table Updater that processes a stream written by the user service. This input is used to store sparse real-time user information, which is later propagated to the tweet being scored by looking up the author of the tweet.\n",
-    "\n",
-    "4. Search Context Features: These features represent the context of the current searcher, like their UI language, their content consumption, and the current time (implied). They are combined with Tweet Data to compute some of the features used in scoring.\n",
-    "\n",
-    "These inputs are then processed by the Heavy Ranker to score and rank candidates based on their relevance and likelihood of engagement by the user. \n",
-    "\n",
-    "-> **Question**: How can one influence Heavy ranker?\n",
-    "\n",
-    "**Answer**: To influence the Heavy Ranker's output or ranking of content, consider the following actions:\n",
-    "\n",
-    "1. Improve content quality: Create high-quality and engaging content that is relevant, informative, and valuable to users. High-quality content is more likely to receive positive user engagement, which the Heavy Ranker considers when ranking content.\n",
-    "\n",
-    "2. Increase user engagement: Encourage users to interact with content through likes, retweets, replies, and comments. Higher engagement levels can lead to better ranking in the Heavy Ranker's output.\n",
-    "\n",
-    "3. Optimize your user profile: A user's reputation, based on factors such as their follower count and follower-to-following ratio, may impact the ranking of their content. Maintain a good reputation by following relevant users, keeping a reasonable follower-to-following ratio and engaging with your followers.\n",
-    "\n",
-    "4. Enhance content discoverability: Use relevant keywords, hashtags, and mentions in your tweets, making it easier for users to find and engage with your content. This increased discoverability may help improve the ranking of your content by the Heavy Ranker.\n",
-    "\n",
-    "5. Leverage multimedia content: Experiment with different content formats, such as videos, images, and GIFs, which may capture users' attention and increase engagement, resulting in better ranking by the Heavy Ranker.\n",
-    "\n",
-    "6. User feedback: Monitor and respond to feedback for your content. Positive feedback may improve your ranking, while negative feedback provides an opportunity to learn and improve.\n",
-    "\n",
-    "Note that the Heavy Ranker uses a combination of machine learning models and various features to rank the content. While the above actions may help influence the ranking, there are no guarantees as the ranking process is determined by a complex algorithm, which evolves over time. \n",
-    "\n",
-    "-> **Question**: why threads and long tweets do so well on the platform?\n",
-    "\n",
-    "**Answer**: Threads and long tweets perform well on the platform for several reasons:\n",
-    "\n",
-    "1. **More content and context**: Threads and long tweets provide more information and context about a topic, which can make the content more engaging and informative for users. People tend to appreciate a well-structured and detailed explanation of a subject or a story, and threads and long tweets can do that effectively.\n",
-    "\n",
-    "2. **Increased user engagement**: As threads and long tweets provide more content, they also encourage users to engage with the tweets through replies, retweets, and likes. This increased engagement can lead to better visibility of the content, as the Twitter algorithm considers user engagement when ranking and surfacing tweets.\n",
-    "\n",
-    "3. **Narrative structure**: Threads enable users to tell stories or present arguments in a step-by-step manner, making the information more accessible and easier to follow. This narrative structure can capture users' attention and encourage them to read through the entire thread and interact with the content.\n",
-    "\n",
-    "4. **Expanded reach**: When users engage with a thread, their interactions can bring the content to the attention of their followers, helping to expand the reach of the thread. This increased visibility can lead to more interactions and higher performance for the threaded tweets.\n",
-    "\n",
-    "5. **Higher content quality**: Generally, threads and long tweets require more thought and effort to create, which may lead to higher quality content. Users are more likely to appreciate and interact with high-quality, well-reasoned content, further improving the performance of these tweets within the platform.\n",
-    "\n",
-    "Overall, threads and long tweets perform well on Twitter because they encourage user engagement and provide a richer, more informative experience that users find valuable. \n",
-    "\n",
-    "-> **Question**: Are thread and long tweet creators building a following that reacts to only threads?\n",
-    "\n",
-    "**Answer**: Based on the provided code and context, there isn't enough information to conclude if the creators of threads and long tweets primarily build a following that engages with only thread-based content. The code provided is focused on Twitter's recommendation and ranking algorithms, as well as infrastructure components like Kafka, partitions, and the Follow Recommendations Service (FRS). To answer your question, data analysis of user engagement and results of specific edge cases would be required. \n",
-    "\n",
-    "-> **Question**: Do you need to follow different strategies to get most followers vs to get most likes and bookmarks per tweet?\n",
-    "\n",
-    "**Answer**: Yes, different strategies need to be followed to maximize the number of followers compared to maximizing likes and bookmarks per tweet. While there may be some overlap in the approaches, they target different aspects of user engagement.\n",
-    "\n",
-    "Maximizing followers: The primary focus is on growing your audience on the platform. Strategies include:\n",
-    "\n",
-    "1. Consistently sharing high-quality content related to your niche or industry.\n",
-    "2. Engaging with others on the platform by replying, retweeting, and mentioning other users.\n",
-    "3. Using relevant hashtags and participating in trending conversations.\n",
-    "4. Collaborating with influencers and other users with a large following.\n",
-    "5. Posting at optimal times when your target audience is most active.\n",
-    "6. Optimizing your profile by using a clear profile picture, catchy bio, and relevant links.\n",
-    "\n",
-    "Maximizing likes and bookmarks per tweet: The focus is on creating content that resonates with your existing audience and encourages engagement. Strategies include:\n",
-    "\n",
-    "1. Crafting engaging and well-written tweets that encourage users to like or save them.\n",
-    "2. Incorporating visually appealing elements, such as images, GIFs, or videos, that capture attention.\n",
-    "3. Asking questions, sharing opinions, or sparking conversations that encourage users to engage with your tweets.\n",
-    "4. Using analytics to understand the type of content that resonates with your audience and tailoring your tweets accordingly.\n",
-    "5. Posting a mix of educational, entertaining, and promotional content to maintain variety and interest.\n",
-    "6. Timing your tweets strategically to maximize engagement, likes, and bookmarks per tweet.\n",
-    "\n",
-    "Both strategies can overlap, and you may need to adapt your approach by understanding your target audience's preferences and analyzing your account's performance. However, it's essential to recognize that maximizing followers and maximizing likes and bookmarks per tweet have different focuses and require specific strategies. \n",
-    "\n",
-    "-> **Question**: Content meta data and how it impacts virality (e.g. ALT in images).\n",
-    "\n",
-    "**Answer**: There is no direct information in the provided context about how content metadata, such as ALT text in images, impacts the virality of a tweet or post. However, it's worth noting that including ALT text can improve the accessibility of your content for users who rely on screen readers, which may lead to increased engagement for a broader audience. Additionally, metadata can be used in search engine optimization, which might improve the visibility of the content, but the context provided does not mention any specific correlation with virality. \n",
-    "\n",
-    "-> **Question**: What are some unexpected fingerprints for spam factors?\n",
-    "\n",
-    "**Answer**: In the provided context, an unusual indicator of spam factors is when a tweet contains a non-media, non-news link. If the tweet has a link but does not have an image URL, video URL, or news URL, it is considered a potential spam vector, and a threshold for user reputation (tweepCredThreshold) is set to MIN_TWEEPCRED_WITH_LINK.\n",
-    "\n",
-    "While this rule may not cover all possible unusual spam indicators, it is derived from the specific codebase and logic shared in the context. \n",
-    "\n",
-    "-> **Question**: Is there any difference between company verified checkmarks and blue verified individual checkmarks?\n",
-    "\n",
-    "**Answer**: Yes, there is a distinction between the verified checkmarks for companies and blue verified checkmarks for individuals. The code snippet provided mentions \"Blue-verified account boost\" which indicates that there is a separate category for blue verified accounts. Typically, blue verified checkmarks are used to indicate notable individuals, while verified checkmarks are for companies or organizations. \n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/extras/use_cases/code_understanding.ipynb b/docs/extras/use_cases/code_understanding.ipynb
new file mode 100644
index 00000000000..b48dc33815e
--- /dev/null
+++ b/docs/extras/use_cases/code_understanding.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Code Understanding\n",
+    "\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/code_understanding.ipynb)\n",
+    "\n",
+    "## Use case\n",
+    "\n",
+    "Source code analysis is one of the most popular LLM applications (e.g., [GitHub Co-Pilot](https://github.com/features/copilot), [Code Interpreter](https://chat.openai.com/auth/login?next=%2F%3Fmodel%3Dgpt-4-code-interpreter), [Codium](https://www.codium.ai/), and [Codeium](https://codeium.com/about)) for use-cases such as:\n",
+    "\n",
+    "- Q&A over the code base to understand how it works\n",
+    "- Using LLMs for suggesting refactors or improvements\n",
+    "- Using LLMs for documenting the code\n",
+    "\n",
+    "![Image description](/img/code_understanding.png)\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "The pipeline for QA over code follows [the steps we do for document question answering](/docs/extras/use_cases/question_answering), with some differences:\n",
+    "\n",
+    "In particular, we can employ a [splitting strategy](https://python.langchain.com/docs/integrations/document_loaders/source_code) that does a few things:\n",
+    "\n",
+    "* Keeps each top-level function and class in the code is loaded into separate documents. \n",
+    "* Puts remaining into a seperate document.\n",
+    "* Retains metadata about where each split comes from\n",
+    "\n",
+    "## Quickstart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install openai tiktoken chromadb langchain\n",
+    "\n",
+    "# Set env var OPENAI_API_KEY or load from a .env file\n",
+    "# import dotenv\n",
+    "\n",
+    "# dotenv.load_env()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'lll follow the structure of [this notebook](https://github.com/cristobalcl/LearningLangChain/blob/master/notebooks/04%20-%20QA%20with%20code.ipynb) and employ [context aware code splitting](https://python.langchain.com/docs/integrations/document_loaders/source_code)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading\n",
+    "\n",
+    "\n",
+    "We will upload all python project files using the `langchain.document_loaders.TextLoader`.\n",
+    "\n",
+    "The following script iterates over the files in the LangChain repository and loads every `.py` file (a.k.a. **documents**):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from git import Repo\n",
+    "from langchain.text_splitter import Language\n",
+    "from langchain.document_loaders.generic import GenericLoader\n",
+    "from langchain.document_loaders.parsers import LanguageParser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clone\n",
+    "repo_path = \"/Users/rlm/Desktop/test_repo\"\n",
+    "repo = Repo.clone_from(\"https://github.com/hwchase17/langchain\", to_path=repo_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We load the py code using [`LanguageParser`](https://python.langchain.com/docs/integrations/document_loaders/source_code), which will:\n",
+    "\n",
+    "* Keep top-level functions and classes together (into a single document)\n",
+    "* Put remaining code into a seperate document\n",
+    "* Retains metadata about where each split comes from"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1293"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load\n",
+    "loader = GenericLoader.from_filesystem(\n",
+    "    repo_path+\"/libs/langchain/langchain\",\n",
+    "    glob=\"**/*\",\n",
+    "    suffixes=[\".py\"],\n",
+    "    parser=LanguageParser(language=Language.PYTHON, parser_threshold=500)\n",
+    ")\n",
+    "documents = loader.load()\n",
+    "len(documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Splittng\n",
+    "\n",
+    "Split the `Document` into chunks for embedding and vector storage.\n",
+    "\n",
+    "We can use `RecursiveCharacterTextSplitter` w/ `language` specified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3748"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "python_splitter = RecursiveCharacterTextSplitter.from_language(language=Language.PYTHON, \n",
+    "                                                               chunk_size=2000, \n",
+    "                                                               chunk_overlap=200)\n",
+    "texts = python_splitter.split_documents(documents)\n",
+    "len(texts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### RetrievalQA\n",
+    "\n",
+    "We need to store the documents in a way we can semantically search for their content. \n",
+    "\n",
+    "The most common approach is to embed the contents of each document then store the embedding and document in a vector store. \n",
+    "\n",
+    "When setting up the vectorstore retriever:\n",
+    "\n",
+    "* We test [max marginal relevance](/docs/extras/use_cases/question_answering) for retrieval\n",
+    "* And 8 documents returned\n",
+    "\n",
+    "#### Go deeper\n",
+    "\n",
+    "- Browse the > 40 vectorstores integrations [here](https://integrations.langchain.com/).\n",
+    "- See further documentation on vectorstores [here](/docs/modules/data_connection/vectorstores/).\n",
+    "- Browse the > 30 text embedding integrations [here](https://integrations.langchain.com/).\n",
+    "- See further documentation on embedding models [here](/docs/modules/data_connection/text_embedding/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "db = Chroma.from_documents(texts, OpenAIEmbeddings(disallowed_special=()))\n",
+    "retriever = db.as_retriever(\n",
+    "    search_type=\"mmr\",  # Also test \"similarity\"\n",
+    "    search_kwargs={\"k\": 8},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Chat\n",
+    "\n",
+    "Test chat, just as we do for [chatbots](/docs/extras/use_cases/chatbots).\n",
+    "\n",
+    "#### Go deeper\n",
+    "\n",
+    "- Browse the > 55 LLM and chat model integrations [here](https://integrations.langchain.com/).\n",
+    "- See further documentation on LLMs and chat models [here](/docs/modules/model_io/models/).\n",
+    "- Use local LLMS: The popularity of [PrivateGPT](https://github.com/imartinez/privateGPT) and [GPT4All](https://github.com/nomic-ai/gpt4all) underscore the importance of running LLMs locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.memory import ConversationSummaryMemory\n",
+    "from langchain.chains import ConversationalRetrievalChain\n",
+    "llm = ChatOpenAI(model_name=\"gpt-4\") \n",
+    "memory = ConversationSummaryMemory(llm=llm,memory_key=\"chat_history\",return_messages=True)\n",
+    "qa = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, memory=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'To load a source code as documents for a QA over code, you can use the `CodeLoader` class. This class allows you to load source code files and split them into classes and functions.\\n\\nHere is an example of how to use the `CodeLoader` class:\\n\\n```python\\nfrom langchain.document_loaders.code import CodeLoader\\n\\n# Specify the path to the source code file\\ncode_file_path = \"path/to/code/file.py\"\\n\\n# Create an instance of the CodeLoader class\\ncode_loader = CodeLoader(code_file_path)\\n\\n# Load the code as documents\\ndocuments = code_loader.load()\\n\\n# Iterate over the documents\\nfor document in documents:\\n    # Access the class or function name\\n    name = document.metadata[\"name\"]\\n    \\n    # Access the code content\\n    code = document.page_content\\n    \\n    # Process the code as needed\\n    # ...\\n```\\n\\nIn the example above, `code_file_path` should be replaced with the actual path to your source code file. The `load()` method of the `CodeLoader` class will return a list of `Document` objects, where each document represents a class or function in the source code. You can access the class or function name using the `metadata[\"name\"]` attribute, and the code content using the `page_content` attribute of each `Document` object.\\n\\nYou can then process the code as needed for your QA task.'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question = \"How can I load a source code as documents, for a QA over code, spliting the code in classes and functions?\"\n",
+    "result = qa(question)\n",
+    "result['answer']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-> **Question**: What is the class hierarchy? \n",
+      "\n",
+      "**Answer**: The class hierarchy in object-oriented programming is the structure that forms when classes are derived from other classes. The derived class is a subclass of the base class also known as the superclass. This hierarchy is formed based on the concept of inheritance in object-oriented programming where a subclass inherits the properties and functionalities of the superclass. \n",
+      "\n",
+      "In the given context, we have the following examples of class hierarchies:\n",
+      "\n",
+      "1. `BaseCallbackHandler --> <name>CallbackHandler` means `BaseCallbackHandler` is a base class and `<name>CallbackHandler` (like `AimCallbackHandler`, `ArgillaCallbackHandler` etc.) are derived classes that inherit from `BaseCallbackHandler`.\n",
+      "\n",
+      "2. `BaseLoader --> <name>Loader` means `BaseLoader` is a base class and `<name>Loader` (like `TextLoader`, `UnstructuredFileLoader` etc.) are derived classes that inherit from `BaseLoader`.\n",
+      "\n",
+      "3. `ToolMetaclass --> BaseTool --> <name>Tool` means `ToolMetaclass` is a base class, `BaseTool` is a derived class that inherits from `ToolMetaclass`, and `<name>Tool` (like `AIPluginTool`, `BaseGraphQLTool` etc.) are further derived classes that inherit from `BaseTool`. \n",
+      "\n",
+      "-> **Question**: What classes are derived from the Chain class? \n",
+      "\n",
+      "**Answer**: The classes that are derived from the Chain class are:\n",
+      "\n",
+      "1. LLMSummarizationCheckerChain\n",
+      "2. MapReduceChain\n",
+      "3. OpenAIModerationChain\n",
+      "4. NatBotChain\n",
+      "5. QAGenerationChain\n",
+      "6. QAWithSourcesChain\n",
+      "7. RetrievalQAWithSourcesChain\n",
+      "8. VectorDBQAWithSourcesChain\n",
+      "9. RetrievalQA\n",
+      "10. VectorDBQA\n",
+      "11. LLMRouterChain\n",
+      "12. MultiPromptChain\n",
+      "13. MultiRetrievalQAChain\n",
+      "14. MultiRouteChain\n",
+      "15. RouterChain\n",
+      "16. SequentialChain\n",
+      "17. SimpleSequentialChain\n",
+      "18. TransformChain\n",
+      "19. BaseConversationalRetrievalChain\n",
+      "20. ConstitutionalChain \n",
+      "\n",
+      "-> **Question**: What one improvement do you propose in code in relation to the class herarchy for the Chain class? \n",
+      "\n",
+      "**Answer**: As an AI model, I don't have personal opinions. However, one suggestion could be to improve the documentation of the Chain class hierarchy. The current comments and docstrings provide some details but it could be helpful to include more explicit explanations about the hierarchy, roles of each subclass, and their relationships with one another. Also, incorporating UML diagrams or other visuals could help developers better understand the structure and interactions of the classes. \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "questions = [\n",
+    "    \"What is the class hierarchy?\",\n",
+    "    \"What classes are derived from the Chain class?\",\n",
+    "    \"What one improvement do you propose in code in relation to the class herarchy for the Chain class?\",\n",
+    "]\n",
+    "\n",
+    "for question in questions:\n",
+    "    result = qa(question)\n",
+    "    print(f\"-> **Question**: {question} \\n\")\n",
+    "    print(f\"**Answer**: {result['answer']} \\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The can look at the [LangSmith trace](https://smith.langchain.com/public/2b23045f-4e49-4d2d-8980-dec85259af36/r) to see what is happening under the hood:\n",
+    "\n",
+    "* In particular, the code well structured and kept together in the retrival output\n",
+    "* The retrieved code and chat history are passed to the LLM for answer distillation\n",
+    "\n",
+    "![Image description](/img/code_retrieval.png)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/extras/use_cases/more/_category_.yml b/docs/extras/use_cases/more/_category_.yml
new file mode 100644
index 00000000000..5e1490ecde0
--- /dev/null
+++ b/docs/extras/use_cases/more/_category_.yml
@@ -0,0 +1,2 @@
+label: 'More'
+position: 1
diff --git a/docs/extras/use_cases/code_writing/cpal.ipynb b/docs/extras/use_cases/more/code_writing/cpal.ipynb
similarity index 100%
rename from docs/extras/use_cases/code_writing/cpal.ipynb
rename to docs/extras/use_cases/more/code_writing/cpal.ipynb
diff --git a/docs/extras/use_cases/code_writing/index.mdx b/docs/extras/use_cases/more/code_writing/index.mdx
similarity index 97%
rename from docs/extras/use_cases/code_writing/index.mdx
rename to docs/extras/use_cases/more/code_writing/index.mdx
index 218b4385159..4dd704b5a0a 100644
--- a/docs/extras/use_cases/code_writing/index.mdx
+++ b/docs/extras/use_cases/more/code_writing/index.mdx
@@ -1,3 +1,7 @@
+---
+sidebar_position: 0
+---
+
 # Code writing
 
 :::warning
diff --git a/docs/extras/use_cases/code_writing/llm_bash.ipynb b/docs/extras/use_cases/more/code_writing/llm_bash.ipynb
similarity index 100%
rename from docs/extras/use_cases/code_writing/llm_bash.ipynb
rename to docs/extras/use_cases/more/code_writing/llm_bash.ipynb
diff --git a/docs/extras/use_cases/code_writing/llm_math.ipynb b/docs/extras/use_cases/more/code_writing/llm_math.ipynb
similarity index 100%
rename from docs/extras/use_cases/code_writing/llm_math.ipynb
rename to docs/extras/use_cases/more/code_writing/llm_math.ipynb
diff --git a/docs/extras/use_cases/code_writing/llm_symbolic_math.ipynb b/docs/extras/use_cases/more/code_writing/llm_symbolic_math.ipynb
similarity index 100%
rename from docs/extras/use_cases/code_writing/llm_symbolic_math.ipynb
rename to docs/extras/use_cases/more/code_writing/llm_symbolic_math.ipynb
diff --git a/docs/extras/use_cases/code_writing/pal.ipynb b/docs/extras/use_cases/more/code_writing/pal.ipynb
similarity index 99%
rename from docs/extras/use_cases/code_writing/pal.ipynb
rename to docs/extras/use_cases/more/code_writing/pal.ipynb
index 7ab94661ee0..4a5e9fc57eb 100644
--- a/docs/extras/use_cases/code_writing/pal.ipynb
+++ b/docs/extras/use_cases/more/code_writing/pal.ipynb
@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains import PALChain\n",
+    "from langchain_experimental.pal_chain import PALChain\n",
     "from langchain import OpenAI"
    ]
   },
diff --git a/docs/extras/use_cases/graph/graph_arangodb_qa.ipynb b/docs/extras/use_cases/more/graph/graph_arangodb_qa.ipynb
similarity index 99%
rename from docs/extras/use_cases/graph/graph_arangodb_qa.ipynb
rename to docs/extras/use_cases/more/graph/graph_arangodb_qa.ipynb
index 25c5ac5607a..e82da530ffc 100644
--- a/docs/extras/use_cases/graph/graph_arangodb_qa.ipynb
+++ b/docs/extras/use_cases/more/graph/graph_arangodb_qa.ipynb
@@ -9,7 +9,7 @@
    "source": [
     "# ArangoDB QA chain\n",
     "\n",
-    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/graph/graph_arangodb_qa.ipynb)\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arangodb/interactive_tutorials/blob/master/notebooks/Langchain.ipynb)\n",
     "\n",
     "This notebook shows how to use LLMs to provide a natural language interface to an [ArangoDB](https://github.com/arangodb/arangodb#readme) database."
    ]
diff --git a/docs/extras/use_cases/graph/graph_cypher_qa.ipynb b/docs/extras/use_cases/more/graph/graph_cypher_qa.ipynb
similarity index 100%
rename from docs/extras/use_cases/graph/graph_cypher_qa.ipynb
rename to docs/extras/use_cases/more/graph/graph_cypher_qa.ipynb
diff --git a/docs/extras/use_cases/graph/graph_hugegraph_qa.ipynb b/docs/extras/use_cases/more/graph/graph_hugegraph_qa.ipynb
similarity index 100%
rename from docs/extras/use_cases/graph/graph_hugegraph_qa.ipynb
rename to docs/extras/use_cases/more/graph/graph_hugegraph_qa.ipynb
diff --git a/docs/extras/use_cases/graph/graph_kuzu_qa.ipynb b/docs/extras/use_cases/more/graph/graph_kuzu_qa.ipynb
similarity index 100%
rename from docs/extras/use_cases/graph/graph_kuzu_qa.ipynb
rename to docs/extras/use_cases/more/graph/graph_kuzu_qa.ipynb
diff --git a/docs/extras/use_cases/graph/graph_nebula_qa.ipynb b/docs/extras/use_cases/more/graph/graph_nebula_qa.ipynb
similarity index 100%
rename from docs/extras/use_cases/graph/graph_nebula_qa.ipynb
rename to docs/extras/use_cases/more/graph/graph_nebula_qa.ipynb
diff --git a/docs/extras/use_cases/graph/graph_qa.ipynb b/docs/extras/use_cases/more/graph/graph_qa.ipynb
similarity index 100%
rename from docs/extras/use_cases/graph/graph_qa.ipynb
rename to docs/extras/use_cases/more/graph/graph_qa.ipynb
diff --git a/docs/extras/use_cases/graph/graph_sparql_qa.ipynb b/docs/extras/use_cases/more/graph/graph_sparql_qa.ipynb
similarity index 100%
rename from docs/extras/use_cases/graph/graph_sparql_qa.ipynb
rename to docs/extras/use_cases/more/graph/graph_sparql_qa.ipynb
diff --git a/docs/extras/use_cases/graph/index.mdx b/docs/extras/use_cases/more/graph/index.mdx
similarity index 100%
rename from docs/extras/use_cases/graph/index.mdx
rename to docs/extras/use_cases/more/graph/index.mdx
diff --git a/docs/extras/use_cases/graph/neptune_cypher_qa.ipynb b/docs/extras/use_cases/more/graph/neptune_cypher_qa.ipynb
similarity index 100%
rename from docs/extras/use_cases/graph/neptune_cypher_qa.ipynb
rename to docs/extras/use_cases/more/graph/neptune_cypher_qa.ipynb
diff --git a/docs/extras/use_cases/graph/tot.ipynb b/docs/extras/use_cases/more/graph/tot.ipynb
similarity index 100%
rename from docs/extras/use_cases/graph/tot.ipynb
rename to docs/extras/use_cases/more/graph/tot.ipynb
diff --git a/docs/extras/use_cases/self_check/index.mdx b/docs/extras/use_cases/more/self_check/index.mdx
similarity index 90%
rename from docs/extras/use_cases/self_check/index.mdx
rename to docs/extras/use_cases/more/self_check/index.mdx
index a424ea43701..9880394eb5a 100644
--- a/docs/extras/use_cases/self_check/index.mdx
+++ b/docs/extras/use_cases/more/self_check/index.mdx
@@ -1,3 +1,7 @@
+---
+sidebar_position: 0
+---
+
 # Self-checking
 
 One of the main issues with using LLMs is that they can often hallucinate and make false claims. One of the surprisingly effective ways to remediate this is to use the LLM itself to check its own answers.
diff --git a/docs/extras/use_cases/self_check/llm_checker.ipynb b/docs/extras/use_cases/more/self_check/llm_checker.ipynb
similarity index 100%
rename from docs/extras/use_cases/self_check/llm_checker.ipynb
rename to docs/extras/use_cases/more/self_check/llm_checker.ipynb
diff --git a/docs/extras/use_cases/self_check/llm_summarization_checker.ipynb b/docs/extras/use_cases/more/self_check/llm_summarization_checker.ipynb
similarity index 100%
rename from docs/extras/use_cases/self_check/llm_summarization_checker.ipynb
rename to docs/extras/use_cases/more/self_check/llm_summarization_checker.ipynb
diff --git a/docs/extras/use_cases/more/self_check/smart_llm.ipynb b/docs/extras/use_cases/more/self_check/smart_llm.ipynb
new file mode 100644
index 00000000000..617a36307ee
--- /dev/null
+++ b/docs/extras/use_cases/more/self_check/smart_llm.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "9e9b7651",
+   "metadata": {},
+   "source": [
+    "# How to use a SmartLLMChain\n",
+    "\n",
+    "A SmartLLMChain is a form of self-critique chain that can help you if have particularly complex questions to answer. Instead of doing a single LLM pass, it instead performs these 3 steps:\n",
+    "1. Ideation: Pass the user prompt n times through the LLM to get n output proposals (called \"ideas\"), where n is a parameter you can set \n",
+    "2. Critique: The LLM critiques all ideas to find possible flaws and picks the best one \n",
+    "3. Resolve: The LLM tries to improve upon the best idea (as chosen in the critique step) and outputs it. This is then the final output.\n",
+    "\n",
+    "SmartLLMChains are based on the SmartGPT workflow proposed in https://youtu.be/wVzuvf9D9BU.\n",
+    "\n",
+    "Note that SmartLLMChains\n",
+    "- use more LLM passes (ie n+2 instead of just 1)\n",
+    "- only work then the underlying LLM has the capability for reflection, whicher smaller models often don't\n",
+    "- only work with underlying models that return exactly 1 output, not multiple\n",
+    "\n",
+    "This notebook demonstrates how to use a SmartLLMChain."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "714dede0",
+   "metadata": {},
+   "source": [
+    "##### Same LLM for all steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d3f7fb22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"...\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "10e5ece6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain_experimental.smart_llm import SmartLLMChain"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "1780da51",
+   "metadata": {},
+   "source": [
+    "As example question, we will use \"I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?\". This is an example from the original SmartGPT video (https://youtu.be/wVzuvf9D9BU?t=384). While this seems like a very easy question, LLMs struggle do these kinds of questions that involve numbers and physical reasoning.\n",
+    "\n",
+    "As we will see, all 3 initial ideas are completely wrong - even though we're using GPT4! Only when using self-reflection do we get a correct answer. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "054af6b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hard_question = \"I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "8049cecd",
+   "metadata": {},
+   "source": [
+    "So, we first create an LLM and prompt template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "811ea8e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = PromptTemplate.from_template(hard_question)\n",
+    "llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "50b602e4",
+   "metadata": {},
+   "source": [
+    "Now we can create a SmartLLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8cd49199",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = SmartLLMChain(llm=llm, prompt=prompt, n_ideas=3, verbose=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "6a72f276",
+   "metadata": {},
+   "source": [
+    "Now we can use the SmartLLM as a drop-in replacement for our LLM. E.g.:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "074e5e75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new SmartLLMChain chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mI have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?\u001b[0m\n",
+      "Idea 1:\n",
+      "\u001b[36;1m\u001b[1;3m1. Fill the 6-liter jug completely.\n",
+      "2. Pour the water from the 6-liter jug into the 12-liter jug.\n",
+      "3. Fill the 6-liter jug again.\n",
+      "4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full.\n",
+      "5. The amount of water left in the 6-liter jug will be exactly 6 liters.\u001b[0m\n",
+      "Idea 2:\n",
+      "\u001b[36;1m\u001b[1;3m1. Fill the 6-liter jug completely.\n",
+      "2. Pour the water from the 6-liter jug into the 12-liter jug.\n",
+      "3. Fill the 6-liter jug again.\n",
+      "4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full.\n",
+      "5. Since the 12-liter jug is now full, there will be 2 liters of water left in the 6-liter jug.\n",
+      "6. Empty the 12-liter jug.\n",
+      "7. Pour the 2 liters of water from the 6-liter jug into the 12-liter jug.\n",
+      "8. Fill the 6-liter jug completely again.\n",
+      "9. Pour the water from the 6-liter jug into the 12-liter jug, which already has 2 liters in it.\n",
+      "10. Now, the 12-liter jug will have exactly 6 liters of water (2 liters from before + 4 liters from the 6-liter jug).\u001b[0m\n",
+      "Idea 3:\n",
+      "\u001b[36;1m\u001b[1;3m1. Fill the 6-liter jug completely.\n",
+      "2. Pour the water from the 6-liter jug into the 12-liter jug.\n",
+      "3. Fill the 6-liter jug again.\n",
+      "4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full.\n",
+      "5. The amount of water left in the 6-liter jug will be exactly 6 liters.\u001b[0m\n",
+      "Critique:\n",
+      "\u001b[33;1m\u001b[1;3mIdea 1:\n",
+      "1. Fill the 6-liter jug completely. (No flaw)\n",
+      "2. Pour the water from the 6-liter jug into the 12-liter jug. (No flaw)\n",
+      "3. Fill the 6-liter jug again. (No flaw)\n",
+      "4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full. (Flaw: The 12-liter jug will never be full in this step, as it can hold 12 liters and we are only pouring 6 liters into it.)\n",
+      "5. The amount of water left in the 6-liter jug will be exactly 6 liters. (Flaw: This statement is incorrect, as there will be no water left in the 6-liter jug after pouring it into the 12-liter jug.)\n",
+      "\n",
+      "Idea 2:\n",
+      "1. Fill the 6-liter jug completely. (No flaw)\n",
+      "2. Pour the water from the 6-liter jug into the 12-liter jug. (No flaw)\n",
+      "3. Fill the 6-liter jug again. (No flaw)\n",
+      "4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full. (Flaw: The 12-liter jug will never be full in this step, as it can hold 12 liters and we are only pouring 6 liters into it.)\n",
+      "5. Since the 12-liter jug is now full, there will be 2 liters of water left in the 6-liter jug. (Flaw: This statement is incorrect, as the 12-liter jug will not be full and there will be no water left in the 6-liter jug.)\n",
+      "6. Empty the 12-liter jug. (No flaw)\n",
+      "7. Pour the 2 liters of water from the 6-liter jug into the 12-liter jug. (Flaw: This step is based on the incorrect assumption that there are 2 liters of water left in the 6-liter jug.)\n",
+      "8. Fill the 6-liter jug completely again. (No flaw)\n",
+      "9. Pour the water from the 6-liter jug into the 12-liter jug, which already has 2 liters in it. (Flaw: This step is based on the incorrect assumption that there are 2 liters of water in the 12-liter jug.)\n",
+      "10. Now, the 12-liter jug will have exactly 6 liters of water (2 liters from before + 4 liters from the 6-liter jug). (Flaw: This conclusion is based on the incorrect assumptions made in the previous steps.)\n",
+      "\n",
+      "Idea 3:\n",
+      "1. Fill the 6-liter jug completely. (No flaw)\n",
+      "2. Pour the water from the 6-liter jug into the 12-liter jug. (No flaw)\n",
+      "3. Fill the 6-liter jug again. (No flaw)\n",
+      "4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full. (Flaw: The 12-liter jug will never be full in this step, as it can hold 12 liters and we are only pouring 6 liters into it.)\n",
+      "5. The amount of water left in the 6-liter jug will be exactly 6 liters. (Flaw: This statement is incorrect, as there will be no water left in the 6-liter jug after pouring it into the 12-liter jug.)\u001b[0m\n",
+      "Resolution:\n",
+      "\u001b[32;1m\u001b[1;3m1. Fill the 12-liter jug completely.\n",
+      "2. Pour the water from the 12-liter jug into the 6-liter jug until the 6-liter jug is full.\n",
+      "3. The amount of water left in the 12-liter jug will be exactly 6 liters.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'1. Fill the 12-liter jug completely.\\n2. Pour the water from the 12-liter jug into the 6-liter jug until the 6-liter jug is full.\\n3. The amount of water left in the 12-liter jug will be exactly 6 liters.'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run({})"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "bbfebea1",
+   "metadata": {},
+   "source": [
+    "##### Different LLM for different steps"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "5be6ec08",
+   "metadata": {},
+   "source": [
+    "You can also use different LLMs for the different steps by passing `ideation_llm`, `critique_llm` and `resolve_llm`. You might want to do this to use a more creative (i.e., high-temperature) model for ideation and a more strict (i.e., low-temperature) model for critique and resolution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9c33fa19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = SmartLLMChain(\n",
+    "    ideation_llm=ChatOpenAI(temperature=0.9, model_name=\"gpt-4\"),\n",
+    "    llm=ChatOpenAI(\n",
+    "        temperature=0, model_name=\"gpt-4\"\n",
+    "    ),  # will be used for critqiue and resolution as no specific llms are given\n",
+    "    prompt=prompt,\n",
+    "    n_ideas=3,\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "886c1cc1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/use_cases/multi_modal/_category_.yml b/docs/extras/use_cases/multi_modal/_category_.yml
index e6d4e549eb4..96ce6ef2bbe 100644
--- a/docs/extras/use_cases/multi_modal/_category_.yml
+++ b/docs/extras/use_cases/multi_modal/_category_.yml
@@ -1 +1,2 @@
 label: 'Multi-modal'
+position: 0
diff --git a/docs/extras/use_cases/question_answering/how_to/code/code-analysis-deeplake.ipynb b/docs/extras/use_cases/question_answering/how_to/code/code-analysis-deeplake.ipynb
new file mode 100644
index 00000000000..69cfb0b9fed
--- /dev/null
+++ b/docs/extras/use_cases/question_answering/how_to/code/code-analysis-deeplake.ipynb
@@ -0,0 +1,1178 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Use LangChain, GPT and Activeloop's Deep Lake to work with code base\n",
+    "In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT to analyze the code base of the LangChain itself. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Design"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Prepare data:\n",
+    "   1. Upload all python project files using the `langchain.document_loaders.TextLoader`. We will call these files the **documents**.\n",
+    "   2. Split all documents to chunks using the `langchain.text_splitter.CharacterTextSplitter`.\n",
+    "   3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain.vectorstores.DeepLake`\n",
+    "2. Question-Answering:\n",
+    "   1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`\n",
+    "   2. Prepare questions.\n",
+    "   3. Get answers running the chain.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementation"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Integration preparations"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to set up keys for external services and install necessary python libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!python3 -m pip install --upgrade langchain deeplake openai"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. \n",
+    "\n",
+    "For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "# Please manually enter OpenAI Key"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the platform at [app.activeloop.ai](https://app.activeloop.ai)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "activeloop_token = getpass(\"Activeloop Token:\")\n",
+    "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prepare data "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load all repository files. Here we assume this notebook is downloaded as the part of the langchain fork and we work with the python files of the `langchain` repo.\n",
+    "\n",
+    "If you want to use files from different repo, change `root_dir` to the root dir of your repo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CITATION.cff  MIGRATE.md  README.md  libs\t  poetry.toml\n",
+      "LICENSE       Makefile\t  docs\t     poetry.lock  pyproject.toml\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls \"../../../../../../libs\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2554\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "\n",
+    "root_dir = \"../../../../../../libs\"\n",
+    "\n",
+    "docs = []\n",
+    "for dirpath, dirnames, filenames in os.walk(root_dir):\n",
+    "    for file in filenames:\n",
+    "        if file.endswith(\".py\") and \"*venv/\" not in dirpath:\n",
+    "            try:\n",
+    "                loader = TextLoader(os.path.join(dirpath, file), encoding=\"utf-8\")\n",
+    "                docs.extend(loader.load_and_split())\n",
+    "            except Exception as e:\n",
+    "                pass\n",
+    "print(f\"{len(docs)}\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, chunk the files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8244\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_documents(docs)\n",
+    "print(f\"{len(texts)}\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then embed chunks and upload them to the DeepLake.\n",
+    "\n",
+    "This can take several minutes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version='', openai_api_base='', openai_api_type='', openai_proxy='', embedding_ctx_length=8191, openai_api_key='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', openai_organization='', allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6, request_timeout=None, headers=None, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={})"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your Deep Lake dataset has been successfully created!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset(path='hub://adilkhan/langchain-code', tensors=['embedding', 'id', 'metadata', 'text'])\n",
+      "\n",
+      "  tensor      htype       shape       dtype  compression\n",
+      "  -------    -------     -------     -------  ------- \n",
+      " embedding  embedding  (8244, 1536)  float32   None   \n",
+      "    id        text      (8244, 1)      str     None   \n",
+      " metadata     json      (8244, 1)      str     None   \n",
+      "   text       text      (8244, 1)      str     None   \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<langchain.vectorstores.deeplake.DeepLake at 0x7fe1b67d7a30>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.vectorstores import DeepLake\n",
+    "\n",
+    "\n",
+    "username = \"<USERNAME_OR_ORG>\"\n",
+    "\n",
+    "\n",
+    "db = DeepLake.from_documents(\n",
+    "    texts, embeddings, dataset_path=f\"hub://{username}/langchain-code\", overwrite=True\n",
+    ")\n",
+    "db"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from langchain.vectorstores import DeepLake\n",
+    "\n",
+    "# db = DeepLake.from_documents(\n",
+    "#     texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\", runtime={\"tensor_db\": True}\n",
+    "# )\n",
+    "# db"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Question Answering\n",
+    "First load the dataset, construct the retriever, then construct the Conversational Chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Deep Lake Dataset in hub://adilkhan/langchain-code already exists, loading from the storage\n"
+     ]
+    }
+   ],
+   "source": [
+    "db = DeepLake(\n",
+    "    dataset_path=f\"hub://{username}/langchain-code\",\n",
+    "    read_only=True,\n",
+    "    embedding=embeddings,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "retriever = db.as_retriever()\n",
+    "retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
+    "retriever.search_kwargs[\"fetch_k\"] = 20\n",
+    "retriever.search_kwargs[\"maximal_marginal_relevance\"] = True\n",
+    "retriever.search_kwargs[\"k\"] = 20"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also specify user defined functions using [Deep Lake filters](https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def filter(x):\n",
+    "    # filter based on source code\n",
+    "    if \"something\" in x[\"text\"].data()[\"value\"]:\n",
+    "        return False\n",
+    "\n",
+    "    # filter based on path e.g. extension\n",
+    "    metadata = x[\"metadata\"].data()[\"value\"]\n",
+    "    return \"only_this\" in metadata[\"source\"] or \"also_that\" in metadata[\"source\"]\n",
+    "\n",
+    "\n",
+    "### turn on below for custom filtering\n",
+    "# retriever.search_kwargs['filter'] = filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains import ConversationalRetrievalChain\n",
+    "\n",
+    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo-0613\")  # 'ada' 'gpt-3.5-turbo-0613' 'gpt-4',\n",
+    "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-> **Question**: What is the class hierarchy? \n",
+      "\n",
+      "**Answer**: The class hierarchy for Memory is as follows:\n",
+      "\n",
+      "    BaseMemory --> BaseChatMemory --> <name>Memory  # Examples: ZepMemory, MotorheadMemory\n",
+      "\n",
+      "The class hierarchy for ChatMessageHistory is as follows:\n",
+      "\n",
+      "    BaseChatMessageHistory --> <name>ChatMessageHistory  # Example: ZepChatMessageHistory\n",
+      "\n",
+      "The class hierarchy for Prompt is as follows:\n",
+      "\n",
+      "    BasePromptTemplate --> PipelinePromptTemplate\n",
+      "                           StringPromptTemplate --> PromptTemplate\n",
+      "                                                    FewShotPromptTemplate\n",
+      "                                                    FewShotPromptWithTemplates\n",
+      "                           BaseChatPromptTemplate --> AutoGPTPrompt\n",
+      "                                                      ChatPromptTemplate --> AgentScratchPadChatPromptTemplate\n",
+      " \n",
+      "\n",
+      "-> **Question**: What classes are derived from the Chain class? \n",
+      "\n",
+      "**Answer**: The classes derived from the Chain class are:\n",
+      "\n",
+      "- APIChain\n",
+      "- OpenAPIEndpointChain\n",
+      "- AnalyzeDocumentChain\n",
+      "- MapReduceDocumentsChain\n",
+      "- MapRerankDocumentsChain\n",
+      "- ReduceDocumentsChain\n",
+      "- RefineDocumentsChain\n",
+      "- StuffDocumentsChain\n",
+      "- ConstitutionalChain\n",
+      "- ConversationChain\n",
+      "- ChatVectorDBChain\n",
+      "- ConversationalRetrievalChain\n",
+      "- FlareChain\n",
+      "- ArangoGraphQAChain\n",
+      "- GraphQAChain\n",
+      "- GraphCypherQAChain\n",
+      "- HugeGraphQAChain\n",
+      "- KuzuQAChain\n",
+      "- NebulaGraphQAChain\n",
+      "- NeptuneOpenCypherQAChain\n",
+      "- GraphSparqlQAChain\n",
+      "- HypotheticalDocumentEmbedder\n",
+      "- LLMChain\n",
+      "- LLMBashChain\n",
+      "- LLMCheckerChain\n",
+      "- LLMMathChain\n",
+      "- LLMRequestsChain\n",
+      "- LLMSummarizationCheckerChain\n",
+      "- MapReduceChain\n",
+      "- OpenAIModerationChain\n",
+      "- NatBotChain\n",
+      "- QAGenerationChain\n",
+      "- QAWithSourcesChain\n",
+      "- RetrievalQAWithSourcesChain\n",
+      "- VectorDBQAWithSourcesChain\n",
+      "- RetrievalQA\n",
+      "- VectorDBQA\n",
+      "- LLMRouterChain\n",
+      "- MultiPromptChain\n",
+      "- MultiRetrievalQAChain\n",
+      "- MultiRouteChain\n",
+      "- RouterChain\n",
+      "- SequentialChain\n",
+      "- SimpleSequentialChain\n",
+      "- TransformChain\n",
+      "- TaskPlaningChain\n",
+      "- QueryChain\n",
+      "- CPALChain\n",
+      " \n",
+      "\n",
+      "-> **Question**: What kind of retrievers does LangChain have? \n",
+      "\n",
+      "**Answer**: The LangChain class includes various types of retrievers such as:\n",
+      "\n",
+      "- ArxivRetriever\n",
+      "- AzureCognitiveSearchRetriever\n",
+      "- BM25Retriever\n",
+      "- ChaindeskRetriever\n",
+      "- ChatGPTPluginRetriever\n",
+      "- ContextualCompressionRetriever\n",
+      "- DocArrayRetriever\n",
+      "- ElasticSearchBM25Retriever\n",
+      "- EnsembleRetriever\n",
+      "- GoogleCloudEnterpriseSearchRetriever\n",
+      "- AmazonKendraRetriever\n",
+      "- KNNRetriever\n",
+      "- LlamaIndexGraphRetriever and LlamaIndexRetriever\n",
+      "- MergerRetriever\n",
+      "- MetalRetriever\n",
+      "- MilvusRetriever\n",
+      "- MultiQueryRetriever\n",
+      "- ParentDocumentRetriever\n",
+      "- PineconeHybridSearchRetriever\n",
+      "- PubMedRetriever\n",
+      "- RePhraseQueryRetriever\n",
+      "- RemoteLangChainRetriever\n",
+      "- SelfQueryRetriever\n",
+      "- SVMRetriever\n",
+      "- TFIDFRetriever\n",
+      "- TimeWeightedVectorStoreRetriever\n",
+      "- VespaRetriever\n",
+      "- WeaviateHybridSearchRetriever\n",
+      "- WebResearchRetriever\n",
+      "- WikipediaRetriever\n",
+      "- ZepRetriever\n",
+      "- ZillizRetriever \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "questions = [\n",
+    "    \"What is the class hierarchy?\",\n",
+    "    \"What classes are derived from the Chain class?\",\n",
+    "    \"What kind of retrievers does LangChain have?\",\n",
+    "]\n",
+    "chat_history = []\n",
+    "qa_dict = {}\n",
+    "\n",
+    "for question in questions:\n",
+    "    result = qa({\"question\": question, \"chat_history\": chat_history})\n",
+    "    chat_history.append((question, result[\"answer\"]))\n",
+    "    qa_dict[question] = result[\"answer\"]\n",
+    "    print(f\"-> **Question**: {question} \\n\")\n",
+    "    print(f\"**Answer**: {result['answer']} \\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'question': 'LangChain possesses a variety of retrievers including:\\n\\n1. ArxivRetriever\\n2. AzureCognitiveSearchRetriever\\n3. BM25Retriever\\n4. ChaindeskRetriever\\n5. ChatGPTPluginRetriever\\n6. ContextualCompressionRetriever\\n7. DocArrayRetriever\\n8. ElasticSearchBM25Retriever\\n9. EnsembleRetriever\\n10. GoogleCloudEnterpriseSearchRetriever\\n11. AmazonKendraRetriever\\n12. KNNRetriever\\n13. LlamaIndexGraphRetriever\\n14. LlamaIndexRetriever\\n15. MergerRetriever\\n16. MetalRetriever\\n17. MilvusRetriever\\n18. MultiQueryRetriever\\n19. ParentDocumentRetriever\\n20. PineconeHybridSearchRetriever\\n21. PubMedRetriever\\n22. RePhraseQueryRetriever\\n23. RemoteLangChainRetriever\\n24. SelfQueryRetriever\\n25. SVMRetriever\\n26. TFIDFRetriever\\n27. TimeWeightedVectorStoreRetriever\\n28. VespaRetriever\\n29. WeaviateHybridSearchRetriever\\n30. WebResearchRetriever\\n31. WikipediaRetriever\\n32. ZepRetriever\\n33. ZillizRetriever\\n\\nIt also includes self query translators like:\\n\\n1. ChromaTranslator\\n2. DeepLakeTranslator\\n3. MyScaleTranslator\\n4. PineconeTranslator\\n5. QdrantTranslator\\n6. WeaviateTranslator\\n\\nAnd remote retrievers like:\\n\\n1. RemoteLangChainRetriever'}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qa_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The class hierarchy for Memory is as follows:\n",
+      "\n",
+      "    BaseMemory --> BaseChatMemory --> <name>Memory  # Examples: ZepMemory, MotorheadMemory\n",
+      "\n",
+      "The class hierarchy for ChatMessageHistory is as follows:\n",
+      "\n",
+      "    BaseChatMessageHistory --> <name>ChatMessageHistory  # Example: ZepChatMessageHistory\n",
+      "\n",
+      "The class hierarchy for Prompt is as follows:\n",
+      "\n",
+      "    BasePromptTemplate --> PipelinePromptTemplate\n",
+      "                           StringPromptTemplate --> PromptTemplate\n",
+      "                                                    FewShotPromptTemplate\n",
+      "                                                    FewShotPromptWithTemplates\n",
+      "                           BaseChatPromptTemplate --> AutoGPTPrompt\n",
+      "                                                      ChatPromptTemplate --> AgentScratchPadChatPromptTemplate\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(qa_dict[\"What is the class hierarchy?\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The classes derived from the Chain class are:\n",
+      "\n",
+      "- APIChain\n",
+      "- OpenAPIEndpointChain\n",
+      "- AnalyzeDocumentChain\n",
+      "- MapReduceDocumentsChain\n",
+      "- MapRerankDocumentsChain\n",
+      "- ReduceDocumentsChain\n",
+      "- RefineDocumentsChain\n",
+      "- StuffDocumentsChain\n",
+      "- ConstitutionalChain\n",
+      "- ConversationChain\n",
+      "- ChatVectorDBChain\n",
+      "- ConversationalRetrievalChain\n",
+      "- FlareChain\n",
+      "- ArangoGraphQAChain\n",
+      "- GraphQAChain\n",
+      "- GraphCypherQAChain\n",
+      "- HugeGraphQAChain\n",
+      "- KuzuQAChain\n",
+      "- NebulaGraphQAChain\n",
+      "- NeptuneOpenCypherQAChain\n",
+      "- GraphSparqlQAChain\n",
+      "- HypotheticalDocumentEmbedder\n",
+      "- LLMChain\n",
+      "- LLMBashChain\n",
+      "- LLMCheckerChain\n",
+      "- LLMMathChain\n",
+      "- LLMRequestsChain\n",
+      "- LLMSummarizationCheckerChain\n",
+      "- MapReduceChain\n",
+      "- OpenAIModerationChain\n",
+      "- NatBotChain\n",
+      "- QAGenerationChain\n",
+      "- QAWithSourcesChain\n",
+      "- RetrievalQAWithSourcesChain\n",
+      "- VectorDBQAWithSourcesChain\n",
+      "- RetrievalQA\n",
+      "- VectorDBQA\n",
+      "- LLMRouterChain\n",
+      "- MultiPromptChain\n",
+      "- MultiRetrievalQAChain\n",
+      "- MultiRouteChain\n",
+      "- RouterChain\n",
+      "- SequentialChain\n",
+      "- SimpleSequentialChain\n",
+      "- TransformChain\n",
+      "- TaskPlaningChain\n",
+      "- QueryChain\n",
+      "- CPALChain\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(qa_dict[\"What classes are derived from the Chain class?\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The LangChain class includes various types of retrievers such as:\n",
+      "\n",
+      "- ArxivRetriever\n",
+      "- AzureCognitiveSearchRetriever\n",
+      "- BM25Retriever\n",
+      "- ChaindeskRetriever\n",
+      "- ChatGPTPluginRetriever\n",
+      "- ContextualCompressionRetriever\n",
+      "- DocArrayRetriever\n",
+      "- ElasticSearchBM25Retriever\n",
+      "- EnsembleRetriever\n",
+      "- GoogleCloudEnterpriseSearchRetriever\n",
+      "- AmazonKendraRetriever\n",
+      "- KNNRetriever\n",
+      "- LlamaIndexGraphRetriever and LlamaIndexRetriever\n",
+      "- MergerRetriever\n",
+      "- MetalRetriever\n",
+      "- MilvusRetriever\n",
+      "- MultiQueryRetriever\n",
+      "- ParentDocumentRetriever\n",
+      "- PineconeHybridSearchRetriever\n",
+      "- PubMedRetriever\n",
+      "- RePhraseQueryRetriever\n",
+      "- RemoteLangChainRetriever\n",
+      "- SelfQueryRetriever\n",
+      "- SVMRetriever\n",
+      "- TFIDFRetriever\n",
+      "- TimeWeightedVectorStoreRetriever\n",
+      "- VespaRetriever\n",
+      "- WeaviateHybridSearchRetriever\n",
+      "- WebResearchRetriever\n",
+      "- WikipediaRetriever\n",
+      "- ZepRetriever\n",
+      "- ZillizRetriever\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(qa_dict[\"What kind of retrievers does LangChain have?\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/extras/use_cases/code/index.mdx b/docs/extras/use_cases/question_answering/how_to/code/index.mdx
similarity index 100%
rename from docs/extras/use_cases/code/index.mdx
rename to docs/extras/use_cases/question_answering/how_to/code/index.mdx
diff --git a/docs/extras/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake.ipynb b/docs/extras/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake.ipynb
new file mode 100644
index 00000000000..c02c0646a77
--- /dev/null
+++ b/docs/extras/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake.ipynb
@@ -0,0 +1,4016 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep Lake\n",
+    "In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT4 to analyze the code base of the twitter algorithm. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. For full documentation of Deep Lake please follow [docs](https://docs.activeloop.ai/) and [API reference](https://docs.deeplake.ai/en/latest/).\n",
+    "\n",
+    "Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the [platform](https://app.activeloop.ai)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import DeepLake\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
+    "activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",
+    "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = OpenAIEmbeddings(disallowed_special=())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "disallowed_special=() is required to avoid `Exception: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte` from tiktoken for some repositories"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Index the code base (optional)\n",
+    "You can directly skip this part and directly jump into using already indexed dataset. To begin with, first we will clone the repository, then parse and chunk the code base and use OpenAI indexing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cloning into 'the-algorithm'...\n",
+      "remote: Enumerating objects: 9142, done.\u001b[K\n",
+      "remote: Counting objects: 100% (2438/2438), done.\u001b[K\n",
+      "remote: Compressing objects: 100% (1662/1662), done.\u001b[K\n",
+      "remote: Total 9142 (delta 597), reused 2349 (delta 593), pack-reused 6704\u001b[K\n",
+      "Receiving objects: 100% (9142/9142), 7.67 MiB | 33.29 MiB/s, done.\n",
+      "Resolving deltas: 100% (2818/2818), done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!git clone https://github.com/twitter/the-algorithm # replace any repository of your choice"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load all files inside the repository"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from langchain.document_loaders import TextLoader\n",
+    "\n",
+    "root_dir = \"./the-algorithm\"\n",
+    "docs = []\n",
+    "for dirpath, dirnames, filenames in os.walk(root_dir):\n",
+    "    for file in filenames:\n",
+    "        try:\n",
+    "            loader = TextLoader(os.path.join(dirpath, file), encoding=\"utf-8\")\n",
+    "            docs.extend(loader.load_and_split())\n",
+    "        except Exception as e:\n",
+    "            pass"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, chunk the files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Created a chunk of size 2549, which is longer than the specified 1000\n",
+      "Created a chunk of size 2095, which is longer than the specified 1000\n",
+      "Created a chunk of size 1983, which is longer than the specified 1000\n",
+      "Created a chunk of size 1531, which is longer than the specified 1000\n",
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+      "Created a chunk of size 1619, which is longer than the specified 1000\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_documents(docs)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Execute the indexing. This will take about ~4 mins to compute embeddings and upload to Activeloop. You can then publish the dataset to be public."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Deep Lake Dataset in hub://adilkhan/twitter-algorithm already exists, loading from the storage\n",
+      "Batch upload: 31310 samples are being uploaded in 32 batches of batch size 1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Evaluating ingest: 28%|██▊       | 9/32 [00:50<02:09Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 851354 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 38%|███▊      | 12/32 [01:13<02:09Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 836180 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 41%|████      | 13/32 [01:29<02:43Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 875259 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 802651 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 44%|████▍     | 14/32 [01:42<02:57Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 884425 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 47%|████▋     | 15/32 [01:51<02:41Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 815327 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 50%|█████     | 16/32 [02:05<02:52Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 867281 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 53%|█████▎    | 17/32 [02:14<02:34Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 908595 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 834375 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 56%|█████▋    | 18/32 [02:26<02:33Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 904522 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 62%|██████▎   | 20/32 [02:44<02:01Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 938638 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 863952 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 66%|██████▌   | 21/32 [02:57<01:58Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 906069 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 833688 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 72%|███████▏  | 23/32 [03:21<01:40Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 855806 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 75%|███████▌  | 24/32 [03:31<01:26Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 845993 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 81%|████████▏ | 26/32 [03:50<01:01Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 904644 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 832413 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 84%|████████▍ | 27/32 [04:03<00:54Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 912569 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 839877 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 91%|█████████ | 29/32 [04:25<00:32Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 890015 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-ciTI4gyz985F6II5qjFfD4Gf on tokens per min. Limit: 1000000 / min. Current: 814898 / min. Contact us through our help center at help.openai.com if you continue to have issues..\n",
+      "Evaluating ingest: 100%|██████████| 32/32 [04:54<00:00\n",
+      "/"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset(path='hub://adilkhan/twitter-algorithm', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+      "\n",
+      "  tensor     htype       shape       dtype  compression\n",
+      "  -------   -------     -------     -------  ------- \n",
+      " embedding  generic  (31310, 1536)   None     None   \n",
+      "    ids      text     (31310, 1)      str     None   \n",
+      " metadata    json     (31310, 1)      str     None   \n",
+      "   text      text     (31310, 1)      str     None   \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " \r"
+     ]
+    },
+    {
+     "data": {
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+       " '081a3fac-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fad-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fae-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3faf-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fb0-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fb1-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fb2-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fb3-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fb4-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fb5-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fb6-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fb7-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fb8-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fb9-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fba-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fbb-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fbc-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fbd-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fbe-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fbf-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fc0-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fc1-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fc2-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fc3-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fc4-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fc5-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fc6-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fc7-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fc8-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fc9-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fca-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fcb-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fcc-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fcd-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fce-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fcf-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fd0-3a8d-11ee-b840-13905694aaaf',\n",
+       " '081a3fd1-3a8d-11ee-b840-13905694aaaf',\n",
+       " '08d1623e-3a8d-11ee-b840-13905694aaaf',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "username = \"<USERNAME_OR_ORG>\"  # replace with your username from app.activeloop.ai\n",
+    "db = DeepLake(\n",
+    "    dataset_path=f\"hub://{username}/twitter-algorithm\",\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "db.add_documents(texts)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# username = \"davitbun\"  # replace with your username from app.activeloop.ai\n",
+    "# db = DeepLake(\n",
+    "#     dataset_path=f\"hub://{username}/twitter-algorithm\",\n",
+    "#     embedding_function=embeddings,\n",
+    "#     runtime={\"tensor_db\": True}\n",
+    "# )\n",
+    "# db.add_documents(texts)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Question Answering on Twitter algorithm codebase\n",
+    "First load the dataset, construct the retriever, then construct the Conversational Chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Deep Lake Dataset in hub://adilkhan/twitter-algorithm already exists, loading from the storage\n"
+     ]
+    }
+   ],
+   "source": [
+    "db = DeepLake(\n",
+    "    dataset_path=f\"hub://{username}/twitter-algorithm\",\n",
+    "    read_only=True,\n",
+    "    embedding=embeddings,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = db.as_retriever()\n",
+    "retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
+    "retriever.search_kwargs[\"fetch_k\"] = 100\n",
+    "retriever.search_kwargs[\"maximal_marginal_relevance\"] = True\n",
+    "retriever.search_kwargs[\"k\"] = 10"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also specify user defined functions using [Deep Lake filters](https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def filter(x):\n",
+    "    # filter based on source code\n",
+    "    if \"com.google\" in x[\"text\"].data()[\"value\"]:\n",
+    "        return False\n",
+    "\n",
+    "    # filter based on path e.g. extension\n",
+    "    metadata = x[\"metadata\"].data()[\"value\"]\n",
+    "    return \"scala\" in metadata[\"source\"] or \"py\" in metadata[\"source\"]\n",
+    "\n",
+    "\n",
+    "### turn on below for custom filtering\n",
+    "# retriever.search_kwargs['filter'] = filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains import ConversationalRetrievalChain\n",
+    "\n",
+    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo-0613\")  # switch to 'gpt-4'\n",
+    "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "questions = [\n",
+    "    \"What does favCountParams do?\",\n",
+    "    \"is it Likes + Bookmarks, or not clear from the code?\",\n",
+    "    \"What are the major negative modifiers that lower your linear ranking parameters?\",\n",
+    "    \"How do you get assigned to SimClusters?\",\n",
+    "    \"What is needed to migrate from one SimClusters to another SimClusters?\",\n",
+    "    \"How much do I get boosted within my cluster?\",\n",
+    "    \"How does Heavy ranker work. what are it’s main inputs?\",\n",
+    "    \"How can one influence Heavy ranker?\",\n",
+    "    \"why threads and long tweets do so well on the platform?\",\n",
+    "    \"Are thread and long tweet creators building a following that reacts to only threads?\",\n",
+    "    \"Do you need to follow different strategies to get most followers vs to get most likes and bookmarks per tweet?\",\n",
+    "    \"Content meta data and how it impacts virality (e.g. ALT in images).\",\n",
+    "    \"What are some unexpected fingerprints for spam factors?\",\n",
+    "    \"Is there any difference between company verified checkmarks and blue verified individual checkmarks?\",\n",
+    "]\n",
+    "chat_history = []\n",
+    "\n",
+    "for question in questions:\n",
+    "    result = qa({\"question\": question, \"chat_history\": chat_history})\n",
+    "    chat_history.append((question, result[\"answer\"]))\n",
+    "    print(f\"-> **Question**: {question} \\n\")\n",
+    "    print(f\"**Answer**: {result['answer']} \\n\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-> **Question**: What does favCountParams do? \n",
+    "\n",
+    "**Answer**: `favCountParams` is an optional ThriftLinearFeatureRankingParams instance that represents the parameters related to the \"favorite count\" feature in the ranking process. It is used to control the weight of the favorite count feature while ranking tweets. The favorite count is the number of times a tweet has been marked as a favorite by users, and it is considered an important signal in the ranking of tweets. By using `favCountParams`, the system can adjust the importance of the favorite count while calculating the final ranking score of a tweet. \n",
+    "\n",
+    "-> **Question**: is it Likes + Bookmarks, or not clear from the code?\n",
+    "\n",
+    "**Answer**: From the provided code, it is not clear if the favorite count metric is determined by the sum of likes and bookmarks. The favorite count is mentioned in the code, but there is no explicit reference to how it is calculated in terms of likes and bookmarks. \n",
+    "\n",
+    "-> **Question**: What are the major negative modifiers that lower your linear ranking parameters?\n",
+    "\n",
+    "**Answer**: In the given code, major negative modifiers that lower the linear ranking parameters are:\n",
+    "\n",
+    "1. `scoringData.querySpecificScore`: This score adjustment is based on the query-specific information. If its value is negative, it will lower the linear ranking parameters.\n",
+    "\n",
+    "2. `scoringData.authorSpecificScore`: This score adjustment is based on the author-specific information. If its value is negative, it will also lower the linear ranking parameters.\n",
+    "\n",
+    "Please note that I cannot provide more information on the exact calculations of these negative modifiers, as the code for their determination is not provided. \n",
+    "\n",
+    "-> **Question**: How do you get assigned to SimClusters?\n",
+    "\n",
+    "**Answer**: The assignment to SimClusters occurs through a Metropolis-Hastings sampling-based community detection algorithm that is run on the Producer-Producer similarity graph. This graph is created by computing the cosine similarity scores between the users who follow each producer. The algorithm identifies communities or clusters of Producers with similar followers, and takes a parameter *k* for specifying the number of communities to be detected.\n",
+    "\n",
+    "After the community detection, different users and content are represented as sparse, interpretable vectors within these identified communities (SimClusters). The resulting SimClusters embeddings can be used for various recommendation tasks. \n",
+    "\n",
+    "-> **Question**: What is needed to migrate from one SimClusters to another SimClusters?\n",
+    "\n",
+    "**Answer**: To migrate from one SimClusters representation to another, you can follow these general steps:\n",
+    "\n",
+    "1. **Prepare the new representation**: Create the new SimClusters representation using any necessary updates or changes in the clustering algorithm, similarity measures, or other model parameters. Ensure that this new representation is properly stored and indexed as needed.\n",
+    "\n",
+    "2. **Update the relevant code and configurations**: Modify the relevant code and configuration files to reference the new SimClusters representation. This may involve updating paths or dataset names to point to the new representation, as well as changing code to use the new clustering method or similarity functions if applicable.\n",
+    "\n",
+    "3. **Test the new representation**: Before deploying the changes to production, thoroughly test the new SimClusters representation to ensure its effectiveness and stability. This may involve running offline jobs like candidate generation and label candidates, validating the output, as well as testing the new representation in the evaluation environment using evaluation tools like TweetSimilarityEvaluationAdhocApp.\n",
+    "\n",
+    "4. **Deploy the changes**: Once the new representation has been tested and validated, deploy the changes to production. This may involve creating a zip file, uploading it to the packer, and then scheduling it with Aurora. Be sure to monitor the system to ensure a smooth transition between representations and verify that the new representation is being used in recommendations as expected.\n",
+    "\n",
+    "5. **Monitor and assess the new representation**: After the new representation has been deployed, continue to monitor its performance and impact on recommendations. Take note of any improvements or issues that arise and be prepared to iterate on the new representation if needed. Always ensure that the results and performance metrics align with the system's goals and objectives. \n",
+    "\n",
+    "-> **Question**: How much do I get boosted within my cluster?\n",
+    "\n",
+    "**Answer**: It's not possible to determine the exact amount your content is boosted within your cluster in the SimClusters representation without specific data about your content and its engagement metrics. However, a combination of factors, such as the favorite score and follow score, alongside other engagement signals and SimCluster calculations, influence the boosting of content. \n",
+    "\n",
+    "-> **Question**: How does Heavy ranker work. what are it’s main inputs?\n",
+    "\n",
+    "**Answer**: The Heavy Ranker is a machine learning model that plays a crucial role in ranking and scoring candidates within the recommendation algorithm. Its primary purpose is to predict the likelihood of a user engaging with a tweet or connecting with another user on the platform.\n",
+    "\n",
+    "Main inputs to the Heavy Ranker consist of:\n",
+    "\n",
+    "1. Static Features: These are features that can be computed directly from a tweet at the time it's created, such as whether it has a URL, has cards, has quotes, etc. These features are produced by the Index Ingester as the tweets are generated and stored in the index.\n",
+    "\n",
+    "2. Real-time Features: These per-tweet features can change after the tweet has been indexed. They mostly consist of social engagements like retweet count, favorite count, reply count, and some spam signals that are computed with later activities. The Signal Ingester, which is part of a Heron topology, processes multiple event streams to collect and compute these real-time features.\n",
+    "\n",
+    "3. User Table Features: These per-user features are obtained from the User Table Updater that processes a stream written by the user service. This input is used to store sparse real-time user information, which is later propagated to the tweet being scored by looking up the author of the tweet.\n",
+    "\n",
+    "4. Search Context Features: These features represent the context of the current searcher, like their UI language, their content consumption, and the current time (implied). They are combined with Tweet Data to compute some of the features used in scoring.\n",
+    "\n",
+    "These inputs are then processed by the Heavy Ranker to score and rank candidates based on their relevance and likelihood of engagement by the user. \n",
+    "\n",
+    "-> **Question**: How can one influence Heavy ranker?\n",
+    "\n",
+    "**Answer**: To influence the Heavy Ranker's output or ranking of content, consider the following actions:\n",
+    "\n",
+    "1. Improve content quality: Create high-quality and engaging content that is relevant, informative, and valuable to users. High-quality content is more likely to receive positive user engagement, which the Heavy Ranker considers when ranking content.\n",
+    "\n",
+    "2. Increase user engagement: Encourage users to interact with content through likes, retweets, replies, and comments. Higher engagement levels can lead to better ranking in the Heavy Ranker's output.\n",
+    "\n",
+    "3. Optimize your user profile: A user's reputation, based on factors such as their follower count and follower-to-following ratio, may impact the ranking of their content. Maintain a good reputation by following relevant users, keeping a reasonable follower-to-following ratio and engaging with your followers.\n",
+    "\n",
+    "4. Enhance content discoverability: Use relevant keywords, hashtags, and mentions in your tweets, making it easier for users to find and engage with your content. This increased discoverability may help improve the ranking of your content by the Heavy Ranker.\n",
+    "\n",
+    "5. Leverage multimedia content: Experiment with different content formats, such as videos, images, and GIFs, which may capture users' attention and increase engagement, resulting in better ranking by the Heavy Ranker.\n",
+    "\n",
+    "6. User feedback: Monitor and respond to feedback for your content. Positive feedback may improve your ranking, while negative feedback provides an opportunity to learn and improve.\n",
+    "\n",
+    "Note that the Heavy Ranker uses a combination of machine learning models and various features to rank the content. While the above actions may help influence the ranking, there are no guarantees as the ranking process is determined by a complex algorithm, which evolves over time. \n",
+    "\n",
+    "-> **Question**: why threads and long tweets do so well on the platform?\n",
+    "\n",
+    "**Answer**: Threads and long tweets perform well on the platform for several reasons:\n",
+    "\n",
+    "1. **More content and context**: Threads and long tweets provide more information and context about a topic, which can make the content more engaging and informative for users. People tend to appreciate a well-structured and detailed explanation of a subject or a story, and threads and long tweets can do that effectively.\n",
+    "\n",
+    "2. **Increased user engagement**: As threads and long tweets provide more content, they also encourage users to engage with the tweets through replies, retweets, and likes. This increased engagement can lead to better visibility of the content, as the Twitter algorithm considers user engagement when ranking and surfacing tweets.\n",
+    "\n",
+    "3. **Narrative structure**: Threads enable users to tell stories or present arguments in a step-by-step manner, making the information more accessible and easier to follow. This narrative structure can capture users' attention and encourage them to read through the entire thread and interact with the content.\n",
+    "\n",
+    "4. **Expanded reach**: When users engage with a thread, their interactions can bring the content to the attention of their followers, helping to expand the reach of the thread. This increased visibility can lead to more interactions and higher performance for the threaded tweets.\n",
+    "\n",
+    "5. **Higher content quality**: Generally, threads and long tweets require more thought and effort to create, which may lead to higher quality content. Users are more likely to appreciate and interact with high-quality, well-reasoned content, further improving the performance of these tweets within the platform.\n",
+    "\n",
+    "Overall, threads and long tweets perform well on Twitter because they encourage user engagement and provide a richer, more informative experience that users find valuable. \n",
+    "\n",
+    "-> **Question**: Are thread and long tweet creators building a following that reacts to only threads?\n",
+    "\n",
+    "**Answer**: Based on the provided code and context, there isn't enough information to conclude if the creators of threads and long tweets primarily build a following that engages with only thread-based content. The code provided is focused on Twitter's recommendation and ranking algorithms, as well as infrastructure components like Kafka, partitions, and the Follow Recommendations Service (FRS). To answer your question, data analysis of user engagement and results of specific edge cases would be required. \n",
+    "\n",
+    "-> **Question**: Do you need to follow different strategies to get most followers vs to get most likes and bookmarks per tweet?\n",
+    "\n",
+    "**Answer**: Yes, different strategies need to be followed to maximize the number of followers compared to maximizing likes and bookmarks per tweet. While there may be some overlap in the approaches, they target different aspects of user engagement.\n",
+    "\n",
+    "Maximizing followers: The primary focus is on growing your audience on the platform. Strategies include:\n",
+    "\n",
+    "1. Consistently sharing high-quality content related to your niche or industry.\n",
+    "2. Engaging with others on the platform by replying, retweeting, and mentioning other users.\n",
+    "3. Using relevant hashtags and participating in trending conversations.\n",
+    "4. Collaborating with influencers and other users with a large following.\n",
+    "5. Posting at optimal times when your target audience is most active.\n",
+    "6. Optimizing your profile by using a clear profile picture, catchy bio, and relevant links.\n",
+    "\n",
+    "Maximizing likes and bookmarks per tweet: The focus is on creating content that resonates with your existing audience and encourages engagement. Strategies include:\n",
+    "\n",
+    "1. Crafting engaging and well-written tweets that encourage users to like or save them.\n",
+    "2. Incorporating visually appealing elements, such as images, GIFs, or videos, that capture attention.\n",
+    "3. Asking questions, sharing opinions, or sparking conversations that encourage users to engage with your tweets.\n",
+    "4. Using analytics to understand the type of content that resonates with your audience and tailoring your tweets accordingly.\n",
+    "5. Posting a mix of educational, entertaining, and promotional content to maintain variety and interest.\n",
+    "6. Timing your tweets strategically to maximize engagement, likes, and bookmarks per tweet.\n",
+    "\n",
+    "Both strategies can overlap, and you may need to adapt your approach by understanding your target audience's preferences and analyzing your account's performance. However, it's essential to recognize that maximizing followers and maximizing likes and bookmarks per tweet have different focuses and require specific strategies. \n",
+    "\n",
+    "-> **Question**: Content meta data and how it impacts virality (e.g. ALT in images).\n",
+    "\n",
+    "**Answer**: There is no direct information in the provided context about how content metadata, such as ALT text in images, impacts the virality of a tweet or post. However, it's worth noting that including ALT text can improve the accessibility of your content for users who rely on screen readers, which may lead to increased engagement for a broader audience. Additionally, metadata can be used in search engine optimization, which might improve the visibility of the content, but the context provided does not mention any specific correlation with virality. \n",
+    "\n",
+    "-> **Question**: What are some unexpected fingerprints for spam factors?\n",
+    "\n",
+    "**Answer**: In the provided context, an unusual indicator of spam factors is when a tweet contains a non-media, non-news link. If the tweet has a link but does not have an image URL, video URL, or news URL, it is considered a potential spam vector, and a threshold for user reputation (tweepCredThreshold) is set to MIN_TWEEPCRED_WITH_LINK.\n",
+    "\n",
+    "While this rule may not cover all possible unusual spam indicators, it is derived from the specific codebase and logic shared in the context. \n",
+    "\n",
+    "-> **Question**: Is there any difference between company verified checkmarks and blue verified individual checkmarks?\n",
+    "\n",
+    "**Answer**: Yes, there is a distinction between the verified checkmarks for companies and blue verified checkmarks for individuals. The code snippet provided mentions \"Blue-verified account boost\" which indicates that there is a separate category for blue verified accounts. Typically, blue verified checkmarks are used to indicate notable individuals, while verified checkmarks are for companies or organizations. \n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/extras/use_cases/question_answering/how_to/multiple_retrieval.ipynb b/docs/extras/use_cases/question_answering/how_to/multiple_retrieval.ipynb
new file mode 100644
index 00000000000..96b6ef6986f
--- /dev/null
+++ b/docs/extras/use_cases/question_answering/how_to/multiple_retrieval.ipynb
@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "66398b75",
+   "metadata": {},
+   "source": [
+    "# Multiple Retrieval Sources\n",
+    "\n",
+    "Often times you may want to do retrieval over multiple sources. These can be different vectorstores (where one contains information about topic X and the other contains info about topic Y). They could also be completely different databases altogether!\n",
+    "\n",
+    "A key part is is doing as much of the retrieval in parrelel as possible. This will keep the latency as low as possible. Luckily, [LangChain Expression Language](../../) supports parrellism out of the box.\n",
+    "\n",
+    "Let's take a look where we do retrieval over a SQL database and a vectorstore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1c5bab6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43a6210f",
+   "metadata": {},
+   "source": [
+    "## Set up SQL query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ab3bf8ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.utilities import SQLDatabase\n",
+    "from langchain.chains import create_sql_query_chain\n",
+    "\n",
+    "db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")\n",
+    "query_chain = create_sql_query_chain(ChatOpenAI(temperature=0), db)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8585120",
+   "metadata": {},
+   "source": [
+    "## Set up vectorstore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b916b0b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.indexes import VectorstoreIndexCreator\n",
+    "from langchain.schema.document import Document\n",
+    "index_creator = VectorstoreIndexCreator()\n",
+    "index = index_creator.from_documents([Document(page_content=\"Foo\")])\n",
+    "retriever = index.vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b91816",
+   "metadata": {},
+   "source": [
+    "## Combine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4423211c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "\n",
+    "system_message = \"\"\"Use the information from the below two sources to answer any questions.\n",
+    "\n",
+    "Source 1: a SQL database about employee data\n",
+    "<source1>\n",
+    "{source1}\n",
+    "</source1>\n",
+    "\n",
+    "Source 2: a text database of random information\n",
+    "<source2>\n",
+    "{source2}\n",
+    "</source2>\n",
+    "\"\"\"\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system_message), (\"human\", \"{question}\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7ff87e0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_chain = {\n",
+    "    \"source1\": {\"question\": lambda x: x[\"question\"]} | query_chain | db.run,\n",
+    "    \"source2\": (lambda x: x['question']) | retriever,\n",
+    "    \"question\": lambda x: x['question'],\n",
+    "} | prompt | ChatOpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d6706410",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content='There are 8 employees.' additional_kwargs={} example=False\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = full_chain.invoke({\"question\":\"How many Employees are there\"})\n",
+    "print(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/extras/use_cases/question_answering/index.mdx b/docs/extras/use_cases/question_answering/index.mdx
index 65421737b72..a9d98d26413 100644
--- a/docs/extras/use_cases/question_answering/index.mdx
+++ b/docs/extras/use_cases/question_answering/index.mdx
@@ -1,5 +1,5 @@
 ---
-sidebar_position: 0
+sidebar_position: -1
 ---
 
 # QA over Documents
diff --git a/docs/extras/use_cases/question_answering/integrations/semantic-search-over-chat.ipynb b/docs/extras/use_cases/question_answering/integrations/semantic-search-over-chat.ipynb
index 800866053b2..73e09dba7c4 100644
--- a/docs/extras/use_cases/question_answering/integrations/semantic-search-over-chat.ipynb
+++ b/docs/extras/use_cases/question_answering/integrations/semantic-search-over-chat.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -11,6 +12,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -27,6 +29,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -34,6 +37,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": []
@@ -69,6 +73,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -78,6 +83,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -96,9 +102,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Document(page_content='Participants:\\n\\nJerry: Loves movies and is a bit of a klutz.\\nSamantha: Enthusiastic about food and always trying new restaurants.\\nBarry: A nature lover, but always manages to get lost.\\nJerry: Hey, guys! You won\\'t believe what happened to me at the Times Square AMC theater. I tripped over my own feet and spilled popcorn everywhere! 🍿💥\\n\\nSamantha: LOL, that\\'s so you, Jerry! Was the floor buttery enough for you to ice skate on after that? 😂\\n\\nBarry: Sounds like a regular Tuesday for you, Jerry. Meanwhile, I tried to find that new hiking trail in Central Park. You know, the one that\\'s supposed to be impossible to get lost on? Well, guess what...\\n\\nJerry: You found a hidden treasure?\\n\\nBarry: No, I got lost. AGAIN. 🧭🙄\\n\\nSamantha: Barry, you\\'d get lost in your own backyard! But speaking of treasures, I found this new sushi place in Little Tokyo. \"Samantha\\'s Sushi Symphony\" it\\'s called. Coincidence? I think not!\\n\\nJerry: Maybe they named it after your ability to eat your body weight in sushi. 🍣', metadata={}), Document(page_content='Barry: How do you even FIND all these places, Samantha?\\n\\nSamantha: Simple, I don\\'t rely on Barry\\'s navigation skills. 😉 But seriously, the wasabi there was hotter than Jerry\\'s love for Marvel movies!\\n\\nJerry: Hey, nothing wrong with a little superhero action. By the way, did you guys see the new \"Captain Crunch: Breakfast Avenger\" trailer?\\n\\nSamantha: Captain Crunch? Are you sure you didn\\'t get that from one of your Saturday morning cereal binges?\\n\\nBarry: Yeah, and did he defeat his arch-enemy, General Mills? 😆\\n\\nJerry: Ha-ha, very funny. Anyway, that sushi place sounds awesome, Samantha. Next time, let\\'s go together, and maybe Barry can guide us... if we want a city-wide tour first.\\n\\nBarry: As long as we\\'re not hiking, I\\'ll get us there... eventually. 😅\\n\\nSamantha: It\\'s a date! But Jerry, you\\'re banned from carrying any food items.\\n\\nJerry: Deal! Just promise me no wasabi challenges. I don\\'t want to end up like the time I tried Sriracha ice cream.', metadata={}), Document(page_content=\"Barry: Wait, what happened with Sriracha ice cream?\\n\\nJerry: Let's just say it was a hot situation. Literally. 🔥\\n\\nSamantha: 🤣 I still have the video!\\n\\nJerry: Samantha, if you value our friendship, that video will never see the light of day.\\n\\nSamantha: No promises, Jerry. No promises. 🤐😈\\n\\nBarry: I foresee a fun weekend ahead! 🎉\", metadata={})]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your Deep Lake dataset has been successfully created!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\\"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset(path='hub://adilkhan/data', tensors=['embedding', 'id', 'metadata', 'text'])\n",
+      "\n",
+      "  tensor      htype      shape     dtype  compression\n",
+      "  -------    -------    -------   -------  ------- \n",
+      " embedding  embedding  (3, 1536)  float32   None   \n",
+      "    id        text      (3, 1)      str     None   \n",
+      " metadata     json      (3, 1)      str     None   \n",
+      "   text       text      (3, 1)      str     None   \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " \r"
+     ]
+    }
+   ],
    "source": [
     "with open(\"messages.txt\") as f:\n",
     "    state_of_the_union = f.read()\n",
@@ -110,7 +159,7 @@
     "\n",
     "print(texts)\n",
     "\n",
-    "dataset_path = \"hub://\" + org + \"/data\"\n",
+    "dataset_path = \"hub://\" + org_id + \"/data\"\n",
     "embeddings = OpenAIEmbeddings()\n",
     "db = DeepLake.from_documents(\n",
     "    texts, embeddings, dataset_path=dataset_path, overwrite=True\n",
@@ -118,6 +167,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -126,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,11 +193,12 @@
     "# dataset_path = \"hub://\" + org + \"/data\"\n",
     "# embeddings = OpenAIEmbeddings()\n",
     "# db = DeepLake.from_documents(\n",
-    "#     texts, embeddings, dataset_path=dataset_path, overwrite=True, runtime=\"tensor_db\"\n",
+    "#     texts, embeddings, dataset_path=dataset_path, overwrite=True, runtime={\"tensor_db\": True}\n",
     "# )"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -162,7 +213,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db = DeepLake(dataset_path=dataset_path, read_only=True, embedding_function=embeddings)\n",
+    "db = DeepLake(dataset_path=dataset_path, read_only=True, embedding=embeddings)\n",
     "\n",
     "retriever = db.as_retriever()\n",
     "retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
@@ -180,13 +231,6 @@
     "\n",
     "print(ans)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -205,7 +249,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/extras/use_cases/tagging.ipynb b/docs/extras/use_cases/tagging.ipynb
index b51e3f6d5ee..d8c3b166312 100644
--- a/docs/extras/use_cases/tagging.ipynb
+++ b/docs/extras/use_cases/tagging.ipynb
@@ -2,97 +2,90 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "a13ea924",
+   "id": "a0507a4b",
    "metadata": {},
    "source": [
     "# Tagging\n",
     "\n",
-    "The tagging chain uses the OpenAI `functions` parameter to specify a schema to tag a document with. This helps us make sure that the model outputs exactly tags that we want, with their appropriate types.\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/tagging.ipynb)\n",
     "\n",
-    "The tagging chain is to be used when we want to tag a passage with a specific attribute (i.e. what is the sentiment of this message?)"
+    "## Use case\n",
+    "\n",
+    "Tagging means labeling a document with classes such as:\n",
+    "\n",
+    "- sentiment\n",
+    "- language\n",
+    "- style (formal, informal etc.)\n",
+    "- covered topics\n",
+    "- political tendency\n",
+    "\n",
+    "![Image description](/img/tagging.png)\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "Tagging has a few components:\n",
+    "\n",
+    "* `function`: Like [extraction](/docs/use_cases/extraction), tagging uses [functions](https://openai.com/blog/function-calling-and-other-api-updates) to specify how the model should tag a document\n",
+    "* `schema`: defines how we want to tag the document\n",
+    "\n",
+    "## Quickstart\n",
+    "\n",
+    "Let's see a very straightforward example of how we can use OpenAI functions for tagging in LangChain."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "bafb496a",
+   "execution_count": null,
+   "id": "dc5cbb6f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.4) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.chains import create_tagging_chain, create_tagging_chain_pydantic\n",
-    "from langchain.prompts import ChatPromptTemplate"
+    "!pip install langchain openai \n",
+    "\n",
+    "# Set env var OPENAI_API_KEY or load from a .env file:\n",
+    "# import dotenv\n",
+    "# dotenv.load_env()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "39f3ce3e",
+   "id": "bafb496a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")"
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.chains import create_tagging_chain, create_tagging_chain_pydantic"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "832ddcd9",
+   "id": "b8ca3f93",
    "metadata": {},
    "source": [
-    "## Simplest approach, only specifying type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4fc8d766",
-   "metadata": {},
-   "source": [
-    "We can start by specifying a few properties with their expected type in our schema"
+    "We specify a few properties with their expected type in our schema."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "8329f943",
+   "execution_count": 4,
+   "id": "39f3ce3e",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Schema\n",
     "schema = {\n",
     "    \"properties\": {\n",
     "        \"sentiment\": {\"type\": \"string\"},\n",
     "        \"aggressiveness\": {\"type\": \"integer\"},\n",
     "        \"language\": {\"type\": \"string\"},\n",
     "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6146ae70",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = create_tagging_chain(schema, llm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e306ca3",
-   "metadata": {},
-   "source": [
-    "As we can see in the examples, it correctly interprets what we want but the results vary so that we get, for example, sentiments in different languages ('positive', 'enojado' etc.).\n",
+    "}\n",
     "\n",
-    "We will see how to control these results in the next section."
+    "# LLM\n",
+    "llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")\n",
+    "chain = create_tagging_chain(schema, llm)"
    ]
   },
   {
@@ -126,7 +119,7 @@
     {
      "data": {
       "text/plain": [
-       "{'sentiment': 'enojado', 'aggressiveness': 1, 'language': 'Spanish'}"
+       "{'sentiment': 'enojado', 'aggressiveness': 1, 'language': 'es'}"
       ]
      },
      "execution_count": 6,
@@ -140,25 +133,15 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "aae85b27",
+   "cell_type": "markdown",
+   "id": "d921bb53",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentiment': 'positive', 'aggressiveness': 0, 'language': 'English'}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "inp = \"Weather is ok here, I can go outside without much more than a coat\"\n",
-    "chain.run(inp)"
+    "As we can see in the examples, it correctly interprets what we want.\n",
+    "\n",
+    "The results vary so that we get, for example, sentiments in different languages ('positive', 'enojado' etc.).\n",
+    "\n",
+    "We will see how to control these results in the next section."
    ]
   },
   {
@@ -166,9 +149,11 @@
    "id": "bebb2f83",
    "metadata": {},
    "source": [
-    "## More control\n",
+    "## Finer control\n",
     "\n",
-    "By being smart about how we define our schema we can have more control over the model's output. Specifically we can define:\n",
+    "Careful schema definition gives us more control over the model's output. \n",
+    "\n",
+    "Specifically, we can define:\n",
     "\n",
     "- possible values for each property\n",
     "- description to make sure that the model understands the property\n",
@@ -180,7 +165,7 @@
    "id": "69ef0b9a",
    "metadata": {},
    "source": [
-    "Following is an example of how we can use _enum_, _description_ and _required_ to control for each of the previously mentioned aspects:"
+    "Here is an example of how we can use `_enum_`, `_description_`, and `_required_` to control for each of the previously mentioned aspects:"
    ]
   },
   {
@@ -192,7 +177,6 @@
    "source": [
     "schema = {\n",
     "    \"properties\": {\n",
-    "        \"sentiment\": {\"type\": \"string\", \"enum\": [\"happy\", \"neutral\", \"sad\"]},\n",
     "        \"aggressiveness\": {\n",
     "            \"type\": \"integer\",\n",
     "            \"enum\": [1, 2, 3, 4, 5],\n",
@@ -234,7 +218,7 @@
     {
      "data": {
       "text/plain": [
-       "{'sentiment': 'happy', 'aggressiveness': 0, 'language': 'spanish'}"
+       "{'aggressiveness': 0, 'language': 'spanish'}"
       ]
      },
      "execution_count": 10,
@@ -256,7 +240,7 @@
     {
      "data": {
       "text/plain": [
-       "{'sentiment': 'sad', 'aggressiveness': 10, 'language': 'spanish'}"
+       "{'aggressiveness': 5, 'language': 'spanish'}"
       ]
      },
      "execution_count": 11,
@@ -278,7 +262,7 @@
     {
      "data": {
       "text/plain": [
-       "{'sentiment': 'neutral', 'aggressiveness': 0, 'language': 'english'}"
+       "{'aggressiveness': 0, 'language': 'english'}"
       ]
      },
      "execution_count": 12,
@@ -291,12 +275,25 @@
     "chain.run(inp)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cf6b7389",
+   "metadata": {},
+   "source": [
+    "The [LangSmith trace](https://smith.langchain.com/public/311e663a-bbe8-4053-843e-5735055c032d/r) lets us peek under the hood:\n",
+    "\n",
+    "* As with [extraction](/docs/use_cases/extraction), we call the `information_extraction` function [here](https://github.com/langchain-ai/langchain/blob/269f85b7b7ffd74b38cd422d4164fc033388c3d0/libs/langchain/langchain/chains/openai_functions/extraction.py#L20) on the input string.\n",
+    "* This OpenAI funtion extraction information based upon the provided schema.\n",
+    "\n",
+    "![Image description](/img/tagging_trace.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e68ad17e",
    "metadata": {},
    "source": [
-    "## Specifying schema with Pydantic"
+    "## Pydantic"
    ]
   },
   {
@@ -304,11 +301,11 @@
    "id": "2f5970ec",
    "metadata": {},
    "source": [
-    "We can also use a Pydantic schema to specify the required properties and types. We can also send other arguments, such as 'enum' or 'description' as can be seen in the example below.\n",
+    "We can also use a Pydantic schema to specify the required properties and types. \n",
     "\n",
-    "By using the `create_tagging_chain_pydantic` function, we can send a Pydantic schema as input and the output will be an instantiated object that respects our desired schema. \n",
+    "We can also send other arguments, such as `enum` or `description`, to each field.\n",
     "\n",
-    "In this way, we can specify our schema in the same manner that we would a new class or function in Python - with purely Pythonic types."
+    "This lets us specify our schema in the same manner that we would a new class or function in Python with purely Pythonic types."
    ]
   },
   {
@@ -371,7 +368,7 @@
     {
      "data": {
       "text/plain": [
-       "Tags(sentiment='sad', aggressiveness=10, language='spanish')"
+       "Tags(sentiment='sad', aggressiveness=5, language='spanish')"
       ]
      },
      "execution_count": 17,
@@ -382,6 +379,17 @@
    "source": [
     "res"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29346d09",
+   "metadata": {},
+   "source": [
+    "### Going deeper\n",
+    "\n",
+    "* You can use the [metadata tagger](https://python.langchain.com/docs/integrations/document_transformers/openai_metadata_tagger) document transformer to extract metadata from a LangChain `Document`. \n",
+    "* This covers the same basic functionality as the tagging chain, only applied to a LangChain `Document`."
+   ]
   }
  ],
  "metadata": {
@@ -400,7 +408,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,
diff --git a/docs/extras/use_cases/web_scraping.ipynb b/docs/extras/use_cases/web_scraping.ipynb
new file mode 100644
index 00000000000..2e6a8e31344
--- /dev/null
+++ b/docs/extras/use_cases/web_scraping.ipynb
@@ -0,0 +1,599 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6605e7f7",
+   "metadata": {},
+   "source": [
+    "# Web Scraping\n",
+    "\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/use_cases/web_scraping.ipynb)\n",
+    "\n",
+    "## Use case\n",
+    "\n",
+    "[Web research](https://blog.langchain.dev/automating-web-research/) is one of the killer LLM applications:\n",
+    "\n",
+    "* Users have [highlighted it](https://twitter.com/GregKamradt/status/1679913813297225729?s=20) as one of his top desired AI tools. \n",
+    "* OSS repos like [gpt-researcher](https://github.com/assafelovic/gpt-researcher) are growing in popularity. \n",
+    " \n",
+    "![Image description](/img/web_scraping.png)\n",
+    " \n",
+    "## Overview\n",
+    "\n",
+    "Gathering content from the web has a few components:\n",
+    "\n",
+    "* `Search`: Query to url (e.g., using `GoogleSearchAPIWrapper`).\n",
+    "* `Loading`: Url to HTML  (e.g., using `AsyncHtmlLoader`, `AsyncChromiumLoader`, etc).\n",
+    "* `Transforming`: HTML to formatted text (e.g., using `HTML2Text` or `Beautiful Soup`).\n",
+    "\n",
+    "## Quickstart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1803c182",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install -q openai langchain playwright beautifulsoup4\n",
+    "playwright install\n",
+    "\n",
+    "# Set env var OPENAI_API_KEY or load from a .env file:\n",
+    "# import dotenv\n",
+    "# dotenv.load_env()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50741083",
+   "metadata": {},
+   "source": [
+    "Scraping HTML content using a headless instance of Chromium.\n",
+    "\n",
+    "* The async nature of the scraping process is handled using Python's asyncio library.\n",
+    "* The actual interaction with the web pages is handled by Playwright."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cd457cb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import AsyncChromiumLoader\n",
+    "from langchain.document_transformers import BeautifulSoupTransformer\n",
+    "\n",
+    "# Load HTML\n",
+    "loader = AsyncChromiumLoader([\"https://www.wsj.com\"])\n",
+    "html = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a879806",
+   "metadata": {},
+   "source": [
+    "Scrape text content tags such as `<p>, <li>, <div>, and <a>` tags from the HTML content:\n",
+    "\n",
+    "* `<p>`: The paragraph tag. It defines a paragraph in HTML and is used to group together related sentences and/or phrases.\n",
+    " \n",
+    "* `<li>`: The list item tag. It is used within ordered (`<ol>`) and unordered (`<ul>`) lists to define individual items within the list.\n",
+    " \n",
+    "* `<div>`: The division tag. It is a block-level element used to group other inline or block-level elements.\n",
+    " \n",
+    "* `<a>`: The anchor tag. It is used to define hyperlinks.\n",
+    "\n",
+    "* `<span>`:  an inline container used to mark up a part of a text, or a part of a document. \n",
+    "\n",
+    "For many news websites (e.g., WSJ, CNN), headlines and summaries are all in `<span>` tags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "141f206b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Transform\n",
+    "bs_transformer = BeautifulSoupTransformer()\n",
+    "docs_transformed = bs_transformer.transform_documents(html,tags_to_extract=[\"span\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "73ddb234",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'English EditionEnglish中文 (Chinese)日本語 (Japanese) More Other Products from WSJBuy Side from WSJWSJ ShopWSJ Wine Other Products from WSJ Search Quotes and Companies Search Quotes and Companies 0.15% 0.03% 0.12% -0.42% 4.102% -0.69% -0.25% -0.15% -1.82% 0.24% 0.19% -1.10% About Evan His Family Reflects His Reporting How You Can Help Write a Message Life in Detention Latest News Get Email Updates Four Americans Released From Iranian Prison The Americans will remain under house arrest until they are '"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Result\n",
+    "docs_transformed[0].page_content[0:500]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d26d185",
+   "metadata": {},
+   "source": [
+    "These `Documents` now are staged for downstream usage in various LLM apps, as discussed below.\n",
+    "\n",
+    "## Loader\n",
+    "\n",
+    "### AsyncHtmlLoader\n",
+    "\n",
+    "The [AsyncHtmlLoader](docs/integrations/document_loaders/async_html) uses the `aiohttp` library to make asynchronous HTTP requests, suitable for simpler and lightweight scraping.\n",
+    "\n",
+    "### AsyncChromiumLoader\n",
+    "\n",
+    "The [AsyncChromiumLoader](docs/integrations/document_loaders/async_chromium) uses Playwright to launch a Chromium instance, which can handle JavaScript rendering and more complex web interactions.\n",
+    "\n",
+    "Chromium is one of the browsers supported by Playwright, a library used to control browser automation. \n",
+    "\n",
+    "Headless mode means that the browser is running without a graphical user interface, which is commonly used for web scrapin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8941e855",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import AsyncHtmlLoader\n",
+    "urls = [\"https://www.espn.com\",\"https://lilianweng.github.io/posts/2023-06-23-agent/\"]\n",
+    "loader = AsyncHtmlLoader(urls)\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e47f4bf0",
+   "metadata": {},
+   "source": [
+    "## Transformer\n",
+    "\n",
+    "### HTML2Text\n",
+    "\n",
+    "[HTML2Text](docs/integrations/document_transformers/html2text) provides a straightforward conversion of HTML content into plain text (with markdown-like formatting) without any specific tag manipulation. \n",
+    "\n",
+    "It's best suited for scenarios where the goal is to extract human-readable text without needing to manipulate specific HTML elements.\n",
+    "\n",
+    "### Beautiful Soup\n",
+    " \n",
+    "Beautiful Soup offers more fine-grained control over HTML content, enabling specific tag extraction, removal, and content cleaning. \n",
+    "\n",
+    "It's suited for cases where you want to extract specific information and clean up the HTML content according to your needs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "99a7e2a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fetching pages: 100%|#############################################################################################################| 2/2 [00:00<00:00,  7.01it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.document_loaders import AsyncHtmlLoader\n",
+    "urls = [\"https://www.espn.com\", \"https://lilianweng.github.io/posts/2023-06-23-agent/\"]\n",
+    "loader = AsyncHtmlLoader(urls)\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a2cd3e8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Skip to main content  Skip to navigation\\n\\n<\\n\\n>\\n\\nMenu\\n\\n## ESPN\\n\\n  * Search\\n\\n  *   * scores\\n\\n  * NFL\\n  * MLB\\n  * NBA\\n  * NHL\\n  * Soccer\\n  * NCAAF\\n  * …\\n\\n    * Women's World Cup\\n    * LLWS\\n    * NCAAM\\n    * NCAAW\\n    * Sports Betting\\n    * Boxing\\n    * CFL\\n    * NCAA\\n    * Cricket\\n    * F1\\n    * Golf\\n    * Horse\\n    * MMA\\n    * NASCAR\\n    * NBA G League\\n    * Olympic Sports\\n    * PLL\\n    * Racing\\n    * RN BB\\n    * RN FB\\n    * Rugby\\n    * Tennis\\n    * WNBA\\n    * WWE\\n    * X Games\\n    * XFL\\n\\n  * More\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.document_transformers import Html2TextTransformer\n",
+    "html2text = Html2TextTransformer()\n",
+    "docs_transformed = html2text.transform_documents(docs)\n",
+    "docs_transformed[0].page_content[0:500]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8aef9861",
+   "metadata": {},
+   "source": [
+    "## Scraping with extraction\n",
+    "\n",
+    "### LLM with function calling\n",
+    "\n",
+    "Web scraping is challenging for many reasons. \n",
+    "\n",
+    "One of them is the changing nature of modern websites' layouts and content, which requires modifying scraping scripts to accommodate the changes.\n",
+    "\n",
+    "Using Function (e.g., OpenAI) with an extraction chain, we avoid having to change your code constantly when websites change. \n",
+    "\n",
+    "We're using `gpt-3.5-turbo-0613` to guarantee access to OpenAI Functions feature (although this might be available to everyone by time of writing). \n",
+    "\n",
+    "We're also keeping `temperature` at `0` to keep randomness of the LLM down."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "52d49f6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc5757ce",
+   "metadata": {},
+   "source": [
+    "### Define a schema\n",
+    "\n",
+    "Next, you define a schema to specify what kind of data you want to extract. \n",
+    "\n",
+    "Here, the key names matter as they tell the LLM what kind of information they want. \n",
+    "\n",
+    "So, be as detailed as possible. \n",
+    "\n",
+    "In this example, we want to scrape only news article's name and summary from The Wall Street Journal website."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "95506f8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import create_extraction_chain\n",
+    "\n",
+    "schema = {\n",
+    "    \"properties\": {\n",
+    "        \"news_article_title\": {\"type\": \"string\"},\n",
+    "        \"news_article_summary\": {\"type\": \"string\"},\n",
+    "    },\n",
+    "    \"required\": [\"news_article_title\", \"news_article_summary\"],\n",
+    "}\n",
+    "\n",
+    "def extract(content: str, schema: dict):\n",
+    "    return create_extraction_chain(schema=schema, llm=llm).run(content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97f7de42",
+   "metadata": {},
+   "source": [
+    "### Run the web scraper w/ BeautifulSoup\n",
+    "\n",
+    "As shown above, we'll using `BeautifulSoupTransformer`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "977560ba",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting content with LLM\n",
+      "[{'news_article_summary': 'The Americans will remain under house arrest until '\n",
+      "                          'they are allowed to return to the U.S. in coming '\n",
+      "                          'weeks, following a monthslong diplomatic push by '\n",
+      "                          'the Biden administration.',\n",
+      "  'news_article_title': 'Four Americans Released From Iranian Prison'},\n",
+      " {'news_article_summary': 'Price pressures continued cooling last month, with '\n",
+      "                          'the CPI rising a mild 0.2% from June, likely '\n",
+      "                          'deterring the Federal Reserve from raising interest '\n",
+      "                          'rates at its September meeting.',\n",
+      "  'news_article_title': 'Cooler July Inflation Opens Door to Fed Pause on '\n",
+      "                        'Rates'},\n",
+      " {'news_article_summary': 'The company has decided to eliminate 27 of its 30 '\n",
+      "                          'clothing labels, such as Lark & Ro and Goodthreads, '\n",
+      "                          'as it works to fend off antitrust scrutiny and cut '\n",
+      "                          'costs.',\n",
+      "  'news_article_title': 'Amazon Cuts Dozens of House Brands'},\n",
+      " {'news_article_summary': 'President Biden’s order comes on top of a slowing '\n",
+      "                          'Chinese economy, Covid lockdowns and rising '\n",
+      "                          'tensions between the two powers.',\n",
+      "  'news_article_title': 'U.S. Investment Ban on China Poised to Deepen Divide'},\n",
+      " {'news_article_summary': 'The proposed trial date in the '\n",
+      "                          'election-interference case comes on the same day as '\n",
+      "                          'the former president’s not guilty plea on '\n",
+      "                          'additional Mar-a-Lago charges.',\n",
+      "  'news_article_title': 'Trump Should Be Tried in January, Prosecutors Tell '\n",
+      "                        'Judge'},\n",
+      " {'news_article_summary': 'The CEO who started in June says the platform has '\n",
+      "                          '“an entirely different road map” for the future.',\n",
+      "  'news_article_title': 'Yaccarino Says X Is Watching Threads but Has Its Own '\n",
+      "                        'Vision'},\n",
+      " {'news_article_summary': 'Students foot the bill for flagship state '\n",
+      "                          'universities that pour money into new buildings and '\n",
+      "                          'programs with little pushback.',\n",
+      "  'news_article_title': 'Colleges Spend Like There’s No Tomorrow. ‘These '\n",
+      "                        'Places Are Just Devouring Money.’'},\n",
+      " {'news_article_summary': 'Wildfires fanned by hurricane winds have torn '\n",
+      "                          'through parts of the Hawaiian island, devastating '\n",
+      "                          'the popular tourist town of Lahaina.',\n",
+      "  'news_article_title': 'Maui Wildfires Leave at Least 36 Dead'},\n",
+      " {'news_article_summary': 'After its large armored push stalled, Kyiv has '\n",
+      "                          'fallen back on the kind of tactics that brought it '\n",
+      "                          'success earlier in the war.',\n",
+      "  'news_article_title': 'Ukraine Uses Small-Unit Tactics to Retake Captured '\n",
+      "                        'Territory'},\n",
+      " {'news_article_summary': 'President Guillermo Lasso says the Aug. 20 election '\n",
+      "                          'will proceed, as the Andean country grapples with '\n",
+      "                          'rising drug gang violence.',\n",
+      "  'news_article_title': 'Ecuador Declares State of Emergency After '\n",
+      "                        'Presidential Hopeful Killed'},\n",
+      " {'news_article_summary': 'This year’s hurricane season, which typically runs '\n",
+      "                          'from June to the end of November, has been '\n",
+      "                          'difficult to predict, climate scientists said.',\n",
+      "  'news_article_title': 'Atlantic Hurricane Season Prediction Increased to '\n",
+      "                        '‘Above Normal,’ NOAA Says'},\n",
+      " {'news_article_summary': 'The NFL is raising the price of its NFL+ streaming '\n",
+      "                          'packages as it adds the NFL Network and RedZone.',\n",
+      "  'news_article_title': 'NFL to Raise Price of NFL+ Streaming Packages as It '\n",
+      "                        'Adds NFL Network, RedZone'},\n",
+      " {'news_article_summary': 'Russia is planning a moon mission as part of the '\n",
+      "                          'new space race.',\n",
+      "  'news_article_title': 'Russia’s Moon Mission and the New Space Race'},\n",
+      " {'news_article_summary': 'Tapestry’s $8.5 billion acquisition of Capri would '\n",
+      "                          'create a conglomerate with more than $12 billion in '\n",
+      "                          'annual sales, but it would still lack the '\n",
+      "                          'high-wattage labels and diversity that have fueled '\n",
+      "                          'LVMH’s success.',\n",
+      "  'news_article_title': \"Why the Coach and Kors Marriage Doesn't Scare LVMH\"},\n",
+      " {'news_article_summary': 'The Supreme Court has blocked Purdue Pharma’s $6 '\n",
+      "                          'billion Sackler opioid settlement.',\n",
+      "  'news_article_title': 'Supreme Court Blocks Purdue Pharma’s $6 Billion '\n",
+      "                        'Sackler Opioid Settlement'},\n",
+      " {'news_article_summary': 'The Social Security COLA is expected to rise in '\n",
+      "                          '2024, but not by a lot.',\n",
+      "  'news_article_title': 'Social Security COLA Expected to Rise in 2024, but '\n",
+      "                        'Not by a Lot'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pprint\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "\n",
+    "def scrape_with_playwright(urls, schema):\n",
+    "    \n",
+    "    loader = AsyncChromiumLoader(urls)\n",
+    "    docs = loader.load()\n",
+    "    bs_transformer = BeautifulSoupTransformer()\n",
+    "    docs_transformed = bs_transformer.transform_documents(docs,tags_to_extract=[\"span\"])\n",
+    "    print(\"Extracting content with LLM\")\n",
+    "    \n",
+    "    # Grab the first 1000 tokens of the site\n",
+    "    splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(chunk_size=1000, \n",
+    "                                                                    chunk_overlap=0)\n",
+    "    splits = splitter.split_documents(docs_transformed)\n",
+    "    \n",
+    "    # Process the first split \n",
+    "    extracted_content = extract(\n",
+    "        schema=schema, content=splits[0].page_content\n",
+    "    )\n",
+    "    pprint.pprint(extracted_content)\n",
+    "    return extracted_content\n",
+    "\n",
+    "urls = [\"https://www.wsj.com\"]\n",
+    "extracted_content = scrape_with_playwright(urls, schema=schema)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b08a8cef",
+   "metadata": {},
+   "source": [
+    "We can compare the headlines scraped to the page:\n",
+    "\n",
+    "![Image description](/img/wsj_page.png)\n",
+    "\n",
+    "Looking at the [LangSmith trace](https://smith.langchain.com/public/c3070198-5b13-419b-87bf-3821cdf34fa6/r), we can see what is going on under the hood:\n",
+    "\n",
+    "* It's following what is explained in the [extraction](docs/use_cases/extraction).\n",
+    "* We call the `information_extraction` function on the input text.\n",
+    "* It will attempt to populate the provided schema from the url content."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5a6f11e",
+   "metadata": {},
+   "source": [
+    "## Research automation\n",
+    "\n",
+    "Related to scraping, we may want to answer specific questions using searched content.\n",
+    "\n",
+    "We can automate the process of [web research](https://blog.langchain.dev/automating-web-research/) using a retriver, such as the `WebResearchRetriever` ([docs](https://python.langchain.com/docs/modules/data_connection/retrievers/web_research)).\n",
+    "\n",
+    "![Image description](/img/web_research.png)\n",
+    "\n",
+    "Copy requirments [from here](https://github.com/langchain-ai/web-explorer/blob/main/requirements.txt):\n",
+    "\n",
+    "`pip install -r requirements.txt`\n",
+    " \n",
+    "Set `GOOGLE_CSE_ID` and `GOOGLE_API_KEY`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "414f0d41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.chat_models.openai import ChatOpenAI\n",
+    "from langchain.utilities import GoogleSearchAPIWrapper\n",
+    "from langchain.retrievers.web_research import WebResearchRetriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "5d1ce098",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Vectorstore\n",
+    "vectorstore = Chroma(embedding_function=OpenAIEmbeddings(),persist_directory=\"./chroma_db_oai\")\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOpenAI(temperature=0)\n",
+    "\n",
+    "# Search \n",
+    "search = GoogleSearchAPIWrapper()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d808b9d",
+   "metadata": {},
+   "source": [
+    "Initialize retriever with the above tools to:\n",
+    "    \n",
+    "* Use an LLM to generate multiple relevant search queries (one LLM call)\n",
+    "* Execute a search for each query\n",
+    "* Choose the top K links per query  (multiple search calls in parallel)\n",
+    "* Load the information from all chosen links (scrape pages in parallel)\n",
+    "* Index those documents into a vectorstore\n",
+    "* Find the most relevant documents for each original generated search query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "e3e3a589",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize\n",
+    "web_research_retriever = WebResearchRetriever.from_llm(\n",
+    "    vectorstore=vectorstore,\n",
+    "    llm=llm, \n",
+    "    search=search)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "20655b74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:langchain.retrievers.web_research:Generating questions for Google Search ...\n",
+      "INFO:langchain.retrievers.web_research:Questions for Google Search (raw): {'question': 'How do LLM Powered Autonomous Agents work?', 'text': LineList(lines=['1. What is the functioning principle of LLM Powered Autonomous Agents?\\n', '2. How do LLM Powered Autonomous Agents operate?\\n'])}\n",
+      "INFO:langchain.retrievers.web_research:Questions for Google Search: ['1. What is the functioning principle of LLM Powered Autonomous Agents?\\n', '2. How do LLM Powered Autonomous Agents operate?\\n']\n",
+      "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+      "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+      "INFO:langchain.retrievers.web_research:Search results: [{'title': 'LLM Powered Autonomous Agents | Hacker News', 'link': 'https://news.ycombinator.com/item?id=36488871', 'snippet': 'Jun 26, 2023 ... Exactly. A temperature of 0 means you always pick the highest probability token (i.e. the \"max\" function), while a temperature of 1 means you\\xa0...'}]\n",
+      "INFO:langchain.retrievers.web_research:Searching for relevat urls ...\n",
+      "INFO:langchain.retrievers.web_research:Search results: [{'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\" , \"What are the subgoals for achieving XYZ?\" , (2) by\\xa0...'}]\n",
+      "INFO:langchain.retrievers.web_research:New URLs to load: []\n",
+      "INFO:langchain.retrievers.web_research:Grabbing most relevant splits from urls...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'question': 'How do LLM Powered Autonomous Agents work?',\n",
+       " 'answer': \"LLM-powered autonomous agents work by using LLM as the agent's brain, complemented by several key components such as planning, memory, and tool use. In terms of planning, the agent breaks down large tasks into smaller subgoals and can reflect and refine its actions based on past experiences. Memory is divided into short-term memory, which is used for in-context learning, and long-term memory, which allows the agent to retain and recall information over extended periods. Tool use involves the agent calling external APIs for additional information. These agents have been used in various applications, including scientific discovery and generative agents simulation.\",\n",
+       " 'sources': ''}"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Run\n",
+    "import logging\n",
+    "logging.basicConfig()\n",
+    "logging.getLogger(\"langchain.retrievers.web_research\").setLevel(logging.INFO)\n",
+    "from langchain.chains import RetrievalQAWithSourcesChain\n",
+    "user_input = \"How do LLM Powered Autonomous Agents work?\"\n",
+    "qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llm,retriever=web_research_retriever)\n",
+    "result = qa_chain({\"question\": user_input})\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff62e5f5",
+   "metadata": {},
+   "source": [
+    "### Going deeper \n",
+    "\n",
+    "* Here's a [app](https://github.com/langchain-ai/web-explorer/tree/main) that wraps this retriver with a lighweight UI."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/snippets/modules/chains/popular/api.mdx b/docs/snippets/modules/chains/popular/api.mdx
deleted file mode 100644
index 3b1a4ec0f57..00000000000
--- a/docs/snippets/modules/chains/popular/api.mdx
+++ /dev/null
@@ -1,105 +0,0 @@
-```python
-from langchain.chains.api.prompt import API_RESPONSE_PROMPT
-```
-
-
-```python
-from langchain.chains import APIChain
-from langchain.prompts.prompt import PromptTemplate
-
-
-from langchain.llms import OpenAI
-
-llm = OpenAI(temperature=0)
-```
-
-## OpenMeteo Example
-
-
-```python
-from langchain.chains.api import open_meteo_docs
-chain_new = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True)
-```
-
-
-```python
-chain_new.run('What is the weather like right now in Munich, Germany in degrees Fahrenheit?')
-```
-
-<CodeOutputBlock lang="python">
-
-```
-
-
-    > Entering new APIChain chain...
-    https://api.open-meteo.com/v1/forecast?latitude=48.1351&longitude=11.5820&temperature_unit=fahrenheit&current_weather=true
-    {"latitude":48.14,"longitude":11.58,"generationtime_ms":0.33104419708251953,"utc_offset_seconds":0,"timezone":"GMT","timezone_abbreviation":"GMT","elevation":521.0,"current_weather":{"temperature":33.4,"windspeed":6.8,"winddirection":198.0,"weathercode":2,"time":"2023-01-16T01:00"}}
-
-    > Finished chain.
-
-
-
-
-
-    ' The current temperature in Munich, Germany is 33.4 degrees Fahrenheit with a windspeed of 6.8 km/h and a wind direction of 198 degrees. The weathercode is 2.'
-```
-
-</CodeOutputBlock>
-
-## TMDB Example
-
-
-```python
-import os
-os.environ['TMDB_BEARER_TOKEN'] = ""
-```
-
-
-```python
-from langchain.chains.api import tmdb_docs
-headers = {"Authorization": f"Bearer {os.environ['TMDB_BEARER_TOKEN']}"}
-chain = APIChain.from_llm_and_api_docs(llm, tmdb_docs.TMDB_DOCS, headers=headers, verbose=True)
-```
-
-
-```python
-chain.run("Search for 'Avatar'")
-```
-
-<CodeOutputBlock lang="python">
-
-```
-
-
-    > Entering new APIChain chain...
-     https://api.themoviedb.org/3/search/movie?query=Avatar&language=en-US
-    {"page":1,"results":[{"adult":false,"backdrop_path":"/o0s4XsEDfDlvit5pDRKjzXR4pp2.jpg","genre_ids":[28,12,14,878],"id":19995,"original_language":"en","original_title":"Avatar","overview":"In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, but becomes torn between following orders and protecting an alien civilization.","popularity":2041.691,"poster_path":"/jRXYjXNq0Cs2TcJjLkki24MLp7u.jpg","release_date":"2009-12-15","title":"Avatar","video":false,"vote_average":7.6,"vote_count":27777},{"adult":false,"backdrop_path":"/s16H6tpK2utvwDtzZ8Qy4qm5Emw.jpg","genre_ids":[878,12,28],"id":76600,"original_language":"en","original_title":"Avatar: The Way of Water","overview":"Set more than a decade after the events of the first film, learn the story of the Sully family (Jake, Neytiri, and their kids), the trouble that follows them, the lengths they go to keep each other safe, the battles they fight to stay alive, and the tragedies they endure.","popularity":3948.296,"poster_path":"/t6HIqrRAclMCA60NsSmeqe9RmNV.jpg","release_date":"2022-12-14","title":"Avatar: The Way of Water","video":false,"vote_average":7.7,"vote_count":4219},{"adult":false,"backdrop_path":"/uEwGFGtao9YG2JolmdvtHLLVbA9.jpg","genre_ids":[99],"id":111332,"original_language":"en","original_title":"Avatar: Creating the World of Pandora","overview":"The Making-of James Cameron's Avatar. It shows interesting parts of the work on the set.","popularity":541.809,"poster_path":"/sjf3xjuofCtDhZghJRzXlTiEjJe.jpg","release_date":"2010-02-07","title":"Avatar: Creating the World of Pandora","video":false,"vote_average":7.3,"vote_count":35},{"adult":false,"backdrop_path":null,"genre_ids":[99],"id":287003,"original_language":"en","original_title":"Avatar: Scene Deconstruction","overview":"The deconstruction of the Avatar scenes and sets","popularity":394.941,"poster_path":"/uCreCQFReeF0RiIXkQypRYHwikx.jpg","release_date":"2009-12-18","title":"Avatar: Scene Deconstruction","video":false,"vote_average":7.8,"vote_count":12},{"adult":false,"backdrop_path":null,"genre_ids":[28,18,878,12,14],"id":83533,"original_language":"en","original_title":"Avatar 3","overview":"","popularity":172.488,"poster_path":"/4rXqTMlkEaMiJjiG0Z2BX6F6Dkm.jpg","release_date":"2024-12-18","title":"Avatar 3","video":false,"vote_average":0,"vote_count":0},{"adult":false,"backdrop_path":null,"genre_ids":[28,878,12,14],"id":216527,"original_language":"en","original_title":"Avatar 4","overview":"","popularity":162.536,"poster_path":"/qzMYKnT4MG1d0gnhwytr4cKhUvS.jpg","release_date":"2026-12-16","title":"Avatar 4","video":false,"vote_average":0,"vote_count":0},{"adult":false,"backdrop_path":null,"genre_ids":[28,12,14,878],"id":393209,"original_language":"en","original_title":"Avatar 5","overview":"","popularity":124.722,"poster_path":"/rtmmvqkIC5zDMEd638Es2woxbz8.jpg","release_date":"2028-12-20","title":"Avatar 5","video":false,"vote_average":0,"vote_count":0},{"adult":false,"backdrop_path":"/nNceJtrrovG1MUBHMAhId0ws9Gp.jpg","genre_ids":[99],"id":183392,"original_language":"en","original_title":"Capturing Avatar","overview":"Capturing Avatar is a feature length behind-the-scenes documentary about the making of Avatar. It uses footage from the film's development, as well as stock footage from as far back as the production of Titanic in 1995. Also included are numerous interviews with cast, artists, and other crew members. The documentary was released as a bonus feature on the extended collector's edition of Avatar.","popularity":109.842,"poster_path":"/26SMEXJl3978dn2svWBSqHbLl5U.jpg","release_date":"2010-11-16","title":"Capturing Avatar","video":false,"vote_average":7.8,"vote_count":39},{"adult":false,"backdrop_path":"/eoAvHxfbaPOcfiQyjqypWIXWxDr.jpg","genre_ids":[99],"id":1059673,"original_language":"en","original_title":"Avatar: The Deep Dive - A Special Edition of 20/20","overview":"An inside look at one of the most anticipated movie sequels ever with James Cameron and cast.","popularity":629.825,"poster_path":"/rtVeIsmeXnpjNbEKnm9Say58XjV.jpg","release_date":"2022-12-14","title":"Avatar: The Deep Dive - A Special Edition of 20/20","video":false,"vote_average":6.5,"vote_count":5},{"adult":false,"backdrop_path":null,"genre_ids":[99],"id":278698,"original_language":"en","original_title":"Avatar Spirits","overview":"Bryan Konietzko and Michael Dante DiMartino, co-creators of the hit television series, Avatar: The Last Airbender, reflect on the creation of the masterful series.","popularity":51.593,"poster_path":"/oBWVyOdntLJd5bBpE0wkpN6B6vy.jpg","release_date":"2010-06-22","title":"Avatar Spirits","video":false,"vote_average":9,"vote_count":16},{"adult":false,"backdrop_path":"/cACUWJKvRfhXge7NC0xxoQnkQNu.jpg","genre_ids":[10402],"id":993545,"original_language":"fr","original_title":"Avatar - Au Hellfest 2022","overview":"","popularity":21.992,"poster_path":"/fw6cPIsQYKjd1YVQanG2vLc5HGo.jpg","release_date":"2022-06-26","title":"Avatar - Au Hellfest 2022","video":false,"vote_average":8,"vote_count":4},{"adult":false,"backdrop_path":null,"genre_ids":[],"id":931019,"original_language":"en","original_title":"Avatar: Enter The World","overview":"A behind the scenes look at the new James Cameron blockbuster “Avatar”, which stars Aussie Sam Worthington. Hastily produced by Australia’s Nine Network following the film’s release.","popularity":30.903,"poster_path":"/9MHY9pYAgs91Ef7YFGWEbP4WJqC.jpg","release_date":"2009-12-05","title":"Avatar: Enter The World","video":false,"vote_average":2,"vote_count":1},{"adult":false,"backdrop_path":null,"genre_ids":[],"id":287004,"original_language":"en","original_title":"Avatar: Production Materials","overview":"Production material overview of what was used in Avatar","popularity":12.389,"poster_path":null,"release_date":"2009-12-18","title":"Avatar: Production Materials","video":true,"vote_average":6,"vote_count":4},{"adult":false,"backdrop_path":"/x43RWEZg9tYRPgnm43GyIB4tlER.jpg","genre_ids":[],"id":740017,"original_language":"es","original_title":"Avatar: Agni Kai","overview":"","popularity":9.462,"poster_path":"/y9PrKMUTA6NfIe5FE92tdwOQ2sH.jpg","release_date":"2020-01-18","title":"Avatar: Agni Kai","video":false,"vote_average":7,"vote_count":1},{"adult":false,"backdrop_path":"/e8mmDO7fKK93T4lnxl4Z2zjxXZV.jpg","genre_ids":[],"id":668297,"original_language":"en","original_title":"The Last Avatar","overview":"The Last Avatar is a mystical adventure film, a story of a young man who leaves Hollywood to find himself. What he finds is beyond his wildest imagination. Based on ancient prophecy, contemporary truth seeking and the future of humanity, The Last Avatar is a film that takes transformational themes and makes them relevant for audiences of all ages. Filled with love, magic, mystery, conspiracy, psychics, underground cities, secret societies, light bodies and much more, The Last Avatar tells the story of the emergence of Kalki Avatar- the final Avatar of our current Age of Chaos. Kalki is also a metaphor for the innate power and potential that lies within humanity to awaken and create a world of truth, harmony and possibility.","popularity":8.786,"poster_path":"/XWz5SS5g5mrNEZjv3FiGhqCMOQ.jpg","release_date":"2014-12-06","title":"The Last Avatar","video":false,"vote_average":4.5,"vote_count":2},{"adult":false,"backdrop_path":null,"genre_ids":[],"id":424768,"original_language":"en","original_title":"Avatar:[2015] Wacken Open Air","overview":"Started in the summer of 2001 by drummer John Alfredsson and vocalist Christian Rimmi under the name Lost Soul.  The band offers a free mp3 download to a song called \"Bloody Knuckles\" if one subscribes to their newsletter.  In 2005 they appeared on the compilation “Listen to Your Inner Voice” together with 17 other bands released by Inner Voice Records.","popularity":6.634,"poster_path":null,"release_date":"2015-08-01","title":"Avatar:[2015] Wacken Open Air","video":false,"vote_average":8,"vote_count":1},{"adult":false,"backdrop_path":null,"genre_ids":[],"id":812836,"original_language":"en","original_title":"Avatar - Live At Graspop 2018","overview":"Live At Graspop Festival Belgium 2018","popularity":9.855,"poster_path":null,"release_date":"","title":"Avatar - Live At Graspop 2018","video":false,"vote_average":9,"vote_count":1},{"adult":false,"backdrop_path":null,"genre_ids":[10402],"id":874770,"original_language":"en","original_title":"Avatar Ages: Memories","overview":"On the night of memories Avatar performed songs from Thoughts of No Tomorrow, Schlacht and Avatar as voted on by the fans.","popularity":2.66,"poster_path":"/xDNNQ2cnxAv3o7u0nT6JJacQrhp.jpg","release_date":"2021-01-30","title":"Avatar Ages: Memories","video":false,"vote_average":10,"vote_count":1},{"adult":false,"backdrop_path":null,"genre_ids":[10402],"id":874768,"original_language":"en","original_title":"Avatar Ages: Madness","overview":"On the night of madness Avatar performed songs from Black Waltz and Hail The Apocalypse as voted on by the fans.","popularity":2.024,"poster_path":"/wVyTuruUctV3UbdzE5cncnpyNoY.jpg","release_date":"2021-01-23","title":"Avatar Ages: Madness","video":false,"vote_average":8,"vote_count":1},{"adult":false,"backdrop_path":"/dj8g4jrYMfK6tQ26ra3IaqOx5Ho.jpg","genre_ids":[10402],"id":874700,"original_language":"en","original_title":"Avatar Ages: Dreams","overview":"On the night of dreams Avatar performed Hunter Gatherer in its entirety, plus a selection of their most popular songs.  Originally aired January 9th 2021","popularity":1.957,"poster_path":"/4twG59wnuHpGIRR9gYsqZnVysSP.jpg","release_date":"2021-01-09","title":"Avatar Ages: Dreams","video":false,"vote_average":0,"vote_count":0}],"total_pages":3,"total_results":57}
-
-    > Finished chain.
-
-
-
-
-
-    ' This response contains 57 movies related to the search query "Avatar". The first movie in the list is the 2009 movie "Avatar" starring Sam Worthington. Other movies in the list include sequels to Avatar, documentaries, and live performances.'
-```
-
-</CodeOutputBlock>
-
-## Listen API Example
-
-
-```python
-import os
-from langchain.llms import OpenAI
-from langchain.chains.api import podcast_docs
-from langchain.chains import APIChain
-
-# Get api key here: https://www.listennotes.com/api/pricing/
-listen_api_key = 'xxx'
-
-llm = OpenAI(temperature=0)
-headers = {"X-ListenAPI-Key": listen_api_key}
-chain = APIChain.from_llm_and_api_docs(llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True)
-chain.run("Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results")
-```
diff --git a/docs/snippets/modules/model_io/models/llms/get_started.mdx b/docs/snippets/modules/model_io/models/llms/get_started.mdx
index 1ef6c060694..5553a7faa21 100644
--- a/docs/snippets/modules/model_io/models/llms/get_started.mdx
+++ b/docs/snippets/modules/model_io/models/llms/get_started.mdx
@@ -43,7 +43,7 @@ llm("Tell me a joke")
 </CodeOutputBlock>
 
 ### `generate`: batch calls, richer outputs
-`generate` lets you can call the model with a list of strings, getting back a more complete response than just the text. This complete response can includes things like multiple top responses and other LLM provider-specific information:
+`generate` lets you can call the model with a list of strings, getting back a more complete response than just the text. This complete response can include things like multiple top responses and other LLM provider-specific information:
 
 ```python
 llm_result = llm.generate(["Tell me a joke", "Tell me a poem"]*15)
diff --git a/docs/snippets/modules/model_io/output_parsers/comma_separated.mdx b/docs/snippets/modules/model_io/output_parsers/comma_separated.mdx
index b53c6d48a47..656bd78f564 100644
--- a/docs/snippets/modules/model_io/output_parsers/comma_separated.mdx
+++ b/docs/snippets/modules/model_io/output_parsers/comma_separated.mdx
@@ -5,34 +5,24 @@ from langchain.llms import OpenAI
 from langchain.chat_models import ChatOpenAI
 
 output_parser = CommaSeparatedListOutputParser()
-```
 
-
-```python
 format_instructions = output_parser.get_format_instructions()
 prompt = PromptTemplate(
     template="List five {subject}.\n{format_instructions}",
     input_variables=["subject"],
     partial_variables={"format_instructions": format_instructions}
 )
-```
 
-
-```python
 model = OpenAI(temperature=0)
-```
 
-
-```python
 _input = prompt.format(subject="ice cream flavors")
 output = model(_input)
-```
 
-
-```python
 output_parser.parse(output)
 ```
 
+The resulting output will be:
+
 <CodeOutputBlock lang="python">
 
 ```
diff --git a/docs/snippets/modules/model_io/prompts/prompt_templates/get_started.mdx b/docs/snippets/modules/model_io/prompts/prompt_templates/get_started.mdx
index d8a234d4da2..dddaf86f41e 100644
--- a/docs/snippets/modules/model_io/prompts/prompt_templates/get_started.mdx
+++ b/docs/snippets/modules/model_io/prompts/prompt_templates/get_started.mdx
@@ -106,9 +106,11 @@ llm(template.format_messages(text='i dont like eating tasty things.'))
 ```
 
 <CodeOutputBlock lang="python">
+
 ```
 AIMessage(content='I absolutely adore indulging in delicious treats!', additional_kwargs={}, example=False)
 ```
+
 </CodeOutputBlock>
 
 This provides you with a lot of flexibility in how you construct your chat prompts.
diff --git a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/agent.py b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/agent.py
index 8638dc5740b..5872f0fd744 100644
--- a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/agent.py
+++ b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/agent.py
@@ -13,7 +13,6 @@ from langchain.schema.messages import AIMessage, HumanMessage, SystemMessage
 from langchain.tools.base import BaseTool
 from langchain.tools.human.tool import HumanInputRun
 from langchain.vectorstores.base import VectorStoreRetriever
-from pydantic import ValidationError
 
 from langchain_experimental.autonomous_agents.autogpt.output_parser import (
     AutoGPTOutputParser,
@@ -23,6 +22,7 @@ from langchain_experimental.autonomous_agents.autogpt.prompt import AutoGPTPromp
 from langchain_experimental.autonomous_agents.autogpt.prompt_generator import (
     FINISH_NAME,
 )
+from langchain_experimental.pydantic_v1 import ValidationError
 
 
 class AutoGPT:
diff --git a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py
index 7978a6f9804..549a9955633 100644
--- a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py
+++ b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py
@@ -2,7 +2,8 @@ from typing import Any, Dict, List
 
 from langchain.memory.chat_memory import BaseChatMemory, get_prompt_input_key
 from langchain.vectorstores.base import VectorStoreRetriever
-from pydantic import Field
+
+from langchain_experimental.pydantic_v1 import Field
 
 
 class AutoGPTMemory(BaseChatMemory):
diff --git a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/prompt.py b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/prompt.py
index ba8fd704878..68da11b0c3b 100644
--- a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/prompt.py
+++ b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/prompt.py
@@ -7,9 +7,9 @@ from langchain.prompts.chat import (
 from langchain.schema.messages import BaseMessage, HumanMessage, SystemMessage
 from langchain.tools.base import BaseTool
 from langchain.vectorstores.base import VectorStoreRetriever
-from pydantic import BaseModel
 
 from langchain_experimental.autonomous_agents.autogpt.prompt_generator import get_prompt
+from langchain_experimental.pydantic_v1 import BaseModel
 
 
 class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel):
diff --git a/libs/experimental/langchain_experimental/autonomous_agents/baby_agi/baby_agi.py b/libs/experimental/langchain_experimental/autonomous_agents/baby_agi/baby_agi.py
index 72ed46d46e1..6dd5d15b146 100644
--- a/libs/experimental/langchain_experimental/autonomous_agents/baby_agi/baby_agi.py
+++ b/libs/experimental/langchain_experimental/autonomous_agents/baby_agi/baby_agi.py
@@ -6,7 +6,6 @@ from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.vectorstores.base import VectorStore
-from pydantic import BaseModel, Field
 
 from langchain_experimental.autonomous_agents.baby_agi.task_creation import (
     TaskCreationChain,
@@ -17,6 +16,7 @@ from langchain_experimental.autonomous_agents.baby_agi.task_execution import (
 from langchain_experimental.autonomous_agents.baby_agi.task_prioritization import (
     TaskPrioritizationChain,
 )
+from langchain_experimental.pydantic_v1 import BaseModel, Field
 
 
 class BabyAGI(Chain, BaseModel):
@@ -145,7 +145,7 @@ class BabyAGI(Chain, BaseModel):
                 self.print_task_result(result)
 
                 # Step 3: Store the result in Pinecone
-                result_id = f"result_{task['task_id']}"
+                result_id = f"result_{task['task_id']}_{num_iters}"
                 self.vectorstore.add_texts(
                     texts=[result],
                     metadatas=[{"task": task["task_name"]}],
diff --git a/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_planner.py b/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_planner.py
index 65ab27fce46..e1228161cf7 100644
--- a/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_planner.py
+++ b/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_planner.py
@@ -13,7 +13,8 @@ from langchain.prompts.chat import (
     SystemMessagePromptTemplate,
 )
 from langchain.tools.base import BaseTool
-from pydantic import BaseModel
+
+from langchain_experimental.pydantic_v1 import BaseModel
 
 DEMONSTRATIONS = [
     {
diff --git a/libs/experimental/langchain_experimental/cpal/models.py b/libs/experimental/langchain_experimental/cpal/models.py
index e818fbdcfc1..62c41e5e14d 100644
--- a/libs/experimental/langchain_experimental/cpal/models.py
+++ b/libs/experimental/langchain_experimental/cpal/models.py
@@ -6,9 +6,15 @@ from typing import Any, Optional, Union
 import duckdb
 import pandas as pd
 from langchain.graphs.networkx_graph import NetworkxEntityGraph
-from pydantic import BaseModel, Field, PrivateAttr, root_validator, validator
 
 from langchain_experimental.cpal.constants import Constant
+from langchain_experimental.pydantic_v1 import (
+    BaseModel,
+    Field,
+    PrivateAttr,
+    root_validator,
+    validator,
+)
 
 
 class NarrativeModel(BaseModel):
diff --git a/libs/experimental/langchain_experimental/generative_agents/generative_agent.py b/libs/experimental/langchain_experimental/generative_agents/generative_agent.py
index cbe0b30e4d3..8fcc4b8413c 100644
--- a/libs/experimental/langchain_experimental/generative_agents/generative_agent.py
+++ b/libs/experimental/langchain_experimental/generative_agents/generative_agent.py
@@ -5,9 +5,9 @@ from typing import Any, Dict, List, Optional, Tuple
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
-from pydantic import BaseModel, Field
 
 from langchain_experimental.generative_agents.memory import GenerativeAgentMemory
+from langchain_experimental.pydantic_v1 import BaseModel, Field
 
 
 class GenerativeAgent(BaseModel):
diff --git a/libs/experimental/langchain_experimental/llms/anthropic_functions.py b/libs/experimental/langchain_experimental/llms/anthropic_functions.py
index e8c362aeb6d..958ba254d56 100644
--- a/libs/experimental/langchain_experimental/llms/anthropic_functions.py
+++ b/libs/experimental/langchain_experimental/llms/anthropic_functions.py
@@ -19,7 +19,8 @@ from langchain.schema.messages import (
     BaseMessage,
     SystemMessage,
 )
-from pydantic import root_validator
+
+from langchain_experimental.pydantic_v1 import root_validator
 
 prompt = """In addition to responding, you can use tools. \
 You have access to the following tools.
diff --git a/libs/experimental/langchain_experimental/llms/jsonformer_decoder.py b/libs/experimental/langchain_experimental/llms/jsonformer_decoder.py
index dcb30238b0a..1cb874fbe05 100644
--- a/libs/experimental/langchain_experimental/llms/jsonformer_decoder.py
+++ b/libs/experimental/langchain_experimental/llms/jsonformer_decoder.py
@@ -6,7 +6,8 @@ from typing import TYPE_CHECKING, Any, List, Optional, cast
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.huggingface_pipeline import HuggingFacePipeline
-from pydantic import Field, root_validator
+
+from langchain_experimental.pydantic_v1 import Field, root_validator
 
 if TYPE_CHECKING:
     import jsonformer
diff --git a/libs/experimental/langchain_experimental/llms/rellm_decoder.py b/libs/experimental/langchain_experimental/llms/rellm_decoder.py
index 6c64533409c..09d7bf4b6b8 100644
--- a/libs/experimental/langchain_experimental/llms/rellm_decoder.py
+++ b/libs/experimental/langchain_experimental/llms/rellm_decoder.py
@@ -6,7 +6,8 @@ from typing import TYPE_CHECKING, Any, List, Optional, cast
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.huggingface_pipeline import HuggingFacePipeline
 from langchain.llms.utils import enforce_stop_tokens
-from pydantic import Field, root_validator
+
+from langchain_experimental.pydantic_v1 import Field, root_validator
 
 if TYPE_CHECKING:
     import rellm
diff --git a/libs/experimental/langchain_experimental/pal_chain/base.py b/libs/experimental/langchain_experimental/pal_chain/base.py
index 8340982efd6..6717c9f4a13 100644
--- a/libs/experimental/langchain_experimental/pal_chain/base.py
+++ b/libs/experimental/langchain_experimental/pal_chain/base.py
@@ -15,10 +15,10 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.utilities import PythonREPL
-from pydantic import Extra, Field
 
 from langchain_experimental.pal_chain.colored_object_prompt import COLORED_OBJECT_PROMPT
 from langchain_experimental.pal_chain.math_prompt import MATH_PROMPT
+from langchain_experimental.pydantic_v1 import Extra, Field
 
 COMMAND_EXECUTION_FUNCTIONS = ["system", "exec", "execfile", "eval"]
 
diff --git a/libs/experimental/langchain_experimental/plan_and_execute/agent_executor.py b/libs/experimental/langchain_experimental/plan_and_execute/agent_executor.py
index 6e598e16f9b..9c50bad650d 100644
--- a/libs/experimental/langchain_experimental/plan_and_execute/agent_executor.py
+++ b/libs/experimental/langchain_experimental/plan_and_execute/agent_executor.py
@@ -5,7 +5,6 @@ from langchain.callbacks.manager import (
     CallbackManagerForChainRun,
 )
 from langchain.chains.base import Chain
-from pydantic import Field
 
 from langchain_experimental.plan_and_execute.executors.base import BaseExecutor
 from langchain_experimental.plan_and_execute.planners.base import BasePlanner
@@ -13,6 +12,7 @@ from langchain_experimental.plan_and_execute.schema import (
     BaseStepContainer,
     ListStepContainer,
 )
+from langchain_experimental.pydantic_v1 import Field
 
 
 class PlanAndExecute(Chain):
diff --git a/libs/experimental/langchain_experimental/plan_and_execute/executors/base.py b/libs/experimental/langchain_experimental/plan_and_execute/executors/base.py
index df660988c0e..a65716de29a 100644
--- a/libs/experimental/langchain_experimental/plan_and_execute/executors/base.py
+++ b/libs/experimental/langchain_experimental/plan_and_execute/executors/base.py
@@ -3,9 +3,9 @@ from typing import Any
 
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.base import Chain
-from pydantic import BaseModel
 
 from langchain_experimental.plan_and_execute.schema import StepResponse
+from langchain_experimental.pydantic_v1 import BaseModel
 
 
 class BaseExecutor(BaseModel):
diff --git a/libs/experimental/langchain_experimental/plan_and_execute/planners/base.py b/libs/experimental/langchain_experimental/plan_and_execute/planners/base.py
index 8689e7ee631..e79c564f807 100644
--- a/libs/experimental/langchain_experimental/plan_and_execute/planners/base.py
+++ b/libs/experimental/langchain_experimental/plan_and_execute/planners/base.py
@@ -3,9 +3,9 @@ from typing import Any, List, Optional
 
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.llm import LLMChain
-from pydantic import BaseModel
 
 from langchain_experimental.plan_and_execute.schema import Plan, PlanOutputParser
+from langchain_experimental.pydantic_v1 import BaseModel
 
 
 class BasePlanner(BaseModel):
diff --git a/libs/experimental/langchain_experimental/plan_and_execute/schema.py b/libs/experimental/langchain_experimental/plan_and_execute/schema.py
index 2a42598418e..41a323880a0 100644
--- a/libs/experimental/langchain_experimental/plan_and_execute/schema.py
+++ b/libs/experimental/langchain_experimental/plan_and_execute/schema.py
@@ -2,7 +2,8 @@ from abc import abstractmethod
 from typing import List, Tuple
 
 from langchain.schema import BaseOutputParser
-from pydantic import BaseModel, Field
+
+from langchain_experimental.pydantic_v1 import BaseModel, Field
 
 
 class Step(BaseModel):
diff --git a/libs/experimental/langchain_experimental/pydantic_v1/__init__.py b/libs/experimental/langchain_experimental/pydantic_v1/__init__.py
new file mode 100644
index 00000000000..859822531ec
--- /dev/null
+++ b/libs/experimental/langchain_experimental/pydantic_v1/__init__.py
@@ -0,0 +1,23 @@
+from importlib import metadata
+
+## Create namespaces for pydantic v1 and v2.
+# This code must stay at the top of the file before other modules may
+# attempt to import pydantic since it adds pydantic_v1 and pydantic_v2 to sys.modules.
+#
+# This hack is done for the following reasons:
+# * Langchain will attempt to remain compatible with both pydantic v1 and v2 since
+#   both dependencies and dependents may be stuck on either version of v1 or v2.
+# * Creating namespaces for pydantic v1 and v2 should allow us to write code that
+#   unambiguously uses either v1 or v2 API.
+# * This change is easier to roll out and roll back.
+
+try:
+    from pydantic.v1 import *  # noqa: F403
+except ImportError:
+    from pydantic import *  # noqa: F403
+
+
+try:
+    _PYDANTIC_MAJOR_VERSION: int = int(metadata.version("pydantic").split(".")[0])
+except metadata.PackageNotFoundError:
+    _PYDANTIC_MAJOR_VERSION = 0
diff --git a/libs/experimental/langchain_experimental/pydantic_v1/dataclasses.py b/libs/experimental/langchain_experimental/pydantic_v1/dataclasses.py
new file mode 100644
index 00000000000..bb7253c29d7
--- /dev/null
+++ b/libs/experimental/langchain_experimental/pydantic_v1/dataclasses.py
@@ -0,0 +1,4 @@
+try:
+    from pydantic.v1.dataclasses import *  # noqa: F403
+except ImportError:
+    from pydantic.dataclasses import *  # noqa: F403
diff --git a/libs/experimental/langchain_experimental/pydantic_v1/main.py b/libs/experimental/langchain_experimental/pydantic_v1/main.py
new file mode 100644
index 00000000000..4b8f1670e13
--- /dev/null
+++ b/libs/experimental/langchain_experimental/pydantic_v1/main.py
@@ -0,0 +1,4 @@
+try:
+    from pydantic.v1.main import *  # noqa: F403
+except ImportError:
+    from pydantic.main import *  # noqa: F403
diff --git a/libs/experimental/langchain_experimental/smart_llm/__init__.py b/libs/experimental/langchain_experimental/smart_llm/__init__.py
new file mode 100644
index 00000000000..925a05cec5b
--- /dev/null
+++ b/libs/experimental/langchain_experimental/smart_llm/__init__.py
@@ -0,0 +1,5 @@
+"""Generalized implementation of SmartGPT (origin: https://youtu.be/wVzuvf9D9BU)"""
+
+from langchain_experimental.smart_llm.base import SmartLLMChain
+
+__all__ = ["SmartLLMChain"]
diff --git a/libs/experimental/langchain_experimental/smart_llm/base.py b/libs/experimental/langchain_experimental/smart_llm/base.py
new file mode 100644
index 00000000000..8aff6a293a3
--- /dev/null
+++ b/libs/experimental/langchain_experimental/smart_llm/base.py
@@ -0,0 +1,324 @@
+"""Chain for applying self-critique using the SmartGPT workflow."""
+from typing import Any, Dict, List, Optional, Tuple, Type
+
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.chains.base import Chain
+from langchain.input import get_colored_text
+from langchain.prompts.base import BasePromptTemplate
+from langchain.prompts.chat import (
+    AIMessagePromptTemplate,
+    BaseMessagePromptTemplate,
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.schema import LLMResult, PromptValue
+
+from langchain_experimental.pydantic_v1 import Extra, root_validator
+
+
+class SmartLLMChain(Chain):
+    """
+    Generalized implementation of SmartGPT (origin: https://youtu.be/wVzuvf9D9BU)
+
+    A SmartLLMChain is an LLMChain that instead of simply passing the prompt to the LLM
+    performs these 3 steps:
+    1. Ideate: Pass the user prompt to an ideation LLM n_ideas times,
+       each result is an "idea"
+    2. Critique: Pass the ideas to a critique LLM which looks for flaws in the ideas
+       & picks the best one
+    3. Resolve: Pass the critique to a resolver LLM which improves upon the best idea
+       & outputs only the (improved version of) the best output
+
+    In total, SmartLLMChain pass will use n_ideas+2 LLM calls
+
+    Note that SmartLLMChain will only improve results (compared to a basic LLMChain),
+    when the underlying models have the capability for reflection, which smaller models
+    often don't.
+
+    Finally, a SmartLLMChain assumes that each underlying LLM outputs exactly 1 result.
+    """
+
+    class SmartLLMChainHistory:
+        question: str = ""
+        ideas: List[str] = []
+        critique: str = ""
+
+        @property
+        def n_ideas(self) -> int:
+            return len(self.ideas)
+
+        def ideation_prompt_inputs(self) -> Dict[str, Any]:
+            return {"question": self.question}
+
+        def critique_prompt_inputs(self) -> Dict[str, Any]:
+            return {
+                "question": self.question,
+                **{f"idea_{i+1}": idea for i, idea in enumerate(self.ideas)},
+            }
+
+        def resolve_prompt_inputs(self) -> Dict[str, Any]:
+            return {
+                "question": self.question,
+                **{f"idea_{i+1}": idea for i, idea in enumerate(self.ideas)},
+                "critique": self.critique,
+            }
+
+    prompt: BasePromptTemplate
+    """Prompt object to use."""
+    ideation_llm: Optional[BaseLanguageModel] = None
+    """LLM to use in ideation step. If None given, 'llm' will be used."""
+    critique_llm: Optional[BaseLanguageModel] = None
+    """LLM to use in critique step. If None given, 'llm' will be used."""
+    resolver_llm: Optional[BaseLanguageModel] = None
+    """LLM to use in resolve step. If None given, 'llm' will be used."""
+    llm: Optional[BaseLanguageModel] = None
+    """LLM to use for each steps, if no specific llm for that step is given. """
+    n_ideas: int = 3
+    """Number of ideas to generate in idea step"""
+    return_intermediate_steps: bool = False
+    """Whether to return ideas and critique, in addition to resolution."""
+    history: SmartLLMChainHistory = SmartLLMChainHistory()
+
+    class Config:
+        extra = Extra.forbid
+
+    @root_validator
+    @classmethod
+    def validate_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Ensure we have an LLM for each step."""
+        llm = values.get("llm")
+        ideation_llm = values.get("ideation_llm")
+        critique_llm = values.get("critique_llm")
+        resolver_llm = values.get("resolver_llm")
+
+        if not llm and not ideation_llm:
+            raise ValueError(
+                "Either ideation_llm or llm needs to be given. Pass llm, "
+                "if you want to use the same llm for all steps, or pass "
+                "ideation_llm, critique_llm and resolver_llm if you want "
+                "to use different llms for each step."
+            )
+        if not llm and not critique_llm:
+            raise ValueError(
+                "Either critique_llm or llm needs to be given. Pass llm, "
+                "if you want to use the same llm for all steps, or pass "
+                "ideation_llm, critique_llm and resolver_llm if you want "
+                "to use different llms for each step."
+            )
+        if not llm and not resolver_llm:
+            raise ValueError(
+                "Either resolve_llm or llm needs to be given. Pass llm, "
+                "if you want to use the same llm for all steps, or pass "
+                "ideation_llm, critique_llm and resolver_llm if you want "
+                "to use different llms for each step."
+            )
+        if llm and ideation_llm and critique_llm and resolver_llm:
+            raise ValueError(
+                "LLMs are given for each step (ideation_llm, critique_llm,"
+                " resolver_llm), but backup LLM (llm) is also given, which"
+                " would not be used."
+            )
+        return values
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Defines the input keys."""
+        return self.prompt.input_variables
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Defines the output keys."""
+        if self.return_intermediate_steps:
+            return ["ideas", "critique", "resolution"]
+        return ["resolution"]
+
+    def prep_prompts(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Tuple[PromptValue, Optional[List[str]]]:
+        """Prepare prompts from inputs."""
+        stop = None
+        if "stop" in inputs:
+            stop = inputs["stop"]
+        selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
+        prompt = self.prompt.format_prompt(**selected_inputs)
+        _colored_text = get_colored_text(prompt.to_string(), "green")
+        _text = "Prompt after formatting:\n" + _colored_text
+        if run_manager:
+            run_manager.on_text(_text, end="\n", verbose=self.verbose)
+        if "stop" in inputs and inputs["stop"] != stop:
+            raise ValueError(
+                "If `stop` is present in any inputs, should be present in all."
+            )
+        return prompt, stop
+
+    def _call(
+        self,
+        input_list: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        prompt, stop = self.prep_prompts(input_list, run_manager=run_manager)
+        self.history.question = prompt.to_string()
+        ideas = self._ideate(stop, run_manager)
+        self.history.ideas = ideas
+        critique = self._critique(stop, run_manager)
+        self.history.critique = critique
+        resolution = self._resolve(stop, run_manager)
+        if self.return_intermediate_steps:
+            return {"ideas": ideas, "critique": critique, "resolution": resolution}
+        return {"resolution": resolution}
+
+    def _get_text_from_llm_result(self, result: LLMResult, step: str) -> str:
+        """Between steps, only the LLM result text is passed, not the LLMResult object.
+        This function extracts the text from an LLMResult."""
+        if len(result.generations) != 1:
+            raise ValueError(
+                f"In SmartLLM the LLM result in step {step} is not "
+                "exactly 1 element. This should never happen"
+            )
+        if len(result.generations[0]) != 1:
+            raise ValueError(
+                f"In SmartLLM the LLM in step {step} returned more than "
+                "1 output. SmartLLM only works with LLMs returning "
+                "exactly 1 output."
+            )
+        return result.generations[0][0].text
+
+    def get_prompt_strings(
+        self, stage: str
+    ) -> List[Tuple[Type[BaseMessagePromptTemplate], str]]:
+        role_strings: List[Tuple[Type[BaseMessagePromptTemplate], str]] = []
+        role_strings.append(
+            (
+                HumanMessagePromptTemplate,
+                "Question: {question}\nAnswer: Let's work this out in a step by "
+                "step way to be sure we have the right answer:",
+            )
+        )
+        if stage == "ideation":
+            return role_strings
+        role_strings.extend(
+            [
+                *[
+                    (
+                        AIMessagePromptTemplate,
+                        "Idea " + str(i + 1) + ": {idea_" + str(i + 1) + "}",
+                    )
+                    for i in range(self.n_ideas)
+                ],
+                (
+                    HumanMessagePromptTemplate,
+                    "You are a researcher tasked with investigating the "
+                    f"{self.n_ideas} response options provided. List the flaws and "
+                    "faulty logic of each answer options. Let'w work this out in a step"
+                    " by step way to be sure we have all the errors:",
+                ),
+            ]
+        )
+        if stage == "critique":
+            return role_strings
+        role_strings.extend(
+            [
+                (AIMessagePromptTemplate, "Critique: {critique}"),
+                (
+                    HumanMessagePromptTemplate,
+                    "You are a resolved tasked with 1) finding which of "
+                    f"the {self.n_ideas} anwer options the researcher thought was  "
+                    "best,2) improving that answer and 3) printing the answer in full. "
+                    "Don't output anything for step 1 or 2, only the full answer in 3. "
+                    "Let's work this out in a step by step way to be sure we have "
+                    "the right answer:",
+                ),
+            ]
+        )
+        if stage == "resolve":
+            return role_strings
+        raise ValueError(
+            "stage should be either 'ideation', 'critique' or 'resolve',"
+            f" but it is '{stage}'. This should never happen."
+        )
+
+    def ideation_prompt(self) -> ChatPromptTemplate:
+        return ChatPromptTemplate.from_strings(self.get_prompt_strings("ideation"))
+
+    def critique_prompt(self) -> ChatPromptTemplate:
+        return ChatPromptTemplate.from_strings(self.get_prompt_strings("critique"))
+
+    def resolve_prompt(self) -> ChatPromptTemplate:
+        return ChatPromptTemplate.from_strings(self.get_prompt_strings("resolve"))
+
+    def _ideate(
+        self,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> List[str]:
+        """Generate n_ideas ideas as response to user prompt."""
+        llm = self.ideation_llm if self.ideation_llm else self.llm
+        prompt = self.ideation_prompt().format_prompt(
+            **self.history.ideation_prompt_inputs()
+        )
+        callbacks = run_manager.get_child() if run_manager else None
+        if llm:
+            ideas = [
+                self._get_text_from_llm_result(
+                    llm.generate_prompt([prompt], stop, callbacks),
+                    step="ideate",
+                )
+                for _ in range(self.n_ideas)
+            ]
+            for i, idea in enumerate(ideas):
+                _colored_text = get_colored_text(idea, "blue")
+                _text = f"Idea {i+1}:\n" + _colored_text
+                if run_manager:
+                    run_manager.on_text(_text, end="\n", verbose=self.verbose)
+            return ideas
+        else:
+            raise ValueError("llm is none, which should never happen")
+
+    def _critique(
+        self,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> str:
+        """Critique each of the ideas from ideation stage & select best one."""
+        llm = self.critique_llm if self.critique_llm else self.llm
+        prompt = self.critique_prompt().format_prompt(
+            **self.history.critique_prompt_inputs()
+        )
+        callbacks = run_manager.handlers if run_manager else None
+        if llm:
+            critique = self._get_text_from_llm_result(
+                llm.generate_prompt([prompt], stop, callbacks), step="critique"
+            )
+            _colored_text = get_colored_text(critique, "yellow")
+            _text = "Critique:\n" + _colored_text
+            if run_manager:
+                run_manager.on_text(_text, end="\n", verbose=self.verbose)
+            return critique
+        else:
+            raise ValueError("llm is none, which should never happen")
+
+    def _resolve(
+        self,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> str:
+        """Improve upon the best idea as chosen in critique step & return it."""
+        llm = self.resolver_llm if self.resolver_llm else self.llm
+        prompt = self.resolve_prompt().format_prompt(
+            **self.history.resolve_prompt_inputs()
+        )
+        callbacks = run_manager.handlers if run_manager else None
+        if llm:
+            resolution = self._get_text_from_llm_result(
+                llm.generate_prompt([prompt], stop, callbacks), step="resolve"
+            )
+            _colored_text = get_colored_text(resolution, "green")
+            _text = "Resolution:\n" + _colored_text
+            if run_manager:
+                run_manager.on_text(_text, end="\n", verbose=self.verbose)
+            return resolution
+        else:
+            raise ValueError("llm is none, which should never happen")
diff --git a/libs/experimental/langchain_experimental/sql/base.py b/libs/experimental/langchain_experimental/sql/base.py
index 86bcf76c3bb..fcc664a75d0 100644
--- a/libs/experimental/langchain_experimental/sql/base.py
+++ b/libs/experimental/langchain_experimental/sql/base.py
@@ -13,7 +13,8 @@ from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.sql_database.prompt import QUERY_CHECKER
 from langchain.utilities.sql_database import SQLDatabase
-from pydantic import Extra, Field, root_validator
+
+from langchain_experimental.pydantic_v1 import Extra, Field, root_validator
 
 INTERMEDIATE_STEPS_KEY = "intermediate_steps"
 
diff --git a/libs/experimental/langchain_experimental/tot/base.py b/libs/experimental/langchain_experimental/tot/base.py
index 027d6c8b4e4..b423525e45c 100644
--- a/libs/experimental/langchain_experimental/tot/base.py
+++ b/libs/experimental/langchain_experimental/tot/base.py
@@ -20,8 +20,8 @@ from langchain.callbacks.manager import (
     CallbackManagerForChainRun,
 )
 from langchain.chains.base import Chain
-from pydantic import Extra
 
+from langchain_experimental.pydantic_v1 import Extra
 from langchain_experimental.tot.checker import ToTChecker
 from langchain_experimental.tot.controller import ToTController
 from langchain_experimental.tot.memory import ToTDFSMemory
diff --git a/libs/experimental/langchain_experimental/tot/thought.py b/libs/experimental/langchain_experimental/tot/thought.py
index d0567c85332..35bc2dfaffa 100644
--- a/libs/experimental/langchain_experimental/tot/thought.py
+++ b/libs/experimental/langchain_experimental/tot/thought.py
@@ -3,7 +3,7 @@ from __future__ import annotations
 from enum import Enum
 from typing import Set
 
-from pydantic import BaseModel, Field
+from langchain_experimental.pydantic_v1 import BaseModel, Field
 
 
 class ThoughtValidity(Enum):
diff --git a/libs/experimental/langchain_experimental/tot/thought_generation.py b/libs/experimental/langchain_experimental/tot/thought_generation.py
index 7c41498467e..8a7a665663b 100644
--- a/libs/experimental/langchain_experimental/tot/thought_generation.py
+++ b/libs/experimental/langchain_experimental/tot/thought_generation.py
@@ -11,8 +11,8 @@ from typing import Any, Dict, List, Tuple
 
 from langchain.chains.llm import LLMChain
 from langchain.prompts.base import BasePromptTemplate
-from pydantic import Field
 
+from langchain_experimental.pydantic_v1 import Field
 from langchain_experimental.tot.prompts import COT_PROMPT, PROPOSE_PROMPT
 
 
diff --git a/libs/experimental/pyproject.toml b/libs/experimental/pyproject.toml
index bc1695dddfd..51669e46f57 100644
--- a/libs/experimental/pyproject.toml
+++ b/libs/experimental/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-experimental"
-version = "0.0.8"
+version = "0.0.10"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
diff --git a/libs/experimental/tests/unit_tests/fake_llm.py b/libs/experimental/tests/unit_tests/fake_llm.py
index 7da86861ea0..a0369418758 100644
--- a/libs/experimental/tests/unit_tests/fake_llm.py
+++ b/libs/experimental/tests/unit_tests/fake_llm.py
@@ -3,7 +3,8 @@ from typing import Any, Dict, List, Mapping, Optional, cast
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
-from pydantic import validator
+
+from langchain_experimental.pydantic_v1 import validator
 
 
 class FakeLLM(LLM):
diff --git a/libs/experimental/tests/unit_tests/test_smartllm.py b/libs/experimental/tests/unit_tests/test_smartllm.py
new file mode 100644
index 00000000000..b969bbdb5db
--- /dev/null
+++ b/libs/experimental/tests/unit_tests/test_smartllm.py
@@ -0,0 +1,120 @@
+"""Test SmartLLM."""
+from langchain.chat_models import FakeListChatModel
+from langchain.llms import FakeListLLM
+from langchain.prompts.prompt import PromptTemplate
+
+from langchain_experimental.smart_llm import SmartLLMChain
+
+
+def test_ideation() -> None:
+    # test that correct responses are returned
+    responses = ["Idea 1", "Idea 2", "Idea 3"]
+    llm = FakeListLLM(responses=responses)
+    prompt = PromptTemplate(
+        input_variables=["product"],
+        template="What is a good name for a company that makes {product}?",
+    )
+    chain = SmartLLMChain(llm=llm, prompt=prompt)
+    prompt_value, _ = chain.prep_prompts({"product": "socks"})
+    chain.history.question = prompt_value.to_string()
+    results = chain._ideate()
+    assert results == responses
+
+    # test that correct number of responses are returned
+    for i in range(1, 5):
+        responses = [f"Idea {j+1}" for j in range(i)]
+        llm = FakeListLLM(responses=responses)
+        chain = SmartLLMChain(llm=llm, prompt=prompt, n_ideas=i)
+        prompt_value, _ = chain.prep_prompts({"product": "socks"})
+        chain.history.question = prompt_value.to_string()
+        results = chain._ideate()
+        assert len(results) == i
+
+
+def test_critique() -> None:
+    response = "Test Critique"
+    llm = FakeListLLM(responses=[response])
+    prompt = PromptTemplate(
+        input_variables=["product"],
+        template="What is a good name for a company that makes {product}?",
+    )
+    chain = SmartLLMChain(llm=llm, prompt=prompt, n_ideas=2)
+    prompt_value, _ = chain.prep_prompts({"product": "socks"})
+    chain.history.question = prompt_value.to_string()
+    chain.history.ideas = ["Test Idea 1", "Test Idea 2"]
+    result = chain._critique()
+    assert result == response
+
+
+def test_resolver() -> None:
+    response = "Test resolution"
+    llm = FakeListLLM(responses=[response])
+    prompt = PromptTemplate(
+        input_variables=["product"],
+        template="What is a good name for a company that makes {product}?",
+    )
+    chain = SmartLLMChain(llm=llm, prompt=prompt, n_ideas=2)
+    prompt_value, _ = chain.prep_prompts({"product": "socks"})
+    chain.history.question = prompt_value.to_string()
+    chain.history.ideas = ["Test Idea 1", "Test Idea 2"]
+    chain.history.critique = "Test Critique"
+    result = chain._resolve()
+    assert result == response
+
+
+def test_all_steps() -> None:
+    joke = "Why did the chicken cross the Mobius strip?"
+    response = "Resolution response"
+    ideation_llm = FakeListLLM(responses=["Ideation response" for _ in range(20)])
+    critique_llm = FakeListLLM(responses=["Critique response" for _ in range(20)])
+    resolver_llm = FakeListLLM(responses=[response for _ in range(20)])
+    prompt = PromptTemplate(
+        input_variables=["joke"],
+        template="Explain this joke to me: {joke}?",
+    )
+    chain = SmartLLMChain(
+        ideation_llm=ideation_llm,
+        critique_llm=critique_llm,
+        resolver_llm=resolver_llm,
+        prompt=prompt,
+    )
+    result = chain(joke)
+    assert result["joke"] == joke
+    assert result["resolution"] == response
+
+
+def test_intermediate_output() -> None:
+    joke = "Why did the chicken cross the Mobius strip?"
+    llm = FakeListLLM(responses=[f"Response {i+1}" for i in range(5)])
+    prompt = PromptTemplate(
+        input_variables=["joke"],
+        template="Explain this joke to me: {joke}?",
+    )
+    chain = SmartLLMChain(llm=llm, prompt=prompt, return_intermediate_steps=True)
+    result = chain(joke)
+    assert result["joke"] == joke
+    assert result["ideas"] == [f"Response {i+1}" for i in range(3)]
+    assert result["critique"] == "Response 4"
+    assert result["resolution"] == "Response 5"
+
+
+def test_all_steps_with_chat_model() -> None:
+    joke = "Why did the chicken cross the Mobius strip?"
+    response = "Resolution response"
+
+    ideation_llm = FakeListChatModel(responses=["Ideation response" for _ in range(20)])
+    critique_llm = FakeListChatModel(responses=["Critique response" for _ in range(20)])
+    resolver_llm = FakeListChatModel(responses=[response for _ in range(20)])
+    prompt = PromptTemplate(
+        input_variables=["joke"],
+        template="Explain this joke to me: {joke}?",
+    )
+    chain = SmartLLMChain(
+        ideation_llm=ideation_llm,
+        critique_llm=critique_llm,
+        resolver_llm=resolver_llm,
+        prompt=prompt,
+    )
+    result = chain(joke)
+    assert result["joke"] == joke
+    assert result["resolution"] == response
diff --git a/libs/langchain/langchain/__init__.py b/libs/langchain/langchain/__init__.py
index 00935e02889..32ad552ecc5 100644
--- a/libs/langchain/langchain/__init__.py
+++ b/libs/langchain/langchain/__init__.py
@@ -1,5 +1,5 @@
+# ruff: noqa: E402
 """Main entrypoint into package."""
-
 from importlib import metadata
 from typing import Optional
 
@@ -67,6 +67,7 @@ llm_cache: Optional[BaseCache] = None
 # For backwards compatibility
 SerpAPIChain = SerpAPIWrapper
 
+
 __all__ = [
     "LLMChain",
     "LLMBashChain",
diff --git a/libs/langchain/langchain/_api/deprecation.py b/libs/langchain/langchain/_api/deprecation.py
index 30164ab67ee..ccc8e397169 100644
--- a/libs/langchain/langchain/_api/deprecation.py
+++ b/libs/langchain/langchain/_api/deprecation.py
@@ -293,6 +293,21 @@ def deprecated(
         else:
             new_doc = f"[*Deprecated*]  {old_doc}"
 
+        # Modify the docstring to include a deprecation notice.
+        notes_header = "\nNotes\n-----"
+        components = [
+            message,
+            f"Use {alternative} instead." if alternative else "",
+            addendum,
+        ]
+        details = " ".join([component.strip() for component in components if component])
+        new_doc += (
+            f"[*Deprecated*] {old_doc}\n"
+            f"{notes_header if notes_header not in old_doc else ''}\n"
+            f".. deprecated:: {since}\n"
+            f"   {details}"
+        )
+
         return finalize(warning_emitting_wrapper, new_doc)
 
     return deprecate
diff --git a/libs/langchain/langchain/adapters/__init__.py b/libs/langchain/langchain/adapters/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/langchain/langchain/adapters/openai.py b/libs/langchain/langchain/adapters/openai.py
new file mode 100644
index 00000000000..e9f18408c17
--- /dev/null
+++ b/libs/langchain/langchain/adapters/openai.py
@@ -0,0 +1,208 @@
+from __future__ import annotations
+
+import importlib
+from typing import (
+    Any,
+    AsyncIterator,
+    Dict,
+    Iterable,
+    List,
+    Mapping,
+    Sequence,
+    Union,
+    overload,
+)
+
+from typing_extensions import Literal
+
+from langchain.schema.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+    BaseMessageChunk,
+    ChatMessage,
+    FunctionMessage,
+    HumanMessage,
+    SystemMessage,
+)
+
+
+async def aenumerate(
+    iterable: AsyncIterator[Any], start: int = 0
+) -> AsyncIterator[tuple[int, Any]]:
+    """Async version of enumerate."""
+    i = start
+    async for x in iterable:
+        yield i, x
+        i += 1
+
+
+def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
+    role = _dict["role"]
+    if role == "user":
+        return HumanMessage(content=_dict["content"])
+    elif role == "assistant":
+        # Fix for azure
+        # Also OpenAI returns None for tool invocations
+        content = _dict.get("content", "") or ""
+        if _dict.get("function_call"):
+            additional_kwargs = {"function_call": dict(_dict["function_call"])}
+        else:
+            additional_kwargs = {}
+        return AIMessage(content=content, additional_kwargs=additional_kwargs)
+    elif role == "system":
+        return SystemMessage(content=_dict["content"])
+    elif role == "function":
+        return FunctionMessage(content=_dict["content"], name=_dict["name"])
+    else:
+        return ChatMessage(content=_dict["content"], role=role)
+
+
+def convert_message_to_dict(message: BaseMessage) -> dict:
+    message_dict: Dict[str, Any]
+    if isinstance(message, ChatMessage):
+        message_dict = {"role": message.role, "content": message.content}
+    elif isinstance(message, HumanMessage):
+        message_dict = {"role": "user", "content": message.content}
+    elif isinstance(message, AIMessage):
+        message_dict = {"role": "assistant", "content": message.content}
+        if "function_call" in message.additional_kwargs:
+            message_dict["function_call"] = message.additional_kwargs["function_call"]
+            # If function call only, content is None not empty string
+            if message_dict["content"] == "":
+                message_dict["content"] = None
+    elif isinstance(message, SystemMessage):
+        message_dict = {"role": "system", "content": message.content}
+    elif isinstance(message, FunctionMessage):
+        message_dict = {
+            "role": "function",
+            "content": message.content,
+            "name": message.name,
+        }
+    else:
+        raise TypeError(f"Got unknown type {message}")
+    if "name" in message.additional_kwargs:
+        message_dict["name"] = message.additional_kwargs["name"]
+    return message_dict
+
+
+def convert_openai_messages(messages: Sequence[Dict[str, Any]]) -> List[BaseMessage]:
+    """Convert dictionaries representing OpenAI messages to LangChain format.
+
+    Args:
+        messages: List of dictionaries representing OpenAI messages
+
+    Returns:
+        List of LangChain BaseMessage objects.
+    """
+    return [convert_dict_to_message(m) for m in messages]
+
+
+def _convert_message_chunk_to_delta(chunk: BaseMessageChunk, i: int) -> Dict[str, Any]:
+    _dict: Dict[str, Any] = {}
+    if isinstance(chunk, AIMessageChunk):
+        if i == 0:
+            # Only shows up in the first chunk
+            _dict["role"] = "assistant"
+        if "function_call" in chunk.additional_kwargs:
+            _dict["function_call"] = chunk.additional_kwargs["function_call"]
+            # If the first chunk is a function call, the content is not empty string,
+            # not missing, but None.
+            if i == 0:
+                _dict["content"] = None
+        else:
+            _dict["content"] = chunk.content
+    else:
+        raise ValueError(f"Got unexpected streaming chunk type: {type(chunk)}")
+    # This only happens at the end of streams, and OpenAI returns as empty dict
+    if _dict == {"content": ""}:
+        _dict = {}
+    return {"choices": [{"delta": _dict}]}
+
+
+class ChatCompletion:
+    @overload
+    @staticmethod
+    def create(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: Literal[False] = False,
+        **kwargs: Any,
+    ) -> dict:
+        ...
+
+    @overload
+    @staticmethod
+    def create(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: Literal[True],
+        **kwargs: Any,
+    ) -> Iterable:
+        ...
+
+    @staticmethod
+    def create(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: bool = False,
+        **kwargs: Any,
+    ) -> Union[dict, Iterable]:
+        models = importlib.import_module("langchain.chat_models")
+        model_cls = getattr(models, provider)
+        model_config = model_cls(**kwargs)
+        converted_messages = convert_openai_messages(messages)
+        if not stream:
+            result = model_config.invoke(converted_messages)
+            return {"choices": [{"message": convert_message_to_dict(result)}]}
+        else:
+            return (
+                _convert_message_chunk_to_delta(c, i)
+                for i, c in enumerate(model_config.stream(converted_messages))
+            )
+
+    @overload
+    @staticmethod
+    async def acreate(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: Literal[False] = False,
+        **kwargs: Any,
+    ) -> dict:
+        ...
+
+    @overload
+    @staticmethod
+    async def acreate(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: Literal[True],
+        **kwargs: Any,
+    ) -> AsyncIterator:
+        ...
+
+    @staticmethod
+    async def acreate(
+        messages: Sequence[Dict[str, Any]],
+        *,
+        provider: str = "ChatOpenAI",
+        stream: bool = False,
+        **kwargs: Any,
+    ) -> Union[dict, AsyncIterator]:
+        models = importlib.import_module("langchain.chat_models")
+        model_cls = getattr(models, provider)
+        model_config = model_cls(**kwargs)
+        converted_messages = convert_openai_messages(messages)
+        if not stream:
+            result = await model_config.ainvoke(converted_messages)
+            return {"choices": [{"message": convert_message_to_dict(result)}]}
+        else:
+            return (
+                _convert_message_chunk_to_delta(c, i)
+                async for i, c in aenumerate(model_config.astream(converted_messages))
+            )
diff --git a/libs/langchain/langchain/agents/agent.py b/libs/langchain/langchain/agents/agent.py
index 9ac57726fd9..1294b1718f8 100644
--- a/libs/langchain/langchain/agents/agent.py
+++ b/libs/langchain/langchain/agents/agent.py
@@ -10,7 +10,6 @@ from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 import yaml
-from pydantic import BaseModel, root_validator
 
 from langchain.agents.agent_iterator import AgentExecutorIterator
 from langchain.agents.agent_types import AgentType
@@ -27,6 +26,7 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.prompts.few_shot import FewShotPromptTemplate
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.schema import (
     AgentAction,
     AgentFinish,
@@ -615,9 +615,9 @@ class Agent(BaseSingleActionAgent):
 class ExceptionTool(BaseTool):
     """Tool that just returns the query."""
 
-    name = "_Exception"
+    name: str = "_Exception"
     """Name of the tool."""
-    description = "Exception tool"
+    description: str = "Exception tool"
     """Description of the tool."""
 
     def _run(
diff --git a/libs/langchain/langchain/agents/agent_toolkits/amadeus/toolkit.py b/libs/langchain/langchain/agents/agent_toolkits/amadeus/toolkit.py
index 28db53bdcb9..9682e122672 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/amadeus/toolkit.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/amadeus/toolkit.py
@@ -2,9 +2,8 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, List
 
-from pydantic import Field
-
 from langchain.agents.agent_toolkits.base import BaseToolkit
+from langchain.pydantic_v1 import Field
 from langchain.tools import BaseTool
 from langchain.tools.amadeus.closest_airport import AmadeusClosestAirport
 from langchain.tools.amadeus.flight_search import AmadeusFlightSearch
diff --git a/libs/langchain/langchain/agents/agent_toolkits/azure_cognitive_services.py b/libs/langchain/langchain/agents/agent_toolkits/azure_cognitive_services.py
index 366444707ab..c733927cd41 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/azure_cognitive_services.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/azure_cognitive_services.py
@@ -19,7 +19,7 @@ class AzureCognitiveServicesToolkit(BaseToolkit):
     def get_tools(self) -> List[BaseTool]:
         """Get the tools in the toolkit."""
 
-        tools = [
+        tools: List[BaseTool] = [
             AzureCogsFormRecognizerTool(),
             AzureCogsSpeech2TextTool(),
             AzureCogsText2SpeechTool(),
diff --git a/libs/langchain/langchain/agents/agent_toolkits/base.py b/libs/langchain/langchain/agents/agent_toolkits/base.py
index f8624cf7e79..5eeccce6b02 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/base.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/base.py
@@ -2,8 +2,7 @@
 from abc import ABC, abstractmethod
 from typing import List
 
-from pydantic import BaseModel
-
+from langchain.pydantic_v1 import BaseModel
 from langchain.tools import BaseTool
 
 
diff --git a/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py b/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py
index 10a8a3ce3f9..43f0a2105d2 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py
@@ -18,5 +18,8 @@ def create_retriever_tool(
         Tool class to pass to an agent
     """
     return Tool(
-        name=name, description=description, func=retriever.get_relevant_documents
+        name=name,
+        description=description,
+        func=retriever.get_relevant_documents,
+        coroutine=retriever.aget_relevant_documents,
     )
diff --git a/libs/langchain/langchain/agents/agent_toolkits/file_management/toolkit.py b/libs/langchain/langchain/agents/agent_toolkits/file_management/toolkit.py
index c78a37c18ae..6f173888d3f 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/file_management/toolkit.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/file_management/toolkit.py
@@ -2,9 +2,8 @@ from __future__ import annotations
 
 from typing import List, Optional
 
-from pydantic import root_validator
-
 from langchain.agents.agent_toolkits.base import BaseToolkit
+from langchain.pydantic_v1 import root_validator
 from langchain.tools import BaseTool
 from langchain.tools.file_management.copy import CopyFileTool
 from langchain.tools.file_management.delete import DeleteFileTool
@@ -15,7 +14,8 @@ from langchain.tools.file_management.read import ReadFileTool
 from langchain.tools.file_management.write import WriteFileTool
 
 _FILE_TOOLS = {
-    tool_cls.__fields__["name"].default: tool_cls
+    # "Type[Runnable[Any, Any]]" has no attribute "__fields__"  [attr-defined]
+    tool_cls.__fields__["name"].default: tool_cls  # type: ignore[attr-defined]
     for tool_cls in [
         CopyFileTool,
         DeleteFileTool,
diff --git a/libs/langchain/langchain/agents/agent_toolkits/gmail/toolkit.py b/libs/langchain/langchain/agents/agent_toolkits/gmail/toolkit.py
index e95f2e6862c..cb456d6aab1 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/gmail/toolkit.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/gmail/toolkit.py
@@ -2,9 +2,8 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, List
 
-from pydantic import Field
-
 from langchain.agents.agent_toolkits.base import BaseToolkit
+from langchain.pydantic_v1 import Field
 from langchain.tools import BaseTool
 from langchain.tools.gmail.create_draft import GmailCreateDraft
 from langchain.tools.gmail.get_message import GmailGetMessage
diff --git a/libs/langchain/langchain/agents/agent_toolkits/nla/toolkit.py b/libs/langchain/langchain/agents/agent_toolkits/nla/toolkit.py
index 523b3929545..38fb1a52197 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/nla/toolkit.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/nla/toolkit.py
@@ -2,10 +2,9 @@ from __future__ import annotations
 
 from typing import Any, List, Optional, Sequence
 
-from pydantic import Field
-
 from langchain.agents.agent_toolkits.base import BaseToolkit
 from langchain.agents.agent_toolkits.nla.tool import NLATool
+from langchain.pydantic_v1 import Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
 from langchain.tools.openapi.utils.openapi_utils import OpenAPISpec
diff --git a/libs/langchain/langchain/agents/agent_toolkits/office365/toolkit.py b/libs/langchain/langchain/agents/agent_toolkits/office365/toolkit.py
index 3e3151e780e..781184863ad 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/office365/toolkit.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/office365/toolkit.py
@@ -2,9 +2,8 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, List
 
-from pydantic import Field
-
 from langchain.agents.agent_toolkits.base import BaseToolkit
+from langchain.pydantic_v1 import Field
 from langchain.tools import BaseTool
 from langchain.tools.office365.create_draft_message import O365CreateDraftMessage
 from langchain.tools.office365.events_search import O365SearchEvents
diff --git a/libs/langchain/langchain/agents/agent_toolkits/openapi/planner.py b/libs/langchain/langchain/agents/agent_toolkits/openapi/planner.py
index 5a5e920e2c1..6e2ab4483d9 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/openapi/planner.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/openapi/planner.py
@@ -5,7 +5,6 @@ from functools import partial
 from typing import Any, Callable, Dict, List, Optional
 
 import yaml
-from pydantic import Field
 
 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.openapi.planner_prompt import (
@@ -33,6 +32,7 @@ from langchain.chains.llm import LLMChain
 from langchain.llms.openai import OpenAI
 from langchain.memory import ReadOnlySharedMemory
 from langchain.prompts import PromptTemplate
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
@@ -66,7 +66,7 @@ def _get_default_llm_chain_factory(
 class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
     """Requests GET tool with LLM-instructed extraction of truncated responses."""
 
-    name = "requests_get"
+    name: str = "requests_get"
     """Tool name."""
     description = REQUESTS_GET_TOOL_DESCRIPTION
     """Tool description."""
@@ -96,7 +96,7 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
 class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
     """Requests POST tool with LLM-instructed extraction of truncated responses."""
 
-    name = "requests_post"
+    name: str = "requests_post"
     """Tool name."""
     description = REQUESTS_POST_TOOL_DESCRIPTION
     """Tool description."""
@@ -125,7 +125,7 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
 class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
     """Requests PATCH tool with LLM-instructed extraction of truncated responses."""
 
-    name = "requests_patch"
+    name: str = "requests_patch"
     """Tool name."""
     description = REQUESTS_PATCH_TOOL_DESCRIPTION
     """Tool description."""
@@ -154,7 +154,7 @@ class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
 class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
     """A tool that sends a DELETE request and parses the response."""
 
-    name = "requests_delete"
+    name: str = "requests_delete"
     """The name of the tool."""
     description = REQUESTS_DELETE_TOOL_DESCRIPTION
     """The description of the tool."""
diff --git a/libs/langchain/langchain/agents/agent_toolkits/playwright/toolkit.py b/libs/langchain/langchain/agents/agent_toolkits/playwright/toolkit.py
index af010e1e22e..8d12d90e54f 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/playwright/toolkit.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/playwright/toolkit.py
@@ -3,9 +3,8 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, List, Optional, Type, cast
 
-from pydantic import Extra, root_validator
-
 from langchain.agents.agent_toolkits.base import BaseToolkit
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.tools.base import BaseTool
 from langchain.tools.playwright.base import (
     BaseBrowserTool,
diff --git a/libs/langchain/langchain/agents/agent_toolkits/powerbi/toolkit.py b/libs/langchain/langchain/agents/agent_toolkits/powerbi/toolkit.py
index e7e66c6a41b..2f24a1cb7ab 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/powerbi/toolkit.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/powerbi/toolkit.py
@@ -1,8 +1,6 @@
 """Toolkit for interacting with a Power BI dataset."""
 from typing import List, Optional, Union
 
-from pydantic import Field
-
 from langchain.agents.agent_toolkits.base import BaseToolkit
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
@@ -13,6 +11,7 @@ from langchain.prompts.chat import (
     HumanMessagePromptTemplate,
     SystemMessagePromptTemplate,
 )
+from langchain.pydantic_v1 import Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import BaseTool
 from langchain.tools.powerbi.prompt import (
diff --git a/libs/langchain/langchain/agents/agent_toolkits/spark_sql/toolkit.py b/libs/langchain/langchain/agents/agent_toolkits/spark_sql/toolkit.py
index 7c89c7dc5f4..3b7e1641c72 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/spark_sql/toolkit.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/spark_sql/toolkit.py
@@ -1,9 +1,8 @@
 """Toolkit for interacting with Spark SQL."""
 from typing import List
 
-from pydantic import Field
-
 from langchain.agents.agent_toolkits.base import BaseToolkit
+from langchain.pydantic_v1 import Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import BaseTool
 from langchain.tools.spark_sql.tool import (
diff --git a/libs/langchain/langchain/agents/agent_toolkits/sql/toolkit.py b/libs/langchain/langchain/agents/agent_toolkits/sql/toolkit.py
index 5fe6e078632..db217a347ae 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/sql/toolkit.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/sql/toolkit.py
@@ -1,9 +1,8 @@
 """Toolkit for interacting with an SQL database."""
 from typing import List
 
-from pydantic import Field
-
 from langchain.agents.agent_toolkits.base import BaseToolkit
+from langchain.pydantic_v1 import Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import BaseTool
 from langchain.tools.sql_database.tool import (
diff --git a/libs/langchain/langchain/agents/agent_toolkits/vectorstore/base.py b/libs/langchain/langchain/agents/agent_toolkits/vectorstore/base.py
index 5ae994a9f1a..82b8c90b9c7 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/vectorstore/base.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/vectorstore/base.py
@@ -22,7 +22,20 @@ def create_vectorstore_agent(
     agent_executor_kwargs: Optional[Dict[str, Any]] = None,
     **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
-    """Construct a VectorStore agent from an LLM and tools."""
+    """Construct a VectorStore agent from an LLM and tools.
+
+    Args:
+        llm (BaseLanguageModel): LLM that will be used by the agent
+        toolkit (VectorStoreToolkit): Set of tools for the agent
+        callback_manager (Optional[BaseCallbackManager], optional): Object to handle the callback [ Defaults to None. ]
+        prefix (str, optional): The prefix prompt for the agent. If not provided uses default PREFIX.
+        verbose (bool, optional): If you want to see the content of the scratchpad. [ Defaults to False ]
+        agent_executor_kwargs (Optional[Dict[str, Any]], optional): If there is any other parameter you want to send to the agent. [ Defaults to None ]
+        **kwargs: Additional named parameters to pass to the ZeroShotAgent.
+
+    Returns:
+        AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response
+    """  # noqa: E501
     tools = toolkit.get_tools()
     prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
     llm_chain = LLMChain(
@@ -50,7 +63,20 @@ def create_vectorstore_router_agent(
     agent_executor_kwargs: Optional[Dict[str, Any]] = None,
     **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
-    """Construct a VectorStore router agent from an LLM and tools."""
+    """Construct a VectorStore router agent from an LLM and tools.
+
+    Args:
+        llm (BaseLanguageModel): LLM that will be used by the agent
+        toolkit (VectorStoreRouterToolkit): Set of tools for the agent which have routing capability with multiple vector stores
+        callback_manager (Optional[BaseCallbackManager], optional): Object to handle the callback [ Defaults to None. ]
+        prefix (str, optional): The prefix prompt for the router agent. If not provided uses default ROUTER_PREFIX.
+        verbose (bool, optional): If you want to see the content of the scratchpad. [ Defaults to False ]
+        agent_executor_kwargs (Optional[Dict[str, Any]], optional): If there is any other parameter you want to send to the agent. [ Defaults to None ]
+        **kwargs: Additional named parameters to pass to the ZeroShotAgent.
+
+    Returns:
+        AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response.
+    """  # noqa: E501
     tools = toolkit.get_tools()
     prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
     llm_chain = LLMChain(
diff --git a/libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py b/libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py
index f706ed215b9..c9399a9359c 100644
--- a/libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py
+++ b/libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py
@@ -1,10 +1,9 @@
 """Toolkit for interacting with a vector store."""
 from typing import List
 
-from pydantic import BaseModel, Field
-
 from langchain.agents.agent_toolkits.base import BaseToolkit
 from langchain.llms.openai import OpenAI
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import BaseTool
 from langchain.tools.vectorstore.tool import (
diff --git a/libs/langchain/langchain/agents/chat/base.py b/libs/langchain/langchain/agents/chat/base.py
index 3217ff1fbb8..dfe97b222f7 100644
--- a/libs/langchain/langchain/agents/chat/base.py
+++ b/libs/langchain/langchain/agents/chat/base.py
@@ -1,7 +1,5 @@
 from typing import Any, List, Optional, Sequence, Tuple
 
-from pydantic import Field
-
 from langchain.agents.agent import Agent, AgentOutputParser
 from langchain.agents.chat.output_parser import ChatOutputParser
 from langchain.agents.chat.prompt import (
@@ -18,6 +16,7 @@ from langchain.prompts.chat import (
     HumanMessagePromptTemplate,
     SystemMessagePromptTemplate,
 )
+from langchain.pydantic_v1 import Field
 from langchain.schema import AgentAction, BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
diff --git a/libs/langchain/langchain/agents/conversational/base.py b/libs/langchain/langchain/agents/conversational/base.py
index 6b9b939410a..ab5f041ec9b 100644
--- a/libs/langchain/langchain/agents/conversational/base.py
+++ b/libs/langchain/langchain/agents/conversational/base.py
@@ -3,8 +3,6 @@ from __future__ import annotations
 
 from typing import Any, List, Optional, Sequence
 
-from pydantic import Field
-
 from langchain.agents.agent import Agent, AgentOutputParser
 from langchain.agents.agent_types import AgentType
 from langchain.agents.conversational.output_parser import ConvoOutputParser
@@ -13,6 +11,7 @@ from langchain.agents.utils import validate_tools_single_input
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
+from langchain.pydantic_v1 import Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
 
diff --git a/libs/langchain/langchain/agents/conversational_chat/base.py b/libs/langchain/langchain/agents/conversational_chat/base.py
index 1ffb7ff1244..5b99593b68e 100644
--- a/libs/langchain/langchain/agents/conversational_chat/base.py
+++ b/libs/langchain/langchain/agents/conversational_chat/base.py
@@ -3,8 +3,6 @@ from __future__ import annotations
 
 from typing import Any, List, Optional, Sequence, Tuple
 
-from pydantic import Field
-
 from langchain.agents.agent import Agent, AgentOutputParser
 from langchain.agents.conversational_chat.output_parser import ConvoOutputParser
 from langchain.agents.conversational_chat.prompt import (
@@ -21,6 +19,7 @@ from langchain.prompts.chat import (
     MessagesPlaceholder,
     SystemMessagePromptTemplate,
 )
+from langchain.pydantic_v1 import Field
 from langchain.schema import AgentAction, BaseOutputParser, BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage
diff --git a/libs/langchain/langchain/agents/mrkl/base.py b/libs/langchain/langchain/agents/mrkl/base.py
index a3939cbeec8..71b33a92f79 100644
--- a/libs/langchain/langchain/agents/mrkl/base.py
+++ b/libs/langchain/langchain/agents/mrkl/base.py
@@ -3,8 +3,6 @@ from __future__ import annotations
 
 from typing import Any, Callable, List, NamedTuple, Optional, Sequence
 
-from pydantic import Field
-
 from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
 from langchain.agents.agent_types import AgentType
 from langchain.agents.mrkl.output_parser import MRKLOutputParser
@@ -14,6 +12,7 @@ from langchain.agents.utils import validate_tools_single_input
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
+from langchain.pydantic_v1 import Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
 
diff --git a/libs/langchain/langchain/agents/openai_functions_agent/agent_token_buffer_memory.py b/libs/langchain/langchain/agents/openai_functions_agent/agent_token_buffer_memory.py
index d4e0fc9dc6f..aa244548042 100644
--- a/libs/langchain/langchain/agents/openai_functions_agent/agent_token_buffer_memory.py
+++ b/libs/langchain/langchain/agents/openai_functions_agent/agent_token_buffer_memory.py
@@ -18,8 +18,8 @@ class AgentTokenBufferMemory(BaseChatMemory):
     """The max number of tokens to keep in the buffer. 
     Once the buffer exceeds this many tokens, the oldest messages will be pruned."""
     return_messages: bool = True
-    output_key = "output"
-    intermediate_steps_key = "intermediate_steps"
+    output_key: str = "output"
+    intermediate_steps_key: str = "intermediate_steps"
 
     @property
     def buffer(self) -> List[BaseMessage]:
diff --git a/libs/langchain/langchain/agents/openai_functions_agent/base.py b/libs/langchain/langchain/agents/openai_functions_agent/base.py
index f3e2fc095ce..19d5ebbc433 100644
--- a/libs/langchain/langchain/agents/openai_functions_agent/base.py
+++ b/libs/langchain/langchain/agents/openai_functions_agent/base.py
@@ -4,8 +4,6 @@ from dataclasses import dataclass
 from json import JSONDecodeError
 from typing import Any, List, Optional, Sequence, Tuple, Union
 
-from pydantic import root_validator
-
 from langchain.agents import BaseSingleActionAgent
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
@@ -16,6 +14,7 @@ from langchain.prompts.chat import (
     HumanMessagePromptTemplate,
     MessagesPlaceholder,
 )
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import (
     AgentAction,
     AgentFinish,
diff --git a/libs/langchain/langchain/agents/openai_functions_multi_agent/base.py b/libs/langchain/langchain/agents/openai_functions_multi_agent/base.py
index 8cf16d81222..fcc51227fda 100644
--- a/libs/langchain/langchain/agents/openai_functions_multi_agent/base.py
+++ b/libs/langchain/langchain/agents/openai_functions_multi_agent/base.py
@@ -4,8 +4,6 @@ from dataclasses import dataclass
 from json import JSONDecodeError
 from typing import Any, List, Optional, Sequence, Tuple, Union
 
-from pydantic import root_validator
-
 from langchain.agents import BaseMultiActionAgent
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
@@ -16,6 +14,7 @@ from langchain.prompts.chat import (
     HumanMessagePromptTemplate,
     MessagesPlaceholder,
 )
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import (
     AgentAction,
     AgentFinish,
diff --git a/libs/langchain/langchain/agents/react/base.py b/libs/langchain/langchain/agents/react/base.py
index db4d67370ad..3d2e73ec20e 100644
--- a/libs/langchain/langchain/agents/react/base.py
+++ b/libs/langchain/langchain/agents/react/base.py
@@ -1,8 +1,6 @@
 """Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
 from typing import Any, List, Optional, Sequence
 
-from pydantic import Field
-
 from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
 from langchain.agents.agent_types import AgentType
 from langchain.agents.react.output_parser import ReActOutputParser
@@ -12,6 +10,7 @@ from langchain.agents.tools import Tool
 from langchain.agents.utils import validate_tools_single_input
 from langchain.docstore.base import Docstore
 from langchain.docstore.document import Document
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
diff --git a/libs/langchain/langchain/agents/self_ask_with_search/base.py b/libs/langchain/langchain/agents/self_ask_with_search/base.py
index 23071af8c90..f2db10b4f72 100644
--- a/libs/langchain/langchain/agents/self_ask_with_search/base.py
+++ b/libs/langchain/langchain/agents/self_ask_with_search/base.py
@@ -1,14 +1,13 @@
 """Chain that does self-ask with search."""
 from typing import Any, Sequence, Union
 
-from pydantic import Field
-
 from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
 from langchain.agents.agent_types import AgentType
 from langchain.agents.self_ask_with_search.output_parser import SelfAskOutputParser
 from langchain.agents.self_ask_with_search.prompt import PROMPT
 from langchain.agents.tools import Tool
 from langchain.agents.utils import validate_tools_single_input
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
diff --git a/libs/langchain/langchain/agents/structured_chat/base.py b/libs/langchain/langchain/agents/structured_chat/base.py
index 8e0eb3725f2..d325e3463ce 100644
--- a/libs/langchain/langchain/agents/structured_chat/base.py
+++ b/libs/langchain/langchain/agents/structured_chat/base.py
@@ -1,8 +1,6 @@
 import re
 from typing import Any, List, Optional, Sequence, Tuple
 
-from pydantic import Field
-
 from langchain.agents.agent import Agent, AgentOutputParser
 from langchain.agents.structured_chat.output_parser import (
     StructuredChatOutputParserWithRetries,
@@ -15,6 +13,7 @@ from langchain.prompts.chat import (
     HumanMessagePromptTemplate,
     SystemMessagePromptTemplate,
 )
+from langchain.pydantic_v1 import Field
 from langchain.schema import AgentAction, BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import BaseTool
diff --git a/libs/langchain/langchain/agents/structured_chat/output_parser.py b/libs/langchain/langchain/agents/structured_chat/output_parser.py
index 6d40848e0ed..3049a531fab 100644
--- a/libs/langchain/langchain/agents/structured_chat/output_parser.py
+++ b/libs/langchain/langchain/agents/structured_chat/output_parser.py
@@ -5,11 +5,10 @@ import logging
 import re
 from typing import Optional, Union
 
-from pydantic import Field
-
 from langchain.agents.agent import AgentOutputParser
 from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS
 from langchain.output_parsers import OutputFixingParser
+from langchain.pydantic_v1 import Field
 from langchain.schema import AgentAction, AgentFinish, OutputParserException
 from langchain.schema.language_model import BaseLanguageModel
 
@@ -19,12 +18,14 @@ logger = logging.getLogger(__name__)
 class StructuredChatOutputParser(AgentOutputParser):
     """Output parser for the structured chat agent."""
 
+    pattern = re.compile(r"```(?:json)?\n(.*?)```", re.DOTALL)
+
     def get_format_instructions(self) -> str:
         return FORMAT_INSTRUCTIONS
 
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
         try:
-            action_match = re.search(r"```(.*?)```?", text, re.DOTALL)
+            action_match = self.pattern.search(text)
             if action_match is not None:
                 response = json.loads(action_match.group(1).strip(), strict=False)
                 if isinstance(response, list):
diff --git a/libs/langchain/langchain/agents/tools.py b/libs/langchain/langchain/agents/tools.py
index e13acc42bd2..91e80bc4ee4 100644
--- a/libs/langchain/langchain/agents/tools.py
+++ b/libs/langchain/langchain/agents/tools.py
@@ -11,8 +11,8 @@ from langchain.tools.base import BaseTool, Tool, tool
 class InvalidTool(BaseTool):
     """Tool that is run when invalid tool name is encountered by agent."""
 
-    name = "invalid_tool"
-    description = "Called when tool name is invalid. Suggests valid tool names."
+    name: str = "invalid_tool"
+    description: str = "Called when tool name is invalid. Suggests valid tool names."
 
     def _run(
         self,
diff --git a/libs/langchain/langchain/cache.py b/libs/langchain/langchain/cache.py
index 2136acaad18..a78c0608011 100644
--- a/libs/langchain/langchain/cache.py
+++ b/libs/langchain/langchain/cache.py
@@ -216,10 +216,25 @@ class SQLiteCache(SQLAlchemyCache):
 class RedisCache(BaseCache):
     """Cache that uses Redis as a backend."""
 
-    # TODO - implement a TTL policy in Redis
+    def __init__(self, redis_: Any, *, ttl: Optional[int] = None):
+        """
+        Initialize an instance of RedisCache.
 
-    def __init__(self, redis_: Any):
-        """Initialize by passing in Redis instance."""
+        This method initializes an object with Redis caching capabilities.
+        It takes a `redis_` parameter, which should be an instance of a Redis
+        client class, allowing the object to interact with a Redis
+        server for caching purposes.
+
+        Parameters:
+            redis_ (Any): An instance of a Redis client class
+                (e.g., redis.Redis) used for caching.
+                This allows the object to communicate with a
+                Redis server for caching operations.
+            ttl (int, optional): Time-to-live (TTL) for cached items in seconds.
+                If provided, it sets the time duration for how long cached
+                items will remain valid. If not provided, cached items will not
+                have an automatic expiration.
+        """
         try:
             from redis import Redis
         except ImportError:
@@ -230,6 +245,7 @@ class RedisCache(BaseCache):
         if not isinstance(redis_, Redis):
             raise ValueError("Please pass in Redis object.")
         self.redis = redis_
+        self.ttl = ttl
 
     def _key(self, prompt: str, llm_string: str) -> str:
         """Compute key from prompt and llm_string"""
@@ -261,12 +277,19 @@ class RedisCache(BaseCache):
                 return
         # Write to a Redis HASH
         key = self._key(prompt, llm_string)
-        self.redis.hset(
-            key,
-            mapping={
-                str(idx): generation.text for idx, generation in enumerate(return_val)
-            },
-        )
+
+        with self.redis.pipeline() as pipe:
+            pipe.hset(
+                key,
+                mapping={
+                    str(idx): generation.text
+                    for idx, generation in enumerate(return_val)
+                },
+            )
+            if self.ttl is not None:
+                pipe.expire(key, self.ttl)
+
+            pipe.execute()
 
     def clear(self, **kwargs: Any) -> None:
         """Clear cache. If `asynchronous` is True, flush asynchronously."""
diff --git a/libs/langchain/langchain/callbacks/__init__.py b/libs/langchain/langchain/callbacks/__init__.py
index 1262222239a..8398741be34 100644
--- a/libs/langchain/langchain/callbacks/__init__.py
+++ b/libs/langchain/langchain/callbacks/__init__.py
@@ -18,6 +18,7 @@ from langchain.callbacks.file import FileCallbackHandler
 from langchain.callbacks.flyte_callback import FlyteCallbackHandler
 from langchain.callbacks.human import HumanApprovalCallbackHandler
 from langchain.callbacks.infino_callback import InfinoCallbackHandler
+from langchain.callbacks.labelstudio_callback import LabelStudioCallbackHandler
 from langchain.callbacks.manager import (
     get_openai_callback,
     tracing_enabled,
@@ -68,4 +69,5 @@ __all__ = [
     "wandb_tracing_enabled",
     "FlyteCallbackHandler",
     "SageMakerCallbackHandler",
+    "LabelStudioCallbackHandler",
 ]
diff --git a/libs/langchain/langchain/callbacks/argilla_callback.py b/libs/langchain/langchain/callbacks/argilla_callback.py
index 1d550461fa1..84a1596386f 100644
--- a/libs/langchain/langchain/callbacks/argilla_callback.py
+++ b/libs/langchain/langchain/callbacks/argilla_callback.py
@@ -2,6 +2,8 @@ import os
 import warnings
 from typing import Any, Dict, List, Optional, Union
 
+from packaging.version import parse
+
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.schema import AgentAction, AgentFinish, LLMResult
 
@@ -19,11 +21,10 @@ class ArgillaCallbackHandler(BaseCallbackHandler):
             default workspace will be used.
         api_url: URL of the Argilla Server that we want to use, and where the
             `FeedbackDataset` lives in. Defaults to `None`, which means that either
-            `ARGILLA_API_URL` environment variable or the default http://localhost:6900
-            will be used.
+            `ARGILLA_API_URL` environment variable or the default will be used.
         api_key: API Key to connect to the Argilla Server. Defaults to `None`, which
             means that either `ARGILLA_API_KEY` environment variable or the default
-            `argilla.apikey` will be used.
+            will be used.
 
     Raises:
         ImportError: if the `argilla` package is not installed.
@@ -51,6 +52,12 @@ class ArgillaCallbackHandler(BaseCallbackHandler):
         "Argilla, no doubt about it."
     """
 
+    REPO_URL: str = "https://github.com/argilla-io/argilla"
+    ISSUES_URL: str = f"{REPO_URL}/issues"
+    BLOG_URL: str = "https://docs.argilla.io/en/latest/guides/llms/practical_guides/use_argilla_callback_in_langchain.html"  # noqa: E501
+
+    DEFAULT_API_URL: str = "http://localhost:6900"
+
     def __init__(
         self,
         dataset_name: str,
@@ -70,11 +77,10 @@ class ArgillaCallbackHandler(BaseCallbackHandler):
                 default workspace will be used.
             api_url: URL of the Argilla Server that we want to use, and where the
                 `FeedbackDataset` lives in. Defaults to `None`, which means that either
-                `ARGILLA_API_URL` environment variable or the default
-                http://localhost:6900 will be used.
+                `ARGILLA_API_URL` environment variable or the default will be used.
             api_key: API Key to connect to the Argilla Server. Defaults to `None`, which
                 means that either `ARGILLA_API_KEY` environment variable or the default
-                `argilla.apikey` will be used.
+                will be used.
 
         Raises:
             ImportError: if the `argilla` package is not installed.
@@ -87,41 +93,58 @@ class ArgillaCallbackHandler(BaseCallbackHandler):
         # Import Argilla (not via `import_argilla` to keep hints in IDEs)
         try:
             import argilla as rg  # noqa: F401
+
+            self.ARGILLA_VERSION = rg.__version__
         except ImportError:
             raise ImportError(
                 "To use the Argilla callback manager you need to have the `argilla` "
                 "Python package installed. Please install it with `pip install argilla`"
             )
 
+        # Check whether the Argilla version is compatible
+        if parse(self.ARGILLA_VERSION) < parse("1.8.0"):
+            raise ImportError(
+                f"The installed `argilla` version is {self.ARGILLA_VERSION} but "
+                "`ArgillaCallbackHandler` requires at least version 1.8.0. Please "
+                "upgrade `argilla` with `pip install --upgrade argilla`."
+            )
+
         # Show a warning message if Argilla will assume the default values will be used
         if api_url is None and os.getenv("ARGILLA_API_URL") is None:
             warnings.warn(
                 (
                     "Since `api_url` is None, and the env var `ARGILLA_API_URL` is not"
-                    " set, it will default to `http://localhost:6900`."
+                    f" set, it will default to `{self.DEFAULT_API_URL}`, which is the"
+                    " default API URL in Argilla Quickstart."
                 ),
             )
+            api_url = self.DEFAULT_API_URL
+
         if api_key is None and os.getenv("ARGILLA_API_KEY") is None:
+            self.DEFAULT_API_KEY = (
+                "admin.apikey"
+                if parse(self.ARGILLA_VERSION) < parse("1.11.0")
+                else "owner.apikey"
+            )
+
             warnings.warn(
                 (
                     "Since `api_key` is None, and the env var `ARGILLA_API_KEY` is not"
-                    " set, it will default to `argilla.apikey`."
+                    f" set, it will default to `{self.DEFAULT_API_KEY}`, which is the"
+                    " default API key in Argilla Quickstart."
                 ),
             )
+            api_url = self.DEFAULT_API_URL
 
         # Connect to Argilla with the provided credentials, if applicable
         try:
-            rg.init(
-                api_key=api_key,
-                api_url=api_url,
-            )
+            rg.init(api_key=api_key, api_url=api_url)
         except Exception as e:
             raise ConnectionError(
                 f"Could not connect to Argilla with exception: '{e}'.\n"
                 "Please check your `api_key` and `api_url`, and make sure that "
                 "the Argilla server is up and running. If the problem persists "
-                "please report it to https://github.com/argilla-io/argilla/issues "
-                "with the label `langchain`."
+                f"please report it to {self.ISSUES_URL} as an `integration` issue."
             ) from e
 
         # Set the Argilla variables
@@ -130,46 +153,47 @@ class ArgillaCallbackHandler(BaseCallbackHandler):
 
         # Retrieve the `FeedbackDataset` from Argilla (without existing records)
         try:
+            extra_args = {}
+            if parse(self.ARGILLA_VERSION) < parse("1.14.0"):
+                warnings.warn(
+                    f"You have Argilla {self.ARGILLA_VERSION}, but Argilla 1.14.0 or"
+                    " higher is recommended.",
+                    UserWarning,
+                )
+                extra_args = {"with_records": False}
             self.dataset = rg.FeedbackDataset.from_argilla(
                 name=self.dataset_name,
                 workspace=self.workspace_name,
-                with_records=False,
+                **extra_args,
             )
         except Exception as e:
             raise FileNotFoundError(
-                "`FeedbackDataset` retrieval from Argilla failed with exception:"
-                f" '{e}'.\nPlease check that the dataset with"
-                f" name={self.dataset_name} in the"
+                f"`FeedbackDataset` retrieval from Argilla failed with exception `{e}`."
+                f"\nPlease check that the dataset with name={self.dataset_name} in the"
                 f" workspace={self.workspace_name} exists in advance. If you need help"
                 " on how to create a `langchain`-compatible `FeedbackDataset` in"
-                " Argilla, please visit"
-                " https://docs.argilla.io/en/latest/guides/llms/practical_guides/use_argilla_callback_in_langchain.html."  # noqa: E501
-                " If the problem persists please report it to"
-                " https://github.com/argilla-io/argilla/issues with the label"
-                " `langchain`."
+                f" Argilla, please visit {self.BLOG_URL}. If the problem persists"
+                f" please report it to {self.ISSUES_URL} as an `integration` issue."
             ) from e
 
         supported_fields = ["prompt", "response"]
         if supported_fields != [field.name for field in self.dataset.fields]:
             raise ValueError(
-                f"`FeedbackDataset` with name={self.dataset_name} in the"
-                f" workspace={self.workspace_name} "
-                "had fields that are not supported yet for the `langchain` integration."
-                " Supported fields are: "
-                f"{supported_fields}, and the current `FeedbackDataset` fields are"
-                f" {[field.name for field in self.dataset.fields]}. "
-                "For more information on how to create a `langchain`-compatible"
-                " `FeedbackDataset` in Argilla, please visit"
-                " https://docs.argilla.io/en/latest/guides/llms/practical_guides/use_argilla_callback_in_langchain.html."  # noqa: E501
+                f"`FeedbackDataset` with name={self.dataset_name} in the workspace="
+                f"{self.workspace_name} had fields that are not supported yet for the"
+                f"`langchain` integration. Supported fields are: {supported_fields},"
+                f" and the current `FeedbackDataset` fields are {[field.name for field in self.dataset.fields]}."  # noqa: E501
+                " For more information on how to create a `langchain`-compatible"
+                f" `FeedbackDataset` in Argilla, please visit {self.BLOG_URL}."
             )
 
         self.prompts: Dict[str, List[str]] = {}
 
         warnings.warn(
             (
-                "The `ArgillaCallbackHandler` is currently in beta and is subject to "
-                "change based on updates to `langchain`. Please report any issues to "
-                "https://github.com/argilla-io/argilla/issues with the tag `langchain`."
+                "The `ArgillaCallbackHandler` is currently in beta and is subject to"
+                " change based on updates to `langchain`. Please report any issues to"
+                f" {self.ISSUES_URL} as an `integration` issue."
             ),
         )
 
@@ -205,12 +229,13 @@ class ArgillaCallbackHandler(BaseCallbackHandler):
                 ]
             )
 
-        # Push the records to Argilla
-        self.dataset.push_to_argilla()
-
         # Pop current run from `self.runs`
         self.prompts.pop(str(kwargs["run_id"]))
 
+        if parse(self.ARGILLA_VERSION) < parse("1.14.0"):
+            # Push the records to Argilla
+            self.dataset.push_to_argilla()
+
     def on_llm_error(
         self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
     ) -> None:
@@ -278,15 +303,16 @@ class ArgillaCallbackHandler(BaseCallbackHandler):
                     ]
                 )
 
-        # Push the records to Argilla
-        self.dataset.push_to_argilla()
-
         # Pop current run from `self.runs`
         if str(kwargs["parent_run_id"]) in self.prompts:
             self.prompts.pop(str(kwargs["parent_run_id"]))
         if str(kwargs["run_id"]) in self.prompts:
             self.prompts.pop(str(kwargs["run_id"]))
 
+        if parse(self.ARGILLA_VERSION) < parse("1.14.0"):
+            # Push the records to Argilla
+            self.dataset.push_to_argilla()
+
     def on_chain_error(
         self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
     ) -> None:
diff --git a/libs/langchain/langchain/callbacks/base.py b/libs/langchain/langchain/callbacks/base.py
index 71025955f87..c03633e2d4d 100644
--- a/libs/langchain/langchain/callbacks/base.py
+++ b/libs/langchain/langchain/callbacks/base.py
@@ -4,6 +4,8 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union
 from uuid import UUID
 
+from tenacity import RetryCallState
+
 if TYPE_CHECKING:
     from langchain.schema.agent import AgentAction, AgentFinish
     from langchain.schema.document import Document
@@ -222,6 +224,16 @@ class RunManagerMixin:
     ) -> Any:
         """Run on arbitrary text."""
 
+    def on_retry(
+        self,
+        retry_state: RetryCallState,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Run on a retry event."""
+
 
 class BaseCallbackHandler(
     LLMManagerMixin,
@@ -414,6 +426,16 @@ class AsyncCallbackHandler(BaseCallbackHandler):
     ) -> None:
         """Run on arbitrary text."""
 
+    async def on_retry(
+        self,
+        retry_state: RetryCallState,
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Run on a retry event."""
+
     async def on_agent_action(
         self,
         action: AgentAction,
diff --git a/libs/langchain/langchain/callbacks/context_callback.py b/libs/langchain/langchain/callbacks/context_callback.py
index 4441f8f9e15..d95db1eceea 100644
--- a/libs/langchain/langchain/callbacks/context_callback.py
+++ b/libs/langchain/langchain/callbacks/context_callback.py
@@ -37,7 +37,7 @@ class ContextCallbackHandler(BaseCallbackHandler):
 
     Keyword Args:
         token (optional): The token with which to authenticate requests to Context.
-            Visit https://go.getcontext.ai/settings to generate a token.
+            Visit https://with.context.ai/settings to generate a token.
             If not provided, the value of the `CONTEXT_TOKEN` environment
             variable will be used.
 
diff --git a/libs/langchain/langchain/callbacks/labelstudio_callback.py b/libs/langchain/langchain/callbacks/labelstudio_callback.py
new file mode 100644
index 00000000000..50e468aa570
--- /dev/null
+++ b/libs/langchain/langchain/callbacks/labelstudio_callback.py
@@ -0,0 +1,392 @@
+import os
+import warnings
+from datetime import datetime
+from enum import Enum
+from typing import Any, Dict, List, Optional, Tuple, Union
+from uuid import UUID
+
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.schema import (
+    AgentAction,
+    AgentFinish,
+    BaseMessage,
+    ChatMessage,
+    Generation,
+    LLMResult,
+)
+
+
+class LabelStudioMode(Enum):
+    PROMPT = "prompt"
+    CHAT = "chat"
+
+
+def get_default_label_configs(
+    mode: Union[str, LabelStudioMode]
+) -> Tuple[str, LabelStudioMode]:
+    _default_label_configs = {
+        LabelStudioMode.PROMPT.value: """
+<View>
+<Style>
+    .prompt-box {
+        background-color: white;
+        border-radius: 10px;
+        box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1);
+        padding: 20px;
+    }
+</Style>
+<View className="root">
+    <View className="prompt-box">
+        <Text name="prompt" value="$prompt"/>
+    </View>
+    <TextArea name="response" toName="prompt"
+              maxSubmissions="1" editable="true"
+              required="true"/>
+</View>
+<Header value="Rate the response:"/>
+<Rating name="rating" toName="prompt"/>
+</View>""",
+        LabelStudioMode.CHAT.value: """
+<View>
+<View className="root">
+     <Paragraphs name="dialogue"
+               value="$prompt"
+               layout="dialogue"
+               textKey="content"
+               nameKey="role"
+               granularity="sentence"/>
+  <Header value="Final response:"/>
+    <TextArea name="response" toName="dialogue"
+              maxSubmissions="1" editable="true"
+              required="true"/>
+</View>
+<Header value="Rate the response:"/>
+<Rating name="rating" toName="dialogue"/>
+</View>""",
+    }
+
+    if isinstance(mode, str):
+        mode = LabelStudioMode(mode)
+
+    return _default_label_configs[mode.value], mode
+
+
+class LabelStudioCallbackHandler(BaseCallbackHandler):
+    """Label Studio callback handler.
+    Provides the ability to send predictions to Label Studio
+    for human evaluation, feedback and annotation.
+
+    Parameters:
+        api_key: Label Studio API key
+        url: Label Studio URL
+        project_id: Label Studio project ID
+        project_name: Label Studio project name
+        project_config: Label Studio project config (XML)
+        mode: Label Studio mode ("prompt" or "chat")
+
+    Examples:
+        >>> from langchain.llms import OpenAI
+        >>> from langchain.callbacks import LabelStudioCallbackHandler
+        >>> handler = LabelStudioCallbackHandler(
+        ...             api_key='<your_key_here>',
+        ...             url='http://localhost:8080',
+        ...             project_name='LangChain-%Y-%m-%d',
+        ...             mode='prompt'
+        ... )
+        >>> llm = OpenAI(callbacks=[handler])
+        >>> llm.predict('Tell me a story about a dog.')
+    """
+
+    DEFAULT_PROJECT_NAME: str = "LangChain-%Y-%m-%d"
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        url: Optional[str] = None,
+        project_id: Optional[int] = None,
+        project_name: str = DEFAULT_PROJECT_NAME,
+        project_config: Optional[str] = None,
+        mode: Union[str, LabelStudioMode] = LabelStudioMode.PROMPT,
+    ):
+        super().__init__()
+
+        # Import LabelStudio SDK
+        try:
+            import label_studio_sdk as ls
+        except ImportError:
+            raise ImportError(
+                f"You're using {self.__class__.__name__} in your code,"
+                f" but you don't have the LabelStudio SDK "
+                f"Python package installed or upgraded to the latest version. "
+                f"Please run `pip install -U label-studio-sdk`"
+                f" before using this callback."
+            )
+
+        # Check if Label Studio API key is provided
+        if not api_key:
+            if os.getenv("LABEL_STUDIO_API_KEY"):
+                api_key = str(os.getenv("LABEL_STUDIO_API_KEY"))
+            else:
+                raise ValueError(
+                    f"You're using {self.__class__.__name__} in your code,"
+                    f" Label Studio API key is not provided. "
+                    f"Please provide Label Studio API key: "
+                    f"go to the Label Studio instance, navigate to "
+                    f"Account & Settings -> Access Token and copy the key. "
+                    f"Use the key as a parameter for the callback: "
+                    f"{self.__class__.__name__}"
+                    f"(label_studio_api_key='<your_key_here>', ...) or "
+                    f"set the environment variable LABEL_STUDIO_API_KEY=<your_key_here>"
+                )
+        self.api_key = api_key
+
+        if not url:
+            if os.getenv("LABEL_STUDIO_URL"):
+                url = os.getenv("LABEL_STUDIO_URL")
+            else:
+                warnings.warn(
+                    f"Label Studio URL is not provided, "
+                    f"using default URL: {ls.LABEL_STUDIO_DEFAULT_URL}"
+                    f"If you want to provide your own URL, use the parameter: "
+                    f"{self.__class__.__name__}"
+                    f"(label_studio_url='<your_url_here>', ...) "
+                    f"or set the environment variable LABEL_STUDIO_URL=<your_url_here>"
+                )
+                url = ls.LABEL_STUDIO_DEFAULT_URL
+        self.url = url
+
+        # Maps run_id to prompts
+        self.payload: Dict[str, Dict] = {}
+
+        self.ls_client = ls.Client(url=self.url, api_key=self.api_key)
+        self.project_name = project_name
+        if project_config:
+            self.project_config = project_config
+            self.mode = None
+        else:
+            self.project_config, self.mode = get_default_label_configs(mode)
+
+        self.project_id = project_id or os.getenv("LABEL_STUDIO_PROJECT_ID")
+        if self.project_id is not None:
+            self.ls_project = self.ls_client.get_project(int(self.project_id))
+        else:
+            project_title = datetime.today().strftime(self.project_name)
+            existing_projects = self.ls_client.get_projects(title=project_title)
+            if existing_projects:
+                self.ls_project = existing_projects[0]
+                self.project_id = self.ls_project.id
+            else:
+                self.ls_project = self.ls_client.create_project(
+                    title=project_title, label_config=self.project_config
+                )
+                self.project_id = self.ls_project.id
+        self.parsed_label_config = self.ls_project.parsed_label_config
+
+        # Find the first TextArea tag
+        # "from_name", "to_name", "value" will be used to create predictions
+        self.from_name, self.to_name, self.value, self.input_type = (
+            None,
+            None,
+            None,
+            None,
+        )
+        for tag_name, tag_info in self.parsed_label_config.items():
+            if tag_info["type"] == "TextArea":
+                self.from_name = tag_name
+                self.to_name = tag_info["to_name"][0]
+                self.value = tag_info["inputs"][0]["value"]
+                self.input_type = tag_info["inputs"][0]["type"]
+                break
+        if not self.from_name:
+            error_message = (
+                f'Label Studio project "{self.project_name}" '
+                f"does not have a TextArea tag. "
+                f"Please add a TextArea tag to the project."
+            )
+            if self.mode == LabelStudioMode.PROMPT:
+                error_message += (
+                    "\nHINT: go to project Settings -> "
+                    "Labeling Interface -> Browse Templates"
+                    ' and select "Generative AI -> '
+                    'Supervised Language Model Fine-tuning" template.'
+                )
+            else:
+                error_message += (
+                    "\nHINT: go to project Settings -> "
+                    "Labeling Interface -> Browse Templates"
+                    " and check available templates under "
+                    '"Generative AI" section.'
+                )
+            raise ValueError(error_message)
+
+    def add_prompts_generations(
+        self, run_id: str, generations: List[List[Generation]]
+    ) -> None:
+        # Create tasks in Label Studio
+        tasks = []
+        prompts = self.payload[run_id]["prompts"]
+        model_version = (
+            self.payload[run_id]["kwargs"]
+            .get("invocation_params", {})
+            .get("model_name")
+        )
+        for prompt, generation in zip(prompts, generations):
+            tasks.append(
+                {
+                    "data": {
+                        self.value: prompt,
+                        "run_id": run_id,
+                    },
+                    "predictions": [
+                        {
+                            "result": [
+                                {
+                                    "from_name": self.from_name,
+                                    "to_name": self.to_name,
+                                    "type": "textarea",
+                                    "value": {"text": [g.text for g in generation]},
+                                }
+                            ],
+                            "model_version": model_version,
+                        }
+                    ],
+                }
+            )
+        self.ls_project.import_tasks(tasks)
+
+    def on_llm_start(
+        self,
+        serialized: Dict[str, Any],
+        prompts: List[str],
+        **kwargs: Any,
+    ) -> None:
+        """Save the prompts in memory when an LLM starts."""
+        if self.input_type != "Text":
+            raise ValueError(
+                f'\nLabel Studio project "{self.project_name}" '
+                f"has an input type <{self.input_type}>. "
+                f'To make it work with the mode="chat", '
+                f"the input type should be <Text>.\n"
+                f"Read more here https://labelstud.io/tags/text"
+            )
+        run_id = str(kwargs["run_id"])
+        self.payload[run_id] = {"prompts": prompts, "kwargs": kwargs}
+
+    def _get_message_role(self, message: BaseMessage) -> str:
+        """Get the role of the message."""
+        if isinstance(message, ChatMessage):
+            return message.role
+        else:
+            return message.__class__.__name__
+
+    def on_chat_model_start(
+        self,
+        serialized: Dict[str, Any],
+        messages: List[List[BaseMessage]],
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Save the prompts in memory when an LLM starts."""
+        if self.input_type != "Paragraphs":
+            raise ValueError(
+                f'\nLabel Studio project "{self.project_name}" '
+                f"has an input type <{self.input_type}>. "
+                f'To make it work with the mode="chat", '
+                f"the input type should be <Paragraphs>.\n"
+                f"Read more here https://labelstud.io/tags/paragraphs"
+            )
+
+        prompts = []
+        for message_list in messages:
+            dialog = []
+            for message in message_list:
+                dialog.append(
+                    {
+                        "role": self._get_message_role(message),
+                        "content": message.content,
+                    }
+                )
+            prompts.append(dialog)
+        self.payload[str(run_id)] = {
+            "prompts": prompts,
+            "tags": tags,
+            "metadata": metadata,
+            "run_id": run_id,
+            "parent_run_id": parent_run_id,
+            "kwargs": kwargs,
+        }
+
+    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
+        """Do nothing when a new token is generated."""
+        pass
+
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        """Create a new Label Studio task for each prompt and generation."""
+        run_id = str(kwargs["run_id"])
+
+        # Submit results to Label Studio
+        self.add_prompts_generations(run_id, response.generations)
+
+        # Pop current run from `self.runs`
+        self.payload.pop(run_id)
+
+    def on_llm_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Do nothing when LLM outputs an error."""
+        pass
+
+    def on_chain_start(
+        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+    ) -> None:
+        pass
+
+    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
+        pass
+
+    def on_chain_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Do nothing when LLM chain outputs an error."""
+        pass
+
+    def on_tool_start(
+        self,
+        serialized: Dict[str, Any],
+        input_str: str,
+        **kwargs: Any,
+    ) -> None:
+        """Do nothing when tool starts."""
+        pass
+
+    def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
+        """Do nothing when agent takes a specific action."""
+        pass
+
+    def on_tool_end(
+        self,
+        output: str,
+        observation_prefix: Optional[str] = None,
+        llm_prefix: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Do nothing when tool ends."""
+        pass
+
+    def on_tool_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> None:
+        """Do nothing when tool outputs an error."""
+        pass
+
+    def on_text(self, text: str, **kwargs: Any) -> None:
+        """Do nothing"""
+        pass
+
+    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
+        """Do nothing"""
+        pass
diff --git a/libs/langchain/langchain/callbacks/tracers/evaluation.py b/libs/langchain/langchain/callbacks/tracers/evaluation.py
index cd8252b49f7..877836b0a5c 100644
--- a/libs/langchain/langchain/callbacks/tracers/evaluation.py
+++ b/libs/langchain/langchain/callbacks/tracers/evaluation.py
@@ -62,7 +62,7 @@ class EvaluatorCallbackHandler(BaseTracer):
         The LangSmith project name to be organize eval chain runs under.
     """
 
-    name = "evaluator_callback_handler"
+    name: str = "evaluator_callback_handler"
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/callbacks/tracers/run_collector.py b/libs/langchain/langchain/callbacks/tracers/run_collector.py
index 26e0174fe88..dc0d6eab1e5 100644
--- a/libs/langchain/langchain/callbacks/tracers/run_collector.py
+++ b/libs/langchain/langchain/callbacks/tracers/run_collector.py
@@ -19,7 +19,7 @@ class RunCollectorCallbackHandler(BaseTracer):
         The ID of the example being traced. It can be either a UUID or a string.
     """
 
-    name = "run-collector_callback_handler"
+    name: str = "run-collector_callback_handler"
 
     def __init__(
         self, example_id: Optional[Union[UUID, str]] = None, **kwargs: Any
diff --git a/libs/langchain/langchain/callbacks/tracers/schemas.py b/libs/langchain/langchain/callbacks/tracers/schemas.py
index 41061e7364b..37f33763ff1 100644
--- a/libs/langchain/langchain/callbacks/tracers/schemas.py
+++ b/libs/langchain/langchain/callbacks/tracers/schemas.py
@@ -8,8 +8,8 @@ from uuid import UUID
 
 from langsmith.schemas import RunBase as BaseRunV2
 from langsmith.schemas import RunTypeEnum as RunTypeEnumDep
-from pydantic import BaseModel, Field, root_validator
 
+from langchain.pydantic_v1 import BaseModel, Field, root_validator
 from langchain.schema import LLMResult
 
 
diff --git a/libs/langchain/langchain/callbacks/tracers/stdout.py b/libs/langchain/langchain/callbacks/tracers/stdout.py
index 2c082196d74..cd05f1898de 100644
--- a/libs/langchain/langchain/callbacks/tracers/stdout.py
+++ b/libs/langchain/langchain/callbacks/tracers/stdout.py
@@ -37,14 +37,14 @@ def elapsed(run: Any) -> str:
     elapsed_time = run.end_time - run.start_time
     milliseconds = elapsed_time.total_seconds() * 1000
     if milliseconds < 1000:
-        return f"{milliseconds}ms"
+        return f"{milliseconds:.0f}ms"
     return f"{(milliseconds / 1000):.2f}s"
 
 
 class FunctionCallbackHandler(BaseTracer):
     """Tracer that calls a function with a single str parameter."""
 
-    name = "function_callback_handler"
+    name: str = "function_callback_handler"
 
     def __init__(self, function: Callable[[str], None], **kwargs: Any) -> None:
         super().__init__(**kwargs)
@@ -78,28 +78,31 @@ class FunctionCallbackHandler(BaseTracer):
     # logging methods
     def _on_chain_start(self, run: Run) -> None:
         crumbs = self.get_breadcrumbs(run)
+        run_type = run.run_type.capitalize()
         self.function_callback(
             f"{get_colored_text('[chain/start]', color='green')} "
-            + get_bolded_text(f"[{crumbs}] Entering Chain run with input:\n")
+            + get_bolded_text(f"[{crumbs}] Entering {run_type} run with input:\n")
             + f"{try_json_stringify(run.inputs, '[inputs]')}"
         )
 
     def _on_chain_end(self, run: Run) -> None:
         crumbs = self.get_breadcrumbs(run)
+        run_type = run.run_type.capitalize()
         self.function_callback(
             f"{get_colored_text('[chain/end]', color='blue')} "
             + get_bolded_text(
-                f"[{crumbs}] [{elapsed(run)}] Exiting Chain run with output:\n"
+                f"[{crumbs}] [{elapsed(run)}] Exiting {run_type} run with output:\n"
             )
             + f"{try_json_stringify(run.outputs, '[outputs]')}"
         )
 
     def _on_chain_error(self, run: Run) -> None:
         crumbs = self.get_breadcrumbs(run)
+        run_type = run.run_type.capitalize()
         self.function_callback(
             f"{get_colored_text('[chain/error]', color='red')} "
             + get_bolded_text(
-                f"[{crumbs}] [{elapsed(run)}] Chain run errored with error:\n"
+                f"[{crumbs}] [{elapsed(run)}] {run_type} run errored with error:\n"
             )
             + f"{try_json_stringify(run.error, '[error]')}"
         )
@@ -169,7 +172,7 @@ class FunctionCallbackHandler(BaseTracer):
 class ConsoleCallbackHandler(FunctionCallbackHandler):
     """Tracer that prints to the console."""
 
-    name = "console_callback_handler"
+    name: str = "console_callback_handler"
 
     def __init__(self, **kwargs: Any) -> None:
         super().__init__(function=print, **kwargs)
diff --git a/libs/langchain/langchain/callbacks/tracers/wandb.py b/libs/langchain/langchain/callbacks/tracers/wandb.py
index d747e765e3e..05f417723d6 100644
--- a/libs/langchain/langchain/callbacks/tracers/wandb.py
+++ b/libs/langchain/langchain/callbacks/tracers/wandb.py
@@ -27,12 +27,21 @@ if TYPE_CHECKING:
 PRINT_WARNINGS = True
 
 
-def _serialize_inputs(run_inputs: dict) -> dict:
-    if "input_documents" in run_inputs:
-        docs = run_inputs["input_documents"]
-        return {f"input_document_{i}": doc.json() for i, doc in enumerate(docs)}
-    else:
-        return run_inputs
+def _serialize_io(run_inputs: dict) -> dict:
+    from google.protobuf.json_format import MessageToJson
+    from google.protobuf.message import Message
+
+    serialized_inputs = {}
+    for key, value in run_inputs.items():
+        if isinstance(value, Message):
+            serialized_inputs[key] = MessageToJson(value)
+        elif key == "input_documents":
+            serialized_inputs.update(
+                {f"input_document_{i}": doc.json() for i, doc in enumerate(value)}
+            )
+        else:
+            serialized_inputs[key] = value
+    return serialized_inputs
 
 
 class RunProcessor:
@@ -117,7 +126,7 @@ class RunProcessor:
 
         base_span.results = [
             self.trace_tree.Result(
-                inputs=_serialize_inputs(run.inputs), outputs=run.outputs
+                inputs=_serialize_io(run.inputs), outputs=_serialize_io(run.outputs)
             )
         ]
         base_span.child_spans = [
@@ -139,7 +148,7 @@ class RunProcessor:
         base_span = self._convert_run_to_wb_span(run)
         base_span.results = [
             self.trace_tree.Result(
-                inputs=_serialize_inputs(run.inputs), outputs=run.outputs
+                inputs=_serialize_io(run.inputs), outputs=_serialize_io(run.outputs)
             )
         ]
         base_span.child_spans = [
@@ -476,29 +485,25 @@ class WandbTracer(BaseTracer):
         If not, will start a new run with the provided run_args.
         """
         if self._wandb.run is None:
-            # Make a shallow copy of the run args, so we don't modify the original
             run_args = self._run_args or {}  # type: ignore
             run_args: dict = {**run_args}  # type: ignore
 
-            # Prefer to run in silent mode since W&B has a lot of output
-            # which can be undesirable when dealing with text-based models.
             if "settings" not in run_args:  # type: ignore
                 run_args["settings"] = {"silent": True}  # type: ignore
 
-            # Start the run and add the stream table
             self._wandb.init(**run_args)
-            if self._wandb.run is not None:
-                if should_print_url:
-                    run_url = self._wandb.run.settings.run_url
-                    self._wandb.termlog(
-                        f"Streaming LangChain activity to W&B at {run_url}\n"
-                        "`WandbTracer` is currently in beta.\n"
-                        "Please report any issues to "
-                        "https://github.com/wandb/wandb/issues with the tag "
-                        "`langchain`."
-                    )
+        if self._wandb.run is not None:
+            if should_print_url:
+                run_url = self._wandb.run.settings.run_url
+                self._wandb.termlog(
+                    f"Streaming LangChain activity to W&B at {run_url}\n"
+                    "`WandbTracer` is currently in beta.\n"
+                    "Please report any issues to "
+                    "https://github.com/wandb/wandb/issues with the tag "
+                    "`langchain`."
+                )
 
-                self._wandb.run._label(repo="langchain")
+            self._wandb.run._label(repo="langchain")
 
     def _persist_run(self, run: "Run") -> None:
         """Persist a run."""
diff --git a/libs/langchain/langchain/chains/api/base.py b/libs/langchain/langchain/chains/api/base.py
index d3e548f395c..52f4afecfe8 100644
--- a/libs/langchain/langchain/chains/api/base.py
+++ b/libs/langchain/langchain/chains/api/base.py
@@ -3,8 +3,6 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional
 
-from pydantic import Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
@@ -12,6 +10,7 @@ from langchain.callbacks.manager import (
 from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.utilities.requests import TextRequestsWrapper
diff --git a/libs/langchain/langchain/chains/api/openapi/chain.py b/libs/langchain/langchain/chains/api/openapi/chain.py
index d3bf442d5ce..1ef5b3c88f3 100644
--- a/libs/langchain/langchain/chains/api/openapi/chain.py
+++ b/libs/langchain/langchain/chains/api/openapi/chain.py
@@ -4,7 +4,6 @@ from __future__ import annotations
 import json
 from typing import Any, Dict, List, NamedTuple, Optional, cast
 
-from pydantic import BaseModel, Field
 from requests import Response
 
 from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
@@ -12,6 +11,7 @@ from langchain.chains.api.openapi.requests_chain import APIRequesterChain
 from langchain.chains.api.openapi.response_chain import APIResponderChain
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.openapi.utils.api_models import APIOperation
 from langchain.utilities.requests import Requests
diff --git a/libs/langchain/langchain/chains/base.py b/libs/langchain/langchain/chains/base.py
index 82093096c09..8a49784f7dc 100644
--- a/libs/langchain/langchain/chains/base.py
+++ b/libs/langchain/langchain/chains/base.py
@@ -10,7 +10,6 @@ from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 import yaml
-from pydantic import Field, root_validator, validator
 
 import langchain
 from langchain.callbacks.base import BaseCallbackManager
@@ -23,6 +22,7 @@ from langchain.callbacks.manager import (
 )
 from langchain.load.dump import dumpd
 from langchain.load.serializable import Serializable
+from langchain.pydantic_v1 import Field, root_validator, validator
 from langchain.schema import RUN_KEY, BaseMemory, RunInfo
 from langchain.schema.runnable import Runnable, RunnableConfig
 
@@ -570,7 +570,7 @@ class Chain(Serializable, Runnable[Dict[str, Any], Dict[str, Any]], ABC):
             A dictionary representation of the chain.
 
         Example:
-            ..code-block:: python
+            .. code-block:: python
 
                 chain.dict(exclude_unset=True)
                 # -> {"_type": "foo", "verbose": False, ...}
diff --git a/libs/langchain/langchain/chains/combine_documents/base.py b/libs/langchain/langchain/chains/combine_documents/base.py
index 0693aac44a0..56b87ce2695 100644
--- a/libs/langchain/langchain/chains/combine_documents/base.py
+++ b/libs/langchain/langchain/chains/combine_documents/base.py
@@ -3,14 +3,13 @@
 from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Optional, Tuple
 
-from pydantic import Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
 )
 from langchain.chains.base import Chain
 from langchain.docstore.document import Document
+from langchain.pydantic_v1 import Field
 from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
 
 
diff --git a/libs/langchain/langchain/chains/combine_documents/map_reduce.py b/libs/langchain/langchain/chains/combine_documents/map_reduce.py
index 9afed3e7e80..335ffd17a5b 100644
--- a/libs/langchain/langchain/chains/combine_documents/map_reduce.py
+++ b/libs/langchain/langchain/chains/combine_documents/map_reduce.py
@@ -4,13 +4,12 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional, Tuple
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.combine_documents.reduce import ReduceDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.docstore.document import Document
+from langchain.pydantic_v1 import Extra, root_validator
 
 
 class MapReduceDocumentsChain(BaseCombineDocumentsChain):
diff --git a/libs/langchain/langchain/chains/combine_documents/map_rerank.py b/libs/langchain/langchain/chains/combine_documents/map_rerank.py
index e2d656d07e0..ddc28a5232a 100644
--- a/libs/langchain/langchain/chains/combine_documents/map_rerank.py
+++ b/libs/langchain/langchain/chains/combine_documents/map_rerank.py
@@ -4,13 +4,12 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.docstore.document import Document
 from langchain.output_parsers.regex import RegexParser
+from langchain.pydantic_v1 import Extra, root_validator
 
 
 class MapRerankDocumentsChain(BaseCombineDocumentsChain):
diff --git a/libs/langchain/langchain/chains/combine_documents/reduce.py b/libs/langchain/langchain/chains/combine_documents/reduce.py
index b70f301ed20..9ad2415b12a 100644
--- a/libs/langchain/langchain/chains/combine_documents/reduce.py
+++ b/libs/langchain/langchain/chains/combine_documents/reduce.py
@@ -4,11 +4,10 @@ from __future__ import annotations
 
 from typing import Any, Callable, List, Optional, Protocol, Tuple
 
-from pydantic import Extra
-
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.docstore.document import Document
+from langchain.pydantic_v1 import Extra
 
 
 class CombineDocsProtocol(Protocol):
diff --git a/libs/langchain/langchain/chains/combine_documents/refine.py b/libs/langchain/langchain/chains/combine_documents/refine.py
index 025e2641655..4cdfa2ad77d 100644
--- a/libs/langchain/langchain/chains/combine_documents/refine.py
+++ b/libs/langchain/langchain/chains/combine_documents/refine.py
@@ -4,8 +4,6 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Tuple
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.combine_documents.base import (
     BaseCombineDocumentsChain,
@@ -13,6 +11,7 @@ from langchain.chains.combine_documents.base import (
 from langchain.chains.llm import LLMChain
 from langchain.docstore.document import Document
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.schema import BasePromptTemplate, format_document
 
 
diff --git a/libs/langchain/langchain/chains/combine_documents/stuff.py b/libs/langchain/langchain/chains/combine_documents/stuff.py
index 2b113c7ab46..7d76b5fa266 100644
--- a/libs/langchain/langchain/chains/combine_documents/stuff.py
+++ b/libs/langchain/langchain/chains/combine_documents/stuff.py
@@ -2,8 +2,6 @@
 
 from typing import Any, Dict, List, Optional, Tuple
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.combine_documents.base import (
     BaseCombineDocumentsChain,
@@ -11,6 +9,7 @@ from langchain.chains.combine_documents.base import (
 from langchain.chains.llm import LLMChain
 from langchain.docstore.document import Document
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.schema import BasePromptTemplate, format_document
 
 
@@ -100,6 +99,13 @@ class StuffDocumentsChain(BaseCombineDocumentsChain):
                 )
         return values
 
+    @property
+    def input_keys(self) -> List[str]:
+        extra_keys = [
+            k for k in self.llm_chain.input_keys if k != self.document_variable_name
+        ]
+        return super().input_keys + extra_keys
+
     def _get_inputs(self, docs: List[Document], **kwargs: Any) -> dict:
         """Construct inputs from kwargs and docs.
 
diff --git a/libs/langchain/langchain/chains/constitutional_ai/models.py b/libs/langchain/langchain/chains/constitutional_ai/models.py
index 912b23a6f9b..74e6a562530 100644
--- a/libs/langchain/langchain/chains/constitutional_ai/models.py
+++ b/libs/langchain/langchain/chains/constitutional_ai/models.py
@@ -1,5 +1,5 @@
 """Models for the Constitutional AI chain."""
-from pydantic import BaseModel
+from langchain.pydantic_v1 import BaseModel
 
 
 class ConstitutionalPrinciple(BaseModel):
diff --git a/libs/langchain/langchain/chains/conversation/base.py b/libs/langchain/langchain/chains/conversation/base.py
index 43d72f91e7c..033e73ffefb 100644
--- a/libs/langchain/langchain/chains/conversation/base.py
+++ b/libs/langchain/langchain/chains/conversation/base.py
@@ -1,11 +1,10 @@
 """Chain that carries on a conversation and calls an LLM."""
 from typing import Dict, List
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.chains.conversation.prompt import PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.memory.buffer import ConversationBufferMemory
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.schema import BaseMemory, BasePromptTemplate
 
 
diff --git a/libs/langchain/langchain/chains/conversational_retrieval/base.py b/libs/langchain/langchain/chains/conversational_retrieval/base.py
index 7f6891ff22e..911094e6d90 100644
--- a/libs/langchain/langchain/chains/conversational_retrieval/base.py
+++ b/libs/langchain/langchain/chains/conversational_retrieval/base.py
@@ -7,8 +7,6 @@ from abc import abstractmethod
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
@@ -20,6 +18,7 @@ from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.chains.question_answering import load_qa_chain
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.schema import BasePromptTemplate, BaseRetriever, Document
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import BaseMessage
diff --git a/libs/langchain/langchain/chains/elasticsearch_database/base.py b/libs/langchain/langchain/chains/elasticsearch_database/base.py
index 17f8ddcdb7b..f4cc3a6837b 100644
--- a/libs/langchain/langchain/chains/elasticsearch_database/base.py
+++ b/libs/langchain/langchain/chains/elasticsearch_database/base.py
@@ -3,13 +3,12 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.elasticsearch_database.prompts import ANSWER_PROMPT, DSL_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.output_parsers.json import SimpleJsonOutputParser
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.schema import BaseLLMOutputParser, BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/chains/flare/base.py b/libs/langchain/langchain/chains/flare/base.py
index 11595dda4f4..4c94df2f379 100644
--- a/libs/langchain/langchain/chains/flare/base.py
+++ b/libs/langchain/langchain/chains/flare/base.py
@@ -5,7 +5,6 @@ from abc import abstractmethod
 from typing import Any, Dict, List, Optional, Sequence, Tuple
 
 import numpy as np
-from pydantic import Field
 
 from langchain.callbacks.manager import (
     CallbackManagerForChainRun,
@@ -18,6 +17,7 @@ from langchain.chains.flare.prompts import (
 )
 from langchain.chains.llm import LLMChain
 from langchain.llms import OpenAI
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate, BaseRetriever, Generation
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/chains/graph_qa/arangodb.py b/libs/langchain/langchain/chains/graph_qa/arangodb.py
index 42eba0b34f0..e9e6767a4cb 100644
--- a/libs/langchain/langchain/chains/graph_qa/arangodb.py
+++ b/libs/langchain/langchain/chains/graph_qa/arangodb.py
@@ -4,8 +4,6 @@ from __future__ import annotations
 import re
 from typing import Any, Dict, List, Optional
 
-from pydantic import Field
-
 from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
@@ -16,6 +14,7 @@ from langchain.chains.graph_qa.prompts import (
 )
 from langchain.chains.llm import LLMChain
 from langchain.graphs.arangodb_graph import ArangoGraph
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate
 
 
@@ -30,10 +29,10 @@ class ArangoGraphQAChain(Chain):
     output_key: str = "result"  #: :meta private:
 
     # Specifies the maximum number of AQL Query Results to return
-    top_k = 10
+    top_k: int = 10
 
     # Specifies the set of AQL Query Examples that promote few-shot-learning
-    aql_examples = ""
+    aql_examples: str = ""
 
     # Specify whether to return the AQL Query in the output dictionary
     return_aql_query: bool = False
@@ -42,7 +41,7 @@ class ArangoGraphQAChain(Chain):
     return_aql_result: bool = False
 
     # Specify the maximum amount of AQL Generation attempts that should be made
-    max_aql_generation_attempts = 3
+    max_aql_generation_attempts: int = 3
 
     @property
     def input_keys(self) -> List[str]:
diff --git a/libs/langchain/langchain/chains/graph_qa/base.py b/libs/langchain/langchain/chains/graph_qa/base.py
index 428104496ee..358a3cf411c 100644
--- a/libs/langchain/langchain/chains/graph_qa/base.py
+++ b/libs/langchain/langchain/chains/graph_qa/base.py
@@ -3,13 +3,12 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import ENTITY_EXTRACTION_PROMPT, GRAPH_QA_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.graphs.networkx_graph import NetworkxEntityGraph, get_entities
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/chains/graph_qa/cypher.py b/libs/langchain/langchain/chains/graph_qa/cypher.py
index a3852dc705c..015ff5f47b2 100644
--- a/libs/langchain/langchain/chains/graph_qa/cypher.py
+++ b/libs/langchain/langchain/chains/graph_qa/cypher.py
@@ -4,13 +4,12 @@ from __future__ import annotations
 import re
 from typing import Any, Dict, List, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.graphs.neo4j_graph import Neo4jGraph
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/chains/graph_qa/hugegraph.py b/libs/langchain/langchain/chains/graph_qa/hugegraph.py
index a1f2f6b8158..9c11016cd1b 100644
--- a/libs/langchain/langchain/chains/graph_qa/hugegraph.py
+++ b/libs/langchain/langchain/chains/graph_qa/hugegraph.py
@@ -3,8 +3,6 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import (
@@ -13,6 +11,7 @@ from langchain.chains.graph_qa.prompts import (
 )
 from langchain.chains.llm import LLMChain
 from langchain.graphs.hugegraph import HugeGraph
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/chains/graph_qa/kuzu.py b/libs/langchain/langchain/chains/graph_qa/kuzu.py
index e532fa64ca0..8246e92588b 100644
--- a/libs/langchain/langchain/chains/graph_qa/kuzu.py
+++ b/libs/langchain/langchain/chains/graph_qa/kuzu.py
@@ -3,13 +3,12 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import CYPHER_QA_PROMPT, KUZU_GENERATION_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.graphs.kuzu_graph import KuzuGraph
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/chains/graph_qa/nebulagraph.py b/libs/langchain/langchain/chains/graph_qa/nebulagraph.py
index 9cfcc960a43..09dd52671ba 100644
--- a/libs/langchain/langchain/chains/graph_qa/nebulagraph.py
+++ b/libs/langchain/langchain/chains/graph_qa/nebulagraph.py
@@ -3,13 +3,12 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import CYPHER_QA_PROMPT, NGQL_GENERATION_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.graphs.nebula_graph import NebulaGraph
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/chains/graph_qa/neptune_cypher.py b/libs/langchain/langchain/chains/graph_qa/neptune_cypher.py
index ae7baa9b215..8809a665fd7 100644
--- a/libs/langchain/langchain/chains/graph_qa/neptune_cypher.py
+++ b/libs/langchain/langchain/chains/graph_qa/neptune_cypher.py
@@ -3,8 +3,6 @@ from __future__ import annotations
 import re
 from typing import Any, Dict, List, Optional
 
-from pydantic import Field
-
 from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
@@ -15,6 +13,7 @@ from langchain.chains.graph_qa.prompts import (
 from langchain.chains.llm import LLMChain
 from langchain.graphs import NeptuneGraph
 from langchain.prompts.base import BasePromptTemplate
+from langchain.pydantic_v1 import Field
 
 INTERMEDIATE_STEPS_KEY = "intermediate_steps"
 
diff --git a/libs/langchain/langchain/chains/graph_qa/sparql.py b/libs/langchain/langchain/chains/graph_qa/sparql.py
index f1a8ff1f8e0..eb8a365d76a 100644
--- a/libs/langchain/langchain/chains/graph_qa/sparql.py
+++ b/libs/langchain/langchain/chains/graph_qa/sparql.py
@@ -5,8 +5,6 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import (
@@ -18,6 +16,7 @@ from langchain.chains.graph_qa.prompts import (
 from langchain.chains.llm import LLMChain
 from langchain.graphs.rdf_graph import RdfGraph
 from langchain.prompts.base import BasePromptTemplate
+from langchain.pydantic_v1 import Field
 from langchain.schema.language_model import BaseLanguageModel
 
 
diff --git a/libs/langchain/langchain/chains/hyde/base.py b/libs/langchain/langchain/chains/hyde/base.py
index 1fb2f3ac4ce..75b8c50666f 100644
--- a/libs/langchain/langchain/chains/hyde/base.py
+++ b/libs/langchain/langchain/chains/hyde/base.py
@@ -7,13 +7,13 @@ from __future__ import annotations
 from typing import Any, Dict, List, Optional
 
 import numpy as np
-from pydantic import Extra
 
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.hyde.prompts import PROMPT_MAP
 from langchain.chains.llm import LLMChain
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import Extra
 from langchain.schema.language_model import BaseLanguageModel
 
 
diff --git a/libs/langchain/langchain/chains/llm.py b/libs/langchain/langchain/chains/llm.py
index b1bbfae78a4..ba8c107305b 100644
--- a/libs/langchain/langchain/chains/llm.py
+++ b/libs/langchain/langchain/chains/llm.py
@@ -4,8 +4,6 @@ from __future__ import annotations
 import warnings
 from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 
-from pydantic import Extra, Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManager,
     AsyncCallbackManagerForChainRun,
@@ -16,6 +14,7 @@ from langchain.callbacks.manager import (
 from langchain.chains.base import Chain
 from langchain.load.dump import dumpd
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import Extra, Field
 from langchain.schema import (
     BaseLLMOutputParser,
     BasePromptTemplate,
@@ -127,6 +126,8 @@ class LLMChain(Chain):
     ) -> Tuple[List[PromptValue], Optional[List[str]]]:
         """Prepare prompts from inputs."""
         stop = None
+        if len(input_list) == 0:
+            return [], stop
         if "stop" in input_list[0]:
             stop = input_list[0]["stop"]
         prompts = []
@@ -151,6 +152,8 @@ class LLMChain(Chain):
     ) -> Tuple[List[PromptValue], Optional[List[str]]]:
         """Prepare prompts from inputs."""
         stop = None
+        if len(input_list) == 0:
+            return [], stop
         if "stop" in input_list[0]:
             stop = input_list[0]["stop"]
         prompts = []
diff --git a/libs/langchain/langchain/chains/llm_bash/base.py b/libs/langchain/langchain/chains/llm_bash/base.py
index 2c7cadf8389..9b1bd568bb5 100644
--- a/libs/langchain/langchain/chains/llm_bash/base.py
+++ b/libs/langchain/langchain/chains/llm_bash/base.py
@@ -5,12 +5,11 @@ import logging
 import warnings
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_bash.prompt import PROMPT
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.schema import BasePromptTemplate, OutputParserException
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.utilities.bash import BashProcess
diff --git a/libs/langchain/langchain/chains/llm_checker/base.py b/libs/langchain/langchain/chains/llm_checker/base.py
index e32a93fc4ad..d72d389398a 100644
--- a/libs/langchain/langchain/chains/llm_checker/base.py
+++ b/libs/langchain/langchain/chains/llm_checker/base.py
@@ -4,8 +4,6 @@ from __future__ import annotations
 import warnings
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
@@ -17,6 +15,7 @@ from langchain.chains.llm_checker.prompt import (
 )
 from langchain.chains.sequential import SequentialChain
 from langchain.prompts import PromptTemplate
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.schema.language_model import BaseLanguageModel
 
 
diff --git a/libs/langchain/langchain/chains/llm_math/base.py b/libs/langchain/langchain/chains/llm_math/base.py
index b5e92620d46..fc54999b910 100644
--- a/libs/langchain/langchain/chains/llm_math/base.py
+++ b/libs/langchain/langchain/chains/llm_math/base.py
@@ -7,7 +7,6 @@ import warnings
 from typing import Any, Dict, List, Optional
 
 import numexpr
-from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
@@ -16,6 +15,7 @@ from langchain.callbacks.manager import (
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_math.prompt import PROMPT
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/chains/llm_requests.py b/libs/langchain/langchain/chains/llm_requests.py
index 2e1a6dd4304..8344c759581 100644
--- a/libs/langchain/langchain/chains/llm_requests.py
+++ b/libs/langchain/langchain/chains/llm_requests.py
@@ -3,11 +3,10 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains import LLMChain
 from langchain.chains.base import Chain
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.utilities.requests import TextRequestsWrapper
 
 DEFAULT_HEADERS = {
diff --git a/libs/langchain/langchain/chains/llm_summarization_checker/base.py b/libs/langchain/langchain/chains/llm_summarization_checker/base.py
index 30e7f8ea853..0728571eb33 100644
--- a/libs/langchain/langchain/chains/llm_summarization_checker/base.py
+++ b/libs/langchain/langchain/chains/llm_summarization_checker/base.py
@@ -6,13 +6,12 @@ import warnings
 from pathlib import Path
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.sequential import SequentialChain
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.schema.language_model import BaseLanguageModel
 
 PROMPTS_DIR = Path(__file__).parent / "prompts"
diff --git a/libs/langchain/langchain/chains/llm_symbolic_math/base.py b/libs/langchain/langchain/chains/llm_symbolic_math/base.py
index 8193085b4fa..c511b8cc373 100644
--- a/libs/langchain/langchain/chains/llm_symbolic_math/base.py
+++ b/libs/langchain/langchain/chains/llm_symbolic_math/base.py
@@ -4,8 +4,6 @@ from __future__ import annotations
 import re
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra
-
 from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
@@ -15,6 +13,7 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_symbolic_math.prompt import PROMPT
 from langchain.prompts.base import BasePromptTemplate
+from langchain.pydantic_v1 import Extra
 
 
 class LLMSymbolicMathChain(Chain):
diff --git a/libs/langchain/langchain/chains/mapreduce.py b/libs/langchain/langchain/chains/mapreduce.py
index e7ba46ba605..0374d12da59 100644
--- a/libs/langchain/langchain/chains/mapreduce.py
+++ b/libs/langchain/langchain/chains/mapreduce.py
@@ -7,8 +7,6 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Extra
-
 from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
 from langchain.chains import ReduceDocumentsChain
 from langchain.chains.base import Chain
@@ -17,6 +15,7 @@ from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChai
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.docstore.document import Document
+from langchain.pydantic_v1 import Extra
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.text_splitter import TextSplitter
diff --git a/libs/langchain/langchain/chains/moderation.py b/libs/langchain/langchain/chains/moderation.py
index e59dfa0cf64..6c31a957f25 100644
--- a/libs/langchain/langchain/chains/moderation.py
+++ b/libs/langchain/langchain/chains/moderation.py
@@ -1,10 +1,9 @@
 """Pass input through a moderation endpoint."""
 from typing import Any, Dict, List, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
+from langchain.pydantic_v1 import root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/chains/natbot/base.py b/libs/langchain/langchain/chains/natbot/base.py
index 2adcea4fd79..0fbffe6885e 100644
--- a/libs/langchain/langchain/chains/natbot/base.py
+++ b/libs/langchain/langchain/chains/natbot/base.py
@@ -4,13 +4,12 @@ from __future__ import annotations
 import warnings
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.natbot.prompt import PROMPT
 from langchain.llms.openai import OpenAI
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.schema.language_model import BaseLanguageModel
 
 
diff --git a/libs/langchain/langchain/chains/openai_functions/base.py b/libs/langchain/langchain/chains/openai_functions/base.py
index ad457d537cf..e023c67b7f3 100644
--- a/libs/langchain/langchain/chains/openai_functions/base.py
+++ b/libs/langchain/langchain/chains/openai_functions/base.py
@@ -12,8 +12,6 @@ from typing import (
     Union,
 )
 
-from pydantic import BaseModel
-
 from langchain.base_language import BaseLanguageModel
 from langchain.chains import LLMChain
 from langchain.output_parsers.openai_functions import (
@@ -22,6 +20,7 @@ from langchain.output_parsers.openai_functions import (
     PydanticOutputFunctionsParser,
 )
 from langchain.prompts import BasePromptTemplate
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema import BaseLLMOutputParser
 
 PYTHON_TO_JSON_TYPES = {
@@ -87,7 +86,9 @@ def _get_python_function_arguments(function: Callable, arg_descriptions: dict) -
         if arg == "return":
             continue
         if isinstance(arg_type, type) and issubclass(arg_type, BaseModel):
-            properties[arg] = arg_type.schema()
+            # Mypy error:
+            # "type" has no attribute "schema"
+            properties[arg] = arg_type.schema()  # type: ignore[attr-defined]
         elif arg_type.__name__ in PYTHON_TO_JSON_TYPES:
             properties[arg] = {"type": PYTHON_TO_JSON_TYPES[arg_type.__name__]}
         if arg in arg_descriptions:
@@ -147,7 +148,9 @@ def convert_to_openai_function(
     if isinstance(function, dict):
         return function
     elif isinstance(function, type) and issubclass(function, BaseModel):
-        schema = function.schema()
+        # Mypy error:
+        # "type" has no attribute "schema"
+        schema = function.schema()  # type: ignore[attr-defined]
         return {
             "name": schema["title"],
             "description": schema["description"],
@@ -188,6 +191,7 @@ def create_openai_fn_chain(
     llm: BaseLanguageModel,
     prompt: BasePromptTemplate,
     *,
+    output_key: str = "function",
     output_parser: Optional[BaseLLMOutputParser] = None,
     **kwargs: Any,
 ) -> LLMChain:
@@ -206,6 +210,7 @@ def create_openai_fn_chain(
             pydantic.BaseModels for arguments.
         llm: Language model to use, assumed to support the OpenAI function-calling API.
         prompt: BasePromptTemplate to pass to the model.
+        output_key: The key to use when returning the output in LLMChain.__call__.
         output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
             will be inferred from the function types. If pydantic.BaseModels are passed
             in, then the OutputParser will try to parse outputs using those. Otherwise
@@ -222,9 +227,9 @@ def create_openai_fn_chain(
 
                 from langchain.chains.openai_functions import create_openai_fn_chain
                 from langchain.chat_models import ChatOpenAI
-                from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
+                from langchain.prompts import ChatPromptTemplate
 
-                from pydantic import BaseModel, Field
+                from pydantic_v1 import BaseModel, Field
 
 
                 class RecordPerson(BaseModel):
@@ -243,17 +248,15 @@ def create_openai_fn_chain(
                     fav_food: Optional[str] = Field(None, description="The dog's favorite food")
 
 
-                llm = ChatOpenAI(model="gpt-3.5-turbo-0613", temperature=0)
-                prompt_msgs = [
-                    SystemMessage(
-                        content="You are a world class algorithm for recording entities"
-                    ),
-                    HumanMessage(content="Make calls to the relevant function to record the entities in the following input:"),
-                    HumanMessagePromptTemplate.from_template("{input}"),
-                    HumanMessage(content="Tips: Make sure to answer in the correct format"),
-                ]
-                prompt = ChatPromptTemplate(messages=prompt_msgs)
-                chain = create_openai_fn_chain([RecordPerson, RecordDog])
+                llm = ChatOpenAI(model="gpt-4", temperature=0)
+                prompt = ChatPromptTemplate.from_messages(
+                    [
+                        ("system", "You are a world class algorithm for recording entities."),
+                        ("human", "Make calls to the relevant function to record the entities in the following input: {input}"),
+                        ("human", "Tip: Make sure to answer in the correct format"),
+                    ]
+                )
+                chain = create_openai_fn_chain([RecordPerson, RecordDog], llm, prompt)
                 chain.run("Harry was a chubby brown beagle who loved chicken")
                 # -> RecordDog(name="Harry", color="brown", fav_food="chicken")
     """  # noqa: E501
@@ -272,7 +275,7 @@ def create_openai_fn_chain(
         prompt=prompt,
         output_parser=output_parser,
         llm_kwargs=llm_kwargs,
-        output_key="function",
+        output_key=output_key,
         **kwargs,
     )
     return llm_chain
@@ -283,6 +286,7 @@ def create_structured_output_chain(
     llm: BaseLanguageModel,
     prompt: BasePromptTemplate,
     *,
+    output_key: str = "function",
     output_parser: Optional[BaseLLMOutputParser] = None,
     **kwargs: Any,
 ) -> LLMChain:
@@ -295,6 +299,7 @@ def create_structured_output_chain(
             the schema represents and descriptions for the parameters.
         llm: Language model to use, assumed to support the OpenAI function-calling API.
         prompt: BasePromptTemplate to pass to the model.
+        output_key: The key to use when returning the output in LLMChain.__call__.
         output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
             will be inferred from the function types. If pydantic.BaseModels are passed
             in, then the OutputParser will try to parse outputs using those. Otherwise
@@ -308,9 +313,9 @@ def create_structured_output_chain(
 
                 from langchain.chains.openai_functions import create_structured_output_chain
                 from langchain.chat_models import ChatOpenAI
-                from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
+                from langchain.prompts import ChatPromptTemplate
 
-                from pydantic import BaseModel, Field
+                from pydantic_v1 import BaseModel, Field
 
                 class Dog(BaseModel):
                     \"\"\"Identifying information about a dog.\"\"\"
@@ -320,15 +325,13 @@ def create_structured_output_chain(
                     fav_food: Optional[str] = Field(None, description="The dog's favorite food")
 
                 llm = ChatOpenAI(model="gpt-3.5-turbo-0613", temperature=0)
-                prompt_msgs = [
-                    SystemMessage(
-                        content="You are a world class algorithm for extracting information in structured formats."
-                    ),
-                    HumanMessage(content="Use the given format to extract information from the following input:"),
-                    HumanMessagePromptTemplate.from_template("{input}"),
-                    HumanMessage(content="Tips: Make sure to answer in the correct format"),
-                ]
-                prompt = ChatPromptTemplate(messages=prompt_msgs)
+                prompt = ChatPromptTemplate.from_messages(
+                    [
+                        ("system", "You are a world class algorithm for extracting information in structured formats."),
+                        ("human", "Use the given format to extract information from the following input: {input}"),
+                        ("human", "Tip: Make sure to answer in the correct format"),
+                    ]
+                )
                 chain = create_structured_output_chain(Dog, llm, prompt)
                 chain.run("Harry was a chubby brown beagle who loved chicken")
                 # -> Dog(name="Harry", color="brown", fav_food="chicken")
@@ -354,5 +357,10 @@ def create_structured_output_chain(
             pydantic_schema=_OutputFormatter, attr_name="output"
         )
     return create_openai_fn_chain(
-        [function], llm, prompt, output_parser=output_parser, **kwargs
+        [function],
+        llm,
+        prompt,
+        output_key=output_key,
+        output_parser=output_parser,
+        **kwargs,
     )
diff --git a/libs/langchain/langchain/chains/openai_functions/citation_fuzzy_match.py b/libs/langchain/langchain/chains/openai_functions/citation_fuzzy_match.py
index 2ea974d331b..421a7599569 100644
--- a/libs/langchain/langchain/chains/openai_functions/citation_fuzzy_match.py
+++ b/libs/langchain/langchain/chains/openai_functions/citation_fuzzy_match.py
@@ -1,13 +1,12 @@
 from typing import Iterator, List
 
-from pydantic import BaseModel, Field
-
 from langchain.chains.llm import LLMChain
 from langchain.chains.openai_functions.utils import get_llm_kwargs
 from langchain.output_parsers.openai_functions import (
     PydanticOutputFunctionsParser,
 )
 from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import HumanMessage, SystemMessage
 
diff --git a/libs/langchain/langchain/chains/openai_functions/extraction.py b/libs/langchain/langchain/chains/openai_functions/extraction.py
index ab4528ca86e..c881537866e 100644
--- a/libs/langchain/langchain/chains/openai_functions/extraction.py
+++ b/libs/langchain/langchain/chains/openai_functions/extraction.py
@@ -1,7 +1,5 @@
 from typing import Any, List
 
-from pydantic import BaseModel
-
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.openai_functions.utils import (
@@ -14,6 +12,7 @@ from langchain.output_parsers.openai_functions import (
     PydanticAttrOutputFunctionsParser,
 )
 from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema.language_model import BaseLanguageModel
 
 
diff --git a/libs/langchain/langchain/chains/openai_functions/openapi.py b/libs/langchain/langchain/chains/openai_functions/openapi.py
index 511123a601e..100e4be28a6 100644
--- a/libs/langchain/langchain/chains/openai_functions/openapi.py
+++ b/libs/langchain/langchain/chains/openai_functions/openapi.py
@@ -1,10 +1,11 @@
+from __future__ import annotations
+
 import json
 import re
 from collections import defaultdict
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
 import requests
-from openapi_schema_pydantic import Parameter
 from requests import Response
 
 from langchain import LLMChain
@@ -20,6 +21,9 @@ from langchain.tools import APIOperation
 from langchain.utilities.openapi import OpenAPISpec
 from langchain.utils.input import get_colored_text
 
+if TYPE_CHECKING:
+    from openapi_schema_pydantic import Parameter
+
 
 def _get_description(o: Any, prefer_short: bool) -> Optional[str]:
     summary = getattr(o, "summary", None)
diff --git a/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py b/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py
index b65296436be..dff17030a36 100644
--- a/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py
+++ b/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py
@@ -1,7 +1,5 @@
 from typing import Any, List, Optional, Type, Union
 
-from pydantic import BaseModel, Field
-
 from langchain.chains.llm import LLMChain
 from langchain.chains.openai_functions.utils import get_llm_kwargs
 from langchain.output_parsers.openai_functions import (
@@ -10,6 +8,7 @@ from langchain.output_parsers.openai_functions import (
 )
 from langchain.prompts import PromptTemplate
 from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema import BaseLLMOutputParser
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import HumanMessage, SystemMessage
diff --git a/libs/langchain/langchain/chains/prompt_selector.py b/libs/langchain/langchain/chains/prompt_selector.py
index d2f6c0554bd..7726ed7ebf0 100644
--- a/libs/langchain/langchain/chains/prompt_selector.py
+++ b/libs/langchain/langchain/chains/prompt_selector.py
@@ -1,10 +1,9 @@
 from abc import ABC, abstractmethod
 from typing import Callable, List, Tuple
 
-from pydantic import BaseModel, Field
-
 from langchain.chat_models.base import BaseChatModel
 from langchain.llms.base import BaseLLM
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/chains/qa_generation/base.py b/libs/langchain/langchain/chains/qa_generation/base.py
index e5efc3d73e0..c081060086f 100644
--- a/libs/langchain/langchain/chains/qa_generation/base.py
+++ b/libs/langchain/langchain/chains/qa_generation/base.py
@@ -3,12 +3,11 @@ from __future__ import annotations
 import json
 from typing import Any, Dict, List, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.qa_generation.prompt import PROMPT_SELECTOR
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
diff --git a/libs/langchain/langchain/chains/qa_with_sources/base.py b/libs/langchain/langchain/chains/qa_with_sources/base.py
index d0f762afbd5..db07d24b233 100644
--- a/libs/langchain/langchain/chains/qa_with_sources/base.py
+++ b/libs/langchain/langchain/chains/qa_with_sources/base.py
@@ -5,9 +5,7 @@ from __future__ import annotations
 import inspect
 import re
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
-
-from pydantic import Extra, root_validator
+from typing import Any, Dict, List, Optional, Tuple
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
@@ -26,6 +24,7 @@ from langchain.chains.qa_with_sources.map_reduce_prompt import (
     QUESTION_PROMPT,
 )
 from langchain.docstore.document import Document
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 
@@ -119,6 +118,15 @@ class BaseQAWithSourcesChain(Chain, ABC):
             values["combine_documents_chain"] = values.pop("combine_document_chain")
         return values
 
+    def _split_sources(self, answer: str) -> Tuple[str, str]:
+        """Split sources from answer."""
+        if re.search(r"SOURCES:\s", answer):
+            answer, sources = re.split(r"SOURCES:\s|QUESTION:\s", answer)[:2]
+            sources = re.split(r"\n", sources)[0]
+        else:
+            sources = ""
+        return answer, sources
+
     @abstractmethod
     def _get_docs(
         self,
@@ -145,10 +153,7 @@ class BaseQAWithSourcesChain(Chain, ABC):
         answer = self.combine_documents_chain.run(
             input_documents=docs, callbacks=_run_manager.get_child(), **inputs
         )
-        if re.search(r"SOURCES:\s", answer):
-            answer, sources = re.split(r"SOURCES:\s", answer)
-        else:
-            sources = ""
+        answer, sources = self._split_sources(answer)
         result: Dict[str, Any] = {
             self.answer_key: answer,
             self.sources_answer_key: sources,
@@ -182,10 +187,7 @@ class BaseQAWithSourcesChain(Chain, ABC):
         answer = await self.combine_documents_chain.arun(
             input_documents=docs, callbacks=_run_manager.get_child(), **inputs
         )
-        if re.search(r"SOURCES:\s", answer):
-            answer, sources = re.split(r"SOURCES:\s", answer)
-        else:
-            sources = ""
+        answer, sources = self._split_sources(answer)
         result: Dict[str, Any] = {
             self.answer_key: answer,
             self.sources_answer_key: sources,
diff --git a/libs/langchain/langchain/chains/qa_with_sources/retrieval.py b/libs/langchain/langchain/chains/qa_with_sources/retrieval.py
index beb36322c9c..c5d587b464c 100644
--- a/libs/langchain/langchain/chains/qa_with_sources/retrieval.py
+++ b/libs/langchain/langchain/chains/qa_with_sources/retrieval.py
@@ -2,8 +2,6 @@
 
 from typing import Any, Dict, List
 
-from pydantic import Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
@@ -11,6 +9,7 @@ from langchain.callbacks.manager import (
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
 from langchain.docstore.document import Document
+from langchain.pydantic_v1 import Field
 from langchain.schema import BaseRetriever
 
 
diff --git a/libs/langchain/langchain/chains/qa_with_sources/vector_db.py b/libs/langchain/langchain/chains/qa_with_sources/vector_db.py
index 5d1689a9b1a..19a491fcb14 100644
--- a/libs/langchain/langchain/chains/qa_with_sources/vector_db.py
+++ b/libs/langchain/langchain/chains/qa_with_sources/vector_db.py
@@ -3,8 +3,6 @@
 import warnings
 from typing import Any, Dict, List
 
-from pydantic import Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
@@ -12,6 +10,7 @@ from langchain.callbacks.manager import (
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
 from langchain.docstore.document import Document
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.vectorstores.base import VectorStore
 
 
diff --git a/libs/langchain/langchain/chains/query_constructor/ir.py b/libs/langchain/langchain/chains/query_constructor/ir.py
index 73707e589f3..04eac8be913 100644
--- a/libs/langchain/langchain/chains/query_constructor/ir.py
+++ b/libs/langchain/langchain/chains/query_constructor/ir.py
@@ -5,7 +5,7 @@ from abc import ABC, abstractmethod
 from enum import Enum
 from typing import Any, List, Optional, Sequence, Union
 
-from pydantic import BaseModel
+from langchain.pydantic_v1 import BaseModel
 
 
 class Visitor(ABC):
diff --git a/libs/langchain/langchain/chains/query_constructor/schema.py b/libs/langchain/langchain/chains/query_constructor/schema.py
index 557ad5ea2a1..54998710615 100644
--- a/libs/langchain/langchain/chains/query_constructor/schema.py
+++ b/libs/langchain/langchain/chains/query_constructor/schema.py
@@ -1,4 +1,4 @@
-from pydantic import BaseModel
+from langchain.pydantic_v1 import BaseModel
 
 
 class AttributeInfo(BaseModel):
diff --git a/libs/langchain/langchain/chains/retrieval_qa/base.py b/libs/langchain/langchain/chains/retrieval_qa/base.py
index e8385d6fe73..481aab5a3be 100644
--- a/libs/langchain/langchain/chains/retrieval_qa/base.py
+++ b/libs/langchain/langchain/chains/retrieval_qa/base.py
@@ -6,8 +6,6 @@ import warnings
 from abc import abstractmethod
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
@@ -20,6 +18,7 @@ from langchain.chains.llm import LLMChain
 from langchain.chains.question_answering import load_qa_chain
 from langchain.chains.question_answering.stuff_prompt import PROMPT_SELECTOR
 from langchain.prompts import PromptTemplate
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.schema import BaseRetriever, Document
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.vectorstores.base import VectorStore
diff --git a/libs/langchain/langchain/chains/router/base.py b/libs/langchain/langchain/chains/router/base.py
index 5ee6fb16a5c..6bc704b8e20 100644
--- a/libs/langchain/langchain/chains/router/base.py
+++ b/libs/langchain/langchain/chains/router/base.py
@@ -4,14 +4,13 @@ from __future__ import annotations
 from abc import ABC
 from typing import Any, Dict, List, Mapping, NamedTuple, Optional
 
-from pydantic import Extra
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
     Callbacks,
 )
 from langchain.chains.base import Chain
+from langchain.pydantic_v1 import Extra
 
 
 class Route(NamedTuple):
diff --git a/libs/langchain/langchain/chains/router/embedding_router.py b/libs/langchain/langchain/chains/router/embedding_router.py
index 53704a987a0..f2e0712f89e 100644
--- a/libs/langchain/langchain/chains/router/embedding_router.py
+++ b/libs/langchain/langchain/chains/router/embedding_router.py
@@ -2,12 +2,11 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional, Sequence, Tuple, Type
 
-from pydantic import Extra
-
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.router.base import RouterChain
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import Extra
 from langchain.vectorstores.base import VectorStore
 
 
diff --git a/libs/langchain/langchain/chains/router/llm_router.py b/libs/langchain/langchain/chains/router/llm_router.py
index b0978646133..74dcf6c0721 100644
--- a/libs/langchain/langchain/chains/router/llm_router.py
+++ b/libs/langchain/langchain/chains/router/llm_router.py
@@ -3,8 +3,6 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional, Type, cast
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
@@ -12,6 +10,7 @@ from langchain.callbacks.manager import (
 from langchain.chains import LLMChain
 from langchain.chains.router.base import RouterChain
 from langchain.output_parsers.json import parse_and_check_json_markdown
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import BaseOutputParser, BasePromptTemplate, OutputParserException
 from langchain.schema.language_model import BaseLanguageModel
 
@@ -83,7 +82,7 @@ class LLMRouterChain(RouterChain):
 
 
 class RouterOutputParser(BaseOutputParser[Dict[str, str]]):
-    """Parser for output of router chain int he multi-prompt chain."""
+    """Parser for output of router chain in the multi-prompt chain."""
 
     default_destination: str = "DEFAULT"
     next_inputs_type: Type = str
diff --git a/libs/langchain/langchain/chains/sequential.py b/libs/langchain/langchain/chains/sequential.py
index 26cbaf7021d..7acf33ef08d 100644
--- a/libs/langchain/langchain/chains/sequential.py
+++ b/libs/langchain/langchain/chains/sequential.py
@@ -1,13 +1,12 @@
 """Chain pipeline where the outputs of one step feed directly into next."""
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
 )
 from langchain.chains.base import Chain
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils.input import get_color_mapping
 
 
diff --git a/libs/langchain/langchain/chains/transform.py b/libs/langchain/langchain/chains/transform.py
index b03bbe22c30..66762fa4ab2 100644
--- a/libs/langchain/langchain/chains/transform.py
+++ b/libs/langchain/langchain/chains/transform.py
@@ -3,13 +3,12 @@ import functools
 import logging
 from typing import Any, Awaitable, Callable, Dict, List, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
 )
 from langchain.chains.base import Chain
+from langchain.pydantic_v1 import Field
 
 logger = logging.getLogger(__name__)
 
diff --git a/libs/langchain/langchain/chat_models/__init__.py b/libs/langchain/langchain/chat_models/__init__.py
index f3b1cbe1618..0f26a852b8e 100644
--- a/libs/langchain/langchain/chat_models/__init__.py
+++ b/libs/langchain/langchain/chat_models/__init__.py
@@ -20,10 +20,12 @@ an interface where "chat messages" are the inputs and outputs.
 from langchain.chat_models.anthropic import ChatAnthropic
 from langchain.chat_models.anyscale import ChatAnyscale
 from langchain.chat_models.azure_openai import AzureChatOpenAI
+from langchain.chat_models.ernie import ErnieBotChat
 from langchain.chat_models.fake import FakeListChatModel
 from langchain.chat_models.google_palm import ChatGooglePalm
 from langchain.chat_models.human import HumanInputChatModel
 from langchain.chat_models.jinachat import JinaChat
+from langchain.chat_models.litellm import ChatLiteLLM
 from langchain.chat_models.mlflow_ai_gateway import ChatMLflowAIGateway
 from langchain.chat_models.openai import ChatOpenAI
 from langchain.chat_models.promptlayer_openai import PromptLayerChatOpenAI
@@ -41,4 +43,6 @@ __all__ = [
     "JinaChat",
     "HumanInputChatModel",
     "ChatAnyscale",
+    "ChatLiteLLM",
+    "ErnieBotChat",
 ]
diff --git a/libs/langchain/langchain/chat_models/anthropic.py b/libs/langchain/langchain/chat_models/anthropic.py
index 5090e9a0ab0..e201e382f98 100644
--- a/libs/langchain/langchain/chat_models/anthropic.py
+++ b/libs/langchain/langchain/chat_models/anthropic.py
@@ -22,7 +22,7 @@ from langchain.schema.output import ChatGenerationChunk
 
 
 class ChatAnthropic(BaseChatModel, _AnthropicCommon):
-    """Anthropic's large language chat model.
+    """`Anthropic` chat large language models.
 
     To use, you should have the ``anthropic`` python package installed, and the
     environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass
diff --git a/libs/langchain/langchain/chat_models/anyscale.py b/libs/langchain/langchain/chat_models/anyscale.py
index 6981b3f45ea..2ccae7ca75f 100644
--- a/libs/langchain/langchain/chat_models/anyscale.py
+++ b/libs/langchain/langchain/chat_models/anyscale.py
@@ -7,13 +7,13 @@ import sys
 from typing import TYPE_CHECKING, Optional, Set
 
 import requests
-from pydantic import Field, root_validator
 
+from langchain.adapters.openai import convert_message_to_dict
 from langchain.chat_models.openai import (
     ChatOpenAI,
-    _convert_message_to_dict,
     _import_tiktoken,
 )
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema.messages import BaseMessage
 from langchain.utils import get_from_dict_or_env
 
@@ -28,7 +28,7 @@ DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf"
 
 
 class ChatAnyscale(ChatOpenAI):
-    """Wrapper around Anyscale Chat large language models.
+    """`Anyscale` Chat large language models.
 
     To use, you should have the ``openai`` python package installed, and the
     environment variable ``ANYSCALE_API_KEY`` set with your API key.
@@ -178,7 +178,7 @@ class ChatAnyscale(ChatOpenAI):
         tokens_per_message = 3
         tokens_per_name = 1
         num_tokens = 0
-        messages_dict = [_convert_message_to_dict(m) for m in messages]
+        messages_dict = [convert_message_to_dict(m) for m in messages]
         for message in messages_dict:
             num_tokens += tokens_per_message
             for key, value in message.items():
diff --git a/libs/langchain/langchain/chat_models/azure_openai.py b/libs/langchain/langchain/chat_models/azure_openai.py
index 71ca287484f..c5090fd8451 100644
--- a/libs/langchain/langchain/chat_models/azure_openai.py
+++ b/libs/langchain/langchain/chat_models/azure_openai.py
@@ -4,9 +4,8 @@ from __future__ import annotations
 import logging
 from typing import Any, Dict, Mapping
 
-from pydantic import root_validator
-
 from langchain.chat_models.openai import ChatOpenAI
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import ChatResult
 from langchain.utils import get_from_dict_or_env
 
@@ -14,7 +13,7 @@ logger = logging.getLogger(__name__)
 
 
 class AzureChatOpenAI(ChatOpenAI):
-    """Wrapper around Azure OpenAI Chat Completion API.
+    """`Azure OpenAI` Chat Completion API.
 
     To use this class you
     must have a deployed model on Azure OpenAI. Use `deployment_name` in the
diff --git a/libs/langchain/langchain/chat_models/azureml_endpoint.py b/libs/langchain/langchain/chat_models/azureml_endpoint.py
index 78a1b3b17a3..9cd57fbcf8c 100644
--- a/libs/langchain/langchain/chat_models/azureml_endpoint.py
+++ b/libs/langchain/langchain/chat_models/azureml_endpoint.py
@@ -1,11 +1,10 @@
 import json
 from typing import Any, Dict, List, Optional
 
-from pydantic import validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.chat_models.base import SimpleChatModel
 from langchain.llms.azureml_endpoint import AzureMLEndpointClient, ContentFormatterBase
+from langchain.pydantic_v1 import validator
 from langchain.schema.messages import (
     AIMessage,
     BaseMessage,
@@ -17,9 +16,9 @@ from langchain.utils import get_from_dict_or_env
 
 
 class LlamaContentFormatter(ContentFormatterBase):
-    """Content formatter for LLaMa"""
+    """Content formatter for `LLaMA`."""
 
-    SUPPORTED_ROLES = ["user", "assistant", "system"]
+    SUPPORTED_ROLES: List[str] = ["user", "assistant", "system"]
 
     @staticmethod
     def _convert_message_to_dict(message: BaseMessage) -> Dict:
@@ -66,7 +65,7 @@ class LlamaContentFormatter(ContentFormatterBase):
 
 
 class AzureMLChatOnlineEndpoint(SimpleChatModel):
-    """Azure ML Chat Online Endpoint models.
+    """`AzureML` Chat models API.
 
     Example:
         .. code-block:: python
diff --git a/libs/langchain/langchain/chat_models/base.py b/libs/langchain/langchain/chat_models/base.py
index c91c8acfdf2..d4c582c19e0 100644
--- a/libs/langchain/langchain/chat_models/base.py
+++ b/libs/langchain/langchain/chat_models/base.py
@@ -14,8 +14,6 @@ from typing import (
     cast,
 )
 
-from pydantic import Field, root_validator
-
 import langchain
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import (
@@ -28,6 +26,7 @@ from langchain.callbacks.manager import (
 from langchain.load.dump import dumpd, dumps
 from langchain.prompts.base import StringPromptValue
 from langchain.prompts.chat import ChatPromptValue
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import (
     ChatGeneration,
     ChatResult,
@@ -51,7 +50,7 @@ def _get_verbosity() -> bool:
 
 
 class BaseChatModel(BaseLanguageModel[BaseMessageChunk], ABC):
-    """Base class for chat models."""
+    """Base class for Chat models."""
 
     cache: Optional[bool] = None
     """Whether to cache the response."""
diff --git a/libs/langchain/langchain/chat_models/ernie.py b/libs/langchain/langchain/chat_models/ernie.py
new file mode 100644
index 00000000000..1ccecf36fe6
--- /dev/null
+++ b/libs/langchain/langchain/chat_models/ernie.py
@@ -0,0 +1,162 @@
+import logging
+import threading
+from typing import Any, Dict, List, Mapping, Optional
+
+import requests
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.chat_models.base import BaseChatModel
+from langchain.pydantic_v1 import root_validator
+from langchain.schema import (
+    AIMessage,
+    BaseMessage,
+    ChatGeneration,
+    ChatMessage,
+    ChatResult,
+    HumanMessage,
+)
+from langchain.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+
+def _convert_message_to_dict(message: BaseMessage) -> dict:
+    if isinstance(message, ChatMessage):
+        message_dict = {"role": message.role, "content": message.content}
+    elif isinstance(message, HumanMessage):
+        message_dict = {"role": "user", "content": message.content}
+    elif isinstance(message, AIMessage):
+        message_dict = {"role": "assistant", "content": message.content}
+    else:
+        raise ValueError(f"Got unknown type {message}")
+    return message_dict
+
+
+class ErnieBotChat(BaseChatModel):
+    """`ERNIE-Bot` large language model.
+
+    ERNIE-Bot is a large language model developed by Baidu,
+    covering a huge amount of Chinese data.
+
+    To use, you should have the `ernie_client_id` and `ernie_client_secret` set,
+    or set the environment variable `ERNIE_CLIENT_ID` and `ERNIE_CLIENT_SECRET`.
+
+    Note:
+    access_token will be automatically generated based on client_id and client_secret,
+    and will be regenerated after expiration (30 days).
+
+    Default model is `ERNIE-Bot-turbo`,
+    currently supported models are `ERNIE-Bot-turbo`, `ERNIE-Bot`
+
+    Example:
+        .. code-block:: python
+
+            from langchain.chat_models import ErnieBotChat
+            chat = ErnieBotChat(model_name='ERNIE-Bot')
+
+    """
+
+    ernie_client_id: Optional[str] = None
+    ernie_client_secret: Optional[str] = None
+    access_token: Optional[str] = None
+
+    model_name: str = "ERNIE-Bot-turbo"
+
+    streaming: Optional[bool] = False
+    top_p: Optional[float] = 0.8
+    temperature: Optional[float] = 0.95
+    penalty_score: Optional[float] = 1
+
+    _lock = threading.Lock()
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        values["ernie_client_id"] = get_from_dict_or_env(
+            values,
+            "ernie_client_id",
+            "ERNIE_CLIENT_ID",
+        )
+        values["ernie_client_secret"] = get_from_dict_or_env(
+            values,
+            "ernie_client_secret",
+            "ERNIE_CLIENT_SECRET",
+        )
+        return values
+
+    def _chat(self, payload: object) -> dict:
+        base_url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat"
+        if self.model_name == "ERNIE-Bot-turbo":
+            url = f"{base_url}/eb-instant"
+        elif self.model_name == "ERNIE-Bot":
+            url = f"{base_url}/completions"
+        else:
+            raise ValueError(f"Got unknown model_name {self.model_name}")
+        resp = requests.post(
+            url,
+            headers={
+                "Content-Type": "application/json",
+            },
+            params={"access_token": self.access_token},
+            json=payload,
+        )
+        return resp.json()
+
+    def _refresh_access_token_with_lock(self) -> None:
+        with self._lock:
+            logger.debug("Refreshing access token")
+            base_url: str = "https://aip.baidubce.com/oauth/2.0/token"
+            resp = requests.post(
+                base_url,
+                headers={
+                    "Content-Type": "application/json",
+                    "Accept": "application/json",
+                },
+                params={
+                    "grant_type": "client_credentials",
+                    "client_id": self.ernie_client_id,
+                    "client_secret": self.ernie_client_secret,
+                },
+            )
+            self.access_token = str(resp.json().get("access_token"))
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if self.streaming:
+            raise ValueError("`streaming` option currently unsupported.")
+
+        if not self.access_token:
+            self._refresh_access_token_with_lock()
+        payload = {
+            "messages": [_convert_message_to_dict(m) for m in messages],
+            "top_p": self.top_p,
+            "temperature": self.temperature,
+            "penalty_score": self.penalty_score,
+            **kwargs,
+        }
+        logger.debug(f"Payload for ernie api is {payload}")
+        resp = self._chat(payload)
+        if resp.get("error_code"):
+            if resp.get("error_code") == 111:
+                logger.debug("access_token expired, refresh it")
+                self._refresh_access_token_with_lock()
+                resp = self._chat(payload)
+            else:
+                raise ValueError(f"Error from ErnieChat api response: {resp}")
+        return self._create_chat_result(resp)
+
+    def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
+        generations = [
+            ChatGeneration(message=AIMessage(content=response.get("result")))
+        ]
+        token_usage = response.get("usage", {})
+        llm_output = {"token_usage": token_usage, "model_name": self.model_name}
+        return ChatResult(generations=generations, llm_output=llm_output)
+
+    @property
+    def _llm_type(self) -> str:
+        return "ernie-bot-chat"
diff --git a/libs/langchain/langchain/chat_models/google_palm.py b/libs/langchain/langchain/chat_models/google_palm.py
index e180030e743..cc7c288f890 100644
--- a/libs/langchain/langchain/chat_models/google_palm.py
+++ b/libs/langchain/langchain/chat_models/google_palm.py
@@ -4,7 +4,6 @@ from __future__ import annotations
 import logging
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional
 
-from pydantic import BaseModel, root_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -18,6 +17,7 @@ from langchain.callbacks.manager import (
     CallbackManagerForLLMRun,
 )
 from langchain.chat_models.base import BaseChatModel
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.schema import (
     ChatGeneration,
     ChatResult,
@@ -38,7 +38,7 @@ logger = logging.getLogger(__name__)
 
 
 class ChatGooglePalmError(Exception):
-    """Error raised when there is an issue with the Google PaLM API."""
+    """Error with the `Google PaLM` API."""
 
 
 def _truncate_at_stop_tokens(
@@ -214,7 +214,7 @@ async def achat_with_retry(llm: ChatGooglePalm, **kwargs: Any) -> Any:
 
 
 class ChatGooglePalm(BaseChatModel, BaseModel):
-    """Wrapper around Google's PaLM Chat API.
+    """`Google PaLM` Chat models API.
 
     To use you must have the google.generativeai Python package installed and
     either:
diff --git a/libs/langchain/langchain/chat_models/human.py b/libs/langchain/langchain/chat_models/human.py
index 1f9bf6eaf14..6c964c9243c 100644
--- a/libs/langchain/langchain/chat_models/human.py
+++ b/libs/langchain/langchain/chat_models/human.py
@@ -5,7 +5,6 @@ from io import StringIO
 from typing import Any, Callable, Dict, List, Mapping, Optional
 
 import yaml
-from pydantic import Field
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
@@ -13,6 +12,7 @@ from langchain.callbacks.manager import (
 )
 from langchain.chat_models.base import BaseChatModel
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Field
 from langchain.schema.messages import (
     BaseMessage,
     HumanMessage,
diff --git a/libs/langchain/langchain/chat_models/jinachat.py b/libs/langchain/langchain/chat_models/jinachat.py
index fd1225a9ffc..7c1e6f90341 100644
--- a/libs/langchain/langchain/chat_models/jinachat.py
+++ b/libs/langchain/langchain/chat_models/jinachat.py
@@ -15,7 +15,6 @@ from typing import (
     Union,
 )
 
-from pydantic import Field, root_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -29,12 +28,14 @@ from langchain.callbacks.manager import (
     CallbackManagerForLLMRun,
 )
 from langchain.chat_models.base import BaseChatModel
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import (
     AIMessage,
     BaseMessage,
     ChatGeneration,
     ChatMessage,
     ChatResult,
+    FunctionMessage,
     HumanMessage,
     SystemMessage,
 )
@@ -125,6 +126,12 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
         message_dict = {"role": "assistant", "content": message.content}
     elif isinstance(message, SystemMessage):
         message_dict = {"role": "system", "content": message.content}
+    elif isinstance(message, FunctionMessage):
+        message_dict = {
+            "role": "function",
+            "name": message.name,
+            "content": message.content,
+        }
     else:
         raise ValueError(f"Got unknown type {message}")
     if "name" in message.additional_kwargs:
@@ -133,8 +140,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
 
 
 class JinaChat(BaseChatModel):
-    """Wrapper for Jina AI's LLM service, providing cost-effective
-    image chat capabilities.
+    """`Jina AI` Chat models API.
 
     To use, you should have the ``openai`` python package installed, and the
     environment variable ``JINACHAT_API_KEY`` set to your API key, which you
diff --git a/libs/langchain/langchain/chat_models/litellm.py b/libs/langchain/langchain/chat_models/litellm.py
new file mode 100644
index 00000000000..d23549dc51c
--- /dev/null
+++ b/libs/langchain/langchain/chat_models/litellm.py
@@ -0,0 +1,450 @@
+"""Wrapper around LiteLLM's model I/O library."""
+from __future__ import annotations
+
+import logging
+from typing import (
+    Any,
+    AsyncIterator,
+    Callable,
+    Dict,
+    Iterator,
+    List,
+    Mapping,
+    Optional,
+    Tuple,
+    Union,
+)
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain.chat_models.base import BaseChatModel
+from langchain.llms.base import create_base_retry_decorator
+from langchain.pydantic_v1 import Field, root_validator
+from langchain.schema import (
+    ChatGeneration,
+    ChatResult,
+)
+from langchain.schema.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+    BaseMessageChunk,
+    ChatMessage,
+    ChatMessageChunk,
+    HumanMessage,
+    HumanMessageChunk,
+    SystemMessage,
+    SystemMessageChunk,
+)
+from langchain.schema.output import ChatGenerationChunk
+from langchain.utils import get_from_dict_or_env
+
+logger = logging.getLogger(__name__)
+
+
+class ChatLiteLLMException(Exception):
+    """Error with the `LiteLLM I/O` library"""
+
+
+def _truncate_at_stop_tokens(
+    text: str,
+    stop: Optional[List[str]],
+) -> str:
+    """Truncates text at the earliest stop token found."""
+    if stop is None:
+        return text
+
+    for stop_token in stop:
+        stop_token_idx = text.find(stop_token)
+        if stop_token_idx != -1:
+            text = text[:stop_token_idx]
+    return text
+
+
+class FunctionMessage(BaseMessage):
+    """Message for passing the result of executing a function back to a model."""
+
+    name: str
+    """The name of the function that was executed."""
+
+    @property
+    def type(self) -> str:
+        """Type of the message, used for serialization."""
+        return "function"
+
+
+class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
+    """Message Chunk for passing the result of executing a function back to a model."""
+
+    pass
+
+
+def _create_retry_decorator(
+    llm: ChatLiteLLM,
+    run_manager: Optional[
+        Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
+    ] = None,
+) -> Callable[[Any], Any]:
+    """Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions"""
+    import openai
+
+    errors = [
+        openai.error.Timeout,
+        openai.error.APIError,
+        openai.error.APIConnectionError,
+        openai.error.RateLimitError,
+        openai.error.ServiceUnavailableError,
+    ]
+    return create_base_retry_decorator(
+        error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
+    )
+
+
+def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
+    role = _dict["role"]
+    if role == "user":
+        return HumanMessage(content=_dict["content"])
+    elif role == "assistant":
+        # Fix for azure
+        # Also OpenAI returns None for tool invocations
+        content = _dict.get("content", "") or ""
+        if _dict.get("function_call"):
+            additional_kwargs = {"function_call": dict(_dict["function_call"])}
+        else:
+            additional_kwargs = {}
+        return AIMessage(content=content, additional_kwargs=additional_kwargs)
+    elif role == "system":
+        return SystemMessage(content=_dict["content"])
+    elif role == "function":
+        return FunctionMessage(content=_dict["content"], name=_dict["name"])
+    else:
+        return ChatMessage(content=_dict["content"], role=role)
+
+
+async def acompletion_with_retry(
+    llm: ChatLiteLLM,
+    run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    **kwargs: Any,
+) -> Any:
+    """Use tenacity to retry the async completion call."""
+    retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
+
+    @retry_decorator
+    async def _completion_with_retry(**kwargs: Any) -> Any:
+        # Use OpenAI's async api https://github.com/openai/openai-python#async-api
+        return await llm.client.acreate(**kwargs)
+
+    return await _completion_with_retry(**kwargs)
+
+
+def _convert_delta_to_message_chunk(
+    _dict: Mapping[str, Any], default_class: type[BaseMessageChunk]
+) -> BaseMessageChunk:
+    role = _dict.get("role")
+    content = _dict.get("content") or ""
+    if _dict.get("function_call"):
+        additional_kwargs = {"function_call": dict(_dict["function_call"])}
+    else:
+        additional_kwargs = {}
+
+    if role == "user" or default_class == HumanMessageChunk:
+        return HumanMessageChunk(content=content)
+    elif role == "assistant" or default_class == AIMessageChunk:
+        return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
+    elif role == "system" or default_class == SystemMessageChunk:
+        return SystemMessageChunk(content=content)
+    elif role == "function" or default_class == FunctionMessageChunk:
+        return FunctionMessageChunk(content=content, name=_dict["name"])
+    elif role or default_class == ChatMessageChunk:
+        return ChatMessageChunk(content=content, role=role)
+    else:
+        return default_class(content=content)
+
+
+def _convert_message_to_dict(message: BaseMessage) -> dict:
+    if isinstance(message, ChatMessage):
+        message_dict = {"role": message.role, "content": message.content}
+    elif isinstance(message, HumanMessage):
+        message_dict = {"role": "user", "content": message.content}
+    elif isinstance(message, AIMessage):
+        message_dict = {"role": "assistant", "content": message.content}
+        if "function_call" in message.additional_kwargs:
+            message_dict["function_call"] = message.additional_kwargs["function_call"]
+    elif isinstance(message, SystemMessage):
+        message_dict = {"role": "system", "content": message.content}
+    elif isinstance(message, FunctionMessage):
+        message_dict = {
+            "role": "function",
+            "content": message.content,
+            "name": message.name,
+        }
+    else:
+        raise ValueError(f"Got unknown type {message}")
+    if "name" in message.additional_kwargs:
+        message_dict["name"] = message.additional_kwargs["name"]
+    return message_dict
+
+
+class ChatLiteLLM(BaseChatModel):
+    """`LiteLLM` Chat models API.
+
+    To use you must have the google.generativeai Python package installed and
+    either:
+
+        1. The ``GOOGLE_API_KEY``` environment variable set with your API key, or
+        2. Pass your API key using the google_api_key kwarg to the ChatGoogle
+           constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.chat_models import ChatGooglePalm
+            chat = ChatGooglePalm()
+
+    """
+
+    client: Any  #: :meta private:
+    model_name: str = "gpt-3.5-turbo"
+    """Model name to use."""
+    openai_api_key: Optional[str] = None
+    azure_api_key: Optional[str] = None
+    anthropic_api_key: Optional[str] = None
+    replicate_api_key: Optional[str] = None
+    cohere_api_key: Optional[str] = None
+    openrouter_api_key: Optional[str] = None
+    streaming: bool = False
+    api_base: Optional[str] = None
+    organization: Optional[str] = None
+    request_timeout: Optional[Union[float, Tuple[float, float]]] = None
+    temperature: Optional[float] = None
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Run inference with this temperature. Must by in the closed
+       interval [0.0, 1.0]."""
+    top_p: Optional[float] = None
+    """Decode using nucleus sampling: consider the smallest set of tokens whose
+       probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
+    top_k: Optional[int] = None
+    """Decode using top-k sampling: consider the set of top_k most probable tokens.
+       Must be positive."""
+    n: int = 1
+    """Number of chat completions to generate for each prompt. Note that the API may
+       not return the full n completions if duplicates are generated."""
+    max_tokens: int = 256
+
+    max_retries: int = 6
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling OpenAI API."""
+        return {
+            "model": self.model_name,
+            "force_timeout": self.request_timeout,
+            "max_tokens": self.max_tokens,
+            "stream": self.streaming,
+            "n": self.n,
+            "temperature": self.temperature,
+            **self.model_kwargs,
+        }
+
+    @property
+    def _client_params(self) -> Dict[str, Any]:
+        """Get the parameters used for the openai client."""
+        self.client.api_base = self.api_base
+        self.client.organization = self.organization
+        creds: Dict[str, Any] = {
+            "model": self.model_name,
+            "force_timeout": self.request_timeout,
+        }
+        return {**self._default_params, **creds}
+
+    def completion_with_retry(
+        self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
+    ) -> Any:
+        """Use tenacity to retry the completion call."""
+        retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
+
+        @retry_decorator
+        def _completion_with_retry(**kwargs: Any) -> Any:
+            return self.client.completion(**kwargs)
+
+        return _completion_with_retry(**kwargs)
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate api key, python package exists, temperature, top_p, and top_k."""
+        try:
+            import litellm
+        except ImportError:
+            raise ChatLiteLLMException(
+                "Could not import google.generativeai python package. "
+                "Please install it with `pip install google-generativeai`"
+            )
+
+        values["openai_api_key"] = get_from_dict_or_env(
+            values, "openai_api_key", "OPENAI_API_KEY", default=""
+        )
+        values["azure_api_key"] = get_from_dict_or_env(
+            values, "azure_api_key", "AZURE_API_KEY", default=""
+        )
+        values["anthropic_api_key"] = get_from_dict_or_env(
+            values, "anthropic_api_key", "ANTHROPIC_API_KEY", default=""
+        )
+        values["replicate_api_key"] = get_from_dict_or_env(
+            values, "replicate_api_key", "REPLICATE_API_KEY", default=""
+        )
+        values["openrouter_api_key"] = get_from_dict_or_env(
+            values, "openrouter_api_key", "OPENROUTER_API_KEY", default=""
+        )
+        values["client"] = litellm
+
+        if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
+            raise ValueError("temperature must be in the range [0.0, 1.0]")
+
+        if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
+            raise ValueError("top_p must be in the range [0.0, 1.0]")
+
+        if values["top_k"] is not None and values["top_k"] <= 0:
+            raise ValueError("top_k must be positive")
+
+        return values
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        stream: Optional[bool] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if stream if stream is not None else self.streaming:
+            generation: Optional[ChatGenerationChunk] = None
+            for chunk in self._stream(
+                messages=messages, stop=stop, run_manager=run_manager, **kwargs
+            ):
+                if generation is None:
+                    generation = chunk
+                else:
+                    generation += chunk
+            assert generation is not None
+            return ChatResult(generations=[generation])
+
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs}
+        response = self.completion_with_retry(
+            messages=message_dicts, run_manager=run_manager, **params
+        )
+        return self._create_chat_result(response)
+
+    def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
+        generations = []
+        for res in response["choices"]:
+            message = _convert_dict_to_message(res["message"])
+            gen = ChatGeneration(
+                message=message,
+                generation_info=dict(finish_reason=res.get("finish_reason")),
+            )
+            generations.append(gen)
+        token_usage = response.get("usage", {})
+        llm_output = {"token_usage": token_usage, "model_name": self.model_name}
+        return ChatResult(generations=generations, llm_output=llm_output)
+
+    def _create_message_dicts(
+        self, messages: List[BaseMessage], stop: Optional[List[str]]
+    ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
+        params = self._client_params
+        if stop is not None:
+            if "stop" in params:
+                raise ValueError("`stop` found in both the input and default params.")
+            params["stop"] = stop
+        message_dicts = [_convert_message_to_dict(m) for m in messages]
+        return message_dicts, params
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs, "stream": True}
+
+        default_chunk_class = AIMessageChunk
+        for chunk in self.completion_with_retry(
+            messages=message_dicts, run_manager=run_manager, **params
+        ):
+            if len(chunk["choices"]) == 0:
+                continue
+            delta = chunk["choices"][0]["delta"]
+            chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
+            default_chunk_class = chunk.__class__
+            yield ChatGenerationChunk(message=chunk)
+            if run_manager:
+                run_manager.on_llm_new_token(chunk.content)
+
+    async def _astream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs, "stream": True}
+
+        default_chunk_class = AIMessageChunk
+        async for chunk in await acompletion_with_retry(
+            self, messages=message_dicts, run_manager=run_manager, **params
+        ):
+            if len(chunk["choices"]) == 0:
+                continue
+            delta = chunk["choices"][0]["delta"]
+            chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
+            default_chunk_class = chunk.__class__
+            yield ChatGenerationChunk(message=chunk)
+            if run_manager:
+                await run_manager.on_llm_new_token(chunk.content)
+
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        stream: Optional[bool] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if stream if stream is not None else self.streaming:
+            generation: Optional[ChatGenerationChunk] = None
+            async for chunk in self._astream(
+                messages=messages, stop=stop, run_manager=run_manager, **kwargs
+            ):
+                if generation is None:
+                    generation = chunk
+                else:
+                    generation += chunk
+            assert generation is not None
+            return ChatResult(generations=[generation])
+
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs}
+        response = await acompletion_with_retry(
+            self, messages=message_dicts, run_manager=run_manager, **params
+        )
+        return self._create_chat_result(response)
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            "model_name": self.model_name,
+            "temperature": self.temperature,
+            "top_p": self.top_p,
+            "top_k": self.top_k,
+            "n": self.n,
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        return "litellm-chat"
diff --git a/libs/langchain/langchain/chat_models/mlflow_ai_gateway.py b/libs/langchain/langchain/chat_models/mlflow_ai_gateway.py
index 188093463c0..a1c02abb7d8 100644
--- a/libs/langchain/langchain/chat_models/mlflow_ai_gateway.py
+++ b/libs/langchain/langchain/chat_models/mlflow_ai_gateway.py
@@ -3,13 +3,12 @@ import logging
 from functools import partial
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain.chat_models.base import BaseChatModel
+from langchain.pydantic_v1 import BaseModel, Extra
 from langchain.schema import (
     ChatGeneration,
     ChatResult,
@@ -26,8 +25,10 @@ from langchain.schema.messages import (
 logger = logging.getLogger(__name__)
 
 
-class ChatParams(BaseModel, extra=Extra.allow):
-    """Parameters for the MLflow AI Gateway LLM."""
+# Ignoring type because below is valid pydantic code
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"  [call-arg]
+class ChatParams(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
+    """Parameters for the `MLflow AI Gateway` LLM."""
 
     temperature: float = 0.0
     candidate_count: int = 1
@@ -37,8 +38,7 @@ class ChatParams(BaseModel, extra=Extra.allow):
 
 
 class ChatMLflowAIGateway(BaseChatModel):
-    """
-    Wrapper around chat LLMs in the MLflow AI Gateway.
+    """`MLflow AI Gateway` chat models API.
 
     To use, you should have the ``mlflow[gateway]`` python package installed.
     For more information, see https://mlflow.org/docs/latest/gateway/index.html.
diff --git a/libs/langchain/langchain/chat_models/openai.py b/libs/langchain/langchain/chat_models/openai.py
index 7e321c8962a..ea9da5131e5 100644
--- a/libs/langchain/langchain/chat_models/openai.py
+++ b/libs/langchain/langchain/chat_models/openai.py
@@ -17,27 +17,22 @@ from typing import (
     Union,
 )
 
-from pydantic import Field, root_validator
-
+from langchain.adapters.openai import convert_dict_to_message, convert_message_to_dict
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain.chat_models.base import BaseChatModel
 from langchain.llms.base import create_base_retry_decorator
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import ChatGeneration, ChatResult
 from langchain.schema.messages import (
-    AIMessage,
     AIMessageChunk,
     BaseMessage,
     BaseMessageChunk,
-    ChatMessage,
     ChatMessageChunk,
-    FunctionMessage,
     FunctionMessageChunk,
-    HumanMessage,
     HumanMessageChunk,
-    SystemMessage,
     SystemMessageChunk,
 )
 from langchain.schema.output import ChatGenerationChunk
@@ -121,65 +116,8 @@ def _convert_delta_to_message_chunk(
         return default_class(content=content)
 
 
-def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
-    role = _dict["role"]
-    if role == "user":
-        return HumanMessage(content=_dict["content"])
-    elif role == "assistant":
-        # Fix for azure
-        # Also OpenAI returns None for tool invocations
-        content = _dict.get("content", "") or ""
-        if _dict.get("function_call"):
-            additional_kwargs = {"function_call": dict(_dict["function_call"])}
-        else:
-            additional_kwargs = {}
-        return AIMessage(content=content, additional_kwargs=additional_kwargs)
-    elif role == "system":
-        return SystemMessage(content=_dict["content"])
-    elif role == "function":
-        return FunctionMessage(content=_dict["content"], name=_dict["name"])
-    else:
-        return ChatMessage(content=_dict["content"], role=role)
-
-
-def convert_openai_messages(messages: List[dict]) -> List[BaseMessage]:
-    """Convert dictionaries representing OpenAI messages to LangChain format.
-
-    Args:
-        messages: List of dictionaries representing OpenAI messages
-
-    Returns:
-        List of LangChain BaseMessage objects.
-    """
-    return [_convert_dict_to_message(m) for m in messages]
-
-
-def _convert_message_to_dict(message: BaseMessage) -> dict:
-    if isinstance(message, ChatMessage):
-        message_dict = {"role": message.role, "content": message.content}
-    elif isinstance(message, HumanMessage):
-        message_dict = {"role": "user", "content": message.content}
-    elif isinstance(message, AIMessage):
-        message_dict = {"role": "assistant", "content": message.content}
-        if "function_call" in message.additional_kwargs:
-            message_dict["function_call"] = message.additional_kwargs["function_call"]
-    elif isinstance(message, SystemMessage):
-        message_dict = {"role": "system", "content": message.content}
-    elif isinstance(message, FunctionMessage):
-        message_dict = {
-            "role": "function",
-            "content": message.content,
-            "name": message.name,
-        }
-    else:
-        raise ValueError(f"Got unknown type {message}")
-    if "name" in message.additional_kwargs:
-        message_dict["name"] = message.additional_kwargs["name"]
-    return message_dict
-
-
 class ChatOpenAI(BaseChatModel):
-    """Wrapper around OpenAI Chat large language models.
+    """`OpenAI` Chat large language models API.
 
     To use, you should have the ``openai`` python package installed, and the
     environment variable ``OPENAI_API_KEY`` set with your API key.
@@ -411,13 +349,13 @@ class ChatOpenAI(BaseChatModel):
             if "stop" in params:
                 raise ValueError("`stop` found in both the input and default params.")
             params["stop"] = stop
-        message_dicts = [_convert_message_to_dict(m) for m in messages]
+        message_dicts = [convert_message_to_dict(m) for m in messages]
         return message_dicts, params
 
     def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
         generations = []
         for res in response["choices"]:
-            message = _convert_dict_to_message(res["message"])
+            message = convert_dict_to_message(res["message"])
             gen = ChatGeneration(
                 message=message,
                 generation_info=dict(finish_reason=res.get("finish_reason")),
@@ -568,7 +506,7 @@ class ChatOpenAI(BaseChatModel):
                 "information on how messages are converted to tokens."
             )
         num_tokens = 0
-        messages_dict = [_convert_message_to_dict(m) for m in messages]
+        messages_dict = [convert_message_to_dict(m) for m in messages]
         for message in messages_dict:
             num_tokens += tokens_per_message
             for key, value in message.items():
diff --git a/libs/langchain/langchain/chat_models/promptlayer_openai.py b/libs/langchain/langchain/chat_models/promptlayer_openai.py
index 092fdaee52c..2f29b548e3a 100644
--- a/libs/langchain/langchain/chat_models/promptlayer_openai.py
+++ b/libs/langchain/langchain/chat_models/promptlayer_openai.py
@@ -12,7 +12,7 @@ from langchain.schema.messages import BaseMessage
 
 
 class PromptLayerChatOpenAI(ChatOpenAI):
-    """Wrapper around OpenAI Chat large language models and PromptLayer.
+    """`PromptLayer` and `OpenAI` Chat large language models API.
 
     To use, you should have the ``openai`` and ``promptlayer`` python
     package installed, and the environment variable ``OPENAI_API_KEY``
diff --git a/libs/langchain/langchain/chat_models/vertexai.py b/libs/langchain/langchain/chat_models/vertexai.py
index e7b5bfbee9d..79a80b6b4c4 100644
--- a/libs/langchain/langchain/chat_models/vertexai.py
+++ b/libs/langchain/langchain/chat_models/vertexai.py
@@ -2,11 +2,10 @@
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.chat_models.base import BaseChatModel
 from langchain.llms.vertexai import _VertexAICommon, is_codey_model
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import (
     ChatGeneration,
     ChatResult,
@@ -93,7 +92,7 @@ def _parse_examples(examples: List[BaseMessage]) -> List["InputOutputTextPair"]:
 
 
 class ChatVertexAI(_VertexAICommon, BaseChatModel):
-    """Wrapper around Vertex AI large language models."""
+    """`Vertex AI` Chat large language models API."""
 
     model_name: str = "chat-bison"
 
diff --git a/libs/langchain/langchain/document_loaders/__init__.py b/libs/langchain/langchain/document_loaders/__init__.py
index a988744d249..237c5b539ad 100644
--- a/libs/langchain/langchain/document_loaders/__init__.py
+++ b/libs/langchain/langchain/document_loaders/__init__.py
@@ -29,6 +29,7 @@ from langchain.document_loaders.airbyte import (
 from langchain.document_loaders.airbyte_json import AirbyteJSONLoader
 from langchain.document_loaders.airtable import AirtableLoader
 from langchain.document_loaders.apify_dataset import ApifyDatasetLoader
+from langchain.document_loaders.arcgis_loader import ArcGISLoader
 from langchain.document_loaders.arxiv import ArxivLoader
 from langchain.document_loaders.async_html import AsyncHtmlLoader
 from langchain.document_loaders.azlyrics import AZLyricsLoader
@@ -52,6 +53,7 @@ from langchain.document_loaders.blockchain import BlockchainDocumentLoader
 from langchain.document_loaders.brave_search import BraveSearchLoader
 from langchain.document_loaders.browserless import BrowserlessLoader
 from langchain.document_loaders.chatgpt import ChatGPTLoader
+from langchain.document_loaders.chromium import AsyncChromiumLoader
 from langchain.document_loaders.college_confidential import CollegeConfidentialLoader
 from langchain.document_loaders.concurrent import ConcurrentLoader
 from langchain.document_loaders.confluence import ConfluenceLoader
@@ -196,6 +198,9 @@ PagedPDFSplitter = PyPDFLoader
 TelegramChatLoader = TelegramChatFileLoader
 
 __all__ = [
+    "AcreomLoader",
+    "AsyncHtmlLoader",
+    "AsyncChromiumLoader",
     "AZLyricsLoader",
     "AcreomLoader",
     "AirbyteCDKLoader",
@@ -210,6 +215,7 @@ __all__ = [
     "AirtableLoader",
     "AmazonTextractPDFLoader",
     "ApifyDatasetLoader",
+    "ArcGISLoader",
     "ArxivLoader",
     "AsyncHtmlLoader",
     "AzureBlobStorageContainerLoader",
diff --git a/libs/langchain/langchain/document_loaders/acreom.py b/libs/langchain/langchain/document_loaders/acreom.py
index b157c4d4e70..c69ba37d0b4 100644
--- a/libs/langchain/langchain/document_loaders/acreom.py
+++ b/libs/langchain/langchain/document_loaders/acreom.py
@@ -1,4 +1,3 @@
-"""Loads acreom vault from a directory."""
 import re
 from pathlib import Path
 from typing import Iterator, List
@@ -8,7 +7,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class AcreomLoader(BaseLoader):
-    """Loader that loads acreom vault from a directory."""
+    """Load `acreom` vault from a directory."""
 
     FRONT_MATTER_REGEX = re.compile(r"^---\n(.*?)\n---\n", re.MULTILINE | re.DOTALL)
     """Regex to match front matter metadata in markdown files."""
@@ -16,6 +15,7 @@ class AcreomLoader(BaseLoader):
     def __init__(
         self, path: str, encoding: str = "UTF-8", collect_metadata: bool = True
     ):
+        """Initialize the loader."""
         self.file_path = path
         """Path to the directory containing the markdown files."""
         self.encoding = encoding
diff --git a/libs/langchain/langchain/document_loaders/airbyte.py b/libs/langchain/langchain/document_loaders/airbyte.py
index 51411f01200..7f9d4b4407a 100644
--- a/libs/langchain/langchain/document_loaders/airbyte.py
+++ b/libs/langchain/langchain/document_loaders/airbyte.py
@@ -1,4 +1,3 @@
-"""Loads local airbyte json files."""
 from typing import Any, Callable, Iterator, List, Mapping, Optional
 
 from langchain.docstore.document import Document
@@ -9,7 +8,7 @@ RecordHandler = Callable[[Any, Optional[str]], Document]
 
 
 class AirbyteCDKLoader(BaseLoader):
-    """Loads records using an Airbyte source connector implemented using the CDK."""
+    """Load with an `Airbyte` source connector implemented using the `CDK`."""
 
     def __init__(
         self,
@@ -61,9 +60,13 @@ class AirbyteCDKLoader(BaseLoader):
             stream_name=self._stream_name, state=self._state
         )
 
+    @property
+    def last_state(self) -> Any:
+        return self._integration.last_state
+
 
 class AirbyteHubspotLoader(AirbyteCDKLoader):
-    """Loads records from Hubspot using an Airbyte source connector."""
+    """Load from `Hubspot` using an `Airbyte` source connector."""
 
     def __init__(
         self,
@@ -95,7 +98,7 @@ class AirbyteHubspotLoader(AirbyteCDKLoader):
 
 
 class AirbyteStripeLoader(AirbyteCDKLoader):
-    """Loads records from Stripe using an Airbyte source connector."""
+    """Load from `Stripe` using an `Airbyte` source connector."""
 
     def __init__(
         self,
@@ -127,7 +130,7 @@ class AirbyteStripeLoader(AirbyteCDKLoader):
 
 
 class AirbyteTypeformLoader(AirbyteCDKLoader):
-    """Loads records from Typeform using an Airbyte source connector."""
+    """Load from `Typeform` using an `Airbyte` source connector."""
 
     def __init__(
         self,
@@ -159,7 +162,7 @@ class AirbyteTypeformLoader(AirbyteCDKLoader):
 
 
 class AirbyteZendeskSupportLoader(AirbyteCDKLoader):
-    """Loads records from Zendesk Support using an Airbyte source connector."""
+    """Load from `Zendesk Support` using an `Airbyte` source connector."""
 
     def __init__(
         self,
@@ -191,7 +194,7 @@ class AirbyteZendeskSupportLoader(AirbyteCDKLoader):
 
 
 class AirbyteShopifyLoader(AirbyteCDKLoader):
-    """Loads records from Shopify using an Airbyte source connector."""
+    """Load from `Shopify` using an `Airbyte` source connector."""
 
     def __init__(
         self,
@@ -223,7 +226,7 @@ class AirbyteShopifyLoader(AirbyteCDKLoader):
 
 
 class AirbyteSalesforceLoader(AirbyteCDKLoader):
-    """Loads records from Salesforce using an Airbyte source connector."""
+    """Load from `Salesforce` using an `Airbyte` source connector."""
 
     def __init__(
         self,
@@ -255,7 +258,7 @@ class AirbyteSalesforceLoader(AirbyteCDKLoader):
 
 
 class AirbyteGongLoader(AirbyteCDKLoader):
-    """Loads records from Gong using an Airbyte source connector."""
+    """Load from `Gong` using an `Airbyte` source connector."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/airbyte_json.py b/libs/langchain/langchain/document_loaders/airbyte_json.py
index b9033b39f26..02bcb9e50ef 100644
--- a/libs/langchain/langchain/document_loaders/airbyte_json.py
+++ b/libs/langchain/langchain/document_loaders/airbyte_json.py
@@ -1,4 +1,3 @@
-"""Loads local airbyte json files."""
 import json
 from typing import List
 
@@ -8,7 +7,7 @@ from langchain.utils import stringify_dict
 
 
 class AirbyteJSONLoader(BaseLoader):
-    """Loads local airbyte json files."""
+    """Load local `Airbyte` json files."""
 
     def __init__(self, file_path: str):
         """Initialize with a file path. This should start with '/tmp/airbyte_local/'."""
diff --git a/libs/langchain/langchain/document_loaders/airtable.py b/libs/langchain/langchain/document_loaders/airtable.py
index 824799b2841..0bec883597b 100644
--- a/libs/langchain/langchain/document_loaders/airtable.py
+++ b/libs/langchain/langchain/document_loaders/airtable.py
@@ -5,7 +5,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class AirtableLoader(BaseLoader):
-    """Loader for Airtable tables."""
+    """Load the `Airtable` tables."""
 
     def __init__(self, api_token: str, table_id: str, base_id: str):
         """Initialize with API token and the IDs for table and base"""
diff --git a/libs/langchain/langchain/document_loaders/apify_dataset.py b/libs/langchain/langchain/document_loaders/apify_dataset.py
index ca3ae6f995c..25658037d6b 100644
--- a/libs/langchain/langchain/document_loaders/apify_dataset.py
+++ b/libs/langchain/langchain/document_loaders/apify_dataset.py
@@ -1,13 +1,13 @@
 from typing import Any, Callable, Dict, List
 
-from pydantic import BaseModel, root_validator
-
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
+from langchain.pydantic_v1 import BaseModel, root_validator
 
 
 class ApifyDatasetLoader(BaseLoader, BaseModel):
-    """Loads datasets from Apify-a web scraping, crawling, and data extraction platform.
+    """Load datasets from `Apify` web scraping, crawling, and data extraction platform.
+
     For details, see https://docs.apify.com/platform/integrations/langchain
 
     Example:
diff --git a/libs/langchain/langchain/document_loaders/arcgis_loader.py b/libs/langchain/langchain/document_loaders/arcgis_loader.py
new file mode 100644
index 00000000000..87222813281
--- /dev/null
+++ b/libs/langchain/langchain/document_loaders/arcgis_loader.py
@@ -0,0 +1,136 @@
+"""Document Loader for ArcGIS FeatureLayers."""
+
+from __future__ import annotations
+
+import json
+import re
+import warnings
+from datetime import datetime, timezone
+from typing import TYPE_CHECKING, Any, Iterator, List, Optional, Union
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+
+if TYPE_CHECKING:
+    import arcgis
+
+_NOT_PROVIDED = "(Not Provided)"
+
+
+class ArcGISLoader(BaseLoader):
+    """Load records from an ArcGIS FeatureLayer."""
+
+    def __init__(
+        self,
+        layer: Union[str, arcgis.features.FeatureLayer],
+        gis: Optional[arcgis.gis.GIS] = None,
+        where: str = "1=1",
+        out_fields: Optional[Union[List[str], str]] = None,
+        return_geometry: bool = False,
+        return_all_records: bool = True,
+        **kwargs: Any,
+    ):
+        try:
+            import arcgis
+        except ImportError as e:
+            raise ImportError(
+                "arcgis is required to use the ArcGIS Loader. "
+                "Install it with pip or conda."
+            ) from e
+
+        try:
+            from bs4 import BeautifulSoup  # type: ignore
+
+            self.BEAUTIFULSOUP = BeautifulSoup
+        except ImportError:
+            warnings.warn("BeautifulSoup not found. HTML will not be parsed.")
+            self.BEAUTIFULSOUP = None
+
+        self.gis = gis or arcgis.gis.GIS()
+
+        if isinstance(layer, str):
+            self.url = layer
+            self.layer = arcgis.features.FeatureLayer(layer, gis=gis)
+        else:
+            self.url = layer.url
+            self.layer = layer
+
+        self.layer_properties = self._get_layer_properties()
+
+        self.where = where
+
+        if isinstance(out_fields, str):
+            self.out_fields = out_fields
+        elif out_fields is None:
+            self.out_fields = "*"
+        else:
+            self.out_fields = ",".join(out_fields)
+
+        self.return_geometry = return_geometry
+        self.return_all_records = return_all_records
+        self.kwargs = kwargs
+
+    def _get_layer_properties(self) -> dict:
+        """Get the layer properties from the FeatureLayer."""
+        import arcgis
+
+        layer_number_pattern = re.compile(r"/\d+$")
+        props = self.layer.properties
+
+        try:
+            if self.BEAUTIFULSOUP:
+                lyr_desc = self.BEAUTIFULSOUP(props["description"]).text
+            else:
+                lyr_desc = props["description"]
+            lyr_desc = lyr_desc or _NOT_PROVIDED
+        except KeyError:
+            lyr_desc = _NOT_PROVIDED
+        try:
+            item_id = props["serviceItemId"]
+            item = self.gis.content.get(item_id) or arcgis.features.FeatureLayer(
+                re.sub(layer_number_pattern, "", self.url),
+            )
+            try:
+                raw_desc = item.description
+            except AttributeError:
+                raw_desc = item.properties.description
+            if self.BEAUTIFULSOUP:
+                item_desc = self.BEAUTIFULSOUP(raw_desc).text
+            else:
+                item_desc = raw_desc
+            item_desc = item_desc or _NOT_PROVIDED
+        except KeyError:
+            item_desc = _NOT_PROVIDED
+        return {
+            "layer_description": lyr_desc,
+            "item_description": item_desc,
+            "layer_properties": props,
+        }
+
+    def lazy_load(self) -> Iterator[Document]:
+        """Lazy load records from FeatureLayer."""
+
+        query_response = self.layer.query(
+            where=self.where,
+            out_fields=self.out_fields,
+            return_geometry=self.return_geometry,
+            return_all_records=self.return_all_records,
+            **self.kwargs,
+        )
+        features = (feature.as_dict["attributes"] for feature in query_response)
+        for feature in features:
+            yield Document(
+                page_content=json.dumps(feature),
+                metadata={
+                    "accessed": f"{datetime.now(timezone.utc).isoformat()}Z",
+                    "name": self.layer_properties["layer_properties"]["name"],
+                    "url": self.url,
+                    "layer_description": self.layer_properties["layer_description"],
+                    "item_description": self.layer_properties["item_description"],
+                    "layer_properties": self.layer_properties["layer_properties"],
+                },
+            )
+
+    def load(self) -> List[Document]:
+        """Load all records from FeatureLayer."""
+        return list(self.lazy_load())
diff --git a/libs/langchain/langchain/document_loaders/arxiv.py b/libs/langchain/langchain/document_loaders/arxiv.py
index 6a7e139ca43..0cfde95afa2 100644
--- a/libs/langchain/langchain/document_loaders/arxiv.py
+++ b/libs/langchain/langchain/document_loaders/arxiv.py
@@ -6,7 +6,7 @@ from langchain.utilities.arxiv import ArxivAPIWrapper
 
 
 class ArxivLoader(BaseLoader):
-    """Loads a query result from arxiv.org into a list of Documents.
+    """Load a query result from `Arxiv`.
 
     The loader converts the original PDF format into the text.
     """
diff --git a/libs/langchain/langchain/document_loaders/async_html.py b/libs/langchain/langchain/document_loaders/async_html.py
index 9ba8a94473e..ce54573ff99 100644
--- a/libs/langchain/langchain/document_loaders/async_html.py
+++ b/libs/langchain/langchain/document_loaders/async_html.py
@@ -24,7 +24,7 @@ default_header_template = {
 
 
 class AsyncHtmlLoader(BaseLoader):
-    """Loads HTML asynchronously."""
+    """Load `HTML` asynchronously."""
 
     def __init__(
         self,
@@ -36,7 +36,7 @@ class AsyncHtmlLoader(BaseLoader):
         requests_kwargs: Dict[str, Any] = {},
         raise_for_status: bool = False,
     ):
-        """Initialize with webpage path."""
+        """Initialize with a webpage path."""
 
         # TODO: Deprecate web_path in favor of web_paths, and remove this
         # left like this because there are a number of loaders that expect single
diff --git a/libs/langchain/langchain/document_loaders/azlyrics.py b/libs/langchain/langchain/document_loaders/azlyrics.py
index d7b6fe28ba0..df355899e88 100644
--- a/libs/langchain/langchain/document_loaders/azlyrics.py
+++ b/libs/langchain/langchain/document_loaders/azlyrics.py
@@ -1,4 +1,3 @@
-"""Loads AZLyrics."""
 from typing import List
 
 from langchain.docstore.document import Document
@@ -6,7 +5,7 @@ from langchain.document_loaders.web_base import WebBaseLoader
 
 
 class AZLyricsLoader(WebBaseLoader):
-    """Loads AZLyrics webpages."""
+    """Load `AZLyrics` webpages."""
 
     def load(self) -> List[Document]:
         """Load webpages into Documents."""
diff --git a/libs/langchain/langchain/document_loaders/azure_blob_storage_container.py b/libs/langchain/langchain/document_loaders/azure_blob_storage_container.py
index 12155d7fd31..da3542a1d85 100644
--- a/libs/langchain/langchain/document_loaders/azure_blob_storage_container.py
+++ b/libs/langchain/langchain/document_loaders/azure_blob_storage_container.py
@@ -1,4 +1,3 @@
-"""Loading logic for loading documents from an Azure Blob Storage container."""
 from typing import List
 
 from langchain.docstore.document import Document
@@ -9,7 +8,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class AzureBlobStorageContainerLoader(BaseLoader):
-    """Loading Documents from Azure Blob Storage."""
+    """Load from `Azure Blob Storage` container."""
 
     def __init__(self, conn_str: str, container: str, prefix: str = ""):
         """Initialize with connection string, container and blob prefix."""
diff --git a/libs/langchain/langchain/document_loaders/azure_blob_storage_file.py b/libs/langchain/langchain/document_loaders/azure_blob_storage_file.py
index 64b7e2d6778..264e7f39ed4 100644
--- a/libs/langchain/langchain/document_loaders/azure_blob_storage_file.py
+++ b/libs/langchain/langchain/document_loaders/azure_blob_storage_file.py
@@ -8,7 +8,7 @@ from langchain.document_loaders.unstructured import UnstructuredFileLoader
 
 
 class AzureBlobStorageFileLoader(BaseLoader):
-    """Loading Documents from Azure Blob Storage."""
+    """Load from `Azure Blob Storage` files."""
 
     def __init__(self, conn_str: str, container: str, blob_name: str):
         """Initialize with connection string, container and blob name."""
diff --git a/libs/langchain/langchain/document_loaders/base.py b/libs/langchain/langchain/document_loaders/base.py
index b41f985d510..448f79a96d9 100644
--- a/libs/langchain/langchain/document_loaders/base.py
+++ b/libs/langchain/langchain/document_loaders/base.py
@@ -8,7 +8,7 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
 
 
 class BaseLoader(ABC):
-    """Interface for loading Documents.
+    """Interface for Document Loader.
 
     Implementations should implement the lazy-loading method using generators
     to avoid loading all Documents into memory at once.
diff --git a/libs/langchain/langchain/document_loaders/bibtex.py b/libs/langchain/langchain/document_loaders/bibtex.py
index d538b1d44ca..1e32ff6b178 100644
--- a/libs/langchain/langchain/document_loaders/bibtex.py
+++ b/libs/langchain/langchain/document_loaders/bibtex.py
@@ -11,7 +11,7 @@ logger = logging.getLogger(__name__)
 
 
 class BibtexLoader(BaseLoader):
-    """Loads a bibtex file into a list of Documents.
+    """Load a `bibtex` file.
 
     Each document represents one entry from the bibtex file.
 
diff --git a/libs/langchain/langchain/document_loaders/bigquery.py b/libs/langchain/langchain/document_loaders/bigquery.py
index b49b1542f2b..abfb171e5c2 100644
--- a/libs/langchain/langchain/document_loaders/bigquery.py
+++ b/libs/langchain/langchain/document_loaders/bigquery.py
@@ -10,7 +10,7 @@ if TYPE_CHECKING:
 
 
 class BigQueryLoader(BaseLoader):
-    """Loads a query result from BigQuery into a list of documents.
+    """Load from the Google Cloud Platform `BigQuery`.
 
     Each document represents one row of the result. The `page_content_columns`
     are written into the `page_content` of the document. The `metadata_columns`
diff --git a/libs/langchain/langchain/document_loaders/bilibili.py b/libs/langchain/langchain/document_loaders/bilibili.py
index 0c1c8151803..e22ac20f8d9 100644
--- a/libs/langchain/langchain/document_loaders/bilibili.py
+++ b/libs/langchain/langchain/document_loaders/bilibili.py
@@ -10,7 +10,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class BiliBiliLoader(BaseLoader):
-    """Loads bilibili transcripts."""
+    """Load `BiliBili` video transcripts."""
 
     def __init__(self, video_urls: List[str]):
         """Initialize with bilibili url.
diff --git a/libs/langchain/langchain/document_loaders/blackboard.py b/libs/langchain/langchain/document_loaders/blackboard.py
index b21db2dfd1b..91bc72d26e2 100644
--- a/libs/langchain/langchain/document_loaders/blackboard.py
+++ b/libs/langchain/langchain/document_loaders/blackboard.py
@@ -1,4 +1,3 @@
-"""Loads all documents from a blackboard course."""
 import contextlib
 import re
 from pathlib import Path
@@ -12,7 +11,7 @@ from langchain.document_loaders.web_base import WebBaseLoader
 
 
 class BlackboardLoader(WebBaseLoader):
-    """Loads all documents from a Blackboard course.
+    """Load a `Blackboard` course.
 
     This loader is not compatible with all Blackboard courses. It is only
     compatible with courses that use the new Blackboard interface.
diff --git a/libs/langchain/langchain/document_loaders/blob_loaders/file_system.py b/libs/langchain/langchain/document_loaders/blob_loaders/file_system.py
index 4705b2c563f..0ac6ad0d84e 100644
--- a/libs/langchain/langchain/document_loaders/blob_loaders/file_system.py
+++ b/libs/langchain/langchain/document_loaders/blob_loaders/file_system.py
@@ -37,7 +37,7 @@ def _make_iterator(
 
 
 class FileSystemBlobLoader(BlobLoader):
-    """Blob loader for the local file system.
+    """Load blobs in the local file system.
 
     Example:
 
@@ -54,15 +54,17 @@ class FileSystemBlobLoader(BlobLoader):
         path: Union[str, Path],
         *,
         glob: str = "**/[!.]*",
+        exclude: Sequence[str] = (),
         suffixes: Optional[Sequence[str]] = None,
         show_progress: bool = False,
     ) -> None:
-        """Initialize with path to directory and how to glob over it.
+        """Initialize with a path to directory and how to glob over it.
 
         Args:
             path: Path to directory to load from
             glob: Glob pattern relative to the specified path
                   by default set to pick up all non-hidden files
+            exclude: patterns to exclude from results, use glob syntax
             suffixes: Provide to keep only files with these suffixes
                       Useful when wanting to keep files with different suffixes
                       Suffixes must include the dot, e.g. ".txt"
@@ -72,16 +74,23 @@ class FileSystemBlobLoader(BlobLoader):
 
         Examples:
 
-        ... code-block:: python
+            .. code-block:: python
 
-            # Recursively load all text files in a directory.
-            loader = FileSystemBlobLoader("/path/to/directory", glob="**/*.txt")
+                # Recursively load all text files in a directory.
+                loader = FileSystemBlobLoader("/path/to/directory", glob="**/*.txt")
 
-            # Recursively load all non-hidden files in a directory.
-            loader = FileSystemBlobLoader("/path/to/directory", glob="**/[!.]*")
+                # Recursively load all non-hidden files in a directory.
+                loader = FileSystemBlobLoader("/path/to/directory", glob="**/[!.]*")
 
-            # Load all files in a directory without recursion.
-            loader = FileSystemBlobLoader("/path/to/directory", glob="*")
+                # Load all files in a directory without recursion.
+                loader = FileSystemBlobLoader("/path/to/directory", glob="*")
+
+                # Recursively load all files in a directory, except for py or pyc files.
+                loader = FileSystemBlobLoader(
+                    "/path/to/directory",
+                    glob="**/*.txt",
+                    exclude=["**/*.py", "**/*.pyc"]
+                )
         """
         if isinstance(path, Path):
             _path = path
@@ -94,6 +103,7 @@ class FileSystemBlobLoader(BlobLoader):
         self.glob = glob
         self.suffixes = set(suffixes or [])
         self.show_progress = show_progress
+        self.exclude = exclude
 
     def yield_blobs(
         self,
@@ -110,6 +120,9 @@ class FileSystemBlobLoader(BlobLoader):
         """Yield paths that match the requested pattern."""
         paths = self.path.glob(self.glob)
         for path in paths:
+            if self.exclude:
+                if any(path.match(glob) for glob in self.exclude):
+                    continue
             if path.is_file():
                 if self.suffixes and path.suffix not in self.suffixes:
                     continue
diff --git a/libs/langchain/langchain/document_loaders/blob_loaders/schema.py b/libs/langchain/langchain/document_loaders/blob_loaders/schema.py
index 6548352c4ca..59e69c31b21 100644
--- a/libs/langchain/langchain/document_loaders/blob_loaders/schema.py
+++ b/libs/langchain/langchain/document_loaders/blob_loaders/schema.py
@@ -13,13 +13,13 @@ from io import BufferedReader, BytesIO
 from pathlib import PurePath
 from typing import Any, Generator, Iterable, Mapping, Optional, Union
 
-from pydantic import BaseModel, root_validator
+from langchain.pydantic_v1 import BaseModel, root_validator
 
 PathLike = Union[str, PurePath]
 
 
 class Blob(BaseModel):
-    """A blob is used to represent raw data by either reference or value.
+    """Blob represents raw data by either reference or value.
 
     Provides an interface to materialize the blob in different representations, and
     help to decouple the development of data loaders from the downstream parsing of
diff --git a/libs/langchain/langchain/document_loaders/blockchain.py b/libs/langchain/langchain/document_loaders/blockchain.py
index 6b103fe1c8c..c79f67d96a8 100644
--- a/libs/langchain/langchain/document_loaders/blockchain.py
+++ b/libs/langchain/langchain/document_loaders/blockchain.py
@@ -20,7 +20,7 @@ class BlockchainType(Enum):
 
 
 class BlockchainDocumentLoader(BaseLoader):
-    """Loads elements from a blockchain smart contract into Langchain documents.
+    """Load elements from a blockchain smart contract.
 
     The supported blockchains are: Ethereum mainnet, Ethereum Goerli testnet,
     Polygon mainnet, and Polygon Mumbai testnet.
diff --git a/libs/langchain/langchain/document_loaders/brave_search.py b/libs/langchain/langchain/document_loaders/brave_search.py
index 2887256e1e0..e3a4821457d 100644
--- a/libs/langchain/langchain/document_loaders/brave_search.py
+++ b/libs/langchain/langchain/document_loaders/brave_search.py
@@ -6,7 +6,7 @@ from langchain.utilities.brave_search import BraveSearchWrapper
 
 
 class BraveSearchLoader(BaseLoader):
-    """Loads a query result from Brave Search engine into a list of Documents."""
+    """Load with `Brave Search` engine."""
 
     def __init__(self, query: str, api_key: str, search_kwargs: Optional[dict] = None):
         """Initializes the BraveLoader.
diff --git a/libs/langchain/langchain/document_loaders/browserless.py b/libs/langchain/langchain/document_loaders/browserless.py
index 1cb7cb6727e..326e7e303ac 100644
--- a/libs/langchain/langchain/document_loaders/browserless.py
+++ b/libs/langchain/langchain/document_loaders/browserless.py
@@ -7,7 +7,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class BrowserlessLoader(BaseLoader):
-    """Loads the content of webpages using Browserless' /content endpoint"""
+    """Load webpages with `Browserless` /content endpoint."""
 
     def __init__(
         self, api_token: str, urls: Union[str, List[str]], text_content: bool = True
diff --git a/libs/langchain/langchain/document_loaders/chatgpt.py b/libs/langchain/langchain/document_loaders/chatgpt.py
index 5dbb90585f3..35d638d6b3f 100644
--- a/libs/langchain/langchain/document_loaders/chatgpt.py
+++ b/libs/langchain/langchain/document_loaders/chatgpt.py
@@ -1,4 +1,3 @@
-"""Load conversations from ChatGPT data export"""
 import datetime
 import json
 from typing import List
@@ -29,7 +28,7 @@ def concatenate_rows(message: dict, title: str) -> str:
 
 
 class ChatGPTLoader(BaseLoader):
-    """Load conversations from exported ChatGPT data."""
+    """Load conversations from exported `ChatGPT` data."""
 
     def __init__(self, log_file: str, num_logs: int = -1):
         """Initialize a class object.
diff --git a/libs/langchain/langchain/document_loaders/chromium.py b/libs/langchain/langchain/document_loaders/chromium.py
new file mode 100644
index 00000000000..fa757f2b001
--- /dev/null
+++ b/libs/langchain/langchain/document_loaders/chromium.py
@@ -0,0 +1,90 @@
+import asyncio
+import logging
+from typing import Iterator, List
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+
+logger = logging.getLogger(__name__)
+
+
+class AsyncChromiumLoader(BaseLoader):
+    """Scrape HTML pages from URLs using a
+    headless instance of the Chromium."""
+
+    def __init__(
+        self,
+        urls: List[str],
+    ):
+        """
+        Initialize the loader with a list of URL paths.
+
+        Args:
+            urls (List[str]): A list of URLs to scrape content from.
+
+        Raises:
+            ImportError: If the required 'playwright' package is not installed.
+        """
+        self.urls = urls
+
+        try:
+            import playwright  # noqa: F401
+        except ImportError:
+            raise ImportError(
+                "playwright is required for AsyncChromiumLoader. "
+                "Please install it with `pip install playwright`."
+            )
+
+    async def ascrape_playwright(self, url: str) -> str:
+        """
+        Asynchronously scrape the content of a given URL using Playwright's async API.
+
+        Args:
+            url (str): The URL to scrape.
+
+        Returns:
+            str: The scraped HTML content or an error message if an exception occurs.
+
+        """
+        from playwright.async_api import async_playwright
+
+        logger.info("Starting scraping...")
+        results = ""
+        async with async_playwright() as p:
+            browser = await p.chromium.launch(headless=True)
+            try:
+                page = await browser.new_page()
+                await page.goto(url)
+                results = await page.content()  # Simply get the HTML content
+                logger.info("Content scraped")
+            except Exception as e:
+                results = f"Error: {e}"
+            await browser.close()
+        return results
+
+    def lazy_load(self) -> Iterator[Document]:
+        """
+        Lazily load text content from the provided URLs.
+
+        This method yields Documents one at a time as they're scraped,
+        instead of waiting to scrape all URLs before returning.
+
+        Yields:
+            Document: The scraped content encapsulated within a Document object.
+
+        """
+        for url in self.urls:
+            html_content = asyncio.run(self.ascrape_playwright(url))
+            metadata = {"source": url}
+            yield Document(page_content=html_content, metadata=metadata)
+
+    def load(self) -> List[Document]:
+        """
+        Load and return all Documents from the provided URLs.
+
+        Returns:
+            List[Document]: A list of Document objects
+            containing the scraped content from each URL.
+
+        """
+        return list(self.lazy_load())
diff --git a/libs/langchain/langchain/document_loaders/college_confidential.py b/libs/langchain/langchain/document_loaders/college_confidential.py
index 5763fdcdf05..d93a53c8462 100644
--- a/libs/langchain/langchain/document_loaders/college_confidential.py
+++ b/libs/langchain/langchain/document_loaders/college_confidential.py
@@ -1,4 +1,3 @@
-"""Loads College Confidential."""
 from typing import List
 
 from langchain.docstore.document import Document
@@ -6,7 +5,7 @@ from langchain.document_loaders.web_base import WebBaseLoader
 
 
 class CollegeConfidentialLoader(WebBaseLoader):
-    """Loads College Confidential webpages."""
+    """Load `College Confidential` webpages."""
 
     def load(self) -> List[Document]:
         """Load webpages as Documents."""
diff --git a/libs/langchain/langchain/document_loaders/concurrent.py b/libs/langchain/langchain/document_loaders/concurrent.py
index 545449527c2..2044dcc7fbb 100644
--- a/libs/langchain/langchain/document_loaders/concurrent.py
+++ b/libs/langchain/langchain/document_loaders/concurrent.py
@@ -16,9 +16,7 @@ DEFAULT = Literal["default"]
 
 
 class ConcurrentLoader(GenericLoader):
-    """
-    A generic document loader that loads and parses documents concurrently.
-    """
+    """Load and pars Documents concurrently."""
 
     def __init__(
         self, blob_loader: BlobLoader, blob_parser: BaseBlobParser, num_workers: int = 4
diff --git a/libs/langchain/langchain/document_loaders/confluence.py b/libs/langchain/langchain/document_loaders/confluence.py
index 8615faa7d8f..98d63cfc18c 100644
--- a/libs/langchain/langchain/document_loaders/confluence.py
+++ b/libs/langchain/langchain/document_loaders/confluence.py
@@ -1,4 +1,3 @@
-"""Load Data from a Confluence Space"""
 import logging
 from enum import Enum
 from io import BytesIO
@@ -33,7 +32,7 @@ class ContentFormat(str, Enum):
 
 
 class ConfluenceLoader(BaseLoader):
-    """Load Confluence pages.
+    """Load `Confluence` pages.
 
     Port of https://llamahub.ai/l/confluence
     This currently supports username/api_key, Oauth2 login or personal access token
diff --git a/libs/langchain/langchain/document_loaders/conllu.py b/libs/langchain/langchain/document_loaders/conllu.py
index d22c8500d2f..a9170c932d0 100644
--- a/libs/langchain/langchain/document_loaders/conllu.py
+++ b/libs/langchain/langchain/document_loaders/conllu.py
@@ -1,4 +1,3 @@
-"""Load CoNLL-U files."""
 import csv
 from typing import List
 
@@ -7,7 +6,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class CoNLLULoader(BaseLoader):
-    """Load CoNLL-U files."""
+    """Load `CoNLL-U` files."""
 
     def __init__(self, file_path: str):
         """Initialize with a file path."""
diff --git a/libs/langchain/langchain/document_loaders/csv_loader.py b/libs/langchain/langchain/document_loaders/csv_loader.py
index f6d9314c9bb..f2ab7c4eaa6 100644
--- a/libs/langchain/langchain/document_loaders/csv_loader.py
+++ b/libs/langchain/langchain/document_loaders/csv_loader.py
@@ -10,7 +10,7 @@ from langchain.document_loaders.unstructured import (
 
 
 class CSVLoader(BaseLoader):
-    """Loads a CSV file into a list of documents.
+    """Load a `CSV` file into a list of Documents.
 
     Each document represents one row of the CSV file. Every row is converted into a
     key/value pair and outputted to a new line in the document's page_content.
@@ -78,7 +78,9 @@ class CSVLoader(BaseLoader):
 
 
 class UnstructuredCSVLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load CSV files. Like other
+    """Load `CSV` files using `Unstructured`.
+
+    Like other
     Unstructured loaders, UnstructuredCSVLoader can be used in both
     "single" and "elements" mode. If you use the loader in "elements"
     mode, the CSV file will be a single Unstructured Table element.
diff --git a/libs/langchain/langchain/document_loaders/cube_semantic.py b/libs/langchain/langchain/document_loaders/cube_semantic.py
index 2d645e9e5c7..a29a41f02fb 100644
--- a/libs/langchain/langchain/document_loaders/cube_semantic.py
+++ b/libs/langchain/langchain/document_loaders/cube_semantic.py
@@ -12,7 +12,7 @@ logger = logging.getLogger(__name__)
 
 
 class CubeSemanticLoader(BaseLoader):
-    """Load Cube semantic layer metadata.
+    """Load `Cube semantic layer` metadata.
 
     Args:
         cube_api_url: REST API endpoint.
diff --git a/libs/langchain/langchain/document_loaders/datadog_logs.py b/libs/langchain/langchain/document_loaders/datadog_logs.py
index 613288a99ea..a79d7975c0b 100644
--- a/libs/langchain/langchain/document_loaders/datadog_logs.py
+++ b/libs/langchain/langchain/document_loaders/datadog_logs.py
@@ -1,4 +1,3 @@
-"""Load Datadog logs."""
 from datetime import datetime, timedelta
 from typing import List, Optional
 
@@ -7,7 +6,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class DatadogLogsLoader(BaseLoader):
-    """Loads a query result from Datadog into a list of documents.
+    """Load `Datadog` logs.
 
     Logs are written into the `page_content` and into the `metadata`.
     """
diff --git a/libs/langchain/langchain/document_loaders/dataframe.py b/libs/langchain/langchain/document_loaders/dataframe.py
index b1a9194c90d..0476f6a2986 100644
--- a/libs/langchain/langchain/document_loaders/dataframe.py
+++ b/libs/langchain/langchain/document_loaders/dataframe.py
@@ -1,4 +1,3 @@
-"""Load from a Dataframe object"""
 from typing import Any, Iterator, List
 
 from langchain.docstore.document import Document
@@ -6,7 +5,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class DataFrameLoader(BaseLoader):
-    """Load Pandas DataFrame."""
+    """Load `Pandas` DataFrame."""
 
     def __init__(self, data_frame: Any, page_content_column: str = "text"):
         """Initialize with dataframe object.
diff --git a/libs/langchain/langchain/document_loaders/diffbot.py b/libs/langchain/langchain/document_loaders/diffbot.py
index e5ef3d5f7fa..3c2bf4c7aa4 100644
--- a/libs/langchain/langchain/document_loaders/diffbot.py
+++ b/libs/langchain/langchain/document_loaders/diffbot.py
@@ -1,4 +1,3 @@
-"""Loader that uses Diffbot to load webpages in text format."""
 import logging
 from typing import Any, List
 
@@ -11,7 +10,7 @@ logger = logging.getLogger(__name__)
 
 
 class DiffbotLoader(BaseLoader):
-    """Loads Diffbot file json."""
+    """Load `Diffbot` json file."""
 
     def __init__(
         self, api_token: str, urls: List[str], continue_on_failure: bool = True
diff --git a/libs/langchain/langchain/document_loaders/directory.py b/libs/langchain/langchain/document_loaders/directory.py
index 729c953236e..bd6ae0bbb1d 100644
--- a/libs/langchain/langchain/document_loaders/directory.py
+++ b/libs/langchain/langchain/document_loaders/directory.py
@@ -1,4 +1,3 @@
-"""Load documents from a directory."""
 import concurrent
 import logging
 import random
@@ -26,7 +25,7 @@ def _is_visible(p: Path) -> bool:
 
 
 class DirectoryLoader(BaseLoader):
-    """Load documents from a directory."""
+    """Load from a directory."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/discord.py b/libs/langchain/langchain/document_loaders/discord.py
index be6f290d704..a9b5b935705 100644
--- a/libs/langchain/langchain/document_loaders/discord.py
+++ b/libs/langchain/langchain/document_loaders/discord.py
@@ -1,4 +1,3 @@
-"""Load from Discord chat dump"""
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, List
@@ -11,7 +10,7 @@ if TYPE_CHECKING:
 
 
 class DiscordChatLoader(BaseLoader):
-    """Load Discord chat logs."""
+    """Load `Discord` chat logs."""
 
     def __init__(self, chat_log: pd.DataFrame, user_id_col: str = "ID"):
         """Initialize with a Pandas DataFrame containing chat logs.
diff --git a/libs/langchain/langchain/document_loaders/docugami.py b/libs/langchain/langchain/document_loaders/docugami.py
index b60326f24cd..5eeaf483f8a 100644
--- a/libs/langchain/langchain/document_loaders/docugami.py
+++ b/libs/langchain/langchain/document_loaders/docugami.py
@@ -1,5 +1,3 @@
-"""Loads processed documents from Docugami."""
-
 import io
 import logging
 import os
@@ -8,10 +6,10 @@ from pathlib import Path
 from typing import Any, Dict, List, Mapping, Optional, Sequence, Union
 
 import requests
-from pydantic import BaseModel, root_validator
 
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
+from langchain.pydantic_v1 import BaseModel, root_validator
 
 TD_NAME = "{http://www.w3.org/1999/xhtml}td"
 TABLE_NAME = "{http://www.w3.org/1999/xhtml}table"
@@ -29,7 +27,7 @@ logger = logging.getLogger(__name__)
 
 
 class DocugamiLoader(BaseLoader, BaseModel):
-    """Loads processed docs from Docugami.
+    """Load from `Docugami`.
 
     To use, you should have the ``lxml`` python package installed.
     """
diff --git a/libs/langchain/langchain/document_loaders/dropbox.py b/libs/langchain/langchain/document_loaders/dropbox.py
index 1197ef59c04..513d19a6d43 100644
--- a/libs/langchain/langchain/document_loaders/dropbox.py
+++ b/libs/langchain/langchain/document_loaders/dropbox.py
@@ -1,5 +1,3 @@
-"""Loads data from Dropbox."""
-
 # Prerequisites:
 # 1. Create a Dropbox app.
 # 2. Give the app these scope permissions: `files.metadata.read`
@@ -13,14 +11,13 @@ import tempfile
 from pathlib import Path
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, root_validator
-
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
+from langchain.pydantic_v1 import BaseModel, root_validator
 
 
 class DropboxLoader(BaseLoader, BaseModel):
-    """Loads files from Dropbox.
+    """Load files from `Dropbox`.
 
     In addition to common files such as text and PDF files, it also supports
     *Dropbox Paper* files.
diff --git a/libs/langchain/langchain/document_loaders/duckdb_loader.py b/libs/langchain/langchain/document_loaders/duckdb_loader.py
index f1805f69e15..11d64ce969b 100644
--- a/libs/langchain/langchain/document_loaders/duckdb_loader.py
+++ b/libs/langchain/langchain/document_loaders/duckdb_loader.py
@@ -5,7 +5,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class DuckDBLoader(BaseLoader):
-    """Loads a query result from DuckDB into a list of documents.
+    """Load from `DuckDB`.
 
     Each document represents one row of the result. The `page_content_columns`
     are written into the `page_content` of the document. The `metadata_columns`
diff --git a/libs/langchain/langchain/document_loaders/email.py b/libs/langchain/langchain/document_loaders/email.py
index 968502b153b..f85f95a9a6d 100644
--- a/libs/langchain/langchain/document_loaders/email.py
+++ b/libs/langchain/langchain/document_loaders/email.py
@@ -1,4 +1,3 @@
-"""Loads email files."""
 import os
 from typing import Any, List
 
@@ -11,7 +10,9 @@ from langchain.document_loaders.unstructured import (
 
 
 class UnstructuredEmailLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load email files. Works with both
+    """Load email files using `Unstructured`.
+
+    Works with both
     .eml and .msg files. You can process attachments in addition to the
     e-mail message itself by passing process_attachments=True into the
     constructor for the loader. By default, attachments will be processed
diff --git a/libs/langchain/langchain/document_loaders/embaas.py b/libs/langchain/langchain/document_loaders/embaas.py
index 89959f9a59d..2b3e6d01a71 100644
--- a/libs/langchain/langchain/document_loaders/embaas.py
+++ b/libs/langchain/langchain/document_loaders/embaas.py
@@ -3,12 +3,12 @@ import warnings
 from typing import Any, Dict, Iterator, List, Optional
 
 import requests
-from pydantic import BaseModel, root_validator, validator
 from typing_extensions import NotRequired, TypedDict
 
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseBlobParser, BaseLoader
 from langchain.document_loaders.blob_loaders import Blob
+from langchain.pydantic_v1 import BaseModel, root_validator, validator
 from langchain.text_splitter import TextSplitter
 from langchain.utils import get_from_dict_or_env
 
@@ -52,14 +52,14 @@ class EmbaasDocumentExtractionPayload(EmbaasDocumentExtractionParameters):
 
 
 class BaseEmbaasLoader(BaseModel):
-    """Base class for embedding a model into an Embaas document extraction API."""
+    """Base loader for `Embaas` document extraction API."""
 
     embaas_api_key: Optional[str] = None
-    """The API key for the embaas document extraction API."""
+    """The API key for the Embaas document extraction API."""
     api_url: str = EMBAAS_DOC_API_URL
-    """The URL of the embaas document extraction API."""
+    """The URL of the Embaas document extraction API."""
     params: EmbaasDocumentExtractionParameters = EmbaasDocumentExtractionParameters()
-    """Additional parameters to pass to the embaas document extraction API."""
+    """Additional parameters to pass to the Embaas document extraction API."""
 
     @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
@@ -72,7 +72,7 @@ class BaseEmbaasLoader(BaseModel):
 
 
 class EmbaasBlobLoader(BaseEmbaasLoader, BaseBlobParser):
-    """Embaas's document byte loader.
+    """Load `Embaas` blob.
 
     To use, you should have the
     environment variable ``EMBAAS_API_KEY`` set with your API key, or pass
@@ -163,13 +163,13 @@ class EmbaasBlobLoader(BaseEmbaasLoader, BaseBlobParser):
         except requests.exceptions.RequestException as e:
             if e.response is None or not e.response.text:
                 raise ValueError(
-                    f"Error raised by embaas document text extraction API: {e}"
+                    f"Error raised by Embaas document text extraction API: {e}"
                 )
 
             parsed_response = e.response.json()
             if "message" in parsed_response:
                 raise ValueError(
-                    f"Validation Error raised by embaas document text extraction API:"
+                    f"Validation Error raised by Embaas document text extraction API:"
                     f" {parsed_response['message']}"
                 )
             raise
@@ -178,7 +178,7 @@ class EmbaasBlobLoader(BaseEmbaasLoader, BaseBlobParser):
 
 
 class EmbaasLoader(BaseEmbaasLoader, BaseLoader):
-    """Embaas's document loader.
+    """Load from `Embaas`.
 
     To use, you should have the
     environment variable ``EMBAAS_API_KEY`` set with your API key, or pass
diff --git a/libs/langchain/langchain/document_loaders/epub.py b/libs/langchain/langchain/document_loaders/epub.py
index 2c2b49721e8..449f0221a02 100644
--- a/libs/langchain/langchain/document_loaders/epub.py
+++ b/libs/langchain/langchain/document_loaders/epub.py
@@ -1,4 +1,3 @@
-"""Loads EPub files."""
 from typing import List
 
 from langchain.document_loaders.unstructured import (
@@ -8,7 +7,7 @@ from langchain.document_loaders.unstructured import (
 
 
 class UnstructuredEPubLoader(UnstructuredFileLoader):
-    """Loader that uses Unstructured to load EPUB files.
+    """Load `EPub` files using `Unstructured`.
 
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
diff --git a/libs/langchain/langchain/document_loaders/etherscan.py b/libs/langchain/langchain/document_loaders/etherscan.py
index d3fa00368de..eed6df98e2d 100644
--- a/libs/langchain/langchain/document_loaders/etherscan.py
+++ b/libs/langchain/langchain/document_loaders/etherscan.py
@@ -9,8 +9,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class EtherscanLoader(BaseLoader):
-    """
-    Load transactions from an account on Ethereum mainnet.
+    """Load transactions from `Ethereum` mainnet.
 
     The Loader use Etherscan API to interact with Ethereum mainnet.
 
diff --git a/libs/langchain/langchain/document_loaders/evernote.py b/libs/langchain/langchain/document_loaders/evernote.py
index d9765e55cac..0f0f6743423 100644
--- a/libs/langchain/langchain/document_loaders/evernote.py
+++ b/libs/langchain/langchain/document_loaders/evernote.py
@@ -15,7 +15,7 @@ logger = logging.getLogger(__name__)
 
 
 class EverNoteLoader(BaseLoader):
-    """EverNote Loader.
+    """Load from `EverNote`.
 
     Loads an EverNote notebook export file e.g. my_notebook.enex into Documents.
     Instructions on producing this file can be found at
diff --git a/libs/langchain/langchain/document_loaders/excel.py b/libs/langchain/langchain/document_loaders/excel.py
index 619a082f5ba..19b3dece878 100644
--- a/libs/langchain/langchain/document_loaders/excel.py
+++ b/libs/langchain/langchain/document_loaders/excel.py
@@ -8,7 +8,9 @@ from langchain.document_loaders.unstructured import (
 
 
 class UnstructuredExcelLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load Excel files. Like other
+    """Load Microsoft Excel files using `Unstructured`.
+
+    Like other
     Unstructured loaders, UnstructuredExcelLoader can be used in both
     "single" and "elements" mode. If you use the loader in "elements"
     mode, each sheet in the Excel file will be a an Unstructured Table
diff --git a/libs/langchain/langchain/document_loaders/facebook_chat.py b/libs/langchain/langchain/document_loaders/facebook_chat.py
index 58de558a810..fa5a5ba4686 100644
--- a/libs/langchain/langchain/document_loaders/facebook_chat.py
+++ b/libs/langchain/langchain/document_loaders/facebook_chat.py
@@ -1,4 +1,3 @@
-"""Loads Facebook chat json dump."""
 import datetime
 import json
 from pathlib import Path
@@ -23,7 +22,7 @@ def concatenate_rows(row: dict) -> str:
 
 
 class FacebookChatLoader(BaseLoader):
-    """Loads Facebook messages json directory dump."""
+    """Load `Facebook Chat` messages directory dump."""
 
     def __init__(self, path: str):
         """Initialize with a path."""
diff --git a/libs/langchain/langchain/document_loaders/fauna.py b/libs/langchain/langchain/document_loaders/fauna.py
index 3782fb97ee6..3c949a70638 100644
--- a/libs/langchain/langchain/document_loaders/fauna.py
+++ b/libs/langchain/langchain/document_loaders/fauna.py
@@ -5,7 +5,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class FaunaLoader(BaseLoader):
-    """FaunaDB Loader.
+    """Load from `FaunaDB`.
 
     Attributes:
         query (str): The FQL query string to execute.
diff --git a/libs/langchain/langchain/document_loaders/figma.py b/libs/langchain/langchain/document_loaders/figma.py
index ff67c9c45c0..5f424be4f70 100644
--- a/libs/langchain/langchain/document_loaders/figma.py
+++ b/libs/langchain/langchain/document_loaders/figma.py
@@ -1,4 +1,3 @@
-"""Loads Figma files json dump."""
 import json
 import urllib.request
 from typing import Any, List
@@ -9,7 +8,7 @@ from langchain.utils import stringify_dict
 
 
 class FigmaFileLoader(BaseLoader):
-    """Loads Figma file json."""
+    """Load `Figma` file."""
 
     def __init__(self, access_token: str, ids: str, key: str):
         """Initialize with access token, ids, and key.
diff --git a/libs/langchain/langchain/document_loaders/gcs_directory.py b/libs/langchain/langchain/document_loaders/gcs_directory.py
index 1820f198b51..a990662897f 100644
--- a/libs/langchain/langchain/document_loaders/gcs_directory.py
+++ b/libs/langchain/langchain/document_loaders/gcs_directory.py
@@ -1,4 +1,3 @@
-"""Loading logic for loading documents from an GCS directory."""
 from typing import Callable, List, Optional
 
 from langchain.docstore.document import Document
@@ -7,7 +6,7 @@ from langchain.document_loaders.gcs_file import GCSFileLoader
 
 
 class GCSDirectoryLoader(BaseLoader):
-    """Loads Documents from GCS."""
+    """Load from GCS directory."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/gcs_file.py b/libs/langchain/langchain/document_loaders/gcs_file.py
index 3dd6950393b..1e6a6da7676 100644
--- a/libs/langchain/langchain/document_loaders/gcs_file.py
+++ b/libs/langchain/langchain/document_loaders/gcs_file.py
@@ -1,4 +1,3 @@
-"""Load documents from a GCS file."""
 import os
 import tempfile
 from typing import Callable, List, Optional
@@ -9,7 +8,7 @@ from langchain.document_loaders.unstructured import UnstructuredFileLoader
 
 
 class GCSFileLoader(BaseLoader):
-    """Load Documents from a GCS file."""
+    """Load from GCS file."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/generic.py b/libs/langchain/langchain/document_loaders/generic.py
index 0625507b278..2728e0ae9fe 100644
--- a/libs/langchain/langchain/document_loaders/generic.py
+++ b/libs/langchain/langchain/document_loaders/generic.py
@@ -15,14 +15,14 @@ DEFAULT = Literal["default"]
 
 
 class GenericLoader(BaseLoader):
-    """A generic document loader.
+    """Generic Document Loader.
 
     A generic document loader that allows combining an arbitrary blob loader with
     a blob parser.
 
     Examples:
 
-        .. code-block:: python
+       .. code-block:: python
 
         from langchain.document_loaders import GenericLoader
         from langchain.document_loaders.blob_loaders import FileSystemBlobLoader
@@ -39,7 +39,7 @@ class GenericLoader(BaseLoader):
 
         Example instantiations to change which files are loaded:
 
-        ... code-block:: python
+        .. code-block:: python
 
             # Recursively load all text files in a directory.
             loader = GenericLoader.from_filesystem("/path/to/dir", glob="**/*.txt")
@@ -52,7 +52,7 @@ class GenericLoader(BaseLoader):
 
         Example instantiations to change which parser is used:
 
-        ... code-block:: python
+        .. code-block:: python
 
             from langchain.document_loaders.parsers.pdf import PyPDFParser
 
diff --git a/libs/langchain/langchain/document_loaders/geodataframe.py b/libs/langchain/langchain/document_loaders/geodataframe.py
index 68998212a1e..70f5fa626e0 100644
--- a/libs/langchain/langchain/document_loaders/geodataframe.py
+++ b/libs/langchain/langchain/document_loaders/geodataframe.py
@@ -1,4 +1,3 @@
-"""Load from Dataframe object"""
 from typing import Any, Iterator, List
 
 from langchain.docstore.document import Document
@@ -6,7 +5,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class GeoDataFrameLoader(BaseLoader):
-    """Load geopandas Dataframe."""
+    """Load `geopandas` Dataframe."""
 
     def __init__(self, data_frame: Any, page_content_column: str = "geometry"):
         """Initialize with geopandas Dataframe.
diff --git a/libs/langchain/langchain/document_loaders/git.py b/libs/langchain/langchain/document_loaders/git.py
index 14b2676a45f..14a8849c7e8 100644
--- a/libs/langchain/langchain/document_loaders/git.py
+++ b/libs/langchain/langchain/document_loaders/git.py
@@ -6,7 +6,8 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class GitLoader(BaseLoader):
-    """Loads files from a Git repository into a list of documents.
+    """Load `Git` repository files.
+
     The Repository can be local on disk available at `repo_path`,
     or remote at `clone_url` that will be cloned to `repo_path`.
     Currently, supports only text files.
diff --git a/libs/langchain/langchain/document_loaders/gitbook.py b/libs/langchain/langchain/document_loaders/gitbook.py
index 1fcec22922d..996a5e12d28 100644
--- a/libs/langchain/langchain/document_loaders/gitbook.py
+++ b/libs/langchain/langchain/document_loaders/gitbook.py
@@ -1,4 +1,3 @@
-"""Loads GitBook."""
 from typing import Any, List, Optional
 from urllib.parse import urljoin, urlparse
 
@@ -7,7 +6,7 @@ from langchain.document_loaders.web_base import WebBaseLoader
 
 
 class GitbookLoader(WebBaseLoader):
-    """Load GitBook data.
+    """Load `GitBook` data.
 
     1. load from either a single page, or
     2. load all (relative) paths in the navbar.
diff --git a/libs/langchain/langchain/document_loaders/github.py b/libs/langchain/langchain/document_loaders/github.py
index 2a55e3d0022..e3dc44b579c 100644
--- a/libs/langchain/langchain/document_loaders/github.py
+++ b/libs/langchain/langchain/document_loaders/github.py
@@ -3,15 +3,15 @@ from datetime import datetime
 from typing import Dict, Iterator, List, Literal, Optional, Union
 
 import requests
-from pydantic import BaseModel, root_validator, validator
 
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
+from langchain.pydantic_v1 import BaseModel, root_validator, validator
 from langchain.utils import get_from_dict_or_env
 
 
 class BaseGitHubLoader(BaseLoader, BaseModel, ABC):
-    """Load issues of a GitHub repository."""
+    """Load `GitHub` repository Issues."""
 
     repo: str
     """Name of repository"""
diff --git a/libs/langchain/langchain/document_loaders/googledrive.py b/libs/langchain/langchain/document_loaders/googledrive.py
index 9a0290a35ef..881bafbd5ae 100644
--- a/libs/langchain/langchain/document_loaders/googledrive.py
+++ b/libs/langchain/langchain/document_loaders/googledrive.py
@@ -1,5 +1,3 @@
-"""Loads data from Google Drive."""
-
 # Prerequisites:
 # 1. Create a Google Cloud project
 # 2. Enable the Google Drive API:
@@ -13,16 +11,15 @@ import os
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Sequence, Union
 
-from pydantic import BaseModel, root_validator, validator
-
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
+from langchain.pydantic_v1 import BaseModel, root_validator, validator
 
 SCOPES = ["https://www.googleapis.com/auth/drive.readonly"]
 
 
 class GoogleDriveLoader(BaseLoader, BaseModel):
-    """Loads Google Docs from Google Drive."""
+    """Load Google Docs from `Google Drive`."""
 
     service_account_key: Path = Path.home() / ".credentials" / "keys.json"
     """Path to the service account key file."""
diff --git a/libs/langchain/langchain/document_loaders/gutenberg.py b/libs/langchain/langchain/document_loaders/gutenberg.py
index bcf1370dd8b..f1280ef651e 100644
--- a/libs/langchain/langchain/document_loaders/gutenberg.py
+++ b/libs/langchain/langchain/document_loaders/gutenberg.py
@@ -1,4 +1,3 @@
-"""Loads .txt web files."""
 from typing import List
 
 from langchain.docstore.document import Document
@@ -6,7 +5,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class GutenbergLoader(BaseLoader):
-    """Loader that uses urllib to load .txt web files."""
+    """Load from `Gutenberg.org`."""
 
     def __init__(self, file_path: str):
         """Initialize with a file path."""
diff --git a/libs/langchain/langchain/document_loaders/helpers.py b/libs/langchain/langchain/document_loaders/helpers.py
index c48d0b8eb18..6e0f8b9bfb9 100644
--- a/libs/langchain/langchain/document_loaders/helpers.py
+++ b/libs/langchain/langchain/document_loaders/helpers.py
@@ -5,7 +5,7 @@ from typing import List, NamedTuple, Optional, cast
 
 
 class FileEncoding(NamedTuple):
-    """A file encoding as the NamedTuple."""
+    """File encoding as the NamedTuple."""
 
     encoding: Optional[str]
     """The encoding of the file."""
diff --git a/libs/langchain/langchain/document_loaders/hn.py b/libs/langchain/langchain/document_loaders/hn.py
index 34b284648b1..a5f21b1f6f0 100644
--- a/libs/langchain/langchain/document_loaders/hn.py
+++ b/libs/langchain/langchain/document_loaders/hn.py
@@ -1,4 +1,3 @@
-"""Loads HN."""
 from typing import Any, List
 
 from langchain.docstore.document import Document
@@ -6,7 +5,9 @@ from langchain.document_loaders.web_base import WebBaseLoader
 
 
 class HNLoader(WebBaseLoader):
-    """Load Hacker News data from either main page results or the comments page."""
+    """Load `Hacker News` data.
+
+    It loads data from either main page results or the comments page."""
 
     def load(self) -> List[Document]:
         """Get important HN webpage information.
diff --git a/libs/langchain/langchain/document_loaders/html.py b/libs/langchain/langchain/document_loaders/html.py
index dce26970766..4cca1bfd1e4 100644
--- a/libs/langchain/langchain/document_loaders/html.py
+++ b/libs/langchain/langchain/document_loaders/html.py
@@ -1,11 +1,10 @@
-"""Loader that uses unstructured to load HTML files."""
 from typing import List
 
 from langchain.document_loaders.unstructured import UnstructuredFileLoader
 
 
 class UnstructuredHTMLLoader(UnstructuredFileLoader):
-    """Loader that uses Unstructured to load HTML files.
+    """Load `HTML` files using `Unstructured`.
 
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
diff --git a/libs/langchain/langchain/document_loaders/html_bs.py b/libs/langchain/langchain/document_loaders/html_bs.py
index cebf3c1baf7..85a5d7ad8f4 100644
--- a/libs/langchain/langchain/document_loaders/html_bs.py
+++ b/libs/langchain/langchain/document_loaders/html_bs.py
@@ -1,5 +1,3 @@
-"""Loader that uses bs4 to load HTML files, enriching metadata with page title."""
-
 import logging
 from typing import Dict, List, Union
 
@@ -10,7 +8,7 @@ logger = logging.getLogger(__name__)
 
 
 class BSHTMLLoader(BaseLoader):
-    """Loader that uses beautiful soup to parse HTML files."""
+    """Load `HTML` files and parse them with `beautiful soup`."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/hugging_face_dataset.py b/libs/langchain/langchain/document_loaders/hugging_face_dataset.py
index 17b823dd21d..a253e720247 100644
--- a/libs/langchain/langchain/document_loaders/hugging_face_dataset.py
+++ b/libs/langchain/langchain/document_loaders/hugging_face_dataset.py
@@ -1,4 +1,3 @@
-"""Loads HuggingFace datasets."""
 from typing import Iterator, List, Mapping, Optional, Sequence, Union
 
 from langchain.docstore.document import Document
@@ -6,7 +5,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class HuggingFaceDatasetLoader(BaseLoader):
-    """Load Documents from the Hugging Face Hub."""
+    """Load from `Hugging Face Hub` datasets."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/ifixit.py b/libs/langchain/langchain/document_loaders/ifixit.py
index 1669dace99c..3507334c566 100644
--- a/libs/langchain/langchain/document_loaders/ifixit.py
+++ b/libs/langchain/langchain/document_loaders/ifixit.py
@@ -1,4 +1,3 @@
-"""Loads iFixit data."""
 from typing import List, Optional
 
 import requests
@@ -11,7 +10,7 @@ IFIXIT_BASE_URL = "https://www.ifixit.com/api/2.0"
 
 
 class IFixitLoader(BaseLoader):
-    """Load iFixit repair guides, device wikis and answers.
+    """Load `iFixit` repair guides, device wikis and answers.
 
     iFixit is the largest, open repair community on the web. The site contains nearly
     100k repair manuals, 200k Questions & Answers on 42k devices, and all the data is
diff --git a/libs/langchain/langchain/document_loaders/image.py b/libs/langchain/langchain/document_loaders/image.py
index 9a31bd00c6c..4d854676d5a 100644
--- a/libs/langchain/langchain/document_loaders/image.py
+++ b/libs/langchain/langchain/document_loaders/image.py
@@ -1,11 +1,10 @@
-"""Loads image files."""
 from typing import List
 
 from langchain.document_loaders.unstructured import UnstructuredFileLoader
 
 
 class UnstructuredImageLoader(UnstructuredFileLoader):
-    """Loader that uses Unstructured to load PNG and JPG files.
+    """Load `PNG` and `JPG` files using `Unstructured`.
 
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
diff --git a/libs/langchain/langchain/document_loaders/image_captions.py b/libs/langchain/langchain/document_loaders/image_captions.py
index 53e3c694e25..40d3de9379b 100644
--- a/libs/langchain/langchain/document_loaders/image_captions.py
+++ b/libs/langchain/langchain/document_loaders/image_captions.py
@@ -1,9 +1,3 @@
-"""Loads image captions.
-
-By default, the loader utilizes the pre-trained BLIP image captioning model.
-https://huggingface.co/Salesforce/blip-image-captioning-base
-
-"""
 from typing import Any, List, Tuple, Union
 
 import requests
@@ -13,7 +7,12 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class ImageCaptionLoader(BaseLoader):
-    """Loads the captions of an image"""
+    """Load image captions.
+
+    By default, the loader utilizes the pre-trained
+    Salesforce BLIP image captioning model.
+    https://huggingface.co/Salesforce/blip-image-captioning-base
+    """
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/imsdb.py b/libs/langchain/langchain/document_loaders/imsdb.py
index 312e25a8508..acf0e1b59be 100644
--- a/libs/langchain/langchain/document_loaders/imsdb.py
+++ b/libs/langchain/langchain/document_loaders/imsdb.py
@@ -1,4 +1,3 @@
-"""Loads IMSDb."""
 from typing import List
 
 from langchain.docstore.document import Document
@@ -6,7 +5,7 @@ from langchain.document_loaders.web_base import WebBaseLoader
 
 
 class IMSDbLoader(WebBaseLoader):
-    """Loads IMSDb webpages."""
+    """Load `IMSDb` webpages."""
 
     def load(self) -> List[Document]:
         """Load webpage."""
diff --git a/libs/langchain/langchain/document_loaders/iugu.py b/libs/langchain/langchain/document_loaders/iugu.py
index 229cf6f6295..c8ee3c35113 100644
--- a/libs/langchain/langchain/document_loaders/iugu.py
+++ b/libs/langchain/langchain/document_loaders/iugu.py
@@ -1,4 +1,3 @@
-"""Loader that fetches data from IUGU"""
 import json
 import urllib.request
 from typing import List, Optional
@@ -17,7 +16,7 @@ IUGU_ENDPOINTS = {
 
 
 class IuguLoader(BaseLoader):
-    """Loader that fetches data from IUGU."""
+    """Load from `IUGU`."""
 
     def __init__(self, resource: str, api_token: Optional[str] = None) -> None:
         """Initialize the IUGU resource.
diff --git a/libs/langchain/langchain/document_loaders/joplin.py b/libs/langchain/langchain/document_loaders/joplin.py
index 0cce9746226..62efe62fd9c 100644
--- a/libs/langchain/langchain/document_loaders/joplin.py
+++ b/libs/langchain/langchain/document_loaders/joplin.py
@@ -11,8 +11,7 @@ LINK_NOTE_TEMPLATE = "joplin://x-callback-url/openNote?id={id}"
 
 
 class JoplinLoader(BaseLoader):
-    """
-    Loader that fetches notes from Joplin.
+    """Load notes from `Joplin`.
 
     In order to use this loader, you need to have Joplin running with the
     Web Clipper enabled (look for "Web Clipper" in the app settings).
diff --git a/libs/langchain/langchain/document_loaders/json_loader.py b/libs/langchain/langchain/document_loaders/json_loader.py
index 9e793798e38..e13ae1bb89c 100644
--- a/libs/langchain/langchain/document_loaders/json_loader.py
+++ b/libs/langchain/langchain/document_loaders/json_loader.py
@@ -1,4 +1,3 @@
-"""Loads data from JSON."""
 import json
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Union
@@ -8,7 +7,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class JSONLoader(BaseLoader):
-    """Loads a JSON file using a jq schema.
+    """Load a `JSON` file using a `jq` schema.
 
     Example:
         [{"text": ...}, {"text": ...}, {"text": ...}] -> schema = .[].text
diff --git a/libs/langchain/langchain/document_loaders/larksuite.py b/libs/langchain/langchain/document_loaders/larksuite.py
index d57c10b8989..1b94ca01752 100644
--- a/libs/langchain/langchain/document_loaders/larksuite.py
+++ b/libs/langchain/langchain/document_loaders/larksuite.py
@@ -1,4 +1,3 @@
-"""Loads LarkSuite (FeiShu) document json dump."""
 import json
 import urllib.request
 from typing import Any, Iterator, List
@@ -8,7 +7,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class LarkSuiteDocLoader(BaseLoader):
-    """Loads LarkSuite (FeiShu) document."""
+    """Load from `LarkSuite` (`FeiShu`)."""
 
     def __init__(self, domain: str, access_token: str, document_id: str):
         """Initialize with domain, access_token (tenant / user), and document_id.
diff --git a/libs/langchain/langchain/document_loaders/markdown.py b/libs/langchain/langchain/document_loaders/markdown.py
index 820b5b53de5..8b3665a4968 100644
--- a/libs/langchain/langchain/document_loaders/markdown.py
+++ b/libs/langchain/langchain/document_loaders/markdown.py
@@ -1,11 +1,10 @@
-"""Loads Markdown files."""
 from typing import List
 
 from langchain.document_loaders.unstructured import UnstructuredFileLoader
 
 
 class UnstructuredMarkdownLoader(UnstructuredFileLoader):
-    """Loader that uses Unstructured to load markdown files.
+    """Load `Markdown` files using `Unstructured`.
 
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
diff --git a/libs/langchain/langchain/document_loaders/mastodon.py b/libs/langchain/langchain/document_loaders/mastodon.py
index ae353790cdf..3ed70e0de1c 100644
--- a/libs/langchain/langchain/document_loaders/mastodon.py
+++ b/libs/langchain/langchain/document_loaders/mastodon.py
@@ -1,4 +1,3 @@
-"""Mastodon document loader."""
 from __future__ import annotations
 
 import os
@@ -23,7 +22,7 @@ def _dependable_mastodon_import() -> mastodon:
 
 
 class MastodonTootsLoader(BaseLoader):
-    """Mastodon toots loader."""
+    """Load the `Mastodon` 'toots'."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/max_compute.py b/libs/langchain/langchain/document_loaders/max_compute.py
index ee3c64ae79d..d75439634bc 100644
--- a/libs/langchain/langchain/document_loaders/max_compute.py
+++ b/libs/langchain/langchain/document_loaders/max_compute.py
@@ -8,7 +8,7 @@ from langchain.utilities.max_compute import MaxComputeAPIWrapper
 
 
 class MaxComputeLoader(BaseLoader):
-    """Loads a query result from Alibaba Cloud MaxCompute table into documents."""
+    """Load from `Alibaba Cloud MaxCompute` table."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/mediawikidump.py b/libs/langchain/langchain/document_loaders/mediawikidump.py
index 370289f1281..d4f7815cec2 100644
--- a/libs/langchain/langchain/document_loaders/mediawikidump.py
+++ b/libs/langchain/langchain/document_loaders/mediawikidump.py
@@ -1,4 +1,3 @@
-"""Load Data from a MediaWiki dump xml."""
 import logging
 from pathlib import Path
 from typing import List, Optional, Sequence, Union
@@ -10,8 +9,8 @@ logger = logging.getLogger(__name__)
 
 
 class MWDumpLoader(BaseLoader):
-    """
-    Load MediaWiki dump from XML file
+    """Load `MediaWiki` dump from an `XML` file.
+
     Example:
         .. code-block:: python
 
diff --git a/libs/langchain/langchain/document_loaders/mhtml.py b/libs/langchain/langchain/document_loaders/mhtml.py
index 4def89a2dd3..c7b7f55ec9a 100644
--- a/libs/langchain/langchain/document_loaders/mhtml.py
+++ b/libs/langchain/langchain/document_loaders/mhtml.py
@@ -1,5 +1,3 @@
-"""Load MHTML files, enriching metadata with page title."""
-
 import email
 import logging
 from typing import Dict, List, Union
@@ -11,7 +9,7 @@ logger = logging.getLogger(__name__)
 
 
 class MHTMLLoader(BaseLoader):
-    """Loader that uses beautiful soup to parse HTML files."""
+    """Parse `MHTML` files with `BeautifulSoup`."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/modern_treasury.py b/libs/langchain/langchain/document_loaders/modern_treasury.py
index 2d12d6bd8e8..21607ba1c89 100644
--- a/libs/langchain/langchain/document_loaders/modern_treasury.py
+++ b/libs/langchain/langchain/document_loaders/modern_treasury.py
@@ -1,4 +1,3 @@
-"""Loader that fetches data from Modern Treasury"""
 import json
 import urllib.request
 from base64 import b64encode
@@ -27,7 +26,7 @@ incoming_payment_details",
 
 
 class ModernTreasuryLoader(BaseLoader):
-    """Loader that fetches data from Modern Treasury."""
+    """Load from `Modern Treasury`."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/news.py b/libs/langchain/langchain/document_loaders/news.py
index 7e4931f94bb..2709a8cd103 100644
--- a/libs/langchain/langchain/document_loaders/news.py
+++ b/libs/langchain/langchain/document_loaders/news.py
@@ -9,7 +9,7 @@ logger = logging.getLogger(__name__)
 
 
 class NewsURLLoader(BaseLoader):
-    """Loader that uses newspaper to load news articles from URLs.
+    """Load news articles from URLs using `Unstructured`.
 
     Args:
         urls: URLs to load. Each is loaded into its own document.
diff --git a/libs/langchain/langchain/document_loaders/notebook.py b/libs/langchain/langchain/document_loaders/notebook.py
index 19522c2a5cb..77a01b6c45c 100644
--- a/libs/langchain/langchain/document_loaders/notebook.py
+++ b/libs/langchain/langchain/document_loaders/notebook.py
@@ -56,7 +56,7 @@ def concatenate_cells(
 
 
 def remove_newlines(x: Any) -> Any:
-    """Recursively removes newlines, no matter the data structure they are stored in."""
+    """Recursively remove newlines, no matter the data structure they are stored in."""
     import pandas as pd
 
     if isinstance(x, str):
@@ -70,7 +70,7 @@ def remove_newlines(x: Any) -> Any:
 
 
 class NotebookLoader(BaseLoader):
-    """Loads .ipynb notebook files."""
+    """Load `Jupyter notebook` (.ipynb) files."""
 
     def __init__(
         self,
@@ -80,7 +80,7 @@ class NotebookLoader(BaseLoader):
         remove_newline: bool = False,
         traceback: bool = False,
     ):
-        """Initialize with path.
+        """Initialize with a path.
 
         Args:
             path: The path to load the notebook from.
diff --git a/libs/langchain/langchain/document_loaders/notion.py b/libs/langchain/langchain/document_loaders/notion.py
index 48010525587..15678ace9e7 100644
--- a/libs/langchain/langchain/document_loaders/notion.py
+++ b/libs/langchain/langchain/document_loaders/notion.py
@@ -1,4 +1,3 @@
-"""Loads Notion directory dump."""
 from pathlib import Path
 from typing import List
 
@@ -7,7 +6,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class NotionDirectoryLoader(BaseLoader):
-    """Loads Notion directory dump."""
+    """Load `Notion directory` dump."""
 
     def __init__(self, path: str):
         """Initialize with a file path."""
diff --git a/libs/langchain/langchain/document_loaders/notiondb.py b/libs/langchain/langchain/document_loaders/notiondb.py
index 16915fba0e2..1f7a67a2a54 100644
--- a/libs/langchain/langchain/document_loaders/notiondb.py
+++ b/libs/langchain/langchain/document_loaders/notiondb.py
@@ -1,5 +1,3 @@
-"""Notion DB loader for langchain"""
-
 from typing import Any, Dict, List, Optional
 
 import requests
@@ -14,7 +12,7 @@ BLOCK_URL = NOTION_BASE_URL + "/blocks/{block_id}/children"
 
 
 class NotionDBLoader(BaseLoader):
-    """Notion DB Loader.
+    """Load from `Notion DB`.
 
     Reads content from pages within a Notion Database.
     Args:
diff --git a/libs/langchain/langchain/document_loaders/nuclia.py b/libs/langchain/langchain/document_loaders/nuclia.py
index 097564ee555..218c11f6338 100644
--- a/libs/langchain/langchain/document_loaders/nuclia.py
+++ b/libs/langchain/langchain/document_loaders/nuclia.py
@@ -1,4 +1,3 @@
-"""Extract text from any file type."""
 import json
 import uuid
 from typing import List
@@ -9,7 +8,7 @@ from langchain.tools.nuclia.tool import NucliaUnderstandingAPI
 
 
 class NucliaLoader(BaseLoader):
-    """Extract text from any file type."""
+    """Load from any file type using `Nuclia Understanding API`."""
 
     def __init__(self, path: str, nuclia_tool: NucliaUnderstandingAPI):
         self.nua = nuclia_tool
diff --git a/libs/langchain/langchain/document_loaders/obs_directory.py b/libs/langchain/langchain/document_loaders/obs_directory.py
index ba30d6a5780..b7af3e330b1 100644
--- a/libs/langchain/langchain/document_loaders/obs_directory.py
+++ b/libs/langchain/langchain/document_loaders/obs_directory.py
@@ -7,7 +7,7 @@ from langchain.document_loaders.obs_file import OBSFileLoader
 
 
 class OBSDirectoryLoader(BaseLoader):
-    """Loading logic for loading documents from Huawei OBS."""
+    """Load from `Huawei OBS directory`."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/obs_file.py b/libs/langchain/langchain/document_loaders/obs_file.py
index b366f4e0e46..69a63808e4a 100644
--- a/libs/langchain/langchain/document_loaders/obs_file.py
+++ b/libs/langchain/langchain/document_loaders/obs_file.py
@@ -10,7 +10,7 @@ from langchain.document_loaders.unstructured import UnstructuredFileLoader
 
 
 class OBSFileLoader(BaseLoader):
-    """Loader for Huawei OBS file."""
+    """Load from the `Huawei OBS file`."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/obsidian.py b/libs/langchain/langchain/document_loaders/obsidian.py
index fd43b075430..ab3a3b188ae 100644
--- a/libs/langchain/langchain/document_loaders/obsidian.py
+++ b/libs/langchain/langchain/document_loaders/obsidian.py
@@ -1,4 +1,3 @@
-"""Loads Obsidian directory dump."""
 import re
 from pathlib import Path
 from typing import List
@@ -8,7 +7,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class ObsidianLoader(BaseLoader):
-    """Loads Obsidian files from disk."""
+    """Load `Obsidian` files from directory."""
 
     FRONT_MATTER_REGEX = re.compile(r"^---\n(.*?)\n---\n", re.MULTILINE | re.DOTALL)
 
diff --git a/libs/langchain/langchain/document_loaders/odt.py b/libs/langchain/langchain/document_loaders/odt.py
index a8641b65623..e8e740fc7b7 100644
--- a/libs/langchain/langchain/document_loaders/odt.py
+++ b/libs/langchain/langchain/document_loaders/odt.py
@@ -1,4 +1,3 @@
-"""Loads OpenOffice ODT files."""
 from typing import Any, List
 
 from langchain.document_loaders.unstructured import (
@@ -8,7 +7,8 @@ from langchain.document_loaders.unstructured import (
 
 
 class UnstructuredODTLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load OpenOffice ODT files.
+    """Load `OpenOffice ODT` files using `Unstructured`.
+
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
     langchain Document object. If you use "elements" mode, the unstructured
diff --git a/libs/langchain/langchain/document_loaders/onedrive.py b/libs/langchain/langchain/document_loaders/onedrive.py
index 77f75e2c07e..f12cc5cd449 100644
--- a/libs/langchain/langchain/document_loaders/onedrive.py
+++ b/libs/langchain/langchain/document_loaders/onedrive.py
@@ -8,11 +8,10 @@ from enum import Enum
 from pathlib import Path
 from typing import TYPE_CHECKING, Dict, List, Optional, Type, Union
 
-from pydantic import BaseModel, BaseSettings, Field, FilePath, SecretStr
-
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
 from langchain.document_loaders.onedrive_file import OneDriveFileLoader
+from langchain.pydantic_v1 import BaseModel, BaseSettings, Field, FilePath, SecretStr
 
 if TYPE_CHECKING:
     from O365 import Account
@@ -60,7 +59,7 @@ class _SupportedFileTypes(BaseModel):
 
 
 class OneDriveLoader(BaseLoader, BaseModel):
-    """Loads data from OneDrive."""
+    """Load from `Microsoft OneDrive`."""
 
     settings: _OneDriveSettings = Field(default_factory=_OneDriveSettings)
     """ The settings for the OneDrive API client."""
diff --git a/libs/langchain/langchain/document_loaders/onedrive_file.py b/libs/langchain/langchain/document_loaders/onedrive_file.py
index c83a2161961..8cc0e7e3753 100644
--- a/libs/langchain/langchain/document_loaders/onedrive_file.py
+++ b/libs/langchain/langchain/document_loaders/onedrive_file.py
@@ -3,11 +3,10 @@ from __future__ import annotations
 import tempfile
 from typing import TYPE_CHECKING, List
 
-from pydantic import BaseModel, Field
-
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
 from langchain.document_loaders.unstructured import UnstructuredFileLoader
+from langchain.pydantic_v1 import BaseModel, Field
 
 if TYPE_CHECKING:
     from O365.drive import File
@@ -16,7 +15,7 @@ CHUNK_SIZE = 1024 * 1024 * 5
 
 
 class OneDriveFileLoader(BaseLoader, BaseModel):
-    """Loads a file from OneDrive."""
+    """Load a file from `Microsoft OneDrive`."""
 
     file: File = Field(...)
     """The file to load."""
diff --git a/libs/langchain/langchain/document_loaders/open_city_data.py b/libs/langchain/langchain/document_loaders/open_city_data.py
index 03801d7995d..60384cf47cb 100644
--- a/libs/langchain/langchain/document_loaders/open_city_data.py
+++ b/libs/langchain/langchain/document_loaders/open_city_data.py
@@ -5,7 +5,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class OpenCityDataLoader(BaseLoader):
-    """Loads Open City data."""
+    """Load from `Open City`."""
 
     def __init__(self, city_id: str, dataset_id: str, limit: int):
         """Initialize with dataset_id.
diff --git a/libs/langchain/langchain/document_loaders/org_mode.py b/libs/langchain/langchain/document_loaders/org_mode.py
index dbb38411fd0..35617675c7e 100644
--- a/libs/langchain/langchain/document_loaders/org_mode.py
+++ b/libs/langchain/langchain/document_loaders/org_mode.py
@@ -1,4 +1,3 @@
-"""Loads Org-Mode files."""
 from typing import Any, List
 
 from langchain.document_loaders.unstructured import (
@@ -8,7 +7,8 @@ from langchain.document_loaders.unstructured import (
 
 
 class UnstructuredOrgModeLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load Org-Mode files.
+    """Load `Org-Mode` files using `Unstructured`.
+
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
     langchain Document object. If you use "elements" mode, the unstructured
diff --git a/libs/langchain/langchain/document_loaders/parsers/generic.py b/libs/langchain/langchain/document_loaders/parsers/generic.py
index 80545281c29..3d4c0a5ee0b 100644
--- a/libs/langchain/langchain/document_loaders/parsers/generic.py
+++ b/libs/langchain/langchain/document_loaders/parsers/generic.py
@@ -10,7 +10,7 @@ from langchain.schema import Document
 
 
 class MimeTypeBasedParser(BaseBlobParser):
-    """A parser that uses mime-types to determine how to parse a blob.
+    """Parser that uses `mime`-types to parse a blob.
 
     This parser is useful for simple pipelines where the mime-type is sufficient
     to determine how to parse a blob.
diff --git a/libs/langchain/langchain/document_loaders/parsers/grobid.py b/libs/langchain/langchain/document_loaders/parsers/grobid.py
index c19b21e23da..ea21ac8a49e 100644
--- a/libs/langchain/langchain/document_loaders/parsers/grobid.py
+++ b/libs/langchain/langchain/document_loaders/parsers/grobid.py
@@ -11,13 +11,13 @@ logger = logging.getLogger(__name__)
 
 
 class ServerUnavailableException(Exception):
-    """Exception raised when the GROBID server is unavailable."""
+    """Exception raised when the Grobid server is unavailable."""
 
     pass
 
 
 class GrobidParser(BaseBlobParser):
-    """Loader that uses Grobid to load article PDF files."""
+    """Load  article `PDF` files using `Grobid`."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/parsers/html/bs4.py b/libs/langchain/langchain/document_loaders/parsers/html/bs4.py
index e56aec31aba..334c1689e5b 100644
--- a/libs/langchain/langchain/document_loaders/parsers/html/bs4.py
+++ b/libs/langchain/langchain/document_loaders/parsers/html/bs4.py
@@ -11,7 +11,7 @@ logger = logging.getLogger(__name__)
 
 
 class BS4HTMLParser(BaseBlobParser):
-    """Parser that uses beautiful soup to parse HTML files."""
+    """Pparse HTML files using `Beautiful Soup`."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/parsers/language/code_segmenter.py b/libs/langchain/langchain/document_loaders/parsers/language/code_segmenter.py
index 100fb78e810..2efb2add448 100644
--- a/libs/langchain/langchain/document_loaders/parsers/language/code_segmenter.py
+++ b/libs/langchain/langchain/document_loaders/parsers/language/code_segmenter.py
@@ -3,7 +3,7 @@ from typing import List
 
 
 class CodeSegmenter(ABC):
-    """The abstract class for the code segmenter."""
+    """Abstract class for the code segmenter."""
 
     def __init__(self, code: str):
         self.code = code
diff --git a/libs/langchain/langchain/document_loaders/parsers/language/javascript.py b/libs/langchain/langchain/document_loaders/parsers/language/javascript.py
index cb53bfb4208..258345f8b9c 100644
--- a/libs/langchain/langchain/document_loaders/parsers/language/javascript.py
+++ b/libs/langchain/langchain/document_loaders/parsers/language/javascript.py
@@ -4,7 +4,7 @@ from langchain.document_loaders.parsers.language.code_segmenter import CodeSegme
 
 
 class JavaScriptSegmenter(CodeSegmenter):
-    """The code segmenter for JavaScript."""
+    """Code segmenter for JavaScript."""
 
     def __init__(self, code: str):
         super().__init__(code)
diff --git a/libs/langchain/langchain/document_loaders/parsers/language/language_parser.py b/libs/langchain/langchain/document_loaders/parsers/language/language_parser.py
index 3b3928cd68c..97d26a99e61 100644
--- a/libs/langchain/langchain/document_loaders/parsers/language/language_parser.py
+++ b/libs/langchain/langchain/document_loaders/parsers/language/language_parser.py
@@ -19,8 +19,7 @@ LANGUAGE_SEGMENTERS: Dict[str, Any] = {
 
 
 class LanguageParser(BaseBlobParser):
-    """
-    Language parser that split code using the respective language syntax.
+    """Parse using the respective programming language syntax.
 
     Each top-level function and class in the code is loaded into separate documents.
     Furthermore, an extra document is generated, containing the remaining top-level code
@@ -35,7 +34,7 @@ class LanguageParser(BaseBlobParser):
 
     Examples:
 
-        .. code-block:: python
+       .. code-block:: python
 
             from langchain.text_splitter.Language
             from langchain.document_loaders.generic import GenericLoader
@@ -51,7 +50,7 @@ class LanguageParser(BaseBlobParser):
 
         Example instantiations to manually select the language:
 
-        ... code-block:: python
+        .. code-block:: python
 
             from langchain.text_splitter import Language
 
@@ -64,7 +63,7 @@ class LanguageParser(BaseBlobParser):
 
         Example instantiations to set number of lines threshold:
 
-        ... code-block:: python
+        .. code-block:: python
 
             loader = GenericLoader.from_filesystem(
                 "./code",
diff --git a/libs/langchain/langchain/document_loaders/parsers/language/python.py b/libs/langchain/langchain/document_loaders/parsers/language/python.py
index 4446b4a21a1..642f6b32c31 100644
--- a/libs/langchain/langchain/document_loaders/parsers/language/python.py
+++ b/libs/langchain/langchain/document_loaders/parsers/language/python.py
@@ -5,7 +5,7 @@ from langchain.document_loaders.parsers.language.code_segmenter import CodeSegme
 
 
 class PythonSegmenter(CodeSegmenter):
-    """The code segmenter for Python."""
+    """Code segmenter for `Python`."""
 
     def __init__(self, code: str):
         super().__init__(code)
diff --git a/libs/langchain/langchain/document_loaders/parsers/pdf.py b/libs/langchain/langchain/document_loaders/parsers/pdf.py
index dde96eb8fb8..00d8d9adea6 100644
--- a/libs/langchain/langchain/document_loaders/parsers/pdf.py
+++ b/libs/langchain/langchain/document_loaders/parsers/pdf.py
@@ -8,7 +8,7 @@ from langchain.schema import Document
 
 
 class PyPDFParser(BaseBlobParser):
-    """Loads a PDF with pypdf and chunks at character level."""
+    """Load `PDF` using `pypdf` and chunk at character level."""
 
     def __init__(self, password: Optional[Union[str, bytes]] = None):
         self.password = password
@@ -29,7 +29,7 @@ class PyPDFParser(BaseBlobParser):
 
 
 class PDFMinerParser(BaseBlobParser):
-    """Parse PDFs with PDFMiner."""
+    """Parse `PDF` using `PDFMiner`."""
 
     def lazy_parse(self, blob: Blob) -> Iterator[Document]:
         """Lazily parse the blob."""
@@ -42,7 +42,7 @@ class PDFMinerParser(BaseBlobParser):
 
 
 class PyMuPDFParser(BaseBlobParser):
-    """Parse PDFs with PyMuPDF."""
+    """Parse `PDF` using `PyMuPDF`."""
 
     def __init__(self, text_kwargs: Optional[Mapping[str, Any]] = None) -> None:
         """Initialize the parser.
@@ -81,7 +81,7 @@ class PyMuPDFParser(BaseBlobParser):
 
 
 class PyPDFium2Parser(BaseBlobParser):
-    """Parse PDFs with PyPDFium2."""
+    """Parse `PDF` with `PyPDFium2`."""
 
     def __init__(self) -> None:
         """Initialize the parser."""
@@ -114,7 +114,7 @@ class PyPDFium2Parser(BaseBlobParser):
 
 
 class PDFPlumberParser(BaseBlobParser):
-    """Parse PDFs with PDFPlumber."""
+    """Parse `PDF` with `PDFPlumber`."""
 
     def __init__(self, text_kwargs: Optional[Mapping[str, Any]] = None) -> None:
         """Initialize the parser.
@@ -153,7 +153,7 @@ class PDFPlumberParser(BaseBlobParser):
 
 
 class AmazonTextractPDFParser(BaseBlobParser):
-    """Sends PDF files to Amazon Textract and parses them to generate Documents.
+    """Send `PDF` files to `Amazon Textract` and parse them.
 
     For parsing multi-page PDFs, they have to reside on S3.
     """
diff --git a/libs/langchain/langchain/document_loaders/pdf.py b/libs/langchain/langchain/document_loaders/pdf.py
index d8eba3d9819..301af6953e1 100644
--- a/libs/langchain/langchain/document_loaders/pdf.py
+++ b/libs/langchain/langchain/document_loaders/pdf.py
@@ -1,4 +1,3 @@
-"""Loads PDF files."""
 import json
 import logging
 import os
@@ -30,7 +29,8 @@ logger = logging.getLogger(__file__)
 
 
 class UnstructuredPDFLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load PDF files.
+    """Load `PDF` files using `Unstructured`.
+
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
     langchain Document object. If you use "elements" mode, the unstructured
@@ -59,7 +59,7 @@ class UnstructuredPDFLoader(UnstructuredFileLoader):
 
 
 class BasePDFLoader(BaseLoader, ABC):
-    """Base loader class for PDF files.
+    """Base Loader class for `PDF` files.
 
     Defaults to check for local file, but if the file is a web path, it will download it
     to a temporary file, use it, then clean up the temporary file after completion
@@ -122,7 +122,7 @@ class BasePDFLoader(BaseLoader, ABC):
 
 
 class OnlinePDFLoader(BasePDFLoader):
-    """Loads online PDFs."""
+    """Load online `PDF`."""
 
     def load(self) -> List[Document]:
         """Load documents."""
@@ -131,7 +131,7 @@ class OnlinePDFLoader(BasePDFLoader):
 
 
 class PyPDFLoader(BasePDFLoader):
-    """Loads a PDF with pypdf and chunks at character level.
+    """Load `PDF using `pypdf` and chunks at character level.
 
     Loader also stores page numbers in metadata.
     """
@@ -162,7 +162,7 @@ class PyPDFLoader(BasePDFLoader):
 
 
 class PyPDFium2Loader(BasePDFLoader):
-    """Loads a PDF with pypdfium2 and chunks at character level."""
+    """Load `PDF` using `pypdfium2` and chunks at character level."""
 
     def __init__(self, file_path: str):
         """Initialize with a file path."""
@@ -182,7 +182,7 @@ class PyPDFium2Loader(BasePDFLoader):
 
 
 class PyPDFDirectoryLoader(BaseLoader):
-    """Loads a directory with PDF files with pypdf and chunks at character level.
+    """Load a directory with `PDF` files using `pypdf` and chunks at character level.
 
     Loader also stores page numbers in metadata.
     """
@@ -227,7 +227,7 @@ class PyPDFDirectoryLoader(BaseLoader):
 
 
 class PDFMinerLoader(BasePDFLoader):
-    """Loader that uses PDFMiner to load PDF files."""
+    """Load `PDF` files using `PDFMiner`."""
 
     def __init__(self, file_path: str) -> None:
         """Initialize with file path."""
@@ -255,7 +255,7 @@ class PDFMinerLoader(BasePDFLoader):
 
 
 class PDFMinerPDFasHTMLLoader(BasePDFLoader):
-    """Loader that uses PDFMiner to load PDF files as HTML content."""
+    """Load `PDF` files as HTML content using `PDFMiner`."""
 
     def __init__(self, file_path: str):
         """Initialize with a file path."""
@@ -289,7 +289,7 @@ class PDFMinerPDFasHTMLLoader(BasePDFLoader):
 
 
 class PyMuPDFLoader(BasePDFLoader):
-    """Loader that uses PyMuPDF to load PDF files."""
+    """Load `PDF` files using `PyMuPDF`."""
 
     def __init__(self, file_path: str) -> None:
         """Initialize with a file path."""
@@ -314,7 +314,7 @@ class PyMuPDFLoader(BasePDFLoader):
 # MathpixPDFLoader implementation taken largely from Daniel Gross's:
 # https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21
 class MathpixPDFLoader(BasePDFLoader):
-    """This class uses Mathpix service to load PDF files."""
+    """Load `PDF` files using `Mathpix` service."""
 
     def __init__(
         self,
@@ -433,7 +433,7 @@ class MathpixPDFLoader(BasePDFLoader):
 
 
 class PDFPlumberLoader(BasePDFLoader):
-    """Loader that uses pdfplumber to load PDF files."""
+    """Load `PDF` files using `pdfplumber`."""
 
     def __init__(
         self, file_path: str, text_kwargs: Optional[Mapping[str, Any]] = None
@@ -459,7 +459,7 @@ class PDFPlumberLoader(BasePDFLoader):
 
 
 class AmazonTextractPDFLoader(BasePDFLoader):
-    """Loads a PDF document from local file system, HTTP or S3.
+    """Load `PDF` files from a local file system, HTTP or S3.
 
     To authenticate, the AWS client uses the following methods to
     automatically load credentials:
diff --git a/libs/langchain/langchain/document_loaders/powerpoint.py b/libs/langchain/langchain/document_loaders/powerpoint.py
index d9c3e7f84d2..f762a56cc0f 100644
--- a/libs/langchain/langchain/document_loaders/powerpoint.py
+++ b/libs/langchain/langchain/document_loaders/powerpoint.py
@@ -1,4 +1,3 @@
-"""Loads PowerPoint files."""
 import os
 from typing import List
 
@@ -6,7 +5,8 @@ from langchain.document_loaders.unstructured import UnstructuredFileLoader
 
 
 class UnstructuredPowerPointLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load PowerPoint files.
+    """Load `Microsoft PowerPoint` files using `Unstructured`.
+
     Works with both .ppt and .pptx files.
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
diff --git a/libs/langchain/langchain/document_loaders/psychic.py b/libs/langchain/langchain/document_loaders/psychic.py
index d1adee8e3c6..88db3cdd059 100644
--- a/libs/langchain/langchain/document_loaders/psychic.py
+++ b/libs/langchain/langchain/document_loaders/psychic.py
@@ -1,4 +1,3 @@
-"""Loads documents from Psychic.dev."""
 from typing import List, Optional
 
 from langchain.docstore.document import Document
@@ -6,7 +5,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class PsychicLoader(BaseLoader):
-    """Loads documents from Psychic.dev."""
+    """Load from `Psychic.dev`."""
 
     def __init__(
         self, api_key: str, account_id: str, connector_id: Optional[str] = None
diff --git a/libs/langchain/langchain/document_loaders/pubmed.py b/libs/langchain/langchain/document_loaders/pubmed.py
index 4e354acfaa2..71d98907310 100644
--- a/libs/langchain/langchain/document_loaders/pubmed.py
+++ b/libs/langchain/langchain/document_loaders/pubmed.py
@@ -6,7 +6,7 @@ from langchain.utilities.pubmed import PubMedAPIWrapper
 
 
 class PubMedLoader(BaseLoader):
-    """Loads a query result from PubMed biomedical library into a list of Documents.
+    """Load from the `PubMed` biomedical library.
 
     Attributes:
         query: The query to be passed to the PubMed API.
diff --git a/libs/langchain/langchain/document_loaders/pyspark_dataframe.py b/libs/langchain/langchain/document_loaders/pyspark_dataframe.py
index 490926fb105..cee1e65492a 100644
--- a/libs/langchain/langchain/document_loaders/pyspark_dataframe.py
+++ b/libs/langchain/langchain/document_loaders/pyspark_dataframe.py
@@ -1,4 +1,3 @@
-"""Load from a Spark Dataframe object"""
 import itertools
 import logging
 import sys
@@ -14,7 +13,7 @@ if TYPE_CHECKING:
 
 
 class PySparkDataFrameLoader(BaseLoader):
-    """Load PySpark DataFrames"""
+    """Load `PySpark` DataFrames."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/python.py b/libs/langchain/langchain/document_loaders/python.py
index e8e238afc69..af970ee1f27 100644
--- a/libs/langchain/langchain/document_loaders/python.py
+++ b/libs/langchain/langchain/document_loaders/python.py
@@ -4,9 +4,7 @@ from langchain.document_loaders.text import TextLoader
 
 
 class PythonLoader(TextLoader):
-    """
-    Load Python files, respecting any non-default encoding if specified.
-    """
+    """Load `Python` files, respecting any non-default encoding if specified."""
 
     def __init__(self, file_path: str):
         """Initialize with a file path.
diff --git a/libs/langchain/langchain/document_loaders/readthedocs.py b/libs/langchain/langchain/document_loaders/readthedocs.py
index 219364bd7de..a123f6a72e2 100644
--- a/libs/langchain/langchain/document_loaders/readthedocs.py
+++ b/libs/langchain/langchain/document_loaders/readthedocs.py
@@ -1,4 +1,3 @@
-"""Loads ReadTheDocs documentation directory dump."""
 from pathlib import Path
 from typing import Any, List, Optional, Tuple, Union
 
@@ -7,7 +6,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class ReadTheDocsLoader(BaseLoader):
-    """Loads ReadTheDocs documentation directory dump."""
+    """Load `ReadTheDocs` documentation directory."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/recursive_url_loader.py b/libs/langchain/langchain/document_loaders/recursive_url_loader.py
index 17cbe8ce8f9..61b9c7032e8 100644
--- a/libs/langchain/langchain/document_loaders/recursive_url_loader.py
+++ b/libs/langchain/langchain/document_loaders/recursive_url_loader.py
@@ -10,7 +10,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class RecursiveUrlLoader(BaseLoader):
-    """Loads all child links from a given url."""
+    """Load all child links from a URL page."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/reddit.py b/libs/langchain/langchain/document_loaders/reddit.py
index 44fa4bb811a..544624a84c6 100644
--- a/libs/langchain/langchain/document_loaders/reddit.py
+++ b/libs/langchain/langchain/document_loaders/reddit.py
@@ -1,4 +1,3 @@
-"""Reddit document loader."""
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Iterable, List, Optional, Sequence
@@ -21,7 +20,8 @@ def _dependable_praw_import() -> praw:
 
 
 class RedditPostsLoader(BaseLoader):
-    """Reddit posts loader.
+    """Load `Reddit` posts.
+
     Read posts on a subreddit.
     First, you need to go to
     https://www.reddit.com/prefs/apps/
diff --git a/libs/langchain/langchain/document_loaders/roam.py b/libs/langchain/langchain/document_loaders/roam.py
index 136bc116d01..df2e4882f4f 100644
--- a/libs/langchain/langchain/document_loaders/roam.py
+++ b/libs/langchain/langchain/document_loaders/roam.py
@@ -1,4 +1,3 @@
-"""Loads Roam directory dump."""
 from pathlib import Path
 from typing import List
 
@@ -7,7 +6,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class RoamLoader(BaseLoader):
-    """Loads Roam files from disk."""
+    """Load `Roam` files from a directory."""
 
     def __init__(self, path: str):
         """Initialize with a path."""
diff --git a/libs/langchain/langchain/document_loaders/rocksetdb.py b/libs/langchain/langchain/document_loaders/rocksetdb.py
index 4355c0a74eb..fd3095d23d9 100644
--- a/libs/langchain/langchain/document_loaders/rocksetdb.py
+++ b/libs/langchain/langchain/document_loaders/rocksetdb.py
@@ -17,7 +17,7 @@ class ColumnNotFoundError(Exception):
 
 
 class RocksetLoader(BaseLoader):
-    """Wrapper around Rockset db
+    """Load from a `Rockset` database.
 
     To use, you should have the `rockset` python package installed.
 
@@ -93,6 +93,12 @@ class RocksetLoader(BaseLoader):
         self.paginator = QueryPaginator
         self.request_model = QueryRequestSql
 
+        try:
+            self.client.set_application("langchain")
+        except AttributeError:
+            # ignore
+            pass
+
     def load(self) -> List[Document]:
         return list(self.lazy_load())
 
diff --git a/libs/langchain/langchain/document_loaders/rss.py b/libs/langchain/langchain/document_loaders/rss.py
index 870849d0ffc..750b8bcccbc 100644
--- a/libs/langchain/langchain/document_loaders/rss.py
+++ b/libs/langchain/langchain/document_loaders/rss.py
@@ -1,4 +1,3 @@
-"""Loader that uses unstructured to load HTML files."""
 import logging
 from typing import Any, Iterator, List, Optional, Sequence
 
@@ -10,7 +9,7 @@ logger = logging.getLogger(__name__)
 
 
 class RSSFeedLoader(BaseLoader):
-    """Loader that uses newspaper to load news articles from RSS feeds.
+    """Load news articles from `RSS` feeds using `Unstructured`.
 
     Args:
         urls: URLs for RSS feeds to load. Each articles in the feed is loaded into its own document.
diff --git a/libs/langchain/langchain/document_loaders/rst.py b/libs/langchain/langchain/document_loaders/rst.py
index a0c0095ec5e..3da16849f57 100644
--- a/libs/langchain/langchain/document_loaders/rst.py
+++ b/libs/langchain/langchain/document_loaders/rst.py
@@ -8,7 +8,8 @@ from langchain.document_loaders.unstructured import (
 
 
 class UnstructuredRSTLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load RST files.
+    """Load `RST` files using `Unstructured`.
+
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
     langchain Document object. If you use "elements" mode, the unstructured
diff --git a/libs/langchain/langchain/document_loaders/rtf.py b/libs/langchain/langchain/document_loaders/rtf.py
index 1cc7b4674d9..47af8009e19 100644
--- a/libs/langchain/langchain/document_loaders/rtf.py
+++ b/libs/langchain/langchain/document_loaders/rtf.py
@@ -8,7 +8,8 @@ from langchain.document_loaders.unstructured import (
 
 
 class UnstructuredRTFLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load RTF files.
+    """Load `RTF` files using `Unstructured`.
+
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
     langchain Document object. If you use "elements" mode, the unstructured
diff --git a/libs/langchain/langchain/document_loaders/s3_directory.py b/libs/langchain/langchain/document_loaders/s3_directory.py
index 60085ee904b..b4d6f2a40a4 100644
--- a/libs/langchain/langchain/document_loaders/s3_directory.py
+++ b/libs/langchain/langchain/document_loaders/s3_directory.py
@@ -1,4 +1,3 @@
-"""Loading logic for loading documents from an AWS S3 directory."""
 from typing import List
 
 from langchain.docstore.document import Document
@@ -7,7 +6,7 @@ from langchain.document_loaders.s3_file import S3FileLoader
 
 
 class S3DirectoryLoader(BaseLoader):
-    """Loading logic for loading documents from an AWS S3."""
+    """Load from `Amazon AWS S3` directory."""
 
     def __init__(self, bucket: str, prefix: str = ""):
         """Initialize with bucket and key name.
diff --git a/libs/langchain/langchain/document_loaders/s3_file.py b/libs/langchain/langchain/document_loaders/s3_file.py
index 28195d8fe6b..509b1ea1eec 100644
--- a/libs/langchain/langchain/document_loaders/s3_file.py
+++ b/libs/langchain/langchain/document_loaders/s3_file.py
@@ -1,4 +1,3 @@
-"""Loading logic for loading documents from an AWS S3 file."""
 import os
 import tempfile
 from typing import List
@@ -9,7 +8,7 @@ from langchain.document_loaders.unstructured import UnstructuredFileLoader
 
 
 class S3FileLoader(BaseLoader):
-    """Loading logic for loading documents from an AWS S3 file."""
+    """Load from `Amazon AWS S3` file."""
 
     def __init__(self, bucket: str, key: str):
         """Initialize with bucket and key name.
diff --git a/libs/langchain/langchain/document_loaders/sitemap.py b/libs/langchain/langchain/document_loaders/sitemap.py
index 158f19e0d2d..a67347a940f 100644
--- a/libs/langchain/langchain/document_loaders/sitemap.py
+++ b/libs/langchain/langchain/document_loaders/sitemap.py
@@ -1,4 +1,3 @@
-"""Loader that fetches a sitemap and loads those URLs."""
 import itertools
 import re
 from typing import Any, Callable, Generator, Iterable, List, Optional
@@ -22,7 +21,7 @@ def _batch_block(iterable: Iterable, size: int) -> Generator[List[dict], None, N
 
 
 class SitemapLoader(WebBaseLoader):
-    """Loader that fetches a sitemap and loads those URLs."""
+    """Load a sitemap and its URLs."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/slack_directory.py b/libs/langchain/langchain/document_loaders/slack_directory.py
index 16aa5b4fc5a..899f4eebd98 100644
--- a/libs/langchain/langchain/document_loaders/slack_directory.py
+++ b/libs/langchain/langchain/document_loaders/slack_directory.py
@@ -1,4 +1,3 @@
-"""Loader for documents from a Slack export."""
 import json
 import zipfile
 from pathlib import Path
@@ -9,7 +8,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class SlackDirectoryLoader(BaseLoader):
-    """Loads documents from a Slack directory dump."""
+    """Load from a `Slack` directory dump."""
 
     def __init__(self, zip_path: str, workspace_url: Optional[str] = None):
         """Initialize the SlackDirectoryLoader.
diff --git a/libs/langchain/langchain/document_loaders/snowflake_loader.py b/libs/langchain/langchain/document_loaders/snowflake_loader.py
index 5ed676a248b..51d98180a71 100644
--- a/libs/langchain/langchain/document_loaders/snowflake_loader.py
+++ b/libs/langchain/langchain/document_loaders/snowflake_loader.py
@@ -7,7 +7,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class SnowflakeLoader(BaseLoader):
-    """Loads a query result from Snowflake into a list of documents.
+    """Load from `Snowflake` API.
 
     Each document represents one row of the result. The `page_content_columns`
     are written into the `page_content` of the document. The `metadata_columns`
diff --git a/libs/langchain/langchain/document_loaders/spreedly.py b/libs/langchain/langchain/document_loaders/spreedly.py
index 2ec0cfc4c02..e95518d106a 100644
--- a/libs/langchain/langchain/document_loaders/spreedly.py
+++ b/libs/langchain/langchain/document_loaders/spreedly.py
@@ -1,4 +1,3 @@
-"""Loader that fetches data from Spreedly API."""
 import json
 import urllib.request
 from typing import List
@@ -20,7 +19,7 @@ SPREEDLY_ENDPOINTS = {
 
 
 class SpreedlyLoader(BaseLoader):
-    """Loader that fetches data from Spreedly API."""
+    """Load from `Spreedly` API."""
 
     def __init__(self, access_token: str, resource: str) -> None:
         """Initialize with an access token and a resource.
diff --git a/libs/langchain/langchain/document_loaders/srt.py b/libs/langchain/langchain/document_loaders/srt.py
index c6114beba90..d110b628f77 100644
--- a/libs/langchain/langchain/document_loaders/srt.py
+++ b/libs/langchain/langchain/document_loaders/srt.py
@@ -1,4 +1,3 @@
-"""Loader for .srt (subtitle) files."""
 from typing import List
 
 from langchain.docstore.document import Document
@@ -6,7 +5,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class SRTLoader(BaseLoader):
-    """Loader for .srt (subtitle) files."""
+    """Load `.srt` (subtitle) files."""
 
     def __init__(self, file_path: str):
         """Initialize with a file path."""
diff --git a/libs/langchain/langchain/document_loaders/stripe.py b/libs/langchain/langchain/document_loaders/stripe.py
index 41f978d1944..95ea8615e9d 100644
--- a/libs/langchain/langchain/document_loaders/stripe.py
+++ b/libs/langchain/langchain/document_loaders/stripe.py
@@ -1,4 +1,3 @@
-"""Loader that fetches data from Stripe"""
 import json
 import urllib.request
 from typing import List, Optional
@@ -18,7 +17,7 @@ STRIPE_ENDPOINTS = {
 
 
 class StripeLoader(BaseLoader):
-    """Loader that fetches data from Stripe."""
+    """Load from `Stripe` API."""
 
     def __init__(self, resource: str, access_token: Optional[str] = None) -> None:
         """Initialize with a resource and an access token.
diff --git a/libs/langchain/langchain/document_loaders/telegram.py b/libs/langchain/langchain/document_loaders/telegram.py
index 88225ecc447..86e857cd37b 100644
--- a/libs/langchain/langchain/document_loaders/telegram.py
+++ b/libs/langchain/langchain/document_loaders/telegram.py
@@ -1,4 +1,3 @@
-"""Loads Telegram chat json dump."""
 from __future__ import annotations
 
 import asyncio
@@ -24,7 +23,7 @@ def concatenate_rows(row: dict) -> str:
 
 
 class TelegramChatFileLoader(BaseLoader):
-    """Loads Telegram chat json directory dump."""
+    """Load from `Telegram chat` dump."""
 
     def __init__(self, path: str):
         """Initialize with a path."""
@@ -48,7 +47,7 @@ class TelegramChatFileLoader(BaseLoader):
 
 
 def text_to_docs(text: Union[str, List[str]]) -> List[Document]:
-    """Converts a string or list of strings to a list of Documents with metadata."""
+    """Convert a string or list of strings to a list of Documents with metadata."""
     if isinstance(text, str):
         # Take a single string as one page
         text = [text]
@@ -79,7 +78,7 @@ def text_to_docs(text: Union[str, List[str]]) -> List[Document]:
 
 
 class TelegramChatApiLoader(BaseLoader):
-    """Loads Telegram chat json directory dump."""
+    """Load `Telegram` chat json directory dump."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/tencent_cos_directory.py b/libs/langchain/langchain/document_loaders/tencent_cos_directory.py
index 14a249f8e16..e4a8ded85f0 100644
--- a/libs/langchain/langchain/document_loaders/tencent_cos_directory.py
+++ b/libs/langchain/langchain/document_loaders/tencent_cos_directory.py
@@ -1,4 +1,3 @@
-"""Loading logic for loading documents from Tencent Cloud COS directory."""
 from typing import Any, Iterator, List
 
 from langchain.docstore.document import Document
@@ -7,7 +6,7 @@ from langchain.document_loaders.tencent_cos_file import TencentCOSFileLoader
 
 
 class TencentCOSDirectoryLoader(BaseLoader):
-    """Loader for Tencent Cloud COS directory."""
+    """Load from `Tencent Cloud COS` directory."""
 
     def __init__(self, conf: Any, bucket: str, prefix: str = ""):
         """Initialize with COS config, bucket and prefix.
diff --git a/libs/langchain/langchain/document_loaders/tencent_cos_file.py b/libs/langchain/langchain/document_loaders/tencent_cos_file.py
index 4d96596716e..664efde2cfe 100644
--- a/libs/langchain/langchain/document_loaders/tencent_cos_file.py
+++ b/libs/langchain/langchain/document_loaders/tencent_cos_file.py
@@ -1,4 +1,3 @@
-"""Loading logic for loading documents from Tencent Cloud COS file."""
 import os
 import tempfile
 from typing import Any, Iterator, List
@@ -9,7 +8,7 @@ from langchain.document_loaders.unstructured import UnstructuredFileLoader
 
 
 class TencentCOSFileLoader(BaseLoader):
-    """Loader for Tencent Cloud COS file."""
+    """Load from `Tencent Cloud COS` file."""
 
     def __init__(self, conf: Any, bucket: str, key: str):
         """Initialize with COS config, bucket and key name.
diff --git a/libs/langchain/langchain/document_loaders/tensorflow_datasets.py b/libs/langchain/langchain/document_loaders/tensorflow_datasets.py
index e908aac8731..82b59d80040 100644
--- a/libs/langchain/langchain/document_loaders/tensorflow_datasets.py
+++ b/libs/langchain/langchain/document_loaders/tensorflow_datasets.py
@@ -6,7 +6,7 @@ from langchain.utilities.tensorflow_datasets import TensorflowDatasets
 
 
 class TensorflowDatasetLoader(BaseLoader):
-    """Loads from TensorFlow Datasets into a list of Documents.
+    """Load from `TensorFlow Dataset`.
 
     Attributes:
         dataset_name: the name of the dataset to load
diff --git a/libs/langchain/langchain/document_loaders/text.py b/libs/langchain/langchain/document_loaders/text.py
index e148e1d4ee0..2bc23c01bbb 100644
--- a/libs/langchain/langchain/document_loaders/text.py
+++ b/libs/langchain/langchain/document_loaders/text.py
@@ -9,7 +9,7 @@ logger = logging.getLogger(__name__)
 
 
 class TextLoader(BaseLoader):
-    """Load text files.
+    """Load text file.
 
 
     Args:
diff --git a/libs/langchain/langchain/document_loaders/tomarkdown.py b/libs/langchain/langchain/document_loaders/tomarkdown.py
index 00ba512dd21..1ffbf3ed00c 100644
--- a/libs/langchain/langchain/document_loaders/tomarkdown.py
+++ b/libs/langchain/langchain/document_loaders/tomarkdown.py
@@ -1,4 +1,3 @@
-"""Loads HTML to markdown using 2markdown."""
 from __future__ import annotations
 
 from typing import Iterator, List
@@ -10,7 +9,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class ToMarkdownLoader(BaseLoader):
-    """Loads HTML to markdown using 2markdown."""
+    """Load `HTML` using `2markdown API`."""
 
     def __init__(self, url: str, api_key: str):
         """Initialize with url and api key."""
diff --git a/libs/langchain/langchain/document_loaders/toml.py b/libs/langchain/langchain/document_loaders/toml.py
index 0f52d314f90..b0916508d69 100644
--- a/libs/langchain/langchain/document_loaders/toml.py
+++ b/libs/langchain/langchain/document_loaders/toml.py
@@ -7,11 +7,10 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class TomlLoader(BaseLoader):
-    """
-    A TOML document loader that inherits from the BaseLoader class.
+    """Load `TOML` files.
 
-    This class can be initialized with either a single source file or a source
-    directory containing TOML files.
+    It can load a single source file or several files in a single
+    directory.
     """
 
     def __init__(self, source: Union[str, Path]):
diff --git a/libs/langchain/langchain/document_loaders/trello.py b/libs/langchain/langchain/document_loaders/trello.py
index 11a59a4f5c2..f199a68295d 100644
--- a/libs/langchain/langchain/document_loaders/trello.py
+++ b/libs/langchain/langchain/document_loaders/trello.py
@@ -1,4 +1,3 @@
-"""Loads cards from Trello"""
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any, List, Literal, Optional, Tuple
@@ -12,7 +11,7 @@ if TYPE_CHECKING:
 
 
 class TrelloLoader(BaseLoader):
-    """Trello loader. Reads all cards from a Trello board."""
+    """Load cards from a `Trello` board."""
 
     def __init__(
         self,
diff --git a/libs/langchain/langchain/document_loaders/tsv.py b/libs/langchain/langchain/document_loaders/tsv.py
index 5a5c7b6d7cf..1de4d9895ca 100644
--- a/libs/langchain/langchain/document_loaders/tsv.py
+++ b/libs/langchain/langchain/document_loaders/tsv.py
@@ -7,7 +7,9 @@ from langchain.document_loaders.unstructured import (
 
 
 class UnstructuredTSVLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load TSV files. Like other
+    """Load `TSV` files using `Unstructured`.
+
+    Like other
     Unstructured loaders, UnstructuredTSVLoader can be used in both
     "single" and "elements" mode. If you use the loader in "elements"
     mode, the TSV file will be a single Unstructured Table element.
diff --git a/libs/langchain/langchain/document_loaders/twitter.py b/libs/langchain/langchain/document_loaders/twitter.py
index 1cf93321a1f..3c681dcf93f 100644
--- a/libs/langchain/langchain/document_loaders/twitter.py
+++ b/libs/langchain/langchain/document_loaders/twitter.py
@@ -1,4 +1,3 @@
-"""Twitter document loader."""
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Sequence, Union
@@ -22,8 +21,9 @@ def _dependable_tweepy_import() -> tweepy:
 
 
 class TwitterTweetLoader(BaseLoader):
-    """Twitter tweets loader.
-    Read tweets of user twitter handle.
+    """Load `Twitter` tweets.
+
+    Read tweets of the user's Twitter handle.
 
     First you need to go to
     `https://developer.twitter.com/en/docs/twitter-api
diff --git a/libs/langchain/langchain/document_loaders/unstructured.py b/libs/langchain/langchain/document_loaders/unstructured.py
index 1c729d1e712..8e55b1dd08f 100644
--- a/libs/langchain/langchain/document_loaders/unstructured.py
+++ b/libs/langchain/langchain/document_loaders/unstructured.py
@@ -1,14 +1,14 @@
 """Loader that uses unstructured to load files."""
 import collections
 from abc import ABC, abstractmethod
-from typing import IO, Any, Callable, Dict, List, Sequence, Union
+from typing import IO, Any, Callable, Dict, List, Optional, Sequence, Union
 
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
 
 
 def satisfies_min_unstructured_version(min_version: str) -> bool:
-    """Checks to see if the installed unstructured version exceeds the minimum version
+    """Check if the installed `Unstructured` version exceeds the minimum version
     for the feature in question."""
     from unstructured.__version__ import __version__ as __unstructured_version__
 
@@ -25,7 +25,7 @@ def satisfies_min_unstructured_version(min_version: str) -> bool:
 
 
 def validate_unstructured_version(min_unstructured_version: str) -> None:
-    """Raises an error if the unstructured version does not exceed the
+    """Raise an error if the `Unstructured` version does not exceed the
     specified minimum."""
     if not satisfies_min_unstructured_version(min_unstructured_version):
         raise ValueError(
@@ -34,12 +34,12 @@ def validate_unstructured_version(min_unstructured_version: str) -> None:
 
 
 class UnstructuredBaseLoader(BaseLoader, ABC):
-    """Loader that uses Unstructured to load files."""
+    """Base Loader that uses `Unstructured`."""
 
     def __init__(
         self,
         mode: str = "single",
-        post_processors: List[Callable] = [],
+        post_processors: Optional[List[Callable]] = None,
         **unstructured_kwargs: Any,
     ):
         """Initialize with file path."""
@@ -62,7 +62,7 @@ class UnstructuredBaseLoader(BaseLoader, ABC):
                 unstructured_kwargs.pop("strategy")
 
         self.unstructured_kwargs = unstructured_kwargs
-        self.post_processors = post_processors
+        self.post_processors = post_processors or []
 
     @abstractmethod
     def _get_elements(self) -> List:
@@ -130,7 +130,7 @@ class UnstructuredBaseLoader(BaseLoader, ABC):
 
 
 class UnstructuredFileLoader(UnstructuredBaseLoader):
-    """Loader that uses Unstructured to load files.
+    """Load files using `Unstructured`.
 
     The file loader uses the
     unstructured partition function and will automatically detect the file
@@ -181,7 +181,7 @@ def get_elements_from_api(
     api_key: str = "",
     **unstructured_kwargs: Any,
 ) -> List:
-    """Retrieves a list of elements from the Unstructured API."""
+    """Retrieve a list of elements from the `Unstructured API`."""
     if isinstance(file, collections.abc.Sequence) or isinstance(file_path, list):
         from unstructured.partition.api import partition_multiple_via_api
 
@@ -211,7 +211,7 @@ def get_elements_from_api(
 
 
 class UnstructuredAPIFileLoader(UnstructuredFileLoader):
-    """Loader that uses the Unstructured API to load files.
+    """Load files using `Unstructured` API.
 
     By default, the loader makes a call to the hosted Unstructured API.
     If you are running the unstructured API locally, you can change the
@@ -275,7 +275,7 @@ class UnstructuredAPIFileLoader(UnstructuredFileLoader):
 
 
 class UnstructuredFileIOLoader(UnstructuredBaseLoader):
-    """Loader that uses Unstructured to load files.
+    """Load files using `Unstructured`.
 
     The file loader
     uses the unstructured partition function and will automatically detect the file
@@ -322,7 +322,7 @@ class UnstructuredFileIOLoader(UnstructuredBaseLoader):
 
 
 class UnstructuredAPIFileIOLoader(UnstructuredFileIOLoader):
-    """Loader that uses the Unstructured API to load files.
+    """Load files using `Unstructured` API.
 
     By default, the loader makes a call to the hosted Unstructured API.
     If you are running the unstructured API locally, you can change the
diff --git a/libs/langchain/langchain/document_loaders/url.py b/libs/langchain/langchain/document_loaders/url.py
index 013ec108f26..e2c48b2d65d 100644
--- a/libs/langchain/langchain/document_loaders/url.py
+++ b/libs/langchain/langchain/document_loaders/url.py
@@ -9,7 +9,8 @@ logger = logging.getLogger(__name__)
 
 
 class UnstructuredURLLoader(BaseLoader):
-    """Loader that use Unstructured to load files from remote URLs.
+    """Load files from remote URLs using `Unstructured`.
+
     Use the unstructured partition function to detect the MIME type
     and route the file to the appropriate partitioner.
 
diff --git a/libs/langchain/langchain/document_loaders/url_playwright.py b/libs/langchain/langchain/document_loaders/url_playwright.py
index ee4a47f2516..0db4168e5c7 100644
--- a/libs/langchain/langchain/document_loaders/url_playwright.py
+++ b/libs/langchain/langchain/document_loaders/url_playwright.py
@@ -10,7 +10,8 @@ logger = logging.getLogger(__name__)
 
 
 class PlaywrightURLLoader(BaseLoader):
-    """Loader that uses Playwright and to load a page and unstructured to load the html.
+    """Load `HTML` pages with `Playwright` and parse with `Unstructured`.
+
     This is useful for loading pages that require javascript to render.
 
     Attributes:
diff --git a/libs/langchain/langchain/document_loaders/url_selenium.py b/libs/langchain/langchain/document_loaders/url_selenium.py
index 5cc3f0ce099..e47da8b5fc7 100644
--- a/libs/langchain/langchain/document_loaders/url_selenium.py
+++ b/libs/langchain/langchain/document_loaders/url_selenium.py
@@ -13,7 +13,8 @@ logger = logging.getLogger(__name__)
 
 
 class SeleniumURLLoader(BaseLoader):
-    """Loader that uses Selenium and to load a page and unstructured to load the html.
+    """Load `HTML` pages with `Selenium` and parse with `Unstructured`.
+
     This is useful for loading pages that require javascript to render.
 
     Attributes:
diff --git a/libs/langchain/langchain/document_loaders/weather.py b/libs/langchain/langchain/document_loaders/weather.py
index 958b6f39c0a..4481cced78d 100644
--- a/libs/langchain/langchain/document_loaders/weather.py
+++ b/libs/langchain/langchain/document_loaders/weather.py
@@ -10,7 +10,7 @@ from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper
 
 
 class WeatherDataLoader(BaseLoader):
-    """Weather Reader.
+    """Load weather data with `Open Weather Map` API.
 
     Reads the forecast & current weather of any location using OpenWeatherMap's free
     API. Checkout 'https://openweathermap.org/appid' for more on how to generate a free
diff --git a/libs/langchain/langchain/document_loaders/web_base.py b/libs/langchain/langchain/document_loaders/web_base.py
index 5ae9482bce9..e6ee1db045d 100644
--- a/libs/langchain/langchain/document_loaders/web_base.py
+++ b/libs/langchain/langchain/document_loaders/web_base.py
@@ -37,7 +37,7 @@ def _build_metadata(soup: Any, url: str) -> dict:
 
 
 class WebBaseLoader(BaseLoader):
-    """Loader that uses urllib and beautiful soup to load webpages."""
+    """Load HTML pages using `urllib` and parse them with `BeautifulSoup'."""
 
     web_paths: List[str]
 
diff --git a/libs/langchain/langchain/document_loaders/whatsapp_chat.py b/libs/langchain/langchain/document_loaders/whatsapp_chat.py
index cad93ac8d02..561f62b743d 100644
--- a/libs/langchain/langchain/document_loaders/whatsapp_chat.py
+++ b/libs/langchain/langchain/document_loaders/whatsapp_chat.py
@@ -12,7 +12,7 @@ def concatenate_rows(date: str, sender: str, text: str) -> str:
 
 
 class WhatsAppChatLoader(BaseLoader):
-    """Loads WhatsApp messages text file."""
+    """Load `WhatsApp` messages text file."""
 
     def __init__(self, path: str):
         """Initialize with path."""
diff --git a/libs/langchain/langchain/document_loaders/wikipedia.py b/libs/langchain/langchain/document_loaders/wikipedia.py
index 5f0bbb73cd5..7140a824744 100644
--- a/libs/langchain/langchain/document_loaders/wikipedia.py
+++ b/libs/langchain/langchain/document_loaders/wikipedia.py
@@ -6,7 +6,8 @@ from langchain.utilities.wikipedia import WikipediaAPIWrapper
 
 
 class WikipediaLoader(BaseLoader):
-    """Loads a query result from www.wikipedia.org into a list of Documents.
+    """Load from `Wikipedia`.
+
     The hard limit on the number of downloaded Documents is 300 for now.
 
     Each wiki page represents one Document.
diff --git a/libs/langchain/langchain/document_loaders/word_document.py b/libs/langchain/langchain/document_loaders/word_document.py
index 66ccfbd0908..3a2ae3a6ae4 100644
--- a/libs/langchain/langchain/document_loaders/word_document.py
+++ b/libs/langchain/langchain/document_loaders/word_document.py
@@ -13,7 +13,7 @@ from langchain.document_loaders.unstructured import UnstructuredFileLoader
 
 
 class Docx2txtLoader(BaseLoader, ABC):
-    """Loads a DOCX with docx2txt and chunks at character level.
+    """Load `DOCX` file using `docx2txt` and chunks at character level.
 
     Defaults to check for local file, but if the file is a web path, it will download it
     to a temporary file, and use that, then clean up the temporary file after completion
@@ -65,7 +65,8 @@ class Docx2txtLoader(BaseLoader, ABC):
 
 
 class UnstructuredWordDocumentLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load word documents.
+    """Load `Microsof Word` file using `Unstructured`.
+
     Works with both .docx and .doc files.
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
diff --git a/libs/langchain/langchain/document_loaders/xml.py b/libs/langchain/langchain/document_loaders/xml.py
index ac6ceeea9a5..df3d364c789 100644
--- a/libs/langchain/langchain/document_loaders/xml.py
+++ b/libs/langchain/langchain/document_loaders/xml.py
@@ -8,7 +8,8 @@ from langchain.document_loaders.unstructured import (
 
 
 class UnstructuredXMLLoader(UnstructuredFileLoader):
-    """Loader that uses unstructured to load XML files.
+    """Load `XML` file using `Unstructured`.
+
     You can run the loader in one of two modes: "single" and "elements".
     If you use "single" mode, the document will be returned as a single
     langchain Document object. If you use "elements" mode, the unstructured
diff --git a/libs/langchain/langchain/document_loaders/xorbits.py b/libs/langchain/langchain/document_loaders/xorbits.py
index e8259d8f0a1..bcc4e680f68 100644
--- a/libs/langchain/langchain/document_loaders/xorbits.py
+++ b/libs/langchain/langchain/document_loaders/xorbits.py
@@ -5,7 +5,7 @@ from langchain.document_loaders.base import BaseLoader
 
 
 class XorbitsLoader(BaseLoader):
-    """Load Xorbits DataFrame."""
+    """Load `Xorbits` DataFrame."""
 
     def __init__(self, data_frame: Any, page_content_column: str = "text"):
         """Initialize with dataframe object.
diff --git a/libs/langchain/langchain/document_loaders/youtube.py b/libs/langchain/langchain/document_loaders/youtube.py
index 86d7c42a8ed..54cd35af216 100644
--- a/libs/langchain/langchain/document_loaders/youtube.py
+++ b/libs/langchain/langchain/document_loaders/youtube.py
@@ -6,11 +6,10 @@ from pathlib import Path
 from typing import Any, Dict, List, Optional, Sequence, Union
 from urllib.parse import parse_qs, urlparse
 
-from pydantic import root_validator
-from pydantic.dataclasses import dataclass
-
 from langchain.docstore.document import Document
 from langchain.document_loaders.base import BaseLoader
+from langchain.pydantic_v1 import root_validator
+from langchain.pydantic_v1.dataclasses import dataclass
 
 logger = logging.getLogger(__name__)
 
@@ -19,7 +18,7 @@ SCOPES = ["https://www.googleapis.com/auth/youtube.readonly"]
 
 @dataclass
 class GoogleApiClient:
-    """A Generic Google Api Client.
+    """Generic Google API Client.
 
     To use, you should have the ``google_auth_oauthlib,youtube_transcript_api,google``
     python package installed.
@@ -140,7 +139,7 @@ def _parse_video_id(url: str) -> Optional[str]:
 
 
 class YoutubeLoader(BaseLoader):
-    """Loads Youtube transcripts."""
+    """Load `YouTube` transcripts."""
 
     def __init__(
         self,
@@ -252,7 +251,7 @@ class YoutubeLoader(BaseLoader):
 
 @dataclass
 class GoogleApiYoutubeLoader(BaseLoader):
-    """Loads all Videos from a Channel
+    """Load all Videos from a `YouTube` Channel.
 
     To use, you should have the ``googleapiclient,youtube_transcript_api``
     python package installed.
diff --git a/libs/langchain/langchain/document_transformers/__init__.py b/libs/langchain/langchain/document_transformers/__init__.py
index e33f5000ebd..e7a51e0445e 100644
--- a/libs/langchain/langchain/document_transformers/__init__.py
+++ b/libs/langchain/langchain/document_transformers/__init__.py
@@ -15,6 +15,9 @@
     Document
 """  # noqa: E501
 
+from langchain.document_transformers.beautiful_soup_transformer import (
+    BeautifulSoupTransformer,
+)
 from langchain.document_transformers.doctran_text_extract import (
     DoctranPropertyExtractor,
 )
@@ -31,6 +34,7 @@ from langchain.document_transformers.nuclia_text_transform import NucliaTextTran
 from langchain.document_transformers.openai_functions import OpenAIMetadataTagger
 
 __all__ = [
+    "BeautifulSoupTransformer",
     "DoctranQATransformer",
     "DoctranTextTranslator",
     "DoctranPropertyExtractor",
diff --git a/libs/langchain/langchain/document_transformers/beautiful_soup_transformer.py b/libs/langchain/langchain/document_transformers/beautiful_soup_transformer.py
new file mode 100644
index 00000000000..d174e950bd3
--- /dev/null
+++ b/libs/langchain/langchain/document_transformers/beautiful_soup_transformer.py
@@ -0,0 +1,143 @@
+from typing import Any, List, Sequence
+
+from langchain.schema import BaseDocumentTransformer, Document
+
+
+class BeautifulSoupTransformer(BaseDocumentTransformer):
+    """Transform HTML content by extracting specific tags and removing unwanted ones.
+
+    Example:
+        .. code-block:: python
+            from langchain.document_transformers import BeautifulSoupTransformer
+            bs4_transformer = BeautifulSoupTransformer()
+            docs_transformed = bs4_transformer.transform_documents(docs)
+    """
+
+    def __init__(self) -> None:
+        """
+        Initialize the transformer.
+
+        This checks if the BeautifulSoup4 package is installed.
+        If not, it raises an ImportError.
+        """
+        try:
+            import bs4  # noqa:F401
+        except ImportError:
+            raise ImportError(
+                "BeautifulSoup4 is required for BeautifulSoupTransformer. "
+                "Please install it with `pip install beautifulsoup4`."
+            )
+
+    def transform_documents(
+        self,
+        documents: Sequence[Document],
+        unwanted_tags: List[str] = ["script", "style"],
+        tags_to_extract: List[str] = ["p", "li", "div", "a"],
+        remove_lines: bool = True,
+        **kwargs: Any,
+    ) -> Sequence[Document]:
+        """
+        Transform a list of Document objects by cleaning their HTML content.
+
+        Args:
+            documents: A sequence of Document objects containing HTML content.
+            unwanted_tags: A list of tags to be removed from the HTML.
+            tags_to_extract: A list of tags whose content will be extracted.
+            remove_lines: If set to True, unnecessary lines will be
+            removed from the HTML content.
+
+        Returns:
+            A sequence of Document objects with transformed content.
+        """
+        for doc in documents:
+            cleaned_content = doc.page_content
+
+            cleaned_content = self.remove_unwanted_tags(cleaned_content, unwanted_tags)
+
+            cleaned_content = self.extract_tags(cleaned_content, tags_to_extract)
+
+            if remove_lines:
+                cleaned_content = self.remove_unnecessary_lines(cleaned_content)
+
+            doc.page_content = cleaned_content
+
+        return documents
+
+    @staticmethod
+    def remove_unwanted_tags(html_content: str, unwanted_tags: List[str]) -> str:
+        """
+        Remove unwanted tags from a given HTML content.
+
+        Args:
+            html_content: The original HTML content string.
+            unwanted_tags: A list of tags to be removed from the HTML.
+
+        Returns:
+            A cleaned HTML string with unwanted tags removed.
+        """
+        from bs4 import BeautifulSoup
+
+        soup = BeautifulSoup(html_content, "html.parser")
+        for tag in unwanted_tags:
+            for element in soup.find_all(tag):
+                element.decompose()
+        return str(soup)
+
+    @staticmethod
+    def extract_tags(html_content: str, tags: List[str]) -> str:
+        """
+        Extract specific tags from a given HTML content.
+
+        Args:
+            html_content: The original HTML content string.
+            tags: A list of tags to be extracted from the HTML.
+
+        Returns:
+            A string combining the content of the extracted tags.
+        """
+        from bs4 import BeautifulSoup
+
+        soup = BeautifulSoup(html_content, "html.parser")
+        text_parts = []
+        for tag in tags:
+            elements = soup.find_all(tag)
+            for element in elements:
+                if tag == "a":
+                    href = element.get("href")
+                    if href:
+                        text_parts.append(f"{element.get_text()} ({href})")
+                    else:
+                        text_parts.append(element.get_text())
+                else:
+                    text_parts.append(element.get_text())
+        return " ".join(text_parts)
+
+    @staticmethod
+    def remove_unnecessary_lines(content: str) -> str:
+        """
+        Clean up the content by removing unnecessary lines.
+
+        Args:
+            content: A string, which may contain unnecessary lines or spaces.
+
+        Returns:
+            A cleaned string with unnecessary lines removed.
+        """
+        lines = content.split("\n")
+        stripped_lines = [line.strip() for line in lines]
+        non_empty_lines = [line for line in stripped_lines if line]
+        seen = set()
+        deduped_lines = []
+        for line in non_empty_lines:
+            if line not in seen:
+                seen.add(line)
+                deduped_lines.append(line)
+        cleaned_content = " ".join(deduped_lines)
+        return cleaned_content
+
+    async def atransform_documents(
+        self,
+        documents: Sequence[Document],
+        **kwargs: Any,
+    ) -> Sequence[Document]:
+        raise NotImplementedError
diff --git a/libs/langchain/langchain/document_transformers/embeddings_redundant_filter.py b/libs/langchain/langchain/document_transformers/embeddings_redundant_filter.py
index a108ce73004..51f166b8c87 100644
--- a/libs/langchain/langchain/document_transformers/embeddings_redundant_filter.py
+++ b/libs/langchain/langchain/document_transformers/embeddings_redundant_filter.py
@@ -2,9 +2,9 @@
 from typing import Any, Callable, List, Sequence
 
 import numpy as np
-from pydantic import BaseModel, Field
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema import BaseDocumentTransformer, Document
 from langchain.utils.math import cosine_similarity
 
@@ -182,7 +182,7 @@ class EmbeddingsClusteringFilter(BaseDocumentTransformer, BaseModel):
     """By default results are re-ordered "grouping" them by cluster, if sorted is true
     result will be ordered by the original position from the retriever"""
 
-    remove_duplicates = False
+    remove_duplicates: bool = False
     """ By default duplicated results are skipped and replaced by the next closest 
     vector in the cluster. If remove_duplicates is true no replacement will be done:
     This could dramatically reduce results when there is a lot of overlap between 
diff --git a/libs/langchain/langchain/document_transformers/long_context_reorder.py b/libs/langchain/langchain/document_transformers/long_context_reorder.py
index 5debbed5ee8..8da4fc441b5 100644
--- a/libs/langchain/langchain/document_transformers/long_context_reorder.py
+++ b/libs/langchain/langchain/document_transformers/long_context_reorder.py
@@ -1,8 +1,7 @@
 """Reorder documents"""
 from typing import Any, List, Sequence
 
-from pydantic import BaseModel
-
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema import BaseDocumentTransformer, Document
 
 
diff --git a/libs/langchain/langchain/document_transformers/openai_functions.py b/libs/langchain/langchain/document_transformers/openai_functions.py
index 8b2d11467f1..f60586410cb 100644
--- a/libs/langchain/langchain/document_transformers/openai_functions.py
+++ b/libs/langchain/langchain/document_transformers/openai_functions.py
@@ -1,11 +1,10 @@
 """Document transformers that use OpenAI Functions models"""
 from typing import Any, Dict, Optional, Sequence, Type, Union
 
-from pydantic import BaseModel
-
 from langchain.chains.llm import LLMChain
 from langchain.chains.openai_functions import create_tagging_chain
 from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema import BaseDocumentTransformer, Document
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/embeddings/__init__.py b/libs/langchain/langchain/embeddings/__init__.py
index f81d1e11731..1c03d2c4042 100644
--- a/libs/langchain/langchain/embeddings/__init__.py
+++ b/libs/langchain/langchain/embeddings/__init__.py
@@ -20,6 +20,7 @@ from langchain.embeddings.aleph_alpha import (
 )
 from langchain.embeddings.awa import AwaEmbeddings
 from langchain.embeddings.bedrock import BedrockEmbeddings
+from langchain.embeddings.cache import CacheBackedEmbeddings
 from langchain.embeddings.clarifai import ClarifaiEmbeddings
 from langchain.embeddings.cohere import CohereEmbeddings
 from langchain.embeddings.dashscope import DashScopeEmbeddings
@@ -62,10 +63,11 @@ logger = logging.getLogger(__name__)
 
 __all__ = [
     "OpenAIEmbeddings",
-    "HuggingFaceEmbeddings",
-    "CohereEmbeddings",
+    "CacheBackedEmbeddings",
     "ClarifaiEmbeddings",
+    "CohereEmbeddings",
     "ElasticsearchEmbeddings",
+    "HuggingFaceEmbeddings",
     "JinaEmbeddings",
     "LlamaCppEmbeddings",
     "HuggingFaceHubEmbeddings",
diff --git a/libs/langchain/langchain/embeddings/aleph_alpha.py b/libs/langchain/langchain/embeddings/aleph_alpha.py
index 1f5c4b8b19b..eb7c28f84a1 100644
--- a/libs/langchain/langchain/embeddings/aleph_alpha.py
+++ b/libs/langchain/langchain/embeddings/aleph_alpha.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/embeddings/awa.py b/libs/langchain/langchain/embeddings/awa.py
index d854eb2f63a..c6f844104be 100644
--- a/libs/langchain/langchain/embeddings/awa.py
+++ b/libs/langchain/langchain/embeddings/awa.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict, List
 
-from pydantic import BaseModel, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, root_validator
 
 
 class AwaEmbeddings(BaseModel, Embeddings):
diff --git a/libs/langchain/langchain/embeddings/bedrock.py b/libs/langchain/langchain/embeddings/bedrock.py
index 93b3d69bbef..6b66c0139b2 100644
--- a/libs/langchain/langchain/embeddings/bedrock.py
+++ b/libs/langchain/langchain/embeddings/bedrock.py
@@ -1,10 +1,11 @@
+import asyncio
 import json
 import os
+from functools import partial
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 
 
 class BedrockEmbeddings(BaseModel, Embeddings):
@@ -128,17 +129,11 @@ class BedrockEmbeddings(BaseModel, Embeddings):
         except Exception as e:
             raise ValueError(f"Error raised by inference endpoint: {e}")
 
-    def embed_documents(
-        self, texts: List[str], chunk_size: int = 1
-    ) -> List[List[float]]:
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Compute doc embeddings using a Bedrock model.
 
         Args:
-            texts: The list of texts to embed.
-            chunk_size: Bedrock currently only allows single string
-                inputs, so chunk size is always 1. This input is here
-                only for compatibility with the embeddings interface.
-
+            texts: The list of texts to embed
 
         Returns:
             List of embeddings, one for each text.
@@ -159,3 +154,31 @@ class BedrockEmbeddings(BaseModel, Embeddings):
             Embeddings for the text.
         """
         return self._embedding_func(text)
+
+    async def aembed_query(self, text: str) -> List[float]:
+        """Asynchronous compute query embeddings using a Bedrock model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+
+        return await asyncio.get_running_loop().run_in_executor(
+            None, partial(self.embed_query, text)
+        )
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Asynchronous compute doc embeddings using a Bedrock model.
+
+        Args:
+            texts: The list of texts to embed
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+
+        result = await asyncio.gather(*[self.aembed_query(text) for text in texts])
+
+        return list(result)
diff --git a/libs/langchain/langchain/embeddings/cache.py b/libs/langchain/langchain/embeddings/cache.py
new file mode 100644
index 00000000000..7acdc287317
--- /dev/null
+++ b/libs/langchain/langchain/embeddings/cache.py
@@ -0,0 +1,175 @@
+"""Module contains code for a cache backed embedder.
+
+The cache backed embedder is a wrapper around an embedder that caches
+embeddings in a key-value store. The cache is used to avoid recomputing
+embeddings for the same text.
+
+The text is hashed and the hash is used as the key in the cache.
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+import uuid
+from functools import partial
+from typing import Callable, List, Sequence, Union, cast
+
+from langchain.embeddings.base import Embeddings
+from langchain.schema import BaseStore
+from langchain.storage.encoder_backed import EncoderBackedStore
+
+NAMESPACE_UUID = uuid.UUID(int=1985)
+
+
+def _hash_string_to_uuid(input_string: str) -> uuid.UUID:
+    """Hash a string and returns the corresponding UUID."""
+    hash_value = hashlib.sha1(input_string.encode("utf-8")).hexdigest()
+    return uuid.uuid5(NAMESPACE_UUID, hash_value)
+
+
+def _key_encoder(key: str, namespace: str) -> str:
+    """Encode a key."""
+    return namespace + str(_hash_string_to_uuid(key))
+
+
+def _create_key_encoder(namespace: str) -> Callable[[str], str]:
+    """Create an encoder for a key."""
+    return partial(_key_encoder, namespace=namespace)
+
+
+def _value_serializer(value: Sequence[float]) -> bytes:
+    """Serialize a value."""
+    return json.dumps(value).encode()
+
+
+def _value_deserializer(serialized_value: bytes) -> List[float]:
+    """Deserialize a value."""
+    return cast(List[float], json.loads(serialized_value.decode()))
+
+
+class CacheBackedEmbeddings(Embeddings):
+    """Interface for caching results from embedding models.
+
+    The interface allows works with any store that implements
+    the abstract store interface accepting keys of type str and values of list of
+    floats.
+
+    If need be, the interface can be extended to accept other implementations
+    of the value serializer and deserializer, as well as the key encoder.
+
+    Examples:
+
+        .. code-block: python
+
+            from langchain.embeddings import CacheBackedEmbeddings, OpenAIEmbeddings
+            from langchain.storage import LocalFileStore
+
+            store = LocalFileStore('./my_cache')
+
+            underlying_embedder = OpenAIEmbeddings()
+            embedder = CacheBackedEmbeddings.from_bytes_store(
+                underlying_embedder, store, namespace=underlying_embedder.model
+            )
+
+            # Embedding is computed and cached
+            embeddings = embedder.embed_documents(["hello", "goodbye"])
+
+            # Embeddings are retrieved from the cache, no computation is done
+            embeddings = embedder.embed_documents(["hello", "goodbye"])
+    """
+
+    def __init__(
+        self,
+        underlying_embeddings: Embeddings,
+        document_embedding_store: BaseStore[str, List[float]],
+    ) -> None:
+        """Initialize the embedder.
+
+        Args:
+            underlying_embeddings: the embedder to use for computing embeddings.
+            document_embedding_store: The store to use for caching document embeddings.
+        """
+        super().__init__()
+        self.document_embedding_store = document_embedding_store
+        self.underlying_embeddings = underlying_embeddings
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of texts.
+
+        The method first checks the cache for the embeddings.
+        If the embeddings are not found, the method uses the underlying embedder
+        to embed the documents and stores the results in the cache.
+
+        Args:
+            texts: A list of texts to embed.
+
+        Returns:
+            A list of embeddings for the given texts.
+        """
+        vectors: List[Union[List[float], None]] = self.document_embedding_store.mget(
+            texts
+        )
+        missing_indices: List[int] = [
+            i for i, vector in enumerate(vectors) if vector is None
+        ]
+        missing_texts = [texts[i] for i in missing_indices]
+
+        if missing_texts:
+            missing_vectors = self.underlying_embeddings.embed_documents(missing_texts)
+            self.document_embedding_store.mset(
+                list(zip(missing_texts, missing_vectors))
+            )
+            for index, updated_vector in zip(missing_indices, missing_vectors):
+                vectors[index] = updated_vector
+
+        return cast(
+            List[List[float]], vectors
+        )  # Nones should have been resolved by now
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query text.
+
+        This method does not support caching at the moment.
+
+        Support for caching queries is easily to implement, but might make
+        sense to hold off to see the most common patterns.
+
+        If the cache has an eviction policy, we may need to be a bit more careful
+        about sharing the cache between documents and queries. Generally,
+        one is OK evicting query caches, but document caches should be kept.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            The embedding for the given text.
+        """
+        return self.underlying_embeddings.embed_query(text)
+
+    @classmethod
+    def from_bytes_store(
+        cls,
+        underlying_embeddings: Embeddings,
+        document_embedding_cache: BaseStore[str, bytes],
+        *,
+        namespace: str = "",
+    ) -> CacheBackedEmbeddings:
+        """On-ramp that adds the necessary serialization and encoding to the store.
+
+        Args:
+            underlying_embeddings: The embedder to use for embedding.
+            document_embedding_cache: The cache to use for storing document embeddings.
+            *,
+            namespace: The namespace to use for document cache.
+                       This namespace is used to avoid collisions with other caches.
+                       For example, set it to the name of the embedding model used.
+        """
+        namespace = namespace
+        key_encoder = _create_key_encoder(namespace)
+        encoder_backed_store = EncoderBackedStore[str, List[float]](
+            document_embedding_cache,
+            key_encoder,
+            _value_serializer,
+            _value_deserializer,
+        )
+        return cls(underlying_embeddings, encoder_backed_store)
diff --git a/libs/langchain/langchain/embeddings/clarifai.py b/libs/langchain/langchain/embeddings/clarifai.py
index 6d97fc075ef..0e0d52356a8 100644
--- a/libs/langchain/langchain/embeddings/clarifai.py
+++ b/libs/langchain/langchain/embeddings/clarifai.py
@@ -1,9 +1,8 @@
 import logging
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/embeddings/cohere.py b/libs/langchain/langchain/embeddings/cohere.py
index 28a3b6cc2a1..2377f416825 100644
--- a/libs/langchain/langchain/embeddings/cohere.py
+++ b/libs/langchain/langchain/embeddings/cohere.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/embeddings/dashscope.py b/libs/langchain/langchain/embeddings/dashscope.py
index 475c7a5940a..4c7b7f2b34c 100644
--- a/libs/langchain/langchain/embeddings/dashscope.py
+++ b/libs/langchain/langchain/embeddings/dashscope.py
@@ -9,7 +9,6 @@ from typing import (
     Optional,
 )
 
-from pydantic import BaseModel, Extra, root_validator
 from requests.exceptions import HTTPError
 from tenacity import (
     before_sleep_log,
@@ -20,6 +19,7 @@ from tenacity import (
 )
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/embeddings/deepinfra.py b/libs/langchain/langchain/embeddings/deepinfra.py
index 2fb15ce1882..79d741b062d 100644
--- a/libs/langchain/langchain/embeddings/deepinfra.py
+++ b/libs/langchain/langchain/embeddings/deepinfra.py
@@ -1,9 +1,9 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import BaseModel, Extra, root_validator
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 DEFAULT_MODEL_ID = "sentence-transformers/clip-ViT-B-32"
diff --git a/libs/langchain/langchain/embeddings/edenai.py b/libs/langchain/langchain/embeddings/edenai.py
index 0f3fe08d9b8..5e10ddb166a 100644
--- a/libs/langchain/langchain/embeddings/edenai.py
+++ b/libs/langchain/langchain/embeddings/edenai.py
@@ -1,8 +1,7 @@
 from typing import Dict, List, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 from langchain.requests import Requests
 from langchain.utils import get_from_dict_or_env
 
diff --git a/libs/langchain/langchain/embeddings/embaas.py b/libs/langchain/langchain/embeddings/embaas.py
index d6985fc68c8..011ed22ad53 100644
--- a/libs/langchain/langchain/embeddings/embaas.py
+++ b/libs/langchain/langchain/embeddings/embaas.py
@@ -1,10 +1,10 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import BaseModel, Extra, root_validator
 from typing_extensions import NotRequired, TypedDict
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 # Currently supported maximum batch size for embedding requests
diff --git a/libs/langchain/langchain/embeddings/fake.py b/libs/langchain/langchain/embeddings/fake.py
index 1b8311dec78..27a91142c8e 100644
--- a/libs/langchain/langchain/embeddings/fake.py
+++ b/libs/langchain/langchain/embeddings/fake.py
@@ -2,9 +2,9 @@ import hashlib
 from typing import List
 
 import numpy as np
-from pydantic import BaseModel
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel
 
 
 class FakeEmbeddings(Embeddings, BaseModel):
diff --git a/libs/langchain/langchain/embeddings/google_palm.py b/libs/langchain/langchain/embeddings/google_palm.py
index 8633ce7a945..825f60d8433 100644
--- a/libs/langchain/langchain/embeddings/google_palm.py
+++ b/libs/langchain/langchain/embeddings/google_palm.py
@@ -3,7 +3,6 @@ from __future__ import annotations
 import logging
 from typing import Any, Callable, Dict, List, Optional
 
-from pydantic import BaseModel, root_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -13,6 +12,7 @@ from tenacity import (
 )
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/embeddings/gpt4all.py b/libs/langchain/langchain/embeddings/gpt4all.py
index 0f109b8453c..8ce636c5d73 100644
--- a/libs/langchain/langchain/embeddings/gpt4all.py
+++ b/libs/langchain/langchain/embeddings/gpt4all.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict, List
 
-from pydantic import BaseModel, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, root_validator
 
 
 class GPT4AllEmbeddings(BaseModel, Embeddings):
diff --git a/libs/langchain/langchain/embeddings/huggingface.py b/libs/langchain/langchain/embeddings/huggingface.py
index b2d4a183333..52afabd79bf 100644
--- a/libs/langchain/langchain/embeddings/huggingface.py
+++ b/libs/langchain/langchain/embeddings/huggingface.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, Field
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, Field
 
 DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
 DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
@@ -47,6 +46,8 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
     """Key word arguments to pass to the model."""
     encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Key word arguments to pass when calling the `encode` method of the model."""
+    multi_process: bool = False
+    """Run encode() on multiple GPUs."""
 
     def __init__(self, **kwargs: Any):
         """Initialize the sentence_transformer."""
@@ -78,8 +79,16 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         Returns:
             List of embeddings, one for each text.
         """
+        import sentence_transformers
+
         texts = list(map(lambda x: x.replace("\n", " "), texts))
-        embeddings = self.client.encode(texts, **self.encode_kwargs)
+        if self.multi_process:
+            pool = self.client.start_multi_process_pool()
+            embeddings = self.client.encode_multi_process(texts, pool)
+            sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
+        else:
+            embeddings = self.client.encode(texts, **self.encode_kwargs)
+
         return embeddings.tolist()
 
     def embed_query(self, text: str) -> List[float]:
@@ -91,9 +100,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         Returns:
             Embeddings for the text.
         """
-        text = text.replace("\n", " ")
-        embedding = self.client.encode(text, **self.encode_kwargs)
-        return embedding.tolist()
+        return self.embed_documents([text])[0]
 
 
 class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
@@ -142,7 +149,7 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
                 self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
             )
         except ImportError as e:
-            raise ValueError("Dependencies for InstructorEmbedding not found.") from e
+            raise ImportError("Dependencies for InstructorEmbedding not found.") from e
 
     class Config:
         """Configuration for this pydantic object."""
diff --git a/libs/langchain/langchain/embeddings/huggingface_hub.py b/libs/langchain/langchain/embeddings/huggingface_hub.py
index 0ca9985a154..e5204eb0efa 100644
--- a/libs/langchain/langchain/embeddings/huggingface_hub.py
+++ b/libs/langchain/langchain/embeddings/huggingface_hub.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 DEFAULT_REPO_ID = "sentence-transformers/all-mpnet-base-v2"
diff --git a/libs/langchain/langchain/embeddings/jina.py b/libs/langchain/langchain/embeddings/jina.py
index 1931a9c4ee4..3d4a4797e6d 100644
--- a/libs/langchain/langchain/embeddings/jina.py
+++ b/libs/langchain/langchain/embeddings/jina.py
@@ -2,9 +2,9 @@ import os
 from typing import Any, Dict, List, Optional
 
 import requests
-from pydantic import BaseModel, root_validator
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/embeddings/llamacpp.py b/libs/langchain/langchain/embeddings/llamacpp.py
index 5c31d53bf32..8c8b2628321 100644
--- a/libs/langchain/langchain/embeddings/llamacpp.py
+++ b/libs/langchain/langchain/embeddings/llamacpp.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 
 
 class LlamaCppEmbeddings(BaseModel, Embeddings):
diff --git a/libs/langchain/langchain/embeddings/localai.py b/libs/langchain/langchain/embeddings/localai.py
index 532cd49eadb..ba7a82ebe56 100644
--- a/libs/langchain/langchain/embeddings/localai.py
+++ b/libs/langchain/langchain/embeddings/localai.py
@@ -15,7 +15,6 @@ from typing import (
     Union,
 )
 
-from pydantic import BaseModel, Extra, Field, root_validator
 from tenacity import (
     AsyncRetrying,
     before_sleep_log,
@@ -26,6 +25,7 @@ from tenacity import (
 )
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/embeddings/minimax.py b/libs/langchain/langchain/embeddings/minimax.py
index 3ac9a7ef430..82402ca12b0 100644
--- a/libs/langchain/langchain/embeddings/minimax.py
+++ b/libs/langchain/langchain/embeddings/minimax.py
@@ -4,7 +4,6 @@ import logging
 from typing import Any, Callable, Dict, List, Optional
 
 import requests
-from pydantic import BaseModel, Extra, root_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -13,6 +12,7 @@ from tenacity import (
 )
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/embeddings/mlflow_gateway.py b/libs/langchain/langchain/embeddings/mlflow_gateway.py
index 16d8f29d682..26c91cf7c7d 100644
--- a/libs/langchain/langchain/embeddings/mlflow_gateway.py
+++ b/libs/langchain/langchain/embeddings/mlflow_gateway.py
@@ -2,9 +2,8 @@ from __future__ import annotations
 
 from typing import Any, Iterator, List, Optional
 
-from pydantic import BaseModel
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel
 
 
 def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
diff --git a/libs/langchain/langchain/embeddings/modelscope_hub.py b/libs/langchain/langchain/embeddings/modelscope_hub.py
index 541aeaa358c..c652e007756 100644
--- a/libs/langchain/langchain/embeddings/modelscope_hub.py
+++ b/libs/langchain/langchain/embeddings/modelscope_hub.py
@@ -1,8 +1,7 @@
 from typing import Any, List, Optional
 
-from pydantic import BaseModel, Extra
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra
 
 
 class ModelScopeEmbeddings(BaseModel, Embeddings):
diff --git a/libs/langchain/langchain/embeddings/mosaicml.py b/libs/langchain/langchain/embeddings/mosaicml.py
index d2e448da7c1..8346bf7cfc1 100644
--- a/libs/langchain/langchain/embeddings/mosaicml.py
+++ b/libs/langchain/langchain/embeddings/mosaicml.py
@@ -1,9 +1,9 @@
 from typing import Any, Dict, List, Mapping, Optional, Tuple
 
 import requests
-from pydantic import BaseModel, Extra, root_validator
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/embeddings/nlpcloud.py b/libs/langchain/langchain/embeddings/nlpcloud.py
index 5a65768a941..ab5372d9ead 100644
--- a/libs/langchain/langchain/embeddings/nlpcloud.py
+++ b/libs/langchain/langchain/embeddings/nlpcloud.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict, List
 
-from pydantic import BaseModel, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/embeddings/octoai_embeddings.py b/libs/langchain/langchain/embeddings/octoai_embeddings.py
index 4a9d0a5120f..c2e1711f12c 100644
--- a/libs/langchain/langchain/embeddings/octoai_embeddings.py
+++ b/libs/langchain/langchain/embeddings/octoai_embeddings.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 DEFAULT_EMBED_INSTRUCTION = "Represent this input: "
diff --git a/libs/langchain/langchain/embeddings/openai.py b/libs/langchain/langchain/embeddings/openai.py
index 038b8bfde81..9c78e2499e8 100644
--- a/libs/langchain/langchain/embeddings/openai.py
+++ b/libs/langchain/langchain/embeddings/openai.py
@@ -16,7 +16,6 @@ from typing import (
 )
 
 import numpy as np
-from pydantic import BaseModel, Extra, Field, root_validator
 from tenacity import (
     AsyncRetrying,
     before_sleep_log,
@@ -27,6 +26,7 @@ from tenacity import (
 )
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/embeddings/sagemaker_endpoint.py b/libs/langchain/langchain/embeddings/sagemaker_endpoint.py
index d0fd864a6bd..ee14eca27af 100644
--- a/libs/langchain/langchain/embeddings/sagemaker_endpoint.py
+++ b/libs/langchain/langchain/embeddings/sagemaker_endpoint.py
@@ -1,9 +1,8 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
 from langchain.embeddings.base import Embeddings
 from langchain.llms.sagemaker_endpoint import ContentHandlerBase
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 
 
 class EmbeddingsContentHandler(ContentHandlerBase[List[str], List[List[float]]]):
diff --git a/libs/langchain/langchain/embeddings/self_hosted.py b/libs/langchain/langchain/embeddings/self_hosted.py
index 3ef31366924..e83ed9e8995 100644
--- a/libs/langchain/langchain/embeddings/self_hosted.py
+++ b/libs/langchain/langchain/embeddings/self_hosted.py
@@ -1,9 +1,8 @@
 from typing import Any, Callable, List
 
-from pydantic import Extra
-
 from langchain.embeddings.base import Embeddings
 from langchain.llms import SelfHostedPipeline
+from langchain.pydantic_v1 import Extra
 
 
 def _embed_documents(pipeline: Any, *args: Any, **kwargs: Any) -> List[List[float]]:
diff --git a/libs/langchain/langchain/embeddings/spacy_embeddings.py b/libs/langchain/langchain/embeddings/spacy_embeddings.py
index ded1fbbd8ac..06a6a0b5dc6 100644
--- a/libs/langchain/langchain/embeddings/spacy_embeddings.py
+++ b/libs/langchain/langchain/embeddings/spacy_embeddings.py
@@ -1,9 +1,8 @@
 import importlib.util
 from typing import Any, Dict, List
 
-from pydantic import BaseModel, Extra, root_validator
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 
 
 class SpacyEmbeddings(BaseModel, Embeddings):
diff --git a/libs/langchain/langchain/embeddings/tensorflow_hub.py b/libs/langchain/langchain/embeddings/tensorflow_hub.py
index 514c7e1c1fd..2048163ba70 100644
--- a/libs/langchain/langchain/embeddings/tensorflow_hub.py
+++ b/libs/langchain/langchain/embeddings/tensorflow_hub.py
@@ -1,8 +1,7 @@
 from typing import Any, List
 
-from pydantic import BaseModel, Extra
-
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseModel, Extra
 
 DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
 
diff --git a/libs/langchain/langchain/embeddings/vertexai.py b/libs/langchain/langchain/embeddings/vertexai.py
index 6ea6d023033..bb1750c2855 100644
--- a/libs/langchain/langchain/embeddings/vertexai.py
+++ b/libs/langchain/langchain/embeddings/vertexai.py
@@ -1,9 +1,8 @@
 from typing import Dict, List
 
-from pydantic import root_validator
-
 from langchain.embeddings.base import Embeddings
 from langchain.llms.vertexai import _VertexAICommon
+from langchain.pydantic_v1 import root_validator
 from langchain.utilities.vertexai import raise_vertex_import_error
 
 
diff --git a/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py b/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py
index 55d0ac1fbc1..805a968d01e 100644
--- a/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py
+++ b/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py
@@ -18,8 +18,6 @@ from typing import (
     cast,
 )
 
-from pydantic import Extra, Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
@@ -32,6 +30,7 @@ from langchain.evaluation.agents.trajectory_eval_prompt import (
     TOOL_FREE_EVAL_CHAT_PROMPT,
 )
 from langchain.evaluation.schema import AgentTrajectoryEvaluator, LLMEvalChain
+from langchain.pydantic_v1 import Extra, Field
 from langchain.schema import AgentAction, BaseOutputParser, OutputParserException
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
diff --git a/libs/langchain/langchain/evaluation/comparison/eval_chain.py b/libs/langchain/langchain/evaluation/comparison/eval_chain.py
index 3ca215324b5..21f623bf0fb 100644
--- a/libs/langchain/langchain/evaluation/comparison/eval_chain.py
+++ b/libs/langchain/langchain/evaluation/comparison/eval_chain.py
@@ -3,8 +3,6 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional, Union
 
-from pydantic import Extra, Field
-
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
 from langchain.chains.llm import LLMChain
@@ -15,6 +13,7 @@ from langchain.evaluation.criteria.eval_chain import (
 )
 from langchain.evaluation.schema import LLMEvalChain, PairwiseStringEvaluator
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import Extra, Field
 from langchain.schema import RUN_KEY, BaseOutputParser
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/evaluation/criteria/eval_chain.py b/libs/langchain/langchain/evaluation/criteria/eval_chain.py
index 09359cf79ed..dbd34cb6e82 100644
--- a/libs/langchain/langchain/evaluation/criteria/eval_chain.py
+++ b/libs/langchain/langchain/evaluation/criteria/eval_chain.py
@@ -3,13 +3,12 @@ from __future__ import annotations
 from enum import Enum
 from typing import Any, Dict, List, Mapping, Optional, Union
 
-from pydantic import Extra, Field
-
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
 from langchain.chains.llm import LLMChain
 from langchain.evaluation.criteria.prompt import PROMPT, PROMPT_WITH_REFERENCES
 from langchain.evaluation.schema import LLMEvalChain, StringEvaluator
+from langchain.pydantic_v1 import Extra, Field
 from langchain.schema import RUN_KEY, BaseOutputParser, BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 
@@ -148,8 +147,8 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
     llm : BaseLanguageModel
         The language model to use for evaluation.
     criteria : Union[Mapping[str, str]]
-        The criteriaor rubric to evaluate the runs against. It can be a mapping of
-        criterion name to its sdescription, or a single criterion name.
+        The criteria or rubric to evaluate the runs against. It can be a mapping of
+        criterion name to its description, or a single criterion name.
     prompt : Optional[BasePromptTemplate], default=None
         The prompt template to use for generating prompts. If not provided, a
         default prompt template will be used based on the value of
diff --git a/libs/langchain/langchain/evaluation/embedding_distance/base.py b/libs/langchain/langchain/evaluation/embedding_distance/base.py
index 3591f45d8be..36fe252b824 100644
--- a/libs/langchain/langchain/evaluation/embedding_distance/base.py
+++ b/libs/langchain/langchain/evaluation/embedding_distance/base.py
@@ -3,7 +3,6 @@ from enum import Enum
 from typing import Any, Dict, List, Optional
 
 import numpy as np
-from pydantic import Field, root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
@@ -14,6 +13,7 @@ from langchain.chains.base import Chain
 from langchain.embeddings.base import Embeddings
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import RUN_KEY
 from langchain.utils.math import cosine_similarity
 
diff --git a/libs/langchain/langchain/evaluation/loading.py b/libs/langchain/langchain/evaluation/loading.py
index c608c259a34..81ffec6d797 100644
--- a/libs/langchain/langchain/evaluation/loading.py
+++ b/libs/langchain/langchain/evaluation/loading.py
@@ -14,8 +14,12 @@ from langchain.evaluation.embedding_distance.base import (
     EmbeddingDistanceEvalChain,
     PairwiseEmbeddingDistanceEvalChain,
 )
+from langchain.evaluation.parsing.base import (
+    JsonEqualityEvaluator,
+    JsonValidityEvaluator,
+)
 from langchain.evaluation.qa import ContextQAEvalChain, CotQAEvalChain, QAEvalChain
-from langchain.evaluation.schema import EvaluatorType, LLMEvalChain
+from langchain.evaluation.schema import EvaluatorType, LLMEvalChain, StringEvaluator
 from langchain.evaluation.string_distance.base import (
     PairwiseStringDistanceEvalChain,
     StringDistanceEvalChain,
@@ -57,7 +61,9 @@ def load_dataset(uri: str) -> List[Dict]:
     return [d for d in dataset["train"]]
 
 
-_EVALUATOR_MAP: Dict[EvaluatorType, Union[Type[LLMEvalChain], Type[Chain]]] = {
+_EVALUATOR_MAP: Dict[
+    EvaluatorType, Union[Type[LLMEvalChain], Type[Chain], Type[StringEvaluator]]
+] = {
     EvaluatorType.QA: QAEvalChain,
     EvaluatorType.COT_QA: CotQAEvalChain,
     EvaluatorType.CONTEXT_QA: ContextQAEvalChain,
@@ -70,6 +76,8 @@ _EVALUATOR_MAP: Dict[EvaluatorType, Union[Type[LLMEvalChain], Type[Chain]]] = {
     EvaluatorType.PAIRWISE_STRING_DISTANCE: PairwiseStringDistanceEvalChain,
     EvaluatorType.EMBEDDING_DISTANCE: EmbeddingDistanceEvalChain,
     EvaluatorType.PAIRWISE_EMBEDDING_DISTANCE: PairwiseEmbeddingDistanceEvalChain,
+    EvaluatorType.JSON_VALIDITY: JsonValidityEvaluator,
+    EvaluatorType.JSON_EQUALITY: JsonEqualityEvaluator,
 }
 
 
@@ -78,7 +86,7 @@ def load_evaluator(
     *,
     llm: Optional[BaseLanguageModel] = None,
     **kwargs: Any,
-) -> Chain:
+) -> Union[Chain, StringEvaluator]:
     """Load the requested evaluation chain specified by a string.
 
     Parameters
@@ -119,7 +127,7 @@ def load_evaluators(
     llm: Optional[BaseLanguageModel] = None,
     config: Optional[dict] = None,
     **kwargs: Any,
-) -> List[Chain]:
+) -> List[Union[Chain, StringEvaluator]]:
     """Load evaluators specified by a list of evaluator types.
 
     Parameters
diff --git a/libs/langchain/langchain/evaluation/parsing/__init__.py b/libs/langchain/langchain/evaluation/parsing/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/langchain/langchain/evaluation/parsing/base.py b/libs/langchain/langchain/evaluation/parsing/base.py
new file mode 100644
index 00000000000..d2233b51481
--- /dev/null
+++ b/libs/langchain/langchain/evaluation/parsing/base.py
@@ -0,0 +1,153 @@
+"""Evaluators for parsing strings."""
+from operator import eq
+from typing import Any, Callable, Optional, Union, cast
+
+from langchain.evaluation.schema import StringEvaluator
+from langchain.output_parsers.json import parse_json_markdown
+
+
+class JsonValidityEvaluator(StringEvaluator):
+    """Evaluates whether the prediction is valid JSON.
+
+    This evaluator checks if the prediction is a valid JSON string. It does not
+        require any input or reference.
+
+    Attributes:
+        requires_input (bool): Whether this evaluator requires an input
+            string. Always False.
+        requires_reference (bool): Whether this evaluator requires a
+            reference string. Always False.
+        evaluation_name (str): The name of the evaluation metric.
+            Always "json".
+
+    Examples:
+        >>> evaluator = JsonValidityEvaluator()
+        >>> prediction = '{"name": "John", "age": 30, "city": "New York"}'
+        >>> evaluator.evaluate(prediction)
+        {'score': 1}
+
+        >>> prediction = '{"name": "John", "age": 30, "city": "New York",}'
+        >>> evaluator.evaluate(prediction)
+        {'score': 0, 'reasoning': 'Expecting property name enclosed in double quotes'}
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        super().__init__()
+
+    @property
+    def requires_input(self) -> bool:
+        return False
+
+    @property
+    def requires_reference(self) -> bool:
+        return False
+
+    @property
+    def evaluation_name(self) -> str:
+        return "json_validity"
+
+    def _evaluate_strings(
+        self,
+        prediction: str,
+        input: Optional[str] = None,
+        reference: Optional[str] = None,
+        **kwargs: Any
+    ) -> dict:
+        """Evaluate the prediction string.
+
+        Args:
+            prediction (str): The prediction string to evaluate.
+            input (str, optional): Not used in this evaluator. Defaults to None.
+            reference (str, optional): Not used in this evaluator. Defaults to None.
+
+        Returns:
+            dict: A dictionary containing the evaluation score. The score is 1 if
+            the prediction is valid JSON, and 0 otherwise.
+                If the prediction is not valid JSON, the dictionary also contains
+                a "reasoning" field with the error message.
+
+        """
+        try:
+            parse_json_markdown(prediction)
+            return {"score": 1}
+        except Exception as e:
+            return {"score": 0, "reasoning": str(e)}
+
+
+class JsonEqualityEvaluator(StringEvaluator):
+    """Evaluates whether the prediction is equal to the reference after
+        parsing both as JSON.
+
+    This evaluator checks if the prediction, after parsing as JSON, is equal
+        to the reference,
+    which is also parsed as JSON. It does not require an input string.
+
+    Attributes:
+        requires_input (bool): Whether this evaluator requires an
+            input string. Always False.
+        requires_reference (bool): Whether this evaluator requires
+            a reference string. Always True.
+        evaluation_name (str): The name of the evaluation metric.
+            Always "parsed_equality".
+
+    Examples:
+        >>> evaluator = JsonEqualityEvaluator()
+        >>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 1}')
+        {'score': True}
+        >>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 2}')
+        {'score': False}
+
+        >>> evaluator = JsonEqualityEvaluator(operator=lambda x, y: x['a'] == y['a'])
+        >>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 1}')
+        {'score': True}
+        >>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 2}')
+        {'score': False}
+
+    """
+
+    def __init__(self, operator: Optional[Callable] = None, **kwargs: Any) -> None:
+        super().__init__()
+        self.operator = operator or eq
+
+    @property
+    def requires_input(self) -> bool:
+        return False
+
+    @property
+    def requires_reference(self) -> bool:
+        return True
+
+    @property
+    def evaluation_name(self) -> str:
+        return "json_equality"
+
+    def _parse_json(
+        self, string: str
+    ) -> Union[dict, list, None, float, bool, int, str]:
+        return parse_json_markdown(string)
+
+    def _evaluate_strings(
+        self,
+        prediction: str,
+        input: Optional[str] = None,
+        reference: Optional[str] = None,
+        **kwargs: Any
+    ) -> dict:
+        """Evaluate the prediction string.
+
+        Args:
+            prediction (str): The prediction string to evaluate.
+            input (str, optional): Not used in this evaluator.
+            reference (str): The reference string to compare against.
+
+        Returns:
+            dict: A dictionary containing the evaluation score.
+        """
+        parsed = self._parse_json(prediction)
+        label = self._parse_json(cast(str, reference))
+        if isinstance(label, list):
+            if not isinstance(parsed, list):
+                return {"score": 0}
+            parsed = sorted(parsed, key=lambda x: str(x))
+            label = sorted(label, key=lambda x: str(x))
+        return {"score": self.operator(parsed, label)}
diff --git a/libs/langchain/langchain/evaluation/qa/eval_chain.py b/libs/langchain/langchain/evaluation/qa/eval_chain.py
index 90b5e8d5dcf..7e3af64168c 100644
--- a/libs/langchain/langchain/evaluation/qa/eval_chain.py
+++ b/libs/langchain/langchain/evaluation/qa/eval_chain.py
@@ -4,13 +4,12 @@ from __future__ import annotations
 import re
 from typing import Any, List, Optional, Sequence
 
-from pydantic import Extra
-
 from langchain import PromptTemplate
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.llm import LLMChain
 from langchain.evaluation.qa.eval_prompt import CONTEXT_PROMPT, COT_PROMPT, PROMPT
 from langchain.evaluation.schema import LLMEvalChain, StringEvaluator
+from langchain.pydantic_v1 import Extra
 from langchain.schema import RUN_KEY
 from langchain.schema.language_model import BaseLanguageModel
 
diff --git a/libs/langchain/langchain/evaluation/qa/generate_chain.py b/libs/langchain/langchain/evaluation/qa/generate_chain.py
index 7891ccccbda..5f925e2e35b 100644
--- a/libs/langchain/langchain/evaluation/qa/generate_chain.py
+++ b/libs/langchain/langchain/evaluation/qa/generate_chain.py
@@ -3,11 +3,10 @@ from __future__ import annotations
 
 from typing import Any
 
-from pydantic import Field
-
 from langchain.chains.llm import LLMChain
 from langchain.evaluation.qa.generate_prompt import PROMPT
 from langchain.output_parsers.regex import RegexParser
+from langchain.pydantic_v1 import Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.output_parser import BaseLLMOutputParser
 
diff --git a/libs/langchain/langchain/evaluation/schema.py b/libs/langchain/langchain/evaluation/schema.py
index ef6344fd460..f08402c8c28 100644
--- a/libs/langchain/langchain/evaluation/schema.py
+++ b/libs/langchain/langchain/evaluation/schema.py
@@ -1,9 +1,11 @@
 """Interfaces to be implemented by general evaluators."""
 from __future__ import annotations
 
+import asyncio
 import logging
 from abc import ABC, abstractmethod
 from enum import Enum
+from functools import partial
 from typing import Any, Optional, Sequence, Tuple
 from warnings import warn
 
@@ -48,6 +50,10 @@ class EvaluatorType(str, Enum):
     """Compare a prediction to a reference label using embedding distance."""
     PAIRWISE_EMBEDDING_DISTANCE = "pairwise_embedding_distance"
     """Compare two predictions using embedding distance."""
+    JSON_VALIDITY = "json_validity"
+    """Check if a prediction is valid JSON."""
+    JSON_EQUALITY = "json_equality"
+    """Check if a prediction is equal to a reference JSON."""
 
 
 class LLMEvalChain(Chain):
@@ -115,7 +121,7 @@ class StringEvaluator(_EvalArgsMixin, ABC):
     @property
     def evaluation_name(self) -> str:
         """The name of the evaluation."""
-        raise NotImplementedError()
+        return self.__class__.__name__
 
     @property
     def requires_reference(self) -> bool:
@@ -168,9 +174,15 @@ class StringEvaluator(_EvalArgsMixin, ABC):
                      - value: the string value of the evaluation, if applicable.
                      - reasoning: the reasoning for the evaluation, if applicable.
         """  # noqa: E501
-        raise NotImplementedError(
-            f"{self.__class__.__name__} hasn't implemented an async "
-            "aevaluate_strings method."
+        return await asyncio.get_running_loop().run_in_executor(
+            None,
+            partial(
+                self._evaluate_strings,
+                prediction=prediction,
+                reference=reference,
+                input=input,
+                **kwargs,
+            ),
         )
 
     def evaluate_strings(
@@ -265,9 +277,16 @@ class PairwiseStringEvaluator(_EvalArgsMixin, ABC):
         Returns:
             dict: A dictionary containing the preference, scores, and/or other information.
         """  # noqa: E501
-        raise NotImplementedError(
-            f"{self.__class__.__name__} hasn't implemented an async "
-            "aevaluate_string_pairs method."
+        return await asyncio.get_running_loop().run_in_executor(
+            None,
+            partial(
+                self._evaluate_string_pairs,
+                prediction=prediction,
+                prediction_b=prediction_b,
+                reference=reference,
+                input=input,
+                **kwargs,
+            ),
         )
 
     def evaluate_string_pairs(
@@ -381,9 +400,16 @@ class AgentTrajectoryEvaluator(_EvalArgsMixin, ABC):
         Returns:
             dict: The evaluation result.
         """
-        raise NotImplementedError(
-            f"{self.__class__.__name__} hasn't implemented an async "
-            "aevaluate_agent_trajectory method."
+        return await asyncio.get_running_loop().run_in_executor(
+            None,
+            partial(
+                self._evaluate_agent_trajectory,
+                prediction=prediction,
+                agent_trajectory=agent_trajectory,
+                reference=reference,
+                input=input,
+                **kwargs,
+            ),
         )
 
     def evaluate_agent_trajectory(
diff --git a/libs/langchain/langchain/evaluation/string_distance/base.py b/libs/langchain/langchain/evaluation/string_distance/base.py
index 57f2958fe54..81aa9a9ebe0 100644
--- a/libs/langchain/langchain/evaluation/string_distance/base.py
+++ b/libs/langchain/langchain/evaluation/string_distance/base.py
@@ -3,8 +3,6 @@
 from enum import Enum
 from typing import Any, Callable, Dict, List, Optional
 
-from pydantic import Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
@@ -12,6 +10,7 @@ from langchain.callbacks.manager import (
 )
 from langchain.chains.base import Chain
 from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import RUN_KEY
 
 
diff --git a/libs/langchain/langchain/graphs/nebula_graph.py b/libs/langchain/langchain/graphs/nebula_graph.py
index cfe07297135..c74b9fb854f 100644
--- a/libs/langchain/langchain/graphs/nebula_graph.py
+++ b/libs/langchain/langchain/graphs/nebula_graph.py
@@ -2,6 +2,8 @@ import logging
 from string import Template
 from typing import Any, Dict
 
+logger = logging.getLogger(__name__)
+
 rel_query = Template(
     """
 MATCH ()-[e:`$edge_type`]->()
@@ -97,7 +99,7 @@ class NebulaGraph:
         try:
             self.session_pool.close()
         except Exception as e:
-            logging.warning(f"Could not close session pool. Error: {e}")
+            logger.warning(f"Could not close session pool. Error: {e}")
 
     @property
     def get_schema(self) -> str:
@@ -112,7 +114,7 @@ class NebulaGraph:
         try:
             result = self.session_pool.execute_parameter(query, params)
             if not result.is_succeeded():
-                logging.warning(
+                logger.warning(
                     f"Error executing query to NebulaGraph. "
                     f"Error: {result.error_msg()}\n"
                     f"Query: {query} \n"
@@ -120,7 +122,7 @@ class NebulaGraph:
             return result
 
         except NoValidSessionException:
-            logging.warning(
+            logger.warning(
                 f"No valid session found in session pool. "
                 f"Please consider increasing the session pool size. "
                 f"Current size: {self.session_pool_size}"
@@ -134,7 +136,7 @@ class NebulaGraph:
         except RuntimeError as e:
             if retry < RETRY_TIMES:
                 retry += 1
-                logging.warning(
+                logger.warning(
                     f"Error executing query to NebulaGraph. "
                     f"Retrying ({retry}/{RETRY_TIMES})...\n"
                     f"query: {query} \n"
@@ -148,7 +150,7 @@ class NebulaGraph:
             # connection issue, try to recreate session pool
             if retry < RETRY_TIMES:
                 retry += 1
-                logging.warning(
+                logger.warning(
                     f"Connection issue with NebulaGraph. "
                     f"Retrying ({retry}/{RETRY_TIMES})...\n to recreate session pool"
                 )
diff --git a/libs/langchain/langchain/hub.py b/libs/langchain/langchain/hub.py
new file mode 100644
index 00000000000..03ef9c552a4
--- /dev/null
+++ b/libs/langchain/langchain/hub.py
@@ -0,0 +1,60 @@
+"""Push and pull to the LangChain Hub."""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Optional
+
+from langchain.load.dump import dumps
+from langchain.load.load import loads
+from langchain.utils import get_from_env
+
+if TYPE_CHECKING:
+    from langchainhub import Client
+
+
+def _get_client(api_url: Optional[str] = None, api_key: Optional[str] = None) -> Client:
+    try:
+        from langchainhub import Client
+    except ImportError as e:
+        raise ImportError(
+            "Could not import langchainhub, please install with `pip install "
+            "langchainhub`."
+        ) from e
+
+    api_url = api_url or get_from_env("api_url", "LANGCHAIN_HUB_API_URL")
+    api_key = api_key or get_from_env("api_key", "LANGCHAIN_HUB_API_KEY", default="")
+    api_key = api_key or get_from_env("api_key", "LANGCHAIN_API_KEY")
+    return Client(api_url, api_key=api_key)
+
+
+def push(
+    repo_full_name: str,
+    object: Any,
+    *,
+    api_url: Optional[str] = None,
+    api_key: Optional[str] = None,
+    parent_commit_hash: Optional[str] = "latest",
+) -> str:
+    """
+    Pushes an object to the hub and returns the URL.
+    """
+    client = _get_client(api_url=api_url, api_key=api_key)
+    manifest_json = dumps(object)
+    resp = client.push(
+        repo_full_name, manifest_json, parent_commit_hash=parent_commit_hash
+    )
+    commit_hash: str = resp["commit"]["commit_hash"]
+    return commit_hash
+
+
+def pull(
+    owner_repo_commit: str,
+    *,
+    api_url: Optional[str] = None,
+    api_key: Optional[str] = None,
+) -> Any:
+    """
+    Pulls an object from the hub and returns it.
+    """
+    client = _get_client(api_url=api_url, api_key=api_key)
+    resp: str = client.pull(owner_repo_commit)
+    return loads(resp)
diff --git a/libs/langchain/langchain/indexes/graph.py b/libs/langchain/langchain/indexes/graph.py
index 64536e003c8..c29f56de5e6 100644
--- a/libs/langchain/langchain/indexes/graph.py
+++ b/libs/langchain/langchain/indexes/graph.py
@@ -1,14 +1,13 @@
 """Graph Index Creator."""
 from typing import Optional, Type
 
-from pydantic import BaseModel
-
 from langchain import BasePromptTemplate
 from langchain.chains.llm import LLMChain
 from langchain.graphs.networkx_graph import NetworkxEntityGraph, parse_triples
 from langchain.indexes.prompts.knowledge_triplet_extraction import (
     KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT,
 )
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema.language_model import BaseLanguageModel
 
 
diff --git a/libs/langchain/langchain/indexes/vectorstore.py b/libs/langchain/langchain/indexes/vectorstore.py
index c9229f9d7ba..f812754f110 100644
--- a/libs/langchain/langchain/indexes/vectorstore.py
+++ b/libs/langchain/langchain/indexes/vectorstore.py
@@ -1,13 +1,12 @@
 from typing import Any, Dict, List, Optional, Type
 
-from pydantic import BaseModel, Extra, Field
-
 from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain
 from langchain.chains.retrieval_qa.base import RetrievalQA
 from langchain.document_loaders.base import BaseLoader
 from langchain.embeddings.base import Embeddings
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.llms.openai import OpenAI
+from langchain.pydantic_v1 import BaseModel, Extra, Field
 from langchain.schema import Document
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
diff --git a/libs/langchain/langchain/llms/__init__.py b/libs/langchain/langchain/llms/__init__.py
index 9afd553dee1..b2728535a63 100644
--- a/libs/langchain/langchain/llms/__init__.py
+++ b/libs/langchain/langchain/llms/__init__.py
@@ -31,6 +31,7 @@ from langchain.llms.base import BaseLLM
 from langchain.llms.baseten import Baseten
 from langchain.llms.beam import Beam
 from langchain.llms.bedrock import Bedrock
+from langchain.llms.bittensor import NIBittensorLLM
 from langchain.llms.cerebriumai import CerebriumAI
 from langchain.llms.chatglm import ChatGLM
 from langchain.llms.clarifai import Clarifai
@@ -38,6 +39,7 @@ from langchain.llms.cohere import Cohere
 from langchain.llms.ctransformers import CTransformers
 from langchain.llms.databricks import Databricks
 from langchain.llms.deepinfra import DeepInfra
+from langchain.llms.deepsparse import DeepSparse
 from langchain.llms.edenai import EdenAI
 from langchain.llms.fake import FakeListLLM
 from langchain.llms.fireworks import Fireworks, FireworksChat
@@ -76,9 +78,10 @@ from langchain.llms.self_hosted_hugging_face import SelfHostedHuggingFaceLLM
 from langchain.llms.stochasticai import StochasticAI
 from langchain.llms.symblai_nebula import Nebula
 from langchain.llms.textgen import TextGen
+from langchain.llms.titan_takeoff import TitanTakeoff
 from langchain.llms.tongyi import Tongyi
 from langchain.llms.vertexai import VertexAI
-from langchain.llms.vllm import VLLM
+from langchain.llms.vllm import VLLM, VLLMOpenAI
 from langchain.llms.writer import Writer
 from langchain.llms.xinference import Xinference
 
@@ -102,6 +105,7 @@ __all__ = [
     "Cohere",
     "Databricks",
     "DeepInfra",
+    "DeepSparse",
     "EdenAI",
     "FakeListLLM",
     "Fireworks",
@@ -124,6 +128,7 @@ __all__ = [
     "Modal",
     "MosaicML",
     "Nebula",
+    "NIBittensorLLM",
     "NLPCloud",
     "Ollama",
     "OpenAI",
@@ -142,9 +147,11 @@ __all__ = [
     "SelfHostedHuggingFaceLLM",
     "SelfHostedPipeline",
     "StochasticAI",
+    "TitanTakeoff",
     "Tongyi",
     "VertexAI",
     "VLLM",
+    "VLLMOpenAI",
     "Writer",
     "OctoAIEndpoint",
     "Xinference",
@@ -170,6 +177,7 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
     "ctransformers": CTransformers,
     "databricks": Databricks,
     "deepinfra": DeepInfra,
+    "deepsparse": DeepSparse,
     "edenai": EdenAI,
     "fake-list": FakeListLLM,
     "forefrontai": ForefrontAI,
@@ -189,6 +197,7 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
     "modal": Modal,
     "mosaic": MosaicML,
     "nebula": Nebula,
+    "nibittensor": NIBittensorLLM,
     "nlpcloud": NLPCloud,
     "ollama": Ollama,
     "openai": OpenAI,
@@ -203,10 +212,12 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
     "self_hosted_hugging_face": SelfHostedHuggingFaceLLM,
     "stochasticai": StochasticAI,
     "tongyi": Tongyi,
+    "titan_takeoff": TitanTakeoff,
     "vertexai": VertexAI,
     "openllm": OpenLLM,
     "openllm_client": OpenLLM,
     "vllm": VLLM,
+    "vllm_openai": VLLMOpenAI,
     "writer": Writer,
     "xinference": Xinference,
 }
diff --git a/libs/langchain/langchain/llms/ai21.py b/libs/langchain/langchain/llms/ai21.py
index 5d2562a6afb..dc079c2010f 100644
--- a/libs/langchain/langchain/llms/ai21.py
+++ b/libs/langchain/langchain/llms/ai21.py
@@ -1,10 +1,10 @@
 from typing import Any, Dict, List, Optional
 
 import requests
-from pydantic import BaseModel, Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/llms/aleph_alpha.py b/libs/langchain/langchain/llms/aleph_alpha.py
index 1df17c3a87a..00235e7760e 100644
--- a/libs/langchain/langchain/llms/aleph_alpha.py
+++ b/libs/langchain/langchain/llms/aleph_alpha.py
@@ -1,10 +1,9 @@
 from typing import Any, Dict, List, Optional, Sequence
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/llms/amazon_api_gateway.py b/libs/langchain/langchain/llms/amazon_api_gateway.py
index 1ba81c11896..3161c0bc2ee 100644
--- a/libs/langchain/langchain/llms/amazon_api_gateway.py
+++ b/libs/langchain/langchain/llms/amazon_api_gateway.py
@@ -1,11 +1,11 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import Extra
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra
 
 
 class ContentHandlerAmazonAPIGateway:
diff --git a/libs/langchain/langchain/llms/anthropic.py b/libs/langchain/langchain/llms/anthropic.py
index 5e5695762f2..afaea04b51c 100644
--- a/libs/langchain/langchain/llms/anthropic.py
+++ b/libs/langchain/langchain/llms/anthropic.py
@@ -2,13 +2,12 @@ import re
 import warnings
 from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Mapping, Optional
 
-from pydantic import Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.output import GenerationChunk
 from langchain.utils import (
diff --git a/libs/langchain/langchain/llms/anyscale.py b/libs/langchain/langchain/llms/anyscale.py
index 5a440b97d4b..810d1730712 100644
--- a/libs/langchain/langchain/llms/anyscale.py
+++ b/libs/langchain/langchain/llms/anyscale.py
@@ -1,11 +1,11 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/llms/aviary.py b/libs/langchain/langchain/llms/aviary.py
index 5e2a38cf714..8444a3a041c 100644
--- a/libs/langchain/langchain/llms/aviary.py
+++ b/libs/langchain/langchain/llms/aviary.py
@@ -3,11 +3,11 @@ import os
 from typing import Any, Dict, List, Mapping, Optional, Union, cast
 
 import requests
-from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 TIMEOUT = 60
diff --git a/libs/langchain/langchain/llms/azureml_endpoint.py b/libs/langchain/langchain/llms/azureml_endpoint.py
index 7432e79e87a..3e7833bc988 100644
--- a/libs/langchain/langchain/llms/azureml_endpoint.py
+++ b/libs/langchain/langchain/llms/azureml_endpoint.py
@@ -4,10 +4,9 @@ import warnings
 from abc import abstractmethod
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import BaseModel, validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/llms/bananadev.py b/libs/langchain/langchain/llms/bananadev.py
index 76dcb7b8af3..f0659118d63 100644
--- a/libs/langchain/langchain/llms/bananadev.py
+++ b/libs/langchain/langchain/llms/bananadev.py
@@ -1,11 +1,10 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/llms/base.py b/libs/langchain/langchain/llms/base.py
index 73efdef16cd..401fe61d067 100644
--- a/libs/langchain/langchain/llms/base.py
+++ b/libs/langchain/langchain/llms/base.py
@@ -27,7 +27,6 @@ from typing import (
 )
 
 import yaml
-from pydantic import Field, root_validator, validator
 from tenacity import (
     RetryCallState,
     before_sleep_log,
@@ -50,6 +49,7 @@ from langchain.callbacks.manager import (
 from langchain.load.dump import dumpd
 from langchain.prompts.base import StringPromptValue
 from langchain.prompts.chat import ChatPromptValue
+from langchain.pydantic_v1 import Field, root_validator, validator
 from langchain.schema import (
     Generation,
     LLMResult,
diff --git a/libs/langchain/langchain/llms/baseten.py b/libs/langchain/langchain/llms/baseten.py
index d07fd63366d..d8f1fb4e94e 100644
--- a/libs/langchain/langchain/llms/baseten.py
+++ b/libs/langchain/langchain/llms/baseten.py
@@ -1,10 +1,9 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Field
 
 logger = logging.getLogger(__name__)
 
diff --git a/libs/langchain/langchain/llms/beam.py b/libs/langchain/langchain/llms/beam.py
index 47bc017dadd..20aa0d0e2a0 100644
--- a/libs/langchain/langchain/llms/beam.py
+++ b/libs/langchain/langchain/llms/beam.py
@@ -7,10 +7,10 @@ import time
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/llms/bedrock.py b/libs/langchain/langchain/llms/bedrock.py
index 481de7f5680..75af53a6a58 100644
--- a/libs/langchain/langchain/llms/bedrock.py
+++ b/libs/langchain/langchain/llms/bedrock.py
@@ -1,11 +1,10 @@
 import json
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 
 
 class LLMInputOutputAdapter:
diff --git a/libs/langchain/langchain/llms/bittensor.py b/libs/langchain/langchain/llms/bittensor.py
new file mode 100644
index 00000000000..883651439ab
--- /dev/null
+++ b/libs/langchain/langchain/llms/bittensor.py
@@ -0,0 +1,173 @@
+import http.client
+import json
+import ssl
+from typing import Any, List, Mapping, Optional
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+
+
+class NIBittensorLLM(LLM):
+    """
+    NIBittensorLLM is created by Neural Internet (https://neuralinternet.ai/),
+    powered by Bittensor, a decentralized network full of different AI models.
+
+    To analyze API_KEYS and logs of your usage visit
+        https://api.neuralinternet.ai/api-keys
+        https://api.neuralinternet.ai/logs
+
+    Example:
+        .. code-block:: python
+
+            from langchain.llms import NIBittensorLLM
+            llm = NIBittensorLLM()
+    """
+
+    system_prompt: Optional[str]
+    """Provide system prompt that you want to supply it to model before every prompt"""
+
+    top_responses: Optional[int] = 0
+    """Provide top_responses to get Top N miner responses on one request.May get delayed
+        Don't use in Production"""
+
+    @property
+    def _llm_type(self) -> str:
+        return "NIBittensorLLM"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """
+        Wrapper around the bittensor top miner models. Its built by Neural Internet.
+
+        Call the Neural Internet's BTVEP Server and return the output.
+
+        Parameters (optional):
+            system_prompt(str): A system prompt defining how your model should respond.
+            top_responses(int): Total top miner responses to retrieve from Bittensor
+                protocol.
+
+        Return:
+            The generated response(s).
+
+        Example:
+            .. code-block:: python
+
+                from langchain.llms import NIBittensorLLM
+                llm = NIBittensorLLM(system_prompt="Act like you are programmer with \
+                5+ years of experience.")
+        """
+
+        # Creating HTTPS connection with SSL
+        context = ssl.create_default_context()
+        context.check_hostname = True
+        conn = http.client.HTTPSConnection("test.neuralinternet.ai", context=context)
+
+        # Sanitizing User Input before passing to API.
+        if isinstance(self.top_responses, int):
+            top_n = min(100, self.top_responses)
+        else:
+            top_n = 0
+
+        default_prompt = "You are an assistant which is created by Neural Internet(NI) \
+            in decentralized network named as a Bittensor."
+        if self.system_prompt is None:
+            system_prompt = (
+                default_prompt
+                + " Your task is to provide accurate response based on user prompt"
+            )
+        else:
+            system_prompt = default_prompt + str(self.system_prompt)
+
+        # Retrieving API KEY to pass into header of each request
+        conn.request("GET", "/admin/api-keys/")
+        api_key_response = conn.getresponse()
+        api_keys_data = (
+            api_key_response.read().decode("utf-8").replace("\n", "").replace("\t", "")
+        )
+        api_keys_json = json.loads(api_keys_data)
+        api_key = api_keys_json[0]["api_key"]
+
+        # Creating Header and getting top benchmark miner uids
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}",
+            "Endpoint-Version": "2023-05-19",
+        }
+        conn.request("GET", "/top_miner_uids", headers=headers)
+        miner_response = conn.getresponse()
+        miner_data = (
+            miner_response.read().decode("utf-8").replace("\n", "").replace("\t", "")
+        )
+        uids = json.loads(miner_data)
+
+        # Condition for benchmark miner response
+        if isinstance(uids, list) and uids and not top_n:
+            for uid in uids:
+                try:
+                    payload = json.dumps(
+                        {
+                            "uids": [uid],
+                            "messages": [
+                                {"role": "system", "content": system_prompt},
+                                {"role": "user", "content": prompt},
+                            ],
+                        }
+                    )
+
+                    conn.request("POST", "/chat", payload, headers)
+                    init_response = conn.getresponse()
+                    init_data = (
+                        init_response.read()
+                        .decode("utf-8")
+                        .replace("\n", "")
+                        .replace("\t", "")
+                    )
+                    init_json = json.loads(init_data)
+                    if "choices" not in init_json:
+                        continue
+                    reply = init_json["choices"][0]["message"]["content"]
+                    conn.close()
+                    return reply
+                except Exception:
+                    continue
+
+        # For top miner based on bittensor response
+        try:
+            payload = json.dumps(
+                {
+                    "top_n": top_n,
+                    "messages": [
+                        {"role": "system", "content": system_prompt},
+                        {"role": "user", "content": prompt},
+                    ],
+                }
+            )
+
+            conn.request("POST", "/chat", payload, headers)
+            response = conn.getresponse()
+            utf_string = (
+                response.read().decode("utf-8").replace("\n", "").replace("\t", "")
+            )
+            if top_n:
+                conn.close()
+                return utf_string
+            json_resp = json.loads(utf_string)
+            reply = json_resp["choices"][0]["message"]["content"]
+            conn.close()
+            return reply
+        except Exception as e:
+            conn.request("GET", f"/error_msg?e={e}&p={prompt}", headers=headers)
+            return "Sorry I am unable to provide response now, Please try again later."
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            "system_prompt": self.system_prompt,
+            "top_responses": self.top_responses,
+        }
diff --git a/libs/langchain/langchain/llms/cerebriumai.py b/libs/langchain/langchain/llms/cerebriumai.py
index 02db1cf33ef..9b8a55eb268 100644
--- a/libs/langchain/langchain/llms/cerebriumai.py
+++ b/libs/langchain/langchain/llms/cerebriumai.py
@@ -1,11 +1,10 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/llms/clarifai.py b/libs/langchain/langchain/llms/clarifai.py
index 2eead835920..33396bfa4e8 100644
--- a/libs/langchain/langchain/llms/clarifai.py
+++ b/libs/langchain/langchain/llms/clarifai.py
@@ -1,11 +1,10 @@
 import logging
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/llms/cohere.py b/libs/langchain/langchain/llms/cohere.py
index bad5a156511..257a69c9ff0 100644
--- a/libs/langchain/langchain/llms/cohere.py
+++ b/libs/langchain/langchain/llms/cohere.py
@@ -3,7 +3,6 @@ from __future__ import annotations
 import logging
 from typing import Any, Callable, Dict, List, Optional
 
-from pydantic import Extra, root_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -18,6 +17,7 @@ from langchain.callbacks.manager import (
 )
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/llms/ctransformers.py b/libs/langchain/langchain/llms/ctransformers.py
index 86c657f6181..ed859a1c80f 100644
--- a/libs/langchain/langchain/llms/ctransformers.py
+++ b/libs/langchain/langchain/llms/ctransformers.py
@@ -1,13 +1,12 @@
 from functools import partial
 from typing import Any, Dict, List, Optional, Sequence
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import root_validator
 
 
 class CTransformers(LLM):
diff --git a/libs/langchain/langchain/llms/databricks.py b/libs/langchain/langchain/llms/databricks.py
index 6ed2a6fa7d6..fb4a3674b37 100644
--- a/libs/langchain/langchain/llms/databricks.py
+++ b/libs/langchain/langchain/llms/databricks.py
@@ -3,10 +3,17 @@ from abc import ABC, abstractmethod
 from typing import Any, Callable, Dict, List, Optional
 
 import requests
-from pydantic import BaseModel, Extra, Field, PrivateAttr, root_validator, validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import (
+    BaseModel,
+    Extra,
+    Field,
+    PrivateAttr,
+    root_validator,
+    validator,
+)
 
 __all__ = ["Databricks"]
 
diff --git a/libs/langchain/langchain/llms/deepinfra.py b/libs/langchain/langchain/llms/deepinfra.py
index 533c236284a..9229d2712d9 100644
--- a/libs/langchain/langchain/llms/deepinfra.py
+++ b/libs/langchain/langchain/llms/deepinfra.py
@@ -1,11 +1,11 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 DEFAULT_MODEL_ID = "google/flan-t5-xl"
diff --git a/libs/langchain/langchain/llms/deepsparse.py b/libs/langchain/langchain/llms/deepsparse.py
new file mode 100644
index 00000000000..ccb753bf6ae
--- /dev/null
+++ b/libs/langchain/langchain/llms/deepsparse.py
@@ -0,0 +1,87 @@
+# flake8: noqa
+from typing import Any, Dict, Optional, List
+
+from langchain.pydantic_v1 import root_validator
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+from langchain.llms.utils import enforce_stop_tokens
+
+
+class DeepSparse(LLM):
+    """Neural Magic DeepSparse LLM interface.
+
+    To use, you should have the ``deepsparse`` or ``deepsparse-nightly``
+    python package installed. See https://github.com/neuralmagic/deepsparse
+
+    This interface let's you deploy optimized LLMs straight from the
+    [SparseZoo](https://sparsezoo.neuralmagic.com/?useCase=text_generation)
+    Example:
+        .. code-block:: python
+            from langchain.llms import DeepSparse
+            llm = DeepSparse(model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none")
+    """  # noqa: E501
+
+    pipeline: Any  #: :meta private:
+
+    model: str
+    """The path to a model file or directory or the name of a SparseZoo model stub."""
+
+    config: Optional[Dict[str, Any]] = None
+    """Key word arguments passed to the pipeline."""
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            "model": self.model,
+            "config": self.config,
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "deepsparse"
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that ``deepsparse`` package is installed."""
+        try:
+            from deepsparse import Pipeline
+        except ImportError:
+            raise ImportError(
+                "Could not import `deepsparse` package. "
+                "Please install it with `pip install deepsparse`"
+            )
+
+        config = values["config"] or {}
+
+        values["pipeline"] = Pipeline.create(
+            task="text_generation",
+            model_path=values["model"],
+            **config,
+        )
+        return values
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Generate text from a prompt.
+        Args:
+            prompt: The prompt to generate text from.
+            stop: A list of strings to stop generation when encountered.
+        Returns:
+            The generated text.
+        Example:
+            .. code-block:: python
+                response = llm("Tell me a joke.")
+        """
+        text = self.pipeline(sequences=prompt).sequences[0]
+
+        if stop is not None:
+            text = enforce_stop_tokens(text, stop)
+        return "".join(text)
diff --git a/libs/langchain/langchain/llms/edenai.py b/libs/langchain/langchain/llms/edenai.py
index 6b0f365b6f4..e63c0776e51 100644
--- a/libs/langchain/langchain/llms/edenai.py
+++ b/libs/langchain/langchain/llms/edenai.py
@@ -3,7 +3,6 @@ import logging
 from typing import Any, Dict, List, Literal, Optional
 
 from aiohttp import ClientSession
-from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
@@ -11,6 +10,7 @@ from langchain.callbacks.manager import (
 )
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.requests import Requests
 from langchain.utils import get_from_dict_or_env
 
@@ -30,7 +30,7 @@ class EdenAI(LLM):
     for api reference check edenai documentation: http://docs.edenai.co.
     """
 
-    base_url = "https://api.edenai.run/v2"
+    base_url: str = "https://api.edenai.run/v2"
 
     edenai_api_key: Optional[str] = None
 
diff --git a/libs/langchain/langchain/llms/fireworks.py b/libs/langchain/langchain/llms/fireworks.py
index 90a9c7d4c04..2b4b5f0ff95 100644
--- a/libs/langchain/langchain/llms/fireworks.py
+++ b/libs/langchain/langchain/llms/fireworks.py
@@ -12,13 +12,13 @@ from typing import (
 )
 
 import requests
-from pydantic import Field, root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain.llms.base import BaseLLM
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import Generation, LLMResult
 from langchain.utils import get_from_dict_or_env
 
@@ -28,9 +28,7 @@ logger = logging.getLogger(__name__)
 class BaseFireworks(BaseLLM):
     """Wrapper around Fireworks large language models."""
 
-    model_id: str = Field(
-        "accounts/fireworks/models/fireworks-llama-v2-7b-chat", alias="model"
-    )
+    model_id: str = Field("accounts/fireworks/models/llama-v2-7b-chat", alias="model")
     """Model name to use."""
     temperature: float = 0.7
     """What sampling temperature to use."""
@@ -174,10 +172,10 @@ class FireworksChat(BaseLLM):
     Example:
         .. code-block:: python
             from langchain.llms import FireworksChat
-            fireworkschat = FireworksChat(model_id=""fireworks-llama-v2-13b-chat"")
+            fireworkschat = FireworksChat(model_id=""llama-v2-13b-chat"")
     """
 
-    model_id: str = "accounts/fireworks/models/fireworks-llama-v2-7b-chat"
+    model_id: str = "accounts/fireworks/models/llama-v2-7b-chat"
     """Model name to use."""
     temperature: float = 0.7
     """What sampling temperature to use."""
@@ -269,7 +267,7 @@ class Fireworks(BaseFireworks):
     Example:
         .. code-block:: python
             from langchain.llms import fireworks
-            llm = Fireworks(model_id="fireworks-llama-v2-13b")
+            llm = Fireworks(model_id="llama-v2-13b")
     """
 
 
diff --git a/libs/langchain/langchain/llms/forefrontai.py b/libs/langchain/langchain/llms/forefrontai.py
index b6c70d5c430..7d82b6896e1 100644
--- a/libs/langchain/langchain/llms/forefrontai.py
+++ b/libs/langchain/langchain/llms/forefrontai.py
@@ -1,11 +1,11 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/llms/google_palm.py b/libs/langchain/langchain/llms/google_palm.py
index 8f45950a29e..1a0a409979c 100644
--- a/libs/langchain/langchain/llms/google_palm.py
+++ b/libs/langchain/langchain/llms/google_palm.py
@@ -3,7 +3,6 @@ from __future__ import annotations
 import logging
 from typing import Any, Callable, Dict, List, Optional
 
-from pydantic import BaseModel, root_validator
 from tenacity import (
     before_sleep_log,
     retry,
@@ -14,6 +13,7 @@ from tenacity import (
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms import BaseLLM
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.schema import Generation, LLMResult
 from langchain.utils import get_from_dict_or_env
 
diff --git a/libs/langchain/langchain/llms/gooseai.py b/libs/langchain/langchain/llms/gooseai.py
index cde043ce221..831cff9a54a 100644
--- a/libs/langchain/langchain/llms/gooseai.py
+++ b/libs/langchain/langchain/llms/gooseai.py
@@ -1,10 +1,9 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/llms/gpt4all.py b/libs/langchain/langchain/llms/gpt4all.py
index c88374c1c09..67aa6d49a75 100644
--- a/libs/langchain/langchain/llms/gpt4all.py
+++ b/libs/langchain/langchain/llms/gpt4all.py
@@ -1,11 +1,10 @@
 from functools import partial
 from typing import Any, Dict, List, Mapping, Optional, Set
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, Field, root_validator
 
 
 class GPT4All(LLM):
diff --git a/libs/langchain/langchain/llms/huggingface_endpoint.py b/libs/langchain/langchain/llms/huggingface_endpoint.py
index ff38a2025a1..a1593287a60 100644
--- a/libs/langchain/langchain/llms/huggingface_endpoint.py
+++ b/libs/langchain/langchain/llms/huggingface_endpoint.py
@@ -1,11 +1,11 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 VALID_TASKS = ("text2text-generation", "text-generation", "summarization")
diff --git a/libs/langchain/langchain/llms/huggingface_hub.py b/libs/langchain/langchain/llms/huggingface_hub.py
index 745f0fdccba..f1bc8977ab9 100644
--- a/libs/langchain/langchain/llms/huggingface_hub.py
+++ b/libs/langchain/langchain/llms/huggingface_hub.py
@@ -1,10 +1,9 @@
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 DEFAULT_REPO_ID = "gpt2"
diff --git a/libs/langchain/langchain/llms/huggingface_pipeline.py b/libs/langchain/langchain/llms/huggingface_pipeline.py
index ef7d428949e..9c32787928d 100644
--- a/libs/langchain/langchain/llms/huggingface_pipeline.py
+++ b/libs/langchain/langchain/llms/huggingface_pipeline.py
@@ -2,11 +2,10 @@ import importlib.util
 import logging
 from typing import Any, List, Mapping, Optional
 
-from pydantic import Extra
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra
 
 DEFAULT_MODEL_ID = "gpt2"
 DEFAULT_TASK = "text-generation"
diff --git a/libs/langchain/langchain/llms/huggingface_text_gen_inference.py b/libs/langchain/langchain/llms/huggingface_text_gen_inference.py
index befe8134808..8e23d935f85 100644
--- a/libs/langchain/langchain/llms/huggingface_text_gen_inference.py
+++ b/libs/langchain/langchain/llms/huggingface_text_gen_inference.py
@@ -1,12 +1,11 @@
 from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.schema.output import GenerationChunk
 
 
diff --git a/libs/langchain/langchain/llms/human.py b/libs/langchain/langchain/llms/human.py
index 87ce68f1522..d6ea10b8ec2 100644
--- a/libs/langchain/langchain/llms/human.py
+++ b/libs/langchain/langchain/llms/human.py
@@ -1,10 +1,9 @@
 from typing import Any, Callable, List, Mapping, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Field
 
 
 def _display_prompt(prompt: str) -> None:
diff --git a/libs/langchain/langchain/llms/llamacpp.py b/libs/langchain/langchain/llms/llamacpp.py
index 518206462a2..d5af66dc4c9 100644
--- a/libs/langchain/langchain/llms/llamacpp.py
+++ b/libs/langchain/langchain/llms/llamacpp.py
@@ -1,10 +1,9 @@
 import logging
 from typing import Any, Dict, Iterator, List, Optional
 
-from pydantic import Field, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema.output import GenerationChunk
 from langchain.utils import get_pydantic_field_names
 from langchain.utils.utils import build_extra_kwargs
diff --git a/libs/langchain/langchain/llms/manifest.py b/libs/langchain/langchain/llms/manifest.py
index 71a89ed00f2..aaaf3a24f76 100644
--- a/libs/langchain/langchain/llms/manifest.py
+++ b/libs/langchain/langchain/llms/manifest.py
@@ -1,9 +1,8 @@
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Extra, root_validator
 
 
 class ManifestWrapper(LLM):
diff --git a/libs/langchain/langchain/llms/minimax.py b/libs/langchain/langchain/llms/minimax.py
index dfcc3c656c3..1fb04709e03 100644
--- a/libs/langchain/langchain/llms/minimax.py
+++ b/libs/langchain/langchain/llms/minimax.py
@@ -10,12 +10,12 @@ from typing import (
 )
 
 import requests
-from pydantic import BaseModel, Extra, Field, PrivateAttr, root_validator
 
 from langchain.callbacks.manager import (
     CallbackManagerForLLMRun,
 )
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import BaseModel, Extra, Field, PrivateAttr, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/llms/mlflow_ai_gateway.py b/libs/langchain/langchain/llms/mlflow_ai_gateway.py
index da20a629784..dcb75c5190b 100644
--- a/libs/langchain/langchain/llms/mlflow_ai_gateway.py
+++ b/libs/langchain/langchain/llms/mlflow_ai_gateway.py
@@ -2,13 +2,14 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import BaseModel, Extra
 
 
-class Params(BaseModel, extra=Extra.allow):
+# Ignoring type because below is valid pydantic code
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
+class Params(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
     """Parameters for the MLflow AI Gateway LLM."""
 
     temperature: float = 0.0
diff --git a/libs/langchain/langchain/llms/modal.py b/libs/langchain/langchain/llms/modal.py
index bf03947bc53..9f1f42269d7 100644
--- a/libs/langchain/langchain/llms/modal.py
+++ b/libs/langchain/langchain/llms/modal.py
@@ -2,11 +2,11 @@ import logging
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, Field, root_validator
 
 logger = logging.getLogger(__name__)
 
diff --git a/libs/langchain/langchain/llms/mosaicml.py b/libs/langchain/langchain/llms/mosaicml.py
index 226b5c3b8b5..780e7a8b4da 100644
--- a/libs/langchain/langchain/llms/mosaicml.py
+++ b/libs/langchain/langchain/llms/mosaicml.py
@@ -1,11 +1,11 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 INSTRUCTION_KEY = "### Instruction:"
diff --git a/libs/langchain/langchain/llms/nlpcloud.py b/libs/langchain/langchain/llms/nlpcloud.py
index 9e5070acf77..1420595eee9 100644
--- a/libs/langchain/langchain/llms/nlpcloud.py
+++ b/libs/langchain/langchain/llms/nlpcloud.py
@@ -1,9 +1,8 @@
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/llms/octoai_endpoint.py b/libs/langchain/langchain/llms/octoai_endpoint.py
index 46c88d51ac0..93bdb36ff93 100644
--- a/libs/langchain/langchain/llms/octoai_endpoint.py
+++ b/libs/langchain/langchain/llms/octoai_endpoint.py
@@ -1,10 +1,9 @@
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/llms/ollama.py b/libs/langchain/langchain/llms/ollama.py
index 8adbfd997e4..ec132c08b88 100644
--- a/libs/langchain/langchain/llms/ollama.py
+++ b/libs/langchain/langchain/llms/ollama.py
@@ -2,10 +2,10 @@ import json
 from typing import Any, Dict, Iterator, List, Mapping, Optional
 
 import requests
-from pydantic import Extra
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import BaseLLM
+from langchain.pydantic_v1 import Extra
 from langchain.schema import LLMResult
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.output import GenerationChunk
@@ -23,7 +23,7 @@ def _stream_response_to_generation_chunk(
 
 
 class _OllamaCommon(BaseLanguageModel):
-    base_url = "http://localhost:11434"
+    base_url: str = "http://localhost:11434"
     """Base url the model is hosted under."""
 
     model: str = "llama2"
diff --git a/libs/langchain/langchain/llms/openai.py b/libs/langchain/langchain/llms/openai.py
index dacafe0f2ea..0ab26529122 100644
--- a/libs/langchain/langchain/llms/openai.py
+++ b/libs/langchain/langchain/llms/openai.py
@@ -20,13 +20,12 @@ from typing import (
     Union,
 )
 
-from pydantic import Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain.llms.base import BaseLLM, create_base_retry_decorator
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import Generation, LLMResult
 from langchain.schema.output import GenerationChunk
 from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
@@ -143,8 +142,8 @@ class BaseOpenAI(BaseLLM):
     def lc_serializable(self) -> bool:
         return True
 
-    client: Any  #: :meta private:
-    model_name: str = Field("text-davinci-003", alias="model")
+    client: Any = None  #: :meta private:
+    model_name: str = Field(default="text-davinci-003", alias="model")
     """Model name to use."""
     temperature: float = 0.7
     """What sampling temperature to use."""
diff --git a/libs/langchain/langchain/llms/openllm.py b/libs/langchain/langchain/llms/openllm.py
index 62b1dde03f0..df8d4bc3818 100644
--- a/libs/langchain/langchain/llms/openllm.py
+++ b/libs/langchain/langchain/llms/openllm.py
@@ -15,13 +15,12 @@ from typing import (
     overload,
 )
 
-from pydantic import PrivateAttr
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import PrivateAttr
 
 if TYPE_CHECKING:
     import openllm
diff --git a/libs/langchain/langchain/llms/openlm.py b/libs/langchain/langchain/llms/openlm.py
index c77badaa3ac..156add03ecf 100644
--- a/libs/langchain/langchain/llms/openlm.py
+++ b/libs/langchain/langchain/llms/openlm.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict
 
-from pydantic import root_validator
-
 from langchain.llms.openai import BaseOpenAI
+from langchain.pydantic_v1 import root_validator
 
 
 class OpenLM(BaseOpenAI):
diff --git a/libs/langchain/langchain/llms/petals.py b/libs/langchain/langchain/llms/petals.py
index 1b74734cd54..9499b602887 100644
--- a/libs/langchain/langchain/llms/petals.py
+++ b/libs/langchain/langchain/llms/petals.py
@@ -1,11 +1,10 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/llms/pipelineai.py b/libs/langchain/langchain/llms/pipelineai.py
index b145cc49474..0d257e33647 100644
--- a/libs/langchain/langchain/llms/pipelineai.py
+++ b/libs/langchain/langchain/llms/pipelineai.py
@@ -1,11 +1,10 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/llms/predibase.py b/libs/langchain/langchain/llms/predibase.py
index 0ab549236e9..98ab06937ae 100644
--- a/libs/langchain/langchain/llms/predibase.py
+++ b/libs/langchain/langchain/llms/predibase.py
@@ -1,9 +1,8 @@
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Field
 
 
 class Predibase(LLM):
diff --git a/libs/langchain/langchain/llms/predictionguard.py b/libs/langchain/langchain/llms/predictionguard.py
index f91541508f2..6977381129b 100644
--- a/libs/langchain/langchain/llms/predictionguard.py
+++ b/libs/langchain/langchain/llms/predictionguard.py
@@ -1,11 +1,10 @@
 import logging
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/llms/replicate.py b/libs/langchain/langchain/llms/replicate.py
index 5ae4638d501..9fa2807b4e2 100644
--- a/libs/langchain/langchain/llms/replicate.py
+++ b/libs/langchain/langchain/llms/replicate.py
@@ -1,10 +1,9 @@
 import logging
 from typing import Any, Dict, List, Mapping, Optional
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
@@ -46,6 +45,14 @@ class Replicate(LLM):
 
         extra = Extra.forbid
 
+    @property
+    def lc_secrets(self) -> Dict[str, str]:
+        return {"replicate_api_token": "REPLICATE_API_TOKEN"}
+
+    @property
+    def lc_serializable(self) -> bool:
+        return True
+
     @root_validator(pre=True)
     def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
         """Build extra kwargs from additional params that were passed in."""
diff --git a/libs/langchain/langchain/llms/rwkv.py b/libs/langchain/langchain/llms/rwkv.py
index 6b717d4a12f..bb54c9d5deb 100644
--- a/libs/langchain/langchain/llms/rwkv.py
+++ b/libs/langchain/langchain/llms/rwkv.py
@@ -5,11 +5,10 @@ Based on https://github.com/saharNooby/rwkv.cpp/blob/master/rwkv/chat_with_bot.p
 """
 from typing import Any, Dict, List, Mapping, Optional, Set
 
-from pydantic import BaseModel, Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 
 
 class RWKV(LLM, BaseModel):
diff --git a/libs/langchain/langchain/llms/sagemaker_endpoint.py b/libs/langchain/langchain/llms/sagemaker_endpoint.py
index 1e28435684d..e0383552b2f 100644
--- a/libs/langchain/langchain/llms/sagemaker_endpoint.py
+++ b/libs/langchain/langchain/llms/sagemaker_endpoint.py
@@ -2,11 +2,10 @@
 from abc import abstractmethod
 from typing import Any, Dict, Generic, List, Mapping, Optional, TypeVar, Union
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 
 INPUT_TYPE = TypeVar("INPUT_TYPE", bound=Union[str, List[str]])
 OUTPUT_TYPE = TypeVar("OUTPUT_TYPE", bound=Union[str, List[List[float]]])
diff --git a/libs/langchain/langchain/llms/self_hosted.py b/libs/langchain/langchain/llms/self_hosted.py
index 42d45c9c545..b43ce822e54 100644
--- a/libs/langchain/langchain/llms/self_hosted.py
+++ b/libs/langchain/langchain/llms/self_hosted.py
@@ -3,11 +3,10 @@ import logging
 import pickle
 from typing import Any, Callable, List, Mapping, Optional
 
-from pydantic import Extra
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra
 
 logger = logging.getLogger(__name__)
 
diff --git a/libs/langchain/langchain/llms/self_hosted_hugging_face.py b/libs/langchain/langchain/llms/self_hosted_hugging_face.py
index 2e55aaf1d2f..0b590cf5192 100644
--- a/libs/langchain/langchain/llms/self_hosted_hugging_face.py
+++ b/libs/langchain/langchain/llms/self_hosted_hugging_face.py
@@ -2,11 +2,10 @@ import importlib.util
 import logging
 from typing import Any, Callable, List, Mapping, Optional
 
-from pydantic import Extra
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.self_hosted import SelfHostedPipeline
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra
 
 DEFAULT_MODEL_ID = "gpt2"
 DEFAULT_TASK = "text-generation"
diff --git a/libs/langchain/langchain/llms/stochasticai.py b/libs/langchain/langchain/llms/stochasticai.py
index 391d9e8c409..2afe7d695e4 100644
--- a/libs/langchain/langchain/llms/stochasticai.py
+++ b/libs/langchain/langchain/llms/stochasticai.py
@@ -3,11 +3,11 @@ import time
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/llms/symblai_nebula.py b/libs/langchain/langchain/llms/symblai_nebula.py
index cec64b55168..d368c3a1a0d 100644
--- a/libs/langchain/langchain/llms/symblai_nebula.py
+++ b/libs/langchain/langchain/llms/symblai_nebula.py
@@ -1,12 +1,21 @@
+import json
 import logging
-from typing import Any, Dict, List, Mapping, Optional
+from typing import Any, Callable, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import Extra, root_validator
+from requests import ConnectTimeout, ReadTimeout, RequestException
+from tenacity import (
+    before_sleep_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+)
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 DEFAULT_NEBULA_SERVICE_URL = "https://api-nebula.symbl.ai"
@@ -19,7 +28,7 @@ class Nebula(LLM):
     """Nebula Service models.
 
     To use, you should have the environment variable ``NEBULA_SERVICE_URL``,
-    ``NEBULA_SERVICE_PATH`` and ``NEBULA_SERVICE_API_KEY`` set with your Nebula
+    ``NEBULA_SERVICE_PATH`` and ``NEBULA_API_KEY`` set with your Nebula
     Service, or pass it as a named parameter to the constructor.
 
     Example:
@@ -28,9 +37,9 @@ class Nebula(LLM):
             from langchain.llms import Nebula
 
             nebula = Nebula(
-                nebula_service_url="SERVICE_URL",
-                nebula_service_path="SERVICE_ROUTE",
-                nebula_api_key="SERVICE_TOKEN",
+                nebula_service_url="NEBULA_SERVICE_URL",
+                nebula_service_path="NEBULA_SERVICE_PATH",
+                nebula_api_key="NEBULA_API_KEY",
             )
     """  # noqa: E501
 
@@ -38,14 +47,19 @@ class Nebula(LLM):
     model_kwargs: Optional[dict] = None
 
     """Optional"""
+
     nebula_service_url: Optional[str] = None
     nebula_service_path: Optional[str] = None
     nebula_api_key: Optional[str] = None
-    conversation: str = ""
-    return_scores: Optional[str] = "false"
-    max_new_tokens: Optional[int] = 2048
-    top_k: Optional[float] = 2
-    penalty_alpha: Optional[float] = 0.1
+    model: Optional[str] = None
+    max_new_tokens: Optional[int] = 128
+    temperature: Optional[float] = 0.6
+    top_p: Optional[float] = 0.95
+    repetition_penalty: Optional[float] = 1.0
+    top_k: Optional[int] = 0
+    penalty_alpha: Optional[float] = 0.0
+    stop_sequences: Optional[List[str]] = None
+    max_retries: Optional[int] = 10
 
     class Config:
         """Configuration for this pydantic object."""
@@ -68,7 +82,7 @@ class Nebula(LLM):
             DEFAULT_NEBULA_SERVICE_PATH,
         )
         nebula_api_key = get_from_dict_or_env(
-            values, "nebula_api_key", "NEBULA_SERVICE_API_KEY", ""
+            values, "nebula_api_key", "NEBULA_API_KEY", None
         )
 
         if nebula_service_url.endswith("/"):
@@ -76,25 +90,24 @@ class Nebula(LLM):
         if not nebula_service_path.startswith("/"):
             nebula_service_path = "/" + nebula_service_path
 
-        """ TODO: Future login"""
-        """
-        try:
-            nebula_service_endpoint = f"{nebula_service_url}{nebula_service_path}"
-            headers = {
-                "Content-Type": "application/json",
-                "ApiKey": "{nebula_api_key}",
-                }
-            requests.get(nebula_service_endpoint, headers=headers)
-        except requests.exceptions.RequestException as e:
-            raise ValueError(e)
-        """
-
         values["nebula_service_url"] = nebula_service_url
         values["nebula_service_path"] = nebula_service_path
         values["nebula_api_key"] = nebula_api_key
 
         return values
 
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling Cohere API."""
+        return {
+            "max_new_tokens": self.max_new_tokens,
+            "temperature": self.temperature,
+            "top_k": self.top_k,
+            "top_p": self.top_p,
+            "repetition_penalty": self.repetition_penalty,
+            "penalty_alpha": self.penalty_alpha,
+        }
+
     @property
     def _identifying_params(self) -> Mapping[str, Any]:
         """Get the identifying parameters."""
@@ -103,7 +116,6 @@ class Nebula(LLM):
             "nebula_service_url": self.nebula_service_url,
             "nebula_service_path": self.nebula_service_path,
             **{"model_kwargs": _model_kwargs},
-            "conversation": self.conversation,
         }
 
     @property
@@ -111,6 +123,25 @@ class Nebula(LLM):
         """Return type of llm."""
         return "nebula"
 
+    def _invocation_params(
+        self, stop_sequences: Optional[List[str]], **kwargs: Any
+    ) -> dict:
+        params = self._default_params
+        if self.stop_sequences is not None and stop_sequences is not None:
+            raise ValueError("`stop` found in both the input and default params.")
+        elif self.stop_sequences is not None:
+            params["stop_sequences"] = self.stop_sequences
+        else:
+            params["stop_sequences"] = stop_sequences
+        return {**params, **kwargs}
+
+    @staticmethod
+    def _process_response(response: Any, stop: Optional[List[str]]) -> str:
+        text = response["output"]["text"]
+        if stop:
+            text = enforce_stop_tokens(text, stop)
+        return text
+
     def _call(
         self,
         prompt: str,
@@ -128,57 +159,84 @@ class Nebula(LLM):
             .. code-block:: python
                 response = nebula("Tell me a joke.")
         """
+        params = self._invocation_params(stop, **kwargs)
+        prompt = prompt.strip()
+        if "\n" in prompt:
+            instruction = prompt.split("\n")[0]
+            conversation = "\n".join(prompt.split("\n")[1:])
+        else:
+            raise ValueError("Prompt must contain instruction and conversation.")
 
-        _model_kwargs = self.model_kwargs or {}
+        response = completion_with_retry(
+            self,
+            instruction=instruction,
+            conversation=conversation,
+            params=params,
+            url=f"{self.nebula_service_url}{self.nebula_service_path}",
+        )
+        _stop = params.get("stop_sequences")
+        return self._process_response(response, _stop)
 
-        nebula_service_endpoint = f"{self.nebula_service_url}{self.nebula_service_path}"
 
-        headers = {
-            "Content-Type": "application/json",
-            "ApiKey": f"{self.nebula_api_key}",
+def make_request(
+    self: Nebula,
+    instruction: str,
+    conversation: str,
+    url: str = f"{DEFAULT_NEBULA_SERVICE_URL}{DEFAULT_NEBULA_SERVICE_PATH}",
+    params: Dict = {},
+) -> Any:
+    """Generate text from the model."""
+    headers = {
+        "Content-Type": "application/json",
+        "ApiKey": f"{self.nebula_api_key}",
+    }
+
+    body = {
+        "prompt": {
+            "instruction": instruction,
+            "conversation": {"text": f"{conversation}"},
         }
+    }
 
-        body = {
-            "prompt": {
-                "instruction": prompt,
-                "conversation": {"text": f"{self.conversation}"},
-            },
-            "return_scores": self.return_scores,
-            "max_new_tokens": self.max_new_tokens,
-            "top_k": self.top_k,
-            "penalty_alpha": self.penalty_alpha,
-        }
+    # add params to body
+    for key, value in params.items():
+        body[key] = value
 
-        if len(self.conversation) == 0:
-            raise ValueError("Error conversation is empty.")
+    # make request
+    response = requests.post(url, headers=headers, json=body)
 
-        logger.debug(f"NEBULA _model_kwargs: {_model_kwargs}")
-        logger.debug(f"NEBULA body: {body}")
-        logger.debug(f"NEBULA kwargs: {kwargs}")
-        logger.debug(f"NEBULA conversation: {self.conversation}")
+    if response.status_code != 200:
+        raise Exception(
+            f"Request failed with status code {response.status_code}"
+            f" and message {response.text}"
+        )
 
-        # call API
-        try:
-            response = requests.post(
-                nebula_service_endpoint, headers=headers, json=body
-            )
-        except requests.exceptions.RequestException as e:
-            raise ValueError(f"Error raised by inference endpoint: {e}")
+    return json.loads(response.text)
 
-        logger.debug(f"NEBULA response: {response}")
 
-        if response.status_code != 200:
-            raise ValueError(
-                f"Error returned by service, status code {response.status_code}"
-            )
+def _create_retry_decorator(llm: Nebula) -> Callable[[Any], Any]:
+    min_seconds = 4
+    max_seconds = 10
+    # Wait 2^x * 1 second between each retry starting with
+    # 4 seconds, then up to 10 seconds, then 10 seconds afterward
+    max_retries = llm.max_retries if llm.max_retries is not None else 3
+    return retry(
+        reraise=True,
+        stop=stop_after_attempt(max_retries),
+        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
+        retry=(
+            retry_if_exception_type((RequestException, ConnectTimeout, ReadTimeout))
+        ),
+        before_sleep=before_sleep_log(logger, logging.WARNING),
+    )
 
-        """ get the result """
-        text = response.text
 
-        """ enforce stop """
-        if stop is not None:
-            # This is required since the stop tokens
-            # are not enforced by the model parameters
-            text = enforce_stop_tokens(text, stop)
+def completion_with_retry(llm: Nebula, **kwargs: Any) -> Any:
+    """Use tenacity to retry the completion call."""
+    retry_decorator = _create_retry_decorator(llm)
 
-        return text
+    @retry_decorator
+    def _completion_with_retry(**_kwargs: Any) -> Any:
+        return make_request(llm, **_kwargs)
+
+    return _completion_with_retry(**kwargs)
diff --git a/libs/langchain/langchain/llms/textgen.py b/libs/langchain/langchain/llms/textgen.py
index 4e4880b82a8..bcee0eb83ed 100644
--- a/libs/langchain/langchain/llms/textgen.py
+++ b/libs/langchain/langchain/llms/textgen.py
@@ -2,10 +2,10 @@ import logging
 from typing import Any, Dict, List, Optional
 
 import requests
-from pydantic import Field
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Field
 
 logger = logging.getLogger(__name__)
 
diff --git a/libs/langchain/langchain/llms/titan_takeoff.py b/libs/langchain/langchain/llms/titan_takeoff.py
new file mode 100644
index 00000000000..517918fa993
--- /dev/null
+++ b/libs/langchain/langchain/llms/titan_takeoff.py
@@ -0,0 +1,157 @@
+from typing import Any, Iterator, List, Mapping, Optional
+
+import requests
+from requests.exceptions import ConnectionError
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+from langchain.llms.utils import enforce_stop_tokens
+from langchain.schema.output import GenerationChunk
+
+
+class TitanTakeoff(LLM):
+    port: int = 8000
+    """Specifies the port to use for the Titan Takeoff API. Default = 8000."""
+
+    generate_max_length: int = 128
+    """Maximum generation length. Default = 128."""
+
+    sampling_topk: int = 1
+    """Sample predictions from the top K most probable candidates. Default = 1."""
+
+    sampling_topp: float = 1.0
+    """Sample from predictions whose cumulative probability exceeds this value.
+    Default = 1.0.
+    """
+
+    sampling_temperature: float = 1.0
+    """Sample with randomness. Bigger temperatures are associated with 
+    more randomness and 'creativity'. Default = 1.0.
+    """
+
+    repetition_penalty: float = 1.0
+    """Penalise the generation of tokens that have been generated before. 
+    Set to > 1 to penalize. Default = 1 (no penalty).
+    """
+
+    no_repeat_ngram_size: int = 0
+    """Prevent repetitions of ngrams of this size. Default = 0 (turned off)."""
+
+    streaming: bool = False
+    """Whether to stream the output. Default = False."""
+
+    @property
+    def _default_params(self) -> Mapping[str, Any]:
+        """Get the default parameters for calling Titan Takeoff Server."""
+        params = {
+            "generate_max_length": self.generate_max_length,
+            "sampling_topk": self.sampling_topk,
+            "sampling_topp": self.sampling_topp,
+            "sampling_temperature": self.sampling_temperature,
+            "repetition_penalty": self.repetition_penalty,
+            "no_repeat_ngram_size": self.no_repeat_ngram_size,
+        }
+        return params
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "titan_takeoff"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call out to Titan Takeoff generate endpoint.
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: Optional list of stop words to use when generating.
+
+        Returns:
+            The string generated by the model.
+
+        Example:
+            .. code-block:: python
+
+                prompt = "What is the capital of the United Kingdom?"
+                response = model(prompt)
+
+        """
+        try:
+            if self.streaming:
+                text_output = ""
+                for chunk in self._stream(
+                    prompt=prompt,
+                    stop=stop,
+                    run_manager=run_manager,
+                ):
+                    text_output += chunk.text
+                return text_output
+
+            url = f"http://localhost:{self.port}/generate"
+            params = {"text": prompt, **self._default_params}
+
+            response = requests.post(url, json=params)
+            response.raise_for_status()
+            response.encoding = "utf-8"
+            text = ""
+
+            if "message" in response.json():
+                text = response.json()["message"]
+            else:
+                raise ValueError("Something went wrong.")
+            if stop is not None:
+                text = enforce_stop_tokens(text, stop)
+            return text
+        except ConnectionError:
+            raise ConnectionError(
+                "Could not connect to Titan Takeoff server. \
+                Please make sure that the server is running."
+            )
+
+    def _stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[GenerationChunk]:
+        """Call out to Titan Takeoff stream endpoint.
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: Optional list of stop words to use when generating.
+
+        Returns:
+            The string generated by the model.
+
+        Yields:
+            A dictionary like object containing a string token.
+
+        Example:
+            .. code-block:: python
+
+                prompt = "What is the capital of the United Kingdom?"
+                response = model(prompt)
+
+        """
+        url = f"http://localhost:{self.port}/generate_stream"
+        params = {"text": prompt, **self._default_params}
+
+        response = requests.post(url, json=params, stream=True)
+        response.encoding = "utf-8"
+        for text in response.iter_content(chunk_size=1, decode_unicode=True):
+            if text:
+                chunk = GenerationChunk(text=text)
+                yield chunk
+                if run_manager:
+                    run_manager.on_llm_new_token(token=chunk.text)
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {"port": self.port, **{}, **self._default_params}
diff --git a/libs/langchain/langchain/llms/tongyi.py b/libs/langchain/langchain/llms/tongyi.py
index 57973861b98..80447a61917 100644
--- a/libs/langchain/langchain/llms/tongyi.py
+++ b/libs/langchain/langchain/llms/tongyi.py
@@ -3,7 +3,6 @@ from __future__ import annotations
 import logging
 from typing import Any, Callable, Dict, List, Optional
 
-from pydantic import Field, root_validator
 from requests.exceptions import HTTPError
 from tenacity import (
     before_sleep_log,
@@ -15,6 +14,7 @@ from tenacity import (
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import Generation, LLMResult
 from langchain.utils import get_from_dict_or_env
 
diff --git a/libs/langchain/langchain/llms/vertexai.py b/libs/langchain/langchain/llms/vertexai.py
index 35fa5734664..cdc541824c8 100644
--- a/libs/langchain/langchain/llms/vertexai.py
+++ b/libs/langchain/langchain/llms/vertexai.py
@@ -4,14 +4,13 @@ import asyncio
 from concurrent.futures import Executor, ThreadPoolExecutor
 from typing import TYPE_CHECKING, Any, Callable, ClassVar, Dict, List, Optional
 
-from pydantic import BaseModel, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
 from langchain.llms.base import LLM, create_base_retry_decorator
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.utilities.vertexai import (
     init_vertexai,
     raise_vertex_import_error,
diff --git a/libs/langchain/langchain/llms/vllm.py b/libs/langchain/langchain/llms/vllm.py
index 0a6c4dbec04..bf82c8c2149 100644
--- a/libs/langchain/langchain/llms/vllm.py
+++ b/libs/langchain/langchain/llms/vllm.py
@@ -1,9 +1,9 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import BaseLLM
+from langchain.llms.openai import BaseOpenAI
+from langchain.pydantic_v1 import root_validator
 from langchain.schema.output import Generation, LLMResult
 
 
@@ -127,3 +127,27 @@ class VLLM(BaseLLM):
     def _llm_type(self) -> str:
         """Return type of llm."""
         return "vllm"
+
+
+class VLLMOpenAI(BaseOpenAI):
+    """vLLM OpenAI-compatible API client"""
+
+    @property
+    def _invocation_params(self) -> Dict[str, Any]:
+        """Get the parameters used to invoke the model."""
+        openai_creds: Dict[str, Any] = {
+            "api_key": self.openai_api_key,
+            "api_base": self.openai_api_base,
+        }
+
+        return {
+            "model": self.model_name,
+            **openai_creds,
+            **self._default_params,
+            "logit_bias": None,
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "vllm-openai"
diff --git a/libs/langchain/langchain/llms/writer.py b/libs/langchain/langchain/llms/writer.py
index 546d09b04c1..5f83d16e694 100644
--- a/libs/langchain/langchain/llms/writer.py
+++ b/libs/langchain/langchain/llms/writer.py
@@ -1,11 +1,11 @@
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
-from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
 from langchain.llms.utils import enforce_stop_tokens
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/load/serializable.py b/libs/langchain/langchain/load/serializable.py
index 8f0e5ccf8dd..535be6476d7 100644
--- a/libs/langchain/langchain/load/serializable.py
+++ b/libs/langchain/langchain/load/serializable.py
@@ -1,7 +1,7 @@
 from abc import ABC
 from typing import Any, Dict, List, Literal, TypedDict, Union, cast
 
-from pydantic import BaseModel, PrivateAttr
+from langchain.pydantic_v1 import BaseModel, PrivateAttr
 
 
 class BaseSerialized(TypedDict):
diff --git a/libs/langchain/langchain/memory/buffer.py b/libs/langchain/langchain/memory/buffer.py
index 50b1468b648..4afad7dffd3 100644
--- a/libs/langchain/langchain/memory/buffer.py
+++ b/libs/langchain/langchain/memory/buffer.py
@@ -1,10 +1,9 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import root_validator
-
 from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
 from langchain.memory.utils import get_prompt_input_key
-from langchain.schema.messages import get_buffer_string
+from langchain.pydantic_v1 import root_validator
+from langchain.schema.messages import BaseMessage, get_buffer_string
 
 
 class ConversationBufferMemory(BaseChatMemory):
@@ -17,14 +16,21 @@ class ConversationBufferMemory(BaseChatMemory):
     @property
     def buffer(self) -> Any:
         """String buffer of memory."""
-        if self.return_messages:
-            return self.chat_memory.messages
-        else:
-            return get_buffer_string(
-                self.chat_memory.messages,
-                human_prefix=self.human_prefix,
-                ai_prefix=self.ai_prefix,
-            )
+        return self.buffer_as_messages if self.return_messages else self.buffer_as_str
+
+    @property
+    def buffer_as_str(self) -> str:
+        """Exposes the buffer as a string in case return_messages is True."""
+        return get_buffer_string(
+            self.chat_memory.messages,
+            human_prefix=self.human_prefix,
+            ai_prefix=self.ai_prefix,
+        )
+
+    @property
+    def buffer_as_messages(self) -> List[BaseMessage]:
+        """Exposes the buffer as a list of messages in case return_messages is False."""
+        return self.chat_memory.messages
 
     @property
     def memory_variables(self) -> List[str]:
diff --git a/libs/langchain/langchain/memory/buffer_window.py b/libs/langchain/langchain/memory/buffer_window.py
index af27f41d33e..05b883e6d7d 100644
--- a/libs/langchain/langchain/memory/buffer_window.py
+++ b/libs/langchain/langchain/memory/buffer_window.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Union
 
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.schema.messages import BaseMessage, get_buffer_string
@@ -14,9 +14,24 @@ class ConversationBufferWindowMemory(BaseChatMemory):
     """Number of messages to store in buffer."""
 
     @property
-    def buffer(self) -> List[BaseMessage]:
+    def buffer(self) -> Union[str, List[BaseMessage]]:
         """String buffer of memory."""
-        return self.chat_memory.messages
+        return self.buffer_as_messages if self.return_messages else self.buffer_as_str
+
+    @property
+    def buffer_as_str(self) -> str:
+        """Exposes the buffer as a string in case return_messages is True."""
+        messages = self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else []
+        return get_buffer_string(
+            messages,
+            human_prefix=self.human_prefix,
+            ai_prefix=self.ai_prefix,
+        )
+
+    @property
+    def buffer_as_messages(self) -> List[BaseMessage]:
+        """Exposes the buffer as a list of messages in case return_messages is False."""
+        return self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else []
 
     @property
     def memory_variables(self) -> List[str]:
@@ -26,14 +41,6 @@ class ConversationBufferWindowMemory(BaseChatMemory):
         """
         return [self.memory_key]
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         """Return history buffer."""
-
-        buffer: Any = self.buffer[-self.k * 2 :] if self.k > 0 else []
-        if not self.return_messages:
-            buffer = get_buffer_string(
-                buffer,
-                human_prefix=self.human_prefix,
-                ai_prefix=self.ai_prefix,
-            )
-        return {self.memory_key: buffer}
+        return {self.memory_key: self.buffer}
diff --git a/libs/langchain/langchain/memory/chat_memory.py b/libs/langchain/langchain/memory/chat_memory.py
index e322c6ddb2d..cb49c12d964 100644
--- a/libs/langchain/langchain/memory/chat_memory.py
+++ b/libs/langchain/langchain/memory/chat_memory.py
@@ -1,10 +1,9 @@
 from abc import ABC
 from typing import Any, Dict, Optional, Tuple
 
-from pydantic import Field
-
 from langchain.memory.chat_message_histories.in_memory import ChatMessageHistory
 from langchain.memory.utils import get_prompt_input_key
+from langchain.pydantic_v1 import Field
 from langchain.schema import BaseChatMessageHistory, BaseMemory
 
 
diff --git a/libs/langchain/langchain/memory/chat_message_histories/in_memory.py b/libs/langchain/langchain/memory/chat_message_histories/in_memory.py
index 38aea5a39eb..d8db3f424b5 100644
--- a/libs/langchain/langchain/memory/chat_message_histories/in_memory.py
+++ b/libs/langchain/langchain/memory/chat_message_histories/in_memory.py
@@ -1,7 +1,6 @@
 from typing import List
 
-from pydantic import BaseModel
-
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema import (
     BaseChatMessageHistory,
 )
diff --git a/libs/langchain/langchain/memory/chat_message_histories/rocksetdb.py b/libs/langchain/langchain/memory/chat_message_histories/rocksetdb.py
index ce19d556934..2e690736917 100644
--- a/libs/langchain/langchain/memory/chat_message_histories/rocksetdb.py
+++ b/libs/langchain/langchain/memory/chat_message_histories/rocksetdb.py
@@ -37,9 +37,9 @@ class RocksetChatMessageHistory(BaseChatMessageHistory):
     # These values are configured for the typical
     # free VI. Read more about VIs here:
     # https://rockset.com/docs/instances
-    SLEEP_INTERVAL_MS = 5
-    ADD_TIMEOUT_MS = 5000
-    CREATE_TIMEOUT_MS = 20000
+    SLEEP_INTERVAL_MS: int = 5
+    ADD_TIMEOUT_MS: int = 5000
+    CREATE_TIMEOUT_MS: int = 20000
 
     def _wait_until(self, method: Callable, timeout: int, **method_params: Any) -> None:
         """Sleeps until meth() evaluates to true. Passes kwargs into
@@ -197,6 +197,12 @@ class RocksetChatMessageHistory(BaseChatMessageHistory):
         self.message_uuid_method = message_uuid_method
         self.sync = sync
 
+        try:
+            self.client.set_application("langchain")
+        except AttributeError:
+            # ignore
+            pass
+
         if not self._collection_exists():
             self._create_collection()
             self._wait_until_collection_created()
diff --git a/libs/langchain/langchain/memory/chat_message_histories/zep.py b/libs/langchain/langchain/memory/chat_message_histories/zep.py
index e349b438abc..853e6bfe1c5 100644
--- a/libs/langchain/langchain/memory/chat_message_histories/zep.py
+++ b/libs/langchain/langchain/memory/chat_message_histories/zep.py
@@ -124,7 +124,7 @@ class ZepChatMessageHistory(BaseChatMessageHistory):
         from zep_python import NotFoundError
 
         try:
-            zep_memory: Memory = self.zep_client.get_memory(self.session_id)
+            zep_memory: Memory = self.zep_client.memory.get_memory(self.session_id)
         except NotFoundError:
             logger.warning(
                 f"Session {self.session_id} not found in Zep. Returning None"
@@ -165,7 +165,7 @@ class ZepChatMessageHistory(BaseChatMessageHistory):
         )
         zep_memory = Memory(messages=[zep_message])
 
-        self.zep_client.add_memory(self.session_id, zep_memory)
+        self.zep_client.memory.add_memory(self.session_id, zep_memory)
 
     def search(
         self, query: str, metadata: Optional[Dict] = None, limit: Optional[int] = None
@@ -177,14 +177,16 @@ class ZepChatMessageHistory(BaseChatMessageHistory):
             text=query, metadata=metadata
         )
 
-        return self.zep_client.search_memory(self.session_id, payload, limit=limit)
+        return self.zep_client.memory.search_memory(
+            self.session_id, payload, limit=limit
+        )
 
     def clear(self) -> None:
         """Clear session memory from Zep. Note that Zep is long-term storage for memory
         and this is not advised unless you have specific data retention requirements.
         """
         try:
-            self.zep_client.delete_memory(self.session_id)
+            self.zep_client.memory.delete_memory(self.session_id)
         except NotFoundError:
             logger.warning(
                 f"Session {self.session_id} not found in Zep. Skipping delete."
diff --git a/libs/langchain/langchain/memory/combined.py b/libs/langchain/langchain/memory/combined.py
index 80316755ae7..8cf76046237 100644
--- a/libs/langchain/langchain/memory/combined.py
+++ b/libs/langchain/langchain/memory/combined.py
@@ -1,9 +1,8 @@
 import warnings
 from typing import Any, Dict, List, Set
 
-from pydantic import validator
-
 from langchain.memory.chat_memory import BaseChatMemory
+from langchain.pydantic_v1 import validator
 from langchain.schema import BaseMemory
 
 
diff --git a/libs/langchain/langchain/memory/entity.py b/libs/langchain/langchain/memory/entity.py
index 54b9574b0dc..cc48026f8c4 100644
--- a/libs/langchain/langchain/memory/entity.py
+++ b/libs/langchain/langchain/memory/entity.py
@@ -3,8 +3,6 @@ from abc import ABC, abstractmethod
 from itertools import islice
 from typing import Any, Dict, Iterable, List, Optional
 
-from pydantic import BaseModel, Field
-
 from langchain.chains.llm import LLMChain
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.prompt import (
@@ -12,6 +10,7 @@ from langchain.memory.prompt import (
     ENTITY_SUMMARIZATION_PROMPT,
 )
 from langchain.memory.utils import get_prompt_input_key
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import BaseMessage, get_buffer_string
diff --git a/libs/langchain/langchain/memory/kg.py b/libs/langchain/langchain/memory/kg.py
index 400ed34987a..4f79bd76a57 100644
--- a/libs/langchain/langchain/memory/kg.py
+++ b/libs/langchain/langchain/memory/kg.py
@@ -1,7 +1,5 @@
 from typing import Any, Dict, List, Type, Union
 
-from pydantic import Field
-
 from langchain.chains.llm import LLMChain
 from langchain.graphs import NetworkxEntityGraph
 from langchain.graphs.networkx_graph import KnowledgeTriple, get_entities, parse_triples
@@ -11,6 +9,7 @@ from langchain.memory.prompt import (
     KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT,
 )
 from langchain.memory.utils import get_prompt_input_key
+from langchain.pydantic_v1 import Field
 from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import BaseMessage, SystemMessage, get_buffer_string
diff --git a/libs/langchain/langchain/memory/motorhead_memory.py b/libs/langchain/langchain/memory/motorhead_memory.py
index 30dfbb7cea9..da9d9d82a3b 100644
--- a/libs/langchain/langchain/memory/motorhead_memory.py
+++ b/libs/langchain/langchain/memory/motorhead_memory.py
@@ -13,8 +13,8 @@ class MotorheadMemory(BaseChatMemory):
     """Chat message memory backed by Motorhead service."""
 
     url: str = MANAGED_URL
-    timeout = 3000
-    memory_key = "history"
+    timeout: int = 3000
+    memory_key: str = "history"
     session_id: str
     context: Optional[str] = None
 
diff --git a/libs/langchain/langchain/memory/summary.py b/libs/langchain/langchain/memory/summary.py
index afa0f9c8ef6..a6cbfa727c3 100644
--- a/libs/langchain/langchain/memory/summary.py
+++ b/libs/langchain/langchain/memory/summary.py
@@ -2,11 +2,10 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Type
 
-from pydantic import BaseModel, root_validator
-
 from langchain.chains.llm import LLMChain
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.prompt import SUMMARY_PROMPT
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.schema import (
     BaseChatMessageHistory,
     BasePromptTemplate,
diff --git a/libs/langchain/langchain/memory/summary_buffer.py b/libs/langchain/langchain/memory/summary_buffer.py
index 0b49797f59a..f0a31b41244 100644
--- a/libs/langchain/langchain/memory/summary_buffer.py
+++ b/libs/langchain/langchain/memory/summary_buffer.py
@@ -1,9 +1,8 @@
 from typing import Any, Dict, List
 
-from pydantic import root_validator
-
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.summary import SummarizerMixin
+from langchain.pydantic_v1 import root_validator
 from langchain.schema.messages import BaseMessage, get_buffer_string
 
 
diff --git a/libs/langchain/langchain/memory/token_buffer.py b/libs/langchain/langchain/memory/token_buffer.py
index a964f2f6b44..864ded2fc53 100644
--- a/libs/langchain/langchain/memory/token_buffer.py
+++ b/libs/langchain/langchain/memory/token_buffer.py
@@ -15,8 +15,22 @@ class ConversationTokenBufferMemory(BaseChatMemory):
     max_token_limit: int = 2000
 
     @property
-    def buffer(self) -> List[BaseMessage]:
+    def buffer(self) -> Any:
         """String buffer of memory."""
+        return self.buffer_as_messages if self.return_messages else self.buffer_as_str
+
+    @property
+    def buffer_as_str(self) -> str:
+        """Exposes the buffer as a string in case return_messages is True."""
+        return get_buffer_string(
+            self.chat_memory.messages,
+            human_prefix=self.human_prefix,
+            ai_prefix=self.ai_prefix,
+        )
+
+    @property
+    def buffer_as_messages(self) -> List[BaseMessage]:
+        """Exposes the buffer as a list of messages in case return_messages is False."""
         return self.chat_memory.messages
 
     @property
@@ -29,16 +43,7 @@ class ConversationTokenBufferMemory(BaseChatMemory):
 
     def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         """Return history buffer."""
-        buffer: Any = self.buffer
-        if self.return_messages:
-            final_buffer: Any = buffer
-        else:
-            final_buffer = get_buffer_string(
-                buffer,
-                human_prefix=self.human_prefix,
-                ai_prefix=self.ai_prefix,
-            )
-        return {self.memory_key: final_buffer}
+        return {self.memory_key: self.buffer}
 
     def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
         """Save context from this conversation to buffer. Pruned."""
diff --git a/libs/langchain/langchain/memory/vectorstore.py b/libs/langchain/langchain/memory/vectorstore.py
index 41f0d8c6719..b368fbab38b 100644
--- a/libs/langchain/langchain/memory/vectorstore.py
+++ b/libs/langchain/langchain/memory/vectorstore.py
@@ -2,10 +2,9 @@
 
 from typing import Any, Dict, List, Optional, Sequence, Union
 
-from pydantic import Field
-
 from langchain.memory.chat_memory import BaseMemory
 from langchain.memory.utils import get_prompt_input_key
+from langchain.pydantic_v1 import Field
 from langchain.schema import Document
 from langchain.vectorstores.base import VectorStoreRetriever
 
diff --git a/libs/langchain/langchain/output_parsers/combining.py b/libs/langchain/langchain/output_parsers/combining.py
index 511d9398f07..3ca3cb96222 100644
--- a/libs/langchain/langchain/output_parsers/combining.py
+++ b/libs/langchain/langchain/output_parsers/combining.py
@@ -2,8 +2,7 @@ from __future__ import annotations
 
 from typing import Any, Dict, List
 
-from pydantic import root_validator
-
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import BaseOutputParser
 
 
diff --git a/libs/langchain/langchain/output_parsers/enum.py b/libs/langchain/langchain/output_parsers/enum.py
index ee3daecfeef..b066e5ca144 100644
--- a/libs/langchain/langchain/output_parsers/enum.py
+++ b/libs/langchain/langchain/output_parsers/enum.py
@@ -1,8 +1,7 @@
 from enum import Enum
 from typing import Any, Dict, List, Type
 
-from pydantic import root_validator
-
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import BaseOutputParser, OutputParserException
 
 
diff --git a/libs/langchain/langchain/output_parsers/format_instructions.py b/libs/langchain/langchain/output_parsers/format_instructions.py
index 23c83281783..11b782a1b76 100644
--- a/libs/langchain/langchain/output_parsers/format_instructions.py
+++ b/libs/langchain/langchain/output_parsers/format_instructions.py
@@ -18,7 +18,7 @@ STRUCTURED_FORMAT_SIMPLE_INSTRUCTIONS = """
 
 PYDANTIC_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
 
-As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}}}}
+As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}}
 the object {{"foo": ["bar", "baz"]}} is a well-formatted instance of the schema. The object {{"properties": {{"foo": ["bar", "baz"]}}}} is not well-formatted.
 
 Here is the output schema:
diff --git a/libs/langchain/langchain/output_parsers/openai_functions.py b/libs/langchain/langchain/output_parsers/openai_functions.py
index 646d895962c..f916c4836ba 100644
--- a/libs/langchain/langchain/output_parsers/openai_functions.py
+++ b/libs/langchain/langchain/output_parsers/openai_functions.py
@@ -2,8 +2,7 @@ import copy
 import json
 from typing import Any, Dict, List, Type, Union
 
-from pydantic import BaseModel, root_validator
-
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.schema import (
     ChatGeneration,
     Generation,
diff --git a/libs/langchain/langchain/output_parsers/pydantic.py b/libs/langchain/langchain/output_parsers/pydantic.py
index b35943e6e2a..f60408eccff 100644
--- a/libs/langchain/langchain/output_parsers/pydantic.py
+++ b/libs/langchain/langchain/output_parsers/pydantic.py
@@ -2,9 +2,8 @@ import json
 import re
 from typing import Type, TypeVar
 
-from pydantic import BaseModel, ValidationError
-
 from langchain.output_parsers.format_instructions import PYDANTIC_FORMAT_INSTRUCTIONS
+from langchain.pydantic_v1 import BaseModel, ValidationError
 from langchain.schema import BaseOutputParser, OutputParserException
 
 T = TypeVar("T", bound=BaseModel)
diff --git a/libs/langchain/langchain/output_parsers/structured.py b/libs/langchain/langchain/output_parsers/structured.py
index 5733c896f6a..b7f53ca32b4 100644
--- a/libs/langchain/langchain/output_parsers/structured.py
+++ b/libs/langchain/langchain/output_parsers/structured.py
@@ -2,13 +2,12 @@ from __future__ import annotations
 
 from typing import Any, List
 
-from pydantic import BaseModel
-
 from langchain.output_parsers.format_instructions import (
     STRUCTURED_FORMAT_INSTRUCTIONS,
     STRUCTURED_FORMAT_SIMPLE_INSTRUCTIONS,
 )
 from langchain.output_parsers.json import parse_and_check_json_markdown
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema import BaseOutputParser
 
 line_template = '\t"{name}": {type}  // {description}'
diff --git a/libs/langchain/langchain/prompts/chat.py b/libs/langchain/langchain/prompts/chat.py
index b724d6b7b5d..b6cd9e0c036 100644
--- a/libs/langchain/langchain/prompts/chat.py
+++ b/libs/langchain/langchain/prompts/chat.py
@@ -3,13 +3,24 @@ from __future__ import annotations
 
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Any, Callable, List, Sequence, Tuple, Type, TypeVar, Union
-
-from pydantic import Field, root_validator
+from typing import (
+    Any,
+    Callable,
+    List,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    overload,
+)
 
+from langchain._api import deprecated
 from langchain.load.serializable import Serializable
 from langchain.prompts.base import StringPromptTemplate
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import (
     BasePromptTemplate,
     PromptValue,
@@ -316,6 +327,16 @@ class BaseChatPromptTemplate(BasePromptTemplate, ABC):
         """Format kwargs into a list of messages."""
 
 
+MessageLike = Union[BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate]
+
+MessageLikeRepresentation = Union[
+    MessageLike,
+    Tuple[str, str],
+    Tuple[Type, str],
+    str,
+]
+
+
 class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
     """A prompt template for chat models.
 
@@ -342,9 +363,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
 
     input_variables: List[str]
     """List of input variables in template messages. Used for validation."""
-    messages: List[
-        Union[BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate]
-    ]
+    messages: List[MessageLike]
     """List of messages consisting of either message prompt templates or messages."""
 
     def __add__(self, other: Any) -> ChatPromptTemplate:
@@ -363,6 +382,9 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
             other, (BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate)
         ):
             return ChatPromptTemplate(messages=self.messages + [other])
+        elif isinstance(other, (list, tuple)):
+            _other = ChatPromptTemplate.from_messages(other)
+            return ChatPromptTemplate(messages=self.messages + _other.messages)
         elif isinstance(other, str):
             prompt = HumanMessagePromptTemplate.from_template(other)
             return ChatPromptTemplate(messages=self.messages + [prompt])
@@ -419,6 +441,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
         return cls.from_messages([message])
 
     @classmethod
+    @deprecated("0.0.260", alternative="from_messages classmethod", pending=True)
     def from_role_strings(
         cls, string_messages: List[Tuple[str, str]]
     ) -> ChatPromptTemplate:
@@ -438,6 +461,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
         )
 
     @classmethod
+    @deprecated("0.0.260", alternative="from_messages classmethod", pending=True)
     def from_strings(
         cls, string_messages: List[Tuple[Type[BaseMessagePromptTemplate], str]]
     ) -> ChatPromptTemplate:
@@ -454,16 +478,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
     @classmethod
     def from_messages(
         cls,
-        messages: Sequence[
-            Union[
-                BaseMessagePromptTemplate,
-                BaseChatPromptTemplate,
-                BaseMessage,
-                Tuple[str, str],
-                Tuple[Type, str],
-                str,
-            ]
-        ],
+        messages: Sequence[MessageLikeRepresentation],
     ) -> ChatPromptTemplate:
         """Create a chat prompt template from a variety of message formats.
 
@@ -502,7 +517,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
         _messages = [_convert_to_message(message) for message in messages]
 
         # Automatically infer input variables from messages
-        input_vars = set()
+        input_vars: Set[str] = set()
         for _message in _messages:
             if isinstance(
                 _message, (BaseChatPromptTemplate, BaseMessagePromptTemplate)
@@ -553,8 +568,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
         return result
 
     def partial(self, **kwargs: Union[str, Callable[[], str]]) -> ChatPromptTemplate:
-        """Return a new ChatPromptTemplate with some of the input variables already
-        filled in.
+        """Get a new ChatPromptTemplate with some input variables already filled in.
 
         Args:
             **kwargs: keyword arguments to use for filling in template variables. Ought
@@ -589,6 +603,41 @@ class ChatPromptTemplate(BaseChatPromptTemplate, ABC):
         prompt_dict["partial_variables"] = {**self.partial_variables, **kwargs}
         return type(self)(**prompt_dict)
 
+    def append(self, message: MessageLikeRepresentation) -> None:
+        """Append message to the end of the chat template.
+
+        Args:
+            message: representation of a message to append.
+        """
+        self.messages.append(_convert_to_message(message))
+
+    def extend(self, messages: Sequence[MessageLikeRepresentation]) -> None:
+        """Extend the chat template with a sequence of messages."""
+        self.messages.extend([_convert_to_message(message) for message in messages])
+
+    @overload
+    def __getitem__(self, index: int) -> MessageLike:
+        ...
+
+    @overload
+    def __getitem__(self, index: slice) -> ChatPromptTemplate:
+        ...
+
+    def __getitem__(
+        self, index: Union[int, slice]
+    ) -> Union[MessageLike, ChatPromptTemplate]:
+        """Use to index into the chat template."""
+        if isinstance(index, slice):
+            start, stop, step = index.indices(len(self.messages))
+            messages = self.messages[start:stop:step]
+            return ChatPromptTemplate.from_messages(messages)
+        else:
+            return self.messages[index]
+
+    def __len__(self) -> int:
+        """Get the length of the chat template."""
+        return len(self.messages)
+
     @property
     def _prompt_type(self) -> str:
         """Name of prompt type."""
@@ -632,14 +681,7 @@ def _create_template_from_message_type(
 
 
 def _convert_to_message(
-    message: Union[
-        BaseMessagePromptTemplate,
-        BaseChatPromptTemplate,
-        BaseMessage,
-        Tuple[str, str],
-        Tuple[Type, str],
-        str,
-    ]
+    message: MessageLikeRepresentation,
 ) -> Union[BaseMessage, BaseMessagePromptTemplate, BaseChatPromptTemplate]:
     """Instantiate a message from a variety of message formats.
 
diff --git a/libs/langchain/langchain/prompts/example_selector/length_based.py b/libs/langchain/langchain/prompts/example_selector/length_based.py
index f6c665de322..19f4a0c5c8e 100644
--- a/libs/langchain/langchain/prompts/example_selector/length_based.py
+++ b/libs/langchain/langchain/prompts/example_selector/length_based.py
@@ -2,10 +2,9 @@
 import re
 from typing import Callable, Dict, List
 
-from pydantic import BaseModel, validator
-
 from langchain.prompts.example_selector.base import BaseExampleSelector
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import BaseModel, validator
 
 
 def _get_length_based(text: str) -> int:
diff --git a/libs/langchain/langchain/prompts/example_selector/ngram_overlap.py b/libs/langchain/langchain/prompts/example_selector/ngram_overlap.py
index 9dd68f00a76..969c81e43d6 100644
--- a/libs/langchain/langchain/prompts/example_selector/ngram_overlap.py
+++ b/libs/langchain/langchain/prompts/example_selector/ngram_overlap.py
@@ -6,10 +6,10 @@ https://aclanthology.org/P02-1040.pdf
 from typing import Dict, List
 
 import numpy as np
-from pydantic import BaseModel, root_validator
 
 from langchain.prompts.example_selector.base import BaseExampleSelector
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import BaseModel, root_validator
 
 
 def ngram_overlap_score(source: List[str], example: List[str]) -> float:
diff --git a/libs/langchain/langchain/prompts/example_selector/semantic_similarity.py b/libs/langchain/langchain/prompts/example_selector/semantic_similarity.py
index 0d66c13673f..2dbd4a7d4d4 100644
--- a/libs/langchain/langchain/prompts/example_selector/semantic_similarity.py
+++ b/libs/langchain/langchain/prompts/example_selector/semantic_similarity.py
@@ -3,10 +3,9 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional, Type
 
-from pydantic import BaseModel, Extra
-
 from langchain.embeddings.base import Embeddings
 from langchain.prompts.example_selector.base import BaseExampleSelector
+from langchain.pydantic_v1 import BaseModel, Extra
 from langchain.vectorstores.base import VectorStore
 
 
diff --git a/libs/langchain/langchain/prompts/few_shot.py b/libs/langchain/langchain/prompts/few_shot.py
index 7a28f10066b..72ba8c13fd7 100644
--- a/libs/langchain/langchain/prompts/few_shot.py
+++ b/libs/langchain/langchain/prompts/few_shot.py
@@ -3,8 +3,6 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional, Union
 
-from pydantic import BaseModel, Extra, Field, root_validator
-
 from langchain.prompts.base import (
     DEFAULT_FORMATTER_MAPPING,
     StringPromptTemplate,
@@ -13,6 +11,7 @@ from langchain.prompts.base import (
 from langchain.prompts.chat import BaseChatPromptTemplate, BaseMessagePromptTemplate
 from langchain.prompts.example_selector.base import BaseExampleSelector
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 from langchain.schema.messages import BaseMessage, get_buffer_string
 
 
diff --git a/libs/langchain/langchain/prompts/few_shot_with_templates.py b/libs/langchain/langchain/prompts/few_shot_with_templates.py
index 5e5330cf949..a66f1a37ee2 100644
--- a/libs/langchain/langchain/prompts/few_shot_with_templates.py
+++ b/libs/langchain/langchain/prompts/few_shot_with_templates.py
@@ -1,11 +1,10 @@
 """Prompt template that contains few shot examples."""
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, StringPromptTemplate
 from langchain.prompts.example_selector.base import BaseExampleSelector
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import Extra, root_validator
 
 
 class FewShotPromptWithTemplates(StringPromptTemplate):
diff --git a/libs/langchain/langchain/prompts/loading.py b/libs/langchain/langchain/prompts/loading.py
index dc8a1bc32a8..55d2013ecd9 100644
--- a/libs/langchain/langchain/prompts/loading.py
+++ b/libs/langchain/langchain/prompts/loading.py
@@ -26,7 +26,10 @@ def load_prompt_from_config(config: dict) -> BasePromptTemplate:
         raise ValueError(f"Loading {config_type} prompt not supported")
 
     prompt_loader = type_to_loader_dict[config_type]
-    return prompt_loader(config)
+    # Unclear why type error is being thrown here.
+    # Incompatible return value type (got "Runnable[Dict[Any, Any], PromptValue]",
+    # expected "BasePromptTemplate")  [return-value]
+    return prompt_loader(config)  # type: ignore[return-value]
 
 
 def _load_template(var_name: str, config: dict) -> dict:
diff --git a/libs/langchain/langchain/prompts/pipeline.py b/libs/langchain/langchain/prompts/pipeline.py
index 28364766a77..4183b97387c 100644
--- a/libs/langchain/langchain/prompts/pipeline.py
+++ b/libs/langchain/langchain/prompts/pipeline.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict, List, Tuple
 
-from pydantic import root_validator
-
 from langchain.prompts.chat import BaseChatPromptTemplate
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import BasePromptTemplate, PromptValue
 
 
diff --git a/libs/langchain/langchain/prompts/prompt.py b/libs/langchain/langchain/prompts/prompt.py
index 11cae9f51af..9f18af8a165 100644
--- a/libs/langchain/langchain/prompts/prompt.py
+++ b/libs/langchain/langchain/prompts/prompt.py
@@ -5,14 +5,13 @@ from pathlib import Path
 from string import Formatter
 from typing import Any, Dict, List, Optional, Union
 
-from pydantic import root_validator
-
 from langchain.prompts.base import (
     DEFAULT_FORMATTER_MAPPING,
     StringPromptTemplate,
     _get_jinja2_variables_from_template,
     check_valid_template,
 )
+from langchain.pydantic_v1 import root_validator
 
 
 class PromptTemplate(StringPromptTemplate):
diff --git a/libs/langchain/langchain/pydantic_v1/__init__.py b/libs/langchain/langchain/pydantic_v1/__init__.py
new file mode 100644
index 00000000000..859822531ec
--- /dev/null
+++ b/libs/langchain/langchain/pydantic_v1/__init__.py
@@ -0,0 +1,23 @@
+from importlib import metadata
+
+## Create namespaces for pydantic v1 and v2.
+# This code must stay at the top of the file before other modules may
+# attempt to import pydantic since it adds pydantic_v1 and pydantic_v2 to sys.modules.
+#
+# This hack is done for the following reasons:
+# * Langchain will attempt to remain compatible with both pydantic v1 and v2 since
+#   both dependencies and dependents may be stuck on either version of v1 or v2.
+# * Creating namespaces for pydantic v1 and v2 should allow us to write code that
+#   unambiguously uses either v1 or v2 API.
+# * This change is easier to roll out and roll back.
+
+try:
+    from pydantic.v1 import *  # noqa: F403
+except ImportError:
+    from pydantic import *  # noqa: F403
+
+
+try:
+    _PYDANTIC_MAJOR_VERSION: int = int(metadata.version("pydantic").split(".")[0])
+except metadata.PackageNotFoundError:
+    _PYDANTIC_MAJOR_VERSION = 0
diff --git a/libs/langchain/langchain/pydantic_v1/dataclasses.py b/libs/langchain/langchain/pydantic_v1/dataclasses.py
new file mode 100644
index 00000000000..bb7253c29d7
--- /dev/null
+++ b/libs/langchain/langchain/pydantic_v1/dataclasses.py
@@ -0,0 +1,4 @@
+try:
+    from pydantic.v1.dataclasses import *  # noqa: F403
+except ImportError:
+    from pydantic.dataclasses import *  # noqa: F403
diff --git a/libs/langchain/langchain/pydantic_v1/main.py b/libs/langchain/langchain/pydantic_v1/main.py
new file mode 100644
index 00000000000..4b8f1670e13
--- /dev/null
+++ b/libs/langchain/langchain/pydantic_v1/main.py
@@ -0,0 +1,4 @@
+try:
+    from pydantic.v1.main import *  # noqa: F403
+except ImportError:
+    from pydantic.main import *  # noqa: F403
diff --git a/libs/langchain/langchain/retrievers/azure_cognitive_search.py b/libs/langchain/langchain/retrievers/azure_cognitive_search.py
index 214f663e95b..45deebfe31e 100644
--- a/libs/langchain/langchain/retrievers/azure_cognitive_search.py
+++ b/libs/langchain/langchain/retrievers/azure_cognitive_search.py
@@ -7,12 +7,12 @@ from typing import Dict, List, Optional
 
 import aiohttp
 import requests
-from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
 )
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.schema import BaseRetriever, Document
 from langchain.utils import get_from_dict_or_env
 
diff --git a/libs/langchain/langchain/retrievers/document_compressors/base.py b/libs/langchain/langchain/retrievers/document_compressors/base.py
index 1e0587c59ce..b44c1e5c73d 100644
--- a/libs/langchain/langchain/retrievers/document_compressors/base.py
+++ b/libs/langchain/langchain/retrievers/document_compressors/base.py
@@ -3,9 +3,8 @@ from abc import ABC, abstractmethod
 from inspect import signature
 from typing import List, Optional, Sequence, Union
 
-from pydantic import BaseModel
-
 from langchain.callbacks.manager import Callbacks
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema import BaseDocumentTransformer, Document
 
 
diff --git a/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py b/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py
index a9fad10c8b0..f6e79a0b3a3 100644
--- a/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py
+++ b/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py
@@ -2,9 +2,8 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, Dict, Optional, Sequence
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import Callbacks
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
 from langchain.schema import Document
 from langchain.utils import get_from_dict_or_env
diff --git a/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py b/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py
index f004a17969a..115eb941fa3 100644
--- a/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py
+++ b/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py
@@ -1,7 +1,6 @@
 from typing import Callable, Dict, Optional, Sequence
 
 import numpy as np
-from pydantic import root_validator
 
 from langchain.callbacks.manager import Callbacks
 from langchain.document_transformers.embeddings_redundant_filter import (
@@ -9,6 +8,7 @@ from langchain.document_transformers.embeddings_redundant_filter import (
     get_stateful_documents,
 )
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import root_validator
 from langchain.retrievers.document_compressors.base import (
     BaseDocumentCompressor,
 )
diff --git a/libs/langchain/langchain/retrievers/ensemble.py b/libs/langchain/langchain/retrievers/ensemble.py
index b01a33fab6b..03f555a686c 100644
--- a/libs/langchain/langchain/retrievers/ensemble.py
+++ b/libs/langchain/langchain/retrievers/ensemble.py
@@ -4,12 +4,11 @@ multiple retrievers by using weighted  Reciprocal Rank Fusion
 """
 from typing import Any, Dict, List
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
 )
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import BaseRetriever, Document
 
 
diff --git a/libs/langchain/langchain/retrievers/google_cloud_enterprise_search.py b/libs/langchain/langchain/retrievers/google_cloud_enterprise_search.py
index 0d76022ef78..826428c26bb 100644
--- a/libs/langchain/langchain/retrievers/google_cloud_enterprise_search.py
+++ b/libs/langchain/langchain/retrievers/google_cloud_enterprise_search.py
@@ -3,9 +3,8 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence
 
-from pydantic import Extra, Field, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
+from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.schema import BaseRetriever, Document
 from langchain.utils import get_from_dict_or_env
 
diff --git a/libs/langchain/langchain/retrievers/kendra.py b/libs/langchain/langchain/retrievers/kendra.py
index c1656a3a6cb..1f43050c49c 100644
--- a/libs/langchain/langchain/retrievers/kendra.py
+++ b/libs/langchain/langchain/retrievers/kendra.py
@@ -2,10 +2,9 @@ import re
 from abc import ABC, abstractmethod
 from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Union
 
-from pydantic import BaseModel, Extra, root_validator, validator
-
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
 from langchain.docstore.document import Document
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator, validator
 from langchain.schema import BaseRetriever
 
 
@@ -49,7 +48,8 @@ DocumentAttributeValueType = Union[str, int, List[str], None]
 """Possible types of a DocumentAttributeValue. Dates are also represented as str."""
 
 
-class Highlight(BaseModel, extra=Extra.allow):
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
+class Highlight(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
     """
     Represents the information that can be
     used to highlight key words in the excerpt.
@@ -65,7 +65,8 @@ class Highlight(BaseModel, extra=Extra.allow):
     """The highlight type: STANDARD or THESAURUS_SYNONYM."""
 
 
-class TextWithHighLights(BaseModel, extra=Extra.allow):
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
+class TextWithHighLights(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
     """Text with highlights."""
 
     Text: str
@@ -74,14 +75,18 @@ class TextWithHighLights(BaseModel, extra=Extra.allow):
     """The highlights."""
 
 
-class AdditionalResultAttributeValue(BaseModel, extra=Extra.allow):
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
+class AdditionalResultAttributeValue(  # type: ignore[call-arg]
+    BaseModel, extra=Extra.allow
+):
     """The value of an additional result attribute."""
 
     TextWithHighlightsValue: TextWithHighLights
     """The text with highlights value."""
 
 
-class AdditionalResultAttribute(BaseModel, extra=Extra.allow):
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
+class AdditionalResultAttribute(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
     """An additional result attribute."""
 
     Key: str
@@ -95,7 +100,8 @@ class AdditionalResultAttribute(BaseModel, extra=Extra.allow):
         return self.Value.TextWithHighlightsValue.Text
 
 
-class DocumentAttributeValue(BaseModel, extra=Extra.allow):
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
+class DocumentAttributeValue(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
     """The value of a document attribute."""
 
     DateValue: Optional[str]
@@ -125,7 +131,8 @@ class DocumentAttributeValue(BaseModel, extra=Extra.allow):
         return None
 
 
-class DocumentAttribute(BaseModel, extra=Extra.allow):
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
+class DocumentAttribute(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
     """A document attribute."""
 
     Key: str
@@ -134,7 +141,8 @@ class DocumentAttribute(BaseModel, extra=Extra.allow):
     """The value of the attribute."""
 
 
-class ResultItem(BaseModel, ABC, extra=Extra.allow):
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
+class ResultItem(BaseModel, ABC, extra=Extra.allow):  # type: ignore[call-arg]
     """Abstract class that represents a result item."""
 
     Id: Optional[str]
@@ -254,7 +262,8 @@ class RetrieveResultItem(ResultItem):
         return self.Content or ""
 
 
-class QueryResult(BaseModel, extra=Extra.allow):
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
+class QueryResult(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
     """
     Represents an Amazon Kendra Query API search result, which is composed of:
         * Relevant suggested answers: either a text excerpt or table excerpt.
@@ -266,7 +275,8 @@ class QueryResult(BaseModel, extra=Extra.allow):
     """The result items."""
 
 
-class RetrieveResult(BaseModel, extra=Extra.allow):
+# Unexpected keyword argument "extra" for "__init_subclass__" of "object"
+class RetrieveResult(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
     """
     Represents an Amazon Kendra Retrieve API search result, which is composed of:
         * relevant passages or text excerpts given an input query.
diff --git a/libs/langchain/langchain/retrievers/llama_index.py b/libs/langchain/langchain/retrievers/llama_index.py
index fe8e4f8b0fc..42ad10d7bf3 100644
--- a/libs/langchain/langchain/retrievers/llama_index.py
+++ b/libs/langchain/langchain/retrievers/llama_index.py
@@ -1,8 +1,7 @@
 from typing import Any, Dict, List, cast
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
+from langchain.pydantic_v1 import Field
 from langchain.schema import BaseRetriever, Document
 
 
diff --git a/libs/langchain/langchain/retrievers/metal.py b/libs/langchain/langchain/retrievers/metal.py
index b4faaeab3df..286de10300a 100644
--- a/libs/langchain/langchain/retrievers/metal.py
+++ b/libs/langchain/langchain/retrievers/metal.py
@@ -1,8 +1,7 @@
 from typing import Any, List, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import BaseRetriever, Document
 
 
diff --git a/libs/langchain/langchain/retrievers/milvus.py b/libs/langchain/langchain/retrievers/milvus.py
index bc35e731842..2d03388e0cc 100644
--- a/libs/langchain/langchain/retrievers/milvus.py
+++ b/libs/langchain/langchain/retrievers/milvus.py
@@ -2,10 +2,9 @@
 import warnings
 from typing import Any, Dict, List, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import BaseRetriever, Document
 from langchain.vectorstores.milvus import Milvus
 
diff --git a/libs/langchain/langchain/retrievers/multi_query.py b/libs/langchain/langchain/retrievers/multi_query.py
index 4d9520c0262..2085e9f2d92 100644
--- a/libs/langchain/langchain/retrievers/multi_query.py
+++ b/libs/langchain/langchain/retrievers/multi_query.py
@@ -1,13 +1,12 @@
 import logging
 from typing import List
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
 from langchain.chains.llm import LLMChain
 from langchain.llms.base import BaseLLM
 from langchain.output_parsers.pydantic import PydanticOutputParser
 from langchain.prompts.prompt import PromptTemplate
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema import BaseRetriever, Document
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/retrievers/parent_document_retriever.py b/libs/langchain/langchain/retrievers/parent_document_retriever.py
index ce690671344..d7ad9acfb12 100644
--- a/libs/langchain/langchain/retrievers/parent_document_retriever.py
+++ b/libs/langchain/langchain/retrievers/parent_document_retriever.py
@@ -1,7 +1,8 @@
 import uuid
-from typing import Any, Dict, List, Optional
+from typing import List, Optional
 
-from langchain.callbacks.base import Callbacks
+from langchain.callbacks.manager import CallbackManagerForRetrieverRun
+from langchain.pydantic_v1 import Field
 from langchain.schema.document import Document
 from langchain.schema.retriever import BaseRetriever
 from langchain.schema.storage import BaseStore
@@ -30,31 +31,32 @@ class ParentDocumentRetriever(BaseRetriever):
     chunk.
 
     Examples:
-        ... code-block:: python
 
-        # Imports
-        from langchain.vectorstores import Chroma
-        from langchain.embeddings import OpenAIEmbeddings
-        from langchain.text_splitter import RecursiveCharacterTextSplitter
-        from langchain.storage import InMemoryStore
+        .. code-block:: python
 
-        # This text splitter is used to create the parent documents
-        parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
-        # This text splitter is used to create the child documents
-        # It should create documents smaller than the parent
-        child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
-        # The vectorstore to use to index the child chunks
-        vectorstore = Chroma(embedding_function=OpenAIEmbeddings())
-        # The storage layer for the parent documents
-        store = InMemoryStore()
+            # Imports
+            from langchain.vectorstores import Chroma
+            from langchain.embeddings import OpenAIEmbeddings
+            from langchain.text_splitter import RecursiveCharacterTextSplitter
+            from langchain.storage import InMemoryStore
 
-        # Initialize the retriever
-        retriever = ParentDocumentRetriever(
-            vectorstore=vectorstore,
-            docstore=store,
-            child_splitter=child_splitter,
-            parent_splitter=parent_splitter,
-        )
+            # This text splitter is used to create the parent documents
+            parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
+            # This text splitter is used to create the child documents
+            # It should create documents smaller than the parent
+            child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
+            # The vectorstore to use to index the child chunks
+            vectorstore = Chroma(embedding_function=OpenAIEmbeddings())
+            # The storage layer for the parent documents
+            store = InMemoryStore()
+
+            # Initialize the retriever
+            retriever = ParentDocumentRetriever(
+                vectorstore=vectorstore,
+                docstore=store,
+                child_splitter=child_splitter,
+                parent_splitter=parent_splitter,
+            )
     """
 
     vectorstore: VectorStore
@@ -70,17 +72,20 @@ class ParentDocumentRetriever(BaseRetriever):
     parent_splitter: Optional[TextSplitter] = None
     """The text splitter to use to create parent documents.
     If none, then the parent documents will be the raw documents passed in."""
+    search_kwargs: dict = Field(default_factory=dict)
+    """Keyword arguments to pass to the search function."""
 
-    def get_relevant_documents(
-        self,
-        query: str,
-        *,
-        callbacks: Callbacks = None,
-        tags: Optional[List[str]] = None,
-        metadata: Optional[Dict[str, Any]] = None,
-        **kwargs: Any,
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
     ) -> List[Document]:
-        sub_docs = self.vectorstore.similarity_search(query)
+        """Get documents relevant to a query.
+        Args:
+            query: String to find relevant documents for
+            run_manager: The callbacks handler to use
+        Returns:
+            List of relevant documents
+        """
+        sub_docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
         # We do this to maintain the order of the ids that are returned
         ids = []
         for d in sub_docs:
diff --git a/libs/langchain/langchain/retrievers/pinecone_hybrid_search.py b/libs/langchain/langchain/retrievers/pinecone_hybrid_search.py
index 97d562421d3..a3653164524 100644
--- a/libs/langchain/langchain/retrievers/pinecone_hybrid_search.py
+++ b/libs/langchain/langchain/retrievers/pinecone_hybrid_search.py
@@ -3,10 +3,9 @@
 import hashlib
 from typing import Any, Dict, List, Optional
 
-from pydantic import Extra, root_validator
-
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import Extra, root_validator
 from langchain.schema import BaseRetriever, Document
 
 
diff --git a/libs/langchain/langchain/retrievers/self_query/base.py b/libs/langchain/langchain/retrievers/self_query/base.py
index 15463751b4f..806d4e1502b 100644
--- a/libs/langchain/langchain/retrievers/self_query/base.py
+++ b/libs/langchain/langchain/retrievers/self_query/base.py
@@ -2,15 +2,15 @@
 
 from typing import Any, Dict, List, Optional, Type, cast
 
-from pydantic import BaseModel, Field, root_validator
-
 from langchain import LLMChain
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
 from langchain.chains.query_constructor.base import load_query_constructor_chain
 from langchain.chains.query_constructor.ir import StructuredQuery, Visitor
 from langchain.chains.query_constructor.schema import AttributeInfo
+from langchain.pydantic_v1 import BaseModel, Field, root_validator
 from langchain.retrievers.self_query.chroma import ChromaTranslator
 from langchain.retrievers.self_query.deeplake import DeepLakeTranslator
+from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator
 from langchain.retrievers.self_query.myscale import MyScaleTranslator
 from langchain.retrievers.self_query.pinecone import PineconeTranslator
 from langchain.retrievers.self_query.qdrant import QdrantTranslator
@@ -20,6 +20,7 @@ from langchain.schema.language_model import BaseLanguageModel
 from langchain.vectorstores import (
     Chroma,
     DeepLake,
+    ElasticsearchStore,
     MyScale,
     Pinecone,
     Qdrant,
@@ -38,6 +39,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
         Qdrant: QdrantTranslator,
         MyScale: MyScaleTranslator,
         DeepLake: DeepLakeTranslator,
+        ElasticsearchStore: ElasticsearchTranslator,
     }
     if vectorstore_cls not in BUILTIN_TRANSLATORS:
         raise ValueError(
diff --git a/libs/langchain/langchain/retrievers/self_query/elasticsearch.py b/libs/langchain/langchain/retrievers/self_query/elasticsearch.py
new file mode 100644
index 00000000000..32a00f071ec
--- /dev/null
+++ b/libs/langchain/langchain/retrievers/self_query/elasticsearch.py
@@ -0,0 +1,93 @@
+from typing import Dict, Tuple, Union
+
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+    StructuredQuery,
+    Visitor,
+)
+
+
+class ElasticsearchTranslator(Visitor):
+    """Translate the internal query language elements to valid filters."""
+
+    allowed_comparators = [
+        Comparator.EQ,
+        Comparator.GT,
+        Comparator.GTE,
+        Comparator.LT,
+        Comparator.LTE,
+        Comparator.CONTAIN,
+        Comparator.LIKE,
+    ]
+    """Subset of allowed logical comparators."""
+
+    allowed_operators = [Operator.AND, Operator.OR, Operator.NOT]
+    """Subset of allowed logical operators."""
+
+    def _format_func(self, func: Union[Operator, Comparator]) -> str:
+        self._validate_func(func)
+        map_dict = {
+            Operator.OR: "should",
+            Operator.NOT: "must_not",
+            Operator.AND: "must",
+            Comparator.EQ: "term",
+            Comparator.GT: "gt",
+            Comparator.GTE: "gte",
+            Comparator.LT: "lt",
+            Comparator.LTE: "lte",
+            Comparator.CONTAIN: "match",
+            Comparator.LIKE: "fuzzy",
+        }
+        return map_dict[func]
+
+    def visit_operation(self, operation: Operation) -> Dict:
+        args = [arg.accept(self) for arg in operation.arguments]
+
+        return {"bool": {self._format_func(operation.operator): args}}
+
+    def visit_comparison(self, comparison: Comparison) -> Dict:
+        # ElasticsearchStore filters require to target
+        # the metadata object field
+        field = f"metadata.{comparison.attribute}"
+
+        is_range_comparator = comparison.comparator in [
+            Comparator.GT,
+            Comparator.GTE,
+            Comparator.LT,
+            Comparator.LTE,
+        ]
+
+        if is_range_comparator:
+            return {
+                "range": {
+                    field: {self._format_func(comparison.comparator): comparison.value}
+                }
+            }
+
+        if comparison.comparator == Comparator.LIKE:
+            return {
+                self._format_func(comparison.comparator): {
+                    field: {"value": comparison.value, "fuzziness": "AUTO"}
+                }
+            }
+
+        if comparison.comparator == Comparator.CONTAIN:
+            return {self._format_func(comparison.comparator): {field: comparison.value}}
+
+        # we assume that if the value is a string,
+        # we want to use the keyword field
+        field = f"{field}.keyword" if isinstance(comparison.value, str) else field
+
+        return {self._format_func(comparison.comparator): {field: comparison.value}}
+
+    def visit_structured_query(
+        self, structured_query: StructuredQuery
+    ) -> Tuple[str, dict]:
+        if structured_query.filter is None:
+            kwargs = {}
+        else:
+            kwargs = {"filter": [structured_query.filter.accept(self)]}
+        return structured_query.query, kwargs
diff --git a/libs/langchain/langchain/retrievers/svm.py b/libs/langchain/langchain/retrievers/svm.py
index 3c65e974ebf..9594ce0dce3 100644
--- a/libs/langchain/langchain/retrievers/svm.py
+++ b/libs/langchain/langchain/retrievers/svm.py
@@ -38,6 +38,8 @@ class SVMRetriever(BaseRetriever):
     """Index of embeddings."""
     texts: List[str]
     """List of texts to index."""
+    metadatas: Optional[List[dict]] = None
+    """List of metadatas corresponding with each text."""
     k: int = 4
     """Number of results to return."""
     relevancy_threshold: Optional[float] = None
@@ -51,10 +53,20 @@ class SVMRetriever(BaseRetriever):
 
     @classmethod
     def from_texts(
-        cls, texts: List[str], embeddings: Embeddings, **kwargs: Any
+        cls,
+        texts: List[str],
+        embeddings: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
     ) -> SVMRetriever:
         index = create_index(texts, embeddings)
-        return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)
+        return cls(
+            embeddings=embeddings,
+            index=index,
+            texts=texts,
+            metadatas=metadatas,
+            **kwargs,
+        )
 
     @classmethod
     def from_documents(
@@ -64,7 +76,9 @@ class SVMRetriever(BaseRetriever):
         **kwargs: Any,
     ) -> SVMRetriever:
         texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
-        return cls.from_texts(texts=texts, embeddings=embeddings, **kwargs)
+        return cls.from_texts(
+            texts=texts, embeddings=embeddings, metadatas=metadatas, **kwargs
+        )
 
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun
@@ -108,5 +122,7 @@ class SVMRetriever(BaseRetriever):
                 self.relevancy_threshold is None
                 or normalized_similarities[row] >= self.relevancy_threshold
             ):
-                top_k_results.append(Document(page_content=self.texts[row - 1]))
+                metadata = self.metadatas[row - 1] if self.metadatas else {}
+                doc = Document(page_content=self.texts[row - 1], metadata=metadata)
+                top_k_results.append(doc)
         return top_k_results
diff --git a/libs/langchain/langchain/retrievers/time_weighted_retriever.py b/libs/langchain/langchain/retrievers/time_weighted_retriever.py
index b2aebfa913b..de4ff2adf29 100644
--- a/libs/langchain/langchain/retrievers/time_weighted_retriever.py
+++ b/libs/langchain/langchain/retrievers/time_weighted_retriever.py
@@ -2,9 +2,8 @@ import datetime
 from copy import deepcopy
 from typing import Any, Dict, List, Optional, Tuple
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
+from langchain.pydantic_v1 import Field
 from langchain.schema import BaseRetriever, Document
 from langchain.vectorstores.base import VectorStore
 
diff --git a/libs/langchain/langchain/retrievers/weaviate_hybrid_search.py b/libs/langchain/langchain/retrievers/weaviate_hybrid_search.py
index 2bd64ed3f17..3251a41cd18 100644
--- a/libs/langchain/langchain/retrievers/weaviate_hybrid_search.py
+++ b/libs/langchain/langchain/retrievers/weaviate_hybrid_search.py
@@ -3,10 +3,9 @@ from __future__ import annotations
 from typing import Any, Dict, List, Optional, cast
 from uuid import uuid4
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
 from langchain.docstore.document import Document
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import BaseRetriever
 
 
diff --git a/libs/langchain/langchain/retrievers/web_research.py b/libs/langchain/langchain/retrievers/web_research.py
index e8ebfd7b435..5c25a411278 100644
--- a/libs/langchain/langchain/retrievers/web_research.py
+++ b/libs/langchain/langchain/retrievers/web_research.py
@@ -2,8 +2,6 @@ import logging
 import re
 from typing import List, Optional
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
@@ -16,6 +14,7 @@ from langchain.llms import LlamaCpp
 from langchain.llms.base import BaseLLM
 from langchain.output_parsers.pydantic import PydanticOutputParser
 from langchain.prompts import BasePromptTemplate, PromptTemplate
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema import BaseRetriever, Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.utilities import GoogleSearchAPIWrapper
@@ -179,15 +178,16 @@ class WebResearchRetriever(BaseRetriever):
         logger.info(f"Questions for Google Search: {questions}")
 
         # Get urls
-        logger.info("Searching for relevat urls ...")
+        logger.info("Searching for relevant urls...")
         urls_to_look = []
         for query in questions:
             # Google search
             search_results = self.search_tool(query, self.num_search_results)
-            logger.info("Searching for relevat urls ...")
+            logger.info("Searching for relevant urls...")
             logger.info(f"Search results: {search_results}")
             for res in search_results:
-                urls_to_look.append(res["link"])
+                if res.get("link", None):
+                    urls_to_look.append(res["link"])
 
         # Relevant urls
         urls = set(urls_to_look)
diff --git a/libs/langchain/langchain/retrievers/zep.py b/libs/langchain/langchain/retrievers/zep.py
index 7d1f18cda1a..5cd0f89799a 100644
--- a/libs/langchain/langchain/retrievers/zep.py
+++ b/libs/langchain/langchain/retrievers/zep.py
@@ -2,12 +2,11 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
 )
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import BaseRetriever, Document
 
 if TYPE_CHECKING:
@@ -77,7 +76,7 @@ class ZepRetriever(BaseRetriever):
             text=query, metadata=metadata
         )
 
-        results: List[MemorySearchResult] = self.zep_client.search_memory(
+        results: List[MemorySearchResult] = self.zep_client.memory.search_memory(
             self.session_id, payload, limit=self.top_k
         )
 
@@ -96,7 +95,7 @@ class ZepRetriever(BaseRetriever):
             text=query, metadata=metadata
         )
 
-        results: List[MemorySearchResult] = await self.zep_client.asearch_memory(
+        results: List[MemorySearchResult] = await self.zep_client.memory.asearch_memory(
             self.session_id, payload, limit=self.top_k
         )
 
diff --git a/libs/langchain/langchain/retrievers/zilliz.py b/libs/langchain/langchain/retrievers/zilliz.py
index e023bac7714..583dbd83731 100644
--- a/libs/langchain/langchain/retrievers/zilliz.py
+++ b/libs/langchain/langchain/retrievers/zilliz.py
@@ -1,10 +1,9 @@
 import warnings
 from typing import Any, Dict, List, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import BaseRetriever, Document
 from langchain.vectorstores.zilliz import Zilliz
 
diff --git a/libs/langchain/langchain/schema/document.py b/libs/langchain/langchain/schema/document.py
index a05df15db62..feaa1acac0a 100644
--- a/libs/langchain/langchain/schema/document.py
+++ b/libs/langchain/langchain/schema/document.py
@@ -3,9 +3,8 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from typing import Any, Sequence
 
-from pydantic import Field
-
 from langchain.load.serializable import Serializable
+from langchain.pydantic_v1 import Field
 
 
 class Document(Serializable):
diff --git a/libs/langchain/langchain/schema/messages.py b/libs/langchain/langchain/schema/messages.py
index 3d6219f7d07..0fbef0ba49e 100644
--- a/libs/langchain/langchain/schema/messages.py
+++ b/libs/langchain/langchain/schema/messages.py
@@ -3,9 +3,8 @@ from __future__ import annotations
 from abc import abstractmethod
 from typing import TYPE_CHECKING, Any, Dict, List, Sequence
 
-from pydantic import Field
-
 from langchain.load.serializable import Serializable
+from langchain.pydantic_v1 import Field
 
 if TYPE_CHECKING:
     from langchain.prompts.chat import ChatPromptTemplate
diff --git a/libs/langchain/langchain/schema/output.py b/libs/langchain/langchain/schema/output.py
index 10b9923bad6..589573ee583 100644
--- a/libs/langchain/langchain/schema/output.py
+++ b/libs/langchain/langchain/schema/output.py
@@ -4,9 +4,8 @@ from copy import deepcopy
 from typing import Any, Dict, List, Optional
 from uuid import UUID
 
-from pydantic import BaseModel, root_validator
-
 from langchain.load.serializable import Serializable
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.schema.messages import BaseMessage, BaseMessageChunk
 
 
diff --git a/libs/langchain/langchain/schema/prompt.py b/libs/langchain/langchain/schema/prompt.py
index d273af0dbfc..951954c5f2e 100644
--- a/libs/langchain/langchain/schema/prompt.py
+++ b/libs/langchain/langchain/schema/prompt.py
@@ -14,6 +14,13 @@ class PromptValue(Serializable, ABC):
         ChatModel inputs.
     """
 
+    @property
+    def lc_serializable(self) -> bool:
+        """
+        Return whether or not the class is serializable.
+        """
+        return True
+
     @abstractmethod
     def to_string(self) -> str:
         """Return prompt value as string."""
diff --git a/libs/langchain/langchain/schema/prompt_template.py b/libs/langchain/langchain/schema/prompt_template.py
index b8eed333193..62c8160c528 100644
--- a/libs/langchain/langchain/schema/prompt_template.py
+++ b/libs/langchain/langchain/schema/prompt_template.py
@@ -6,9 +6,9 @@ from pathlib import Path
 from typing import Any, Callable, Dict, List, Mapping, Optional, Union
 
 import yaml
-from pydantic import Field, root_validator
 
 from langchain.load.serializable import Serializable
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema.document import Document
 from langchain.schema.output_parser import BaseOutputParser
 from langchain.schema.prompt import PromptValue
diff --git a/libs/langchain/langchain/schema/runnable/__init__.py b/libs/langchain/langchain/schema/runnable/__init__.py
new file mode 100644
index 00000000000..0dbabd1579d
--- /dev/null
+++ b/libs/langchain/langchain/schema/runnable/__init__.py
@@ -0,0 +1,24 @@
+from langchain.schema.runnable.base import (
+    Runnable,
+    RunnableBinding,
+    RunnableLambda,
+    RunnableMap,
+    RunnableSequence,
+    RunnableWithFallbacks,
+)
+from langchain.schema.runnable.config import RunnableConfig
+from langchain.schema.runnable.passthrough import RunnablePassthrough
+from langchain.schema.runnable.router import RouterInput, RouterRunnable
+
+__all__ = [
+    "RouterInput",
+    "RouterRunnable",
+    "Runnable",
+    "RunnableBinding",
+    "RunnableConfig",
+    "RunnableMap",
+    "RunnableLambda",
+    "RunnablePassthrough",
+    "RunnableSequence",
+    "RunnableWithFallbacks",
+]
diff --git a/libs/langchain/langchain/schema/runnable.py b/libs/langchain/langchain/schema/runnable/base.py
similarity index 83%
rename from libs/langchain/langchain/schema/runnable.py
rename to libs/langchain/langchain/schema/runnable/base.py
index 38a84ead2a1..9354355fa22 100644
--- a/libs/langchain/langchain/schema/runnable.py
+++ b/libs/langchain/langchain/schema/runnable/base.py
@@ -10,7 +10,6 @@ from typing import (
     AsyncIterator,
     Awaitable,
     Callable,
-    Coroutine,
     Dict,
     Generic,
     Iterator,
@@ -20,61 +19,22 @@ from typing import (
     Sequence,
     Tuple,
     Type,
-    TypedDict,
     TypeVar,
     Union,
     cast,
 )
 
-from pydantic import Field
-
-from langchain.callbacks.base import BaseCallbackManager, Callbacks
+from langchain.callbacks.base import BaseCallbackManager
 from langchain.load.dump import dumpd
 from langchain.load.serializable import Serializable
+from langchain.pydantic_v1 import Field
+from langchain.schema.runnable.config import RunnableConfig
+from langchain.schema.runnable.utils import (
+    gather_with_concurrency,
+)
 from langchain.utils.aiter import atee, py_anext
 
 
-async def _gated_coro(semaphore: asyncio.Semaphore, coro: Coroutine) -> Any:
-    async with semaphore:
-        return await coro
-
-
-async def _gather_with_concurrency(n: Union[int, None], *coros: Coroutine) -> list:
-    if n is None:
-        return await asyncio.gather(*coros)
-
-    semaphore = asyncio.Semaphore(n)
-
-    return await asyncio.gather(*(_gated_coro(semaphore, c) for c in coros))
-
-
-class RunnableConfig(TypedDict, total=False):
-    """Configuration for a Runnable."""
-
-    tags: List[str]
-    """
-    Tags for this call and any sub-calls (eg. a Chain calling an LLM).
-    You can use these to filter calls.
-    """
-
-    metadata: Dict[str, Any]
-    """
-    Metadata for this call and any sub-calls (eg. a Chain calling an LLM).
-    Keys should be strings, values should be JSON-serializable.
-    """
-
-    callbacks: Callbacks
-    """
-    Callbacks for this call and any sub-calls (eg. a Chain calling an LLM).
-    Tags are passed to all callbacks, metadata is passed to handle*Start callbacks.
-    """
-
-    _locals: Dict[str, Any]
-    """
-    Local variables
-    """
-
-
 def _empty_config() -> RunnableConfig:
     return RunnableConfig(tags=[], metadata={}, callbacks=None, _locals={})
 
@@ -117,7 +77,7 @@ class Runnable(Generic[Input, Output], ABC):
             Mapping[str, Union[Runnable[Any, Other], Callable[[Any], Other]]],
         ],
     ) -> RunnableSequence[Input, Other]:
-        return RunnableSequence(first=self, last=_coerce_to_runnable(other))
+        return RunnableSequence(first=self, last=coerce_to_runnable(other))
 
     def __ror__(
         self,
@@ -127,7 +87,7 @@ class Runnable(Generic[Input, Output], ABC):
             Mapping[str, Union[Runnable[Other, Any], Callable[[Other], Any]]],
         ],
     ) -> RunnableSequence[Other, Output]:
-        return RunnableSequence(first=_coerce_to_runnable(other), last=self)
+        return RunnableSequence(first=coerce_to_runnable(other), last=self)
 
     """ --- Public API --- """
 
@@ -180,7 +140,7 @@ class Runnable(Generic[Input, Output], ABC):
         configs = self._get_config_list(config, len(inputs))
         coros = map(self.ainvoke, inputs, configs)
 
-        return await _gather_with_concurrency(max_concurrency, *coros)
+        return await gather_with_concurrency(max_concurrency, *coros)
 
     def stream(
         self, input: Input, config: Optional[RunnableConfig] = None
@@ -303,8 +263,11 @@ class Runnable(Generic[Input, Output], ABC):
             run_manager.on_chain_error(e)
             raise
         else:
+            output_for_tracer = dumpd(output)
             run_manager.on_chain_end(
-                output if isinstance(output, dict) else {"output": output}
+                output_for_tracer
+                if isinstance(output_for_tracer, dict)
+                else {"output": output_for_tracer}
             )
             return output
 
@@ -330,8 +293,11 @@ class Runnable(Generic[Input, Output], ABC):
             await run_manager.on_chain_error(e)
             raise
         else:
+            output_for_tracer = dumpd(output)
             await run_manager.on_chain_end(
-                output if isinstance(output, dict) else {"output": output}
+                output_for_tracer
+                if isinstance(output_for_tracer, dict)
+                else {"output": output_for_tracer}
             )
             return output
 
@@ -478,6 +444,14 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]):
     class Config:
         arbitrary_types_allowed = True
 
+    @property
+    def lc_serializable(self) -> bool:
+        return True
+
+    @property
+    def lc_namespace(self) -> List[str]:
+        return self.__class__.__module__.split(".")[:-1]
+
     @property
     def runnables(self) -> Iterator[Runnable[Input, Output]]:
         yield self.runnable
@@ -496,7 +470,7 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]):
             try:
                 output = runnable.invoke(
                     input,
-                    _patch_config(config, run_manager.get_child()),
+                    patch_config(config, run_manager.get_child()),
                 )
             except self.exceptions_to_handle as e:
                 if first_error is None:
@@ -530,7 +504,7 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]):
             try:
                 output = await runnable.ainvoke(
                     input,
-                    _patch_config(config, run_manager.get_child()),
+                    patch_config(config, run_manager.get_child()),
                 )
             except self.exceptions_to_handle as e:
                 if first_error is None:
@@ -586,7 +560,7 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]):
                     inputs,
                     [
                         # each step a child run of the corresponding root run
-                        _patch_config(config, rm.get_child())
+                        patch_config(config, rm.get_child())
                         for rm, config in zip(run_managers, configs)
                     ],
                     max_concurrency=max_concurrency,
@@ -653,7 +627,7 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]):
                     inputs,
                     [
                         # each step a child run of the corresponding root run
-                        _patch_config(config, rm.get_child())
+                        patch_config(config, rm.get_child())
                         for rm, config in zip(run_managers, configs)
                     ],
                     max_concurrency=max_concurrency,
@@ -736,6 +710,10 @@ class RunnableSequence(Serializable, Runnable[Input, Output]):
     def lc_serializable(self) -> bool:
         return True
 
+    @property
+    def lc_namespace(self) -> List[str]:
+        return self.__class__.__module__.split(".")[:-1]
+
     class Config:
         arbitrary_types_allowed = True
 
@@ -757,7 +735,7 @@ class RunnableSequence(Serializable, Runnable[Input, Output]):
             return RunnableSequence(
                 first=self.first,
                 middle=self.middle + [self.last],
-                last=_coerce_to_runnable(other),
+                last=coerce_to_runnable(other),
             )
 
     def __ror__(
@@ -776,7 +754,7 @@ class RunnableSequence(Serializable, Runnable[Input, Output]):
             )
         else:
             return RunnableSequence(
-                first=_coerce_to_runnable(other),
+                first=coerce_to_runnable(other),
                 middle=[self.first] + self.middle,
                 last=self.last,
             )
@@ -792,11 +770,12 @@ class RunnableSequence(Serializable, Runnable[Input, Output]):
 
         # invoke all steps in sequence
         try:
-            callbacks = run_manager.get_child()
             for step in self.steps:
-                # mark each step as child run
-                step_config = _patch_config(config, callbacks)
-                input = step.invoke(input, step_config)
+                input = step.invoke(
+                    input,
+                    # mark each step as a child run
+                    patch_config(config, run_manager.get_child()),
+                )
         # finish the root run
         except (KeyboardInterrupt, Exception) as e:
             run_manager.on_chain_error(e)
@@ -824,7 +803,7 @@ class RunnableSequence(Serializable, Runnable[Input, Output]):
                 input = await step.ainvoke(
                     input,
                     # mark each step as a child run
-                    _patch_config(config, run_manager.get_child()),
+                    patch_config(config, run_manager.get_child()),
                 )
         # finish the root run
         except (KeyboardInterrupt, Exception) as e:
@@ -874,7 +853,7 @@ class RunnableSequence(Serializable, Runnable[Input, Output]):
                     inputs,
                     [
                         # each step a child run of the corresponding root run
-                        _patch_config(config, rm.get_child())
+                        patch_config(config, rm.get_child())
                         for rm, config in zip(run_managers, configs)
                     ],
                     max_concurrency=max_concurrency,
@@ -933,7 +912,7 @@ class RunnableSequence(Serializable, Runnable[Input, Output]):
                     inputs,
                     [
                         # each step a child run of the corresponding root run
-                        _patch_config(config, rm.get_child())
+                        patch_config(config, rm.get_child())
                         for rm, config in zip(run_managers, configs)
                     ],
                     max_concurrency=max_concurrency,
@@ -979,7 +958,7 @@ class RunnableSequence(Serializable, Runnable[Input, Output]):
                 input = step.invoke(
                     input,
                     # mark each step as a child run
-                    _patch_config(config, run_manager.get_child()),
+                    patch_config(config, run_manager.get_child()),
                 )
         except (KeyboardInterrupt, Exception) as e:
             run_manager.on_chain_error(e)
@@ -991,12 +970,12 @@ class RunnableSequence(Serializable, Runnable[Input, Output]):
         try:
             # stream the first of the last steps with non-streaming input
             final_pipeline = steps[streaming_start_index].stream(
-                input, _patch_config(config, run_manager.get_child())
+                input, patch_config(config, run_manager.get_child())
             )
             # stream the rest of the last steps with streaming input
             for step in steps[streaming_start_index + 1 :]:
                 final_pipeline = step.transform(
-                    final_pipeline, _patch_config(config, run_manager.get_child())
+                    final_pipeline, patch_config(config, run_manager.get_child())
                 )
             for output in final_pipeline:
                 yield output
@@ -1046,7 +1025,7 @@ class RunnableSequence(Serializable, Runnable[Input, Output]):
                 input = await step.ainvoke(
                     input,
                     # mark each step as a child run
-                    _patch_config(config, run_manager.get_child()),
+                    patch_config(config, run_manager.get_child()),
                 )
         except (KeyboardInterrupt, Exception) as e:
             await run_manager.on_chain_error(e)
@@ -1058,12 +1037,12 @@ class RunnableSequence(Serializable, Runnable[Input, Output]):
         try:
             # stream the first of the last steps with non-streaming input
             final_pipeline = steps[streaming_start_index].astream(
-                input, _patch_config(config, run_manager.get_child())
+                input, patch_config(config, run_manager.get_child())
             )
             # stream the rest of the last steps with streaming input
             for step in steps[streaming_start_index + 1 :]:
                 final_pipeline = step.atransform(
-                    final_pipeline, _patch_config(config, run_manager.get_child())
+                    final_pipeline, patch_config(config, run_manager.get_child())
                 )
             async for output in final_pipeline:
                 yield output
@@ -1107,14 +1086,16 @@ class RunnableMap(Serializable, Runnable[Input, Dict[str, Any]]):
             ],
         ],
     ) -> None:
-        super().__init__(
-            steps={key: _coerce_to_runnable(r) for key, r in steps.items()}
-        )
+        super().__init__(steps={key: coerce_to_runnable(r) for key, r in steps.items()})
 
     @property
     def lc_serializable(self) -> bool:
         return True
 
+    @property
+    def lc_namespace(self) -> List[str]:
+        return self.__class__.__module__.split(".")[:-1]
+
     class Config:
         arbitrary_types_allowed = True
 
@@ -1147,7 +1128,7 @@ class RunnableMap(Serializable, Runnable[Input, Dict[str, Any]]):
                         step.invoke,
                         input,
                         # mark each step as a child run
-                        _patch_config(config, run_manager.get_child()),
+                        patch_config(config, run_manager.get_child()),
                     )
                     for step in steps.values()
                 ]
@@ -1180,7 +1161,7 @@ class RunnableMap(Serializable, Runnable[Input, Dict[str, Any]]):
                     step.ainvoke(
                         input,
                         # mark each step as a child run
-                        _patch_config(config, run_manager.get_child()),
+                        patch_config(config, run_manager.get_child()),
                     )
                     for step in steps.values()
                 )
@@ -1219,19 +1200,6 @@ class RunnableLambda(Runnable[Input, Output]):
         return self._call_with_config(self.func, input, config)
 
 
-class RunnablePassthrough(Serializable, Runnable[Input, Input]):
-    """
-    A runnable that passes through the input.
-    """
-
-    @property
-    def lc_serializable(self) -> bool:
-        return True
-
-    def invoke(self, input: Input, config: Optional[RunnableConfig] = None) -> Input:
-        return self._call_with_config(lambda x: x, input, config)
-
-
 class RunnableBinding(Serializable, Runnable[Input, Output]):
     """
     A runnable that delegates calls to another runnable with a set of kwargs.
@@ -1248,6 +1216,10 @@ class RunnableBinding(Serializable, Runnable[Input, Output]):
     def lc_serializable(self) -> bool:
         return True
 
+    @property
+    def lc_namespace(self) -> List[str]:
+        return self.__class__.__module__.split(".")[:-1]
+
     def bind(self, **kwargs: Any) -> Runnable[Input, Output]:
         return self.__class__(bound=self.bound, kwargs={**self.kwargs, **kwargs})
 
@@ -1304,160 +1276,7 @@ class RunnableBinding(Serializable, Runnable[Input, Output]):
             yield item
 
 
-class RouterInput(TypedDict):
-    """A Router input.
-
-    Attributes:
-        key: The key to route on.
-        input: The input to pass to the selected runnable.
-    """
-
-    key: str
-    input: Any
-
-
-class RouterRunnable(
-    Serializable, Generic[Input, Output], Runnable[RouterInput, Output]
-):
-    """
-    A runnable that routes to a set of runnables based on Input['key'].
-    Returns the output of the selected runnable.
-    """
-
-    runnables: Mapping[str, Runnable[Input, Output]]
-
-    def __init__(
-        self,
-        runnables: Mapping[
-            str, Union[Runnable[Input, Output], Callable[[Input], Output]]
-        ],
-    ) -> None:
-        super().__init__(
-            runnables={key: _coerce_to_runnable(r) for key, r in runnables.items()}
-        )
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    @property
-    def lc_serializable(self) -> bool:
-        return True
-
-    def __or__(
-        self,
-        other: Union[
-            Runnable[Any, Other],
-            Callable[[Any], Other],
-            Mapping[str, Union[Runnable[Any, Other], Callable[[Any], Other]]],
-            Mapping[str, Any],
-        ],
-    ) -> RunnableSequence[RouterInput, Other]:
-        return RunnableSequence(first=self, last=_coerce_to_runnable(other))
-
-    def __ror__(
-        self,
-        other: Union[
-            Runnable[Other, Any],
-            Callable[[Any], Other],
-            Mapping[str, Union[Runnable[Other, Any], Callable[[Other], Any]]],
-            Mapping[str, Any],
-        ],
-    ) -> RunnableSequence[Other, Output]:
-        return RunnableSequence(first=_coerce_to_runnable(other), last=self)
-
-    def invoke(
-        self, input: RouterInput, config: Optional[RunnableConfig] = None
-    ) -> Output:
-        key = input["key"]
-        actual_input = input["input"]
-        if key not in self.runnables:
-            raise ValueError(f"No runnable associated with key '{key}'")
-
-        runnable = self.runnables[key]
-        return runnable.invoke(actual_input, config)
-
-    async def ainvoke(
-        self, input: RouterInput, config: Optional[RunnableConfig] = None
-    ) -> Output:
-        key = input["key"]
-        actual_input = input["input"]
-        if key not in self.runnables:
-            raise ValueError(f"No runnable associated with key '{key}'")
-
-        runnable = self.runnables[key]
-        return await runnable.ainvoke(actual_input, config)
-
-    def batch(
-        self,
-        inputs: List[RouterInput],
-        config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
-        *,
-        max_concurrency: Optional[int] = None,
-    ) -> List[Output]:
-        keys = [input["key"] for input in inputs]
-        actual_inputs = [input["input"] for input in inputs]
-        if any(key not in self.runnables for key in keys):
-            raise ValueError("One or more keys do not have a corresponding runnable")
-
-        runnables = [self.runnables[key] for key in keys]
-        configs = self._get_config_list(config, len(inputs))
-        with ThreadPoolExecutor(max_workers=max_concurrency) as executor:
-            return list(
-                executor.map(
-                    lambda runnable, input, config: runnable.invoke(input, config),
-                    runnables,
-                    actual_inputs,
-                    configs,
-                )
-            )
-
-    async def abatch(
-        self,
-        inputs: List[RouterInput],
-        config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
-        *,
-        max_concurrency: Optional[int] = None,
-    ) -> List[Output]:
-        keys = [input["key"] for input in inputs]
-        actual_inputs = [input["input"] for input in inputs]
-        if any(key not in self.runnables for key in keys):
-            raise ValueError("One or more keys do not have a corresponding runnable")
-
-        runnables = [self.runnables[key] for key in keys]
-        configs = self._get_config_list(config, len(inputs))
-        return await _gather_with_concurrency(
-            max_concurrency,
-            *(
-                runnable.ainvoke(input, config)
-                for runnable, input, config in zip(runnables, actual_inputs, configs)
-            ),
-        )
-
-    def stream(
-        self, input: RouterInput, config: Optional[RunnableConfig] = None
-    ) -> Iterator[Output]:
-        key = input["key"]
-        actual_input = input["input"]
-        if key not in self.runnables:
-            raise ValueError(f"No runnable associated with key '{key}'")
-
-        runnable = self.runnables[key]
-        yield from runnable.stream(actual_input, config)
-
-    async def astream(
-        self, input: RouterInput, config: Optional[RunnableConfig] = None
-    ) -> AsyncIterator[Output]:
-        key = input["key"]
-        actual_input = input["input"]
-        if key not in self.runnables:
-            raise ValueError(f"No runnable associated with key '{key}'")
-
-        runnable = self.runnables[key]
-        async for output in runnable.astream(actual_input, config):
-            yield output
-
-
-def _patch_config(
+def patch_config(
     config: RunnableConfig,
     callback_manager: BaseCallbackManager,
     _locals: Optional[Dict[str, Any]] = None,
@@ -1469,7 +1288,7 @@ def _patch_config(
     return config
 
 
-def _coerce_to_runnable(
+def coerce_to_runnable(
     thing: Union[
         Runnable[Input, Output],
         Callable[[Input], Output],
@@ -1481,7 +1300,7 @@ def _coerce_to_runnable(
     elif callable(thing):
         return RunnableLambda(thing)
     elif isinstance(thing, dict):
-        runnables = {key: _coerce_to_runnable(r) for key, r in thing.items()}
+        runnables = {key: coerce_to_runnable(r) for key, r in thing.items()}
         return cast(Runnable[Input, Output], RunnableMap(steps=runnables))
     else:
         raise TypeError(
diff --git a/libs/langchain/langchain/schema/runnable/config.py b/libs/langchain/langchain/schema/runnable/config.py
new file mode 100644
index 00000000000..f2bf28fcb57
--- /dev/null
+++ b/libs/langchain/langchain/schema/runnable/config.py
@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+from typing import Any, Dict, List, TypedDict
+
+from langchain.callbacks.base import Callbacks
+
+
+class RunnableConfig(TypedDict, total=False):
+    """Configuration for a Runnable."""
+
+    tags: List[str]
+    """
+    Tags for this call and any sub-calls (eg. a Chain calling an LLM).
+    You can use these to filter calls.
+    """
+
+    metadata: Dict[str, Any]
+    """
+    Metadata for this call and any sub-calls (eg. a Chain calling an LLM).
+    Keys should be strings, values should be JSON-serializable.
+    """
+
+    callbacks: Callbacks
+    """
+    Callbacks for this call and any sub-calls (eg. a Chain calling an LLM).
+    Tags are passed to all callbacks, metadata is passed to handle*Start callbacks.
+    """
+
+    _locals: Dict[str, Any]
+    """
+    Local variables
+    """
diff --git a/libs/langchain/langchain/schema/runnable/passthrough.py b/libs/langchain/langchain/schema/runnable/passthrough.py
new file mode 100644
index 00000000000..a97e708b64b
--- /dev/null
+++ b/libs/langchain/langchain/schema/runnable/passthrough.py
@@ -0,0 +1,47 @@
+from __future__ import annotations
+
+from typing import AsyncIterator, Iterator, List, Optional
+
+from langchain.load.serializable import Serializable
+from langchain.schema.runnable.base import Input, Runnable
+from langchain.schema.runnable.config import RunnableConfig
+
+
+def identity(x: Input) -> Input:
+    return x
+
+
+async def aidentity(x: Input) -> Input:
+    return x
+
+
+class RunnablePassthrough(Serializable, Runnable[Input, Input]):
+    """
+    A runnable that passes through the input.
+    """
+
+    @property
+    def lc_serializable(self) -> bool:
+        return True
+
+    @property
+    def lc_namespace(self) -> List[str]:
+        return self.__class__.__module__.split(".")[:-1]
+
+    def invoke(self, input: Input, config: Optional[RunnableConfig] = None) -> Input:
+        return self._call_with_config(identity, input, config)
+
+    async def ainvoke(
+        self, input: Input, config: RunnableConfig | None = None
+    ) -> Input:
+        return await self._acall_with_config(aidentity, input, config)
+
+    def transform(
+        self, input: Iterator[Input], config: RunnableConfig | None = None
+    ) -> Iterator[Input]:
+        return self._transform_stream_with_config(input, identity, config)
+
+    def atransform(
+        self, input: AsyncIterator[Input], config: RunnableConfig | None = None
+    ) -> AsyncIterator[Input]:
+        return self._atransform_stream_with_config(input, identity, config)
diff --git a/libs/langchain/langchain/schema/runnable/router.py b/libs/langchain/langchain/schema/runnable/router.py
new file mode 100644
index 00000000000..a844a01972a
--- /dev/null
+++ b/libs/langchain/langchain/schema/runnable/router.py
@@ -0,0 +1,184 @@
+from __future__ import annotations
+
+from concurrent.futures import ThreadPoolExecutor
+from typing import (
+    Any,
+    AsyncIterator,
+    Callable,
+    Generic,
+    Iterator,
+    List,
+    Mapping,
+    Optional,
+    TypedDict,
+    Union,
+)
+
+from langchain.load.serializable import Serializable
+from langchain.schema.runnable.base import (
+    Input,
+    Other,
+    Output,
+    Runnable,
+    RunnableSequence,
+    coerce_to_runnable,
+)
+from langchain.schema.runnable.config import RunnableConfig
+from langchain.schema.runnable.utils import gather_with_concurrency
+
+
+class RouterInput(TypedDict):
+    """A Router input.
+
+    Attributes:
+        key: The key to route on.
+        input: The input to pass to the selected runnable.
+    """
+
+    key: str
+    input: Any
+
+
+class RouterRunnable(
+    Serializable, Generic[Input, Output], Runnable[RouterInput, Output]
+):
+    """
+    A runnable that routes to a set of runnables based on Input['key'].
+    Returns the output of the selected runnable.
+    """
+
+    runnables: Mapping[str, Runnable[Input, Output]]
+
+    def __init__(
+        self,
+        runnables: Mapping[
+            str, Union[Runnable[Input, Output], Callable[[Input], Output]]
+        ],
+    ) -> None:
+        super().__init__(
+            runnables={key: coerce_to_runnable(r) for key, r in runnables.items()}
+        )
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    @property
+    def lc_serializable(self) -> bool:
+        return True
+
+    @property
+    def lc_namespace(self) -> List[str]:
+        return self.__class__.__module__.split(".")[:-1]
+
+    def __or__(
+        self,
+        other: Union[
+            Runnable[Any, Other],
+            Callable[[Any], Other],
+            Mapping[str, Union[Runnable[Any, Other], Callable[[Any], Other]]],
+            Mapping[str, Any],
+        ],
+    ) -> RunnableSequence[RouterInput, Other]:
+        return RunnableSequence(first=self, last=coerce_to_runnable(other))
+
+    def __ror__(
+        self,
+        other: Union[
+            Runnable[Other, Any],
+            Callable[[Any], Other],
+            Mapping[str, Union[Runnable[Other, Any], Callable[[Other], Any]]],
+            Mapping[str, Any],
+        ],
+    ) -> RunnableSequence[Other, Output]:
+        return RunnableSequence(first=coerce_to_runnable(other), last=self)
+
+    def invoke(
+        self, input: RouterInput, config: Optional[RunnableConfig] = None
+    ) -> Output:
+        key = input["key"]
+        actual_input = input["input"]
+        if key not in self.runnables:
+            raise ValueError(f"No runnable associated with key '{key}'")
+
+        runnable = self.runnables[key]
+        return runnable.invoke(actual_input, config)
+
+    async def ainvoke(
+        self, input: RouterInput, config: Optional[RunnableConfig] = None
+    ) -> Output:
+        key = input["key"]
+        actual_input = input["input"]
+        if key not in self.runnables:
+            raise ValueError(f"No runnable associated with key '{key}'")
+
+        runnable = self.runnables[key]
+        return await runnable.ainvoke(actual_input, config)
+
+    def batch(
+        self,
+        inputs: List[RouterInput],
+        config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
+        *,
+        max_concurrency: Optional[int] = None,
+    ) -> List[Output]:
+        keys = [input["key"] for input in inputs]
+        actual_inputs = [input["input"] for input in inputs]
+        if any(key not in self.runnables for key in keys):
+            raise ValueError("One or more keys do not have a corresponding runnable")
+
+        runnables = [self.runnables[key] for key in keys]
+        configs = self._get_config_list(config, len(inputs))
+        with ThreadPoolExecutor(max_workers=max_concurrency) as executor:
+            return list(
+                executor.map(
+                    lambda runnable, input, config: runnable.invoke(input, config),
+                    runnables,
+                    actual_inputs,
+                    configs,
+                )
+            )
+
+    async def abatch(
+        self,
+        inputs: List[RouterInput],
+        config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
+        *,
+        max_concurrency: Optional[int] = None,
+    ) -> List[Output]:
+        keys = [input["key"] for input in inputs]
+        actual_inputs = [input["input"] for input in inputs]
+        if any(key not in self.runnables for key in keys):
+            raise ValueError("One or more keys do not have a corresponding runnable")
+
+        runnables = [self.runnables[key] for key in keys]
+        configs = self._get_config_list(config, len(inputs))
+        return await gather_with_concurrency(
+            max_concurrency,
+            *(
+                runnable.ainvoke(input, config)
+                for runnable, input, config in zip(runnables, actual_inputs, configs)
+            ),
+        )
+
+    def stream(
+        self, input: RouterInput, config: Optional[RunnableConfig] = None
+    ) -> Iterator[Output]:
+        key = input["key"]
+        actual_input = input["input"]
+        if key not in self.runnables:
+            raise ValueError(f"No runnable associated with key '{key}'")
+
+        runnable = self.runnables[key]
+        yield from runnable.stream(actual_input, config)
+
+    async def astream(
+        self, input: RouterInput, config: Optional[RunnableConfig] = None
+    ) -> AsyncIterator[Output]:
+        key = input["key"]
+        actual_input = input["input"]
+        if key not in self.runnables:
+            raise ValueError(f"No runnable associated with key '{key}'")
+
+        runnable = self.runnables[key]
+        async for output in runnable.astream(actual_input, config):
+            yield output
diff --git a/libs/langchain/langchain/schema/runnable/utils.py b/libs/langchain/langchain/schema/runnable/utils.py
new file mode 100644
index 00000000000..0602413bd68
--- /dev/null
+++ b/libs/langchain/langchain/schema/runnable/utils.py
@@ -0,0 +1,18 @@
+from __future__ import annotations
+
+import asyncio
+from typing import Any, Coroutine, Union
+
+
+async def gated_coro(semaphore: asyncio.Semaphore, coro: Coroutine) -> Any:
+    async with semaphore:
+        return await coro
+
+
+async def gather_with_concurrency(n: Union[int, None], *coros: Coroutine) -> list:
+    if n is None:
+        return await asyncio.gather(*coros)
+
+    semaphore = asyncio.Semaphore(n)
+
+    return await asyncio.gather(*(gated_coro(semaphore, c) for c in coros))
diff --git a/libs/langchain/langchain/smith/evaluation/config.py b/libs/langchain/langchain/smith/evaluation/config.py
index 21cb4848216..9348e2d81cb 100644
--- a/libs/langchain/langchain/smith/evaluation/config.py
+++ b/libs/langchain/langchain/smith/evaluation/config.py
@@ -3,7 +3,6 @@
 from typing import Any, Dict, List, Optional, Union
 
 from langsmith import RunEvaluator
-from pydantic import BaseModel, Field
 
 from langchain.embeddings.base import Embeddings
 from langchain.evaluation.criteria.eval_chain import CRITERIA_TYPE
@@ -14,6 +13,7 @@ from langchain.evaluation.schema import EvaluatorType, StringEvaluator
 from langchain.evaluation.string_distance.base import (
     StringDistance as StringDistanceEnum,
 )
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.prompt_template import BasePromptTemplate
 
@@ -83,7 +83,9 @@ class RunEvalConfig(BaseModel):
         The language model to pass to any evaluators that use a language model.
     """  # noqa: E501
 
-    evaluators: List[Union[EvaluatorType, EvalConfig]] = Field(default_factory=list)
+    evaluators: List[Union[EvaluatorType, str, EvalConfig]] = Field(
+        default_factory=list
+    )
     """Configurations for which evaluators to apply to the dataset run.
     Each can be the string of an
     :class:`EvaluatorType <langchain.evaluation.schema.EvaluatorType>`, such
@@ -239,4 +241,22 @@ class RunEvalConfig(BaseModel):
         llm: Optional[BaseLanguageModel] = None
         prompt: Optional[BasePromptTemplate] = None
 
+    class JsonValidity(EvalConfig):
+        """Configuration for a json validity evaluator.
+
+        Parameters
+        ----------
+        """
+
+        evaluator_type: EvaluatorType = EvaluatorType.JSON_VALIDITY
+
+    class JsonEqualityEvaluator(EvalConfig):
+        """Configuration for a json equality evaluator.
+
+        Parameters
+        ----------
+        """
+
+        evaluator_type: EvaluatorType = EvaluatorType.JSON_EQUALITY
+
     # TODO: Trajectory
diff --git a/libs/langchain/langchain/smith/evaluation/runner_utils.py b/libs/langchain/langchain/smith/evaluation/runner_utils.py
index 7f883c49794..cc3c46dff14 100644
--- a/libs/langchain/langchain/smith/evaluation/runner_utils.py
+++ b/libs/langchain/langchain/smith/evaluation/runner_utils.py
@@ -462,7 +462,7 @@ def _determine_reference_key(
 
 
 def _construct_run_evaluator(
-    eval_config: Union[EvaluatorType, EvalConfig],
+    eval_config: Union[EvaluatorType, str, EvalConfig],
     eval_llm: BaseLanguageModel,
     run_type: str,
     data_type: DataType,
@@ -471,7 +471,9 @@ def _construct_run_evaluator(
     input_key: Optional[str],
     prediction_key: Optional[str],
 ) -> RunEvaluator:
-    if isinstance(eval_config, EvaluatorType):
+    if isinstance(eval_config, (EvaluatorType, str)):
+        if not isinstance(eval_config, EvaluatorType):
+            eval_config = EvaluatorType(eval_config)
         evaluator_ = load_evaluator(eval_config, llm=eval_llm)
         eval_type_tag = eval_config.value
     else:
@@ -1314,7 +1316,7 @@ def _handle_coroutine(coro: Coroutine) -> Any:
     except RuntimeError:  # No event loop
         return asyncio.run(coro)
     if loop.is_running():
-        return loop.create_task(coro)
+        return loop.run_until_complete(coro)
     else:
         return asyncio.run(coro)
 
diff --git a/libs/langchain/langchain/smith/evaluation/string_run_evaluator.py b/libs/langchain/langchain/smith/evaluation/string_run_evaluator.py
index 4016482c191..d8133b19d87 100644
--- a/libs/langchain/langchain/smith/evaluation/string_run_evaluator.py
+++ b/libs/langchain/langchain/smith/evaluation/string_run_evaluator.py
@@ -313,17 +313,30 @@ class StringRunEvaluatorChain(Chain, RunEvaluator):
         self, run: Run, example: Optional[Example] = None
     ) -> EvaluationResult:
         """Evaluate an example."""
-        result = self({"run": run, "example": example}, include_run_info=True)
-        return self._prepare_evaluator_output(result)
+        try:
+            result = self({"run": run, "example": example}, include_run_info=True)
+            return self._prepare_evaluator_output(result)
+        except Exception as e:
+            return EvaluationResult(
+                key=self.string_evaluator.evaluation_name,
+                comment=f"Error evaluating run {run.id}: {e}",
+                # TODO: Add run ID once we can declare it via callbacks
+            )
 
     async def aevaluate_run(
         self, run: Run, example: Optional[Example] = None
     ) -> EvaluationResult:
         """Evaluate an example."""
-        result = await self.acall(
-            {"run": run, "example": example}, include_run_info=True
-        )
-        return self._prepare_evaluator_output(result)
+        try:
+            result = await self.acall(
+                {"run": run, "example": example}, include_run_info=True
+            )
+            return self._prepare_evaluator_output(result)
+        except Exception as e:
+            return EvaluationResult(
+                key=self.string_evaluator.evaluation_name,
+                comment=f"Error evaluating run {run.id}: {e}",
+            )
 
     @classmethod
     def from_run_and_data_type(
@@ -375,7 +388,11 @@ class StringRunEvaluatorChain(Chain, RunEvaluator):
             )
 
         # Configure how example rows are fed as a reference string to the evaluator
-        if reference_key is not None or data_type in (DataType.llm, DataType.chat):
+        if (
+            reference_key is not None
+            or data_type in (DataType.llm, DataType.chat)
+            or evaluator.requires_reference
+        ):
             example_mapper = StringExampleMapper(reference_key=reference_key)
         elif evaluator.requires_reference:
             raise ValueError(
diff --git a/libs/langchain/langchain/storage/__init__.py b/libs/langchain/langchain/storage/__init__.py
index 6b0511aba33..ecc2e817f24 100644
--- a/libs/langchain/langchain/storage/__init__.py
+++ b/libs/langchain/langchain/storage/__init__.py
@@ -9,9 +9,11 @@ The primary goal of these storages is to support implementation of caching.
 from langchain.storage.encoder_backed import EncoderBackedStore
 from langchain.storage.file_system import LocalFileStore
 from langchain.storage.in_memory import InMemoryStore
+from langchain.storage.redis import RedisStore
 
 __all__ = [
     "EncoderBackedStore",
-    "LocalFileStore",
     "InMemoryStore",
+    "LocalFileStore",
+    "RedisStore",
 ]
diff --git a/libs/langchain/langchain/storage/in_memory.py b/libs/langchain/langchain/storage/in_memory.py
index 906d08e853b..3350f75f4d4 100644
--- a/libs/langchain/langchain/storage/in_memory.py
+++ b/libs/langchain/langchain/storage/in_memory.py
@@ -16,7 +16,8 @@ class InMemoryStore(BaseStore[str, Any]):
             the key-value pairs.
 
     Examples:
-        ... code-block:: python
+
+        .. code-block:: python
 
             from langchain.storage import InMemoryStore
 
diff --git a/libs/langchain/langchain/storage/redis.py b/libs/langchain/langchain/storage/redis.py
new file mode 100644
index 00000000000..bfb3e4a4ae0
--- /dev/null
+++ b/libs/langchain/langchain/storage/redis.py
@@ -0,0 +1,140 @@
+from typing import Any, Iterator, List, Optional, Sequence, Tuple, cast
+
+from langchain.schema import BaseStore
+from langchain.utilities.redis import get_client
+
+
+class RedisStore(BaseStore[str, bytes]):
+    """BaseStore implementation using Redis as the underlying store.
+
+    Examples:
+        Create a RedisStore instance and perform operations on it:
+
+        .. code-block:: python
+
+            # Instantiate the RedisStore with a Redis connection
+            from langchain.storage import RedisStore
+            from langchain.utilities.redis import get_client
+
+            client = get_client('redis://localhost:6379')
+            redis_store = RedisStore(client)
+
+            # Set values for keys
+            redis_store.mset([("key1", b"value1"), ("key2", b"value2")])
+
+            # Get values for keys
+            values = redis_store.mget(["key1", "key2"])
+            # [b"value1", b"value2"]
+
+            # Delete keys
+            redis_store.mdelete(["key1"])
+
+            # Iterate over keys
+            for key in redis_store.yield_keys():
+                print(key)
+    """
+
+    def __init__(
+        self,
+        *,
+        client: Any = None,
+        redis_url: Optional[str] = None,
+        client_kwargs: Optional[dict] = None,
+        ttl: Optional[int] = None,
+        namespace: Optional[str] = None,
+    ) -> None:
+        """Initialize the RedisStore with a Redis connection.
+
+        Must provide either a Redis client or a redis_url with optional client_kwargs.
+
+        Args:
+            client: A Redis connection instance
+            redis_url: redis url
+            client_kwargs: Keyword arguments to pass to the Redis client
+            ttl: time to expire keys in seconds if provided,
+                 if None keys will never expire
+            namespace: if provided, all keys will be prefixed with this namespace
+        """
+        try:
+            from redis import Redis
+        except ImportError as e:
+            raise ImportError(
+                "The RedisStore requires the redis library to be installed. "
+                "pip install redis"
+            ) from e
+
+        if client and redis_url or client and client_kwargs:
+            raise ValueError(
+                "Either a Redis client or a redis_url with optional client_kwargs "
+                "must be provided, but not both."
+            )
+
+        if client:
+            if not isinstance(client, Redis):
+                raise TypeError(
+                    f"Expected Redis client, got {type(client).__name__} instead."
+                )
+            _client = client
+        else:
+            if not redis_url:
+                raise ValueError(
+                    "Either a Redis client or a redis_url must be provided."
+                )
+            _client = get_client(redis_url, **(client_kwargs or {}))
+
+        self.client = _client
+
+        if not isinstance(ttl, int) and ttl is not None:
+            raise TypeError(f"Expected int or None, got {type(ttl)} instead.")
+
+        self.ttl = ttl
+        self.namespace = namespace
+
+    def _get_prefixed_key(self, key: str) -> str:
+        """Get the key with the namespace prefix.
+
+        Args:
+            key (str): The original key.
+
+        Returns:
+            str: The key with the namespace prefix.
+        """
+        delimiter = "/"
+        if self.namespace:
+            return f"{self.namespace}{delimiter}{key}"
+        return key
+
+    def mget(self, keys: Sequence[str]) -> List[Optional[bytes]]:
+        """Get the values associated with the given keys."""
+        return cast(
+            List[Optional[bytes]],
+            self.client.mget([self._get_prefixed_key(key) for key in keys]),
+        )
+
+    def mset(self, key_value_pairs: Sequence[Tuple[str, bytes]]) -> None:
+        """Set the given key-value pairs."""
+        pipe = self.client.pipeline()
+
+        for key, value in key_value_pairs:
+            pipe.set(self._get_prefixed_key(key), value, ex=self.ttl)
+        pipe.execute()
+
+    def mdelete(self, keys: Sequence[str]) -> None:
+        """Delete the given keys."""
+        _keys = [self._get_prefixed_key(key) for key in keys]
+        self.client.delete(*_keys)
+
+    def yield_keys(self, *, prefix: Optional[str] = None) -> Iterator[str]:
+        """Yield keys in the store."""
+        if prefix:
+            pattern = self._get_prefixed_key(prefix)
+        else:
+            pattern = self._get_prefixed_key("*")
+        scan_iter = cast(Iterator[bytes], self.client.scan_iter(match=pattern))
+        for key in scan_iter:
+            decoded_key = key.decode("utf-8")
+            if self.namespace:
+                relative_key = decoded_key[len(self.namespace) + 1 :]
+                yield relative_key
+            else:
+                yield decoded_key
diff --git a/libs/langchain/langchain/text_splitter.py b/libs/langchain/langchain/text_splitter.py
index 638f0c830df..e804b93be9e 100644
--- a/libs/langchain/langchain/text_splitter.py
+++ b/libs/langchain/langchain/text_splitter.py
@@ -592,7 +592,7 @@ class SentenceTransformersTokenTextSplitter(TextSplitter):
     def count_tokens(self, *, text: str) -> int:
         return len(self._encode(text))
 
-    _max_length_equal_32_bit_integer = 2**32
+    _max_length_equal_32_bit_integer: int = 2**32
 
     def _encode(self, text: str) -> List[int]:
         token_ids_with_start_and_end_token_ids = self.tokenizer.encode(
diff --git a/libs/langchain/langchain/tools/amadeus/base.py b/libs/langchain/langchain/tools/amadeus/base.py
index fc0c907e9c9..c2db135135a 100644
--- a/libs/langchain/langchain/tools/amadeus/base.py
+++ b/libs/langchain/langchain/tools/amadeus/base.py
@@ -3,8 +3,7 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from pydantic import Field
-
+from langchain.pydantic_v1 import Field
 from langchain.tools.amadeus.utils import authenticate
 from langchain.tools.base import BaseTool
 
diff --git a/libs/langchain/langchain/tools/amadeus/closest_airport.py b/libs/langchain/langchain/tools/amadeus/closest_airport.py
index a92066e4028..ff55239e04d 100644
--- a/libs/langchain/langchain/tools/amadeus/closest_airport.py
+++ b/libs/langchain/langchain/tools/amadeus/closest_airport.py
@@ -1,10 +1,9 @@
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
 from langchain.chains import LLMChain
 from langchain.chat_models import ChatOpenAI
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.amadeus.base import AmadeusBaseTool
 
 
diff --git a/libs/langchain/langchain/tools/amadeus/flight_search.py b/libs/langchain/langchain/tools/amadeus/flight_search.py
index 819cd1d296b..6a82e9bc2e7 100644
--- a/libs/langchain/langchain/tools/amadeus/flight_search.py
+++ b/libs/langchain/langchain/tools/amadeus/flight_search.py
@@ -2,9 +2,8 @@ import logging
 from datetime import datetime as dt
 from typing import Dict, Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.amadeus.base import AmadeusBaseTool
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/tools/arxiv/tool.py b/libs/langchain/langchain/tools/arxiv/tool.py
index 768e4eb2a25..c9b2f5caa9c 100644
--- a/libs/langchain/langchain/tools/arxiv/tool.py
+++ b/libs/langchain/langchain/tools/arxiv/tool.py
@@ -2,9 +2,8 @@
 
 from typing import Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
 from langchain.utilities.arxiv import ArxivAPIWrapper
 
@@ -12,8 +11,8 @@ from langchain.utilities.arxiv import ArxivAPIWrapper
 class ArxivQueryRun(BaseTool):
     """Tool that searches the Arxiv API."""
 
-    name = "arxiv"
-    description = (
+    name: str = "arxiv"
+    description: str = (
         "A wrapper around Arxiv.org "
         "Useful for when you need to answer questions about Physics, Mathematics, "
         "Computer Science, Quantitative Biology, Quantitative Finance, Statistics, "
diff --git a/libs/langchain/langchain/tools/azure_cognitive_services/form_recognizer.py b/libs/langchain/langchain/tools/azure_cognitive_services/form_recognizer.py
index 0dfb4d2b57f..b5dce896e39 100644
--- a/libs/langchain/langchain/tools/azure_cognitive_services/form_recognizer.py
+++ b/libs/langchain/langchain/tools/azure_cognitive_services/form_recognizer.py
@@ -3,9 +3,8 @@ from __future__ import annotations
 import logging
 from typing import Any, Dict, List, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import root_validator
 from langchain.tools.azure_cognitive_services.utils import detect_file_src_type
 from langchain.tools.base import BaseTool
 from langchain.utils import get_from_dict_or_env
@@ -24,8 +23,8 @@ class AzureCogsFormRecognizerTool(BaseTool):
     azure_cogs_endpoint: str = ""  #: :meta private:
     doc_analysis_client: Any  #: :meta private:
 
-    name = "azure_cognitive_services_form_recognizer"
-    description = (
+    name: str = "azure_cognitive_services_form_recognizer"
+    description: str = (
         "A wrapper around Azure Cognitive Services Form Recognizer. "
         "Useful for when you need to "
         "extract text, tables, and key-value pairs from documents. "
diff --git a/libs/langchain/langchain/tools/azure_cognitive_services/image_analysis.py b/libs/langchain/langchain/tools/azure_cognitive_services/image_analysis.py
index ecac290e393..b2032fcb994 100644
--- a/libs/langchain/langchain/tools/azure_cognitive_services/image_analysis.py
+++ b/libs/langchain/langchain/tools/azure_cognitive_services/image_analysis.py
@@ -3,9 +3,8 @@ from __future__ import annotations
 import logging
 from typing import Any, Dict, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import root_validator
 from langchain.tools.azure_cognitive_services.utils import detect_file_src_type
 from langchain.tools.base import BaseTool
 from langchain.utils import get_from_dict_or_env
@@ -25,8 +24,8 @@ class AzureCogsImageAnalysisTool(BaseTool):
     vision_service: Any  #: :meta private:
     analysis_options: Any  #: :meta private:
 
-    name = "azure_cognitive_services_image_analysis"
-    description = (
+    name: str = "azure_cognitive_services_image_analysis"
+    description: str = (
         "A wrapper around Azure Cognitive Services Image Analysis. "
         "Useful for when you need to analyze images. "
         "Input should be a url to an image."
diff --git a/libs/langchain/langchain/tools/azure_cognitive_services/speech2text.py b/libs/langchain/langchain/tools/azure_cognitive_services/speech2text.py
index 98b3e760558..0f7f82937e7 100644
--- a/libs/langchain/langchain/tools/azure_cognitive_services/speech2text.py
+++ b/libs/langchain/langchain/tools/azure_cognitive_services/speech2text.py
@@ -4,9 +4,8 @@ import logging
 import time
 from typing import Any, Dict, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import root_validator
 from langchain.tools.azure_cognitive_services.utils import (
     detect_file_src_type,
     download_audio_from_url,
@@ -29,8 +28,8 @@ class AzureCogsSpeech2TextTool(BaseTool):
     speech_language: str = "en-US"  #: :meta private:
     speech_config: Any  #: :meta private:
 
-    name = "azure_cognitive_services_speech2text"
-    description = (
+    name: str = "azure_cognitive_services_speech2text"
+    description: str = (
         "A wrapper around Azure Cognitive Services Speech2Text. "
         "Useful for when you need to transcribe audio to text. "
         "Input should be a url to an audio file."
diff --git a/libs/langchain/langchain/tools/azure_cognitive_services/text2speech.py b/libs/langchain/langchain/tools/azure_cognitive_services/text2speech.py
index cd51a78c12c..fee35591f38 100644
--- a/libs/langchain/langchain/tools/azure_cognitive_services/text2speech.py
+++ b/libs/langchain/langchain/tools/azure_cognitive_services/text2speech.py
@@ -4,9 +4,8 @@ import logging
 import tempfile
 from typing import Any, Dict, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import root_validator
 from langchain.tools.base import BaseTool
 from langchain.utils import get_from_dict_or_env
 
@@ -25,8 +24,8 @@ class AzureCogsText2SpeechTool(BaseTool):
     speech_language: str = "en-US"  #: :meta private:
     speech_config: Any  #: :meta private:
 
-    name = "azure_cognitive_services_text2speech"
-    description = (
+    name: str = "azure_cognitive_services_text2speech"
+    description: str = (
         "A wrapper around Azure Cognitive Services Text2Speech. "
         "Useful for when you need to convert text to speech. "
     )
diff --git a/libs/langchain/langchain/tools/base.py b/libs/langchain/langchain/tools/base.py
index f8607d51443..7c24c01b3ae 100644
--- a/libs/langchain/langchain/tools/base.py
+++ b/libs/langchain/langchain/tools/base.py
@@ -8,16 +8,6 @@ from functools import partial
 from inspect import signature
 from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, Type, Union
 
-from pydantic import (
-    BaseModel,
-    Extra,
-    Field,
-    create_model,
-    root_validator,
-    validate_arguments,
-)
-from pydantic.main import ModelMetaclass
-
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import (
     AsyncCallbackManager,
@@ -26,6 +16,14 @@ from langchain.callbacks.manager import (
     CallbackManagerForToolRun,
     Callbacks,
 )
+from langchain.pydantic_v1 import (
+    BaseModel,
+    Extra,
+    Field,
+    create_model,
+    root_validator,
+    validate_arguments,
+)
 from langchain.schema.runnable import Runnable, RunnableConfig
 
 
@@ -33,40 +31,6 @@ class SchemaAnnotationError(TypeError):
     """Raised when 'args_schema' is missing or has an incorrect type annotation."""
 
 
-class ToolMetaclass(ModelMetaclass):
-    """Metaclass for BaseTool to ensure the provided args_schema
-
-    doesn't silently ignore."""
-
-    def __new__(
-        cls: Type[ToolMetaclass], name: str, bases: Tuple[Type, ...], dct: dict
-    ) -> ToolMetaclass:
-        """Create the definition of the new tool class."""
-        schema_type: Optional[Type[BaseModel]] = dct.get("args_schema")
-        if schema_type is not None:
-            schema_annotations = dct.get("__annotations__", {})
-            args_schema_type = schema_annotations.get("args_schema", None)
-            if args_schema_type is None or args_schema_type == BaseModel:
-                # Throw errors for common mis-annotations.
-                # TODO: Use get_args / get_origin and fully
-                # specify valid annotations.
-                typehint_mandate = """
-class ChildTool(BaseTool):
-    ...
-    args_schema: Type[BaseModel] = SchemaClass
-    ..."""
-                raise SchemaAnnotationError(
-                    f"Tool definition for {name} must include valid type annotations"
-                    f" for argument 'args_schema' to behave as expected.\n"
-                    f"Expected annotation of 'Type[BaseModel]'"
-                    f" but got '{args_schema_type}'.\n"
-                    f"Expected class looks like:\n"
-                    f"{typehint_mandate}"
-                )
-        # Pass through to Pydantic's metaclass
-        return super().__new__(cls, name, bases, dct)
-
-
 def _create_subset_model(
     name: str, model: BaseModel, field_names: list
 ) -> Type[BaseModel]:
@@ -91,8 +55,8 @@ def _get_filtered_args(
 class _SchemaConfig:
     """Configuration for the pydantic model."""
 
-    extra = Extra.forbid
-    arbitrary_types_allowed = True
+    extra: Any = Extra.forbid
+    arbitrary_types_allowed: bool = True
 
 
 def create_schema_from_function(
@@ -132,9 +96,35 @@ class ToolException(Exception):
     pass
 
 
-class BaseTool(BaseModel, Runnable[Union[str, Dict], Any], metaclass=ToolMetaclass):
+class BaseTool(BaseModel, Runnable[Union[str, Dict], Any]):
     """Interface LangChain tools must implement."""
 
+    def __init_subclass__(cls, **kwargs: Any) -> None:
+        """Create the definition of the new tool class."""
+        super().__init_subclass__(**kwargs)
+
+        args_schema_type = cls.__annotations__.get("args_schema", None)
+
+        if args_schema_type is not None:
+            if args_schema_type is None or args_schema_type == BaseModel:
+                # Throw errors for common mis-annotations.
+                # TODO: Use get_args / get_origin and fully
+                # specify valid annotations.
+                typehint_mandate = """
+class ChildTool(BaseTool):
+    ...
+    args_schema: Type[BaseModel] = SchemaClass
+    ..."""
+                name = cls.__name__
+                raise SchemaAnnotationError(
+                    f"Tool definition for {name} must include valid type annotations"
+                    f" for argument 'args_schema' to behave as expected.\n"
+                    f"Expected annotation of 'Type[BaseModel]'"
+                    f" but got '{args_schema_type}'.\n"
+                    f"Expected class looks like:\n"
+                    f"{typehint_mandate}"
+                )
+
     name: str
     """The unique name of the tool that clearly communicates its purpose."""
     description: str
@@ -663,7 +653,9 @@ class StructuredTool(BaseTool):
             The tool
 
         Examples:
-            ... code-block:: python
+
+            .. code-block:: python
+
                 def add(a: int, b: int) -> int:
                     \"\"\"Add two numbers\"\"\"
                     return a + b
diff --git a/libs/langchain/langchain/tools/bing_search/tool.py b/libs/langchain/langchain/tools/bing_search/tool.py
index a8556ae30bf..4d73b5a0d93 100644
--- a/libs/langchain/langchain/tools/bing_search/tool.py
+++ b/libs/langchain/langchain/tools/bing_search/tool.py
@@ -10,8 +10,8 @@ from langchain.utilities.bing_search import BingSearchAPIWrapper
 class BingSearchRun(BaseTool):
     """Tool that queries the Bing search API."""
 
-    name = "bing_search"
-    description = (
+    name: str = "bing_search"
+    description: str = (
         "A wrapper around Bing Search. "
         "Useful for when you need to answer questions about current events. "
         "Input should be a search query."
@@ -30,8 +30,8 @@ class BingSearchRun(BaseTool):
 class BingSearchResults(BaseTool):
     """Tool that queries the Bing Search API and gets back json."""
 
-    name = "Bing Search Results JSON"
-    description = (
+    name: str = "Bing Search Results JSON"
+    description: str = (
         "A wrapper around Bing Search. "
         "Useful for when you need to answer questions about current events. "
         "Input should be a search query. Output is a JSON array of the query results"
diff --git a/libs/langchain/langchain/tools/brave_search/tool.py b/libs/langchain/langchain/tools/brave_search/tool.py
index 8a0f887fbfe..15ec34f9420 100644
--- a/libs/langchain/langchain/tools/brave_search/tool.py
+++ b/libs/langchain/langchain/tools/brave_search/tool.py
@@ -10,8 +10,8 @@ from langchain.utilities.brave_search import BraveSearchWrapper
 class BraveSearch(BaseTool):
     """Tool that queries the BraveSearch."""
 
-    name = "brave_search"
-    description = (
+    name: str = "brave_search"
+    description: str = (
         "a search engine. "
         "useful for when you need to answer questions about current events."
         " input should be a search query."
diff --git a/libs/langchain/langchain/tools/dataforseo_api_search/tool.py b/libs/langchain/langchain/tools/dataforseo_api_search/tool.py
index c22c7254ea5..6b2cf5a3e92 100644
--- a/libs/langchain/langchain/tools/dataforseo_api_search/tool.py
+++ b/libs/langchain/langchain/tools/dataforseo_api_search/tool.py
@@ -2,12 +2,11 @@
 
 from typing import Optional
 
-from pydantic.fields import Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
 from langchain.utilities.dataforseo_api_search import DataForSeoAPIWrapper
 
@@ -15,8 +14,8 @@ from langchain.utilities.dataforseo_api_search import DataForSeoAPIWrapper
 class DataForSeoAPISearchRun(BaseTool):
     """Tool that queries the DataForSeo Google search API."""
 
-    name = "dataforseo_api_search"
-    description = (
+    name: str = "dataforseo_api_search"
+    description: str = (
         "A robust Google Search API provided by DataForSeo."
         "This tool is handy when you need information about trending topics "
         "or current events."
@@ -44,8 +43,8 @@ class DataForSeoAPISearchResults(BaseTool):
     """Tool that queries the DataForSeo Google Search API
     and get back json."""
 
-    name = "DataForSeo Results JSON"
-    description = (
+    name: str = "DataForSeo Results JSON"
+    description: str = (
         "A comprehensive Google Search API provided by DataForSeo."
         "This tool is useful for obtaining real-time data on current events "
         "or popular searches."
diff --git a/libs/langchain/langchain/tools/ddg_search/tool.py b/libs/langchain/langchain/tools/ddg_search/tool.py
index 53aef10aee3..d148bf468c3 100644
--- a/libs/langchain/langchain/tools/ddg_search/tool.py
+++ b/libs/langchain/langchain/tools/ddg_search/tool.py
@@ -3,9 +3,8 @@
 import warnings
 from typing import Any, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
 from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
 
@@ -13,8 +12,8 @@ from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
 class DuckDuckGoSearchRun(BaseTool):
     """Tool that queries the DuckDuckGo search API."""
 
-    name = "duckduckgo_search"
-    description = (
+    name: str = "duckduckgo_search"
+    description: str = (
         "A wrapper around DuckDuckGo Search. "
         "Useful for when you need to answer questions about current events. "
         "Input should be a search query."
@@ -35,8 +34,8 @@ class DuckDuckGoSearchRun(BaseTool):
 class DuckDuckGoSearchResults(BaseTool):
     """Tool that queries the DuckDuckGo search API and gets back json."""
 
-    name = "DuckDuckGo Results JSON"
-    description = (
+    name: str = "DuckDuckGo Results JSON"
+    description: str = (
         "A wrapper around Duck Duck Go Search. "
         "Useful for when you need to answer questions about current events. "
         "Input should be a search query. Output is a JSON array of the query results"
diff --git a/libs/langchain/langchain/tools/file_management/copy.py b/libs/langchain/langchain/tools/file_management/copy.py
index 525e03a4fe8..48fabe3be50 100644
--- a/libs/langchain/langchain/tools/file_management/copy.py
+++ b/libs/langchain/langchain/tools/file_management/copy.py
@@ -1,9 +1,8 @@
 import shutil
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 from langchain.tools.file_management.utils import (
     INVALID_PATH_TEMPLATE,
diff --git a/libs/langchain/langchain/tools/file_management/delete.py b/libs/langchain/langchain/tools/file_management/delete.py
index f1a097641cf..07be6e9e10f 100644
--- a/libs/langchain/langchain/tools/file_management/delete.py
+++ b/libs/langchain/langchain/tools/file_management/delete.py
@@ -1,9 +1,8 @@
 import os
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 from langchain.tools.file_management.utils import (
     INVALID_PATH_TEMPLATE,
diff --git a/libs/langchain/langchain/tools/file_management/file_search.py b/libs/langchain/langchain/tools/file_management/file_search.py
index c197f03a492..aabdb389534 100644
--- a/libs/langchain/langchain/tools/file_management/file_search.py
+++ b/libs/langchain/langchain/tools/file_management/file_search.py
@@ -2,9 +2,8 @@ import fnmatch
 import os
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 from langchain.tools.file_management.utils import (
     INVALID_PATH_TEMPLATE,
diff --git a/libs/langchain/langchain/tools/file_management/list_dir.py b/libs/langchain/langchain/tools/file_management/list_dir.py
index 621a11947dc..5399be28dd0 100644
--- a/libs/langchain/langchain/tools/file_management/list_dir.py
+++ b/libs/langchain/langchain/tools/file_management/list_dir.py
@@ -1,9 +1,8 @@
 import os
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 from langchain.tools.file_management.utils import (
     INVALID_PATH_TEMPLATE,
diff --git a/libs/langchain/langchain/tools/file_management/move.py b/libs/langchain/langchain/tools/file_management/move.py
index 6d9c441daa2..4d348821a86 100644
--- a/libs/langchain/langchain/tools/file_management/move.py
+++ b/libs/langchain/langchain/tools/file_management/move.py
@@ -1,9 +1,8 @@
 import shutil
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 from langchain.tools.file_management.utils import (
     INVALID_PATH_TEMPLATE,
diff --git a/libs/langchain/langchain/tools/file_management/read.py b/libs/langchain/langchain/tools/file_management/read.py
index 11a7a923ee1..1d1a6edf4d8 100644
--- a/libs/langchain/langchain/tools/file_management/read.py
+++ b/libs/langchain/langchain/tools/file_management/read.py
@@ -1,8 +1,7 @@
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 from langchain.tools.file_management.utils import (
     INVALID_PATH_TEMPLATE,
diff --git a/libs/langchain/langchain/tools/file_management/utils.py b/libs/langchain/langchain/tools/file_management/utils.py
index 788823fecd7..21b0807e51a 100644
--- a/libs/langchain/langchain/tools/file_management/utils.py
+++ b/libs/langchain/langchain/tools/file_management/utils.py
@@ -2,7 +2,7 @@ import sys
 from pathlib import Path
 from typing import Optional
 
-from pydantic import BaseModel
+from langchain.pydantic_v1 import BaseModel
 
 
 def is_relative_to(path: Path, root: Path) -> bool:
diff --git a/libs/langchain/langchain/tools/file_management/write.py b/libs/langchain/langchain/tools/file_management/write.py
index 478fca2cc11..09bac8a3547 100644
--- a/libs/langchain/langchain/tools/file_management/write.py
+++ b/libs/langchain/langchain/tools/file_management/write.py
@@ -1,8 +1,7 @@
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 from langchain.tools.file_management.utils import (
     INVALID_PATH_TEMPLATE,
diff --git a/libs/langchain/langchain/tools/github/tool.py b/libs/langchain/langchain/tools/github/tool.py
index 524b2a4f88c..ec67fd2b335 100644
--- a/libs/langchain/langchain/tools/github/tool.py
+++ b/libs/langchain/langchain/tools/github/tool.py
@@ -9,9 +9,8 @@ To use this tool, you must first set as environment variables:
 """
 from typing import Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
 from langchain.utilities.github import GitHubAPIWrapper
 
@@ -21,8 +20,8 @@ class GitHubAction(BaseTool):
 
     api_wrapper: GitHubAPIWrapper = Field(default_factory=GitHubAPIWrapper)
     mode: str
-    name = ""
-    description = ""
+    name: str = ""
+    description: str = ""
 
     def _run(
         self,
diff --git a/libs/langchain/langchain/tools/gmail/base.py b/libs/langchain/langchain/tools/gmail/base.py
index c306dce9120..8ce5e8e85b0 100644
--- a/libs/langchain/langchain/tools/gmail/base.py
+++ b/libs/langchain/langchain/tools/gmail/base.py
@@ -3,8 +3,7 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from pydantic import Field
-
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
 from langchain.tools.gmail.utils import build_resource_service
 
diff --git a/libs/langchain/langchain/tools/gmail/create_draft.py b/libs/langchain/langchain/tools/gmail/create_draft.py
index 2c944270b6f..10d0de57843 100644
--- a/libs/langchain/langchain/tools/gmail/create_draft.py
+++ b/libs/langchain/langchain/tools/gmail/create_draft.py
@@ -2,9 +2,8 @@ import base64
 from email.message import EmailMessage
 from typing import List, Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.gmail.base import GmailBaseTool
 
 
diff --git a/libs/langchain/langchain/tools/gmail/get_message.py b/libs/langchain/langchain/tools/gmail/get_message.py
index 262dbd063b5..e1cca7b13eb 100644
--- a/libs/langchain/langchain/tools/gmail/get_message.py
+++ b/libs/langchain/langchain/tools/gmail/get_message.py
@@ -2,9 +2,8 @@ import base64
 import email
 from typing import Dict, Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.gmail.base import GmailBaseTool
 from langchain.tools.gmail.utils import clean_email_body
 
diff --git a/libs/langchain/langchain/tools/gmail/get_thread.py b/libs/langchain/langchain/tools/gmail/get_thread.py
index 0db1f8cd58b..61754a43a81 100644
--- a/libs/langchain/langchain/tools/gmail/get_thread.py
+++ b/libs/langchain/langchain/tools/gmail/get_thread.py
@@ -1,8 +1,7 @@
 from typing import Dict, Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.gmail.base import GmailBaseTool
 
 
diff --git a/libs/langchain/langchain/tools/gmail/search.py b/libs/langchain/langchain/tools/gmail/search.py
index 16041a4aaf8..045834abc15 100644
--- a/libs/langchain/langchain/tools/gmail/search.py
+++ b/libs/langchain/langchain/tools/gmail/search.py
@@ -3,9 +3,8 @@ import email
 from enum import Enum
 from typing import Any, Dict, List, Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.gmail.base import GmailBaseTool
 from langchain.tools.gmail.utils import clean_email_body
 
diff --git a/libs/langchain/langchain/tools/gmail/send_message.py b/libs/langchain/langchain/tools/gmail/send_message.py
index 76576149fa6..7121a83d3c8 100644
--- a/libs/langchain/langchain/tools/gmail/send_message.py
+++ b/libs/langchain/langchain/tools/gmail/send_message.py
@@ -4,9 +4,8 @@ from email.mime.multipart import MIMEMultipart
 from email.mime.text import MIMEText
 from typing import Any, Dict, List, Optional, Union
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.gmail.base import GmailBaseTool
 
 
diff --git a/libs/langchain/langchain/tools/golden_query/tool.py b/libs/langchain/langchain/tools/golden_query/tool.py
index 9b12c1af12f..c84bec91886 100644
--- a/libs/langchain/langchain/tools/golden_query/tool.py
+++ b/libs/langchain/langchain/tools/golden_query/tool.py
@@ -10,8 +10,8 @@ from langchain.utilities.golden_query import GoldenQueryAPIWrapper
 class GoldenQueryRun(BaseTool):
     """Tool that adds the capability to query using the Golden API and get back JSON."""
 
-    name = "Golden Query"
-    description = (
+    name: str = "Golden Query"
+    description: str = (
         "A wrapper around Golden Query API."
         " Useful for getting entities that match"
         " a natural language query from Golden's Knowledge Base."
diff --git a/libs/langchain/langchain/tools/google_places/tool.py b/libs/langchain/langchain/tools/google_places/tool.py
index 644ba134c70..c34c5198762 100644
--- a/libs/langchain/langchain/tools/google_places/tool.py
+++ b/libs/langchain/langchain/tools/google_places/tool.py
@@ -2,9 +2,8 @@
 
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 from langchain.utilities.google_places_api import GooglePlacesAPIWrapper
 
@@ -18,8 +17,8 @@ class GooglePlacesSchema(BaseModel):
 class GooglePlacesTool(BaseTool):
     """Tool that queries the Google places API."""
 
-    name = "google_places"
-    description = (
+    name: str = "google_places"
+    description: str = (
         "A wrapper around Google Places. "
         "Useful for when you need to validate or "
         "discover addressed from ambiguous text. "
diff --git a/libs/langchain/langchain/tools/google_search/tool.py b/libs/langchain/langchain/tools/google_search/tool.py
index 038b496e8a8..3f9a268e3d0 100644
--- a/libs/langchain/langchain/tools/google_search/tool.py
+++ b/libs/langchain/langchain/tools/google_search/tool.py
@@ -10,8 +10,8 @@ from langchain.utilities.google_search import GoogleSearchAPIWrapper
 class GoogleSearchRun(BaseTool):
     """Tool that queries the Google search API."""
 
-    name = "google_search"
-    description = (
+    name: str = "google_search"
+    description: str = (
         "A wrapper around Google Search. "
         "Useful for when you need to answer questions about current events. "
         "Input should be a search query."
@@ -30,8 +30,8 @@ class GoogleSearchRun(BaseTool):
 class GoogleSearchResults(BaseTool):
     """Tool that queries the Google Search API and gets back json."""
 
-    name = "Google Search Results JSON"
-    description = (
+    name: str = "Google Search Results JSON"
+    description: str = (
         "A wrapper around Google Search. "
         "Useful for when you need to answer questions about current events. "
         "Input should be a search query. Output is a JSON array of the query results"
diff --git a/libs/langchain/langchain/tools/google_serper/tool.py b/libs/langchain/langchain/tools/google_serper/tool.py
index f139263edd9..f2489e294f2 100644
--- a/libs/langchain/langchain/tools/google_serper/tool.py
+++ b/libs/langchain/langchain/tools/google_serper/tool.py
@@ -2,12 +2,11 @@
 
 from typing import Optional
 
-from pydantic.fields import Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
 from langchain.utilities.google_serper import GoogleSerperAPIWrapper
 
@@ -15,8 +14,8 @@ from langchain.utilities.google_serper import GoogleSerperAPIWrapper
 class GoogleSerperRun(BaseTool):
     """Tool that queries the Serper.dev Google search API."""
 
-    name = "google_serper"
-    description = (
+    name: str = "google_serper"
+    description: str = (
         "A low-cost Google Search API."
         "Useful for when you need to answer questions about current events."
         "Input should be a search query."
@@ -44,8 +43,8 @@ class GoogleSerperResults(BaseTool):
     """Tool that queries the Serper.dev Google Search API
     and get back json."""
 
-    name = "google_serrper_results_json"
-    description = (
+    name: str = "google_serrper_results_json"
+    description: str = (
         "A low-cost Google Search API."
         "Useful for when you need to answer questions about current events."
         "Input should be a search query. Output is a JSON object of the query results"
diff --git a/libs/langchain/langchain/tools/graphql/tool.py b/libs/langchain/langchain/tools/graphql/tool.py
index b73ed3178fe..bad3bcd8225 100644
--- a/libs/langchain/langchain/tools/graphql/tool.py
+++ b/libs/langchain/langchain/tools/graphql/tool.py
@@ -11,8 +11,8 @@ class BaseGraphQLTool(BaseTool):
 
     graphql_wrapper: GraphQLAPIWrapper
 
-    name = "query_graphql"
-    description = """\
+    name: str = "query_graphql"
+    description: str = """\
     Input to this tool is a detailed and correct GraphQL query, output is a result from the API.
     If the query is not correct, an error message will be returned.
     If an error is returned with 'Bad request' in it, rewrite the query and try again.
diff --git a/libs/langchain/langchain/tools/human/tool.py b/libs/langchain/langchain/tools/human/tool.py
index 4d8f5fd9019..30591c004de 100644
--- a/libs/langchain/langchain/tools/human/tool.py
+++ b/libs/langchain/langchain/tools/human/tool.py
@@ -2,9 +2,8 @@
 
 from typing import Callable, Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
 
 
@@ -16,8 +15,8 @@ def _print_func(text: str) -> None:
 class HumanInputRun(BaseTool):
     """Tool that asks user for input."""
 
-    name = "human"
-    description = (
+    name: str = "human"
+    description: str = (
         "You can ask a human for guidance when you think you "
         "got stuck or you are not sure what to do next. "
         "The input should be a question for the human."
diff --git a/libs/langchain/langchain/tools/jira/tool.py b/libs/langchain/langchain/tools/jira/tool.py
index 26a9c0d914f..533b701b8c3 100644
--- a/libs/langchain/langchain/tools/jira/tool.py
+++ b/libs/langchain/langchain/tools/jira/tool.py
@@ -30,9 +30,8 @@ agent = initialize_agent(
 """
 from typing import Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
 from langchain.utilities.jira import JiraAPIWrapper
 
@@ -42,8 +41,8 @@ class JiraAction(BaseTool):
 
     api_wrapper: JiraAPIWrapper = Field(default_factory=JiraAPIWrapper)
     mode: str
-    name = ""
-    description = ""
+    name: str = ""
+    description: str = ""
 
     def _run(
         self,
diff --git a/libs/langchain/langchain/tools/json/tool.py b/libs/langchain/langchain/tools/json/tool.py
index 6f6473d51e6..9f82ce91ff4 100644
--- a/libs/langchain/langchain/tools/json/tool.py
+++ b/libs/langchain/langchain/tools/json/tool.py
@@ -7,7 +7,7 @@ import re
 from pathlib import Path
 from typing import Dict, List, Optional, Union
 
-from pydantic import BaseModel
+from langchain.pydantic_v1 import BaseModel
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
@@ -20,7 +20,7 @@ def _parse_input(text: str) -> List[Union[str, int]]:
     """Parse input of the form data["key1"][0]["key2"] into a list of keys."""
     _res = re.findall(r"\[.*?]", text)
     # strip the brackets and quotes, convert to int if possible
-    res = [i[1:-1].replace('"', "") for i in _res]
+    res = [i[1:-1].replace('"', "").replace("'", "") for i in _res]
     res = [int(i) if i.isdigit() else i for i in res]
     return res
 
@@ -84,8 +84,8 @@ class JsonSpec(BaseModel):
 class JsonListKeysTool(BaseTool):
     """Tool for listing keys in a JSON spec."""
 
-    name = "json_spec_list_keys"
-    description = """
+    name: str = "json_spec_list_keys"
+    description: str = """
     Can be used to list all keys at a given path. 
     Before calling this you should be SURE that the path to this exists.
     The input is a text representation of the path to the dict in Python syntax (e.g. data["key1"][0]["key2"]).
@@ -110,8 +110,8 @@ class JsonListKeysTool(BaseTool):
 class JsonGetValueTool(BaseTool):
     """Tool for getting a value in a JSON spec."""
 
-    name = "json_spec_get_value"
-    description = """
+    name: str = "json_spec_get_value"
+    description: str = """
     Can be used to see value in string format at a given path.
     Before calling this you should be SURE that the path to this exists.
     The input is a text representation of the path to the dict in Python syntax (e.g. data["key1"][0]["key2"]).
diff --git a/libs/langchain/langchain/tools/metaphor_search/tool.py b/libs/langchain/langchain/tools/metaphor_search/tool.py
index 64cb3cd2358..e957aeb43c3 100644
--- a/libs/langchain/langchain/tools/metaphor_search/tool.py
+++ b/libs/langchain/langchain/tools/metaphor_search/tool.py
@@ -13,8 +13,8 @@ from langchain.utilities.metaphor_search import MetaphorSearchAPIWrapper
 class MetaphorSearchResults(BaseTool):
     """Tool that queries the Metaphor Search API and gets back json."""
 
-    name = "metaphor_search_results_json"
-    description = (
+    name: str = "metaphor_search_results_json"
+    description: str = (
         "A wrapper around Metaphor Search. "
         "Input should be a Metaphor-optimized query. "
         "Output is a JSON array of the query results"
diff --git a/libs/langchain/langchain/tools/multion/create_session.py b/libs/langchain/langchain/tools/multion/create_session.py
index b0551e49cab..58a1951b592 100644
--- a/libs/langchain/langchain/tools/multion/create_session.py
+++ b/libs/langchain/langchain/tools/multion/create_session.py
@@ -1,8 +1,7 @@
 from typing import TYPE_CHECKING, Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 
 if TYPE_CHECKING:
@@ -40,9 +39,14 @@ class MultionCreateSession(BaseTool):
     """
 
     name: str = "create_multion_session"
-    description: str = """Use this tool to create a new Multion Browser Window \
-        with provided fields.Always the first step to run \
-            any activities that can be done using browser."""
+    description: str = """
+        Create a new web browsing session based on a user's command or request. \
+        The command should include the full info required for the session. \
+        Also include an url (defaults to google.com if no better option) \
+        to start the session. \
+        Use this tool to create a new Browser Window with provided fields. \
+        Always the first step to run any activities that can be done using browser.
+        """
     args_schema: Type[CreateSessionSchema] = CreateSessionSchema
 
     def _run(
diff --git a/libs/langchain/langchain/tools/multion/update_session.py b/libs/langchain/langchain/tools/multion/update_session.py
index 1f20d70d65d..12e148d19a0 100644
--- a/libs/langchain/langchain/tools/multion/update_session.py
+++ b/libs/langchain/langchain/tools/multion/update_session.py
@@ -1,8 +1,7 @@
 from typing import TYPE_CHECKING, Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 
 if TYPE_CHECKING:
@@ -44,10 +43,10 @@ class MultionUpdateSession(BaseTool):
 
     name: str = "update_multion_session"
     description: str = """Use this tool to update \
-        a existing corresponding \
-    Multion Browser Window with provided fields. \
-        Note:TabId is got from one of the previous Browser window creation."""
+an existing corresponding Multion Browser Window with provided fields. \
+Note: TabId must be received from previous Browser window creation."""
     args_schema: Type[UpdateSessionSchema] = UpdateSessionSchema
+    tabId: str = ""
 
     def _run(
         self,
@@ -63,9 +62,10 @@ class MultionUpdateSession(BaseTool):
                 self.tabId = tabId
                 return content
             except Exception as e:
-                print(f"{e}, creating a new session")
-                response = multion.new_session({"input": query, "url": url})
-                self.tabID = response["tabId"]
-                return {"tabId": response["tabId"], "Response": response["message"]}
+                print(f"{e}, retrying...")
+                return {"error": f"{e}", "Response": "retrying..."}
+                # response = multion.new_session({"input": query, "url": url})
+                # self.tabID = response["tabId"]
+                # return {"tabId": response["tabId"], "Response": response["message"]}
         except Exception as e:
             raise Exception(f"An error occurred: {e}")
diff --git a/libs/langchain/langchain/tools/nuclia/tool.py b/libs/langchain/langchain/tools/nuclia/tool.py
index 054a439dbb0..e4aba0fa72c 100644
--- a/libs/langchain/langchain/tools/nuclia/tool.py
+++ b/libs/langchain/langchain/tools/nuclia/tool.py
@@ -16,12 +16,12 @@ import os
 from typing import Any, Dict, Optional, Type, Union
 
 import requests
-from pydantic import BaseModel, Field
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 
 logger = logging.getLogger(__name__)
@@ -58,8 +58,8 @@ class NUASchema(BaseModel):
 class NucliaUnderstandingAPI(BaseTool):
     """Tool to process files with the Nuclia Understanding API."""
 
-    name = "nuclia_understanding_api"
-    description = (
+    name: str = "nuclia_understanding_api"
+    description: str = (
         "A wrapper around Nuclia Understanding API endpoints. "
         "Useful for when you need to extract text from any kind of files. "
     )
diff --git a/libs/langchain/langchain/tools/office365/base.py b/libs/langchain/langchain/tools/office365/base.py
index f7f6659e12d..35407c663d1 100644
--- a/libs/langchain/langchain/tools/office365/base.py
+++ b/libs/langchain/langchain/tools/office365/base.py
@@ -3,8 +3,7 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from pydantic import Field
-
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
 from langchain.tools.office365.utils import authenticate
 
diff --git a/libs/langchain/langchain/tools/office365/create_draft_message.py b/libs/langchain/langchain/tools/office365/create_draft_message.py
index 68b161310b6..c3d69bf9547 100644
--- a/libs/langchain/langchain/tools/office365/create_draft_message.py
+++ b/libs/langchain/langchain/tools/office365/create_draft_message.py
@@ -1,8 +1,7 @@
 from typing import List, Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.office365.base import O365BaseTool
 
 
diff --git a/libs/langchain/langchain/tools/office365/events_search.py b/libs/langchain/langchain/tools/office365/events_search.py
index fba8fcb4f01..7e1b1bd1306 100644
--- a/libs/langchain/langchain/tools/office365/events_search.py
+++ b/libs/langchain/langchain/tools/office365/events_search.py
@@ -7,9 +7,8 @@ https://learn.microsoft.com/en-us/graph/auth/
 from datetime import datetime as dt
 from typing import Any, Dict, List, Optional, Type
 
-from pydantic import BaseModel, Extra, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Extra, Field
 from langchain.tools.office365.base import O365BaseTool
 from langchain.tools.office365.utils import clean_body
 
diff --git a/libs/langchain/langchain/tools/office365/messages_search.py b/libs/langchain/langchain/tools/office365/messages_search.py
index 435ba58d6ac..eca8ed33f6f 100644
--- a/libs/langchain/langchain/tools/office365/messages_search.py
+++ b/libs/langchain/langchain/tools/office365/messages_search.py
@@ -6,9 +6,8 @@ https://learn.microsoft.com/en-us/graph/auth/
 
 from typing import Any, Dict, List, Optional, Type
 
-from pydantic import BaseModel, Extra, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Extra, Field
 from langchain.tools.office365.base import O365BaseTool
 from langchain.tools.office365.utils import clean_body
 
diff --git a/libs/langchain/langchain/tools/office365/send_event.py b/libs/langchain/langchain/tools/office365/send_event.py
index f19992b3217..6151dde1439 100644
--- a/libs/langchain/langchain/tools/office365/send_event.py
+++ b/libs/langchain/langchain/tools/office365/send_event.py
@@ -7,9 +7,8 @@ https://learn.microsoft.com/en-us/graph/auth/
 from datetime import datetime as dt
 from typing import List, Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.office365.base import O365BaseTool
 
 
diff --git a/libs/langchain/langchain/tools/office365/send_message.py b/libs/langchain/langchain/tools/office365/send_message.py
index 9104d1685cf..3590ba406fa 100644
--- a/libs/langchain/langchain/tools/office365/send_message.py
+++ b/libs/langchain/langchain/tools/office365/send_message.py
@@ -1,8 +1,7 @@
 from typing import List, Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.office365.base import O365BaseTool
 
 
diff --git a/libs/langchain/langchain/tools/openapi/utils/api_models.py b/libs/langchain/langchain/tools/openapi/utils/api_models.py
index 42e8f13a358..11606ca8e24 100644
--- a/libs/langchain/langchain/tools/openapi/utils/api_models.py
+++ b/libs/langchain/langchain/tools/openapi/utils/api_models.py
@@ -1,11 +1,21 @@
 """Pydantic models for parsing an OpenAPI spec."""
+from __future__ import annotations
+
 import logging
 from enum import Enum
-from typing import Any, Dict, List, Optional, Sequence, Tuple, Type, Union
-
-from openapi_schema_pydantic import MediaType, Parameter, Reference, RequestBody, Schema
-from pydantic import BaseModel, Field
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+)
 
+from langchain.pydantic_v1 import _PYDANTIC_MAJOR_VERSION, BaseModel, Field
 from langchain.tools.openapi.utils.openapi_utils import HTTPVerb, OpenAPISpec
 
 logger = logging.getLogger(__name__)
@@ -85,499 +95,561 @@ class APIPropertyBase(BaseModel):
     """The description of the property."""
 
 
-class APIProperty(APIPropertyBase):
-    """A model for a property in the query, path, header, or cookie params."""
-
-    location: APIPropertyLocation = Field(alias="location")
-    """The path/how it's being passed to the endpoint."""
-
-    @staticmethod
-    def _cast_schema_list_type(schema: Schema) -> Optional[Union[str, Tuple[str, ...]]]:
-        type_ = schema.type
-        if not isinstance(type_, list):
-            return type_
-        else:
-            return tuple(type_)
-
-    @staticmethod
-    def _get_schema_type_for_enum(parameter: Parameter, schema: Schema) -> Enum:
-        """Get the schema type when the parameter is an enum."""
-        param_name = f"{parameter.name}Enum"
-        return Enum(param_name, {str(v): v for v in schema.enum})
-
-    @staticmethod
-    def _get_schema_type_for_array(
-        schema: Schema,
-    ) -> Optional[Union[str, Tuple[str, ...]]]:
-        items = schema.items
-        if isinstance(items, Schema):
-            schema_type = APIProperty._cast_schema_list_type(items)
-        elif isinstance(items, Reference):
-            ref_name = items.ref.split("/")[-1]
-            schema_type = ref_name  # TODO: Add ref definitions to make his valid
-        else:
-            raise ValueError(f"Unsupported array items: {items}")
-
-        if isinstance(schema_type, str):
-            # TODO: recurse
-            schema_type = (schema_type,)
-
-        return schema_type
-
-    @staticmethod
-    def _get_schema_type(parameter: Parameter, schema: Optional[Schema]) -> SCHEMA_TYPE:
-        if schema is None:
-            return None
-        schema_type: SCHEMA_TYPE = APIProperty._cast_schema_list_type(schema)
-        if schema_type == "array":
-            schema_type = APIProperty._get_schema_type_for_array(schema)
-        elif schema_type == "object":
-            # TODO: Resolve array and object types to components.
-            raise NotImplementedError("Objects not yet supported")
-        elif schema_type in PRIMITIVE_TYPES:
-            if schema.enum:
-                schema_type = APIProperty._get_schema_type_for_enum(parameter, schema)
-            else:
-                # Directly use the primitive type
-                pass
-        else:
-            raise NotImplementedError(f"Unsupported type: {schema_type}")
-
-        return schema_type
-
-    @staticmethod
-    def _validate_location(location: APIPropertyLocation, name: str) -> None:
-        if location not in SUPPORTED_LOCATIONS:
-            raise NotImplementedError(
-                INVALID_LOCATION_TEMPL.format(location=location, name=name)
-            )
-
-    @staticmethod
-    def _validate_content(content: Optional[Dict[str, MediaType]]) -> None:
-        if content:
-            raise ValueError(
-                "API Properties with media content not supported. "
-                "Media content only supported within APIRequestBodyProperty's"
-            )
-
-    @staticmethod
-    def _get_schema(parameter: Parameter, spec: OpenAPISpec) -> Optional[Schema]:
-        schema = parameter.param_schema
-        if isinstance(schema, Reference):
-            schema = spec.get_referenced_schema(schema)
-        elif schema is None:
-            return None
-        elif not isinstance(schema, Schema):
-            raise ValueError(f"Error dereferencing schema: {schema}")
-
-        return schema
-
-    @staticmethod
-    def is_supported_location(location: str) -> bool:
-        """Return whether the provided location is supported."""
-        try:
-            return APIPropertyLocation.from_str(location) in SUPPORTED_LOCATIONS
-        except ValueError:
-            return False
-
-    @classmethod
-    def from_parameter(cls, parameter: Parameter, spec: OpenAPISpec) -> "APIProperty":
-        """Instantiate from an OpenAPI Parameter."""
-        location = APIPropertyLocation.from_str(parameter.param_in)
-        cls._validate_location(
-            location,
-            parameter.name,
-        )
-        cls._validate_content(parameter.content)
-        schema = cls._get_schema(parameter, spec)
-        schema_type = cls._get_schema_type(parameter, schema)
-        default_val = schema.default if schema is not None else None
-        return cls(
-            name=parameter.name,
-            location=location,
-            default=default_val,
-            description=parameter.description,
-            required=parameter.required,
-            type=schema_type,
+if _PYDANTIC_MAJOR_VERSION == 1:
+    if TYPE_CHECKING:
+        from openapi_schema_pydantic import (
+            MediaType,
+            Parameter,
+            RequestBody,
+            Schema,
         )
 
+    class APIProperty(APIPropertyBase):
+        """A model for a property in the query, path, header, or cookie params."""
 
-class APIRequestBodyProperty(APIPropertyBase):
-    """A model for a request body property."""
+        location: APIPropertyLocation = Field(alias="location")
+        """The path/how it's being passed to the endpoint."""
 
-    properties: List["APIRequestBodyProperty"] = Field(alias="properties")
-    """The sub-properties of the property."""
-
-    # This is useful for handling nested property cycles.
-    # We can define separate types in that case.
-    references_used: List[str] = Field(alias="references_used")
-    """The references used by the property."""
-
-    @classmethod
-    def _process_object_schema(
-        cls, schema: Schema, spec: OpenAPISpec, references_used: List[str]
-    ) -> Tuple[Union[str, List[str], None], List["APIRequestBodyProperty"]]:
-        properties = []
-        required_props = schema.required or []
-        if schema.properties is None:
-            raise ValueError(
-                f"No properties found when processing object schema: {schema}"
-            )
-        for prop_name, prop_schema in schema.properties.items():
-            if isinstance(prop_schema, Reference):
-                ref_name = prop_schema.ref.split("/")[-1]
-                if ref_name not in references_used:
-                    references_used.append(ref_name)
-                    prop_schema = spec.get_referenced_schema(prop_schema)
-                else:
-                    continue
-
-            properties.append(
-                cls.from_schema(
-                    schema=prop_schema,
-                    name=prop_name,
-                    required=prop_name in required_props,
-                    spec=spec,
-                    references_used=references_used,
-                )
-            )
-        return schema.type, properties
-
-    @classmethod
-    def _process_array_schema(
-        cls, schema: Schema, name: str, spec: OpenAPISpec, references_used: List[str]
-    ) -> str:
-        items = schema.items
-        if items is not None:
-            if isinstance(items, Reference):
-                ref_name = items.ref.split("/")[-1]
-                if ref_name not in references_used:
-                    references_used.append(ref_name)
-                    items = spec.get_referenced_schema(items)
-                else:
-                    pass
-                return f"Array<{ref_name}>"
+        @staticmethod
+        def _cast_schema_list_type(
+            schema: Schema,
+        ) -> Optional[Union[str, Tuple[str, ...]]]:
+            type_ = schema.type
+            if not isinstance(type_, list):
+                return type_
             else:
-                pass
+                return tuple(type_)
 
+        @staticmethod
+        def _get_schema_type_for_enum(parameter: Parameter, schema: Schema) -> Enum:
+            """Get the schema type when the parameter is an enum."""
+            param_name = f"{parameter.name}Enum"
+            return Enum(param_name, {str(v): v for v in schema.enum})
+
+        @staticmethod
+        def _get_schema_type_for_array(
+            schema: Schema,
+        ) -> Optional[Union[str, Tuple[str, ...]]]:
+            from openapi_schema_pydantic import (
+                Reference,
+                Schema,
+            )
+
+            items = schema.items
             if isinstance(items, Schema):
-                array_type = cls.from_schema(
-                    schema=items,
-                    name=f"{name}Item",
-                    required=True,  # TODO: Add required
-                    spec=spec,
-                    references_used=references_used,
+                schema_type = APIProperty._cast_schema_list_type(items)
+            elif isinstance(items, Reference):
+                ref_name = items.ref.split("/")[-1]
+                schema_type = ref_name  # TODO: Add ref definitions to make his valid
+            else:
+                raise ValueError(f"Unsupported array items: {items}")
+
+            if isinstance(schema_type, str):
+                # TODO: recurse
+                schema_type = (schema_type,)
+
+            return schema_type
+
+        @staticmethod
+        def _get_schema_type(
+            parameter: Parameter, schema: Optional[Schema]
+        ) -> SCHEMA_TYPE:
+            if schema is None:
+                return None
+            schema_type: SCHEMA_TYPE = APIProperty._cast_schema_list_type(schema)
+            if schema_type == "array":
+                schema_type = APIProperty._get_schema_type_for_array(schema)
+            elif schema_type == "object":
+                # TODO: Resolve array and object types to components.
+                raise NotImplementedError("Objects not yet supported")
+            elif schema_type in PRIMITIVE_TYPES:
+                if schema.enum:
+                    schema_type = APIProperty._get_schema_type_for_enum(
+                        parameter, schema
+                    )
+                else:
+                    # Directly use the primitive type
+                    pass
+            else:
+                raise NotImplementedError(f"Unsupported type: {schema_type}")
+
+            return schema_type
+
+        @staticmethod
+        def _validate_location(location: APIPropertyLocation, name: str) -> None:
+            if location not in SUPPORTED_LOCATIONS:
+                raise NotImplementedError(
+                    INVALID_LOCATION_TEMPL.format(location=location, name=name)
                 )
-                return f"Array<{array_type.type}>"
 
-        return "array"
+        @staticmethod
+        def _validate_content(content: Optional[Dict[str, MediaType]]) -> None:
+            if content:
+                raise ValueError(
+                    "API Properties with media content not supported. "
+                    "Media content only supported within APIRequestBodyProperty's"
+                )
 
-    @classmethod
-    def from_schema(
-        cls,
-        schema: Schema,
-        name: str,
-        required: bool,
-        spec: OpenAPISpec,
-        references_used: Optional[List[str]] = None,
-    ) -> "APIRequestBodyProperty":
-        """Recursively populate from an OpenAPI Schema."""
-        if references_used is None:
-            references_used = []
-
-        schema_type = schema.type
-        properties: List[APIRequestBodyProperty] = []
-        if schema_type == "object" and schema.properties:
-            schema_type, properties = cls._process_object_schema(
-                schema, spec, references_used
+        @staticmethod
+        def _get_schema(parameter: Parameter, spec: OpenAPISpec) -> Optional[Schema]:
+            from openapi_schema_pydantic import (
+                Reference,
+                Schema,
             )
-        elif schema_type == "array":
-            schema_type = cls._process_array_schema(schema, name, spec, references_used)
-        elif schema_type in PRIMITIVE_TYPES:
-            # Use the primitive type directly
-            pass
-        elif schema_type is None:
-            # No typing specified/parsed. WIll map to 'any'
-            pass
-        else:
-            raise ValueError(f"Unsupported type: {schema_type}")
 
-        return cls(
-            name=name,
-            required=required,
-            type=schema_type,
-            default=schema.default,
-            description=schema.description,
-            properties=properties,
-            references_used=references_used,
-        )
+            schema = parameter.param_schema
+            if isinstance(schema, Reference):
+                schema = spec.get_referenced_schema(schema)
+            elif schema is None:
+                return None
+            elif not isinstance(schema, Schema):
+                raise ValueError(f"Error dereferencing schema: {schema}")
 
+            return schema
 
-class APIRequestBody(BaseModel):
-    """A model for a request body."""
+        @staticmethod
+        def is_supported_location(location: str) -> bool:
+            """Return whether the provided location is supported."""
+            try:
+                return APIPropertyLocation.from_str(location) in SUPPORTED_LOCATIONS
+            except ValueError:
+                return False
 
-    description: Optional[str] = Field(alias="description")
-    """The description of the request body."""
-
-    properties: List[APIRequestBodyProperty] = Field(alias="properties")
-
-    # E.g., application/json - we only support JSON at the moment.
-    media_type: str = Field(alias="media_type")
-    """The media type of the request body."""
-
-    @classmethod
-    def _process_supported_media_type(
-        cls,
-        media_type_obj: MediaType,
-        spec: OpenAPISpec,
-    ) -> List[APIRequestBodyProperty]:
-        """Process the media type of the request body."""
-        references_used = []
-        schema = media_type_obj.media_type_schema
-        if isinstance(schema, Reference):
-            references_used.append(schema.ref.split("/")[-1])
-            schema = spec.get_referenced_schema(schema)
-        if schema is None:
-            raise ValueError(
-                f"Could not resolve schema for media type: {media_type_obj}"
+        @classmethod
+        def from_parameter(
+            cls, parameter: Parameter, spec: OpenAPISpec
+        ) -> "APIProperty":
+            """Instantiate from an OpenAPI Parameter."""
+            location = APIPropertyLocation.from_str(parameter.param_in)
+            cls._validate_location(
+                location,
+                parameter.name,
             )
-        api_request_body_properties = []
-        required_properties = schema.required or []
-        if schema.type == "object" and schema.properties:
+            cls._validate_content(parameter.content)
+            schema = cls._get_schema(parameter, spec)
+            schema_type = cls._get_schema_type(parameter, schema)
+            default_val = schema.default if schema is not None else None
+            return cls(
+                name=parameter.name,
+                location=location,
+                default=default_val,
+                description=parameter.description,
+                required=parameter.required,
+                type=schema_type,
+            )
+
+    class APIRequestBodyProperty(APIPropertyBase):
+        """A model for a request body property."""
+
+        properties: List["APIRequestBodyProperty"] = Field(alias="properties")
+        """The sub-properties of the property."""
+
+        # This is useful for handling nested property cycles.
+        # We can define separate types in that case.
+        references_used: List[str] = Field(alias="references_used")
+        """The references used by the property."""
+
+        @classmethod
+        def _process_object_schema(
+            cls, schema: Schema, spec: OpenAPISpec, references_used: List[str]
+        ) -> Tuple[Union[str, List[str], None], List["APIRequestBodyProperty"]]:
+            from openapi_schema_pydantic import (
+                Reference,
+            )
+
+            properties = []
+            required_props = schema.required or []
+            if schema.properties is None:
+                raise ValueError(
+                    f"No properties found when processing object schema: {schema}"
+                )
             for prop_name, prop_schema in schema.properties.items():
                 if isinstance(prop_schema, Reference):
-                    prop_schema = spec.get_referenced_schema(prop_schema)
+                    ref_name = prop_schema.ref.split("/")[-1]
+                    if ref_name not in references_used:
+                        references_used.append(ref_name)
+                        prop_schema = spec.get_referenced_schema(prop_schema)
+                    else:
+                        continue
 
-                api_request_body_properties.append(
-                    APIRequestBodyProperty.from_schema(
+                properties.append(
+                    cls.from_schema(
                         schema=prop_schema,
                         name=prop_name,
-                        required=prop_name in required_properties,
+                        required=prop_name in required_props,
                         spec=spec,
+                        references_used=references_used,
                     )
                 )
-        else:
-            api_request_body_properties.append(
-                APIRequestBodyProperty(
-                    name="body",
-                    required=True,
-                    type=schema.type,
-                    default=schema.default,
-                    description=schema.description,
-                    properties=[],
-                    references_used=references_used,
-                )
-            )
+            return schema.type, properties
 
-        return api_request_body_properties
+        @classmethod
+        def _process_array_schema(
+            cls,
+            schema: Schema,
+            name: str,
+            spec: OpenAPISpec,
+            references_used: List[str],
+        ) -> str:
+            from openapi_schema_pydantic import Reference, Schema
 
-    @classmethod
-    def from_request_body(
-        cls, request_body: RequestBody, spec: OpenAPISpec
-    ) -> "APIRequestBody":
-        """Instantiate from an OpenAPI RequestBody."""
-        properties = []
-        for media_type, media_type_obj in request_body.content.items():
-            if media_type not in _SUPPORTED_MEDIA_TYPES:
-                continue
-            api_request_body_properties = cls._process_supported_media_type(
-                media_type_obj,
-                spec,
-            )
-            properties.extend(api_request_body_properties)
+            items = schema.items
+            if items is not None:
+                if isinstance(items, Reference):
+                    ref_name = items.ref.split("/")[-1]
+                    if ref_name not in references_used:
+                        references_used.append(ref_name)
+                        items = spec.get_referenced_schema(items)
+                    else:
+                        pass
+                    return f"Array<{ref_name}>"
+                else:
+                    pass
 
-        return cls(
-            description=request_body.description,
-            properties=properties,
-            media_type=media_type,
-        )
-
-
-class APIOperation(BaseModel):
-    """A model for a single API operation."""
-
-    operation_id: str = Field(alias="operation_id")
-    """The unique identifier of the operation."""
-
-    description: Optional[str] = Field(alias="description")
-    """The description of the operation."""
-
-    base_url: str = Field(alias="base_url")
-    """The base URL of the operation."""
-
-    path: str = Field(alias="path")
-    """The path of the operation."""
-
-    method: HTTPVerb = Field(alias="method")
-    """The HTTP method of the operation."""
-
-    properties: Sequence[APIProperty] = Field(alias="properties")
-
-    # TODO: Add parse in used components to be able to specify what type of
-    # referenced object it is.
-    # """The properties of the operation."""
-    # components: Dict[str, BaseModel] = Field(alias="components")
-
-    request_body: Optional[APIRequestBody] = Field(alias="request_body")
-    """The request body of the operation."""
-
-    @staticmethod
-    def _get_properties_from_parameters(
-        parameters: List[Parameter], spec: OpenAPISpec
-    ) -> List[APIProperty]:
-        """Get the properties of the operation."""
-        properties = []
-        for param in parameters:
-            if APIProperty.is_supported_location(param.param_in):
-                properties.append(APIProperty.from_parameter(param, spec))
-            elif param.required:
-                raise ValueError(
-                    INVALID_LOCATION_TEMPL.format(
-                        location=param.param_in, name=param.name
+                if isinstance(items, Schema):
+                    array_type = cls.from_schema(
+                        schema=items,
+                        name=f"{name}Item",
+                        required=True,  # TODO: Add required
+                        spec=spec,
+                        references_used=references_used,
                     )
+                    return f"Array<{array_type.type}>"
+
+            return "array"
+
+        @classmethod
+        def from_schema(
+            cls,
+            schema: Schema,
+            name: str,
+            required: bool,
+            spec: OpenAPISpec,
+            references_used: Optional[List[str]] = None,
+        ) -> "APIRequestBodyProperty":
+            """Recursively populate from an OpenAPI Schema."""
+            if references_used is None:
+                references_used = []
+
+            schema_type = schema.type
+            properties: List[APIRequestBodyProperty] = []
+            if schema_type == "object" and schema.properties:
+                schema_type, properties = cls._process_object_schema(
+                    schema, spec, references_used
                 )
-            else:
-                logger.warning(
-                    INVALID_LOCATION_TEMPL.format(
-                        location=param.param_in, name=param.name
-                    )
-                    + " Ignoring optional parameter"
+            elif schema_type == "array":
+                schema_type = cls._process_array_schema(
+                    schema, name, spec, references_used
                 )
+            elif schema_type in PRIMITIVE_TYPES:
+                # Use the primitive type directly
                 pass
-        return properties
+            elif schema_type is None:
+                # No typing specified/parsed. WIll map to 'any'
+                pass
+            else:
+                raise ValueError(f"Unsupported type: {schema_type}")
 
-    @classmethod
-    def from_openapi_url(
-        cls,
-        spec_url: str,
-        path: str,
-        method: str,
-    ) -> "APIOperation":
-        """Create an APIOperation from an OpenAPI URL."""
-        spec = OpenAPISpec.from_url(spec_url)
-        return cls.from_openapi_spec(spec, path, method)
+            return cls(
+                name=name,
+                required=required,
+                type=schema_type,
+                default=schema.default,
+                description=schema.description,
+                properties=properties,
+                references_used=references_used,
+            )
 
-    @classmethod
-    def from_openapi_spec(
-        cls,
-        spec: OpenAPISpec,
-        path: str,
-        method: str,
-    ) -> "APIOperation":
-        """Create an APIOperation from an OpenAPI spec."""
-        operation = spec.get_operation(path, method)
-        parameters = spec.get_parameters_for_operation(operation)
-        properties = cls._get_properties_from_parameters(parameters, spec)
-        operation_id = OpenAPISpec.get_cleaned_operation_id(operation, path, method)
-        request_body = spec.get_request_body_for_operation(operation)
-        api_request_body = (
-            APIRequestBody.from_request_body(request_body, spec)
-            if request_body is not None
-            else None
-        )
-        description = operation.description or operation.summary
-        if not description and spec.paths is not None:
-            description = spec.paths[path].description or spec.paths[path].summary
-        return cls(
-            operation_id=operation_id,
-            description=description or "",
-            base_url=spec.base_url,
-            path=path,
-            method=method,
-            properties=properties,
-            request_body=api_request_body,
-        )
+    # class APIRequestBodyProperty(APIPropertyBase):
+    class APIRequestBody(BaseModel):
+        """A model for a request body."""
 
-    @staticmethod
-    def ts_type_from_python(type_: SCHEMA_TYPE) -> str:
-        if type_ is None:
-            # TODO: Handle Nones better. These often result when
-            # parsing specs that are < v3
-            return "any"
-        elif isinstance(type_, str):
-            return {
-                "str": "string",
-                "integer": "number",
-                "float": "number",
-                "date-time": "string",
-            }.get(type_, type_)
-        elif isinstance(type_, tuple):
-            return f"Array<{APIOperation.ts_type_from_python(type_[0])}>"
-        elif isinstance(type_, type) and issubclass(type_, Enum):
-            return " | ".join([f"'{e.value}'" for e in type_])
-        else:
-            return str(type_)
+        description: Optional[str] = Field(alias="description")
+        """The description of the request body."""
 
-    def _format_nested_properties(
-        self, properties: List[APIRequestBodyProperty], indent: int = 2
-    ) -> str:
-        """Format nested properties."""
-        formatted_props = []
+        properties: List[APIRequestBodyProperty] = Field(alias="properties")
 
-        for prop in properties:
-            prop_name = prop.name
-            prop_type = self.ts_type_from_python(prop.type)
-            prop_required = "" if prop.required else "?"
-            prop_desc = f"/* {prop.description} */" if prop.description else ""
+        # E.g., application/json - we only support JSON at the moment.
+        media_type: str = Field(alias="media_type")
+        """The media type of the request body."""
 
-            if prop.properties:
-                nested_props = self._format_nested_properties(
-                    prop.properties, indent + 2
+        @classmethod
+        def _process_supported_media_type(
+            cls,
+            media_type_obj: MediaType,
+            spec: OpenAPISpec,
+        ) -> List[APIRequestBodyProperty]:
+            """Process the media type of the request body."""
+            from openapi_schema_pydantic import Reference
+
+            references_used = []
+            schema = media_type_obj.media_type_schema
+            if isinstance(schema, Reference):
+                references_used.append(schema.ref.split("/")[-1])
+                schema = spec.get_referenced_schema(schema)
+            if schema is None:
+                raise ValueError(
+                    f"Could not resolve schema for media type: {media_type_obj}"
                 )
-                prop_type = f"{{\n{nested_props}\n{' ' * indent}}}"
+            api_request_body_properties = []
+            required_properties = schema.required or []
+            if schema.type == "object" and schema.properties:
+                for prop_name, prop_schema in schema.properties.items():
+                    if isinstance(prop_schema, Reference):
+                        prop_schema = spec.get_referenced_schema(prop_schema)
 
-            formatted_props.append(
-                f"{prop_desc}\n{' ' * indent}{prop_name}{prop_required}: {prop_type},"
+                    api_request_body_properties.append(
+                        APIRequestBodyProperty.from_schema(
+                            schema=prop_schema,
+                            name=prop_name,
+                            required=prop_name in required_properties,
+                            spec=spec,
+                        )
+                    )
+            else:
+                api_request_body_properties.append(
+                    APIRequestBodyProperty(
+                        name="body",
+                        required=True,
+                        type=schema.type,
+                        default=schema.default,
+                        description=schema.description,
+                        properties=[],
+                        references_used=references_used,
+                    )
+                )
+
+            return api_request_body_properties
+
+        @classmethod
+        def from_request_body(
+            cls, request_body: RequestBody, spec: OpenAPISpec
+        ) -> "APIRequestBody":
+            """Instantiate from an OpenAPI RequestBody."""
+            properties = []
+            for media_type, media_type_obj in request_body.content.items():
+                if media_type not in _SUPPORTED_MEDIA_TYPES:
+                    continue
+                api_request_body_properties = cls._process_supported_media_type(
+                    media_type_obj,
+                    spec,
+                )
+                properties.extend(api_request_body_properties)
+
+            return cls(
+                description=request_body.description,
+                properties=properties,
+                media_type=media_type,
             )
 
-        return "\n".join(formatted_props)
+    # class APIRequestBodyProperty(APIPropertyBase):
+    #     class APIRequestBody(BaseModel):
+    class APIOperation(BaseModel):
+        """A model for a single API operation."""
 
-    def to_typescript(self) -> str:
-        """Get typescript string representation of the operation."""
-        operation_name = self.operation_id
-        params = []
+        operation_id: str = Field(alias="operation_id")
+        """The unique identifier of the operation."""
 
-        if self.request_body:
-            formatted_request_body_props = self._format_nested_properties(
-                self.request_body.properties
+        description: Optional[str] = Field(alias="description")
+        """The description of the operation."""
+
+        base_url: str = Field(alias="base_url")
+        """The base URL of the operation."""
+
+        path: str = Field(alias="path")
+        """The path of the operation."""
+
+        method: HTTPVerb = Field(alias="method")
+        """The HTTP method of the operation."""
+
+        properties: Sequence[APIProperty] = Field(alias="properties")
+
+        # TODO: Add parse in used components to be able to specify what type of
+        # referenced object it is.
+        # """The properties of the operation."""
+        # components: Dict[str, BaseModel] = Field(alias="components")
+
+        request_body: Optional[APIRequestBody] = Field(alias="request_body")
+        """The request body of the operation."""
+
+        @staticmethod
+        def _get_properties_from_parameters(
+            parameters: List[Parameter], spec: OpenAPISpec
+        ) -> List[APIProperty]:
+            """Get the properties of the operation."""
+            properties = []
+            for param in parameters:
+                if APIProperty.is_supported_location(param.param_in):
+                    properties.append(APIProperty.from_parameter(param, spec))
+                elif param.required:
+                    raise ValueError(
+                        INVALID_LOCATION_TEMPL.format(
+                            location=param.param_in, name=param.name
+                        )
+                    )
+                else:
+                    logger.warning(
+                        INVALID_LOCATION_TEMPL.format(
+                            location=param.param_in, name=param.name
+                        )
+                        + " Ignoring optional parameter"
+                    )
+                    pass
+            return properties
+
+        @classmethod
+        def from_openapi_url(
+            cls,
+            spec_url: str,
+            path: str,
+            method: str,
+        ) -> "APIOperation":
+            """Create an APIOperation from an OpenAPI URL."""
+            spec = OpenAPISpec.from_url(spec_url)
+            return cls.from_openapi_spec(spec, path, method)
+
+        @classmethod
+        def from_openapi_spec(
+            cls,
+            spec: OpenAPISpec,
+            path: str,
+            method: str,
+        ) -> "APIOperation":
+            """Create an APIOperation from an OpenAPI spec."""
+            operation = spec.get_operation(path, method)
+            parameters = spec.get_parameters_for_operation(operation)
+            properties = cls._get_properties_from_parameters(parameters, spec)
+            operation_id = OpenAPISpec.get_cleaned_operation_id(operation, path, method)
+            request_body = spec.get_request_body_for_operation(operation)
+            api_request_body = (
+                APIRequestBody.from_request_body(request_body, spec)
+                if request_body is not None
+                else None
+            )
+            description = operation.description or operation.summary
+            if not description and spec.paths is not None:
+                description = spec.paths[path].description or spec.paths[path].summary
+            return cls(
+                operation_id=operation_id,
+                description=description or "",
+                base_url=spec.base_url,
+                path=path,
+                method=method,
+                properties=properties,
+                request_body=api_request_body,
             )
-            params.append(formatted_request_body_props)
 
-        for prop in self.properties:
-            prop_name = prop.name
-            prop_type = self.ts_type_from_python(prop.type)
-            prop_required = "" if prop.required else "?"
-            prop_desc = f"/* {prop.description} */" if prop.description else ""
-            params.append(f"{prop_desc}\n\t\t{prop_name}{prop_required}: {prop_type},")
+        @staticmethod
+        def ts_type_from_python(type_: SCHEMA_TYPE) -> str:
+            if type_ is None:
+                # TODO: Handle Nones better. These often result when
+                # parsing specs that are < v3
+                return "any"
+            elif isinstance(type_, str):
+                return {
+                    "str": "string",
+                    "integer": "number",
+                    "float": "number",
+                    "date-time": "string",
+                }.get(type_, type_)
+            elif isinstance(type_, tuple):
+                return f"Array<{APIOperation.ts_type_from_python(type_[0])}>"
+            elif isinstance(type_, type) and issubclass(type_, Enum):
+                return " | ".join([f"'{e.value}'" for e in type_])
+            else:
+                return str(type_)
 
-        formatted_params = "\n".join(params).strip()
-        description_str = f"/* {self.description} */" if self.description else ""
-        typescript_definition = f"""
-{description_str}
-type {operation_name} = (_: {{
-{formatted_params}
-}}) => any;
-"""
-        return typescript_definition.strip()
+        def _format_nested_properties(
+            self, properties: List[APIRequestBodyProperty], indent: int = 2
+        ) -> str:
+            """Format nested properties."""
+            formatted_props = []
 
-    @property
-    def query_params(self) -> List[str]:
-        return [
-            property.name
-            for property in self.properties
-            if property.location == APIPropertyLocation.QUERY
-        ]
+            for prop in properties:
+                prop_name = prop.name
+                prop_type = self.ts_type_from_python(prop.type)
+                prop_required = "" if prop.required else "?"
+                prop_desc = f"/* {prop.description} */" if prop.description else ""
 
-    @property
-    def path_params(self) -> List[str]:
-        return [
-            property.name
-            for property in self.properties
-            if property.location == APIPropertyLocation.PATH
-        ]
+                if prop.properties:
+                    nested_props = self._format_nested_properties(
+                        prop.properties, indent + 2
+                    )
+                    prop_type = f"{{\n{nested_props}\n{' ' * indent}}}"
 
-    @property
-    def body_params(self) -> List[str]:
-        if self.request_body is None:
-            return []
-        return [prop.name for prop in self.request_body.properties]
+                formatted_props.append(
+                    f"{prop_desc}\n{' ' * indent}{prop_name}"
+                    f"{prop_required}: {prop_type},"
+                )
+
+            return "\n".join(formatted_props)
+
+        def to_typescript(self) -> str:
+            """Get typescript string representation of the operation."""
+            operation_name = self.operation_id
+            params = []
+
+            if self.request_body:
+                formatted_request_body_props = self._format_nested_properties(
+                    self.request_body.properties
+                )
+                params.append(formatted_request_body_props)
+
+            for prop in self.properties:
+                prop_name = prop.name
+                prop_type = self.ts_type_from_python(prop.type)
+                prop_required = "" if prop.required else "?"
+                prop_desc = f"/* {prop.description} */" if prop.description else ""
+                params.append(
+                    f"{prop_desc}\n\t\t{prop_name}{prop_required}: {prop_type},"
+                )
+
+            formatted_params = "\n".join(params).strip()
+            description_str = f"/* {self.description} */" if self.description else ""
+            typescript_definition = f"""
+    {description_str}
+    type {operation_name} = (_: {{
+    {formatted_params}
+    }}) => any;
+    """
+            return typescript_definition.strip()
+
+        @property
+        def query_params(self) -> List[str]:
+            return [
+                property.name
+                for property in self.properties
+                if property.location == APIPropertyLocation.QUERY
+            ]
+
+        @property
+        def path_params(self) -> List[str]:
+            return [
+                property.name
+                for property in self.properties
+                if property.location == APIPropertyLocation.PATH
+            ]
+
+        @property
+        def body_params(self) -> List[str]:
+            if self.request_body is None:
+                return []
+            return [prop.name for prop in self.request_body.properties]
+
+else:
+
+    class APIProperty(APIPropertyBase):  # type: ignore[no-redef]
+        def __init__(self, *args: Any, **kwargs: Any) -> None:
+            raise NotImplementedError("Only supported for pydantic v1")
+
+    class APIRequestBodyProperty(APIPropertyBase):  # type: ignore[no-redef]
+        def __init__(self, *args: Any, **kwargs: Any) -> None:
+            raise NotImplementedError("Only supported for pydantic v1")
+
+    class APIRequestBody(BaseModel):  # type: ignore[no-redef]
+        def __init__(self, *args: Any, **kwargs: Any) -> None:
+            raise NotImplementedError("Only supported for pydantic v1")
+
+    class APIOperation(BaseModel):  # type: ignore[no-redef]
+        def __init__(self, *args: Any, **kwargs: Any) -> None:
+            raise NotImplementedError("Only supported for pydantic v1")
diff --git a/libs/langchain/langchain/tools/openapi/utils/openapi_utils.py b/libs/langchain/langchain/tools/openapi/utils/openapi_utils.py
index f37b41b9d1e..eb376819263 100644
--- a/libs/langchain/langchain/tools/openapi/utils/openapi_utils.py
+++ b/libs/langchain/langchain/tools/openapi/utils/openapi_utils.py
@@ -1,2 +1,4 @@
 """Utility functions for parsing an OpenAPI spec. Kept for backwards compat."""
-from langchain.utilities.openapi import HTTPVerb, OpenAPISpec  # noqa: F401
+from langchain.utilities.openapi import HTTPVerb, OpenAPISpec
+
+__all__ = ["HTTPVerb", "OpenAPISpec"]
diff --git a/libs/langchain/langchain/tools/openweathermap/tool.py b/libs/langchain/langchain/tools/openweathermap/tool.py
index 0290e144a7c..4e122b13d2b 100644
--- a/libs/langchain/langchain/tools/openweathermap/tool.py
+++ b/libs/langchain/langchain/tools/openweathermap/tool.py
@@ -2,9 +2,8 @@
 
 from typing import Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
 from langchain.utilities import OpenWeatherMapAPIWrapper
 
@@ -16,8 +15,8 @@ class OpenWeatherMapQueryRun(BaseTool):
         default_factory=OpenWeatherMapAPIWrapper
     )
 
-    name = "OpenWeatherMap"
-    description = (
+    name: str = "OpenWeatherMap"
+    description: str = (
         "A wrapper around OpenWeatherMap API. "
         "Useful for fetching current weather information for a specified location. "
         "Input should be a location string (e.g. London,GB)."
diff --git a/libs/langchain/langchain/tools/playwright/base.py b/libs/langchain/langchain/tools/playwright/base.py
index 7930a29fca9..bf9209fff8a 100644
--- a/libs/langchain/langchain/tools/playwright/base.py
+++ b/libs/langchain/langchain/tools/playwright/base.py
@@ -2,8 +2,7 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, Optional, Tuple, Type
 
-from pydantic import root_validator
-
+from langchain.pydantic_v1 import root_validator
 from langchain.tools.base import BaseTool
 
 if TYPE_CHECKING:
diff --git a/libs/langchain/langchain/tools/playwright/click.py b/libs/langchain/langchain/tools/playwright/click.py
index d87248f73a3..b93b69ca126 100644
--- a/libs/langchain/langchain/tools/playwright/click.py
+++ b/libs/langchain/langchain/tools/playwright/click.py
@@ -2,12 +2,11 @@ from __future__ import annotations
 
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.playwright.base import BaseBrowserTool
 from langchain.tools.playwright.utils import (
     aget_current_page,
diff --git a/libs/langchain/langchain/tools/playwright/current_page.py b/libs/langchain/langchain/tools/playwright/current_page.py
index 6ac77e31456..b26383f3445 100644
--- a/libs/langchain/langchain/tools/playwright/current_page.py
+++ b/libs/langchain/langchain/tools/playwright/current_page.py
@@ -2,12 +2,11 @@ from __future__ import annotations
 
 from typing import Optional, Type
 
-from pydantic import BaseModel
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel
 from langchain.tools.playwright.base import BaseBrowserTool
 from langchain.tools.playwright.utils import aget_current_page, get_current_page
 
diff --git a/libs/langchain/langchain/tools/playwright/extract_hyperlinks.py b/libs/langchain/langchain/tools/playwright/extract_hyperlinks.py
index 885059786ab..c1c4292c2f2 100644
--- a/libs/langchain/langchain/tools/playwright/extract_hyperlinks.py
+++ b/libs/langchain/langchain/tools/playwright/extract_hyperlinks.py
@@ -3,12 +3,11 @@ from __future__ import annotations
 import json
 from typing import TYPE_CHECKING, Any, Optional, Type
 
-from pydantic import BaseModel, Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel, Field, root_validator
 from langchain.tools.playwright.base import BaseBrowserTool
 from langchain.tools.playwright.utils import aget_current_page, get_current_page
 
diff --git a/libs/langchain/langchain/tools/playwright/extract_text.py b/libs/langchain/langchain/tools/playwright/extract_text.py
index 210cde907ec..86d7a4b556f 100644
--- a/libs/langchain/langchain/tools/playwright/extract_text.py
+++ b/libs/langchain/langchain/tools/playwright/extract_text.py
@@ -2,12 +2,11 @@ from __future__ import annotations
 
 from typing import Optional, Type
 
-from pydantic import BaseModel, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.tools.playwright.base import BaseBrowserTool
 from langchain.tools.playwright.utils import aget_current_page, get_current_page
 
diff --git a/libs/langchain/langchain/tools/playwright/get_elements.py b/libs/langchain/langchain/tools/playwright/get_elements.py
index 9a7c33d848a..f5098c39d66 100644
--- a/libs/langchain/langchain/tools/playwright/get_elements.py
+++ b/libs/langchain/langchain/tools/playwright/get_elements.py
@@ -3,12 +3,11 @@ from __future__ import annotations
 import json
 from typing import TYPE_CHECKING, List, Optional, Sequence, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.playwright.base import BaseBrowserTool
 from langchain.tools.playwright.utils import aget_current_page, get_current_page
 
diff --git a/libs/langchain/langchain/tools/playwright/navigate.py b/libs/langchain/langchain/tools/playwright/navigate.py
index 14065d6c7bf..ac91f1f2418 100644
--- a/libs/langchain/langchain/tools/playwright/navigate.py
+++ b/libs/langchain/langchain/tools/playwright/navigate.py
@@ -2,12 +2,11 @@ from __future__ import annotations
 
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.playwright.base import BaseBrowserTool
 from langchain.tools.playwright.utils import (
     aget_current_page,
diff --git a/libs/langchain/langchain/tools/playwright/navigate_back.py b/libs/langchain/langchain/tools/playwright/navigate_back.py
index da4d3577549..41f9ee0323c 100644
--- a/libs/langchain/langchain/tools/playwright/navigate_back.py
+++ b/libs/langchain/langchain/tools/playwright/navigate_back.py
@@ -2,12 +2,11 @@ from __future__ import annotations
 
 from typing import Optional, Type
 
-from pydantic import BaseModel
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel
 from langchain.tools.playwright.base import BaseBrowserTool
 from langchain.tools.playwright.utils import (
     aget_current_page,
diff --git a/libs/langchain/langchain/tools/plugin.py b/libs/langchain/langchain/tools/plugin.py
index eafca8491a3..a9b8e19c1fc 100644
--- a/libs/langchain/langchain/tools/plugin.py
+++ b/libs/langchain/langchain/tools/plugin.py
@@ -5,12 +5,12 @@ from typing import Optional, Type
 
 import requests
 import yaml
-from pydantic import BaseModel
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel
 from langchain.tools.base import BaseTool
 
 
diff --git a/libs/langchain/langchain/tools/powerbi/tool.py b/libs/langchain/langchain/tools/powerbi/tool.py
index a201875cd98..c52f695c214 100644
--- a/libs/langchain/langchain/tools/powerbi/tool.py
+++ b/libs/langchain/langchain/tools/powerbi/tool.py
@@ -3,14 +3,13 @@ import logging
 from time import perf_counter
 from typing import Any, Dict, Optional, Tuple
 
-from pydantic import Field, validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
 from langchain.chains.llm import LLMChain
 from langchain.chat_models.openai import _import_tiktoken
+from langchain.pydantic_v1 import Field, validator
 from langchain.tools.base import BaseTool
 from langchain.tools.powerbi.prompt import (
     BAD_REQUEST_RESPONSE,
@@ -25,8 +24,8 @@ logger = logging.getLogger(__name__)
 class QueryPowerBITool(BaseTool):
     """Tool for querying a Power BI Dataset."""
 
-    name = "query_powerbi"
-    description = """
+    name: str = "query_powerbi"
+    description: str = """
     Input to this tool is a detailed question about the dataset, output is a result from the dataset. It will try to answer the question using the dataset, and if it cannot, it will ask for clarification.
 
     Example Input: "How many rows are in table1?"
@@ -218,8 +217,8 @@ class QueryPowerBITool(BaseTool):
 class InfoPowerBITool(BaseTool):
     """Tool for getting metadata about a PowerBI Dataset."""
 
-    name = "schema_powerbi"
-    description = """
+    name: str = "schema_powerbi"
+    description: str = """
     Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.
     Be sure that the tables actually exist by calling list_tables_powerbi first!
 
@@ -251,8 +250,8 @@ class InfoPowerBITool(BaseTool):
 class ListPowerBITool(BaseTool):
     """Tool for getting tables names."""
 
-    name = "list_tables_powerbi"
-    description = "Input is an empty string, output is a comma separated list of tables in the database."  # noqa: E501 # pylint: disable=C0301
+    name: str = "list_tables_powerbi"
+    description: str = "Input is an empty string, output is a comma separated list of tables in the database."  # noqa: E501 # pylint: disable=C0301
     powerbi: PowerBIDataset = Field(exclude=True)
 
     class Config:
diff --git a/libs/langchain/langchain/tools/pubmed/tool.py b/libs/langchain/langchain/tools/pubmed/tool.py
index dac103de73d..84bfa72db64 100644
--- a/libs/langchain/langchain/tools/pubmed/tool.py
+++ b/libs/langchain/langchain/tools/pubmed/tool.py
@@ -1,8 +1,7 @@
 from typing import Optional
 
-from pydantic import Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
 from langchain.utilities.pubmed import PubMedAPIWrapper
 
@@ -10,8 +9,8 @@ from langchain.utilities.pubmed import PubMedAPIWrapper
 class PubmedQueryRun(BaseTool):
     """Tool that searches the PubMed API."""
 
-    name = "PubMed"
-    description = (
+    name: str = "PubMed"
+    description: str = (
         "A wrapper around PubMed. "
         "Useful for when you need to answer questions about medicine, health, "
         "and biomedical topics "
diff --git a/libs/langchain/langchain/tools/python/tool.py b/libs/langchain/langchain/tools/python/tool.py
index d5ae9509274..402a115ce90 100644
--- a/libs/langchain/langchain/tools/python/tool.py
+++ b/libs/langchain/langchain/tools/python/tool.py
@@ -8,12 +8,11 @@ from contextlib import redirect_stdout
 from io import StringIO
 from typing import Any, Dict, Optional
 
-from pydantic import Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.tools.base import BaseTool
 from langchain.utilities import PythonREPL
 
@@ -43,8 +42,8 @@ def sanitize_input(query: str) -> str:
 class PythonREPLTool(BaseTool):
     """A tool for running python code in a REPL."""
 
-    name = "Python_REPL"
-    description = (
+    name: str = "Python_REPL"
+    description: str = (
         "A Python shell. Use this to execute python commands. "
         "Input should be a valid python command. "
         "If you want to see the output of a value, you should print it out "
@@ -81,8 +80,8 @@ class PythonREPLTool(BaseTool):
 class PythonAstREPLTool(BaseTool):
     """A tool for running python code in a REPL."""
 
-    name = "python_repl_ast"
-    description = (
+    name: str = "python_repl_ast"
+    description: str = (
         "A Python shell. Use this to execute python commands. "
         "Input should be a valid python command. "
         "When using this tool, sometimes output is abbreviated - "
@@ -131,3 +130,15 @@ class PythonAstREPLTool(BaseTool):
                 return io_buffer.getvalue()
         except Exception as e:
             return "{}: {}".format(type(e).__name__, str(e))
+
+    async def _arun(
+        self,
+        query: str,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> Any:
+        """Use the tool asynchronously."""
+
+        loop = asyncio.get_running_loop()
+        result = await loop.run_in_executor(None, self._run, query)
+
+        return result
diff --git a/libs/langchain/langchain/tools/requests/tool.py b/libs/langchain/langchain/tools/requests/tool.py
index bc032cbd166..b47cd98da53 100644
--- a/libs/langchain/langchain/tools/requests/tool.py
+++ b/libs/langchain/langchain/tools/requests/tool.py
@@ -3,7 +3,7 @@
 import json
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel
+from langchain.pydantic_v1 import BaseModel
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
@@ -32,8 +32,8 @@ class BaseRequestsTool(BaseModel):
 class RequestsGetTool(BaseRequestsTool, BaseTool):
     """Tool for making a GET request to an API endpoint."""
 
-    name = "requests_get"
-    description = "A portal to the internet. Use this when you need to get specific content from a website. Input should be a  url (i.e. https://www.google.com). The output will be the text response of the GET request."
+    name: str = "requests_get"
+    description: str = "A portal to the internet. Use this when you need to get specific content from a website. Input should be a  url (i.e. https://www.google.com). The output will be the text response of the GET request."
 
     def _run(
         self, url: str, run_manager: Optional[CallbackManagerForToolRun] = None
@@ -53,8 +53,8 @@ class RequestsGetTool(BaseRequestsTool, BaseTool):
 class RequestsPostTool(BaseRequestsTool, BaseTool):
     """Tool for making a POST request to an API endpoint."""
 
-    name = "requests_post"
-    description = """Use this when you want to POST to a website.
+    name: str = "requests_post"
+    description: str = """Use this when you want to POST to a website.
     Input should be a json string with two keys: "url" and "data".
     The value of "url" should be a string, and the value of "data" should be a dictionary of 
     key-value pairs you want to POST to the url.
@@ -90,8 +90,8 @@ class RequestsPostTool(BaseRequestsTool, BaseTool):
 class RequestsPatchTool(BaseRequestsTool, BaseTool):
     """Tool for making a PATCH request to an API endpoint."""
 
-    name = "requests_patch"
-    description = """Use this when you want to PATCH to a website.
+    name: str = "requests_patch"
+    description: str = """Use this when you want to PATCH to a website.
     Input should be a json string with two keys: "url" and "data".
     The value of "url" should be a string, and the value of "data" should be a dictionary of 
     key-value pairs you want to PATCH to the url.
@@ -127,8 +127,8 @@ class RequestsPatchTool(BaseRequestsTool, BaseTool):
 class RequestsPutTool(BaseRequestsTool, BaseTool):
     """Tool for making a PUT request to an API endpoint."""
 
-    name = "requests_put"
-    description = """Use this when you want to PUT to a website.
+    name: str = "requests_put"
+    description: str = """Use this when you want to PUT to a website.
     Input should be a json string with two keys: "url" and "data".
     The value of "url" should be a string, and the value of "data" should be a dictionary of 
     key-value pairs you want to PUT to the url.
@@ -164,8 +164,8 @@ class RequestsPutTool(BaseRequestsTool, BaseTool):
 class RequestsDeleteTool(BaseRequestsTool, BaseTool):
     """Tool for making a DELETE request to an API endpoint."""
 
-    name = "requests_delete"
-    description = "A portal to the internet. Use this when you need to make a DELETE request to a URL. Input should be a specific url, and the output will be the text response of the DELETE request."
+    name: str = "requests_delete"
+    description: str = "A portal to the internet. Use this when you need to make a DELETE request to a URL. Input should be a specific url, and the output will be the text response of the DELETE request."
 
     def _run(
         self,
diff --git a/libs/langchain/langchain/tools/scenexplain/tool.py b/libs/langchain/langchain/tools/scenexplain/tool.py
index 7ad619c1e8a..59d03125b4f 100644
--- a/libs/langchain/langchain/tools/scenexplain/tool.py
+++ b/libs/langchain/langchain/tools/scenexplain/tool.py
@@ -1,9 +1,8 @@
 """Tool for the SceneXplain API."""
 from typing import Optional
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 from langchain.utilities.scenexplain import SceneXplainAPIWrapper
 
@@ -17,8 +16,8 @@ class SceneXplainInput(BaseModel):
 class SceneXplainTool(BaseTool):
     """Tool that explains images."""
 
-    name = "image_explainer"
-    description = (
+    name: str = "image_explainer"
+    description: str = (
         "An Image Captioning Tool: Use this tool to generate a detailed caption "
         "for an image. The input can be an image file of any format, and "
         "the output will be a text description that covers every detail of the image."
diff --git a/libs/langchain/langchain/tools/searx_search/tool.py b/libs/langchain/langchain/tools/searx_search/tool.py
index 3de5521c658..7dc1dcbd0ca 100644
--- a/libs/langchain/langchain/tools/searx_search/tool.py
+++ b/libs/langchain/langchain/tools/searx_search/tool.py
@@ -1,12 +1,11 @@
 """Tool for the SearxNG search API."""
 from typing import Optional
 
-from pydantic import Extra
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import Extra
 from langchain.tools.base import BaseTool, Field
 from langchain.utilities.searx_search import SearxSearchWrapper
 
@@ -14,8 +13,8 @@ from langchain.utilities.searx_search import SearxSearchWrapper
 class SearxSearchRun(BaseTool):
     """Tool that queries a Searx instance."""
 
-    name = "searx_search"
-    description = (
+    name: str = "searx_search"
+    description: str = (
         "A meta search engine."
         "Useful for when you need to answer questions about current events."
         "Input should be a search query."
@@ -43,8 +42,8 @@ class SearxSearchRun(BaseTool):
 class SearxSearchResults(BaseTool):
     """Tool that queries a Searx instance and gets back json."""
 
-    name = "Searx Search Results"
-    description = (
+    name: str = "Searx Search Results"
+    description: str = (
         "A meta search engine."
         "Useful for when you need to answer questions about current events."
         "Input should be a search query. Output is a JSON array of the query results"
diff --git a/libs/langchain/langchain/tools/shell/tool.py b/libs/langchain/langchain/tools/shell/tool.py
index 28f2edcf225..3ad4c3ed980 100644
--- a/libs/langchain/langchain/tools/shell/tool.py
+++ b/libs/langchain/langchain/tools/shell/tool.py
@@ -3,12 +3,11 @@ import platform
 import warnings
 from typing import List, Optional, Type, Union
 
-from pydantic import BaseModel, Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel, Field, root_validator
 from langchain.tools.base import BaseTool
 from langchain.utilities.bash import BashProcess
 
diff --git a/libs/langchain/langchain/tools/sleep/tool.py b/libs/langchain/langchain/tools/sleep/tool.py
index 707cc329b73..91906c9f088 100644
--- a/libs/langchain/langchain/tools/sleep/tool.py
+++ b/libs/langchain/langchain/tools/sleep/tool.py
@@ -3,12 +3,11 @@ from asyncio import sleep as asleep
 from time import sleep
 from typing import Optional, Type
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.base import BaseTool
 
 
@@ -21,9 +20,9 @@ class SleepInput(BaseModel):
 class SleepTool(BaseTool):
     """Tool that adds the capability to sleep."""
 
-    name = "sleep"
+    name: str = "sleep"
     args_schema: Type[BaseModel] = SleepInput
-    description = "Make agent sleep for a specified number of seconds."
+    description: str = "Make agent sleep for a specified number of seconds."
 
     def _run(
         self,
diff --git a/libs/langchain/langchain/tools/spark_sql/tool.py b/libs/langchain/langchain/tools/spark_sql/tool.py
index 89ea7dbf75a..4a650f7d518 100644
--- a/libs/langchain/langchain/tools/spark_sql/tool.py
+++ b/libs/langchain/langchain/tools/spark_sql/tool.py
@@ -2,7 +2,7 @@
 """Tools for interacting with Spark SQL."""
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.callbacks.manager import (
@@ -33,8 +33,8 @@ class BaseSparkSQLTool(BaseModel):
 class QuerySparkSQLTool(BaseSparkSQLTool, BaseTool):
     """Tool for querying a Spark SQL."""
 
-    name = "query_sql_db"
-    description = """
+    name: str = "query_sql_db"
+    description: str = """
     Input to this tool is a detailed and correct SQL query, output is a result from the Spark SQL.
     If the query is not correct, an error message will be returned.
     If an error is returned, rewrite the query, check the query, and try again.
@@ -52,8 +52,8 @@ class QuerySparkSQLTool(BaseSparkSQLTool, BaseTool):
 class InfoSparkSQLTool(BaseSparkSQLTool, BaseTool):
     """Tool for getting metadata about a Spark SQL."""
 
-    name = "schema_sql_db"
-    description = """
+    name: str = "schema_sql_db"
+    description: str = """
     Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.
     Be sure that the tables actually exist by calling list_tables_sql_db first!
 
@@ -72,8 +72,8 @@ class InfoSparkSQLTool(BaseSparkSQLTool, BaseTool):
 class ListSparkSQLTool(BaseSparkSQLTool, BaseTool):
     """Tool for getting tables names."""
 
-    name = "list_tables_sql_db"
-    description = "Input is an empty string, output is a comma separated list of tables in the Spark SQL."
+    name: str = "list_tables_sql_db"
+    description: str = "Input is an empty string, output is a comma separated list of tables in the Spark SQL."
 
     def _run(
         self,
@@ -91,8 +91,8 @@ class QueryCheckerTool(BaseSparkSQLTool, BaseTool):
     template: str = QUERY_CHECKER
     llm: BaseLanguageModel
     llm_chain: LLMChain = Field(init=False)
-    name = "query_checker_sql_db"
-    description = """
+    name: str = "query_checker_sql_db"
+    description: str = """
     Use this tool to double check if your query is correct before executing it.
     Always use this tool before executing a query with query_sql_db!
     """
diff --git a/libs/langchain/langchain/tools/sql_database/tool.py b/libs/langchain/langchain/tools/sql_database/tool.py
index 7ea40eb4b1d..f60275bcafb 100644
--- a/libs/langchain/langchain/tools/sql_database/tool.py
+++ b/libs/langchain/langchain/tools/sql_database/tool.py
@@ -2,7 +2,7 @@
 """Tools for interacting with a SQL database."""
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel, Extra, Field, root_validator
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.callbacks.manager import (
@@ -33,8 +33,8 @@ class BaseSQLDatabaseTool(BaseModel):
 class QuerySQLDataBaseTool(BaseSQLDatabaseTool, BaseTool):
     """Tool for querying a SQL database."""
 
-    name = "sql_db_query"
-    description = """
+    name: str = "sql_db_query"
+    description: str = """
     Input to this tool is a detailed and correct SQL query, output is a result from the database.
     If the query is not correct, an error message will be returned.
     If an error is returned, rewrite the query, check the query, and try again.
@@ -52,8 +52,8 @@ class QuerySQLDataBaseTool(BaseSQLDatabaseTool, BaseTool):
 class InfoSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
     """Tool for getting metadata about a SQL database."""
 
-    name = "sql_db_schema"
-    description = """
+    name: str = "sql_db_schema"
+    description: str = """
     Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.    
 
     Example Input: "table1, table2, table3"
@@ -71,8 +71,8 @@ class InfoSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
 class ListSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
     """Tool for getting tables names."""
 
-    name = "sql_db_list_tables"
-    description = "Input is an empty string, output is a comma separated list of tables in the database."
+    name: str = "sql_db_list_tables"
+    description: str = "Input is an empty string, output is a comma separated list of tables in the database."
 
     def _run(
         self,
@@ -90,8 +90,8 @@ class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool):
     template: str = QUERY_CHECKER
     llm: BaseLanguageModel
     llm_chain: LLMChain = Field(init=False)
-    name = "sql_db_query_checker"
-    description = """
+    name: str = "sql_db_query_checker"
+    description: str = """
     Use this tool to double check if your query is correct before executing it.
     Always use this tool before executing a query with query_sql_db!
     """
diff --git a/libs/langchain/langchain/tools/steamship_image_generation/tool.py b/libs/langchain/langchain/tools/steamship_image_generation/tool.py
index 1c9188a2074..abd38c8d29b 100644
--- a/libs/langchain/langchain/tools/steamship_image_generation/tool.py
+++ b/libs/langchain/langchain/tools/steamship_image_generation/tool.py
@@ -16,9 +16,8 @@ from __future__ import annotations
 from enum import Enum
 from typing import TYPE_CHECKING, Dict, Optional
 
-from pydantic import root_validator
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.pydantic_v1 import root_validator
 from langchain.tools import BaseTool
 from langchain.tools.steamship_image_generation.utils import make_image_public
 from langchain.utils import get_from_dict_or_env
@@ -49,8 +48,8 @@ class SteamshipImageGenerationTool(BaseTool):
     steamship: Steamship
     return_urls: Optional[bool] = False
 
-    name = "GenerateImage"
-    description = (
+    name: str = "GenerateImage"
+    description: str = (
         "Useful for when you need to generate an image."
         "Input: A detailed text-2-image prompt describing an image"
         "Output: the UUID of a generated image"
diff --git a/libs/langchain/langchain/tools/vectorstore/tool.py b/libs/langchain/langchain/tools/vectorstore/tool.py
index ae77fa12f26..59c4be604a0 100644
--- a/libs/langchain/langchain/tools/vectorstore/tool.py
+++ b/libs/langchain/langchain/tools/vectorstore/tool.py
@@ -3,11 +3,10 @@
 import json
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel, Field
-
 from langchain.callbacks.manager import CallbackManagerForToolRun
 from langchain.chains import RetrievalQA, RetrievalQAWithSourcesChain
 from langchain.llms.openai import OpenAI
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
 from langchain.vectorstores.base import VectorStore
diff --git a/libs/langchain/langchain/tools/wikipedia/tool.py b/libs/langchain/langchain/tools/wikipedia/tool.py
index f9e3d653c02..60db606d1aa 100644
--- a/libs/langchain/langchain/tools/wikipedia/tool.py
+++ b/libs/langchain/langchain/tools/wikipedia/tool.py
@@ -10,8 +10,8 @@ from langchain.utilities.wikipedia import WikipediaAPIWrapper
 class WikipediaQueryRun(BaseTool):
     """Tool that searches the Wikipedia API."""
 
-    name = "Wikipedia"
-    description = (
+    name: str = "Wikipedia"
+    description: str = (
         "A wrapper around Wikipedia. "
         "Useful for when you need to answer general questions about "
         "people, places, companies, facts, historical events, or other subjects. "
diff --git a/libs/langchain/langchain/tools/wolfram_alpha/tool.py b/libs/langchain/langchain/tools/wolfram_alpha/tool.py
index dd7be399ece..2d5353e5ea7 100644
--- a/libs/langchain/langchain/tools/wolfram_alpha/tool.py
+++ b/libs/langchain/langchain/tools/wolfram_alpha/tool.py
@@ -10,8 +10,8 @@ from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
 class WolframAlphaQueryRun(BaseTool):
     """Tool that queries using the Wolfram Alpha SDK."""
 
-    name = "wolfram_alpha"
-    description = (
+    name: str = "wolfram_alpha"
+    description: str = (
         "A wrapper around Wolfram Alpha. "
         "Useful for when you need to answer questions about Math, "
         "Science, Technology, Culture, Society and Everyday Life. "
diff --git a/libs/langchain/langchain/tools/youtube/search.py b/libs/langchain/langchain/tools/youtube/search.py
index 6d66b3b2cc0..310eb37aa08 100644
--- a/libs/langchain/langchain/tools/youtube/search.py
+++ b/libs/langchain/langchain/tools/youtube/search.py
@@ -18,8 +18,8 @@ from langchain.tools import BaseTool
 class YouTubeSearchTool(BaseTool):
     """Tool that queries YouTube."""
 
-    name = "youtube_search"
-    description = (
+    name: str = "youtube_search"
+    description: str = (
         "search for youtube videos associated with a person. "
         "the input to this tool should be a comma separated list, "
         "the first part contains a person name and the second a "
diff --git a/libs/langchain/langchain/tools/zapier/tool.py b/libs/langchain/langchain/tools/zapier/tool.py
index 96b6209287b..636ea3c6366 100644
--- a/libs/langchain/langchain/tools/zapier/tool.py
+++ b/libs/langchain/langchain/tools/zapier/tool.py
@@ -79,12 +79,11 @@ agent.run(("Summarize the last email I received regarding Silicon Valley Bank. "
 """
 from typing import Any, Dict, Optional
 
-from pydantic import Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.tools.base import BaseTool
 from langchain.tools.zapier.prompt import BASE_ZAPIER_TOOL_PROMPT
 from langchain.utilities.zapier import ZapierNLAWrapper
@@ -109,8 +108,8 @@ class ZapierNLARunAction(BaseTool):
     base_prompt: str = BASE_ZAPIER_TOOL_PROMPT
     zapier_description: str
     params_schema: Dict[str, str] = Field(default_factory=dict)
-    name = ""
-    description = ""
+    name: str = ""
+    description: str = ""
 
     @root_validator
     def set_name_description(cls, values: Dict[str, Any]) -> Dict[str, Any]:
@@ -168,8 +167,8 @@ class ZapierNLAListActions(BaseTool):
 
     """
 
-    name = "ZapierNLA_list_actions"
-    description = BASE_ZAPIER_TOOL_PROMPT + (
+    name: str = "ZapierNLA_list_actions"
+    description: str = BASE_ZAPIER_TOOL_PROMPT + (
         "This tool returns a list of the user's exposed actions."
     )
     api_wrapper: ZapierNLAWrapper = Field(default_factory=ZapierNLAWrapper)
diff --git a/libs/langchain/langchain/utilities/__init__.py b/libs/langchain/langchain/utilities/__init__.py
index 167ff1f8fd5..3365c60442b 100644
--- a/libs/langchain/langchain/utilities/__init__.py
+++ b/libs/langchain/langchain/utilities/__init__.py
@@ -3,6 +3,7 @@
 Other LangChain classes use **Utilities** to interact with third-part systems
 and packages.
 """
+from langchain.utilities.alpha_vantage import AlphaVantageAPIWrapper
 from langchain.utilities.arxiv import ArxivAPIWrapper
 from langchain.utilities.awslambda import LambdaWrapper
 from langchain.utilities.bash import BashProcess
@@ -36,6 +37,7 @@ from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
 from langchain.utilities.zapier import ZapierNLAWrapper
 
 __all__ = [
+    "AlphaVantageAPIWrapper",
     "ArxivAPIWrapper",
     "BashProcess",
     "BibtexparserWrapper",
diff --git a/libs/langchain/langchain/utilities/alpha_vantage.py b/libs/langchain/langchain/utilities/alpha_vantage.py
new file mode 100644
index 00000000000..4a1c6381a7c
--- /dev/null
+++ b/libs/langchain/langchain/utilities/alpha_vantage.py
@@ -0,0 +1,65 @@
+"""Util that calls AlphaVantage for Currency Exchange Rate."""
+from typing import Any, Dict, List, Optional
+
+import requests
+
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
+from langchain.utils import get_from_dict_or_env
+
+
+class AlphaVantageAPIWrapper(BaseModel):
+    """Wrapper for AlphaVantage API for Currency Exchange Rate.
+
+    Docs for using:
+
+    1. Go to AlphaVantage and sign up for an API key
+    2. Save your API KEY into ALPHAVANTAGE_API_KEY env variable
+    """
+
+    alphavantage_api_key: Optional[str] = None
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key exists in environment."""
+        values["alphavantage_api_key"] = get_from_dict_or_env(
+            values, "alphavantage_api_key", "ALPHAVANTAGE_API_KEY"
+        )
+        return values
+
+    def _get_exchange_rate(
+        self, from_currency: str, to_currency: str
+    ) -> Dict[str, Any]:
+        """Make a request to the AlphaVantage API to get the exchange rate."""
+        response = requests.get(
+            "https://www.alphavantage.co/query/",
+            params={
+                "function": "CURRENCY_EXCHANGE_RATE",
+                "from_currency": from_currency,
+                "to_currency": to_currency,
+                "apikey": self.alphavantage_api_key,
+            },
+        )
+        response.raise_for_status()
+        data = response.json()
+
+        if "Error Message" in data:
+            raise ValueError(f"API Error: {data['Error Message']}")
+
+        return data
+
+    @property
+    def standard_currencies(self) -> List[str]:
+        return ["USD", "EUR", "GBP", "JPY", "CHF", "CAD", "AUD", "NZD"]
+
+    def run(self, from_currency: str, to_currency: str) -> str:
+        """Get the current exchange rate for a specified currency pair."""
+        if to_currency not in self.standard_currencies:
+            from_currency, to_currency = to_currency, from_currency
+
+        data = self._get_exchange_rate(from_currency, to_currency)
+        return data["Realtime Currency Exchange Rate"]
diff --git a/libs/langchain/langchain/utilities/arxiv.py b/libs/langchain/langchain/utilities/arxiv.py
index 246ed890453..3a048908508 100644
--- a/libs/langchain/langchain/utilities/arxiv.py
+++ b/libs/langchain/langchain/utilities/arxiv.py
@@ -3,8 +3,7 @@ import logging
 import os
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, root_validator
-
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.schema import Document
 
 logger = logging.getLogger(__name__)
@@ -50,7 +49,7 @@ class ArxivAPIWrapper(BaseModel):
     arxiv_search: Any  #: :meta private:
     arxiv_exceptions: Any  # :meta private:
     top_k_results: int = 3
-    ARXIV_MAX_QUERY_LENGTH = 300
+    ARXIV_MAX_QUERY_LENGTH: int = 300
     load_max_docs: int = 100
     load_all_available_meta: bool = False
     doc_content_chars_max: Optional[int] = 4000
@@ -128,6 +127,8 @@ class ArxivAPIWrapper(BaseModel):
             )
 
         try:
+            # Remove the ":" and "-" from the query, as they can cause search problems
+            query = query.replace(":", "").replace("-", "")
             results = self.arxiv_search(  # type: ignore
                 query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.load_max_docs
             ).results()
@@ -141,7 +142,7 @@ class ArxivAPIWrapper(BaseModel):
                 doc_file_name: str = result.download_pdf()
                 with fitz.open(doc_file_name) as doc_file:
                     text: str = "".join(page.get_text() for page in doc_file)
-            except FileNotFoundError as f_ex:
+            except (FileNotFoundError, fitz.fitz.FileDataError) as f_ex:
                 logger.debug(f_ex)
                 continue
             if self.load_all_available_meta:
diff --git a/libs/langchain/langchain/utilities/awslambda.py b/libs/langchain/langchain/utilities/awslambda.py
index 64782905e33..f75cf3eec28 100644
--- a/libs/langchain/langchain/utilities/awslambda.py
+++ b/libs/langchain/langchain/utilities/awslambda.py
@@ -2,7 +2,7 @@
 import json
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 
 
 class LambdaWrapper(BaseModel):
diff --git a/libs/langchain/langchain/utilities/bibtex.py b/libs/langchain/langchain/utilities/bibtex.py
index afafaef2208..8dcbda88518 100644
--- a/libs/langchain/langchain/utilities/bibtex.py
+++ b/libs/langchain/langchain/utilities/bibtex.py
@@ -2,7 +2,7 @@
 import logging
 from typing import Any, Dict, List, Mapping
 
-from pydantic import BaseModel, Extra, root_validator
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 
 logger = logging.getLogger(__name__)
 
diff --git a/libs/langchain/langchain/utilities/bing_search.py b/libs/langchain/langchain/utilities/bing_search.py
index 6dc2fe4073e..75dd964dbb2 100644
--- a/libs/langchain/langchain/utilities/bing_search.py
+++ b/libs/langchain/langchain/utilities/bing_search.py
@@ -6,8 +6,8 @@ https://levelup.gitconnected.com/api-tutorial-how-to-use-bing-web-search-api-in-
 from typing import Dict, List
 
 import requests
-from pydantic import BaseModel, Extra, root_validator
 
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/brave_search.py b/libs/langchain/langchain/utilities/brave_search.py
index 0e870b8f62d..b3d8b40fe76 100644
--- a/libs/langchain/langchain/utilities/brave_search.py
+++ b/libs/langchain/langchain/utilities/brave_search.py
@@ -2,8 +2,8 @@ import json
 from typing import List
 
 import requests
-from pydantic import BaseModel, Field
 
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema import Document
 
 
@@ -14,7 +14,7 @@ class BraveSearchWrapper(BaseModel):
     """The API key to use for the Brave search engine."""
     search_kwargs: dict = Field(default_factory=dict)
     """Additional keyword arguments to pass to the search request."""
-    base_url = "https://api.search.brave.com/res/v1/web/search"
+    base_url: str = "https://api.search.brave.com/res/v1/web/search"
     """The base URL for the Brave search engine."""
 
     def run(self, query: str) -> str:
diff --git a/libs/langchain/langchain/utilities/dalle_image_generator.py b/libs/langchain/langchain/utilities/dalle_image_generator.py
index 9f4d8c19c84..dc25c44b735 100644
--- a/libs/langchain/langchain/utilities/dalle_image_generator.py
+++ b/libs/langchain/langchain/utilities/dalle_image_generator.py
@@ -1,8 +1,7 @@
-"""Util that calls OpenAI's Dall-E Image Generator."""
+"""Utility that calls OpenAI's Dall-E Image Generator."""
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/dataforseo_api_search.py b/libs/langchain/langchain/utilities/dataforseo_api_search.py
index b8fc9ce864f..e774ff7c935 100644
--- a/libs/langchain/langchain/utilities/dataforseo_api_search.py
+++ b/libs/langchain/langchain/utilities/dataforseo_api_search.py
@@ -4,8 +4,8 @@ from urllib.parse import quote
 
 import aiohttp
 import requests
-from pydantic import BaseModel, Extra, Field, root_validator
 
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/duckduckgo_search.py b/libs/langchain/langchain/utilities/duckduckgo_search.py
index 0d9e127058d..b293ea6d7d6 100644
--- a/libs/langchain/langchain/utilities/duckduckgo_search.py
+++ b/libs/langchain/langchain/utilities/duckduckgo_search.py
@@ -5,8 +5,7 @@ https://pypi.org/project/duckduckgo-search/
 """
 from typing import Dict, List, Optional
 
-from pydantic import BaseModel, Extra
-from pydantic.class_validators import root_validator
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 
 
 class DuckDuckGoSearchAPIWrapper(BaseModel):
@@ -31,7 +30,7 @@ class DuckDuckGoSearchAPIWrapper(BaseModel):
         try:
             from duckduckgo_search import DDGS  # noqa: F401
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import duckduckgo-search python package. "
                 "Please install it with `pip install duckduckgo-search`."
             )
diff --git a/libs/langchain/langchain/utilities/github.py b/libs/langchain/langchain/utilities/github.py
index 7d350a5592d..7ef1c2e4b78 100644
--- a/libs/langchain/langchain/utilities/github.py
+++ b/libs/langchain/langchain/utilities/github.py
@@ -4,8 +4,7 @@ from __future__ import annotations
 import json
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 if TYPE_CHECKING:
diff --git a/libs/langchain/langchain/utilities/golden_query.py b/libs/langchain/langchain/utilities/golden_query.py
index df7e505faab..e94b49c1c0b 100644
--- a/libs/langchain/langchain/utilities/golden_query.py
+++ b/libs/langchain/langchain/utilities/golden_query.py
@@ -3,8 +3,8 @@ import json
 from typing import Dict, Optional
 
 import requests
-from pydantic import BaseModel, Extra, root_validator
 
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 GOLDEN_BASE_URL = "https://golden.com"
diff --git a/libs/langchain/langchain/utilities/google_places_api.py b/libs/langchain/langchain/utilities/google_places_api.py
index 8b8ea7a93a9..22d4d0e6267 100644
--- a/libs/langchain/langchain/utilities/google_places_api.py
+++ b/libs/langchain/langchain/utilities/google_places_api.py
@@ -4,8 +4,7 @@
 import logging
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/google_search.py b/libs/langchain/langchain/utilities/google_search.py
index 2c118dd165f..8095de8c6fa 100644
--- a/libs/langchain/langchain/utilities/google_search.py
+++ b/libs/langchain/langchain/utilities/google_search.py
@@ -1,8 +1,7 @@
 """Util that calls Google Search."""
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/google_serper.py b/libs/langchain/langchain/utilities/google_serper.py
index 9db376c4792..951c00b8fac 100644
--- a/libs/langchain/langchain/utilities/google_serper.py
+++ b/libs/langchain/langchain/utilities/google_serper.py
@@ -3,10 +3,9 @@ from typing import Any, Dict, List, Optional
 
 import aiohttp
 import requests
-from pydantic.class_validators import root_validator
-from pydantic.main import BaseModel
 from typing_extensions import Literal
 
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/graphql.py b/libs/langchain/langchain/utilities/graphql.py
index 1e8a7b20392..5bc548e881b 100644
--- a/libs/langchain/langchain/utilities/graphql.py
+++ b/libs/langchain/langchain/utilities/graphql.py
@@ -1,7 +1,7 @@
 import json
 from typing import Any, Callable, Dict, Optional
 
-from pydantic import BaseModel, Extra, root_validator
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 
 
 class GraphQLAPIWrapper(BaseModel):
diff --git a/libs/langchain/langchain/utilities/jira.py b/libs/langchain/langchain/utilities/jira.py
index d59a76b4722..ccfd4dc7b9f 100644
--- a/libs/langchain/langchain/utilities/jira.py
+++ b/libs/langchain/langchain/utilities/jira.py
@@ -1,8 +1,7 @@
 """Util that calls Jira."""
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/metaphor_search.py b/libs/langchain/langchain/utilities/metaphor_search.py
index d5d36c74496..6dea326934a 100644
--- a/libs/langchain/langchain/utilities/metaphor_search.py
+++ b/libs/langchain/langchain/utilities/metaphor_search.py
@@ -7,8 +7,8 @@ from typing import Dict, List, Optional
 
 import aiohttp
 import requests
-from pydantic import BaseModel, Extra, root_validator
 
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 METAPHOR_API_URL = "https://api.metaphor.systems"
@@ -163,10 +163,10 @@ class MetaphorSearchAPIWrapper(BaseModel):
         for result in raw_search_results:
             cleaned_results.append(
                 {
-                    "title": result["title"],
-                    "url": result["url"],
-                    "author": result["author"],
-                    "published_date": result["publishedDate"],
+                    "title": result.get("title", "Unknown Title"),
+                    "url": result.get("url", "Unknown URL"),
+                    "author": result.get("author", "Unknown Author"),
+                    "published_date": result.get("publishedDate", "Unknown Date"),
                 }
             )
         return cleaned_results
diff --git a/libs/langchain/langchain/utilities/openapi.py b/libs/langchain/langchain/utilities/openapi.py
index 06d0f666325..34224a2bbf0 100644
--- a/libs/langchain/langchain/utilities/openapi.py
+++ b/libs/langchain/langchain/utilities/openapi.py
@@ -1,26 +1,18 @@
 """Utility functions for parsing an OpenAPI spec."""
+from __future__ import annotations
+
 import copy
 import json
 import logging
 import re
 from enum import Enum
 from pathlib import Path
-from typing import Dict, List, Optional, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Union
 
 import requests
 import yaml
-from openapi_schema_pydantic import (
-    Components,
-    OpenAPI,
-    Operation,
-    Parameter,
-    PathItem,
-    Paths,
-    Reference,
-    RequestBody,
-    Schema,
-)
-from pydantic import ValidationError
+
+from langchain.pydantic_v1 import _PYDANTIC_MAJOR_VERSION, ValidationError
 
 logger = logging.getLogger(__name__)
 
@@ -38,7 +30,7 @@ class HTTPVerb(str, Enum):
     TRACE = "trace"
 
     @classmethod
-    def from_str(cls, verb: str) -> "HTTPVerb":
+    def from_str(cls, verb: str) -> HTTPVerb:
         """Parse an HTTP verb."""
         try:
             return cls(verb)
@@ -46,240 +38,288 @@ class HTTPVerb(str, Enum):
             raise ValueError(f"Invalid HTTP verb. Valid values are {cls.__members__}")
 
 
-class OpenAPISpec(OpenAPI):
-    """OpenAPI Model that removes misformatted parts of the spec."""
-
-    @property
-    def _paths_strict(self) -> Paths:
-        if not self.paths:
-            raise ValueError("No paths found in spec")
-        return self.paths
-
-    def _get_path_strict(self, path: str) -> PathItem:
-        path_item = self._paths_strict.get(path)
-        if not path_item:
-            raise ValueError(f"No path found for {path}")
-        return path_item
-
-    @property
-    def _components_strict(self) -> Components:
-        """Get components or err."""
-        if self.components is None:
-            raise ValueError("No components found in spec. ")
-        return self.components
-
-    @property
-    def _parameters_strict(self) -> Dict[str, Union[Parameter, Reference]]:
-        """Get parameters or err."""
-        parameters = self._components_strict.parameters
-        if parameters is None:
-            raise ValueError("No parameters found in spec. ")
-        return parameters
-
-    @property
-    def _schemas_strict(self) -> Dict[str, Schema]:
-        """Get the dictionary of schemas or err."""
-        schemas = self._components_strict.schemas
-        if schemas is None:
-            raise ValueError("No schemas found in spec. ")
-        return schemas
-
-    @property
-    def _request_bodies_strict(self) -> Dict[str, Union[RequestBody, Reference]]:
-        """Get the request body or err."""
-        request_bodies = self._components_strict.requestBodies
-        if request_bodies is None:
-            raise ValueError("No request body found in spec. ")
-        return request_bodies
-
-    def _get_referenced_parameter(self, ref: Reference) -> Union[Parameter, Reference]:
-        """Get a parameter (or nested reference) or err."""
-        ref_name = ref.ref.split("/")[-1]
-        parameters = self._parameters_strict
-        if ref_name not in parameters:
-            raise ValueError(f"No parameter found for {ref_name}")
-        return parameters[ref_name]
-
-    def _get_root_referenced_parameter(self, ref: Reference) -> Parameter:
-        """Get the root reference or err."""
-        parameter = self._get_referenced_parameter(ref)
-        while isinstance(parameter, Reference):
-            parameter = self._get_referenced_parameter(parameter)
-        return parameter
-
-    def get_referenced_schema(self, ref: Reference) -> Schema:
-        """Get a schema (or nested reference) or err."""
-        ref_name = ref.ref.split("/")[-1]
-        schemas = self._schemas_strict
-        if ref_name not in schemas:
-            raise ValueError(f"No schema found for {ref_name}")
-        return schemas[ref_name]
-
-    def get_schema(self, schema: Union[Reference, Schema]) -> Schema:
-        if isinstance(schema, Reference):
-            return self.get_referenced_schema(schema)
-        return schema
-
-    def _get_root_referenced_schema(self, ref: Reference) -> Schema:
-        """Get the root reference or err."""
-        schema = self.get_referenced_schema(ref)
-        while isinstance(schema, Reference):
-            schema = self.get_referenced_schema(schema)
-        return schema
-
-    def _get_referenced_request_body(
-        self, ref: Reference
-    ) -> Optional[Union[Reference, RequestBody]]:
-        """Get a request body (or nested reference) or err."""
-        ref_name = ref.ref.split("/")[-1]
-        request_bodies = self._request_bodies_strict
-        if ref_name not in request_bodies:
-            raise ValueError(f"No request body found for {ref_name}")
-        return request_bodies[ref_name]
-
-    def _get_root_referenced_request_body(
-        self, ref: Reference
-    ) -> Optional[RequestBody]:
-        """Get the root request Body or err."""
-        request_body = self._get_referenced_request_body(ref)
-        while isinstance(request_body, Reference):
-            request_body = self._get_referenced_request_body(request_body)
-        return request_body
-
-    @staticmethod
-    def _alert_unsupported_spec(obj: dict) -> None:
-        """Alert if the spec is not supported."""
-        warning_message = (
-            " This may result in degraded performance."
-            + " Convert your OpenAPI spec to 3.1.* spec"
-            + " for better support."
+if _PYDANTIC_MAJOR_VERSION == 1:
+    if TYPE_CHECKING:
+        from openapi_schema_pydantic import (
+            Components,
+            Operation,
+            Parameter,
+            PathItem,
+            Paths,
+            Reference,
+            RequestBody,
+            Schema,
         )
-        swagger_version = obj.get("swagger")
-        openapi_version = obj.get("openapi")
-        if isinstance(openapi_version, str):
-            if openapi_version != "3.1.0":
+
+    try:
+        from openapi_schema_pydantic import OpenAPI
+    except ImportError:
+        OpenAPI = object
+
+    class OpenAPISpec(OpenAPI):
+        """OpenAPI Model that removes mis-formatted parts of the spec."""
+
+        @property
+        def _paths_strict(self) -> Paths:
+            if not self.paths:
+                raise ValueError("No paths found in spec")
+            return self.paths
+
+        def _get_path_strict(self, path: str) -> PathItem:
+            path_item = self._paths_strict.get(path)
+            if not path_item:
+                raise ValueError(f"No path found for {path}")
+            return path_item
+
+        @property
+        def _components_strict(self) -> Components:
+            """Get components or err."""
+            if self.components is None:
+                raise ValueError("No components found in spec. ")
+            return self.components
+
+        @property
+        def _parameters_strict(self) -> Dict[str, Union[Parameter, Reference]]:
+            """Get parameters or err."""
+            parameters = self._components_strict.parameters
+            if parameters is None:
+                raise ValueError("No parameters found in spec. ")
+            return parameters
+
+        @property
+        def _schemas_strict(self) -> Dict[str, Schema]:
+            """Get the dictionary of schemas or err."""
+            schemas = self._components_strict.schemas
+            if schemas is None:
+                raise ValueError("No schemas found in spec. ")
+            return schemas
+
+        @property
+        def _request_bodies_strict(self) -> Dict[str, Union[RequestBody, Reference]]:
+            """Get the request body or err."""
+            request_bodies = self._components_strict.requestBodies
+            if request_bodies is None:
+                raise ValueError("No request body found in spec. ")
+            return request_bodies
+
+        def _get_referenced_parameter(
+            self, ref: Reference
+        ) -> Union[Parameter, Reference]:
+            """Get a parameter (or nested reference) or err."""
+            ref_name = ref.ref.split("/")[-1]
+            parameters = self._parameters_strict
+            if ref_name not in parameters:
+                raise ValueError(f"No parameter found for {ref_name}")
+            return parameters[ref_name]
+
+        def _get_root_referenced_parameter(self, ref: Reference) -> Parameter:
+            """Get the root reference or err."""
+            from openapi_schema_pydantic import Reference
+
+            parameter = self._get_referenced_parameter(ref)
+            while isinstance(parameter, Reference):
+                parameter = self._get_referenced_parameter(parameter)
+            return parameter
+
+        def get_referenced_schema(self, ref: Reference) -> Schema:
+            """Get a schema (or nested reference) or err."""
+            ref_name = ref.ref.split("/")[-1]
+            schemas = self._schemas_strict
+            if ref_name not in schemas:
+                raise ValueError(f"No schema found for {ref_name}")
+            return schemas[ref_name]
+
+        def get_schema(self, schema: Union[Reference, Schema]) -> Schema:
+            from openapi_schema_pydantic import Reference
+
+            if isinstance(schema, Reference):
+                return self.get_referenced_schema(schema)
+            return schema
+
+        def _get_root_referenced_schema(self, ref: Reference) -> Schema:
+            """Get the root reference or err."""
+            from openapi_schema_pydantic import Reference
+
+            schema = self.get_referenced_schema(ref)
+            while isinstance(schema, Reference):
+                schema = self.get_referenced_schema(schema)
+            return schema
+
+        def _get_referenced_request_body(
+            self, ref: Reference
+        ) -> Optional[Union[Reference, RequestBody]]:
+            """Get a request body (or nested reference) or err."""
+            ref_name = ref.ref.split("/")[-1]
+            request_bodies = self._request_bodies_strict
+            if ref_name not in request_bodies:
+                raise ValueError(f"No request body found for {ref_name}")
+            return request_bodies[ref_name]
+
+        def _get_root_referenced_request_body(
+            self, ref: Reference
+        ) -> Optional[RequestBody]:
+            """Get the root request Body or err."""
+            from openapi_schema_pydantic import Reference
+
+            request_body = self._get_referenced_request_body(ref)
+            while isinstance(request_body, Reference):
+                request_body = self._get_referenced_request_body(request_body)
+            return request_body
+
+        @staticmethod
+        def _alert_unsupported_spec(obj: dict) -> None:
+            """Alert if the spec is not supported."""
+            warning_message = (
+                " This may result in degraded performance."
+                + " Convert your OpenAPI spec to 3.1.* spec"
+                + " for better support."
+            )
+            swagger_version = obj.get("swagger")
+            openapi_version = obj.get("openapi")
+            if isinstance(openapi_version, str):
+                if openapi_version != "3.1.0":
+                    logger.warning(
+                        f"Attempting to load an OpenAPI {openapi_version}"
+                        f" spec. {warning_message}"
+                    )
+                else:
+                    pass
+            elif isinstance(swagger_version, str):
                 logger.warning(
-                    f"Attempting to load an OpenAPI {openapi_version}"
+                    f"Attempting to load a Swagger {swagger_version}"
                     f" spec. {warning_message}"
                 )
             else:
-                pass
-        elif isinstance(swagger_version, str):
-            logger.warning(
-                f"Attempting to load a Swagger {swagger_version}"
-                f" spec. {warning_message}"
-            )
-        else:
-            raise ValueError(
-                "Attempting to load an unsupported spec:"
-                f"\n\n{obj}\n{warning_message}"
-            )
+                raise ValueError(
+                    "Attempting to load an unsupported spec:"
+                    f"\n\n{obj}\n{warning_message}"
+                )
 
-    @classmethod
-    def parse_obj(cls, obj: dict) -> "OpenAPISpec":
-        try:
-            cls._alert_unsupported_spec(obj)
-            return super().parse_obj(obj)
-        except ValidationError as e:
-            # We are handling possibly misconfigured specs and want to do a best-effort
-            # job to get a reasonable interface out of it.
-            new_obj = copy.deepcopy(obj)
-            for error in e.errors():
-                keys = error["loc"]
-                item = new_obj
-                for key in keys[:-1]:
-                    item = item[key]
-                item.pop(keys[-1], None)
-            return cls.parse_obj(new_obj)
+        @classmethod
+        def parse_obj(cls, obj: dict) -> OpenAPISpec:
+            try:
+                cls._alert_unsupported_spec(obj)
+                return super().parse_obj(obj)
+            except ValidationError as e:
+                # We are handling possibly misconfigured specs and
+                # want to do a best-effort job to get a reasonable interface out of it.
+                new_obj = copy.deepcopy(obj)
+                for error in e.errors():
+                    keys = error["loc"]
+                    item = new_obj
+                    for key in keys[:-1]:
+                        item = item[key]
+                    item.pop(keys[-1], None)
+                return cls.parse_obj(new_obj)
 
-    @classmethod
-    def from_spec_dict(cls, spec_dict: dict) -> "OpenAPISpec":
-        """Get an OpenAPI spec from a dict."""
-        return cls.parse_obj(spec_dict)
+        @classmethod
+        def from_spec_dict(cls, spec_dict: dict) -> OpenAPISpec:
+            """Get an OpenAPI spec from a dict."""
+            return cls.parse_obj(spec_dict)
 
-    @classmethod
-    def from_text(cls, text: str) -> "OpenAPISpec":
-        """Get an OpenAPI spec from a text."""
-        try:
-            spec_dict = json.loads(text)
-        except json.JSONDecodeError:
-            spec_dict = yaml.safe_load(text)
-        return cls.from_spec_dict(spec_dict)
+        @classmethod
+        def from_text(cls, text: str) -> OpenAPISpec:
+            """Get an OpenAPI spec from a text."""
+            try:
+                spec_dict = json.loads(text)
+            except json.JSONDecodeError:
+                spec_dict = yaml.safe_load(text)
+            return cls.from_spec_dict(spec_dict)
 
-    @classmethod
-    def from_file(cls, path: Union[str, Path]) -> "OpenAPISpec":
-        """Get an OpenAPI spec from a file path."""
-        path_ = path if isinstance(path, Path) else Path(path)
-        if not path_.exists():
-            raise FileNotFoundError(f"{path} does not exist")
-        with path_.open("r") as f:
-            return cls.from_text(f.read())
+        @classmethod
+        def from_file(cls, path: Union[str, Path]) -> OpenAPISpec:
+            """Get an OpenAPI spec from a file path."""
+            path_ = path if isinstance(path, Path) else Path(path)
+            if not path_.exists():
+                raise FileNotFoundError(f"{path} does not exist")
+            with path_.open("r") as f:
+                return cls.from_text(f.read())
 
-    @classmethod
-    def from_url(cls, url: str) -> "OpenAPISpec":
-        """Get an OpenAPI spec from a URL."""
-        response = requests.get(url)
-        return cls.from_text(response.text)
+        @classmethod
+        def from_url(cls, url: str) -> OpenAPISpec:
+            """Get an OpenAPI spec from a URL."""
+            response = requests.get(url)
+            return cls.from_text(response.text)
 
-    @property
-    def base_url(self) -> str:
-        """Get the base url."""
-        return self.servers[0].url
+        @property
+        def base_url(self) -> str:
+            """Get the base url."""
+            return self.servers[0].url
 
-    def get_methods_for_path(self, path: str) -> List[str]:
-        """Return a list of valid methods for the specified path."""
-        path_item = self._get_path_strict(path)
-        results = []
-        for method in HTTPVerb:
-            operation = getattr(path_item, method.value, None)
-            if isinstance(operation, Operation):
-                results.append(method.value)
-        return results
+        def get_methods_for_path(self, path: str) -> List[str]:
+            """Return a list of valid methods for the specified path."""
+            from openapi_schema_pydantic import Operation
 
-    def get_parameters_for_path(self, path: str) -> List[Parameter]:
-        path_item = self._get_path_strict(path)
-        parameters = []
-        if not path_item.parameters:
-            return []
-        for parameter in path_item.parameters:
-            if isinstance(parameter, Reference):
-                parameter = self._get_root_referenced_parameter(parameter)
-            parameters.append(parameter)
-        return parameters
+            path_item = self._get_path_strict(path)
+            results = []
+            for method in HTTPVerb:
+                operation = getattr(path_item, method.value, None)
+                if isinstance(operation, Operation):
+                    results.append(method.value)
+            return results
 
-    def get_operation(self, path: str, method: str) -> Operation:
-        """Get the operation object for a given path and HTTP method."""
-        path_item = self._get_path_strict(path)
-        operation_obj = getattr(path_item, method, None)
-        if not isinstance(operation_obj, Operation):
-            raise ValueError(f"No {method} method found for {path}")
-        return operation_obj
+        def get_parameters_for_path(self, path: str) -> List[Parameter]:
+            from openapi_schema_pydantic import Reference
 
-    def get_parameters_for_operation(self, operation: Operation) -> List[Parameter]:
-        """Get the components for a given operation."""
-        parameters = []
-        if operation.parameters:
-            for parameter in operation.parameters:
+            path_item = self._get_path_strict(path)
+            parameters = []
+            if not path_item.parameters:
+                return []
+            for parameter in path_item.parameters:
                 if isinstance(parameter, Reference):
                     parameter = self._get_root_referenced_parameter(parameter)
                 parameters.append(parameter)
-        return parameters
+            return parameters
 
-    def get_request_body_for_operation(
-        self, operation: Operation
-    ) -> Optional[RequestBody]:
-        """Get the request body for a given operation."""
-        request_body = operation.requestBody
-        if isinstance(request_body, Reference):
-            request_body = self._get_root_referenced_request_body(request_body)
-        return request_body
+        def get_operation(self, path: str, method: str) -> Operation:
+            """Get the operation object for a given path and HTTP method."""
+            from openapi_schema_pydantic import Operation
 
-    @staticmethod
-    def get_cleaned_operation_id(operation: Operation, path: str, method: str) -> str:
-        """Get a cleaned operation id from an operation id."""
-        operation_id = operation.operationId
-        if operation_id is None:
-            # Replace all punctuation of any kind with underscore
-            path = re.sub(r"[^a-zA-Z0-9]", "_", path.lstrip("/"))
-            operation_id = f"{path}_{method}"
-        return operation_id.replace("-", "_").replace(".", "_").replace("/", "_")
+            path_item = self._get_path_strict(path)
+            operation_obj = getattr(path_item, method, None)
+            if not isinstance(operation_obj, Operation):
+                raise ValueError(f"No {method} method found for {path}")
+            return operation_obj
+
+        def get_parameters_for_operation(self, operation: Operation) -> List[Parameter]:
+            """Get the components for a given operation."""
+            from openapi_schema_pydantic import Reference
+
+            parameters = []
+            if operation.parameters:
+                for parameter in operation.parameters:
+                    if isinstance(parameter, Reference):
+                        parameter = self._get_root_referenced_parameter(parameter)
+                    parameters.append(parameter)
+            return parameters
+
+        def get_request_body_for_operation(
+            self, operation: Operation
+        ) -> Optional[RequestBody]:
+            """Get the request body for a given operation."""
+            from openapi_schema_pydantic import Reference
+
+            request_body = operation.requestBody
+            if isinstance(request_body, Reference):
+                request_body = self._get_root_referenced_request_body(request_body)
+            return request_body
+
+        @staticmethod
+        def get_cleaned_operation_id(
+            operation: Operation, path: str, method: str
+        ) -> str:
+            """Get a cleaned operation id from an operation id."""
+            operation_id = operation.operationId
+            if operation_id is None:
+                # Replace all punctuation of any kind with underscore
+                path = re.sub(r"[^a-zA-Z0-9]", "_", path.lstrip("/"))
+                operation_id = f"{path}_{method}"
+            return operation_id.replace("-", "_").replace(".", "_").replace("/", "_")
+
+else:
+
+    class OpenAPISpec:  # type: ignore[no-redef]
+        """Shim for pydantic version >=2"""
+
+        def __init__(self) -> None:
+            raise NotImplementedError("Only supported for pydantic version 1")
diff --git a/libs/langchain/langchain/utilities/openweathermap.py b/libs/langchain/langchain/utilities/openweathermap.py
index 7b157625e0b..8b64a7dff64 100644
--- a/libs/langchain/langchain/utilities/openweathermap.py
+++ b/libs/langchain/langchain/utilities/openweathermap.py
@@ -1,8 +1,7 @@
 """Util that calls OpenWeatherMap using PyOWM."""
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/powerbi.py b/libs/langchain/langchain/utilities/powerbi.py
index 5c00d017b10..f201bc49850 100644
--- a/libs/langchain/langchain/utilities/powerbi.py
+++ b/libs/langchain/langchain/utilities/powerbi.py
@@ -9,10 +9,11 @@ from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
 import aiohttp
 import requests
 from aiohttp import ServerTimeoutError
-from pydantic import BaseModel, Field, root_validator, validator
 from requests.exceptions import Timeout
 
-_LOGGER = logging.getLogger(__name__)
+from langchain.pydantic_v1 import BaseModel, Field, root_validator, validator
+
+logger = logging.getLogger(__name__)
 
 BASE_URL = os.getenv("POWERBI_BASE_URL", "https://api.powerbi.com/v1.0/myorg")
 
@@ -120,7 +121,7 @@ class PowerBIDataset(BaseModel):
                     table for table in fixed_tables if table not in self.table_names
                 ]
                 if non_existing_tables:
-                    _LOGGER.warning(
+                    logger.warning(
                         "Table(s) %s not found in dataset.",
                         ", ".join(non_existing_tables),
                     )
@@ -130,7 +131,7 @@ class PowerBIDataset(BaseModel):
                 return tables if tables else None
             if isinstance(table_names, str) and table_names != "":
                 if table_names not in self.table_names:
-                    _LOGGER.warning("Table %s not found in dataset.", table_names)
+                    logger.warning("Table %s not found in dataset.", table_names)
                     return None
                 return [fix_table_name(table_names)]
         return self.table_names
@@ -177,10 +178,10 @@ class PowerBIDataset(BaseModel):
             )
             self.schemas[table] = json_to_md(result["results"][0]["tables"][0]["rows"])
         except Timeout:
-            _LOGGER.warning("Timeout while getting table info for %s", table)
+            logger.warning("Timeout while getting table info for %s", table)
             self.schemas[table] = "unknown"
         except Exception as exc:  # pylint: disable=broad-exception-caught
-            _LOGGER.warning("Error while getting table info for %s: %s", table, exc)
+            logger.warning("Error while getting table info for %s: %s", table, exc)
             self.schemas[table] = "unknown"
 
     async def _aget_schema(self, table: str) -> None:
@@ -191,10 +192,10 @@ class PowerBIDataset(BaseModel):
             )
             self.schemas[table] = json_to_md(result["results"][0]["tables"][0]["rows"])
         except ServerTimeoutError:
-            _LOGGER.warning("Timeout while getting table info for %s", table)
+            logger.warning("Timeout while getting table info for %s", table)
             self.schemas[table] = "unknown"
         except Exception as exc:  # pylint: disable=broad-exception-caught
-            _LOGGER.warning("Error while getting table info for %s: %s", table, exc)
+            logger.warning("Error while getting table info for %s: %s", table, exc)
             self.schemas[table] = "unknown"
 
     def _create_json_content(self, command: str) -> dict[str, Any]:
@@ -207,7 +208,7 @@ class PowerBIDataset(BaseModel):
 
     def run(self, command: str) -> Any:
         """Execute a DAX command and return a json representing the results."""
-        _LOGGER.debug("Running command: %s", command)
+        logger.debug("Running command: %s", command)
         response = requests.post(
             self.request_url,
             json=self._create_json_content(command),
@@ -222,7 +223,7 @@ class PowerBIDataset(BaseModel):
 
     async def arun(self, command: str) -> Any:
         """Execute a DAX command and return the result asynchronously."""
-        _LOGGER.debug("Running command: %s", command)
+        logger.debug("Running command: %s", command)
         if self.aiosession:
             async with self.aiosession.post(
                 self.request_url,
diff --git a/libs/langchain/langchain/utilities/pubmed.py b/libs/langchain/langchain/utilities/pubmed.py
index 261358a6b35..2bb26b7a417 100644
--- a/libs/langchain/langchain/utilities/pubmed.py
+++ b/libs/langchain/langchain/utilities/pubmed.py
@@ -5,9 +5,7 @@ import urllib.error
 import urllib.request
 from typing import Any, Dict, Iterator, List
 
-from pydantic import BaseModel
-from pydantic.class_validators import root_validator
-
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.schema import Document
 
 logger = logging.getLogger(__name__)
@@ -36,14 +34,16 @@ class PubMedAPIWrapper(BaseModel):
 
     parse: Any  #: :meta private:
 
-    base_url_esearch = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?"
-    base_url_efetch = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?"
-    max_retry = 5
-    sleep_time = 0.2
+    base_url_esearch: str = (
+        "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?"
+    )
+    base_url_efetch: str = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?"
+    max_retry: int = 5
+    sleep_time: float = 0.2
 
     # Default values for the parameters
     top_k_results: int = 3
-    MAX_QUERY_LENGTH = 300
+    MAX_QUERY_LENGTH: int = 300
     doc_content_chars_max: int = 2000
     email: str = "your_email@example.com"
 
diff --git a/libs/langchain/langchain/utilities/python.py b/libs/langchain/langchain/utilities/python.py
index 348fd274b6c..d2f5d2fb72a 100644
--- a/libs/langchain/langchain/utilities/python.py
+++ b/libs/langchain/langchain/utilities/python.py
@@ -5,7 +5,7 @@ import sys
 from io import StringIO
 from typing import Dict, Optional
 
-from pydantic import BaseModel, Field
+from langchain.pydantic_v1 import BaseModel, Field
 
 logger = logging.getLogger(__name__)
 
diff --git a/libs/langchain/langchain/utilities/redis.py b/libs/langchain/langchain/utilities/redis.py
index 9255179999d..a9535c44259 100644
--- a/libs/langchain/langchain/utilities/redis.py
+++ b/libs/langchain/langchain/utilities/redis.py
@@ -68,14 +68,17 @@ def get_client(redis_url: str, **kwargs: Any) -> RedisType:
     # check if normal redis:// or redis+sentinel:// url
     if redis_url.startswith("redis+sentinel"):
         redis_client = _redis_sentinel_client(redis_url, **kwargs)
-    if redis_url.startswith("rediss+sentinel"):  # sentinel with TLS support enables
+    elif redis_url.startswith("rediss+sentinel"):  # sentinel with TLS support enables
         kwargs["ssl"] = True
         if "ssl_cert_reqs" not in kwargs:
             kwargs["ssl_cert_reqs"] = "none"
         redis_client = _redis_sentinel_client(redis_url, **kwargs)
     else:
-        # connect to redis server from url
+        # connect to redis server from url, reconnect with cluster client if needed
         redis_client = redis.from_url(redis_url, **kwargs)
+        if _check_for_cluster(redis_client):
+            redis_client.close()
+            redis_client = _redis_cluster_client(redis_url, **kwargs)
     return redis_client
 
 
@@ -138,3 +141,19 @@ answered NO PASSWORD NEEDED - Please check Sentinel configuration"
             raise ae
 
     return sentinel_client.master_for(service_name)
+
+
+def _check_for_cluster(redis_client: RedisType) -> bool:
+    import redis
+
+    try:
+        cluster_info = redis_client.info("cluster")
+        return cluster_info["cluster_enabled"] == 1
+    except redis.exceptions.RedisError:
+        return False
+
+
+def _redis_cluster_client(redis_url: str, **kwargs: Any) -> RedisType:
+    from redis.cluster import RedisCluster
+
+    return RedisCluster.from_url(redis_url, **kwargs)
diff --git a/libs/langchain/langchain/utilities/requests.py b/libs/langchain/langchain/utilities/requests.py
index 2891701c499..21f988a02c8 100644
--- a/libs/langchain/langchain/utilities/requests.py
+++ b/libs/langchain/langchain/utilities/requests.py
@@ -4,7 +4,8 @@ from typing import Any, AsyncGenerator, Dict, Optional
 
 import aiohttp
 import requests
-from pydantic import BaseModel, Extra
+
+from langchain.pydantic_v1 import BaseModel, Extra
 
 
 class Requests(BaseModel):
diff --git a/libs/langchain/langchain/utilities/scenexplain.py b/libs/langchain/langchain/utilities/scenexplain.py
index 1150a34dc3f..c43348ec8b8 100644
--- a/libs/langchain/langchain/utilities/scenexplain.py
+++ b/libs/langchain/langchain/utilities/scenexplain.py
@@ -8,8 +8,8 @@ You can obtain a key by following the steps below.
 from typing import Dict
 
 import requests
-from pydantic import BaseModel, BaseSettings, Field, root_validator
 
+from langchain.pydantic_v1 import BaseModel, BaseSettings, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/searx_search.py b/libs/langchain/langchain/utilities/searx_search.py
index e73e81c3804..5c0b6fddc67 100644
--- a/libs/langchain/langchain/utilities/searx_search.py
+++ b/libs/langchain/langchain/utilities/searx_search.py
@@ -132,8 +132,15 @@ from typing import Any, Dict, List, Optional
 
 import aiohttp
 import requests
-from pydantic import BaseModel, Extra, Field, PrivateAttr, root_validator, validator
 
+from langchain.pydantic_v1 import (
+    BaseModel,
+    Extra,
+    Field,
+    PrivateAttr,
+    root_validator,
+    validator,
+)
 from langchain.utils import get_from_dict_or_env
 
 
@@ -144,7 +151,7 @@ def _get_default_params() -> dict:
 class SearxResults(dict):
     """Dict like wrapper around search api results."""
 
-    _data = ""
+    _data: str = ""
 
     def __init__(self, data: str):
         """Take a raw result from Searx and make it into a dict like object."""
diff --git a/libs/langchain/langchain/utilities/serpapi.py b/libs/langchain/langchain/utilities/serpapi.py
index 98f4214ce06..ab132b53afb 100644
--- a/libs/langchain/langchain/utilities/serpapi.py
+++ b/libs/langchain/langchain/utilities/serpapi.py
@@ -7,8 +7,8 @@ import sys
 from typing import Any, Dict, Optional, Tuple
 
 import aiohttp
-from pydantic import BaseModel, Extra, Field, root_validator
 
+from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/tensorflow_datasets.py b/libs/langchain/langchain/utilities/tensorflow_datasets.py
index 1a7073d2f7a..680fe51d871 100644
--- a/libs/langchain/langchain/utilities/tensorflow_datasets.py
+++ b/libs/langchain/langchain/utilities/tensorflow_datasets.py
@@ -1,8 +1,7 @@
 import logging
 from typing import Any, Callable, Dict, Iterator, List, Optional
 
-from pydantic import BaseModel, root_validator
-
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.schema import Document
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/utilities/twilio.py b/libs/langchain/langchain/utilities/twilio.py
index 45e009bb2b2..07f937357c4 100644
--- a/libs/langchain/langchain/utilities/twilio.py
+++ b/libs/langchain/langchain/utilities/twilio.py
@@ -1,8 +1,7 @@
 """Util that calls Twilio."""
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/wikipedia.py b/libs/langchain/langchain/utilities/wikipedia.py
index 1202f8b24e1..dd38408e627 100644
--- a/libs/langchain/langchain/utilities/wikipedia.py
+++ b/libs/langchain/langchain/utilities/wikipedia.py
@@ -2,8 +2,7 @@
 import logging
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, root_validator
-
+from langchain.pydantic_v1 import BaseModel, root_validator
 from langchain.schema import Document
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/utilities/wolfram_alpha.py b/libs/langchain/langchain/utilities/wolfram_alpha.py
index a27aec051f4..1af50eaf4f3 100644
--- a/libs/langchain/langchain/utilities/wolfram_alpha.py
+++ b/libs/langchain/langchain/utilities/wolfram_alpha.py
@@ -1,8 +1,7 @@
 """Util that calls WolframAlpha."""
 from typing import Any, Dict, Optional
 
-from pydantic import BaseModel, Extra, root_validator
-
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utilities/zapier.py b/libs/langchain/langchain/utilities/zapier.py
index 5884f5e4e35..72321d27bb1 100644
--- a/libs/langchain/langchain/utilities/zapier.py
+++ b/libs/langchain/langchain/utilities/zapier.py
@@ -16,9 +16,9 @@ from typing import Any, Dict, List, Optional
 
 import aiohttp
 import requests
-from pydantic import BaseModel, Extra, root_validator
 from requests import Request, Session
 
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain.utils import get_from_dict_or_env
 
 
diff --git a/libs/langchain/langchain/utils/pydantic.py b/libs/langchain/langchain/utils/pydantic.py
new file mode 100644
index 00000000000..80ddb81fcb9
--- /dev/null
+++ b/libs/langchain/langchain/utils/pydantic.py
@@ -0,0 +1,14 @@
+"""Utilities for tests."""
+
+
+def get_pydantic_major_version() -> int:
+    """Get the major version of Pydantic."""
+    try:
+        import pydantic
+
+        return int(pydantic.__version__.split(".")[0])
+    except ImportError:
+        return 0
+
+
+PYDANTIC_MAJOR_VERSION = get_pydantic_major_version()
diff --git a/libs/langchain/langchain/vectorstores/__init__.py b/libs/langchain/langchain/vectorstores/__init__.py
index aefd5e69caa..f79ca1d77f9 100644
--- a/libs/langchain/langchain/vectorstores/__init__.py
+++ b/libs/langchain/langchain/vectorstores/__init__.py
@@ -27,17 +27,21 @@ from langchain.vectorstores.annoy import Annoy
 from langchain.vectorstores.atlas import AtlasDB
 from langchain.vectorstores.awadb import AwaDB
 from langchain.vectorstores.azuresearch import AzureSearch
+from langchain.vectorstores.bageldb import Bagel
 from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.cassandra import Cassandra
 from langchain.vectorstores.chroma import Chroma
 from langchain.vectorstores.clarifai import Clarifai
 from langchain.vectorstores.clickhouse import Clickhouse, ClickhouseSettings
+from langchain.vectorstores.dashvector import DashVector
 from langchain.vectorstores.deeplake import DeepLake
+from langchain.vectorstores.dingo import Dingo
 from langchain.vectorstores.docarray import DocArrayHnswSearch, DocArrayInMemorySearch
 from langchain.vectorstores.elastic_vector_search import (
     ElasticKnnSearch,
     ElasticVectorSearch,
 )
+from langchain.vectorstores.elasticsearch import ElasticsearchStore
 from langchain.vectorstores.faiss import FAISS
 from langchain.vectorstores.hologres import Hologres
 from langchain.vectorstores.lancedb import LanceDB
@@ -65,6 +69,7 @@ from langchain.vectorstores.typesense import Typesense
 from langchain.vectorstores.usearch import USearch
 from langchain.vectorstores.vectara import Vectara
 from langchain.vectorstores.weaviate import Weaviate
+from langchain.vectorstores.zep import ZepVectorStore
 from langchain.vectorstores.zilliz import Zilliz
 
 __all__ = [
@@ -75,15 +80,19 @@ __all__ = [
     "AtlasDB",
     "AwaDB",
     "AzureSearch",
+    "Bagel",
     "Cassandra",
     "Chroma",
     "Clickhouse",
     "ClickhouseSettings",
+    "DashVector",
     "DeepLake",
+    "Dingo",
     "DocArrayHnswSearch",
     "DocArrayInMemorySearch",
     "ElasticVectorSearch",
     "ElasticKnnSearch",
+    "ElasticsearchStore",
     "FAISS",
     "PGEmbedding",
     "Hologres",
@@ -119,6 +128,7 @@ __all__ = [
     "Vectara",
     "VectorStore",
     "Weaviate",
+    "ZepVectorStore",
     "Zilliz",
     "PGVector",
     "USearch",
diff --git a/libs/langchain/langchain/vectorstores/azuresearch.py b/libs/langchain/langchain/vectorstores/azuresearch.py
index b20274b868f..5f2a45e9361 100644
--- a/libs/langchain/langchain/vectorstores/azuresearch.py
+++ b/libs/langchain/langchain/vectorstores/azuresearch.py
@@ -18,7 +18,6 @@ from typing import (
 )
 
 import numpy as np
-from pydantic import root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -26,6 +25,7 @@ from langchain.callbacks.manager import (
 )
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import root_validator
 from langchain.schema import BaseRetriever
 from langchain.utils import get_from_env
 from langchain.vectorstores.base import VectorStore
diff --git a/libs/langchain/langchain/vectorstores/bageldb.py b/libs/langchain/langchain/vectorstores/bageldb.py
new file mode 100644
index 00000000000..ab0e7868caa
--- /dev/null
+++ b/libs/langchain/langchain/vectorstores/bageldb.py
@@ -0,0 +1,432 @@
+"""BagelDB integration"""
+from __future__ import annotations
+
+import uuid
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Tuple,
+    Type,
+)
+
+if TYPE_CHECKING:
+    import bagel
+    import bagel.config
+    from bagel.api.types import ID, OneOrMany, Where, WhereDocument
+
+from langchain.docstore.document import Document
+from langchain.embeddings.base import Embeddings
+from langchain.utils import xor_args
+from langchain.vectorstores.base import VectorStore
+
+DEFAULT_K = 5
+
+
+def _results_to_docs(results: Any) -> List[Document]:
+    return [doc for doc, _ in _results_to_docs_and_scores(results)]
+
+
+def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
+    return [
+        (Document(page_content=result[0], metadata=result[1] or {}), result[2])
+        for result in zip(
+            results["documents"][0],
+            results["metadatas"][0],
+            results["distances"][0],
+        )
+    ]
+
+
+class Bagel(VectorStore):
+    """Wrapper around BagelDB.ai vector store.
+
+    To use, you should have the ``betabageldb`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+                from langchain.vectorstores import Bagel
+                vectorstore = Bagel(cluster_name="langchain_store")
+    """
+
+    _LANGCHAIN_DEFAULT_CLUSTER_NAME = "langchain"
+
+    def __init__(
+        self,
+        cluster_name: str = _LANGCHAIN_DEFAULT_CLUSTER_NAME,
+        client_settings: Optional[bagel.config.Settings] = None,
+        embedding_function: Optional[Embeddings] = None,
+        cluster_metadata: Optional[Dict] = None,
+        client: Optional[bagel.Client] = None,
+        relevance_score_fn: Optional[Callable[[float], float]] = None,
+    ) -> None:
+        """Initialize with bagel client"""
+        try:
+            import bagel
+            import bagel.config
+        except ImportError:
+            raise ValueError("Please install bagel `pip install betabageldb`.")
+        if client is not None:
+            self._client_settings = client_settings
+            self._client = client
+        else:
+            if client_settings:
+                _client_settings = client_settings
+            else:
+                _client_settings = bagel.config.Settings(
+                    bagel_api_impl="rest",
+                    bagel_server_host="api.bageldb.ai",
+                )
+            self._client_settings = _client_settings
+            self._client = bagel.Client(_client_settings)
+
+        self._cluster = self._client.get_or_create_cluster(
+            name=cluster_name,
+            metadata=cluster_metadata,
+        )
+        self.override_relevance_score_fn = relevance_score_fn
+        self._embedding_function = embedding_function
+
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        return self._embedding_function
+
+    @xor_args(("query_texts", "query_embeddings"))
+    def __query_cluster(
+        self,
+        query_texts: Optional[List[str]] = None,
+        query_embeddings: Optional[List[List[float]]] = None,
+        n_results: int = 4,
+        where: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Query the BagelDB cluster based on the provided parameters."""
+        try:
+            import bagel  # noqa: F401
+        except ImportError:
+            raise ValueError("Please install bagel `pip install betabageldb`.")
+        return self._cluster.find(
+            query_texts=query_texts,
+            query_embeddings=query_embeddings,
+            n_results=n_results,
+            where=where,
+            **kwargs,
+        )
+
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+        embeddings: Optional[List[List[float]]] = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        """
+        Add texts along with their corresponding embeddings and optional
+        metadata to the BagelDB cluster.
+
+        Args:
+            texts (Iterable[str]): Texts to be added.
+            embeddings (Optional[List[float]]): List of embeddingvectors
+            metadatas (Optional[List[dict]]): Optional list of metadatas.
+            ids (Optional[List[str]]): List of unique ID for the texts.
+
+        Returns:
+            List[str]: List of unique ID representing the added texts.
+        """
+        # creating unique ids if None
+        if ids is None:
+            ids = [str(uuid.uuid1()) for _ in texts]
+
+        texts = list(texts)
+        if self._embedding_function and embeddings is None and texts:
+            embeddings = self._embedding_function.embed_documents(texts)
+        if metadatas:
+            length_diff = len(texts) - len(metadatas)
+            if length_diff:
+                metadatas = metadatas + [{}] * length_diff
+            empty_ids = []
+            non_empty_ids = []
+            for idx, metadata in enumerate(metadatas):
+                if metadata:
+                    non_empty_ids.append(idx)
+                else:
+                    empty_ids.append(idx)
+            if non_empty_ids:
+                metadatas = [metadatas[idx] for idx in non_empty_ids]
+                texts_with_metadatas = [texts[idx] for idx in non_empty_ids]
+                embeddings_with_metadatas = (
+                    [embeddings[idx] for idx in non_empty_ids] if embeddings else None
+                )
+                ids_with_metadata = [ids[idx] for idx in non_empty_ids]
+                self._cluster.upsert(
+                    embeddings=embeddings_with_metadatas,
+                    metadatas=metadatas,
+                    documents=texts_with_metadatas,
+                    ids=ids_with_metadata,
+                )
+            if empty_ids:
+                texts_without_metadatas = [texts[j] for j in empty_ids]
+                embeddings_without_metadatas = (
+                    [embeddings[j] for j in empty_ids] if embeddings else None
+                )
+                ids_without_metadatas = [ids[j] for j in empty_ids]
+                self._cluster.upsert(
+                    embeddings=embeddings_without_metadatas,
+                    documents=texts_without_metadatas,
+                    ids=ids_without_metadatas,
+                )
+        else:
+            metadatas = [{}] * len(texts)
+            self._cluster.upsert(
+                embeddings=embeddings,
+                documents=texts,
+                metadatas=metadatas,
+                ids=ids,
+            )
+        return ids
+
+    def similarity_search(
+        self,
+        query: str,
+        k: int = DEFAULT_K,
+        where: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """
+        Run a similarity search with BagelDB.
+
+        Args:
+            query (str): The query text to search for similar documents/texts.
+            k (int): The number of results to return.
+            where (Optional[Dict[str, str]]): Metadata filters to narrow down.
+
+        Returns:
+            List[Document]: List of documents objects representing
+            the documents most similar to the query text.
+        """
+        docs_and_scores = self.similarity_search_with_score(query, k, where=where)
+        return [doc for doc, _ in docs_and_scores]
+
+    def similarity_search_with_score(
+        self,
+        query: str,
+        k: int = DEFAULT_K,
+        where: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """
+        Run a similarity search with BagelDB and return documents with their
+        corresponding similarity scores.
+
+        Args:
+            query (str): The query text to search for similar documents.
+            k (int): The number of results to return.
+            where (Optional[Dict[str, str]]): Filter using metadata.
+
+        Returns:
+            List[Tuple[Document, float]]: List of tuples, each containing a
+            Document object representing a similar document and its
+            corresponding similarity score.
+
+        """
+        results = self.__query_cluster(query_texts=[query], n_results=k, where=where)
+        return _results_to_docs_and_scores(results)
+
+    @classmethod
+    def from_texts(
+        cls: Type[Bagel],
+        texts: List[str],
+        embedding: Optional[Embeddings] = None,
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+        cluster_name: str = _LANGCHAIN_DEFAULT_CLUSTER_NAME,
+        client_settings: Optional[bagel.config.Settings] = None,
+        cluster_metadata: Optional[Dict] = None,
+        client: Optional[bagel.Client] = None,
+        text_embeddings: Optional[List[List[float]]] = None,
+        **kwargs: Any,
+    ) -> Bagel:
+        """
+        Create and initialize a Bagel instance from list of texts.
+
+        Args:
+            texts (List[str]): List of text content to be added.
+            cluster_name (str): The name of the BagelDB cluster.
+            client_settings (Optional[bagel.config.Settings]): Client settings.
+            cluster_metadata (Optional[Dict]): Metadata of the cluster.
+            embeddings (Optional[Embeddings]): List of embedding.
+            metadatas (Optional[List[dict]]): List of metadata.
+            ids (Optional[List[str]]): List of unique ID. Defaults to None.
+            client (Optional[bagel.Client]): Bagel client instance.
+
+        Returns:
+            Bagel: Bagel vectorstore.
+        """
+        bagel_cluster = cls(
+            cluster_name=cluster_name,
+            embedding_function=embedding,
+            client_settings=client_settings,
+            client=client,
+            cluster_metadata=cluster_metadata,
+            **kwargs,
+        )
+        _ = bagel_cluster.add_texts(
+            texts=texts, embeddings=text_embeddings, metadatas=metadatas, ids=ids
+        )
+        return bagel_cluster
+
+    def delete_cluster(self) -> None:
+        """Delete the cluster."""
+        self._client.delete_cluster(self._cluster.name)
+
+    def similarity_search_by_vector_with_relevance_scores(
+        self,
+        query_embeddings: List[float],
+        k: int = DEFAULT_K,
+        where: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """
+        Return docs most similar to embedding vector and similarity score.
+        """
+        results = self.__query_cluster(
+            query_embeddings=query_embeddings, n_results=k, where=where
+        )
+        return _results_to_docs_and_scores(results)
+
+    def similarity_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = DEFAULT_K,
+        where: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs most similar to embedding vector."""
+        results = self.__query_cluster(
+            query_embeddings=embedding, n_results=k, where=where
+        )
+        return _results_to_docs(results)
+
+    def _select_relevance_score_fn(self) -> Callable[[float], float]:
+        """
+        Select and return the appropriate relevance score function based
+        on the distance metric used in the BagelDB cluster.
+        """
+        if self.override_relevance_score_fn:
+            return self.override_relevance_score_fn
+
+        distance = "l2"
+        distance_key = "hnsw:space"
+        metadata = self._cluster.metadata
+
+        if metadata and distance_key in metadata:
+            distance = metadata[distance_key]
+
+        if distance == "cosine":
+            return self._cosine_relevance_score_fn
+        elif distance == "l2":
+            return self._euclidean_relevance_score_fn
+        elif distance == "ip":
+            return self._max_inner_product_relevance_score_fn
+        else:
+            raise ValueError(
+                "No supported normalization function for distance"
+                f" metric of type: {distance}. Consider providing"
+                " relevance_score_fn to Bagel constructor."
+            )
+
+    @classmethod
+    def from_documents(
+        cls: Type[Bagel],
+        documents: List[Document],
+        embedding: Optional[Embeddings] = None,
+        ids: Optional[List[str]] = None,
+        cluster_name: str = _LANGCHAIN_DEFAULT_CLUSTER_NAME,
+        client_settings: Optional[bagel.config.Settings] = None,
+        client: Optional[bagel.Client] = None,
+        cluster_metadata: Optional[Dict] = None,
+        **kwargs: Any,
+    ) -> Bagel:
+        """
+        Create a Bagel vectorstore from a list of documents.
+
+        Args:
+            documents (List[Document]): List of Document objects to add to the
+                                        Bagel vectorstore.
+            embedding (Optional[List[float]]): List of embedding.
+            ids (Optional[List[str]]): List of IDs. Defaults to None.
+            cluster_name (str): The name of the BagelDB cluster.
+            client_settings (Optional[bagel.config.Settings]): Client settings.
+            client (Optional[bagel.Client]): Bagel client instance.
+            cluster_metadata (Optional[Dict]): Metadata associated with the
+                                               Bagel cluster. Defaults to None.
+
+        Returns:
+            Bagel: Bagel vectorstore.
+        """
+        texts = [doc.page_content for doc in documents]
+        metadatas = [doc.metadata for doc in documents]
+        return cls.from_texts(
+            texts=texts,
+            embedding=embedding,
+            metadatas=metadatas,
+            ids=ids,
+            cluster_name=cluster_name,
+            client_settings=client_settings,
+            client=client,
+            cluster_metadata=cluster_metadata,
+            **kwargs,
+        )
+
+    def update_document(self, document_id: str, document: Document) -> None:
+        """Update a document in the cluster.
+
+        Args:
+            document_id (str): ID of the document to update.
+            document (Document): Document to update.
+        """
+        text = document.page_content
+        metadata = document.metadata
+        self._cluster.update(
+            ids=[document_id],
+            documents=[text],
+            metadatas=[metadata],
+        )
+
+    def get(
+        self,
+        ids: Optional[OneOrMany[ID]] = None,
+        where: Optional[Where] = None,
+        limit: Optional[int] = None,
+        offset: Optional[int] = None,
+        where_document: Optional[WhereDocument] = None,
+        include: Optional[List[str]] = None,
+    ) -> Dict[str, Any]:
+        """Gets the collection."""
+        kwargs = {
+            "ids": ids,
+            "where": where,
+            "limit": limit,
+            "offset": offset,
+            "where_document": where_document,
+        }
+
+        if include is not None:
+            kwargs["include"] = include
+
+        return self._cluster.get(**kwargs)
+
+    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
+        """
+        Delete by IDs.
+
+        Args:
+            ids: List of ids to delete.
+        """
+        self._cluster.delete(ids=ids)
diff --git a/libs/langchain/langchain/vectorstores/base.py b/libs/langchain/langchain/vectorstores/base.py
index 8f27ce20b59..5bc624add4d 100644
--- a/libs/langchain/langchain/vectorstores/base.py
+++ b/libs/langchain/langchain/vectorstores/base.py
@@ -22,14 +22,13 @@ from typing import (
     TypeVar,
 )
 
-from pydantic import Field, root_validator
-
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
 )
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import Field, root_validator
 from langchain.schema import BaseRetriever
 
 logger = logging.getLogger(__name__)
diff --git a/libs/langchain/langchain/vectorstores/chroma.py b/libs/langchain/langchain/vectorstores/chroma.py
index 59edb26831f..9457419c56d 100644
--- a/libs/langchain/langchain/vectorstores/chroma.py
+++ b/libs/langchain/langchain/vectorstores/chroma.py
@@ -205,12 +205,22 @@ class Chroma(VectorStore):
                     [embeddings[idx] for idx in non_empty_ids] if embeddings else None
                 )
                 ids_with_metadata = [ids[idx] for idx in non_empty_ids]
-                self._collection.upsert(
-                    metadatas=metadatas,
-                    embeddings=embeddings_with_metadatas,
-                    documents=texts_with_metadatas,
-                    ids=ids_with_metadata,
-                )
+                try:
+                    self._collection.upsert(
+                        metadatas=metadatas,
+                        embeddings=embeddings_with_metadatas,
+                        documents=texts_with_metadatas,
+                        ids=ids_with_metadata,
+                    )
+                except ValueError as e:
+                    if "Expected metadata value to be" in str(e):
+                        msg = (
+                            "Try filtering complex metadata from the document using "
+                            "langchain.vectorstore.utils.filter_complex_metadata."
+                        )
+                        raise ValueError(e.args[0] + "\n\n" + msg)
+                    else:
+                        raise e
             if empty_ids:
                 texts_without_metadatas = [texts[j] for j in empty_ids]
                 embeddings_without_metadatas = (
diff --git a/libs/langchain/langchain/vectorstores/clickhouse.py b/libs/langchain/langchain/vectorstores/clickhouse.py
index 2544d45fd78..c0916ec446b 100644
--- a/libs/langchain/langchain/vectorstores/clickhouse.py
+++ b/libs/langchain/langchain/vectorstores/clickhouse.py
@@ -8,10 +8,9 @@ from hashlib import sha1
 from threading import Thread
 from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 
-from pydantic import BaseSettings
-
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseSettings
 from langchain.vectorstores.base import VectorStore
 
 logger = logging.getLogger()
diff --git a/libs/langchain/langchain/vectorstores/dashvector.py b/libs/langchain/langchain/vectorstores/dashvector.py
new file mode 100644
index 00000000000..714c76c0083
--- /dev/null
+++ b/libs/langchain/langchain/vectorstores/dashvector.py
@@ -0,0 +1,365 @@
+"""Wrapper around DashVector vector database."""
+from __future__ import annotations
+
+import logging
+import uuid
+from typing import (
+    Any,
+    Iterable,
+    List,
+    Optional,
+    Tuple,
+)
+
+import numpy as np
+
+from langchain.docstore.document import Document
+from langchain.embeddings.base import Embeddings
+from langchain.utils import get_from_env
+from langchain.vectorstores.base import VectorStore
+from langchain.vectorstores.utils import maximal_marginal_relevance
+
+logger = logging.getLogger(__name__)
+
+
+class DashVector(VectorStore):
+    """Wrapper around DashVector vector database.
+
+    To use, you should have the ``dashvector`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.vectorstores import dashvector
+            from langchain.embeddings.openai import OpenAIEmbeddings
+            import dashvector
+
+            client = dashvector.Client.init(api_key="***")
+            client.create("langchain")
+            collection = client.get("langchain")
+            embeddings = OpenAIEmbeddings()
+            vectorstore = Dashvector(collection, embeddings.embed_query, "text")
+    """
+
+    def __init__(
+        self,
+        collection: Any,
+        embedding: Embeddings,
+        text_field: str,
+    ):
+        """Initialize with DashVector collection."""
+
+        try:
+            import dashvector
+        except ImportError:
+            raise ValueError(
+                "Could not import dashvector python package. "
+                "Please install it with `pip install dashvector`."
+            )
+
+        if not isinstance(collection, dashvector.Collection):
+            raise ValueError(
+                f"collection should be an instance of dashvector.Collection, "
+                f"bug got {type(collection)}"
+            )
+
+        self._collection = collection
+        self._embedding = embedding
+        self._text_field = text_field
+
+    def _similarity_search_with_score_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        filter: Optional[str] = None,
+    ) -> List[Tuple[Document, float]]:
+        """Return docs most similar to query vector, along with scores"""
+
+        # query by vector
+        ret = self._collection.query(embedding, topk=k, filter=filter)
+        if not ret:
+            raise ValueError(
+                f"Fail to query docs by vector, error {self._collection.message}"
+            )
+
+        docs = []
+        for doc in ret:
+            metadata = doc.fields
+            text = metadata.pop(self._text_field)
+            score = doc.score
+            docs.append((Document(page_content=text, metadata=metadata), score))
+        return docs
+
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+        batch_size: int = 25,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the vectorstore.
+
+        Args:
+            texts: Iterable of strings to add to the vectorstore.
+            metadatas: Optional list of metadatas associated with the texts.
+            ids: Optional list of ids associated with the texts.
+            batch_size: Optional batch size to upsert docs.
+            kwargs: vectorstore specific parameters
+
+        Returns:
+            List of ids from adding the texts into the vectorstore.
+        """
+        ids = ids or [str(uuid.uuid4().hex) for _ in texts]
+        text_list = list(texts)
+        for i in range(0, len(text_list), batch_size):
+            # batch end
+            end = min(i + batch_size, len(text_list))
+
+            batch_texts = text_list[i:end]
+            batch_ids = ids[i:end]
+            batch_embeddings = self._embedding.embed_documents(list(batch_texts))
+
+            # batch metadatas
+            if metadatas:
+                batch_metadatas = metadatas[i:end]
+            else:
+                batch_metadatas = [{} for _ in range(i, end)]
+            for metadata, text in zip(batch_metadatas, batch_texts):
+                metadata[self._text_field] = text
+
+            # batch upsert to collection
+            docs = list(zip(batch_ids, batch_embeddings, batch_metadatas))
+            ret = self._collection.upsert(docs)
+            if not ret:
+                raise ValueError(
+                    f"Fail to upsert docs to dashvector vector database,"
+                    f"Error: {ret.message}"
+                )
+        return ids
+
+    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool:
+        """Delete by vector ID.
+
+        Args:
+            ids: List of ids to delete.
+
+        Returns:
+            True if deletion is successful,
+            False otherwise.
+        """
+        return bool(self._collection.delete(ids))
+
+    def similarity_search(
+        self,
+        query: str,
+        k: int = 4,
+        filter: Optional[str] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs most similar to query.
+
+        Args:
+            query: Text to search documents similar to.
+            k: Number of documents to return. Default to 4.
+            filter: Doc fields filter conditions that meet the SQL where clause
+                    specification.
+
+        Returns:
+            List of Documents most similar to the query text.
+        """
+
+        docs_and_scores = self.similarity_search_with_relevance_scores(query, k, filter)
+        return [doc for doc, _ in docs_and_scores]
+
+    def similarity_search_with_relevance_scores(
+        self,
+        query: str,
+        k: int = 4,
+        filter: Optional[str] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Return docs most similar to query text , alone with relevance scores.
+
+        Less is more similar, more is more dissimilar.
+
+        Args:
+            query: input text
+            k: Number of Documents to return. Defaults to 4.
+            filter: Doc fields filter conditions that meet the SQL where clause
+                    specification.
+
+        Returns:
+            List of Tuples of (doc, similarity_score)
+        """
+
+        embedding = self._embedding.embed_query(query)
+        return self._similarity_search_with_score_by_vector(
+            embedding, k=k, filter=filter
+        )
+
+    def similarity_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        filter: Optional[str] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs most similar to embedding vector.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            filter: Doc fields filter conditions that meet the SQL where clause
+                    specification.
+
+        Returns:
+            List of Documents most similar to the query vector.
+        """
+        docs_and_scores = self._similarity_search_with_score_by_vector(
+            embedding, k, filter
+        )
+        return [doc for doc, _ in docs_and_scores]
+
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        filter: Optional[dict] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+            filter: Doc fields filter conditions that meet the SQL where clause
+                    specification.
+
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        embedding = self._embedding.embed_query(query)
+        return self.max_marginal_relevance_search_by_vector(
+            embedding, k, fetch_k, lambda_mult, filter
+        )
+
+    def max_marginal_relevance_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        filter: Optional[dict] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+            filter: Doc fields filter conditions that meet the SQL where clause
+                    specification.
+
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+
+        # query by vector
+        ret = self._collection.query(
+            embedding, topk=fetch_k, filter=filter, include_vector=True
+        )
+        if not ret:
+            raise ValueError(
+                f"Fail to query docs by vector, error {self._collection.message}"
+            )
+
+        candidate_embeddings = [doc.vector for doc in ret]
+        mmr_selected = maximal_marginal_relevance(
+            np.array(embedding), candidate_embeddings, lambda_mult, k
+        )
+
+        metadatas = [ret.output[i].fields for i in mmr_selected]
+        return [
+            Document(page_content=metadata.pop(self._text_field), metadata=metadata)
+            for metadata in metadatas
+        ]
+
+    @classmethod
+    def from_texts(
+        cls,
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        dashvector_api_key: Optional[str] = None,
+        collection_name: str = "langchain",
+        text_field: str = "text",
+        batch_size: int = 25,
+        ids: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> DashVector:
+        """Return DashVector VectorStore initialized from texts and embeddings.
+
+        This is the quick way to get started with dashvector vector store.
+
+        Example:
+            .. code-block:: python
+
+            from langchain.vectorstores import DashVector
+            from langchain.embeddings import OpenAIEmbeddings
+            import dashvector
+
+            embeddings = OpenAIEmbeddings()
+            dashvector = DashVector.from_documents(
+                docs,
+                embeddings,
+                dashvector_api_key="{DASHVECTOR_API_KEY}"
+            )
+        """
+        try:
+            import dashvector
+        except ImportError:
+            raise ValueError(
+                "Could not import dashvector python package. "
+                "Please install it with `pip install dashvector`."
+            )
+
+        dashvector_api_key = dashvector_api_key or get_from_env(
+            "dashvector_api_key", "DASHVECTOR_API_KEY"
+        )
+
+        dashvector_client = dashvector.Client(api_key=dashvector_api_key)
+        dashvector_client.delete(collection_name)
+        collection = dashvector_client.get(collection_name)
+        if not collection:
+            dim = len(embedding.embed_query(texts[0]))
+            # create collection if not existed
+            resp = dashvector_client.create(collection_name, dimension=dim)
+            if resp:
+                collection = dashvector_client.get(collection_name)
+            else:
+                raise ValueError(
+                    "Fail to create collection. " f"Error: {resp.message}."
+                )
+
+        dashvector_vector_db = cls(collection, embedding, text_field)
+        dashvector_vector_db.add_texts(texts, metadatas, ids, batch_size)
+        return dashvector_vector_db
diff --git a/libs/langchain/langchain/vectorstores/deeplake.py b/libs/langchain/langchain/vectorstores/deeplake.py
index d4acdf55b95..c04d71aced6 100644
--- a/libs/langchain/langchain/vectorstores/deeplake.py
+++ b/libs/langchain/langchain/vectorstores/deeplake.py
@@ -142,10 +142,11 @@ class DeepLake(VectorStore):
 
         self.dataset_path = dataset_path
 
-        logger.warning(
-            "Using embedding function is deprecated and will be removed "
-            "in the future. Please use embedding instead."
-        )
+        if embedding_function:
+            logger.warning(
+                "Using embedding function is deprecated and will be removed "
+                "in the future. Please use embedding instead."
+            )
 
         self.vectorstore = DeepLakeVectorStore(
             path=self.dataset_path,
diff --git a/libs/langchain/langchain/vectorstores/dingo.py b/libs/langchain/langchain/vectorstores/dingo.py
new file mode 100644
index 00000000000..44765cdd310
--- /dev/null
+++ b/libs/langchain/langchain/vectorstores/dingo.py
@@ -0,0 +1,359 @@
+"""Wrapper around the Dingo vector database."""
+from __future__ import annotations
+
+import logging
+import uuid
+from typing import Any, Iterable, List, Optional, Tuple
+
+import numpy as np
+
+from langchain.docstore.document import Document
+from langchain.embeddings.base import Embeddings
+from langchain.vectorstores.base import VectorStore
+from langchain.vectorstores.utils import maximal_marginal_relevance
+
+logger = logging.getLogger(__name__)
+
+
+class Dingo(VectorStore):
+    """Wrapper around Dingo vector database.
+
+    To use, you should have the ``dingodb`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.vectorstores import Dingo
+            from langchain.embeddings.openai import OpenAIEmbeddings
+
+            embeddings = OpenAIEmbeddings()
+            dingo = Dingo(embeddings, "text")
+    """
+
+    def __init__(
+        self,
+        embedding: Embeddings,
+        text_key: str,
+        *,
+        client: Any = None,
+        index_name: Optional[str] = None,
+        host: Optional[List[str]] = None,
+        user: str = "root",
+        password: str = "123123",
+        self_id: bool = False,
+    ):
+        """Initialize with Dingo client."""
+        try:
+            import dingodb
+        except ImportError:
+            raise ImportError(
+                "Could not import dingo python package. "
+                "Please install it with `pip install dingodb."
+            )
+
+        host = host if host is not None else ["172.20.31.10:13000"]
+
+        # collection
+        if client is not None:
+            dingo_client = client
+        else:
+            try:
+                # connect to dingo db
+                dingo_client = dingodb.DingoDB(user, password, host)
+            except ValueError as e:
+                raise ValueError(f"Dingo failed to connect: {e}")
+
+        self._text_key = text_key
+        self._client = dingo_client
+
+        if index_name is not None and index_name not in dingo_client.get_index():
+            if self_id is True:
+                dingo_client.create_index(index_name, 1024, auto_id=False)
+            else:
+                dingo_client.create_index(index_name, 1024)
+
+        self._index_name = index_name
+        self._embedding = embedding
+
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        return self._embedding
+
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+        text_key: str = "text",
+        batch_size: int = 500,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the vectorstore.
+
+        Args:
+            texts: Iterable of strings to add to the vectorstore.
+            metadatas: Optional list of metadatas associated with the texts.
+            ids: Optional list of ids to associate with the texts.
+
+        Returns:
+            List of ids from adding the texts into the vectorstore.
+
+        """
+
+        # Embed and create the documents
+        ids = ids or [str(uuid.uuid1().int)[:13] for _ in texts]
+        metadatas_list = []
+        texts = list(texts)
+        embeds = self._embedding.embed_documents(texts)
+        for i, text in enumerate(texts):
+            metadata = metadatas[i] if metadatas else {}
+            metadata[self._text_key] = text
+            metadatas_list.append(metadata)
+        # upsert to Dingo
+        for i in range(0, len(list(texts)), batch_size):
+            j = i + batch_size
+            add_res = self._client.vector_add(
+                self._index_name, metadatas_list[i:j], embeds[i:j], ids[i:j]
+            )
+            if not add_res:
+                raise Exception("vector add fail")
+
+        return ids
+
+    def similarity_search(
+        self,
+        query: str,
+        k: int = 4,
+        search_params: Optional[dict] = None,
+        timeout: Optional[int] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return Dingo documents most similar to query, along with scores.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            search_params: Dictionary of argument(s) to filter on metadata
+
+        Returns:
+            List of Documents most similar to the query and score for each
+        """
+        docs_and_scores = self.similarity_search_with_score(
+            query, k=k, search_params=search_params
+        )
+        return [doc for doc, _ in docs_and_scores]
+
+    def similarity_search_with_score(
+        self,
+        query: str,
+        k: int = 4,
+        search_params: Optional[dict] = None,
+        timeout: Optional[int] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Return Dingo documents most similar to query, along with scores.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            search_params: Dictionary of argument(s) to filter on metadata
+
+        Returns:
+            List of Documents most similar to the query and score for each
+        """
+        docs = []
+        query_obj = self._embedding.embed_query(query)
+        results = self._client.vector_search(
+            self._index_name, xq=query_obj, top_k=k, search_params=search_params
+        )
+
+        if not results:
+            return []
+
+        for res in results[0]["vectorWithDistances"]:
+            metadatas = res["scalarData"]
+            id = res["id"]
+            score = res["distance"]
+            text = metadatas[self._text_key]["fields"][0]["data"]
+
+            metadata = {"id": id, "text": text, "score": score}
+            docs.append((Document(page_content=text, metadata=metadata), score))
+
+        return docs
+
+    def max_marginal_relevance_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        search_params: Optional[dict] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        results = self._client.vector_search(
+            self._index_name, [embedding], search_params=search_params, top_k=k
+        )
+
+        mmr_selected = maximal_marginal_relevance(
+            np.array([embedding], dtype=np.float32),
+            [
+                item["vector"]["floatValues"]
+                for item in results[0]["vectorWithDistances"]
+            ],
+            k=k,
+            lambda_mult=lambda_mult,
+        )
+        selected = []
+        for i in mmr_selected:
+            meta_data = {}
+            for k, v in results[0]["vectorWithDistances"][i]["scalarData"].items():
+                meta_data.update({str(k): v["fields"][0]["data"]})
+            selected.append(meta_data)
+        return [
+            Document(page_content=metadata.pop(self._text_key), metadata=metadata)
+            for metadata in selected
+        ]
+
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        search_params: Optional[dict] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        embedding = self._embedding.embed_query(query)
+        return self.max_marginal_relevance_search_by_vector(
+            embedding, k, fetch_k, lambda_mult, search_params
+        )
+
+    @classmethod
+    def from_texts(
+        cls,
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+        text_key: str = "text",
+        index_name: Optional[str] = None,
+        client: Any = None,
+        host: List[str] = ["172.20.31.10:13000"],
+        user: str = "root",
+        password: str = "123123",
+        batch_size: int = 500,
+        **kwargs: Any,
+    ) -> Dingo:
+        """Construct Dingo wrapper from raw documents.
+
+                This is a user friendly interface that:
+                    1. Embeds documents.
+                    2. Adds the documents to a provided Dingo index
+
+                This is intended to be a quick way to get started.
+
+                Example:
+                    .. code-block:: python
+
+                        from langchain import Dingo
+                        from langchain.embeddings import OpenAIEmbeddings
+                        import dingodb
+        sss
+                        embeddings = OpenAIEmbeddings()
+                        dingo = Dingo.from_texts(
+                            texts,
+                            embeddings,
+                            index_name="langchain-demo"
+                        )
+        """
+        try:
+            import dingodb
+        except ImportError:
+            raise ImportError(
+                "Could not import dingo python package. "
+                "Please install it with `pip install dingodb`."
+            )
+
+        if client is not None:
+            dingo_client = client
+        else:
+            try:
+                # connect to dingo db
+                dingo_client = dingodb.DingoDB(user, password, host)
+            except ValueError as e:
+                raise ValueError(f"Dingo failed to connect: {e}")
+        if kwargs is not None and kwargs.get("self_id") is True:
+            if index_name not in dingo_client.get_index():
+                dingo_client.create_index(index_name, 1024, auto_id=False)
+        else:
+            if index_name not in dingo_client.get_index():
+                dingo_client.create_index(index_name, 1024)
+            # dingo_client.create_index(index_name, 1024, index_type="hnsw")
+
+        # Embed and create the documents
+
+        ids = ids or [str(uuid.uuid1().int)[:13] for _ in texts]
+        metadatas_list = []
+        texts = list(texts)
+        embeds = embedding.embed_documents(texts)
+        for i, text in enumerate(texts):
+            metadata = metadatas[i] if metadatas else {}
+            metadata[text_key] = text
+            metadatas_list.append(metadata)
+
+        # upsert to Dingo
+        for i in range(0, len(list(texts)), batch_size):
+            j = i + batch_size
+            add_res = dingo_client.vector_add(
+                index_name, metadatas_list[i:j], embeds[i:j], ids[i:j]
+            )
+            if not add_res:
+                raise Exception("vector add fail")
+        return cls(embedding, text_key, client=dingo_client, index_name=index_name)
+
+    def delete(
+        self,
+        ids: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Delete by vector IDs or filter.
+        Args:
+            ids: List of ids to delete.
+        """
+
+        if ids is None:
+            raise ValueError("No ids provided to delete.")
+
+        return self._client.vector_delete(self._index_name, ids=ids)
diff --git a/libs/langchain/langchain/vectorstores/docarray/base.py b/libs/langchain/langchain/vectorstores/docarray/base.py
index fe58f49fcb2..2e693bcde14 100644
--- a/libs/langchain/langchain/vectorstores/docarray/base.py
+++ b/libs/langchain/langchain/vectorstores/docarray/base.py
@@ -2,9 +2,9 @@ from abc import ABC
 from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
 
 import numpy as np
-from pydantic import Field
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import Field
 from langchain.schema import Document
 from langchain.vectorstores import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
diff --git a/libs/langchain/langchain/vectorstores/elastic_vector_search.py b/libs/langchain/langchain/vectorstores/elastic_vector_search.py
index 7be146c161d..b77743012bf 100644
--- a/libs/langchain/langchain/vectorstores/elastic_vector_search.py
+++ b/libs/langchain/langchain/vectorstores/elastic_vector_search.py
@@ -1,7 +1,8 @@
-"""Wrapper around Elasticsearch vector database."""
+"""[DEPRECATED] Please use ElasticsearchStore instead."""
 from __future__ import annotations
 
 import uuid
+import warnings
 from abc import ABC
 from typing import (
     TYPE_CHECKING,
@@ -15,6 +16,7 @@ from typing import (
     Union,
 )
 
+from langchain._api import deprecated
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
 from langchain.utils import get_from_dict_or_env
@@ -50,13 +52,7 @@ def _default_script_query(query_vector: List[float], filter: Optional[dict]) ->
     }
 
 
-# ElasticVectorSearch is a concrete implementation of the abstract base class
-# VectorStore, which defines a common interface for all vector database
-# implementations. By inheriting from the ABC class, ElasticVectorSearch can be
-# defined as an abstract base class itself, allowing the creation of subclasses with
-# their own specific implementations. If you plan to subclass ElasticVectorSearch,
-# you can inherit from it and define your own implementation of the necessary methods
-# and attributes.
+@deprecated("0.0.265", alternative="ElasticsearchStore class.", pending=True)
 class ElasticVectorSearch(VectorStore, ABC):
     """Wrapper around Elasticsearch as a vector database.
 
@@ -136,6 +132,11 @@ class ElasticVectorSearch(VectorStore, ABC):
         ssl_verify: Optional[Dict[str, Any]] = None,
     ):
         """Initialize with necessary components."""
+        warnings.warn(
+            "ElasticVectorSearch will be removed in a future release. See"
+            "Elasticsearch integration docs on how to upgrade."
+        )
+
         try:
             import elasticsearch
         except ImportError:
@@ -392,6 +393,11 @@ class ElasticKnnSearch(VectorStore, ABC):
                 "Please install it with `pip install elasticsearch`."
             )
 
+        warnings.warn(
+            "ElasticKnnSearch will be removed in a future release."
+            "Use ElasticsearchStore instead. See Elasticsearch "
+            "integration docs on how to upgrade."
+        )
         self.embedding = embedding
         self.index_name = index_name
         self.query_field = query_field
diff --git a/libs/langchain/langchain/vectorstores/elasticsearch.py b/libs/langchain/langchain/vectorstores/elasticsearch.py
new file mode 100644
index 00000000000..c8dd5990189
--- /dev/null
+++ b/libs/langchain/langchain/vectorstores/elasticsearch.py
@@ -0,0 +1,1078 @@
+"""Wrapper around Elasticsearch vector database."""
+
+import logging
+import uuid
+from abc import ABC, abstractmethod
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Literal,
+    Optional,
+    Tuple,
+    Union,
+)
+
+from langchain.docstore.document import Document
+from langchain.embeddings.base import Embeddings
+from langchain.vectorstores.base import VectorStore
+from langchain.vectorstores.utils import DistanceStrategy
+
+if TYPE_CHECKING:
+    from elasticsearch import Elasticsearch
+
+logger = logging.getLogger(__name__)
+
+
+class BaseRetrievalStrategy(ABC):
+    @abstractmethod
+    def query(
+        self,
+        query_vector: Union[List[float], None],
+        query: Union[str, None],
+        *,
+        k: int,
+        fetch_k: int,
+        vector_query_field: str,
+        text_field: str,
+        filter: List[dict],
+        similarity: Union[DistanceStrategy, None],
+    ) -> Dict:
+        """
+        Executes when a search is performed on the store.
+
+        Args:
+            query_vector: The query vector,
+                          or None if not using vector-based query.
+            query: The text query, or None if not using text-based query.
+            k: The total number of results to retrieve.
+            fetch_k: The number of results to fetch initially.
+            vector_query_field: The field containing the vector
+                                representations in the index.
+            text_field: The field containing the text data in the index.
+            filter: List of filter clauses to apply to the query.
+            similarity: The similarity strategy to use, or None if not using one.
+
+        Returns:
+            Dict: The Elasticsearch query body.
+        """
+
+    @abstractmethod
+    def index(
+        self,
+        dims_length: Union[int, None],
+        vector_query_field: str,
+        similarity: Union[DistanceStrategy, None],
+    ) -> Dict:
+        """
+        Executes when the index is created.
+
+        Args:
+            dims_length: Numeric length of the embedding vectors,
+                        or None if not using vector-based query.
+            vector_query_field: The field containing the vector
+                                representations in the index.
+            similarity: The similarity strategy to use,
+                        or None if not using one.
+
+        Returns:
+            Dict: The Elasticsearch settings and mappings for the strategy.
+        """
+
+    def before_index_setup(
+        self, client: "Elasticsearch", text_field: str, vector_query_field: str
+    ) -> None:
+        """
+        Executes before the index is created. Used for setting up
+        any required Elasticsearch resources like a pipeline.
+
+        Args:
+            client: The Elasticsearch client.
+            text_field: The field containing the text data in the index.
+            vector_query_field: The field containing the vector
+                                representations in the index.
+        """
+
+    def require_inference(self) -> bool:
+        """
+        Returns whether or not the strategy requires inference
+        to be performed on the text before it is added to the index.
+
+        Returns:
+            bool: Whether or not the strategy requires inference
+            to be performed on the text before it is added to the index.
+        """
+        return True
+
+
+class ApproxRetrievalStrategy(BaseRetrievalStrategy):
+    def __init__(
+        self,
+        query_model_id: Optional[str] = None,
+        hybrid: Optional[bool] = False,
+    ):
+        self.query_model_id = query_model_id
+        self.hybrid = hybrid
+
+    def query(
+        self,
+        query_vector: Union[List[float], None],
+        query: Union[str, None],
+        k: int,
+        fetch_k: int,
+        vector_query_field: str,
+        text_field: str,
+        filter: List[dict],
+        similarity: Union[DistanceStrategy, None],
+    ) -> Dict:
+        knn = {
+            "filter": filter,
+            "field": vector_query_field,
+            "k": k,
+            "num_candidates": fetch_k,
+        }
+
+        # Embedding provided via the embedding function
+        if query_vector and not self.query_model_id:
+            knn["query_vector"] = query_vector
+
+        # Case 2: Used when model has been deployed to
+        # Elasticsearch and can infer the query vector from the query text
+        elif query and self.query_model_id:
+            knn["query_vector_builder"] = {
+                "text_embedding": {
+                    "model_id": self.query_model_id,  # use 'model_id' argument
+                    "model_text": query,  # use 'query' argument
+                }
+            }
+
+        else:
+            raise ValueError(
+                "You must provide an embedding function or a"
+                " query_model_id to perform a similarity search."
+            )
+
+        # If hybrid, add a query to the knn query
+        # RRF is used to even the score from the knn query and text query
+        if self.hybrid:
+            return {
+                "knn": knn,
+                "query": {
+                    "bool": {
+                        "must": [
+                            {
+                                "match": {
+                                    text_field: {
+                                        "query": query,
+                                    }
+                                }
+                            }
+                        ],
+                        "filter": filter,
+                    }
+                },
+                "rank": {"rrf": {}},
+            }
+        else:
+            return {"knn": knn}
+
+    def index(
+        self,
+        dims_length: Union[int, None],
+        vector_query_field: str,
+        similarity: Union[DistanceStrategy, None],
+    ) -> Dict:
+        """Create the mapping for the Elasticsearch index."""
+
+        if similarity is DistanceStrategy.COSINE:
+            similarityAlgo = "cosine"
+        elif similarity is DistanceStrategy.EUCLIDEAN_DISTANCE:
+            similarityAlgo = "l2_norm"
+        elif similarity is DistanceStrategy.DOT_PRODUCT:
+            similarityAlgo = "dot_product"
+        else:
+            raise ValueError(f"Similarity {similarity} not supported.")
+
+        return {
+            "mappings": {
+                "properties": {
+                    vector_query_field: {
+                        "type": "dense_vector",
+                        "dims": dims_length,
+                        "index": True,
+                        "similarity": similarityAlgo,
+                    },
+                }
+            }
+        }
+
+
+class ExactRetrievalStrategy(BaseRetrievalStrategy):
+    def query(
+        self,
+        query_vector: Union[List[float], None],
+        query: Union[str, None],
+        k: int,
+        fetch_k: int,
+        vector_query_field: str,
+        text_field: str,
+        filter: Union[List[dict], None],
+        similarity: Union[DistanceStrategy, None],
+    ) -> Dict:
+        if similarity is DistanceStrategy.COSINE:
+            similarityAlgo = (
+                f"cosineSimilarity(params.query_vector, '{vector_query_field}') + 1.0"
+            )
+        elif similarity is DistanceStrategy.EUCLIDEAN_DISTANCE:
+            similarityAlgo = (
+                f"1 / (1 + l2norm(params.query_vector, '{vector_query_field}'))"
+            )
+        elif similarity is DistanceStrategy.DOT_PRODUCT:
+            similarityAlgo = f"""
+            double value = dotProduct(params.query_vector, '{vector_query_field}');
+            return sigmoid(1, Math.E, -value); 
+            """
+        else:
+            raise ValueError(f"Similarity {similarity} not supported.")
+
+        queryBool: Dict = {"match_all": {}}
+        if filter:
+            queryBool = {"bool": {"filter": filter}}
+
+        return {
+            "query": {
+                "script_score": {
+                    "query": queryBool,
+                    "script": {
+                        "source": similarityAlgo,
+                        "params": {"query_vector": query_vector},
+                    },
+                },
+            }
+        }
+
+    def index(
+        self,
+        dims_length: Union[int, None],
+        vector_query_field: str,
+        similarity: Union[DistanceStrategy, None],
+    ) -> Dict:
+        """Create the mapping for the Elasticsearch index."""
+
+        return {
+            "mappings": {
+                "properties": {
+                    vector_query_field: {
+                        "type": "dense_vector",
+                        "dims": dims_length,
+                        "index": False,
+                    },
+                }
+            }
+        }
+
+
+class SparseRetrievalStrategy(BaseRetrievalStrategy):
+    def __init__(self, model_id: Optional[str] = None):
+        self.model_id = model_id or ".elser_model_1"
+
+    def query(
+        self,
+        query_vector: Union[List[float], None],
+        query: Union[str, None],
+        k: int,
+        fetch_k: int,
+        vector_query_field: str,
+        text_field: str,
+        filter: List[dict],
+        similarity: Union[DistanceStrategy, None],
+    ) -> Dict:
+        return {
+            "query": {
+                "bool": {
+                    "must": [
+                        {
+                            "text_expansion": {
+                                f"{vector_query_field}.tokens": {
+                                    "model_id": self.model_id,
+                                    "model_text": query,
+                                }
+                            }
+                        }
+                    ],
+                    "filter": filter,
+                }
+            }
+        }
+
+    def _get_pipeline_name(self) -> str:
+        return f"{self.model_id}_sparse_embedding"
+
+    def before_index_setup(
+        self, client: "Elasticsearch", text_field: str, vector_query_field: str
+    ) -> None:
+        # If model_id is provided, create a pipeline for the model
+        if self.model_id:
+            client.ingest.put_pipeline(
+                id=self._get_pipeline_name(),
+                description="Embedding pipeline for langchain vectorstore",
+                processors=[
+                    {
+                        "inference": {
+                            "model_id": self.model_id,
+                            "target_field": vector_query_field,
+                            "field_map": {text_field: "text_field"},
+                            "inference_config": {
+                                "text_expansion": {"results_field": "tokens"}
+                            },
+                        }
+                    }
+                ],
+            )
+
+    def index(
+        self,
+        dims_length: Union[int, None],
+        vector_query_field: str,
+        similarity: Union[DistanceStrategy, None],
+    ) -> Dict:
+        return {
+            "mappings": {
+                "properties": {
+                    vector_query_field: {
+                        "properties": {"tokens": {"type": "rank_features"}}
+                    }
+                }
+            },
+            "settings": {"default_pipeline": self._get_pipeline_name()},
+        }
+
+    def require_inference(self) -> bool:
+        return False
+
+
+class ElasticsearchStore(VectorStore):
+
+    """Wrapper around Elasticsearch search database.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.vectorstores import ElasticsearchStore
+            from langchain.embeddings.openai import OpenAIEmbeddings
+
+            embeddings = OpenAIEmbeddings()
+            vectorstore = ElasticsearchStore(
+                embedding=OpenAIEmbeddings(),
+                index_name="langchain-demo",
+                es_url="http://localhost:9200"
+            )
+
+    Args:
+        index_name: Name of the Elasticsearch index to create.
+        es_url: URL of the Elasticsearch instance to connect to.
+        cloud_id: Cloud ID of the Elasticsearch instance to connect to.
+        es_user: Username to use when connecting to Elasticsearch.
+        es_password: Password to use when connecting to Elasticsearch.
+        es_api_key: API key to use when connecting to Elasticsearch.
+        es_connection: Optional pre-existing Elasticsearch connection.
+        vector_query_field: Optional. Name of the field to store
+                            the embedding vectors in.
+        query_field: Optional. Name of the field to store the texts in.
+        strategy: Optional. Retrieval strategy to use when searching the index.
+                 Defaults to ApproxRetrievalStrategy. Can be one of
+                 ExactRetrievalStrategy, ApproxRetrievalStrategy,
+                 or SparseRetrievalStrategy.
+        distance_strategy: Optional. Distance strategy to use when
+                            searching the index.
+                            Defaults to COSINE. Can be one of COSINE,
+                            EUCLIDEAN_DISTANCE, or DOT_PRODUCT.
+
+    If you want to use a cloud hosted Elasticsearch instance, you can pass in the
+    cloud_id argument instead of the es_url argument.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.vectorstores import ElasticsearchStore
+            from langchain.embeddings.openai import OpenAIEmbeddings
+
+            vectorstore = ElasticsearchStore(
+                embedding=OpenAIEmbeddings(),
+                index_name="langchain-demo",
+                es_cloud_id="<cloud_id>"
+                es_user="elastic",
+                es_password="<password>"
+            )
+
+    You can also connect to an existing Elasticsearch instance by passing in a
+    pre-existing Elasticsearch connection via the es_connection argument.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.vectorstores import ElasticsearchStore
+            from langchain.embeddings.openai import OpenAIEmbeddings
+
+            from elasticsearch import Elasticsearch
+
+            es_connection = Elasticsearch("http://localhost:9200")
+
+            vectorstore = ElasticsearchStore(
+                embedding=OpenAIEmbeddings(),
+                index_name="langchain-demo",
+                es_connection=es_connection
+            )
+
+    ElasticsearchStore by default uses the ApproxRetrievalStrategy, which uses the
+    HNSW algorithm to perform approximate nearest neighbor search. This is the
+    fastest and most memory efficient algorithm.
+
+    If you want to use the Brute force / Exact strategy for searching vectors, you
+    can pass in the ExactRetrievalStrategy to the ElasticsearchStore constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.vectorstores import ElasticsearchStore
+            from langchain.embeddings.openai import OpenAIEmbeddings
+
+            vectorstore = ElasticsearchStore(
+                embedding=OpenAIEmbeddings(),
+                index_name="langchain-demo",
+                es_url="http://localhost:9200",
+                strategy=ElasticsearchStore.ExactRetrievalStrategy()
+            )
+
+    Both strategies require that you know the similarity metric you want to use
+    when creating the index. The default is cosine similarity, but you can also
+    use dot product or euclidean distance.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.vectorstores import ElasticsearchStore
+            from langchain.embeddings.openai import OpenAIEmbeddings
+            from langchain.vectorstores.utils import DistanceStrategy
+
+            vectorstore = ElasticsearchStore(
+                embedding=OpenAIEmbeddings(),
+                index_name="langchain-demo",
+                es_url="http://localhost:9200",
+                distance_strategy="DOT_PRODUCT"
+            )
+
+    """
+
+    def __init__(
+        self,
+        index_name: str,
+        *,
+        embedding: Optional[Embeddings] = None,
+        es_connection: Optional["Elasticsearch"] = None,
+        es_url: Optional[str] = None,
+        es_cloud_id: Optional[str] = None,
+        es_user: Optional[str] = None,
+        es_api_key: Optional[str] = None,
+        es_password: Optional[str] = None,
+        vector_query_field: str = "vector",
+        query_field: str = "text",
+        distance_strategy: Optional[
+            Literal[
+                DistanceStrategy.COSINE,
+                DistanceStrategy.DOT_PRODUCT,
+                DistanceStrategy.EUCLIDEAN_DISTANCE,
+            ]
+        ] = None,
+        strategy: BaseRetrievalStrategy = ApproxRetrievalStrategy(),
+    ):
+        self.embedding = embedding
+        self.index_name = index_name
+        self.query_field = query_field
+        self.vector_query_field = vector_query_field
+        self.distance_strategy = (
+            DistanceStrategy.COSINE
+            if distance_strategy is None
+            else DistanceStrategy[distance_strategy]
+        )
+        self.strategy = strategy
+
+        if es_connection is not None:
+            self.client = es_connection
+        elif es_url is not None or es_cloud_id is not None:
+            self.client = ElasticsearchStore.connect_to_elasticsearch(
+                es_url=es_url,
+                username=es_user,
+                password=es_password,
+                cloud_id=es_cloud_id,
+                api_key=es_api_key,
+            )
+        else:
+            raise ValueError(
+                """Either provide a pre-existing Elasticsearch connection, \
+                or valid credentials for creating a new connection."""
+            )
+
+    @staticmethod
+    def connect_to_elasticsearch(
+        *,
+        es_url: Optional[str] = None,
+        cloud_id: Optional[str] = None,
+        api_key: Optional[str] = None,
+        username: Optional[str] = None,
+        password: Optional[str] = None,
+    ) -> "Elasticsearch":
+        try:
+            import elasticsearch
+        except ImportError:
+            raise ImportError(
+                "Could not import elasticsearch python package. "
+                "Please install it with `pip install elasticsearch`."
+            )
+
+        if es_url and cloud_id:
+            raise ValueError(
+                "Both es_url and cloud_id are defined. Please provide only one."
+            )
+
+        connection_params: Dict[str, Any] = {}
+
+        if es_url:
+            connection_params["hosts"] = [es_url]
+        elif cloud_id:
+            connection_params["cloud_id"] = cloud_id
+        else:
+            raise ValueError("Please provide either elasticsearch_url or cloud_id.")
+
+        if api_key:
+            connection_params["api_key"] = api_key
+        elif username and password:
+            connection_params["basic_auth"] = (username, password)
+
+        es_client = elasticsearch.Elasticsearch(**connection_params)
+        try:
+            es_client.info()
+        except Exception as e:
+            logger.error(f"Error connecting to Elasticsearch: {e}")
+            raise e
+
+        return es_client
+
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        return self.embedding
+
+    def similarity_search(
+        self,
+        query: str,
+        k: int = 4,
+        filter: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return Elasticsearch documents most similar to query.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            filter: Array of Elasticsearch filter clauses to apply to the query.
+
+        Returns:
+            List of Documents most similar to the query,
+            in descending order of similarity.
+        """
+
+        results = self._search(query=query, k=k, filter=filter, **kwargs)
+        return [doc for doc, _ in results]
+
+    def similarity_search_with_score(
+        self, query: str, k: int = 4, filter: Optional[List[dict]] = None, **kwargs: Any
+    ) -> List[Tuple[Document, float]]:
+        """Return Elasticsearch documents most similar to query, along with scores.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            filter: Array of Elasticsearch filter clauses to apply to the query.
+
+        Returns:
+            List of Documents most similar to the query and score for each
+        """
+        return self._search(query=query, k=k, filter=filter, **kwargs)
+
+    def similarity_search_by_vector_with_relevance_scores(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        filter: Optional[List[Dict]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Return Elasticsearch documents most similar to query, along with scores.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            filter: Array of Elasticsearch filter clauses to apply to the query.
+
+        Returns:
+            List of Documents most similar to the embedding and score for each
+        """
+        return self._search(query_vector=embedding, k=k, filter=filter, **kwargs)
+
+    def _search(
+        self,
+        query: Optional[str] = None,
+        k: int = 4,
+        query_vector: Union[List[float], None] = None,
+        fetch_k: int = 50,
+        fields: Optional[List[str]] = None,
+        filter: Optional[List[dict]] = None,
+        custom_query: Optional[Callable[[Dict, Union[str, None]], Dict]] = None,
+    ) -> List[Tuple[Document, float]]:
+        """Return Elasticsearch documents most similar to query, along with scores.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            query_vector: Embedding to look up documents similar to.
+            fetch_k: Number of candidates to fetch from each shard.
+                    Defaults to 50.
+            fields: List of fields to return from Elasticsearch.
+                    Defaults to only returning the text field.
+            filter: Array of Elasticsearch filter clauses to apply to the query.
+            custom_query: Function to modify the Elasticsearch
+                         query body before it is sent to Elasticsearch.
+
+        Returns:
+            List of Documents most similar to the query and score for each
+        """
+        if fields is None:
+            fields = ["metadata"]
+
+        if self.query_field not in fields:
+            fields.append(self.query_field)
+
+        if self.embedding and query is not None:
+            query_vector = self.embedding.embed_query(query)
+
+        query_body = self.strategy.query(
+            query_vector=query_vector,
+            query=query,
+            k=k,
+            fetch_k=fetch_k,
+            vector_query_field=self.vector_query_field,
+            text_field=self.query_field,
+            filter=filter or [],
+            similarity=self.distance_strategy,
+        )
+
+        logger.debug(f"Query body: {query_body}")
+
+        if custom_query is not None:
+            query_body = custom_query(query_body, query)
+            logger.debug(f"Calling custom_query, Query body now: {query_body}")
+
+        # Perform the kNN search on the Elasticsearch index and return the results.
+        response = self.client.search(
+            index=self.index_name,
+            **query_body,
+            size=k,
+            source=fields,
+        )
+
+        hits = [hit for hit in response["hits"]["hits"]]
+        docs_and_scores = [
+            (
+                Document(
+                    page_content=hit["_source"][self.query_field],
+                    metadata=hit["_source"]["metadata"],
+                ),
+                hit["_score"],
+            )
+            for hit in hits
+        ]
+
+        return docs_and_scores
+
+    def delete(
+        self,
+        ids: Optional[List[str]] = None,
+        refresh_indices: Optional[bool] = True,
+        **kwargs: Any,
+    ) -> Optional[bool]:
+        """Delete documents from the Elasticsearch index.
+
+        Args:
+            ids: List of ids of documents to delete.
+            refresh_indices: Whether to refresh the index
+                            after deleting documents. Defaults to True.
+        """
+        try:
+            from elasticsearch.helpers import bulk
+        except ImportError:
+            raise ImportError(
+                "Could not import elasticsearch python package. "
+                "Please install it with `pip install elasticsearch`."
+            )
+
+        body = []
+
+        if ids is None:
+            raise ValueError("ids must be provided.")
+
+        for _id in ids:
+            body.append({"_op_type": "delete", "_index": self.index_name, "_id": _id})
+
+        if len(body) > 0:
+            try:
+                bulk(self.client, body, refresh=refresh_indices, ignore_status=404)
+                logger.debug(f"Deleted {len(body)} texts from index")
+
+                return True
+            except Exception as e:
+                logger.error(f"Error deleting texts: {e}")
+                raise e
+
+        else:
+            logger.debug("No texts to delete from index")
+            return False
+
+    def _create_index_if_not_exists(
+        self, index_name: str, dims_length: Optional[int] = None
+    ) -> None:
+        """Create the Elasticsearch index if it doesn't already exist.
+
+        Args:
+            index_name: Name of the Elasticsearch index to create.
+            dims_length: Length of the embedding vectors.
+        """
+
+        if self.client.indices.exists(index=index_name):
+            logger.debug(f"Index {index_name} already exists. Skipping creation.")
+
+        else:
+            if dims_length is None and self.strategy.require_inference():
+                raise ValueError(
+                    "Cannot create index without specifying dims_length "
+                    "when the index doesn't already exist. We infer "
+                    "dims_length from the first embedding. Check that "
+                    "you have provided an embedding function."
+                )
+
+            self.strategy.before_index_setup(
+                client=self.client,
+                text_field=self.query_field,
+                vector_query_field=self.vector_query_field,
+            )
+
+            indexSettings = self.strategy.index(
+                vector_query_field=self.vector_query_field,
+                dims_length=dims_length,
+                similarity=self.distance_strategy,
+            )
+            logger.debug(
+                f"Creating index {index_name} with mappings {indexSettings['mappings']}"
+            )
+            self.client.indices.create(index=index_name, **indexSettings)
+
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[Dict[Any, Any]]] = None,
+        ids: Optional[List[str]] = None,
+        refresh_indices: bool = True,
+        create_index_if_not_exists: bool = True,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the vectorstore.
+
+        Args:
+            texts: Iterable of strings to add to the vectorstore.
+            metadatas: Optional list of metadatas associated with the texts.
+            ids: Optional list of ids to associate with the texts.
+            refresh_indices: Whether to refresh the Elasticsearch indices
+                            after adding the texts.
+            create_index_if_not_exists: Whether to create the Elasticsearch
+                                        index if it doesn't already exist.
+
+        Returns:
+            List of ids from adding the texts into the vectorstore.
+        """
+        try:
+            from elasticsearch.helpers import bulk
+        except ImportError:
+            raise ImportError(
+                "Could not import elasticsearch python package. "
+                "Please install it with `pip install elasticsearch`."
+            )
+
+        embeddings = []
+        ids = ids or [str(uuid.uuid4()) for _ in texts]
+        requests = []
+
+        if self.embedding is not None:
+            # If no search_type requires inference, we use the provided
+            # embedding function to embed the texts.
+            embeddings = self.embedding.embed_documents(list(texts))
+            dims_length = len(embeddings[0])
+
+            if create_index_if_not_exists:
+                self._create_index_if_not_exists(
+                    index_name=self.index_name, dims_length=dims_length
+                )
+
+            for i, (text, vector) in enumerate(zip(texts, embeddings)):
+                metadata = metadatas[i] if metadatas else {}
+
+                requests.append(
+                    {
+                        "_op_type": "index",
+                        "_index": self.index_name,
+                        self.query_field: text,
+                        self.vector_query_field: vector,
+                        "metadata": metadata,
+                        "_id": ids[i],
+                    }
+                )
+
+        else:
+            # the search_type doesn't require inference, so we don't need to
+            # embed the texts.
+            if create_index_if_not_exists:
+                self._create_index_if_not_exists(index_name=self.index_name)
+
+            for i, text in enumerate(texts):
+                metadata = metadatas[i] if metadatas else {}
+
+                requests.append(
+                    {
+                        "_op_type": "index",
+                        "_index": self.index_name,
+                        self.query_field: text,
+                        "metadata": metadata,
+                        "_id": ids[i],
+                    }
+                )
+
+        if len(requests) > 0:
+            try:
+                success, failed = bulk(
+                    self.client, requests, stats_only=True, refresh=refresh_indices
+                )
+                logger.debug(
+                    f"Added {success} and failed to add {failed} texts to index"
+                )
+
+                logger.debug(f"added texts {ids} to index")
+                return ids
+            except Exception as e:
+                logger.error(f"Error adding texts: {e}")
+                raise e
+
+        else:
+            logger.debug("No texts to add to index")
+            return []
+
+    @classmethod
+    def from_texts(
+        cls,
+        texts: List[str],
+        embedding: Optional[Embeddings] = None,
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        **kwargs: Any,
+    ) -> "ElasticsearchStore":
+        """Construct ElasticsearchStore wrapper from raw documents.
+
+        Example:
+            .. code-block:: python
+
+                from langchain.vectorstores import ElasticsearchStore
+                from langchain.embeddings.openai import OpenAIEmbeddings
+
+                db = ElasticsearchStore.from_texts(
+                    texts,
+                    // embeddings optional if using
+                    // a strategy that doesn't require inference
+                    embeddings,
+                    index_name="langchain-demo",
+                    es_url="http://localhost:9200"
+                )
+
+        Args:
+            texts: List of texts to add to the Elasticsearch index.
+            embedding: Embedding function to use to embed the texts.
+            metadatas: Optional list of metadatas associated with the texts.
+            index_name: Name of the Elasticsearch index to create.
+            es_url: URL of the Elasticsearch instance to connect to.
+            cloud_id: Cloud ID of the Elasticsearch instance to connect to.
+            es_user: Username to use when connecting to Elasticsearch.
+            es_password: Password to use when connecting to Elasticsearch.
+            es_api_key: API key to use when connecting to Elasticsearch.
+            es_connection: Optional pre-existing Elasticsearch connection.
+            vector_query_field: Optional. Name of the field to
+                                store the embedding vectors in.
+            query_field: Optional. Name of the field to store the texts in.
+            distance_strategy: Optional. Name of the distance
+                                strategy to use. Defaults to "COSINE".
+                                can be one of "COSINE",
+                                "EUCLIDEAN_DISTANCE", "DOT_PRODUCT".
+        """
+
+        elasticsearchStore = ElasticsearchStore._create_cls_from_kwargs(
+            embedding=embedding, **kwargs
+        )
+
+        # Encode the provided texts and add them to the newly created index.
+        elasticsearchStore.add_texts(texts, metadatas=metadatas)
+
+        return elasticsearchStore
+
+    @staticmethod
+    def _create_cls_from_kwargs(
+        embedding: Optional[Embeddings] = None, **kwargs: Any
+    ) -> "ElasticsearchStore":
+        index_name = kwargs.get("index_name")
+
+        if index_name is None:
+            raise ValueError("Please provide an index_name.")
+
+        es_connection = kwargs.get("es_connection")
+        es_cloud_id = kwargs.get("es_cloud_id")
+        es_url = kwargs.get("es_url")
+        es_user = kwargs.get("es_user")
+        es_password = kwargs.get("es_password")
+        es_api_key = kwargs.get("es_api_key")
+        vector_query_field = kwargs.get("vector_query_field")
+        query_field = kwargs.get("query_field")
+        distance_strategy = kwargs.get("distance_strategy")
+        strategy = kwargs.get("strategy", ElasticsearchStore.ApproxRetrievalStrategy())
+
+        optional_args = {}
+
+        if vector_query_field is not None:
+            optional_args["vector_query_field"] = vector_query_field
+
+        if query_field is not None:
+            optional_args["query_field"] = query_field
+
+        return ElasticsearchStore(
+            index_name=index_name,
+            embedding=embedding,
+            es_url=es_url,
+            es_connection=es_connection,
+            es_cloud_id=es_cloud_id,
+            es_user=es_user,
+            es_password=es_password,
+            es_api_key=es_api_key,
+            strategy=strategy,
+            distance_strategy=distance_strategy,
+            **optional_args,
+        )
+
+    @classmethod
+    def from_documents(
+        cls,
+        documents: List[Document],
+        embedding: Optional[Embeddings] = None,
+        **kwargs: Any,
+    ) -> "ElasticsearchStore":
+        """Construct ElasticsearchStore wrapper from documents.
+
+        Example:
+            .. code-block:: python
+
+                from langchain.vectorstores import ElasticsearchStore
+                from langchain.embeddings.openai import OpenAIEmbeddings
+
+                db = ElasticsearchStore.from_documents(
+                    texts,
+                    embeddings,
+                    index_name="langchain-demo",
+                    es_url="http://localhost:9200"
+                )
+
+        Args:
+            texts: List of texts to add to the Elasticsearch index.
+            embedding: Embedding function to use to embed the texts.
+                      Do not provide if using a strategy
+                      that doesn't require inference.
+            metadatas: Optional list of metadatas associated with the texts.
+            index_name: Name of the Elasticsearch index to create.
+            es_url: URL of the Elasticsearch instance to connect to.
+            cloud_id: Cloud ID of the Elasticsearch instance to connect to.
+            es_user: Username to use when connecting to Elasticsearch.
+            es_password: Password to use when connecting to Elasticsearch.
+            es_api_key: API key to use when connecting to Elasticsearch.
+            es_connection: Optional pre-existing Elasticsearch connection.
+            vector_query_field: Optional. Name of the field
+                                to store the embedding vectors in.
+            query_field: Optional. Name of the field to store the texts in.
+        """
+
+        elasticsearchStore = ElasticsearchStore._create_cls_from_kwargs(
+            embedding=embedding, **kwargs
+        )
+        # Encode the provided texts and add them to the newly created index.
+        elasticsearchStore.add_documents(documents)
+
+        return elasticsearchStore
+
+    @staticmethod
+    def ExactRetrievalStrategy() -> "ExactRetrievalStrategy":
+        """Used to perform brute force / exact
+        nearest neighbor search via script_score."""
+        return ExactRetrievalStrategy()
+
+    @staticmethod
+    def ApproxRetrievalStrategy(
+        query_model_id: Optional[str] = None,
+        hybrid: Optional[bool] = False,
+    ) -> "ApproxRetrievalStrategy":
+        """Used to perform approximate nearest neighbor search
+        using the HNSW algorithm.
+
+        At build index time, this strategy will create a
+        dense vector field in the index and store the
+        embedding vectors in the index.
+
+        At query time, the text will either be embedded using the
+        provided embedding function or the query_model_id
+        will be used to embed the text using the model
+        deployed to Elasticsearch.
+
+        if query_model_id is used, do not provide an embedding function.
+
+        Args:
+            query_model_id: Optional. ID of the model to use to
+                            embed the query text within the stack. Requires
+                            embedding model to be deployed to Elasticsearch.
+            hybrid: Optional. If True, will perform a hybrid search
+                    using both the knn query and a text query.
+                    Defaults to False.
+        """
+        return ApproxRetrievalStrategy(query_model_id=query_model_id, hybrid=hybrid)
+
+    @staticmethod
+    def SparseVectorRetrievalStrategy(
+        model_id: Optional[str] = None,
+    ) -> "SparseRetrievalStrategy":
+        """Used to perform sparse vector search via text_expansion.
+        Used for when you want to use ELSER model to perform document search.
+
+        At build index time, this strategy will create a pipeline that
+        will embed the text using the ELSER model and store the
+        resulting tokens in the index.
+
+        At query time, the text will be embedded using the ELSER
+        model and the resulting tokens will be used to
+        perform a text_expansion query.
+
+        Args:
+            model_id: Optional. Default is ".elser_model_1".
+                    ID of the model to use to embed the query text
+                    within the stack. Requires embedding model to be
+                    deployed to Elasticsearch.
+        """
+        return SparseRetrievalStrategy(model_id=model_id)
diff --git a/libs/langchain/langchain/vectorstores/faiss.py b/libs/langchain/langchain/vectorstores/faiss.py
index 898a96f1558..7e9cb109e34 100644
--- a/libs/langchain/langchain/vectorstores/faiss.py
+++ b/libs/langchain/langchain/vectorstores/faiss.py
@@ -7,7 +7,16 @@ import pickle
 import uuid
 import warnings
 from pathlib import Path
-from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Sized,
+    Tuple,
+)
 
 import numpy as np
 
@@ -46,16 +55,29 @@ def dependable_faiss_import(no_avx2: Optional[bool] = None) -> Any:
     return faiss
 
 
+def _len_check_if_sized(x: Any, y: Any, x_name: str, y_name: str) -> None:
+    if isinstance(x, Sized) and isinstance(y, Sized) and len(x) != len(y):
+        raise ValueError(
+            f"{x_name} and {y_name} expected to be equal length but "
+            f"len({x_name})={len(x)} and len({y_name})={len(y)}"
+        )
+    return
+
+
 class FAISS(VectorStore):
     """Wrapper around FAISS vector database.
 
-    To use, you should have the ``faiss`` python package installed.
+    To use, you must have the ``faiss`` python package installed.
 
     Example:
         .. code-block:: python
 
-            from langchain import FAISS
-            faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id)
+            from langchain.embeddings.openai import OpenAIEmbeddings
+            from langchain.vectorstores import FAISS
+
+            embeddings = OpenAIEmbeddings()
+            texts = ["FAISS is an important library", "LangChain supports FAISS"]
+            faiss = FAISS.from_texts(texts, embeddings)
 
     """
 
@@ -87,44 +109,43 @@ class FAISS(VectorStore):
                 )
             )
 
-    @property
-    def embeddings(self) -> Optional[Embeddings]:
-        # TODO: Accept embeddings object directly
-        return None
-
     def __add(
         self,
         texts: Iterable[str],
         embeddings: Iterable[List[float]],
-        metadatas: Optional[List[dict]] = None,
+        metadatas: Optional[Iterable[dict]] = None,
         ids: Optional[List[str]] = None,
-        **kwargs: Any,
     ) -> List[str]:
+        faiss = dependable_faiss_import()
+
         if not isinstance(self.docstore, AddableMixin):
             raise ValueError(
                 "If trying to add texts, the underlying docstore should support "
                 f"adding items, which {self.docstore} does not"
             )
-        documents = []
-        for i, text in enumerate(texts):
-            metadata = metadatas[i] if metadatas else {}
-            documents.append(Document(page_content=text, metadata=metadata))
-        if ids is None:
-            ids = [str(uuid.uuid4()) for _ in texts]
-        # Add to the index, the index_to_id mapping, and the docstore.
-        starting_len = len(self.index_to_docstore_id)
-        faiss = dependable_faiss_import()
+
+        _len_check_if_sized(texts, metadatas, "texts", "metadatas")
+        _metadatas = metadatas or ({} for _ in texts)
+        documents = [
+            Document(page_content=t, metadata=m) for t, m in zip(texts, _metadatas)
+        ]
+
+        _len_check_if_sized(documents, embeddings, "documents", "embeddings")
+        _len_check_if_sized(documents, ids, "documents", "ids")
+
+        # Add to the index.
         vector = np.array(embeddings, dtype=np.float32)
         if self._normalize_L2:
             faiss.normalize_L2(vector)
         self.index.add(vector)
-        # Get list of index, id, and docs.
-        full_info = [(starting_len + i, ids[i], doc) for i, doc in enumerate(documents)]
+
         # Add information to docstore and index.
-        self.docstore.add({_id: doc for _, _id, doc in full_info})
-        index_to_id = {index: _id for index, _id, _ in full_info}
+        ids = ids or [str(uuid.uuid4()) for _ in texts]
+        self.docstore.add({id_: doc for id_, doc in zip(ids, documents)})
+        starting_len = len(self.index_to_docstore_id)
+        index_to_id = {starting_len + j: id_ for j, id_ in enumerate(ids)}
         self.index_to_docstore_id.update(index_to_id)
-        return [_id for _, _id, _ in full_info]
+        return ids
 
     def add_texts(
         self,
@@ -143,14 +164,8 @@ class FAISS(VectorStore):
         Returns:
             List of ids from adding the texts into the vectorstore.
         """
-        if not isinstance(self.docstore, AddableMixin):
-            raise ValueError(
-                "If trying to add texts, the underlying docstore should support "
-                f"adding items, which {self.docstore} does not"
-            )
-        # Embed and create the documents.
         embeddings = [self.embedding_function(text) for text in texts]
-        return self.__add(texts, embeddings, metadatas=metadatas, ids=ids, **kwargs)
+        return self.__add(texts, embeddings, metadatas=metadatas, ids=ids)
 
     def add_embeddings(
         self,
@@ -170,15 +185,9 @@ class FAISS(VectorStore):
         Returns:
             List of ids from adding the texts into the vectorstore.
         """
-        if not isinstance(self.docstore, AddableMixin):
-            raise ValueError(
-                "If trying to add texts, the underlying docstore should support "
-                f"adding items, which {self.docstore} does not"
-            )
         # Embed and create the documents.
         texts, embeddings = zip(*text_embeddings)
-
-        return self.__add(texts, embeddings, metadatas=metadatas, ids=ids, **kwargs)
+        return self.__add(texts, embeddings, metadatas=metadatas, ids=ids)
 
     def similarity_search_with_score_by_vector(
         self,
@@ -480,22 +489,26 @@ class FAISS(VectorStore):
         """
         if ids is None:
             raise ValueError("No ids provided to delete.")
+        missing_ids = set(ids).difference(self.index_to_docstore_id.values())
+        if missing_ids:
+            raise ValueError(
+                f"Some specified ids do not exist in the current store. Ids not found: "
+                f"{missing_ids}"
+            )
 
-        overlapping = set(ids).intersection(self.index_to_docstore_id.values())
-        if not overlapping:
-            raise ValueError("ids do not exist in the current object")
+        reversed_index = {id_: idx for idx, id_ in self.index_to_docstore_id.items()}
+        index_to_delete = [reversed_index[id_] for id_ in ids]
 
-        _reversed_index = {v: k for k, v in self.index_to_docstore_id.items()}
-
-        index_to_delete = [_reversed_index[i] for i in ids]
-
-        # Removing ids from index.
         self.index.remove_ids(np.array(index_to_delete, dtype=np.int64))
-        for _id in index_to_delete:
-            del self.index_to_docstore_id[_id]
-
-        # Remove items from docstore.
         self.docstore.delete(ids)
+
+        remaining_ids = [
+            id_
+            for i, id_ in sorted(self.index_to_docstore_id.items())
+            if i not in index_to_delete
+        ]
+        self.index_to_docstore_id = {i: id_ for i, id_ in enumerate(remaining_ids)}
+
         return True
 
     def merge_from(self, target: FAISS) -> None:
@@ -533,50 +546,32 @@ class FAISS(VectorStore):
     @classmethod
     def __from(
         cls,
-        texts: List[str],
+        texts: Iterable[str],
         embeddings: List[List[float]],
         embedding: Embeddings,
-        metadatas: Optional[List[dict]] = None,
+        metadatas: Optional[Iterable[dict]] = None,
         ids: Optional[List[str]] = None,
         normalize_L2: bool = False,
+        distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE,
         **kwargs: Any,
     ) -> FAISS:
         faiss = dependable_faiss_import()
-        distance_strategy = kwargs.get(
-            "distance_strategy", DistanceStrategy.EUCLIDEAN_DISTANCE
-        )
         if distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
             index = faiss.IndexFlatIP(len(embeddings[0]))
         else:
             # Default to L2, currently other metric types not initialized.
             index = faiss.IndexFlatL2(len(embeddings[0]))
-        vector = np.array(embeddings, dtype=np.float32)
-        if normalize_L2 and distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
-            faiss.normalize_L2(vector)
-        index.add(vector)
-        documents = []
-        if ids is None:
-            ids = [str(uuid.uuid4()) for _ in texts]
-        for i, text in enumerate(texts):
-            metadata = metadatas[i] if metadatas else {}
-            documents.append(Document(page_content=text, metadata=metadata))
-        index_to_id = dict(enumerate(ids))
-
-        if len(index_to_id) != len(documents):
-            raise Exception(
-                f"{len(index_to_id)} ids provided for {len(documents)} documents."
-                " Each document should have an id."
-            )
-
-        docstore = InMemoryDocstore(dict(zip(index_to_id.values(), documents)))
-        return cls(
+        vecstore = cls(
             embedding.embed_query,
             index,
-            docstore,
-            index_to_id,
+            InMemoryDocstore(),
+            {},
             normalize_L2=normalize_L2,
+            distance_strategy=distance_strategy,
             **kwargs,
         )
+        vecstore.__add(texts, embeddings, metadatas=metadatas, ids=ids)
+        return vecstore
 
     @classmethod
     def from_texts(
@@ -601,6 +596,7 @@ class FAISS(VectorStore):
 
                 from langchain import FAISS
                 from langchain.embeddings import OpenAIEmbeddings
+
                 embeddings = OpenAIEmbeddings()
                 faiss = FAISS.from_texts(texts, embeddings)
         """
@@ -617,9 +613,9 @@ class FAISS(VectorStore):
     @classmethod
     def from_embeddings(
         cls,
-        text_embeddings: List[Tuple[str, List[float]]],
+        text_embeddings: Iterable[Tuple[str, List[float]]],
         embedding: Embeddings,
-        metadatas: Optional[List[dict]] = None,
+        metadatas: Optional[Iterable[dict]] = None,
         ids: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> FAISS:
@@ -637,9 +633,10 @@ class FAISS(VectorStore):
 
                 from langchain import FAISS
                 from langchain.embeddings import OpenAIEmbeddings
+
                 embeddings = OpenAIEmbeddings()
                 text_embeddings = embeddings.embed_documents(texts)
-                text_embedding_pairs = list(zip(texts, text_embeddings))
+                text_embedding_pairs = zip(texts, text_embeddings)
                 faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings)
         """
         texts = [t[0] for t in text_embeddings]
diff --git a/libs/langchain/langchain/vectorstores/myscale.py b/libs/langchain/langchain/vectorstores/myscale.py
index 28e0824e131..7bbb20dcfb9 100644
--- a/libs/langchain/langchain/vectorstores/myscale.py
+++ b/libs/langchain/langchain/vectorstores/myscale.py
@@ -7,10 +7,9 @@ from hashlib import sha1
 from threading import Thread
 from typing import Any, Dict, Iterable, List, Optional, Tuple
 
-from pydantic import BaseSettings
-
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseSettings
 from langchain.vectorstores.base import VectorStore
 
 logger = logging.getLogger()
diff --git a/libs/langchain/langchain/vectorstores/pinecone.py b/libs/langchain/langchain/vectorstores/pinecone.py
index f9cc2303a60..6ecf0b291b2 100644
--- a/libs/langchain/langchain/vectorstores/pinecone.py
+++ b/libs/langchain/langchain/vectorstores/pinecone.py
@@ -3,7 +3,8 @@ from __future__ import annotations
 
 import logging
 import uuid
-from typing import Any, Callable, Iterable, List, Optional, Tuple
+import warnings
+from typing import Any, Callable, Iterable, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -38,7 +39,7 @@ class Pinecone(VectorStore):
     def __init__(
         self,
         index: Any,
-        embedding_function: Callable,
+        embedding: Union[Embeddings, Callable],
         text_key: str,
         namespace: Optional[str] = None,
         distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE,
@@ -47,7 +48,7 @@ class Pinecone(VectorStore):
         try:
             import pinecone
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import pinecone python package. "
                 "Please install it with `pip install pinecone-client`."
             )
@@ -56,17 +57,36 @@ class Pinecone(VectorStore):
                 f"client should be an instance of pinecone.index.Index, "
                 f"got {type(index)}"
             )
+        if not isinstance(embedding, Embeddings):
+            warnings.warn(
+                "Passing in `embedding` as a Callable is deprecated. Please pass in an"
+                " Embeddings object instead."
+            )
         self._index = index
-        self._embedding_function = embedding_function
+        self._embedding = embedding
         self._text_key = text_key
         self._namespace = namespace
         self.distance_strategy = distance_strategy
 
     @property
     def embeddings(self) -> Optional[Embeddings]:
-        # TODO: Accept this object directly
+        """Access the query embedding object if available."""
+        if isinstance(self._embedding, Embeddings):
+            return self._embedding
         return None
 
+    def _embed_documents(self, texts: Iterable[str]) -> List[List[float]]:
+        """Embed search docs."""
+        if isinstance(self._embedding, Embeddings):
+            return self._embedding.embed_documents(list(texts))
+        return [self._embedding(t) for t in texts]
+
+    def _embed_query(self, text: str) -> List[float]:
+        """Embed query text."""
+        if isinstance(self._embedding, Embeddings):
+            return self._embedding.embed_query(text)
+        return self._embedding(text)
+
     def add_texts(
         self,
         texts: Iterable[str],
@@ -93,8 +113,8 @@ class Pinecone(VectorStore):
         # Embed and create the documents
         docs = []
         ids = ids or [str(uuid.uuid4()) for _ in texts]
-        for i, text in enumerate(texts):
-            embedding = self._embedding_function(text)
+        embeddings = self._embed_documents(texts)
+        for i, (text, embedding) in enumerate(zip(texts, embeddings)):
             metadata = metadatas[i] if metadatas else {}
             metadata[self._text_key] = text
             docs.append((ids[i], embedding, metadata))
@@ -122,12 +142,25 @@ class Pinecone(VectorStore):
         Returns:
             List of Documents most similar to the query and score for each
         """
+        return self.similarity_search_by_vector_with_score(
+            self._embed_query(query), k=k, filter=filter, namespace=namespace
+        )
+
+    def similarity_search_by_vector_with_score(
+        self,
+        embedding: List[float],
+        *,
+        k: int = 4,
+        filter: Optional[dict] = None,
+        namespace: Optional[str] = None,
+    ) -> List[Tuple[Document, float]]:
+        """Return pinecone documents most similar to embedding, along with scores."""
+
         if namespace is None:
             namespace = self._namespace
-        query_obj = self._embedding_function(query)
         docs = []
         results = self._index.query(
-            [query_obj],
+            [embedding],
             top_k=k,
             include_metadata=True,
             namespace=namespace,
@@ -265,7 +298,7 @@ class Pinecone(VectorStore):
         Returns:
             List of Documents selected by maximal marginal relevance.
         """
-        embedding = self._embedding_function(query)
+        embedding = self._embed_query(query)
         return self.max_marginal_relevance_search_by_vector(
             embedding, k, fetch_k, lambda_mult, filter, namespace
         )
@@ -356,7 +389,7 @@ class Pinecone(VectorStore):
             # upsert to Pinecone
             _upsert_kwargs = upsert_kwargs or {}
             index.upsert(vectors=list(to_upsert), namespace=namespace, **_upsert_kwargs)
-        return cls(index, embedding.embed_query, text_key, namespace, **kwargs)
+        return cls(index, embedding, text_key, namespace, **kwargs)
 
     @classmethod
     def from_existing_index(
@@ -375,9 +408,7 @@ class Pinecone(VectorStore):
                 "Please install it with `pip install pinecone-client`."
             )
 
-        return cls(
-            pinecone.Index(index_name), embedding.embed_query, text_key, namespace
-        )
+        return cls(pinecone.Index(index_name), embedding, text_key, namespace)
 
     def delete(
         self,
diff --git a/libs/langchain/langchain/vectorstores/redis.py b/libs/langchain/langchain/vectorstores/redis.py
index f6ed5c4011e..198e5d706a3 100644
--- a/libs/langchain/langchain/vectorstores/redis.py
+++ b/libs/langchain/langchain/vectorstores/redis.py
@@ -20,7 +20,6 @@ from typing import (
 )
 
 import numpy as np
-from pydantic import root_validator
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -28,6 +27,7 @@ from langchain.callbacks.manager import (
 )
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import root_validator
 from langchain.utilities.redis import get_client
 from langchain.utils import get_from_dict_or_env
 from langchain.vectorstores.base import VectorStore, VectorStoreRetriever
diff --git a/libs/langchain/langchain/vectorstores/rocksetdb.py b/libs/langchain/langchain/vectorstores/rocksetdb.py
index b71c216fcd2..5b2fc234a46 100644
--- a/libs/langchain/langchain/vectorstores/rocksetdb.py
+++ b/libs/langchain/langchain/vectorstores/rocksetdb.py
@@ -84,6 +84,12 @@ class Rockset(VectorStore):
         self._embedding_key = embedding_key
         self._workspace = workspace
 
+        try:
+            self._client.set_application("langchain")
+        except AttributeError:
+            # ignore
+            pass
+
     @property
     def embeddings(self) -> Embeddings:
         return self._embeddings
diff --git a/libs/langchain/langchain/vectorstores/starrocks.py b/libs/langchain/langchain/vectorstores/starrocks.py
index 0076a4cf062..d6abc40af88 100644
--- a/libs/langchain/langchain/vectorstores/starrocks.py
+++ b/libs/langchain/langchain/vectorstores/starrocks.py
@@ -8,10 +8,9 @@ from hashlib import sha1
 from threading import Thread
 from typing import Any, Dict, Iterable, List, Optional, Tuple
 
-from pydantic import BaseSettings
-
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import BaseSettings
 from langchain.vectorstores.base import VectorStore
 
 logger = logging.getLogger()
diff --git a/libs/langchain/langchain/vectorstores/utils.py b/libs/langchain/langchain/vectorstores/utils.py
index 539a1feda5c..64ecf137713 100644
--- a/libs/langchain/langchain/vectorstores/utils.py
+++ b/libs/langchain/langchain/vectorstores/utils.py
@@ -1,10 +1,11 @@
 """Utility functions for working with vectors and vectorstores."""
 
 from enum import Enum
-from typing import List
+from typing import List, Tuple, Type
 
 import numpy as np
 
+from langchain.docstore.document import Document
 from langchain.utils.math import cosine_similarity
 
 
@@ -51,3 +52,23 @@ def maximal_marginal_relevance(
         idxs.append(idx_to_add)
         selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
     return idxs
+
+
+def filter_complex_metadata(
+    documents: List[Document],
+    *,
+    allowed_types: Tuple[Type, ...] = (str, bool, int, float)
+) -> List[Document]:
+    """Filter out metadata types that are not supported for a vector store."""
+    updated_documents = []
+    for document in documents:
+        filtered_metadata = {}
+        for key, value in document.metadata.items():
+            if not isinstance(value, allowed_types):
+                continue
+            filtered_metadata[key] = value
+
+        document.metadata = filtered_metadata
+        updated_documents.append(document)
+
+    return updated_documents
diff --git a/libs/langchain/langchain/vectorstores/vectara.py b/libs/langchain/langchain/vectorstores/vectara.py
index 8c7b0ac2d3b..f263ebeac84 100644
--- a/libs/langchain/langchain/vectorstores/vectara.py
+++ b/libs/langchain/langchain/vectorstores/vectara.py
@@ -8,9 +8,9 @@ from hashlib import md5
 from typing import Any, Iterable, List, Optional, Tuple, Type
 
 import requests
-from pydantic import Field
 
 from langchain.embeddings.base import Embeddings
+from langchain.pydantic_v1 import Field
 from langchain.schema import Document
 from langchain.vectorstores.base import VectorStore, VectorStoreRetriever
 
@@ -117,7 +117,7 @@ class Vectara(VectorStore):
 
         response = self._session.post(
             headers=self._get_post_headers(),
-            url="https://api.vectara.io/v1/core/index",
+            url="https://api.vectara.io/v1/index",
             data=json.dumps(request),
             timeout=self.vectara_api_timeout,
             verify=True,
@@ -223,11 +223,12 @@ class Vectara(VectorStore):
         doc = {
             "document_id": doc_id,
             "metadataJson": json.dumps(doc_metadata),
-            "parts": [
+            "section": [
                 {"text": text, "metadataJson": json.dumps(md)}
                 for text, md in zip(texts, metadatas)
             ],
         }
+
         success_str = self._index_doc(doc)
         if success_str == "E_ALREADY_EXISTS":
             self._delete_doc(doc_id)
@@ -245,7 +246,7 @@ class Vectara(VectorStore):
         k: int = 5,
         lambda_val: float = 0.025,
         filter: Optional[str] = None,
-        n_sentence_context: int = 0,
+        n_sentence_context: int = 2,
         **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         """Return Vectara documents most similar to query, along with scores.
@@ -259,7 +260,7 @@ class Vectara(VectorStore):
                 https://docs.vectara.com/docs/search-apis/sql/filter-overview
                 for more details.
             n_sentence_context: number of sentences before/after the matching segment
-                to add
+                to add, defaults to 2
 
         Returns:
             List of Documents most similar to the query and score for each.
@@ -304,6 +305,7 @@ class Vectara(VectorStore):
             return []
 
         result = response.json()
+
         responses = result["responseSet"][0]["response"]
         vectara_default_metadata = ["lang", "len", "offset"]
         docs = [
@@ -328,7 +330,7 @@ class Vectara(VectorStore):
         k: int = 5,
         lambda_val: float = 0.025,
         filter: Optional[str] = None,
-        n_sentence_context: int = 0,
+        n_sentence_context: int = 2,
         **kwargs: Any,
     ) -> List[Document]:
         """Return Vectara documents most similar to query, along with scores.
@@ -341,7 +343,7 @@ class Vectara(VectorStore):
                 https://docs.vectara.com/docs/search-apis/sql/filter-overview for more
                 details.
             n_sentence_context: number of sentences before/after the matching segment
-                to add
+                to add, defaults to 2
 
         Returns:
             List of Documents most similar to the query
@@ -427,7 +429,7 @@ class VectaraRetriever(VectorStoreRetriever):
             "lambda_val": 0.025,
             "k": 5,
             "filter": "",
-            "n_sentence_context": "0",
+            "n_sentence_context": "2",
         }
     )
     """Search params.
diff --git a/libs/langchain/langchain/vectorstores/zep.py b/libs/langchain/langchain/vectorstores/zep.py
new file mode 100644
index 00000000000..faf04a139fe
--- /dev/null
+++ b/libs/langchain/langchain/vectorstores/zep.py
@@ -0,0 +1,674 @@
+from __future__ import annotations
+
+import logging
+import warnings
+from dataclasses import asdict, dataclass
+from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple
+
+import numpy as np
+
+from langchain.docstore.document import Document
+from langchain.embeddings.base import Embeddings
+from langchain.vectorstores.base import VectorStore
+from langchain.vectorstores.utils import maximal_marginal_relevance
+
+if TYPE_CHECKING:
+    from zep_python.document import Document as ZepDocument
+    from zep_python.document import DocumentCollection
+
+
+logger = logging.getLogger()
+
+
+@dataclass
+class CollectionConfig:
+    """
+    A configuration class for a Zep Collection.
+
+    If the collection does not exist, it will be created.
+
+    Attributes:
+        name (str): The name of the collection.
+        description (Optional[str]): An optional description of the collection.
+        metadata (Optional[Dict[str, Any]]): Optional metadata for the collection.
+        embedding_dimensions (int): The number of dimensions for the embeddings in
+            the collection. This should match the Zep server configuration
+            if auto-embed is true.
+        is_auto_embedded (bool): A flag indicating whether the collection is
+            automatically embedded by Zep.
+    """
+
+    name: str
+    description: Optional[str]
+    metadata: Optional[Dict[str, Any]]
+    embedding_dimensions: int
+    is_auto_embedded: bool
+
+
+class ZepVectorStore(VectorStore):
+    """
+    ZepVectorStore is a VectorStore implementation that uses the Zep long-term memory
+    store as a backend. It provides methods for adding texts or documents to the store,
+    searching for similar documents, and deleting documents.
+
+    Search scores are calculated using cosine similarity normalized to [0, 1].
+
+    Args:
+        api_url (str): The URL of the Zep API.
+        collection_name (str): The name of the collection in the Zep store.
+        api_key (Optional[str]): The API key for the Zep API.
+        config (Optional[CollectionConfig]): The configuration for the collection.
+            Required if the collection does not already exist.
+        embedding (Optional[Embeddings]): Optional embedding function to use to
+            embed the texts. Required if the collection is not auto-embedded.
+    """
+
+    def __init__(
+        self,
+        collection_name: str,
+        api_url: str,
+        *,
+        api_key: Optional[str] = None,
+        config: Optional[CollectionConfig] = None,
+        embedding: Optional[Embeddings] = None,
+    ) -> None:
+        super().__init__()
+        if not collection_name:
+            raise ValueError(
+                "collection_name must be specified when using ZepVectorStore."
+            )
+        try:
+            from zep_python import ZepClient
+        except ImportError:
+            raise ImportError(
+                "Could not import zep-python python package. "
+                "Please install it with `pip install zep-python`."
+            )
+        self._client = ZepClient(api_url, api_key=api_key)
+
+        self.collection_name = collection_name
+        # If for some reason the collection name is not the same as the one in the
+        # config, update it.
+        if config and config.name != self.collection_name:
+            config.name = self.collection_name
+
+        self._collection_config = config
+        self._collection = self._load_collection()
+        self._embedding = embedding
+
+        # self.add_texts(texts, metadatas=metadatas, **kwargs)
+
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        """Access the query embedding object if available."""
+        return self._embedding
+
+    def _load_collection(self) -> DocumentCollection:
+        """
+        Load the collection from the Zep backend.
+        """
+        from zep_python import NotFoundError
+
+        try:
+            collection = self._client.document.get_collection(self.collection_name)
+        except NotFoundError:
+            logger.info(
+                f"Collection {self.collection_name} not found. "
+                "Creating new collection."
+            )
+            collection = self._create_collection()
+
+        return collection
+
+    def _create_collection(self) -> DocumentCollection:
+        """
+        Create a new collection in the Zep backend.
+        """
+        if not self._collection_config:
+            raise ValueError(
+                "Collection config must be specified when creating a new collection."
+            )
+        collection = self._client.document.add_collection(
+            **asdict(self._collection_config)
+        )
+        return collection
+
+    def _generate_documents_to_add(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[Dict[Any, Any]]] = None,
+        document_ids: Optional[List[str]] = None,
+    ) -> List[ZepDocument]:
+        from zep_python.document import Document as ZepDocument
+
+        embeddings = None
+        if self._collection and self._collection.is_auto_embedded:
+            if self._embedding is not None:
+                warnings.warn(
+                    """The collection is set to auto-embed and an embedding 
+                function is present. Ignoring the embedding function.""",
+                    stacklevel=2,
+                )
+        elif self._embedding is not None:
+            embeddings = self._embedding.embed_documents(list(texts))
+            if self._collection and self._collection.embedding_dimensions != len(
+                embeddings[0]
+            ):
+                raise ValueError(
+                    "The embedding dimensions of the collection and the embedding"
+                    " function do not match. Collection dimensions:"
+                    f" {self._collection.embedding_dimensions}, Embedding dimensions:"
+                    f" {len(embeddings[0])}"
+                )
+        else:
+            pass
+
+        documents: List[ZepDocument] = []
+        for i, d in enumerate(texts):
+            documents.append(
+                ZepDocument(
+                    content=d,
+                    metadata=metadatas[i] if metadatas else None,
+                    document_id=document_ids[i] if document_ids else None,
+                    embedding=embeddings[i] if embeddings else None,
+                )
+            )
+        return documents
+
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        document_ids: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the vectorstore.
+
+        Args:
+            texts: Iterable of strings to add to the vectorstore.
+            metadatas: Optional list of metadatas associated with the texts.
+            document_ids: Optional list of document ids associated with the texts.
+            kwargs: vectorstore specific parameters
+
+        Returns:
+            List of ids from adding the texts into the vectorstore.
+        """
+        if not self._collection:
+            raise ValueError(
+                "collection should be an instance of a Zep DocumentCollection"
+            )
+
+        documents = self._generate_documents_to_add(texts, metadatas, document_ids)
+        uuids = self._collection.add_documents(documents)
+
+        return uuids
+
+    async def aadd_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        document_ids: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the vectorstore."""
+        if not self._collection:
+            raise ValueError(
+                "collection should be an instance of a Zep DocumentCollection"
+            )
+
+        documents = self._generate_documents_to_add(texts, metadatas, document_ids)
+        uuids = await self._collection.aadd_documents(documents)
+
+        return uuids
+
+    def search(
+        self,
+        query: str,
+        search_type: str,
+        metadata: Optional[Dict[str, Any]] = None,
+        k: int = 3,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs most similar to query using specified search type."""
+        if search_type == "similarity":
+            return self.similarity_search(query, k=k, metadata=metadata, **kwargs)
+        elif search_type == "mmr":
+            return self.max_marginal_relevance_search(
+                query, k=k, metadata=metadata, **kwargs
+            )
+        else:
+            raise ValueError(
+                f"search_type of {search_type} not allowed. Expected "
+                "search_type to be 'similarity' or 'mmr'."
+            )
+
+    async def asearch(
+        self,
+        query: str,
+        search_type: str,
+        metadata: Optional[Dict[str, Any]] = None,
+        k: int = 3,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs most similar to query using specified search type."""
+        if search_type == "similarity":
+            return await self.asimilarity_search(
+                query, k=k, metadata=metadata, **kwargs
+            )
+        elif search_type == "mmr":
+            return await self.amax_marginal_relevance_search(
+                query, k=k, metadata=metadata, **kwargs
+            )
+        else:
+            raise ValueError(
+                f"search_type of {search_type} not allowed. Expected "
+                "search_type to be 'similarity' or 'mmr'."
+            )
+
+    def similarity_search(
+        self,
+        query: str,
+        k: int = 4,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs most similar to query."""
+
+        results = self._similarity_search_with_relevance_scores(
+            query, k=k, metadata=metadata, **kwargs
+        )
+        return [doc for doc, _ in results]
+
+    def similarity_search_with_score(
+        self,
+        query: str,
+        k: int = 4,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Run similarity search with distance."""
+
+        return self._similarity_search_with_relevance_scores(
+            query, k=k, metadata=metadata, **kwargs
+        )
+
+    def _similarity_search_with_relevance_scores(
+        self,
+        query: str,
+        k: int = 4,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """
+        Default similarity search with relevance scores. Modify if necessary
+        in subclass.
+        Return docs and relevance scores in the range [0, 1].
+
+        0 is dissimilar, 1 is most similar.
+
+        Args:
+            query: input text
+            k: Number of Documents to return. Defaults to 4.
+            metadata: Optional, metadata filter
+            **kwargs: kwargs to be passed to similarity search. Should include:
+                score_threshold: Optional, a floating point value between 0 to 1 and
+                    filter the resulting set of retrieved docs
+
+        Returns:
+            List of Tuples of (doc, similarity_score)
+        """
+
+        if not self._collection:
+            raise ValueError(
+                "collection should be an instance of a Zep DocumentCollection"
+            )
+
+        if not self._collection.is_auto_embedded and self._embedding:
+            query_vector = self._embedding.embed_query(query)
+            results = self._collection.search(
+                embedding=query_vector, limit=k, metadata=metadata, **kwargs
+            )
+        else:
+            results = self._collection.search(
+                query, limit=k, metadata=metadata, **kwargs
+            )
+
+        return [
+            (
+                Document(
+                    page_content=doc.content,
+                    metadata=doc.metadata,
+                ),
+                doc.score or 0.0,
+            )
+            for doc in results
+        ]
+
+    async def asimilarity_search_with_relevance_scores(
+        self,
+        query: str,
+        k: int = 4,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Return docs most similar to query."""
+
+        if not self._collection:
+            raise ValueError(
+                "collection should be an instance of a Zep DocumentCollection"
+            )
+
+        if not self._collection.is_auto_embedded and self._embedding:
+            query_vector = self._embedding.embed_query(query)
+            results = await self._collection.asearch(
+                embedding=query_vector, limit=k, metadata=metadata, **kwargs
+            )
+        else:
+            results = await self._collection.asearch(
+                query, limit=k, metadata=metadata, **kwargs
+            )
+
+        return [
+            (
+                Document(
+                    page_content=doc.content,
+                    metadata=doc.metadata,
+                ),
+                doc.score or 0.0,
+            )
+            for doc in results
+        ]
+
+    async def asimilarity_search(
+        self,
+        query: str,
+        k: int = 4,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs most similar to query."""
+
+        results = await self.asimilarity_search_with_relevance_scores(
+            query, k, metadata=metadata, **kwargs
+        )
+
+        return [doc for doc, _ in results]
+
+    def similarity_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs most similar to embedding vector.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            metadata: Optional, metadata filter
+
+        Returns:
+            List of Documents most similar to the query vector.
+        """
+        if not self._collection:
+            raise ValueError(
+                "collection should be an instance of a Zep DocumentCollection"
+            )
+
+        results = self._collection.search(
+            embedding=embedding, limit=k, metadata=metadata, **kwargs
+        )
+
+        return [
+            Document(
+                page_content=doc.content,
+                metadata=doc.metadata,
+            )
+            for doc in results
+        ]
+
+    async def asimilarity_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs most similar to embedding vector."""
+        if not self._collection:
+            raise ValueError(
+                "collection should be an instance of a Zep DocumentCollection"
+            )
+
+        results = self._collection.search(
+            embedding=embedding, limit=k, metadata=metadata, **kwargs
+        )
+
+        return [
+            Document(
+                page_content=doc.content,
+                metadata=doc.metadata,
+            )
+            for doc in results
+        ]
+
+    def _max_marginal_relevance_selection(
+        self,
+        query_vector: List[float],
+        results: List["ZepDocument"],
+        *,
+        lambda_mult: float = 0.5,
+        k: int = 4,
+    ) -> List[Document]:
+        mmr_selected = maximal_marginal_relevance(
+            np.array([query_vector], dtype=np.float32),
+            [d.embedding for d in results],
+            lambda_mult=lambda_mult,
+            k=k,
+        )
+        selected = [results[i] for i in mmr_selected]
+        return [Document(page_content=d.content, metadata=d.metadata) for d in selected]
+
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+            metadata: Optional, metadata to filter the resulting set of retrieved docs
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+
+        if not self._collection:
+            raise ValueError(
+                "collection should be an instance of a Zep DocumentCollection"
+            )
+
+        if not self._collection.is_auto_embedded and self._embedding:
+            query_vector = self._embedding.embed_query(query)
+            results = self._collection.search(
+                embedding=query_vector, limit=k, metadata=metadata, **kwargs
+            )
+        else:
+            results, query_vector = self._collection.search_return_query_vector(
+                query, limit=k, metadata=metadata, **kwargs
+            )
+
+        return self._max_marginal_relevance_selection(
+            query_vector, results, k=k, lambda_mult=lambda_mult
+        )
+
+    async def amax_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance."""
+
+        if not self._collection:
+            raise ValueError(
+                "collection should be an instance of a Zep DocumentCollection"
+            )
+
+        if not self._collection.is_auto_embedded and self._embedding:
+            query_vector = self._embedding.embed_query(query)
+            results = await self._collection.asearch(
+                embedding=query_vector, limit=k, metadata=metadata, **kwargs
+            )
+        else:
+            results, query_vector = await self._collection.asearch_return_query_vector(
+                query, limit=k, metadata=metadata, **kwargs
+            )
+
+        return self._max_marginal_relevance_selection(
+            query_vector, results, k=k, lambda_mult=lambda_mult
+        )
+
+    def max_marginal_relevance_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+            metadata: Optional, metadata to filter the resulting set of retrieved docs
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        if not self._collection:
+            raise ValueError(
+                "collection should be an instance of a Zep DocumentCollection"
+            )
+
+        results = self._collection.search(
+            embedding=embedding, limit=k, metadata=metadata, **kwargs
+        )
+
+        return self._max_marginal_relevance_selection(
+            embedding, results, k=k, lambda_mult=lambda_mult
+        )
+
+    async def amax_marginal_relevance_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance."""
+        if not self._collection:
+            raise ValueError(
+                "collection should be an instance of a Zep DocumentCollection"
+            )
+
+        results = await self._collection.asearch(
+            embedding=embedding, limit=k, metadata=metadata, **kwargs
+        )
+
+        return self._max_marginal_relevance_selection(
+            embedding, results, k=k, lambda_mult=lambda_mult
+        )
+
+    @classmethod
+    def from_texts(
+        cls,
+        texts: List[str],
+        embedding: Optional[Embeddings] = None,
+        metadatas: Optional[List[dict]] = None,
+        collection_name: str = "",
+        api_url: str = "",
+        api_key: Optional[str] = None,
+        config: Optional[CollectionConfig] = None,
+        **kwargs: Any,
+    ) -> ZepVectorStore:
+        """
+        Class method that returns a ZepVectorStore instance initialized from texts.
+
+        If the collection does not exist, it will be created.
+
+        Args:
+            texts (List[str]): The list of texts to add to the vectorstore.
+            embedding (Optional[Embeddings]): Optional embedding function to use to
+               embed the texts.
+            metadatas (Optional[List[Dict[str, Any]]]): Optional list of metadata
+               associated with the texts.
+            collection_name (str): The name of the collection in the Zep store.
+            api_url (str): The URL of the Zep API.
+            api_key (Optional[str]): The API key for the Zep API.
+            config (Optional[CollectionConfig]): The configuration for the collection.
+            **kwargs: Additional parameters specific to the vectorstore.
+
+        Returns:
+            ZepVectorStore: An instance of ZepVectorStore.
+        """
+        vecstore = cls(
+            collection_name,
+            api_url,
+            api_key=api_key,
+            config=config,
+            embedding=embedding,
+        )
+        vecstore.add_texts(texts, metadatas)
+        return vecstore
+
+    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
+        """Delete by Zep vector UUIDs.
+
+        Parameters
+        ----------
+        ids : Optional[List[str]]
+            The UUIDs of the vectors to delete.
+
+        Raises
+        ------
+        ValueError
+            If no UUIDs are provided.
+        """
+
+        if ids is None or len(ids) == 0:
+            raise ValueError("No uuids provided to delete.")
+
+        if self._collection is None:
+            raise ValueError("No collection name provided.")
+
+        for u in ids:
+            self._collection.delete_document(u)
diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock
index 712a4406bff..9305a0838f8 100644
--- a/libs/langchain/poetry.lock
+++ b/libs/langchain/poetry.lock
@@ -12,41 +12,12 @@ files = [
     {file = "absl_py-1.4.0-py3-none-any.whl", hash = "sha256:0d3fe606adfa4f7db64792dd4c7aee4ee0c38ab75dfd353b7a83ed3e957fcb47"},
 ]
 
-[[package]]
-name = "accelerate"
-version = "0.20.3"
-description = "Accelerate"
-category = "main"
-optional = true
-python-versions = ">=3.7.0"
-files = [
-    {file = "accelerate-0.20.3-py3-none-any.whl", hash = "sha256:147183e7a2215f7bd45a7af3b986a963daa8a61fa58b0912b9473049e011ad15"},
-    {file = "accelerate-0.20.3.tar.gz", hash = "sha256:79a896978c20dac270083d42bf033f4c9a80dcdd6b946f1ca92d8d6d0f0f5ba9"},
-]
-
-[package.dependencies]
-numpy = ">=1.17"
-packaging = ">=20.0"
-psutil = "*"
-pyyaml = "*"
-torch = ">=1.6.0"
-
-[package.extras]
-dev = ["black (>=23.1,<24.0)", "datasets", "deepspeed", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.0.241)", "scikit-learn", "scipy", "tqdm", "transformers", "urllib3 (<2.0.0)"]
-quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.0.241)", "urllib3 (<2.0.0)"]
-rich = ["rich"]
-sagemaker = ["sagemaker"]
-test-dev = ["datasets", "deepspeed", "evaluate", "scikit-learn", "scipy", "tqdm", "transformers"]
-test-prod = ["parameterized", "pytest", "pytest-subtests", "pytest-xdist"]
-test-trackers = ["comet-ml", "tensorboard", "wandb"]
-testing = ["datasets", "deepspeed", "evaluate", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "tqdm", "transformers"]
-
 [[package]]
 name = "aioboto3"
 version = "11.2.0"
 description = "Async boto3 wrapper"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7,<4.0"
 files = [
     {file = "aioboto3-11.2.0-py3-none-any.whl", hash = "sha256:df4b83c3943b009a4dcd9f397f9f0491a374511b1ef37545082a771ca1e549fb"},
@@ -65,7 +36,7 @@ name = "aiobotocore"
 version = "2.5.0"
 description = "Async client for aws services using botocore and aiohttp"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "aiobotocore-2.5.0-py3-none-any.whl", hash = "sha256:9a2a022d7b78ec9a2af0de589916d2721cddbf96264401b78d7a73c1a1435f3b"},
@@ -239,7 +210,7 @@ name = "aioitertools"
 version = "0.11.0"
 description = "itertools and builtins for AsyncIO and mixed iterables"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6"
 files = [
     {file = "aioitertools-0.11.0-py3-none-any.whl", hash = "sha256:04b95e3dab25b449def24d7df809411c10e62aab0cbe31a50ca4e68748c43394"},
@@ -264,18 +235,6 @@ files = [
 [package.dependencies]
 frozenlist = ">=1.1.0"
 
-[[package]]
-name = "aiostream"
-version = "0.4.5"
-description = "Generator-based operators for asynchronous iteration"
-category = "main"
-optional = true
-python-versions = "*"
-files = [
-    {file = "aiostream-0.4.5-py3-none-any.whl", hash = "sha256:25b7c2d9c83570d78c0ef5a20e949b7d0b8ea3b0b0a4f22c49d3f721105a6057"},
-    {file = "aiostream-0.4.5.tar.gz", hash = "sha256:3ecbf87085230fbcd9605c32ca20c4fb41af02c71d076eab246ea22e35947d88"},
-]
-
 [[package]]
 name = "aleph-alpha-client"
 version = "2.17.0"
@@ -374,26 +333,6 @@ files = [
 boto3 = "*"
 marshmallow = ">=3.14,<4"
 
-[[package]]
-name = "anthropic"
-version = "0.3.2"
-description = "Client library for the anthropic API"
-category = "main"
-optional = true
-python-versions = ">=3.7,<4.0"
-files = [
-    {file = "anthropic-0.3.2-py3-none-any.whl", hash = "sha256:43ad86df406bf91419e3c651e20dcc69ae273c932c92c26973a1621a72ff1d86"},
-    {file = "anthropic-0.3.2.tar.gz", hash = "sha256:f968e970bb0dfa38b1ec59db7bb4162fd1e0f2bef95c3203e926effe62bfcf38"},
-]
-
-[package.dependencies]
-anyio = ">=3.5.0"
-distro = ">=1.7.0"
-httpx = ">=0.23.0"
-pydantic = ">=1.9.0,<2.0.0"
-tokenizers = ">=0.13.0"
-typing-extensions = ">=4.1.1"
-
 [[package]]
 name = "anyio"
 version = "3.7.0"
@@ -518,7 +457,7 @@ name = "arxiv"
 version = "1.4.7"
 description = "Python wrapper for the arXiv API: http://arxiv.org/help/api/"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "arxiv-1.4.7-py3-none-any.whl", hash = "sha256:22b8f610957bb6859a25fac9dc205ab6ba76d521791119a5762ea52625e398a0"},
@@ -528,24 +467,6 @@ files = [
 [package.dependencies]
 feedparser = "*"
 
-[[package]]
-name = "asgiref"
-version = "3.7.2"
-description = "ASGI specs, helper code, and adapters"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "asgiref-3.7.2-py3-none-any.whl", hash = "sha256:89b2ef2247e3b562a16eef663bc0e2e703ec6468e2fa8a5cd61cd449786d4f6e"},
-    {file = "asgiref-3.7.2.tar.gz", hash = "sha256:9e0ce3aa93a819ba5b45120216b23878cf6e8525eb3848653452b4192b92afed"},
-]
-
-[package.dependencies]
-typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""}
-
-[package.extras]
-tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
-
 [[package]]
 name = "asttokens"
 version = "2.2.1"
@@ -648,7 +569,7 @@ name = "authlib"
 version = "1.2.0"
 description = "The ultimate Python library in building OAuth and OpenID Connect servers and clients."
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "Authlib-1.2.0-py2.py3-none-any.whl", hash = "sha256:4ddf4fd6cfa75c9a460b361d4bd9dac71ffda0be879dbe4292a02e92349ad55a"},
@@ -832,7 +753,7 @@ name = "backoff"
 version = "2.2.1"
 description = "Function decoration for backoff and retry"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7,<4.0"
 files = [
     {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"},
@@ -887,83 +808,6 @@ soupsieve = ">1.2"
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
-[[package]]
-name = "bentoml"
-version = "1.0.22"
-description = "BentoML: The Unified Model Serving Framework"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "bentoml-1.0.22-py3-none-any.whl", hash = "sha256:630e7af986e8472d15d2ba65322a0c535c685ae28e77486f9c9af5716eaee625"},
-    {file = "bentoml-1.0.22.tar.gz", hash = "sha256:1a204a9c938b52a6735156fedf43f93687fa51239b8fbb3f9cdb06868b86e819"},
-]
-
-[package.dependencies]
-aiohttp = "*"
-attrs = ">=21.1.0"
-cattrs = ">=22.1.0"
-circus = ">=0.17.0,<0.17.2 || >0.17.2"
-click = ">=7.0"
-click-option-group = "*"
-cloudpickle = "*"
-deepmerge = "*"
-filetype = {version = "*", optional = true, markers = "extra == \"io-file\""}
-fs = "*"
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-
-[package.dependencies]
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-
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-uvicorn = {version = ">=0.18.3", extras = ["standard"]}
-
-[[package]]
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-description = "Circus is a program that will let you run and watch multiple processes and sockets."
-category = "main"
-optional = true
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-
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-
 [[package]]
 name = "clarifai"
 version = "9.1.0"
@@ -1732,26 +1416,6 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
-[[package]]
-name = "click-option-group"
-version = "0.5.6"
-description = "Option groups missing in Click"
-category = "main"
-optional = true
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-tests-cov = ["coverage", "coveralls", "pytest", "pytest-cov"]
-
 [[package]]
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 version = "1.1.1"
@@ -1775,7 +1439,7 @@ name = "clickhouse-connect"
 version = "0.5.25"
 description = "ClickHouse core driver, SqlAlchemy, and Superset libraries"
 category = "main"
-optional = false
+optional = true
 python-versions = "~=3.7"
 files = [
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@@ -1878,18 +1542,6 @@ click = ">=4.0"
 [package.extras]
 test = ["pytest-cov"]
 
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-
 [[package]]
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 version = "2.2.5"
@@ -1951,24 +1603,6 @@ files = [
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-category = "main"
-optional = false
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-
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-
 [[package]]
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 version = "0.1.3"
@@ -1989,93 +1623,6 @@ lint = ["black (>=22.6.0)", "mdformat (>0.7)", "mdformat-gfm (>=0.3.5)", "ruff (
 test = ["pytest"]
 typing = ["mypy (>=0.990)"]
 
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-test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
-
 [[package]]
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 version = "7.2.7"
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-
 [[package]]
 name = "dataclasses-json"
 version = "0.5.8"
@@ -2276,49 +1773,6 @@ typing-inspect = ">=0.4.0"
 [package.extras]
 dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest (>=7.2.0)", "simplejson", "types-dataclasses"]
 
-[[package]]
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-version = "2.13.0"
-description = "HuggingFace community-driven open-source library of datasets"
-category = "main"
-optional = true
-python-versions = ">=3.7.0"
-files = [
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-
-[package.dependencies]
-aiohttp = "*"
-dill = ">=0.3.0,<0.3.7"
-fsspec = {version = ">=2021.11.1", extras = ["http"]}
-huggingface-hub = ">=0.11.0,<1.0.0"
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-pyarrow = ">=8.0.0"
-pyyaml = ">=5.1"
-requests = ">=2.19.0"
-tqdm = ">=4.62.1"
-xxhash = "*"
-
-[package.extras]
-apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"]
-audio = ["librosa", "soundfile (>=0.12.1)"]
-benchmarks = ["numpy (==1.18.5)", "protobuf (==3.20.3)", "tensorflow (==2.3.0)", "torch (==1.7.1)", "transformers (==3.0.2)"]
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-quality = ["black (>=23.1,<24.0)", "pyyaml (>=5.3.1)", "ruff (>=0.0.241)"]
-s3 = ["s3fs"]
-tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
-tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
-tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
-torch = ["torch"]
-vision = ["Pillow (>=6.2.1)"]
-
 [[package]]
 name = "debugpy"
 version = "1.6.7"
@@ -2364,7 +1818,7 @@ name = "deeplake"
 version = "3.6.11"
 description = "Activeloop Deep Lake"
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "deeplake-3.6.11.tar.gz", hash = "sha256:97e22aa56e0a48dc935597f46a6f968393a4f95e799fa1df8bc7997231a40892"},
@@ -2397,23 +1851,11 @@ point-cloud = ["laspy"]
 video = ["av (>=8.1.0)"]
 visualizer = ["IPython", "flask"]
 
-[[package]]
-name = "deepmerge"
-version = "1.1.0"
-description = "a toolset to deeply merge python dictionaries."
-category = "main"
-optional = true
-python-versions = "*"
-files = [
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-    {file = "deepmerge-1.1.0.tar.gz", hash = "sha256:4c27a0db5de285e1a7ceac7dbc1531deaa556b627dea4900c8244581ecdfea2d"},
-]
-
 [[package]]
 name = "defusedxml"
 version = "0.7.1"
 description = "XML bomb protection for Python stdlib modules"
-category = "main"
+category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
@@ -2426,7 +1868,7 @@ name = "deprecated"
 version = "1.2.14"
 description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
     {file = "Deprecated-1.2.14-py2.py3-none-any.whl", hash = "sha256:6fac8b097794a90302bdbb17b9b815e732d3c4720583ff1b198499d78470466c"},
@@ -2459,7 +1901,7 @@ name = "dill"
 version = "0.3.6"
 description = "serialize all of python"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
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@@ -2469,24 +1911,12 @@ files = [
 [package.extras]
 graph = ["objgraph (>=1.7.2)"]
 
-[[package]]
-name = "distro"
-version = "1.8.0"
-description = "Distro - an OS platform information API"
-category = "main"
-optional = true
-python-versions = ">=3.6"
-files = [
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-    {file = "distro-1.8.0.tar.gz", hash = "sha256:02e111d1dc6a50abb8eed6bf31c3e48ed8b0830d1ea2a1b78c61765c2513fdd8"},
-]
-
 [[package]]
 name = "dnspython"
 version = "2.3.0"
 description = "DNS toolkit"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7,<4.0"
 files = [
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@@ -2671,31 +2101,12 @@ click = ">=8.1.3"
 httpx = {version = ">=0.24.1", extras = ["brotli", "http2", "socks"]}
 lxml = ">=4.9.2"
 
-[[package]]
-name = "ecdsa"
-version = "0.18.0"
-description = "ECDSA cryptographic signature library (pure python)"
-category = "main"
-optional = true
-python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
-files = [
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-    {file = "ecdsa-0.18.0.tar.gz", hash = "sha256:190348041559e21b22a1d65cee485282ca11a6f81d503fddb84d5017e9ed1e49"},
-]
-
-[package.dependencies]
-six = ">=1.9.0"
-
-[package.extras]
-gmpy = ["gmpy"]
-gmpy2 = ["gmpy2"]
-
 [[package]]
 name = "elastic-transport"
 version = "8.4.0"
 description = "Transport classes and utilities shared among Python Elastic client libraries"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6"
 files = [
     {file = "elastic-transport-8.4.0.tar.gz", hash = "sha256:b9ad708ceb7fcdbc6b30a96f886609a109f042c0b9d9f2e44403b3133ba7ff10"},
@@ -2714,7 +2125,7 @@ name = "elasticsearch"
 version = "8.8.0"
 description = "Python client for Elasticsearch"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6, <4"
 files = [
     {file = "elasticsearch-8.8.0-py3-none-any.whl", hash = "sha256:2223ee9daaa3c80c25b28ec3f7c48e66fce6b767a338333d9a81886046a07df6"},
@@ -2722,7 +2133,6 @@ files = [
 ]
 
 [package.dependencies]
-aiohttp = {version = ">=3,<4", optional = true, markers = "extra == \"async\""}
 elastic-transport = ">=8,<9"
 
 [package.extras]
@@ -2817,28 +2227,6 @@ files = [
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 ]
 
-[[package]]
-name = "fastapi"
-version = "0.95.2"
-description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
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-    {file = "fastapi-0.95.2.tar.gz", hash = "sha256:4d9d3e8c71c73f11874bcf5e33626258d143252e329a01002f767306c64fb982"},
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-
-[package.dependencies]
-pydantic = ">=1.6.2,<1.7 || >1.7,<1.7.1 || >1.7.1,<1.7.2 || >1.7.2,<1.7.3 || >1.7.3,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0"
-starlette = ">=0.27.0,<0.28.0"
-
-[package.extras]
-all = ["email-validator (>=1.1.1)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "python-multipart (>=0.0.5)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
-dev = ["pre-commit (>=2.17.0,<3.0.0)", "ruff (==0.0.138)", "uvicorn[standard] (>=0.12.0,<0.21.0)"]
-doc = ["mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-markdownextradata-plugin (>=0.1.7,<0.3.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pyyaml (>=5.3.1,<7.0.0)", "typer-cli (>=0.0.13,<0.0.14)", "typer[all] (>=0.6.1,<0.8.0)"]
-test = ["anyio[trio] (>=3.2.1,<4.0.0)", "black (==23.1.0)", "coverage[toml] (>=6.5.0,<8.0)", "databases[sqlite] (>=0.3.2,<0.7.0)", "email-validator (>=1.1.1,<2.0.0)", "flask (>=1.1.2,<3.0.0)", "httpx (>=0.23.0,<0.24.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.982)", "orjson (>=3.2.1,<4.0.0)", "passlib[bcrypt] (>=1.7.2,<2.0.0)", "peewee (>=3.13.3,<4.0.0)", "pytest (>=7.1.3,<8.0.0)", "python-jose[cryptography] (>=3.3.0,<4.0.0)", "python-multipart (>=0.0.5,<0.0.7)", "pyyaml (>=5.3.1,<7.0.0)", "ruff (==0.0.138)", "sqlalchemy (>=1.3.18,<1.4.43)", "types-orjson (==3.6.2)", "types-ujson (==5.7.0.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0,<6.0.0)"]
-
 [[package]]
 name = "fastavro"
 version = "1.7.4"
@@ -2922,7 +2310,7 @@ name = "feedparser"
 version = "6.0.10"
 description = "Universal feed parser, handles RSS 0.9x, RSS 1.0, RSS 2.0, CDF, Atom 0.3, and Atom 1.0 feeds"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6"
 files = [
     {file = "feedparser-6.0.10-py3-none-any.whl", hash = "sha256:79c257d526d13b944e965f6095700587f27388e50ea16fd245babe4dfae7024f"},
@@ -2932,23 +2320,12 @@ files = [
 [package.dependencies]
 sgmllib3k = "*"
 
-[[package]]
-name = "ffmpy"
-version = "0.3.1"
-description = "A simple Python wrapper for ffmpeg"
-category = "main"
-optional = true
-python-versions = "*"
-files = [
-    {file = "ffmpy-0.3.1.tar.gz", hash = "sha256:a173b8f42c7c669ff722df7fb31e1e870067713697f745224fa6e621b82f0004"},
-]
-
 [[package]]
 name = "filelock"
 version = "3.12.2"
 description = "A platform independent file lock."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "filelock-3.12.2-py3-none-any.whl", hash = "sha256:cbb791cdea2a72f23da6ac5b5269ab0a0d161e9ef0100e653b69049a7706d1ec"},
@@ -2959,18 +2336,6 @@ files = [
 docs = ["furo (>=2023.5.20)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
 testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"]
 
-[[package]]
-name = "filetype"
-version = "1.2.0"
-description = "Infer file type and MIME type of any file/buffer. No external dependencies."
-category = "main"
-optional = true
-python-versions = "*"
-files = [
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-]
-
 [[package]]
 name = "fiona"
 version = "1.9.4.post1"
@@ -3021,86 +2386,13 @@ name = "flatbuffers"
 version = "23.5.26"
 description = "The FlatBuffers serialization format for Python"
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "flatbuffers-23.5.26-py2.py3-none-any.whl", hash = "sha256:c0ff356da363087b915fde4b8b45bdda73432fc17cddb3c8157472eab1422ad1"},
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 ]
 
-[[package]]
-name = "fluent-logger"
-version = "0.10.0"
-description = "A Python logging handler for Fluentd event collector"
-category = "main"
-optional = true
-python-versions = ">=3.5"
-files = [
-    {file = "fluent-logger-0.10.0.tar.gz", hash = "sha256:678bda90c513ff0393964b64544ce41ef25669d2089ce6c3b63d9a18554b9bfa"},
-    {file = "fluent_logger-0.10.0-py2.py3-none-any.whl", hash = "sha256:543637e5e62ec3fc3c92b44e5a4e148a3cea88a0f8ca4fae26c7e60fda7564c1"},
-]
-
-[package.dependencies]
-msgpack = ">1.0"
-
-[[package]]
-name = "fonttools"
-version = "4.41.1"
-description = "Tools to manipulate font files"
-category = "main"
-optional = true
-python-versions = ">=3.8"
-files = [
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-    {file = "fonttools-4.41.1-cp39-cp39-win32.whl", hash = "sha256:afce2aeb80be72b4da7dd114f10f04873ff512793d13ce0b19d12b2a4c44c0f0"},
-    {file = "fonttools-4.41.1-cp39-cp39-win_amd64.whl", hash = "sha256:1df1b6f4c7c4bc8201eb47f3b268adbf2539943aa43c400f84556557e3e109c0"},
-    {file = "fonttools-4.41.1-py3-none-any.whl", hash = "sha256:952cb405f78734cf6466252fec42e206450d1a6715746013f64df9cbd4f896fa"},
-    {file = "fonttools-4.41.1.tar.gz", hash = "sha256:e16a9449f21a93909c5be2f5ed5246420f2316e94195dbfccb5238aaa38f9751"},
-]
-
-[package.extras]
-all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.0.0)", "xattr", "zopfli (>=0.1.4)"]
-graphite = ["lz4 (>=1.7.4.2)"]
-interpolatable = ["munkres", "scipy"]
-lxml = ["lxml (>=4.0,<5)"]
-pathops = ["skia-pathops (>=0.5.0)"]
-plot = ["matplotlib"]
-repacker = ["uharfbuzz (>=0.23.0)"]
-symfont = ["sympy"]
-type1 = ["xattr"]
-ufo = ["fs (>=2.2.0,<3)"]
-unicode = ["unicodedata2 (>=15.0.0)"]
-woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
-
 [[package]]
 name = "fqdn"
 version = "1.5.1"
@@ -3212,42 +2504,18 @@ files = [
     {file = "frozenlist-1.3.3.tar.gz", hash = "sha256:58bcc55721e8a90b88332d6cd441261ebb22342e238296bb330968952fbb3a6a"},
 ]
 
-[[package]]
-name = "fs"
-version = "2.4.16"
-description = "Python's filesystem abstraction layer"
-category = "main"
-optional = true
-python-versions = "*"
-files = [
-    {file = "fs-2.4.16-py2.py3-none-any.whl", hash = "sha256:660064febbccda264ae0b6bace80a8d1be9e089e0a5eb2427b7d517f9a91545c"},
-    {file = "fs-2.4.16.tar.gz", hash = "sha256:ae97c7d51213f4b70b6a958292530289090de3a7e15841e108fbe144f069d313"},
-]
-
-[package.dependencies]
-appdirs = ">=1.4.3,<1.5.0"
-setuptools = "*"
-six = ">=1.10,<2.0"
-
-[package.extras]
-scandir = ["scandir (>=1.5,<2.0)"]
-
 [[package]]
 name = "fsspec"
 version = "2023.6.0"
 description = "File-system specification"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "fsspec-2023.6.0-py3-none-any.whl", hash = "sha256:1cbad1faef3e391fba6dc005ae9b5bdcbf43005c9167ce78c915549c352c869a"},
     {file = "fsspec-2023.6.0.tar.gz", hash = "sha256:d0b2f935446169753e7a5c5c55681c54ea91996cc67be93c39a154fb3a2742af"},
 ]
 
-[package.dependencies]
-aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-requests = {version = "*", optional = true, markers = "extra == \"http\""}
-
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
@@ -3529,7 +2797,7 @@ name = "gptcache"
 version = "0.1.32"
 description = "GPTCache, a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service. GPTCache works as a memcache for AIGC applications, similar to how Redis works for traditional applications."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8.1"
 files = [
     {file = "gptcache-0.1.32-py3-none-any.whl", hash = "sha256:e85eb40ac54ebd8c0db1d67ba962b505d87321f287446083cfd80c4f205d2d10"},
@@ -3568,80 +2836,6 @@ test = ["aiofiles", "aiohttp (>=3.7.1,<3.9.0)", "botocore (>=1.21,<2)", "mock (=
 test-no-transport = ["aiofiles", "mock (==4.0.2)", "parse (==1.15.0)", "pytest (==6.2.5)", "pytest-asyncio (==0.16.0)", "pytest-console-scripts (==1.3.1)", "pytest-cov (==3.0.0)", "vcrpy (==4.0.2)"]
 websockets = ["websockets (>=10,<11)", "websockets (>=9,<10)"]
 
-[[package]]
-name = "gradio"
-version = "3.38.0"
-description = "Python library for easily interacting with trained machine learning models"
-category = "main"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "gradio-3.38.0-py3-none-any.whl", hash = "sha256:3ac954349ab00e1d78c6b87940e49853e5fb800a1ef35ff0a24796eb2f4803f5"},
-    {file = "gradio-3.38.0.tar.gz", hash = "sha256:cc08db00efd8ab00fef2d655f80ee18d4330fe08d0d412914cb0606b4a05881b"},
-]
-
-[package.dependencies]
-aiofiles = ">=22.0,<24.0"
-aiohttp = ">=3.0,<4.0"
-altair = ">=4.2.0,<6.0"
-fastapi = "*"
-ffmpy = "*"
-gradio-client = ">=0.2.10"
-httpx = "*"
-huggingface-hub = ">=0.14.0"
-jinja2 = "<4.0"
-markdown-it-py = {version = ">=2.0.0", extras = ["linkify"]}
-markupsafe = ">=2.0,<3.0"
-matplotlib = ">=3.0,<4.0"
-mdit-py-plugins = "<=0.3.3"
-numpy = ">=1.0,<2.0"
-orjson = ">=3.0,<4.0"
-packaging = "*"
-pandas = ">=1.0,<3.0"
-pillow = ">=8.0,<11.0"
-pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<3.0.0"
-pydub = "*"
-python-multipart = "*"
-pyyaml = ">=5.0,<7.0"
-requests = ">=2.0,<3.0"
-semantic-version = ">=2.0,<3.0"
-typing-extensions = ">=4.0,<5.0"
-uvicorn = ">=0.14.0"
-websockets = ">=10.0,<12.0"
-
-[[package]]
-name = "gradio-client"
-version = "0.2.10"
-description = "Python library for easily interacting with trained machine learning models"
-category = "main"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "gradio_client-0.2.10-py3-none-any.whl", hash = "sha256:8b1e32093f766812b91c65756e85ad8bbe6d60b885b87b47908a72359c8d72a0"},
-    {file = "gradio_client-0.2.10.tar.gz", hash = "sha256:d4f93c86649f7662ec16861506ae864d18667422e8c8ecc227360f2aedcffdc9"},
-]
-
-[package.dependencies]
-fsspec = "*"
-httpx = "*"
-huggingface-hub = ">=0.13.0"
-packaging = "*"
-requests = ">=2.0,<3.0"
-typing-extensions = ">=4.0,<5.0"
-websockets = ">=10.0,<12.0"
-
-[[package]]
-name = "graphlib-backport"
-version = "1.0.3"
-description = "Backport of the Python 3.9 graphlib module for Python 3.6+"
-category = "dev"
-optional = false
-python-versions = ">=3.6,<4.0"
-files = [
-    {file = "graphlib_backport-1.0.3-py3-none-any.whl", hash = "sha256:24246967b9e7e6a91550bc770e6169585d35aa32790258579a8a3899a8c18fde"},
-    {file = "graphlib_backport-1.0.3.tar.gz", hash = "sha256:7bb8fc7757b8ae4e6d8000a26cd49e9232aaa9a3aa57edb478474b8424bfaae2"},
-]
-
 [[package]]
 name = "graphql-core"
 version = "3.2.3"
@@ -3733,7 +2927,7 @@ name = "grpcio"
 version = "1.47.5"
 description = "HTTP/2-based RPC framework"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6"
 files = [
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@@ -3790,38 +2984,6 @@ six = ">=1.5.2"
 [package.extras]
 protobuf = ["grpcio-tools (>=1.47.5)"]
 
-[[package]]
-name = "grpcio-health-checking"
-version = "1.47.5"
-description = "Standard Health Checking Service for gRPC"
-category = "main"
-optional = true
-python-versions = ">=3.6"
-files = [
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-
-[package.dependencies]
-grpcio = ">=1.47.5"
-protobuf = ">=3.12.0"
-
-[[package]]
-name = "grpcio-reflection"
-version = "1.47.5"
-description = "Standard Protobuf Reflection Service for gRPC"
-category = "main"
-optional = true
-python-versions = ">=3.6"
-files = [
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-[package.dependencies]
-grpcio = ">=1.47.5"
-protobuf = ">=3.12.0"
-
 [[package]]
 name = "grpcio-tools"
 version = "1.47.5"
@@ -3888,7 +3050,7 @@ name = "h11"
 version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
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@@ -4024,60 +3186,6 @@ files = [
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-[[package]]
-name = "httptools"
-version = "0.5.0"
-description = "A collection of framework independent HTTP protocol utils."
-category = "main"
-optional = false
-python-versions = ">=3.5.0"
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-    {file = "httptools-0.5.0-cp39-cp39-win_amd64.whl", hash = "sha256:1af91b3650ce518d226466f30bbba5b6376dbd3ddb1b2be8b0658c6799dd450b"},
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-]
-
-[package.extras]
-test = ["Cython (>=0.29.24,<0.30.0)"]
-
 [[package]]
 name = "httpx"
 version = "0.24.1"
@@ -4111,7 +3219,7 @@ name = "huggingface-hub"
 version = "0.15.1"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7.0"
 files = [
     {file = "huggingface_hub-0.15.1-py3-none-any.whl", hash = "sha256:05b0fb0abbf1f625dfee864648ac3049fe225ac4371c7bafaca0c2d3a2f83445"},
@@ -4138,27 +3246,12 @@ testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "gradio", "jedi", "numpy"
 torch = ["torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
 
-[[package]]
-name = "humanfriendly"
-version = "10.0"
-description = "Human friendly output for text interfaces using Python"
-category = "main"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-files = [
-    {file = "humanfriendly-10.0-py2.py3-none-any.whl", hash = "sha256:1697e1a8a8f550fd43c2865cd84542fc175a61dcb779b6fee18cf6b6ccba1477"},
-    {file = "humanfriendly-10.0.tar.gz", hash = "sha256:6b0b831ce8f15f7300721aa49829fc4e83921a9a301cc7f606be6686a2288ddc"},
-]
-
-[package.dependencies]
-pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_version >= \"3.8\""}
-
 [[package]]
 name = "humbug"
 version = "0.3.1"
 description = "Humbug: Do you build developer tools? Humbug helps you know your users."
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "humbug-0.3.1-py3-none-any.whl", hash = "sha256:f9e3c8dd60a8ba943194f7ed45caa66e5db43d99f3745c60030ec40e6313a927"},
@@ -4236,18 +3329,6 @@ zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
 
-[[package]]
-name = "inflection"
-version = "0.5.1"
-description = "A port of Ruby on Rails inflector to Python"
-category = "main"
-optional = true
-python-versions = ">=3.5"
-files = [
-    {file = "inflection-0.5.1-py2.py3-none-any.whl", hash = "sha256:f38b2b640938a4f35ade69ac3d053042959b62a0f1076a5bbaa1b9526605a8a2"},
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-]
-
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -4414,29 +3495,6 @@ files = [
 docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
 
-[[package]]
-name = "jcloud"
-version = "0.2.12"
-description = "Simplify deploying and managing Jina projects on Jina Cloud"
-category = "main"
-optional = true
-python-versions = "*"
-files = [
-    {file = "jcloud-0.2.12.tar.gz", hash = "sha256:b7bae0909e4f09267aaa681c59e86ae27d3812fb7b63ad018bfbbcfdf8c0ad1e"},
-]
-
-[package.dependencies]
-aiohttp = ">=3.8.0"
-jina-hubble-sdk = ">=0.26.10"
-packaging = "*"
-python-dateutil = "*"
-python-dotenv = "*"
-pyyaml = "*"
-rich = ">=12.0.0"
-
-[package.extras]
-test = ["black (==22.3.0)", "jina (>=3.7.0)", "mock", "pytest", "pytest-asyncio", "pytest-cov", "pytest-custom_exit_code", "pytest-env", "pytest-mock", "pytest-repeat", "pytest-reraise", "pytest-timeout"]
-
 [[package]]
 name = "jedi"
 version = "0.18.2"
@@ -4468,148 +3526,6 @@ files = [
     {file = "jieba3k-0.35.1.zip", hash = "sha256:980a4f2636b778d312518066be90c7697d410dd5a472385f5afced71a2db1c10"},
 ]
 
-[[package]]
-name = "jina"
-version = "3.14.1"
-description = "Build multimodal AI services via cloud native technologies · Neural Search · Generative AI · MLOps"
-category = "main"
-optional = true
-python-versions = "*"
-files = [
-    {file = "jina-3.14.1.tar.gz", hash = "sha256:00b1f5995b13c9a49a2287bd534bd32eb8c05706064752035d569e616a15b411"},
-]
-
-[package.dependencies]
-aiofiles = "*"
-aiohttp = "*"
-aiostream = "*"
-docarray = ">=0.16.4"
-docker = "*"
-fastapi = ">=0.76.0"
-filelock = "*"
-grpcio = ">=1.46.0,<1.48.1"
-grpcio-health-checking = ">=1.46.0,<1.48.1"
-grpcio-reflection = ">=1.46.0,<1.48.1"
-jcloud = ">=0.0.35"
-jina-hubble-sdk = ">=0.30.4"
-numpy = "*"
-opentelemetry-api = ">=1.12.0"
-opentelemetry-exporter-otlp = ">=1.12.0"
-opentelemetry-exporter-otlp-proto-grpc = ">=1.13.0"
-opentelemetry-exporter-prometheus = ">=1.12.0rc1"
-opentelemetry-instrumentation-aiohttp-client = ">=0.33b0"
-opentelemetry-instrumentation-fastapi = ">=0.33b0"
-opentelemetry-instrumentation-grpc = ">=0.35b0"
-opentelemetry-sdk = ">=1.14.0"
-packaging = ">=20.0"
-pathspec = "*"
-prometheus_client = ">=0.12.0"
-protobuf = ">=3.19.0"
-pydantic = "*"
-python-multipart = "*"
-pyyaml = ">=5.3.1"
-requests = "*"
-uvicorn = {version = "*", extras = ["standard"]}
-uvloop = "*"
-websockets = "*"
-
-[package.extras]
-aiofiles = ["aiofiles"]
-aiohttp = ["aiohttp"]
-aiostream = ["aiostream"]
-all = ["Pillow", "aiofiles", "aiohttp", "aiostream", "black (==22.3.0)", "bs4", "coverage (==6.2)", "docarray (>=0.16.4)", "docker", "fastapi (>=0.76.0)", "filelock", "flaky", "grpcio (>=1.46.0,<1.48.1)", "grpcio-health-checking (>=1.46.0,<1.48.1)", "grpcio-reflection (>=1.46.0,<1.48.1)", "jcloud (>=0.0.35)", "jina-hubble-sdk (>=0.30.4)", "jsonschema", "kubernetes (>=18.20.0)", "mock", "numpy", "opentelemetry-api (>=1.12.0)", "opentelemetry-exporter-otlp (>=1.12.0)", "opentelemetry-exporter-otlp-proto-grpc (>=1.13.0)", "opentelemetry-exporter-prometheus (>=1.12.0rc1)", "opentelemetry-instrumentation-aiohttp-client (>=0.33b0)", "opentelemetry-instrumentation-fastapi (>=0.33b0)", "opentelemetry-instrumentation-grpc (>=0.35b0)", "opentelemetry-sdk (>=1.14.0)", "opentelemetry-test-utils (>=0.33b0)", "packaging (>=20.0)", "pathspec", "portforward (>=0.2.4)", "prometheus-api-client (>=0.5.1)", "prometheus_client (>=0.12.0)", "protobuf (>=3.19.0)", "psutil", "pydantic", "pytest", "pytest-asyncio", "pytest-cov (==3.0.0)", "pytest-custom_exit_code", "pytest-kind (==22.11.1)", "pytest-lazy-fixture", "pytest-mock", "pytest-repeat", "pytest-reraise", "pytest-timeout", "python-multipart", "pyyaml (>=5.3.1)", "requests", "requests-mock", "scipy (>=1.6.1)", "sgqlc", "strawberry-graphql (>=0.96.0)", "tensorflow (>=2.0)", "torch", "uvicorn[standard]", "uvloop", "watchfiles (>=0.18.0)", "websockets"]
-black = ["black (==22.3.0)"]
-bs4 = ["bs4"]
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-
 [[package]]
 name = "lancedb"
 version = "0.1.8"
@@ -5095,21 +3933,6 @@ dev = ["black", "pre-commit", "ruff"]
 docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]"]
 tests = ["doctest", "pytest", "pytest-mock"]
 
-[[package]]
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-version = "3.3.0"
-description = "Tools for labeling human languages with IETF language tags"
-category = "main"
-optional = true
-python-versions = ">=3.6"
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-
-[package.extras]
-data = ["language-data (>=1.1,<2.0)"]
-
 [[package]]
 name = "langkit"
 version = "0.0.6"
@@ -5132,18 +3955,18 @@ all = ["datasets (>=2.12.0,<3.0.0)", "nltk (>=3.8.1,<4.0.0)", "openai (>=0.27.6,
 
 [[package]]
 name = "langsmith"
-version = "0.0.11"
+version = "0.0.22"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 category = "main"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
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 [package.dependencies]
-pydantic = ">=1,<2"
+pydantic = ">=1,<3"
 requests = ">=2,<3"
 
 [[package]]
@@ -5202,7 +4025,7 @@ name = "libdeeplake"
 version = "0.0.60"
 description = "C++ backend for Deep Lake"
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
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@@ -5262,27 +4085,6 @@ display = ["matplotlib (>=3.3.0)"]
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-description = "Links recognition library with FULL unicode support."
-category = "main"
-optional = true
-python-versions = ">=3.7"
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-
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-
 [[package]]
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 version = "0.40.1"
@@ -5322,7 +4124,7 @@ name = "loguru"
 version = "0.7.0"
 description = "Python logging made (stupidly) simple"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.5"
 files = [
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@@ -5434,7 +4236,7 @@ name = "lz4"
 version = "4.3.2"
 description = "LZ4 Bindings for Python"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
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@@ -5531,7 +4333,6 @@ files = [
 ]
 
 [package.dependencies]
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 mdurl = ">=0.1,<1.0"
 
 [package.extras]
@@ -5657,95 +4458,6 @@ files = [
 [package.dependencies]
 marshmallow = ">=2.0.0"
 
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-category = "dev"
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-python-versions = "*"
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-
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-
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-
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.6"
@@ -5761,26 +4473,6 @@ files = [
 [package.dependencies]
 traitlets = "*"
 
-[[package]]
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-version = "0.3.3"
-description = "Collection of plugins for markdown-it-py"
-category = "main"
-optional = true
-python-versions = ">=3.7"
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-
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -5810,7 +4502,7 @@ name = "mmh3"
 version = "3.1.0"
 description = "Python wrapper for MurmurHash (MurmurHash3), a set of fast and robust hash functions."
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "mmh3-3.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:16ee043b1bac040b4324b8baee39df9fdca480a560a6d74f2eef66a5009a234e"},
@@ -5855,7 +4547,7 @@ name = "momento"
 version = "1.6.0"
 description = "SDK for Momento"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7,<4.0"
 files = [
     {file = "momento-1.6.0-py3-none-any.whl", hash = "sha256:a7f9a85a4372274bd5eafba95d6ab72bafa2d947abfbb16f8d0afb4b6501e4fb"},
@@ -5872,7 +4564,7 @@ name = "momento-wire-types"
 version = "0.64.1"
 description = "Momento Client Proto Generated Files"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7,<4.0"
 files = [
     {file = "momento_wire_types-0.64.1-py3-none-any.whl", hash = "sha256:b6fb773831e7aaf95c60223e450e985606076a45df120814ecb9a97904948fe5"},
@@ -5883,18 +4575,6 @@ files = [
 grpcio = "*"
 protobuf = ">=3,<5"
 
-[[package]]
-name = "monotonic"
-version = "1.6"
-description = "An implementation of time.monotonic() for Python 2 & < 3.3"
-category = "dev"
-optional = false
-python-versions = "*"
-files = [
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-]
-
 [[package]]
 name = "more-itertools"
 version = "9.1.0"
@@ -5912,7 +4592,7 @@ name = "mpmath"
 version = "1.3.0"
 description = "Python library for arbitrary-precision floating-point arithmetic"
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
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@@ -6148,7 +4828,7 @@ name = "multiprocess"
 version = "0.70.14"
 description = "better multiprocessing and multithreading in python"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
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@@ -6170,44 +4850,6 @@ files = [
 [package.dependencies]
 dill = ">=0.3.6"
 
-[[package]]
-name = "murmurhash"
-version = "1.0.9"
-description = "Cython bindings for MurmurHash"
-category = "main"
-optional = true
-python-versions = ">=3.6"
-files = [
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-    {file = "murmurhash-1.0.9-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b129e1c5ebd772e6ff5ef925bcce695df13169bd885337e6074b923ab6edcfc8"},
-    {file = "murmurhash-1.0.9-cp39-cp39-win_amd64.whl", hash = "sha256:379bf6b414bd27dd36772dd1570565a7d69918e980457370838bd514df0d91e9"},
-    {file = "murmurhash-1.0.9.tar.gz", hash = "sha256:fe7a38cb0d3d87c14ec9dddc4932ffe2dbc77d75469ab80fd5014689b0e07b58"},
-]
-
 [[package]]
 name = "mwcli"
 version = "0.0.3"
@@ -6609,7 +5251,7 @@ name = "nltk"
 version = "3.8.1"
 description = "Natural Language Toolkit"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "nltk-3.8.1-py3-none-any.whl", hash = "sha256:fd5c9109f976fa86bcadba8f91e47f5e9293bd034474752e92a520f81c93dda5"},
@@ -6755,7 +5397,7 @@ name = "numcodecs"
 version = "0.11.0"
 description = "A Python package providing buffer compression and transformation codecs for use"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "numcodecs-0.11.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c0bc116752be45b4f9dca4315e5a2b4185e3b46f68c997dbb84aef334ceb5a1d"},
@@ -6869,7 +5511,7 @@ name = "nvidia-cublas-cu11"
 version = "11.10.3.66"
 description = "CUBLAS native runtime libraries"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_cublas_cu11-11.10.3.66-py3-none-manylinux1_x86_64.whl", hash = "sha256:d32e4d75f94ddfb93ea0a5dda08389bcc65d8916a25cb9f37ac89edaeed3bded"},
@@ -6885,7 +5527,7 @@ name = "nvidia-cuda-nvrtc-cu11"
 version = "11.7.99"
 description = "NVRTC native runtime libraries"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_cuda_nvrtc_cu11-11.7.99-2-py3-none-manylinux1_x86_64.whl", hash = "sha256:9f1562822ea264b7e34ed5930567e89242d266448e936b85bc97a3370feabb03"},
@@ -6902,7 +5544,7 @@ name = "nvidia-cuda-runtime-cu11"
 version = "11.7.99"
 description = "CUDA Runtime native Libraries"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_cuda_runtime_cu11-11.7.99-py3-none-manylinux1_x86_64.whl", hash = "sha256:cc768314ae58d2641f07eac350f40f99dcb35719c4faff4bc458a7cd2b119e31"},
@@ -6918,7 +5560,7 @@ name = "nvidia-cudnn-cu11"
 version = "8.5.0.96"
 description = "cuDNN runtime libraries"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3"
 files = [
     {file = "nvidia_cudnn_cu11-8.5.0.96-2-py3-none-manylinux1_x86_64.whl", hash = "sha256:402f40adfc6f418f9dae9ab402e773cfed9beae52333f6d86ae3107a1b9527e7"},
@@ -6967,72 +5609,6 @@ rsa = ["cryptography (>=3.0.0)"]
 signals = ["blinker (>=1.4.0)"]
 signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 
-[[package]]
-name = "octoai-sdk"
-version = "0.1.1"
-description = "A runtime library for OctoAI."
-category = "main"
-optional = true
-python-versions = ">=3.8.1,<4.0.0"
-files = [
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-]
-
-[package.dependencies]
-click = ">=8.1.3,<9.0.0"
-fastapi = ">=0.95.2,<0.96.0"
-httpx = ">=0.24.0,<0.25.0"
-numpy = ">=1.24.3,<2.0.0"
-pillow = ">=9.5.0,<10.0.0"
-pydantic = ">=1.10.8,<2.0.0"
-pyyaml = ">=6.0,<7.0"
-soundfile = ">=0.12.1,<0.13.0"
-types-pyyaml = ">=6.0.12.10,<7.0.0.0"
-uvicorn = ">=0.22.0,<0.23.0"
-
-[[package]]
-name = "onnxruntime"
-version = "1.15.1"
-description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
-category = "dev"
-optional = false
-python-versions = "*"
-files = [
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-]
-
-[package.dependencies]
-coloredlogs = "*"
-flatbuffers = "*"
-numpy = ">=1.21.6"
-packaging = "*"
-protobuf = "*"
-sympy = "*"
-
 [[package]]
 name = "openai"
 version = "0.27.8"
@@ -7061,7 +5637,7 @@ name = "openapi-schema-pydantic"
 version = "1.2.4"
 description = "OpenAPI (v3) specification schema as pydantic class"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6.1"
 files = [
     {file = "openapi-schema-pydantic-1.2.4.tar.gz", hash = "sha256:3e22cf58b74a69f752cc7e5f1537f6e44164282db2700cbbcd3bb99ddd065196"},
@@ -7071,41 +5647,6 @@ files = [
 [package.dependencies]
 pydantic = ">=1.8.2"
 
-[[package]]
-name = "openllm"
-version = "0.1.19"
-description = "OpenLLM: REST/gRPC API server for running any open Large-Language Model - StableLM, Llama, Alpaca, Dolly, Flan-T5, Custom"
-category = "main"
-optional = true
-python-versions = ">=3.8"
-files = [
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-    {file = "openllm-0.1.19.tar.gz", hash = "sha256:fbb91294d17f9ff1496f4871208e2bdfed5f51e48e7e4bbc1803209983b1cc00"},
-]
-
-[package.dependencies]
-attrs = ">=23.1.0"
-bentoml = {version = ">=1.0.22", extras = ["grpc", "io"]}
-cattrs = ">=23.1.0"
-httpx = "*"
-inflection = "*"
-optimum = "*"
-orjson = "*"
-tabulate = {version = ">=0.9.0", extras = ["widechars"]}
-transformers = {version = ">=4.29.0", extras = ["accelerate", "tokenizers", "torch"]}
-typing-extensions = "*"
-
-[package.extras]
-agents = ["diffusers", "soundfile", "transformers[agents] (>=4.30)"]
-all = ["openllm[agents]", "openllm[chatglm]", "openllm[falcon]", "openllm[fine-tune]", "openllm[flan-t5]", "openllm[mpt]", "openllm[openai]", "openllm[starcoder]"]
-chatglm = ["cpm-kernels", "sentencepiece"]
-falcon = ["einops", "safetensors", "xformers"]
-fine-tune = ["accelerate", "bitsandbytes", "datasets", "deepspeed", "peft", "trl"]
-flan-t5 = ["flax", "jax", "jaxlib", "keras", "tensorflow"]
-mpt = ["triton"]
-openai = ["openai", "tiktoken"]
-starcoder = ["bitsandbytes"]
-
 [[package]]
 name = "openlm"
 version = "0.0.5"
@@ -7146,268 +5687,6 @@ develop = ["black", "botocore", "coverage (<7.0.0)", "jinja2", "mock", "myst-par
 docs = ["myst-parser", "sphinx", "sphinx-copybutton", "sphinx-rtd-theme"]
 kerberos = ["requests-kerberos"]
 
-[[package]]
-name = "opentelemetry-api"
-version = "1.17.0"
-description = "OpenTelemetry Python API"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_api-1.17.0-py3-none-any.whl", hash = "sha256:b41d9b2a979607b75d2683b9bbf97062a683d190bc696969fb2122fa60aeaabc"},
-    {file = "opentelemetry_api-1.17.0.tar.gz", hash = "sha256:3480fcf6b783be5d440a226a51db979ccd7c49a2e98d1c747c991031348dcf04"},
-]
-
-[package.dependencies]
-deprecated = ">=1.2.6"
-importlib-metadata = ">=6.0.0,<6.1.0"
-setuptools = ">=16.0"
-
-[[package]]
-name = "opentelemetry-exporter-otlp"
-version = "1.17.0"
-description = "OpenTelemetry Collector Exporters"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_exporter_otlp-1.17.0-py3-none-any.whl", hash = "sha256:9708d2b74c9205a7bd9b46e24acec0e3b362465d9a77b62347ea0459d4358044"},
-    {file = "opentelemetry_exporter_otlp-1.17.0.tar.gz", hash = "sha256:d0fa02b512127b44493c75d12a2dc2557bf251b7f76b354cfaa58b0f820d04ae"},
-]
-
-[package.dependencies]
-opentelemetry-exporter-otlp-proto-grpc = "1.17.0"
-opentelemetry-exporter-otlp-proto-http = "1.17.0"
-
-[[package]]
-name = "opentelemetry-exporter-otlp-proto-grpc"
-version = "1.17.0"
-description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.17.0-py3-none-any.whl", hash = "sha256:192d781b668a74edb49152b8b5f4f7e25bcb4307a9cf4b2dfcf87e68feac98bd"},
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.17.0.tar.gz", hash = "sha256:f01476ae89484bc6210e50d7a4d93c293b3a12aff562253b94f588a85af13f70"},
-]
-
-[package.dependencies]
-backoff = {version = ">=1.10.0,<3.0.0", markers = "python_version >= \"3.7\""}
-googleapis-common-protos = ">=1.52,<2.0"
-grpcio = ">=1.0.0,<2.0.0"
-opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-proto = "1.17.0"
-opentelemetry-sdk = ">=1.17.0,<1.18.0"
-
-[package.extras]
-test = ["pytest-grpc"]
-
-[[package]]
-name = "opentelemetry-exporter-otlp-proto-http"
-version = "1.17.0"
-description = "OpenTelemetry Collector Protobuf over HTTP Exporter"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_exporter_otlp_proto_http-1.17.0-py3-none-any.whl", hash = "sha256:81959249b75bd36c3b73c885a9ce36aa21e8022618e8e95fa41ae69609f0c799"},
-    {file = "opentelemetry_exporter_otlp_proto_http-1.17.0.tar.gz", hash = "sha256:329984da861ee2cc42c4bc5ae1b80092fb76a0ea5a24b3519bc3b52572197fec"},
-]
-
-[package.dependencies]
-backoff = {version = ">=1.10.0,<3.0.0", markers = "python_version >= \"3.7\""}
-googleapis-common-protos = ">=1.52,<2.0"
-opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-proto = "1.17.0"
-opentelemetry-sdk = ">=1.17.0,<1.18.0"
-requests = ">=2.7,<3.0"
-
-[package.extras]
-test = ["responses (==0.22.0)"]
-
-[[package]]
-name = "opentelemetry-exporter-prometheus"
-version = "1.12.0rc1"
-description = "Prometheus Metric Exporter for OpenTelemetry"
-category = "main"
-optional = true
-python-versions = ">=3.6"
-files = [
-    {file = "opentelemetry-exporter-prometheus-1.12.0rc1.tar.gz", hash = "sha256:f10c6c243d69d5b63f755885b36a4ce8ef2cdf3e737c4e6bf00f32e87668f0a9"},
-    {file = "opentelemetry_exporter_prometheus-1.12.0rc1-py3-none-any.whl", hash = "sha256:1f0c8f93d62e1575313966ceb8abf11e9a46e1839fda9ee4269b06d40494280f"},
-]
-
-[package.dependencies]
-opentelemetry-api = ">=1.10.0"
-opentelemetry-sdk = ">=1.10.0"
-prometheus-client = ">=0.5.0,<1.0.0"
-
-[[package]]
-name = "opentelemetry-instrumentation"
-version = "0.38b0"
-description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_instrumentation-0.38b0-py3-none-any.whl", hash = "sha256:48eed87e5db9d2cddd57a8ea359bd15318560c0ffdd80d90a5fc65816e15b7f4"},
-    {file = "opentelemetry_instrumentation-0.38b0.tar.gz", hash = "sha256:3dbe93248eec7652d5725d3c6d2f9dd048bb8fda6b0505aadbc99e51638d833c"},
-]
-
-[package.dependencies]
-opentelemetry-api = ">=1.4,<2.0"
-setuptools = ">=16.0"
-wrapt = ">=1.0.0,<2.0.0"
-
-[[package]]
-name = "opentelemetry-instrumentation-aiohttp-client"
-version = "0.38b0"
-description = "OpenTelemetry aiohttp client instrumentation"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_instrumentation_aiohttp_client-0.38b0-py3-none-any.whl", hash = "sha256:093987f5c96518ac6999eb7480af168655bc3538752ae67d4d9a5807eaad1ee0"},
-    {file = "opentelemetry_instrumentation_aiohttp_client-0.38b0.tar.gz", hash = "sha256:9c3e637e742b5d8e5c8a76fae4f3812dde5e58f85598d119abd0149cb1c82ec0"},
-]
-
-[package.dependencies]
-opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.38b0"
-opentelemetry-semantic-conventions = "0.38b0"
-opentelemetry-util-http = "0.38b0"
-wrapt = ">=1.0.0,<2.0.0"
-
-[package.extras]
-instruments = ["aiohttp (>=3.0,<4.0)"]
-test = ["opentelemetry-instrumentation-aiohttp-client[instruments]"]
-
-[[package]]
-name = "opentelemetry-instrumentation-asgi"
-version = "0.38b0"
-description = "ASGI instrumentation for OpenTelemetry"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_instrumentation_asgi-0.38b0-py3-none-any.whl", hash = "sha256:c5bba11505008a3cd1b2c42b72f85f3f4f5af50ab931eddd0b01bde376dc5971"},
-    {file = "opentelemetry_instrumentation_asgi-0.38b0.tar.gz", hash = "sha256:32d1034c253de6048d0d0166b304f9125267ca9329e374202ebe011a206eba53"},
-]
-
-[package.dependencies]
-asgiref = ">=3.0,<4.0"
-opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.38b0"
-opentelemetry-semantic-conventions = "0.38b0"
-opentelemetry-util-http = "0.38b0"
-
-[package.extras]
-instruments = ["asgiref (>=3.0,<4.0)"]
-test = ["opentelemetry-instrumentation-asgi[instruments]", "opentelemetry-test-utils (==0.38b0)"]
-
-[[package]]
-name = "opentelemetry-instrumentation-fastapi"
-version = "0.38b0"
-description = "OpenTelemetry FastAPI Instrumentation"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_instrumentation_fastapi-0.38b0-py3-none-any.whl", hash = "sha256:91139586732e437b1c3d5cf838dc5be910bce27b4b679612112be03fcc4fa2aa"},
-    {file = "opentelemetry_instrumentation_fastapi-0.38b0.tar.gz", hash = "sha256:8946fd414084b305ad67556a1907e2d4a497924d023effc5ea3b4b1b0c55b256"},
-]
-
-[package.dependencies]
-opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.38b0"
-opentelemetry-instrumentation-asgi = "0.38b0"
-opentelemetry-semantic-conventions = "0.38b0"
-opentelemetry-util-http = "0.38b0"
-
-[package.extras]
-instruments = ["fastapi (>=0.58,<1.0)"]
-test = ["httpx (>=0.22,<1.0)", "opentelemetry-instrumentation-fastapi[instruments]", "opentelemetry-test-utils (==0.38b0)", "requests (>=2.23,<3.0)"]
-
-[[package]]
-name = "opentelemetry-instrumentation-grpc"
-version = "0.38b0"
-description = "OpenTelemetry gRPC instrumentation"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_instrumentation_grpc-0.38b0-py3-none-any.whl", hash = "sha256:64978d158f233c45df809d927f62a79e0bbb1c433d63ae5f7b38133a515397d8"},
-    {file = "opentelemetry_instrumentation_grpc-0.38b0.tar.gz", hash = "sha256:d6a45e4c64aa4a2f3c29b6ca673b04d88e8ef4c2d0273e9b23209f9248f30325"},
-]
-
-[package.dependencies]
-opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.38b0"
-opentelemetry-sdk = ">=1.12,<2.0"
-opentelemetry-semantic-conventions = "0.38b0"
-wrapt = ">=1.0.0,<2.0.0"
-
-[package.extras]
-instruments = ["grpcio (>=1.27,<2.0)"]
-test = ["opentelemetry-instrumentation-grpc[instruments]", "opentelemetry-sdk (>=1.12,<2.0)", "opentelemetry-test-utils (==0.38b0)", "protobuf (>=3.13,<4.0)"]
-
-[[package]]
-name = "opentelemetry-proto"
-version = "1.17.0"
-description = "OpenTelemetry Python Proto"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_proto-1.17.0-py3-none-any.whl", hash = "sha256:c7c0f748668102598e84ca4d51975f87ebf66865aa7469fc2c5e8bdaab813e93"},
-    {file = "opentelemetry_proto-1.17.0.tar.gz", hash = "sha256:8501fdc3bc76c03a2ed11603a4d9fce6e5a97eeaebd7a20ad84bba7bd79cc9f8"},
-]
-
-[package.dependencies]
-protobuf = ">=3.19,<5.0"
-
-[[package]]
-name = "opentelemetry-sdk"
-version = "1.17.0"
-description = "OpenTelemetry Python SDK"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_sdk-1.17.0-py3-none-any.whl", hash = "sha256:07424cbcc8c012bc120ed573d5443e7322f3fb393512e72866c30111070a8c37"},
-    {file = "opentelemetry_sdk-1.17.0.tar.gz", hash = "sha256:99bb9a787006774f865a4b24f8179900347d03a214c362a6cb70191f77dd6132"},
-]
-
-[package.dependencies]
-opentelemetry-api = "1.17.0"
-opentelemetry-semantic-conventions = "0.38b0"
-setuptools = ">=16.0"
-typing-extensions = ">=3.7.4"
-
-[[package]]
-name = "opentelemetry-semantic-conventions"
-version = "0.38b0"
-description = "OpenTelemetry Semantic Conventions"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_semantic_conventions-0.38b0-py3-none-any.whl", hash = "sha256:b0ba36e8b70bfaab16ee5a553d809309cc11ff58aec3d2550d451e79d45243a7"},
-    {file = "opentelemetry_semantic_conventions-0.38b0.tar.gz", hash = "sha256:37f09e47dd5fc316658bf9ee9f37f9389b21e708faffa4a65d6a3de484d22309"},
-]
-
-[[package]]
-name = "opentelemetry-util-http"
-version = "0.38b0"
-description = "Web util for OpenTelemetry"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "opentelemetry_util_http-0.38b0-py3-none-any.whl", hash = "sha256:8e5f0451eeb5307b2c628dd799886adc5e113fb13a7207c29c672e8d168eabd8"},
-    {file = "opentelemetry_util_http-0.38b0.tar.gz", hash = "sha256:85eb032b6129c4d7620583acf574e99fe2e73c33d60e256b54af436f76ceb5ae"},
-]
-
 [[package]]
 name = "opt-einsum"
 version = "3.3.0"
@@ -7427,47 +5706,6 @@ numpy = ">=1.7"
 docs = ["numpydoc", "sphinx (==1.2.3)", "sphinx-rtd-theme", "sphinxcontrib-napoleon"]
 tests = ["pytest", "pytest-cov", "pytest-pep8"]
 
-[[package]]
-name = "optimum"
-version = "1.8.8"
-description = "Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality."
-category = "main"
-optional = true
-python-versions = ">=3.7.0"
-files = [
-    {file = "optimum-1.8.8.tar.gz", hash = "sha256:07a5bd64d3d9dcdbc3926e7a421fbe7fce206fff0d9488a6aad7de1315287449"},
-]
-
-[package.dependencies]
-coloredlogs = "*"
-datasets = "*"
-huggingface_hub = ">=0.8.0"
-numpy = "*"
-packaging = "*"
-sympy = "*"
-torch = ">=1.9"
-torchvision = "*"
-transformers = {version = ">=4.26.0", extras = ["sentencepiece"]}
-
-[package.extras]
-benchmark = ["evaluate (>=0.2.0)", "optuna", "scikit-learn", "seqeval", "torchvision", "tqdm"]
-dev = ["Pillow", "black (>=23.1,<24.0)", "diffusers (>=0.17.0)", "parameterized", "pytest", "pytest-xdist", "requests", "ruff (>=0.0.241)", "sacremoses", "torchaudio", "torchvision"]
-exporters = ["onnx (<1.14.0)", "onnxruntime", "timm"]
-exporters-gpu = ["onnx (<1.14.0)", "onnxruntime-gpu", "timm"]
-exporters-tf = ["h5py", "numpy (<1.24.0)", "onnx", "onnxruntime", "tensorflow (>=2.4,<2.11)", "tf2onnx", "timm"]
-graphcore = ["optimum-graphcore"]
-habana = ["optimum-habana", "transformers (<4.29.0)"]
-intel = ["optimum-intel"]
-neural-compressor = ["optimum-intel[neural-compressor]"]
-neuron = ["optimum-neuron[neuron]"]
-neuronx = ["optimum-neuron[neuronx]"]
-nncf = ["optimum-intel[nncf]"]
-onnxruntime = ["datasets (>=1.2.1)", "evaluate", "onnx (<1.14.0)", "onnxruntime (>=1.9.0)", "protobuf (>=3.20.1)"]
-onnxruntime-gpu = ["datasets (>=1.2.1)", "evaluate", "onnx (<1.14.0)", "onnxruntime-gpu (>=1.9.0)", "protobuf (>=3.20.1)"]
-openvino = ["optimum-intel[openvino]"]
-quality = ["black (>=23.1,<24.0)", "ruff (>=0.0.241)"]
-tests = ["Pillow", "diffusers (>=0.17.0)", "parameterized", "pytest", "pytest-xdist", "requests", "sacremoses", "torchaudio", "torchvision"]
-
 [[package]]
 name = "orjson"
 version = "3.9.1"
@@ -7661,7 +5899,7 @@ name = "pathos"
 version = "0.3.0"
 description = "parallel graph management and execution in heterogeneous computing"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "pathos-0.3.0-py3-none-any.whl", hash = "sha256:b1f5a79b1c79a594330d451832642ee5bb61dd77dc75ba9e5c72087c77e8994c"},
@@ -7678,7 +5916,7 @@ ppft = ">=1.7.6.6"
 name = "pathspec"
 version = "0.11.1"
 description = "Utility library for gitignore style pattern matching of file paths."
-category = "main"
+category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -7686,29 +5924,6 @@ files = [
     {file = "pathspec-0.11.1.tar.gz", hash = "sha256:2798de800fa92780e33acca925945e9a19a133b715067cf165b8866c15a31687"},
 ]
 
-[[package]]
-name = "pathy"
-version = "0.10.1"
-description = "pathlib.Path subclasses for local and cloud bucket storage"
-category = "main"
-optional = true
-python-versions = ">= 3.6"
-files = [
-    {file = "pathy-0.10.1-py3-none-any.whl", hash = "sha256:a7613ee2d99a0a3300e1d836322e2d947c85449fde59f52906f995dbff67dad4"},
-    {file = "pathy-0.10.1.tar.gz", hash = "sha256:4cd6e71b4cd5ff875cfbb949ad9fa5519d8d1dbe69d5fc1d1b23aa3cb049618b"},
-]
-
-[package.dependencies]
-smart-open = ">=5.2.1,<7.0.0"
-typer = ">=0.3.0,<1.0.0"
-
-[package.extras]
-all = ["azure-storage-blob", "boto3", "google-cloud-storage (>=1.26.0,<2.0.0)", "mock", "pytest", "pytest-coverage", "typer-cli"]
-azure = ["azure-storage-blob"]
-gcs = ["google-cloud-storage (>=1.26.0,<2.0.0)"]
-s3 = ["boto3"]
-test = ["mock", "pytest", "pytest-coverage", "typer-cli"]
-
 [[package]]
 name = "pdfminer-six"
 version = "20221105"
@@ -7776,7 +5991,7 @@ name = "pillow"
 version = "9.5.0"
 description = "Python Imaging Library (Fork)"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "Pillow-9.5.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:ace6ca218308447b9077c14ea4ef381ba0b67ee78d64046b3f19cf4e1139ad16"},
@@ -7856,7 +6071,7 @@ name = "pinecone-client"
 version = "2.2.2"
 description = "Pinecone client and SDK"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "pinecone-client-2.2.2.tar.gz", hash = "sha256:391fe413754efd4e0ef00154b44271d63c4cdd4bedf088d23111a5725d863210"},
@@ -7882,7 +6097,7 @@ name = "pinecone-text"
 version = "0.4.2"
 description = "Text utilities library by Pinecone.io"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8,<4.0"
 files = [
     {file = "pinecone_text-0.4.2-py3-none-any.whl", hash = "sha256:79468c197b2fc7738c1511a6b5b8e7697fad613604ad935661a438f621ad2004"},
@@ -7897,61 +6112,6 @@ torch = ">=1.13.1,<2.0.0"
 transformers = ">=4.26.1,<5.0.0"
 wget = ">=3.2,<4.0"
 
-[[package]]
-name = "pip"
-version = "23.1.2"
-description = "The PyPA recommended tool for installing Python packages."
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "pip-23.1.2-py3-none-any.whl", hash = "sha256:3ef6ac33239e4027d9a5598a381b9d30880a1477e50039db2eac6e8a8f6d1b18"},
-    {file = "pip-23.1.2.tar.gz", hash = "sha256:0e7c86f486935893c708287b30bd050a36ac827ec7fe5e43fe7cb198dd835fba"},
-]
-
-[[package]]
-name = "pip-requirements-parser"
-version = "32.0.1"
-description = "pip requirements parser - a mostly correct pip requirements parsing library because it uses pip's own code."
-category = "main"
-optional = true
-python-versions = ">=3.6.0"
-files = [
-    {file = "pip-requirements-parser-32.0.1.tar.gz", hash = "sha256:b4fa3a7a0be38243123cf9d1f3518da10c51bdb165a2b2985566247f9155a7d3"},
-    {file = "pip_requirements_parser-32.0.1-py3-none-any.whl", hash = "sha256:4659bc2a667783e7a15d190f6fccf8b2486685b6dba4c19c3876314769c57526"},
-]
-
-[package.dependencies]
-packaging = "*"
-pyparsing = "*"
-
-[package.extras]
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-
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-
 [[package]]
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@@ -8060,36 +6220,12 @@ docs = ["sphinx (>=1.7.1)"]
 redis = ["redis"]
 tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "pytest-timeout (>=2.1.0)", "redis", "sphinx (>=6.0.0)"]
 
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-
 [[package]]
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 description = "utilities for filesystem exploration and automated builds"
 category = "main"
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 python-versions = ">=3.7"
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 description = "distributed and parallel python"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
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 [[package]]
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 version = "0.17.0"
 description = "Python client for the Prometheus monitoring system."
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+category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -8183,21 +6277,6 @@ files = [
 [package.dependencies]
 wcwidth = "*"
 
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@@ -8237,7 +6316,7 @@ files = [
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 version = "5.9.5"
 description = "Cross-platform lib for process and system monitoring in Python."
-category = "main"
+category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
@@ -8359,54 +6438,6 @@ files = [
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-
 [[package]]
 name = "pure-eval"
 version = "0.2.2"
@@ -8621,48 +6652,48 @@ files = [
 
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 python-versions = ">=3.7"
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 [package.dependencies]
 typing-extensions = "*"
 
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 description = "JSON Web Token implementation in Python"
 category = "main"
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 python-versions = ">=3.7"
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 description = "Python driver for MongoDB <http://www.mongodb.org>"
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-python-versions = ">=3.6"
-files = [
-    {file = "pynvml-11.5.0-py3-none-any.whl", hash = "sha256:5cce014ac01b098d08f06178f86c37be409b80b2e903a5a03ce15eed60f55e25"},
-    {file = "pynvml-11.5.0.tar.gz", hash = "sha256:d027b21b95b1088b9fc278117f9f61b7c67f8e33a787e9f83f735f0f71ac32d0"},
-]
-
 [[package]]
 name = "pyowm"
 version = "3.3.0"
@@ -9076,17 +7038,6 @@ files = [
 doc = ["sphinx", "sphinx_rtd_theme"]
 test = ["flake8", "isort", "pytest"]
 
-[[package]]
-name = "pypika"
-version = "0.48.9"
-description = "A SQL query builder API for Python"
-category = "dev"
-optional = false
-python-versions = "*"
-files = [
-    {file = "PyPika-0.48.9.tar.gz", hash = "sha256:838836a61747e7c8380cd1b7ff638694b7a7335345d0f559b04b2cd832ad5378"},
-]
-
 [[package]]
 name = "pyproj"
 version = "3.5.0"
@@ -9150,18 +7101,6 @@ files = [
 [package.dependencies]
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
 
-[[package]]
-name = "pyreadline3"
-version = "3.4.1"
-description = "A python implementation of GNU readline."
-category = "main"
-optional = false
-python-versions = "*"
-files = [
-    {file = "pyreadline3-3.4.1-py3-none-any.whl", hash = "sha256:b0efb6516fd4fb07b45949053826a62fa4cb353db5be2bbb4a7aa1fdd1e345fb"},
-    {file = "pyreadline3-3.4.1.tar.gz", hash = "sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae"},
-]
-
 [[package]]
 name = "pyrsistent"
 version = "0.19.3"
@@ -9444,33 +7383,11 @@ files = [
 [package.extras]
 cli = ["click (>=5.0)"]
 
-[[package]]
-name = "python-jose"
-version = "3.3.0"
-description = "JOSE implementation in Python"
-category = "main"
-optional = true
-python-versions = "*"
-files = [
-    {file = "python-jose-3.3.0.tar.gz", hash = "sha256:55779b5e6ad599c6336191246e95eb2293a9ddebd555f796a65f838f07e5d78a"},
-    {file = "python_jose-3.3.0-py2.py3-none-any.whl", hash = "sha256:9b1376b023f8b298536eedd47ae1089bcdb848f1535ab30555cd92002d78923a"},
-]
-
-[package.dependencies]
-ecdsa = "!=0.15"
-pyasn1 = "*"
-rsa = "*"
-
-[package.extras]
-cryptography = ["cryptography (>=3.4.0)"]
-pycrypto = ["pyasn1", "pycrypto (>=2.6.0,<2.7.0)"]
-pycryptodome = ["pyasn1", "pycryptodome (>=3.3.1,<4.0.0)"]
-
 [[package]]
 name = "python-json-logger"
 version = "2.0.7"
 description = "A python library adding a json log formatter"
-category = "main"
+category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -9478,46 +7395,6 @@ files = [
     {file = "python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd"},
 ]
 
-[[package]]
-name = "python-magic"
-version = "0.4.27"
-description = "File type identification using libmagic"
-category = "dev"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-files = [
-    {file = "python-magic-0.4.27.tar.gz", hash = "sha256:c1ba14b08e4a5f5c31a302b7721239695b2f0f058d125bd5ce1ee36b9d9d3c3b"},
-    {file = "python_magic-0.4.27-py2.py3-none-any.whl", hash = "sha256:c212960ad306f700aa0d01e5d7a325d20548ff97eb9920dcd29513174f0294d3"},
-]
-
-[[package]]
-name = "python-magic-bin"
-version = "0.4.14"
-description = "File type identification using libmagic binary package"
-category = "dev"
-optional = false
-python-versions = "*"
-files = [
-    {file = "python_magic_bin-0.4.14-py2.py3-none-macosx_10_6_intel.whl", hash = "sha256:7b1743b3dbf16601d6eedf4e7c2c9a637901b0faaf24ad4df4d4527e7d8f66a4"},
-    {file = "python_magic_bin-0.4.14-py2.py3-none-win32.whl", hash = "sha256:34a788c03adde7608028203e2dbb208f1f62225ad91518787ae26d603ae68892"},
-    {file = "python_magic_bin-0.4.14-py2.py3-none-win_amd64.whl", hash = "sha256:90be6206ad31071a36065a2fc169c5afb5e0355cbe6030e87641c6c62edc2b69"},
-]
-
-[[package]]
-name = "python-multipart"
-version = "0.0.6"
-description = "A streaming multipart parser for Python"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "python_multipart-0.0.6-py3-none-any.whl", hash = "sha256:ee698bab5ef148b0a760751c261902cd096e57e10558e11aca17646b74ee1c18"},
-    {file = "python_multipart-0.0.6.tar.gz", hash = "sha256:e9925a80bb668529f1b67c7fdb0a5dacdd7cbfc6fb0bff3ea443fe22bdd62132"},
-]
-
-[package.extras]
-dev = ["atomicwrites (==1.2.1)", "attrs (==19.2.0)", "coverage (==6.5.0)", "hatch", "invoke (==1.7.3)", "more-itertools (==4.3.0)", "pbr (==4.3.0)", "pluggy (==1.0.0)", "py (==1.11.0)", "pytest (==7.2.0)", "pytest-cov (==4.0.0)", "pytest-timeout (==2.1.0)", "pyyaml (==5.1)"]
-
 [[package]]
 name = "pytz"
 version = "2023.3"
@@ -9665,7 +7542,7 @@ files = [
 name = "pyzmq"
 version = "25.1.0"
 description = "Python bindings for 0MQ"
-category = "main"
+category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -9753,14 +7630,14 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
 name = "qdrant-client"
-version = "1.3.1"
+version = "1.4.0"
 description = "Client library for the Qdrant vector search engine"
 category = "main"
 optional = true
 python-versions = ">=3.7,<3.12"
 files = [
-    {file = "qdrant_client-1.3.1-py3-none-any.whl", hash = "sha256:9640855585d1f532094e342f07e0f2ef00652a60fc5d903c92ca3989a1e86318"},
-    {file = "qdrant_client-1.3.1.tar.gz", hash = "sha256:a999358b10e611d71b4b04c6ded36a6cfc963e56b4c3f99d9c1a603ca524a82e"},
+    {file = "qdrant_client-1.4.0-py3-none-any.whl", hash = "sha256:2f9e563955b5163da98016f2ed38d9aea5058576c7c5844e9aa205d28155f56d"},
+    {file = "qdrant_client-1.4.0.tar.gz", hash = "sha256:2e54f5a80eb1e7e67f4603b76365af4817af15fb3d0c0f44de4fd93afbbe5537"},
 ]
 
 [package.dependencies]
@@ -9769,8 +7646,7 @@ grpcio-tools = ">=1.41.0"
 httpx = {version = ">=0.14.0", extras = ["http2"]}
 numpy = {version = ">=1.21", markers = "python_version >= \"3.8\""}
 portalocker = ">=2.7.0,<3.0.0"
-pydantic = ">=1.8,<2.0"
-typing-extensions = ">=4.0.0,<4.6.0"
+pydantic = ">=1.10.8"
 urllib3 = ">=1.26.14,<2.0.0"
 
 [[package]]
@@ -9983,7 +7859,7 @@ name = "redis"
 version = "4.5.5"
 description = "Python client for Redis database and key-value store"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "redis-4.5.5-py3-none-any.whl", hash = "sha256:77929bc7f5dab9adf3acba2d3bb7d7658f1e0c2f1cafe7eb36434e751c471119"},
@@ -10299,7 +8175,7 @@ name = "s3transfer"
 version = "0.6.1"
 description = "An Amazon S3 Transfer Manager"
 category = "main"
-optional = false
+optional = true
 python-versions = ">= 3.7"
 files = [
     {file = "s3transfer-0.6.1-py3-none-any.whl", hash = "sha256:3c0da2d074bf35d6870ef157158641178a4204a6e689e82546083e31e0311346"},
@@ -10317,7 +8193,7 @@ name = "safetensors"
 version = "0.3.1"
 description = "Fast and Safe Tensor serialization"
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "safetensors-0.3.1-cp310-cp310-macosx_10_11_x86_64.whl", hash = "sha256:2ae9b7dd268b4bae6624729dac86deb82104820e9786429b0583e5168db2f770"},
@@ -10373,27 +8249,12 @@ tensorflow = ["tensorflow (>=2.11.0)"]
 testing = ["h5py (>=3.7.0)", "huggingface-hub (>=0.12.1)", "numpy (>=1.21.6)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "setuptools-rust (>=1.5.2)"]
 torch = ["torch (>=1.10)"]
 
-[[package]]
-name = "schema"
-version = "0.7.5"
-description = "Simple data validation library"
-category = "main"
-optional = true
-python-versions = "*"
-files = [
-    {file = "schema-0.7.5-py2.py3-none-any.whl", hash = "sha256:f3ffdeeada09ec34bf40d7d79996d9f7175db93b7a5065de0faa7f41083c1e6c"},
-    {file = "schema-0.7.5.tar.gz", hash = "sha256:f06717112c61895cabc4707752b88716e8420a8819d71404501e114f91043197"},
-]
-
-[package.dependencies]
-contextlib2 = ">=0.5.5"
-
 [[package]]
 name = "scikit-learn"
 version = "1.2.2"
 description = "A set of python modules for machine learning and data mining"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "scikit-learn-1.2.2.tar.gz", hash = "sha256:8429aea30ec24e7a8c7ed8a3fa6213adf3814a6efbea09e16e0a0c71e1a1a3d7"},
@@ -10431,44 +8292,12 @@ docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)"
 examples = ["matplotlib (>=3.1.3)", "pandas (>=1.0.5)", "plotly (>=5.10.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"]
 tests = ["black (>=22.3.0)", "flake8 (>=3.8.2)", "matplotlib (>=3.1.3)", "mypy (>=0.961)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pytest (>=5.3.1)", "pytest-cov (>=2.9.0)", "scikit-image (>=0.16.2)"]
 
-[[package]]
-name = "scipy"
-version = "1.6.1"
-description = "SciPy: Scientific Library for Python"
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "scipy-1.6.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:a15a1f3fc0abff33e792d6049161b7795909b40b97c6cc2934ed54384017ab76"},
-    {file = "scipy-1.6.1-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:e79570979ccdc3d165456dd62041d9556fb9733b86b4b6d818af7a0afc15f092"},
-    {file = "scipy-1.6.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:a423533c55fec61456dedee7b6ee7dce0bb6bfa395424ea374d25afa262be261"},
-    {file = "scipy-1.6.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:33d6b7df40d197bdd3049d64e8e680227151673465e5d85723b3b8f6b15a6ced"},
-    {file = "scipy-1.6.1-cp37-cp37m-win32.whl", hash = "sha256:6725e3fbb47da428794f243864f2297462e9ee448297c93ed1dcbc44335feb78"},
-    {file = "scipy-1.6.1-cp37-cp37m-win_amd64.whl", hash = "sha256:5fa9c6530b1661f1370bcd332a1e62ca7881785cc0f80c0d559b636567fab63c"},
-    {file = "scipy-1.6.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:bd50daf727f7c195e26f27467c85ce653d41df4358a25b32434a50d8870fc519"},
-    {file = "scipy-1.6.1-cp38-cp38-manylinux1_i686.whl", hash = "sha256:f46dd15335e8a320b0fb4685f58b7471702234cba8bb3442b69a3e1dc329c345"},
-    {file = "scipy-1.6.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:0e5b0ccf63155d90da576edd2768b66fb276446c371b73841e3503be1d63fb5d"},
-    {file = "scipy-1.6.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:2481efbb3740977e3c831edfd0bd9867be26387cacf24eb5e366a6a374d3d00d"},
-    {file = "scipy-1.6.1-cp38-cp38-win32.whl", hash = "sha256:68cb4c424112cd4be886b4d979c5497fba190714085f46b8ae67a5e4416c32b4"},
-    {file = "scipy-1.6.1-cp38-cp38-win_amd64.whl", hash = "sha256:5f331eeed0297232d2e6eea51b54e8278ed8bb10b099f69c44e2558c090d06bf"},
-    {file = "scipy-1.6.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0c8a51d33556bf70367452d4d601d1742c0e806cd0194785914daf19775f0e67"},
-    {file = "scipy-1.6.1-cp39-cp39-manylinux1_i686.whl", hash = "sha256:83bf7c16245c15bc58ee76c5418e46ea1811edcc2e2b03041b804e46084ab627"},
-    {file = "scipy-1.6.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:794e768cc5f779736593046c9714e0f3a5940bc6dcc1dba885ad64cbfb28e9f0"},
-    {file = "scipy-1.6.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:5da5471aed911fe7e52b86bf9ea32fb55ae93e2f0fac66c32e58897cfb02fa07"},
-    {file = "scipy-1.6.1-cp39-cp39-win32.whl", hash = "sha256:8e403a337749ed40af60e537cc4d4c03febddcc56cd26e774c9b1b600a70d3e4"},
-    {file = "scipy-1.6.1-cp39-cp39-win_amd64.whl", hash = "sha256:a5193a098ae9f29af283dcf0041f762601faf2e595c0db1da929875b7570353f"},
-    {file = "scipy-1.6.1.tar.gz", hash = "sha256:c4fceb864890b6168e79b0e714c585dbe2fd4222768ee90bc1aa0f8218691b11"},
-]
-
-[package.dependencies]
-numpy = ">=1.16.5"
-
 [[package]]
 name = "scipy"
 version = "1.9.3"
 description = "Fundamental algorithms for scientific computing in Python"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "scipy-1.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0"},
@@ -10502,34 +8331,6 @@ dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"]
 doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
 test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
-[[package]]
-name = "semantic-version"
-version = "2.10.0"
-description = "A library implementing the 'SemVer' scheme."
-category = "main"
-optional = true
-python-versions = ">=2.7"
-files = [
-    {file = "semantic_version-2.10.0-py2.py3-none-any.whl", hash = "sha256:de78a3b8e0feda74cabc54aab2da702113e33ac9d9eb9d2389bcf1f58b7d9177"},
-    {file = "semantic_version-2.10.0.tar.gz", hash = "sha256:bdabb6d336998cbb378d4b9db3a4b56a1e3235701dc05ea2690d9a997ed5041c"},
-]
-
-[package.extras]
-dev = ["Django (>=1.11)", "check-manifest", "colorama (<=0.4.1)", "coverage", "flake8", "nose2", "readme-renderer (<25.0)", "tox", "wheel", "zest.releaser[recommended]"]
-doc = ["Sphinx", "sphinx-rtd-theme"]
-
-[[package]]
-name = "semver"
-version = "3.0.1"
-description = "Python helper for Semantic Versioning (https://semver.org)"
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "semver-3.0.1-py3-none-any.whl", hash = "sha256:2a23844ba1647362c7490fe3995a86e097bb590d16f0f32dfc383008f19e4cdf"},
-    {file = "semver-3.0.1.tar.gz", hash = "sha256:9ec78c5447883c67b97f98c3b6212796708191d22e4ad30f4570f840171cbce1"},
-]
-
 [[package]]
 name = "send2trash"
 version = "1.8.2"
@@ -10552,7 +8353,7 @@ name = "sentence-transformers"
 version = "2.2.2"
 description = "Multilingual text embeddings"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6.0"
 files = [
     {file = "sentence-transformers-2.2.2.tar.gz", hash = "sha256:dbc60163b27de21076c9a30d24b5b7b6fa05141d68cf2553fa9a77bf79a29136"},
@@ -10575,7 +8376,7 @@ name = "sentencepiece"
 version = "0.1.99"
 description = "SentencePiece python wrapper"
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "sentencepiece-0.1.99-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0eb528e70571b7c02723e5804322469b82fe7ea418c96051d0286c0fa028db73"},
@@ -10647,7 +8448,7 @@ name = "sgmllib3k"
 version = "1.0.0"
 description = "Py3k port of sgmllib."
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "sgmllib3k-1.0.0.tar.gz", hash = "sha256:7868fb1c8bfa764c1ac563d3cf369c381d1325d36124933a726f29fcdaa812e9"},
@@ -10708,21 +8509,6 @@ numpy = ">=1.14"
 docs = ["matplotlib", "numpydoc (>=1.1.0,<1.2.0)", "sphinx", "sphinx-book-theme", "sphinx-remove-toctrees"]
 test = ["pytest", "pytest-cov"]
 
-[[package]]
-name = "simple-di"
-version = "0.1.5"
-description = "simple dependency injection library"
-category = "main"
-optional = true
-python-versions = ">=3.6.1"
-files = [
-    {file = "simple_di-0.1.5-py3-none-any.whl", hash = "sha256:e3fb6242f18f389a3c2d571dd51ade47c74cdbc4550590894664ad59bfb2a345"},
-    {file = "simple_di-0.1.5.tar.gz", hash = "sha256:192b999dee4cd4fb11a5d861165caad02d8f0617c0f806fc5b09f905f1a03ca0"},
-]
-
-[package.extras]
-test = ["mypy", "pytest"]
-
 [[package]]
 name = "singlestoredb"
 version = "0.7.1"
@@ -10768,28 +8554,6 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
-[[package]]
-name = "smart-open"
-version = "6.3.0"
-description = "Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...)"
-category = "main"
-optional = true
-python-versions = ">=3.6,<4.0"
-files = [
-    {file = "smart_open-6.3.0-py3-none-any.whl", hash = "sha256:b4c9ae193ad6d3e7add50944b86afa0d150bd821ab8ec21edb26d9a06b66f6a8"},
-    {file = "smart_open-6.3.0.tar.gz", hash = "sha256:d5238825fe9a9340645fac3d75b287c08fbb99fb2b422477de781c9f5f09e019"},
-]
-
-[package.extras]
-all = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "paramiko", "requests"]
-azure = ["azure-common", "azure-core", "azure-storage-blob"]
-gcs = ["google-cloud-storage (>=2.6.0)"]
-http = ["requests"]
-s3 = ["boto3"]
-ssh = ["paramiko"]
-test = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "moto[server]", "paramiko", "pytest", "pytest-rerunfailures", "requests", "responses"]
-webhdfs = ["requests"]
-
 [[package]]
 name = "smmap"
 version = "5.0.0"
@@ -10907,117 +8671,6 @@ numpy = "*"
 docs = ["linkify-it-py", "myst-parser", "sphinx", "sphinx-book-theme"]
 test = ["pytest"]
 
-[[package]]
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 [[package]]
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 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
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 [[package]]
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 version = "1.22.0"
@@ -11295,7 +8850,7 @@ name = "sympy"
 version = "1.12"
 description = "Computer algebra system (CAS) in Python"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
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@@ -11672,88 +9182,12 @@ files = [
 [package.dependencies]
 pyphen = "*"
 
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-wasabi = ">=0.8.1,<1.2.0"
-
-[package.extras]
-cuda = ["cupy (>=5.0.0b4)"]
-cuda-autodetect = ["cupy-wheel (>=11.0.0)"]
-cuda100 = ["cupy-cuda100 (>=5.0.0b4)"]
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-cuda114 = ["cupy-cuda114 (>=5.0.0b4)"]
-cuda115 = ["cupy-cuda115 (>=5.0.0b4)"]
-cuda116 = ["cupy-cuda116 (>=5.0.0b4)"]
-cuda117 = ["cupy-cuda117 (>=5.0.0b4)"]
-cuda11x = ["cupy-cuda11x (>=11.0.0)"]
-cuda80 = ["cupy-cuda80 (>=5.0.0b4)"]
-cuda90 = ["cupy-cuda90 (>=5.0.0b4)"]
-cuda91 = ["cupy-cuda91 (>=5.0.0b4)"]
-cuda92 = ["cupy-cuda92 (>=5.0.0b4)"]
-datasets = ["ml-datasets (>=0.2.0,<0.3.0)"]
-mxnet = ["mxnet (>=1.5.1,<1.6.0)"]
-tensorflow = ["tensorflow (>=2.0.0,<2.6.0)"]
-torch = ["torch (>=1.6.0)"]
-
 [[package]]
 name = "threadpoolctl"
 version = "3.1.0"
 description = "threadpoolctl"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6"
 files = [
     {file = "threadpoolctl-3.1.0-py3-none-any.whl", hash = "sha256:8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b"},
@@ -11875,7 +9309,7 @@ name = "tokenizers"
 version = "0.13.3"
 description = "Fast and Customizable Tokenizers"
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "tokenizers-0.13.3-cp310-cp310-macosx_10_11_x86_64.whl", hash = "sha256:f3835c5be51de8c0a092058a4d4380cb9244fb34681fd0a295fbf0a52a5fdf33"},
@@ -11966,7 +9400,7 @@ name = "torch"
 version = "1.13.1"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7.0"
 files = [
     {file = "torch-1.13.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:fd12043868a34a8da7d490bf6db66991108b00ffbeecb034228bfcbbd4197143"},
@@ -12007,7 +9441,7 @@ name = "torchvision"
 version = "0.14.1"
 description = "image and video datasets and models for torch deep learning"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "torchvision-0.14.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:eeb05dd9dd3af5428fee525400759daf8da8e4caec45ddd6908cfb36571f6433"},
@@ -12104,7 +9538,7 @@ name = "transformers"
 version = "4.30.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7.0"
 files = [
     {file = "transformers-4.30.2-py3-none-any.whl", hash = "sha256:c332e3a3097f9ed89ce556b403251235931c00237b8bc2d7adaa19d226c13f1d"},
@@ -12112,19 +9546,15 @@ files = [
 ]
 
 [package.dependencies]
-accelerate = {version = ">=0.20.2", optional = true, markers = "extra == \"accelerate\" or extra == \"torch\""}
 filelock = "*"
 huggingface-hub = ">=0.14.1,<1.0"
 numpy = ">=1.17"
 packaging = ">=20.0"
-protobuf = {version = "<=3.20.3", optional = true, markers = "extra == \"sentencepiece\""}
 pyyaml = ">=5.1"
 regex = "!=2019.12.17"
 requests = "*"
 safetensors = ">=0.3.1"
-sentencepiece = {version = ">=0.1.91,<0.1.92 || >0.1.92", optional = true, markers = "extra == \"sentencepiece\""}
 tokenizers = ">=0.11.1,<0.11.3 || >0.11.3,<0.14"
-torch = {version = ">=1.9,<1.12.0 || >1.12.0", optional = true, markers = "extra == \"torch\""}
 tqdm = ">=4.27"
 
 [package.extras]
@@ -12173,27 +9603,6 @@ torchhub = ["filelock", "huggingface-hub (>=0.14.1,<1.0)", "importlib-metadata",
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow"]
 
-[[package]]
-name = "typer"
-version = "0.7.0"
-description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
-category = "main"
-optional = true
-python-versions = ">=3.6"
-files = [
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-
-[package.dependencies]
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-
-[package.extras]
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-dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
-doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
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-
 [[package]]
 name = "types-chardet"
 version = "5.0.4.6"
@@ -12249,7 +9658,7 @@ files = [
 name = "types-pyyaml"
 version = "6.0.12.10"
 description = "Typing stubs for PyYAML"
-category = "main"
+category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -12314,14 +9723,14 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.5.0"
+version = "4.7.1"
 description = "Backported and Experimental Type Hints for Python 3.7+"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"},
 ]
 
 [[package]]
@@ -12372,21 +9781,6 @@ tzdata = {version = "*", markers = "platform_system == \"Windows\""}
 [package.extras]
 devenv = ["black", "check-manifest", "flake8", "pyroma", "pytest (>=4.3)", "pytest-cov", "pytest-mock (>=3.3)", "zest.releaser"]
 
-[[package]]
-name = "uc-micro-py"
-version = "1.0.2"
-description = "Micro subset of unicode data files for linkify-it-py projects."
-category = "main"
-optional = true
-python-versions = ">=3.7"
-files = [
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-]
-
-[package.extras]
-test = ["coverage", "pytest", "pytest-cov"]
-
 [[package]]
 name = "uri-template"
 version = "1.2.0"
@@ -12431,83 +9825,12 @@ brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
 secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
 socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
-[[package]]
-name = "uvicorn"
-version = "0.22.0"
-description = "The lightning-fast ASGI server."
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
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-    {file = "uvicorn-0.22.0.tar.gz", hash = "sha256:79277ae03db57ce7d9aa0567830bbb51d7a612f54d6e1e3e92da3ef24c2c8ed8"},
-]
-
-[package.dependencies]
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-colorama = {version = ">=0.4", optional = true, markers = "sys_platform == \"win32\" and extra == \"standard\""}
-h11 = ">=0.8"
-httptools = {version = ">=0.5.0", optional = true, markers = "extra == \"standard\""}
-python-dotenv = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
-pyyaml = {version = ">=5.1", optional = true, markers = "extra == \"standard\""}
-uvloop = {version = ">=0.14.0,<0.15.0 || >0.15.0,<0.15.1 || >0.15.1", optional = true, markers = "sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\" and extra == \"standard\""}
-watchfiles = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
-websockets = {version = ">=10.4", optional = true, markers = "extra == \"standard\""}
-
-[package.extras]
-standard = ["colorama (>=0.4)", "httptools (>=0.5.0)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"]
-
-[[package]]
-name = "uvloop"
-version = "0.17.0"
-description = "Fast implementation of asyncio event loop on top of libuv"
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
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-
 [[package]]
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 version = "0.20.0"
 description = "Python Data Validation for Humans™."
 category = "main"
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 python-versions = ">=3.4"
 files = [
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 wrapt = "*"
 yarl = "*"
 
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-
 [[package]]
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 version = "3.0.0"
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 [[package]]
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 version = "0.2.6"
 description = "Measures the displayed width of unicode strings in a terminal"
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 optional = false
 python-versions = "*"
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 version = "3.20.1"
 description = "A python native Weaviate client"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
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 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
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 description = "pure python download utility"
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 version = "1.9.2"
@@ -13510,22 +10395,6 @@ files = [
 idna = ">=2.0"
 multidict = ">=4.0"
 
-[[package]]
-name = "zep-python"
-version = "0.32"
-description = "Zep stores, manages, enriches, indexes, and searches long-term memory for conversational AI applications. This is the Python client for the Zep service."
-category = "main"
-optional = true
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-
 [[package]]
 name = "zipp"
 version = "3.15.0"
@@ -13547,7 +10416,7 @@ name = "zstandard"
 version = "0.21.0"
 description = "Zstandard bindings for Python"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
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 cffi = ["cffi (>=1.11)"]
 
 [extras]
-all = ["O365", "aleph-alpha-client", "amadeus", "anthropic", "arxiv", "atlassian-python-api", "awadb", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-cosmos", "azure-identity", "beautifulsoup4", "clarifai", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "esprima", "faiss-cpu", "google-api-python-client", "google-auth", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "langkit", "lark", "libdeeplake", "librosa", "lxml", "manifest-ml", "marqo", "momento", "nebula3-python", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "octoai-sdk", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pymongo", "pyowm", "pypdf", "pytesseract", "python-arango", "pyvespa", "qdrant-client", "rdflib", "redis", "requests-toolbelt", "sentence-transformers", "singlestoredb", "spacy", "steamship", "tensorflow-text", "tigrisdb", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha", "xinference"]
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 clarifai = ["clarifai"]
 cohere = ["cohere"]
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 embeddings = ["sentence-transformers"]
-extended-testing = ["amazon-textract-caller", "anthropic", "atlassian-python-api", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "esprima", "feedparser", "geopandas", "gitpython", "gql", "html2text", "jinja2", "jq", "lxml", "mwparserfromhell", "mwxml", "newspaper3k", "openai", "openai", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "requests-toolbelt", "scikit-learn", "streamlit", "sympy", "telethon", "tqdm", "xata", "xinference", "xmltodict", "zep-python"]
+extended-testing = ["amazon-textract-caller", "atlassian-python-api", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "esprima", "faiss-cpu", "feedparser", "geopandas", "gitpython", "gql", "html2text", "jinja2", "jq", "lxml", "mwparserfromhell", "mwxml", "newspaper3k", "openai", "openai", "openapi-schema-pydantic", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "requests-toolbelt", "scikit-learn", "streamlit", "sympy", "telethon", "tqdm", "xata", "xmltodict"]
 javascript = ["esprima"]
-llms = ["anthropic", "clarifai", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "openllm", "openlm", "torch", "transformers", "xinference"]
+llms = ["clarifai", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "openlm", "torch", "transformers"]
 openai = ["openai", "tiktoken"]
 qdrant = ["qdrant-client"]
-scheduled-testing = ["openai"]
 text-helpers = ["chardet"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "a8fd5dbcab821e39c502724e13a2f85b718f3e06c7c3f98062de01a44cf1ff6e"
+content-hash = "a5e3458dd0cabcefd83caec6eb33b6fb593c2c347ca1d33c1f182341e852a9c8"
diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml
index 3fb30ee0d6c..c928ad800d7 100644
--- a/libs/langchain/pyproject.toml
+++ b/libs/langchain/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain"
-version = "0.0.260"
+version = "0.0.267"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -12,21 +12,20 @@ langchain-server = "langchain.server:main"
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
-pydantic = "^1"
+pydantic = ">=1,<3"
 SQLAlchemy = ">=1.4,<3"
 requests = "^2"
 PyYAML = ">=5.3"
 numpy = "^1"
 azure-core = {version = "^1.26.4", optional=true}
 tqdm = {version = ">=4.48.0", optional = true}
-openapi-schema-pydantic = "^1.2"
+openapi-schema-pydantic = {version = "^1.2", optional = true}
 faiss-cpu = {version = "^1", optional = true}
 wikipedia = {version = "^1", optional = true}
 elasticsearch = {version = "^8", optional = true}
 opensearch-py = {version = "^2.0.0", optional = true}
 redis = {version = "^4", optional = true}
 manifest-ml = {version = "^0.0.1", optional = true}
-spacy = {version = "^3", optional = true}
 nltk = {version = "^3", optional = true}
 transformers = {version = "^4", optional = true}
 beautifulsoup4 = {version = "^4", optional = true}
@@ -42,7 +41,6 @@ marqo = {version = "^0.11.0", optional=true}
 google-api-python-client = {version = "2.70.0", optional = true}
 google-auth = {version = "^2.18.1", optional = true}
 wolframalpha = {version = "5.0.0", optional = true}
-anthropic = {version = "^0.3", optional = true}
 qdrant-client = {version = "^1.3.1", optional = true, python = ">=3.8.1,<3.12"}
 dataclasses-json = "^0.5.7"
 tensorflow-text = {version = "^2.11.0", optional = true, python = "^3.10, <3.12"}
@@ -52,8 +50,6 @@ openai = {version = "^0", optional = true}
 nlpcloud = {version = "^1", optional = true}
 nomic = {version = "^1.0.43", optional = true}
 huggingface_hub = {version = "^0", optional = true}
-octoai-sdk = {version = "^0.1.1", optional = true}
-jina = {version = "^3.14", optional = true}
 google-search-results = {version = "^2", optional = true}
 sentence-transformers = {version = "^2", optional = true}
 aiohttp = "^3.8.3"
@@ -81,7 +77,6 @@ pexpect = {version = "^4.8.0", optional = true}
 pyvespa = {version = "^0.33.0", optional = true}
 O365 = {version = "^2.0.26", optional = true}
 jq = {version = "^1.4.1", optional = true}
-steamship = {version = "^2.16.9", optional = true}
 pdfminer-six = {version = "^20221105", optional = true}
 docarray = {version="^0.32.0", extras=["hnswlib"], optional=true}
 lxml = {version = "^4.9.2", optional = true}
@@ -91,7 +86,6 @@ gql = {version = "^3.4.1", optional = true}
 pandas = {version = "^2.0.1", optional = true}
 telethon = {version = "^1.28.5", optional = true}
 neo4j = {version = "^5.8.1", optional = true}
-zep-python = {version=">=0.32", optional=true}
 langkit = {version = ">=0.0.6, <0.1.0", optional = true}
 chardet = {version="^5.1.0", optional=true}
 requests-toolbelt = {version = "^1.0.0", optional = true}
@@ -113,18 +107,16 @@ mwxml = {version = "^0.3.3", optional = true}
 awadb = {version = "^0.3.9", optional = true}
 azure-search-documents = {version = "11.4.0b6", optional = true}
 esprima = {version = "^4.0.1", optional = true}
-openllm = {version = ">=0.1.19", optional = true}
 streamlit = {version = "^1.18.0", optional = true, python = ">=3.8.1,<3.9.7 || >3.9.7,<4.0"}
 psychicapi = {version = "^0.8.0", optional = true}
 cassio = {version = "^0.0.7", optional = true}
 rdflib = {version = "^6.3.2", optional = true}
 sympy = {version = "^1.12", optional = true}
 rapidfuzz = {version = "^3.1.1", optional = true}
-langsmith = "~0.0.11"
+langsmith = "~0.0.21"
 rank-bm25 = {version = "^0.2.2", optional = true}
 amadeus = {version = ">=8.1.0", optional = true}
 geopandas = {version = "^0.13.1", optional = true}
-xinference = {version = "^0.0.6", optional = true}
 python-arango = {version = "^7.5.9", optional = true}
 gitpython = {version = "^3.1.32", optional = true}
 librosa = {version="^0.10.0.post2", optional = true }
@@ -134,6 +126,7 @@ amazon-textract-caller = {version = "<2", optional = true}
 xata = {version = "^1.0.0a7", optional = true}
 xmltodict = {version = "^0.13.0", optional = true}
 
+
 [tool.poetry.group.test.dependencies]
 # The only dependencies that should be added are
 # dependencies used for running tests (e.g., pytest, freezegun, response).
@@ -177,32 +170,9 @@ optional = true
 pytest-vcr = "^1.0.2"
 wrapt = "^1.15.0"
 openai = "^0.27.4"
-elasticsearch = {extras = ["async"], version = "^8.6.2"}
-redis = "^4.5.4"
-pinecone-client = "^2.2.1"
-pinecone-text = "^0.4.2"
-pymongo = "^4.3.3"
-clickhouse-connect = "^0.5.14"
-transformers = "^4.27.4"
-deeplake = "^3.6.8"
-libdeeplake = "^0.0.60"
-weaviate-client = "^3.15.5"
-torch = "^1.0.0"
-chromadb = "^0.4.0"
-tiktoken = "^0.3.3"
 python-dotenv = "^1.0.0"
-sentence-transformers = "^2"
-gptcache = "^0.1.9"
-promptlayer = "^0.1.80"
-tair = "^1.3.3"
-wikipedia = "^1"
 cassio = "^0.0.7"
-arxiv = "^1.4"
-mastodon-py = "^1.8.1"
-momento = "^1.5.0"
-# Please do not add any dependencies in the test_integration group
-# See instructions above ^^
-pygithub = "^1.59.0"
+tiktoken = "^0.3.2"
 
 [tool.poetry.group.lint.dependencies]
 ruff = "^0.0.249"
@@ -227,7 +197,7 @@ playwright = "^1.28.0"
 setuptools = "^67.6.1"
 
 [tool.poetry.extras]
-llms = ["anthropic", "clarifai", "cohere", "openai", "openllm", "openlm", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers", "xinference"]
+llms = ["clarifai", "cohere", "openai", "openlm", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
 qdrant = ["qdrant-client"]
 openai = ["openai", "tiktoken"]
 text_helpers = ["chardet"]
@@ -247,13 +217,11 @@ azure = [
     "azure-search-documents",
 ]
 all = [
-    "anthropic",
     "clarifai",
     "cohere",
     "openai",
     "nlpcloud",
     "huggingface_hub",
-    "jina",
     "manifest-ml",
     "elasticsearch",
     "opensearch-py",
@@ -261,7 +229,6 @@ all = [
     "faiss-cpu",
     "sentence-transformers",
     "transformers",
-    "spacy",
     "nltk",
     "wikipedia",
     "beautifulsoup4",
@@ -305,7 +272,6 @@ all = [
     "O365",
     "jq",
     "docarray",
-    "steamship",
     "pdfminer-six",
     "lxml",
     "requests-toolbelt",
@@ -320,10 +286,8 @@ all = [
     "nebula3-python",
     "awadb",
     "esprima",
-    "octoai-sdk",
     "rdflib",
     "amadeus",
-    "xinference",
     "librosa",
     "python-arango",
 ]
@@ -352,7 +316,6 @@ extended_testing = [
  "pandas",
  "telethon",
  "psychicapi",
- "zep-python",
  "gql",
  "requests_toolbelt",
  "html2text",
@@ -367,17 +330,13 @@ extended_testing = [
  "rank_bm25",
  "geopandas",
  "jinja2",
- "xinference",
  "gitpython",
  "newspaper3k",
  "feedparser",
  "xata",
  "xmltodict",
- "anthropic",
-]
-
-scheduled_testing = [
- "openai",
+ "faiss-cpu",
+ "openapi-schema-pydantic",
 ]
 
 [tool.ruff]
diff --git a/libs/langchain/tests/integration_tests/adapters/__init__.py b/libs/langchain/tests/integration_tests/adapters/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/langchain/tests/integration_tests/adapters/test_openai.py b/libs/langchain/tests/integration_tests/adapters/test_openai.py
new file mode 100644
index 00000000000..2e27f036506
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/adapters/test_openai.py
@@ -0,0 +1,107 @@
+from typing import Any
+
+import openai
+import pytest
+
+from langchain.adapters import openai as lcopenai
+
+
+def _test_no_stream(**kwargs: Any) -> None:
+    result = openai.ChatCompletion.create(**kwargs)
+    lc_result = lcopenai.ChatCompletion.create(**kwargs)
+    if isinstance(lc_result, dict):
+        if isinstance(result, dict):
+            result_dict = result["choices"][0]["message"].to_dict_recursive()
+            lc_result_dict = lc_result["choices"][0]["message"]
+            assert result_dict == lc_result_dict
+    return
+
+
+def _test_stream(**kwargs: Any) -> None:
+    result = []
+    for c in openai.ChatCompletion.create(**kwargs):
+        result.append(c["choices"][0]["delta"].to_dict_recursive())
+
+    lc_result = []
+    for c in lcopenai.ChatCompletion.create(**kwargs):
+        lc_result.append(c["choices"][0]["delta"])
+    assert result == lc_result
+
+
+async def _test_async(**kwargs: Any) -> None:
+    result = await openai.ChatCompletion.acreate(**kwargs)
+    lc_result = await lcopenai.ChatCompletion.acreate(**kwargs)
+    if isinstance(lc_result, dict):
+        if isinstance(result, dict):
+            result_dict = result["choices"][0]["message"].to_dict_recursive()
+            lc_result_dict = lc_result["choices"][0]["message"]
+            assert result_dict == lc_result_dict
+    return
+
+
+async def _test_astream(**kwargs: Any) -> None:
+    result = []
+    async for c in await openai.ChatCompletion.acreate(**kwargs):
+        result.append(c["choices"][0]["delta"].to_dict_recursive())
+
+    lc_result = []
+    async for c in await lcopenai.ChatCompletion.acreate(**kwargs):
+        lc_result.append(c["choices"][0]["delta"])
+    assert result == lc_result
+
+
+FUNCTIONS = [
+    {
+        "name": "get_current_weather",
+        "description": "Get the current weather in a given location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, CA",
+                },
+                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+            },
+            "required": ["location"],
+        },
+    }
+]
+
+
+async def _test_module(**kwargs: Any) -> None:
+    _test_no_stream(**kwargs)
+    await _test_async(**kwargs)
+    _test_stream(stream=True, **kwargs)
+    await _test_astream(stream=True, **kwargs)
+
+
+@pytest.mark.asyncio
+async def test_normal_call() -> None:
+    await _test_module(
+        messages=[{"role": "user", "content": "hi"}],
+        model="gpt-3.5-turbo",
+        temperature=0,
+    )
+
+
+@pytest.mark.asyncio
+async def test_function_calling() -> None:
+    await _test_module(
+        messages=[{"role": "user", "content": "whats the weather in boston"}],
+        model="gpt-3.5-turbo",
+        functions=FUNCTIONS,
+        temperature=0,
+    )
+
+
+@pytest.mark.asyncio
+async def test_answer_with_function_calling() -> None:
+    await _test_module(
+        messages=[
+            {"role": "user", "content": "say hi, then whats the weather in boston"}
+        ],
+        model="gpt-3.5-turbo",
+        functions=FUNCTIONS,
+        temperature=0,
+    )
diff --git a/libs/langchain/tests/integration_tests/cache/test_redis_cache.py b/libs/langchain/tests/integration_tests/cache/test_redis_cache.py
index 7d43c9a4058..5d51a12e8e1 100644
--- a/libs/langchain/tests/integration_tests/cache/test_redis_cache.py
+++ b/libs/langchain/tests/integration_tests/cache/test_redis_cache.py
@@ -11,6 +11,15 @@ from tests.unit_tests.llms.fake_llm import FakeLLM
 REDIS_TEST_URL = "redis://localhost:6379"
 
 
+def test_redis_cache_ttl() -> None:
+    import redis
+
+    langchain.llm_cache = RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL), ttl=1)
+    langchain.llm_cache.update("foo", "bar", [Generation(text="fizz")])
+    key = langchain.llm_cache._key("foo", "bar")
+    assert langchain.llm_cache.redis.pttl(key) > 0
+
+
 def test_redis_cache() -> None:
     import redis
 
diff --git a/libs/langchain/tests/integration_tests/chat_models/test_anthropic.py b/libs/langchain/tests/integration_tests/chat_models/test_anthropic.py
index 6704bde096a..0237b055d12 100644
--- a/libs/langchain/tests/integration_tests/chat_models/test_anthropic.py
+++ b/libs/langchain/tests/integration_tests/chat_models/test_anthropic.py
@@ -95,3 +95,19 @@ def test_formatting() -> None:
     chat_messages = [HumanMessage(content="Hello"), AIMessage(content="Answer:")]
     result = chat._convert_messages_to_prompt(chat_messages)
     assert result == "\n\nHuman: Hello\n\nAssistant: Answer:"
+
+
+def test_anthropic_model_kwargs() -> None:
+    llm = ChatAnthropic(model_kwargs={"foo": "bar"})
+    assert llm.model_kwargs == {"foo": "bar"}
+
+
+def test_anthropic_invalid_model_kwargs() -> None:
+    with pytest.raises(ValueError):
+        ChatAnthropic(model_kwargs={"max_tokens_to_sample": 5})
+
+
+def test_anthropic_incorrect_field() -> None:
+    with pytest.warns(match="not default parameter"):
+        llm = ChatAnthropic(foo="bar")
+    assert llm.model_kwargs == {"foo": "bar"}
diff --git a/libs/langchain/tests/unit_tests/chat_models/test_anthropic.py b/libs/langchain/tests/integration_tests/chat_models/test_anthropic_2.py
similarity index 100%
rename from libs/langchain/tests/unit_tests/chat_models/test_anthropic.py
rename to libs/langchain/tests/integration_tests/chat_models/test_anthropic_2.py
diff --git a/libs/langchain/tests/integration_tests/chat_models/test_ernie.py b/libs/langchain/tests/integration_tests/chat_models/test_ernie.py
new file mode 100644
index 00000000000..a8a80ed9bbd
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/chat_models/test_ernie.py
@@ -0,0 +1,56 @@
+import pytest
+
+from langchain.chat_models.ernie import ErnieBotChat
+from langchain.schema.messages import AIMessage, HumanMessage
+
+
+def test_chat_ernie_bot() -> None:
+    chat = ErnieBotChat()
+    message = HumanMessage(content="Hello")
+    response = chat([message])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+def test_chat_ernie_bot_with_model_name() -> None:
+    chat = ErnieBotChat(model_name="ERNIE-Bot")
+    message = HumanMessage(content="Hello")
+    response = chat([message])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+def test_chat_ernie_bot_with_temperature() -> None:
+    chat = ErnieBotChat(model_name="ERNIE-Bot", temperature=1.0)
+    message = HumanMessage(content="Hello")
+    response = chat([message])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+def test_chat_ernie_bot_with_kwargs() -> None:
+    chat = ErnieBotChat()
+    message = HumanMessage(content="Hello")
+    response = chat([message], temperature=0.88, top_p=0.7)
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+def test_extra_kwargs() -> None:
+    chat = ErnieBotChat(temperature=0.88, top_p=0.7)
+    assert chat.temperature == 0.88
+    assert chat.top_p == 0.7
+
+
+def test_wrong_temperature_1() -> None:
+    chat = ErnieBotChat()
+    message = HumanMessage(content="Hello")
+    with pytest.raises(ValueError):
+        chat([message], temperature=1.2)
+
+
+def test_wrong_temperature_2() -> None:
+    chat = ErnieBotChat()
+    message = HumanMessage(content="Hello")
+    with pytest.raises(ValueError):
+        chat([message], temperature=0)
diff --git a/libs/langchain/tests/integration_tests/chat_models/test_litellm.py b/libs/langchain/tests/integration_tests/chat_models/test_litellm.py
new file mode 100644
index 00000000000..4f252453da6
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/chat_models/test_litellm.py
@@ -0,0 +1,64 @@
+"""Test Anthropic API wrapper."""
+from typing import List
+
+from langchain.callbacks.manager import (
+    CallbackManager,
+)
+from langchain.chat_models.litellm import ChatLiteLLM
+from langchain.schema import (
+    ChatGeneration,
+    LLMResult,
+)
+from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage
+from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
+
+
+def test_litellm_call() -> None:
+    """Test valid call to litellm."""
+    chat = ChatLiteLLM(
+        model="test",
+    )
+    message = HumanMessage(content="Hello")
+    response = chat([message])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+def test_litellm_generate() -> None:
+    """Test generate method of anthropic."""
+    chat = ChatLiteLLM(model="test")
+    chat_messages: List[List[BaseMessage]] = [
+        [HumanMessage(content="How many toes do dogs have?")]
+    ]
+    messages_copy = [messages.copy() for messages in chat_messages]
+    result: LLMResult = chat.generate(chat_messages)
+    assert isinstance(result, LLMResult)
+    for response in result.generations[0]:
+        assert isinstance(response, ChatGeneration)
+        assert isinstance(response.text, str)
+        assert response.text == response.message.content
+    assert chat_messages == messages_copy
+
+
+def test_litellm_streaming() -> None:
+    """Test streaming tokens from anthropic."""
+    chat = ChatLiteLLM(model="test", streaming=True)
+    message = HumanMessage(content="Hello")
+    response = chat([message])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+def test_litellm_streaming_callback() -> None:
+    """Test that streaming correctly invokes on_llm_new_token callback."""
+    callback_handler = FakeCallbackHandler()
+    callback_manager = CallbackManager([callback_handler])
+    chat = ChatLiteLLM(
+        model="test",
+        streaming=True,
+        callback_manager=callback_manager,
+        verbose=True,
+    )
+    message = HumanMessage(content="Write me a sentence with 10 words.")
+    chat([message])
+    assert callback_handler.llm_streams > 1
diff --git a/libs/langchain/tests/integration_tests/chat_models/test_openai.py b/libs/langchain/tests/integration_tests/chat_models/test_openai.py
index ff7332c88cb..19adbf1cd41 100644
--- a/libs/langchain/tests/integration_tests/chat_models/test_openai.py
+++ b/libs/langchain/tests/integration_tests/chat_models/test_openai.py
@@ -14,6 +14,7 @@ from langchain.schema.messages import BaseMessage, HumanMessage, SystemMessage
 from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
 
 
+@pytest.mark.scheduled
 def test_chat_openai() -> None:
     """Test ChatOpenAI wrapper."""
     chat = ChatOpenAI(max_tokens=10)
@@ -41,6 +42,7 @@ def test_chat_openai_system_message() -> None:
     assert isinstance(response.content, str)
 
 
+@pytest.mark.scheduled
 def test_chat_openai_generate() -> None:
     """Test ChatOpenAI wrapper with generate."""
     chat = ChatOpenAI(max_tokens=10, n=2)
@@ -56,6 +58,7 @@ def test_chat_openai_generate() -> None:
             assert generation.text == generation.message.content
 
 
+@pytest.mark.scheduled
 def test_chat_openai_multiple_completions() -> None:
     """Test ChatOpenAI wrapper with multiple completions."""
     chat = ChatOpenAI(max_tokens=10, n=5)
@@ -68,6 +71,7 @@ def test_chat_openai_multiple_completions() -> None:
         assert isinstance(generation.message.content, str)
 
 
+@pytest.mark.scheduled
 def test_chat_openai_streaming() -> None:
     """Test that streaming correctly invokes on_llm_new_token callback."""
     callback_handler = FakeCallbackHandler()
@@ -114,6 +118,7 @@ def test_chat_openai_invalid_streaming_params() -> None:
         )
 
 
+@pytest.mark.scheduled
 @pytest.mark.asyncio
 async def test_async_chat_openai() -> None:
     """Test async generation."""
@@ -130,6 +135,7 @@ async def test_async_chat_openai() -> None:
             assert generation.text == generation.message.content
 
 
+@pytest.mark.scheduled
 @pytest.mark.asyncio
 async def test_async_chat_openai_streaming() -> None:
     """Test that streaming correctly invokes on_llm_new_token callback."""
@@ -179,6 +185,7 @@ def test_chat_openai_extra_kwargs() -> None:
         ChatOpenAI(model_kwargs={"model": "text-davinci-003"})
 
 
+@pytest.mark.scheduled
 def test_openai_streaming() -> None:
     """Test streaming tokens from OpenAI."""
     llm = ChatOpenAI(max_tokens=10)
@@ -187,6 +194,7 @@ def test_openai_streaming() -> None:
         assert isinstance(token.content, str)
 
 
+@pytest.mark.scheduled
 @pytest.mark.asyncio
 async def test_openai_astream() -> None:
     """Test streaming tokens from OpenAI."""
@@ -196,6 +204,7 @@ async def test_openai_astream() -> None:
         assert isinstance(token.content, str)
 
 
+@pytest.mark.scheduled
 @pytest.mark.asyncio
 async def test_openai_abatch() -> None:
     """Test streaming tokens from ChatOpenAI."""
@@ -206,6 +215,7 @@ async def test_openai_abatch() -> None:
         assert isinstance(token.content, str)
 
 
+@pytest.mark.scheduled
 @pytest.mark.asyncio
 async def test_openai_abatch_tags() -> None:
     """Test batch tokens from ChatOpenAI."""
@@ -218,6 +228,7 @@ async def test_openai_abatch_tags() -> None:
         assert isinstance(token.content, str)
 
 
+@pytest.mark.scheduled
 def test_openai_batch() -> None:
     """Test batch tokens from ChatOpenAI."""
     llm = ChatOpenAI(max_tokens=10)
@@ -227,6 +238,7 @@ def test_openai_batch() -> None:
         assert isinstance(token.content, str)
 
 
+@pytest.mark.scheduled
 @pytest.mark.asyncio
 async def test_openai_ainvoke() -> None:
     """Test invoke tokens from ChatOpenAI."""
@@ -236,6 +248,7 @@ async def test_openai_ainvoke() -> None:
     assert isinstance(result.content, str)
 
 
+@pytest.mark.scheduled
 def test_openai_invoke() -> None:
     """Test invoke tokens from ChatOpenAI."""
     llm = ChatOpenAI(max_tokens=10)
diff --git a/libs/langchain/tests/integration_tests/document_loaders/test_arxiv.py b/libs/langchain/tests/integration_tests/document_loaders/test_arxiv.py
index 60315e52b81..fbd5cf45fd8 100644
--- a/libs/langchain/tests/integration_tests/document_loaders/test_arxiv.py
+++ b/libs/langchain/tests/integration_tests/document_loaders/test_arxiv.py
@@ -1,5 +1,7 @@
 from typing import List
 
+import pytest
+
 from langchain.document_loaders.arxiv import ArxivLoader
 from langchain.schema import Document
 
@@ -53,3 +55,29 @@ def test_load_returns_full_set_of_metadata() -> None:
         )
         print(doc.metadata)
         assert len(set(doc.metadata)) > 4
+
+
+@pytest.mark.skip(reason="test could be flaky")
+def test_load_issue_9046() -> None:
+    """Test for the fixed issue 9046"""
+    expected_docs = 3
+
+    # ":" character could not be an issue
+    loader = ArxivLoader(
+        query="MetaGPT: Meta Programming for Multi-Agent Collaborative Framework",
+        load_max_docs=expected_docs,
+    )
+    docs = loader.load()
+
+    assert_docs(docs)
+    assert "MetaGPT" in docs[0].metadata["Title"]
+
+    # "-" character could not be an issue
+    loader = ArxivLoader(
+        query="MetaGPT - Meta Programming for Multi-Agent Collaborative Framework",
+        load_max_docs=expected_docs,
+    )
+    docs = loader.load()
+
+    assert_docs(docs)
+    assert "MetaGPT" in docs[0].metadata["Title"]
diff --git a/libs/langchain/tests/integration_tests/document_loaders/test_geodataframe.py b/libs/langchain/tests/integration_tests/document_loaders/test_geodataframe.py
index 37fbbe5ff72..2dbf2777478 100644
--- a/libs/langchain/tests/integration_tests/document_loaders/test_geodataframe.py
+++ b/libs/langchain/tests/integration_tests/document_loaders/test_geodataframe.py
@@ -1,6 +1,7 @@
+from __future__ import annotations
+
 from typing import TYPE_CHECKING
 
-import geopandas
 import pytest
 
 from langchain.document_loaders import GeoDataFrameLoader
@@ -14,6 +15,8 @@ else:
 
 @pytest.mark.requires("geopandas")
 def sample_gdf() -> GeoDataFrame:
+    import geopandas
+
     path_to_data = geopandas.datasets.get_path("nybb")
     gdf = geopandas.read_file(path_to_data)
     gdf["area"] = gdf.area
diff --git a/libs/langchain/tests/integration_tests/document_loaders/test_news.py b/libs/langchain/tests/integration_tests/document_loaders/test_news.py
index d52655b73f5..2507df34f68 100644
--- a/libs/langchain/tests/integration_tests/document_loaders/test_news.py
+++ b/libs/langchain/tests/integration_tests/document_loaders/test_news.py
@@ -2,12 +2,13 @@ import random
 
 import pytest
 import requests
-from bs4 import BeautifulSoup
 
 from langchain.document_loaders import NewsURLLoader
 
 
 def get_random_news_url() -> str:
+    from bs4 import BeautifulSoup
+
     response = requests.get("https://news.google.com")
     soup = BeautifulSoup(response.text, "html.parser")
 
diff --git a/libs/langchain/tests/integration_tests/document_loaders/test_recursive_url_loader.py b/libs/langchain/tests/integration_tests/document_loaders/test_recursive_url_loader.py
new file mode 100644
index 00000000000..c31bd369d72
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/document_loaders/test_recursive_url_loader.py
@@ -0,0 +1,30 @@
+from langchain.document_loaders.recursive_url_loader import RecursiveUrlLoader
+
+
+def test_async_recursive_url_loader() -> None:
+    url = "https://docs.python.org/3.9/"
+    loader = RecursiveUrlLoader(
+        url=url, extractor=lambda _: "placeholder", use_async=True, max_depth=1
+    )
+    docs = loader.load()
+    assert len(docs) == 24
+    assert docs[0].page_content == "placeholder"
+
+
+def test_sync_recursive_url_loader() -> None:
+    url = "https://docs.python.org/3.9/"
+    loader = RecursiveUrlLoader(
+        url=url, extractor=lambda _: "placeholder", use_async=False, max_depth=1
+    )
+    docs = loader.load()
+    assert len(docs) == 24
+    assert docs[0].page_content == "placeholder"
+
+
+def test_loading_invalid_url() -> None:
+    url = "https://this.url.is.invalid/this/is/a/test"
+    loader = RecursiveUrlLoader(
+        url=url, max_depth=1, extractor=lambda _: "placeholder", use_async=False
+    )
+    docs = loader.load()
+    assert len(docs) == 0
diff --git a/libs/langchain/tests/integration_tests/document_loaders/test_tensorflow_datasets.py b/libs/langchain/tests/integration_tests/document_loaders/test_tensorflow_datasets.py
index 50a5f7e96f9..d36e91a2169 100644
--- a/libs/langchain/tests/integration_tests/document_loaders/test_tensorflow_datasets.py
+++ b/libs/langchain/tests/integration_tests/document_loaders/test_tensorflow_datasets.py
@@ -1,18 +1,16 @@
 """Integration tests for the TensorFlow Dataset Loader."""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
 
 import pytest
-from pydantic.error_wrappers import ValidationError
 
 from langchain.document_loaders.tensorflow_datasets import TensorflowDatasetLoader
+from langchain.pydantic_v1 import ValidationError
 from langchain.schema.document import Document
 
-# adding tensorflow and tensorflow_datasets to pyproject.toml is not working
-# these tests can be run in isolation only
-tensorflow = pytest.importorskip("tensorflow")
-tensorflow_datasets = pytest.importorskip("tensorflow_datasets")
-
-# placed here after checking for tensorflow package installation
-import tensorflow as tf  # noqa: E402
+if TYPE_CHECKING:
+    import tensorflow as tf  # noqa: E402
 
 
 def decode_to_str(item: tf.Tensor) -> str:
diff --git a/libs/langchain/tests/integration_tests/embeddings/test_openai.py b/libs/langchain/tests/integration_tests/embeddings/test_openai.py
index 2d117a1fc3f..1288bb463e5 100644
--- a/libs/langchain/tests/integration_tests/embeddings/test_openai.py
+++ b/libs/langchain/tests/integration_tests/embeddings/test_openai.py
@@ -1,4 +1,6 @@
 """Test openai embeddings."""
+import os
+
 import numpy as np
 import openai
 import pytest
@@ -6,6 +8,7 @@ import pytest
 from langchain.embeddings.openai import OpenAIEmbeddings
 
 
+@pytest.mark.scheduled
 def test_openai_embedding_documents() -> None:
     """Test openai embeddings."""
     documents = ["foo bar"]
@@ -15,6 +18,7 @@ def test_openai_embedding_documents() -> None:
     assert len(output[0]) == 1536
 
 
+@pytest.mark.scheduled
 def test_openai_embedding_documents_multiple() -> None:
     """Test openai embeddings."""
     documents = ["foo bar", "bar foo", "foo"]
@@ -27,6 +31,7 @@ def test_openai_embedding_documents_multiple() -> None:
     assert len(output[2]) == 1536
 
 
+@pytest.mark.scheduled
 @pytest.mark.asyncio
 async def test_openai_embedding_documents_async_multiple() -> None:
     """Test openai embeddings."""
@@ -40,6 +45,7 @@ async def test_openai_embedding_documents_async_multiple() -> None:
     assert len(output[2]) == 1536
 
 
+@pytest.mark.scheduled
 def test_openai_embedding_query() -> None:
     """Test openai embeddings."""
     document = "foo bar"
@@ -48,6 +54,7 @@ def test_openai_embedding_query() -> None:
     assert len(output) == 1536
 
 
+@pytest.mark.scheduled
 @pytest.mark.asyncio
 async def test_openai_embedding_async_query() -> None:
     """Test openai embeddings."""
@@ -57,6 +64,7 @@ async def test_openai_embedding_async_query() -> None:
     assert len(output) == 1536
 
 
+@pytest.mark.scheduled
 def test_openai_embedding_with_empty_string() -> None:
     """Test openai embeddings with empty string."""
     document = ["", "abc"]
@@ -71,11 +79,36 @@ def test_openai_embedding_with_empty_string() -> None:
     assert len(output[1]) == 1536
 
 
+@pytest.mark.scheduled
 def test_embed_documents_normalized() -> None:
     output = OpenAIEmbeddings().embed_documents(["foo walked to the market"])
     assert np.isclose(np.linalg.norm(output[0]), 1.0)
 
 
+@pytest.mark.scheduled
 def test_embed_query_normalized() -> None:
     output = OpenAIEmbeddings().embed_query("foo walked to the market")
     assert np.isclose(np.linalg.norm(output), 1.0)
+
+
+def test_azure_openai_embeddings() -> None:
+    from openai import error
+
+    os.environ["OPENAI_API_TYPE"] = "azure"
+    os.environ["OPENAI_API_BASE"] = "https://your-endpoint.openai.azure.com/"
+    os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
+    os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
+
+    embeddings = OpenAIEmbeddings(deployment="your-embeddings-deployment-name")
+    text = "This is a test document."
+
+    try:
+        embeddings.embed_query(text)
+    except error.InvalidRequestError as e:
+        if "Must provide an 'engine' or 'deployment_id' parameter" in str(e):
+            assert (
+                False
+            ), "deployment was provided to but openai.Embeddings didn't get it."
+    except Exception:
+        # Expected to fail because endpoint doesn't exist.
+        pass
diff --git a/libs/langchain/tests/integration_tests/embeddings/test_self_hosted.py b/libs/langchain/tests/integration_tests/embeddings/test_self_hosted.py
index 055f7343346..cb317d11c28 100644
--- a/libs/langchain/tests/integration_tests/embeddings/test_self_hosted.py
+++ b/libs/langchain/tests/integration_tests/embeddings/test_self_hosted.py
@@ -1,8 +1,6 @@
 """Test self-hosted embeddings."""
 from typing import Any
 
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
 from langchain.embeddings import (
     SelfHostedEmbeddings,
     SelfHostedHuggingFaceEmbeddings,
@@ -59,6 +57,8 @@ def test_self_hosted_huggingface_instructor_embedding_query() -> None:
 
 def get_pipeline() -> Any:
     """Get pipeline for testing."""
+    from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
     model_id = "facebook/bart-base"
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     model = AutoModelForCausalLM.from_pretrained(model_id)
diff --git a/libs/langchain/tests/integration_tests/llms/test_anthropic.py b/libs/langchain/tests/integration_tests/llms/test_anthropic.py
index 1d0a9475b54..3604f61969c 100644
--- a/libs/langchain/tests/integration_tests/llms/test_anthropic.py
+++ b/libs/langchain/tests/integration_tests/llms/test_anthropic.py
@@ -11,14 +11,14 @@ from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
 
 def test_anthropic_call() -> None:
     """Test valid call to anthropic."""
-    llm = Anthropic(model="test")
+    llm = Anthropic(model="claude-instant-1")
     output = llm("Say foo:")
     assert isinstance(output, str)
 
 
 def test_anthropic_streaming() -> None:
     """Test streaming tokens from anthropic."""
-    llm = Anthropic(model="test")
+    llm = Anthropic(model="claude-instant-1")
     generator = llm.stream("I'm Pickle Rick")
 
     assert isinstance(generator, Generator)
diff --git a/libs/langchain/tests/integration_tests/llms/test_bittensor.py b/libs/langchain/tests/integration_tests/llms/test_bittensor.py
new file mode 100644
index 00000000000..891708efded
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/llms/test_bittensor.py
@@ -0,0 +1,10 @@
+"""Test Bittensor Validator Endpoint wrapper."""
+
+from langchain.llms import NIBittensorLLM
+
+
+def test_bittensor_call() -> None:
+    """Test valid call to validator endpoint."""
+    llm = NIBittensorLLM(system_prompt="Your task is to answer user prompt.")
+    output = llm("Say foo:")
+    assert isinstance(output, str)
diff --git a/libs/langchain/tests/integration_tests/llms/test_deepsparse.py b/libs/langchain/tests/integration_tests/llms/test_deepsparse.py
new file mode 100644
index 00000000000..3b6480cef8d
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/llms/test_deepsparse.py
@@ -0,0 +1,17 @@
+"""Test DeepSparse wrapper."""
+from langchain.llms import DeepSparse
+
+
+def test_deepsparse_call() -> None:
+    """Test valid call to DeepSparse."""
+    config = {"max_generated_tokens": 5, "use_deepsparse_cache": False}
+
+    llm = DeepSparse(
+        model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none",
+        config=config,
+    )
+
+    output = llm("def ")
+    assert isinstance(output, str)
+    assert len(output) > 1
+    assert output == "ids_to_names"
diff --git a/libs/langchain/tests/integration_tests/llms/test_huggingface_pipeline.py b/libs/langchain/tests/integration_tests/llms/test_huggingface_pipeline.py
index 8dfc9c8b219..46fbeb91b30 100644
--- a/libs/langchain/tests/integration_tests/llms/test_huggingface_pipeline.py
+++ b/libs/langchain/tests/integration_tests/llms/test_huggingface_pipeline.py
@@ -2,8 +2,6 @@
 
 from pathlib import Path
 
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
 from langchain.llms.huggingface_pipeline import HuggingFacePipeline
 from langchain.llms.loading import load_llm
 from tests.integration_tests.llms.utils import assert_llm_equality
@@ -48,6 +46,8 @@ def test_saving_loading_llm(tmp_path: Path) -> None:
 
 def test_init_with_pipeline() -> None:
     """Test initialization with a HF pipeline."""
+    from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
     model_id = "gpt2"
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     model = AutoModelForCausalLM.from_pretrained(model_id)
diff --git a/libs/langchain/tests/integration_tests/llms/test_self_hosted_llm.py b/libs/langchain/tests/integration_tests/llms/test_self_hosted_llm.py
index 73457a01c39..1419b1efed1 100644
--- a/libs/langchain/tests/integration_tests/llms/test_self_hosted_llm.py
+++ b/libs/langchain/tests/integration_tests/llms/test_self_hosted_llm.py
@@ -2,8 +2,6 @@
 import pickle
 from typing import Any, List, Optional
 
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
 from langchain.llms import SelfHostedHuggingFaceLLM, SelfHostedPipeline
 
 model_reqs = ["pip:./", "transformers", "torch"]
@@ -58,6 +56,8 @@ def test_self_hosted_huggingface_pipeline_summarization() -> None:
 
 def load_pipeline() -> Any:
     """Load pipeline for testing."""
+    from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
     model_id = "gpt2"
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     model = AutoModelForCausalLM.from_pretrained(model_id)
diff --git a/libs/langchain/tests/integration_tests/llms/test_symblai_nebula.py b/libs/langchain/tests/integration_tests/llms/test_symblai_nebula.py
index 618ad4ed0d8..35ebd968383 100644
--- a/libs/langchain/tests/integration_tests/llms/test_symblai_nebula.py
+++ b/libs/langchain/tests/integration_tests/llms/test_symblai_nebula.py
@@ -1,56 +1,46 @@
 """Test Nebula API wrapper."""
-
 from langchain import LLMChain, PromptTemplate
 from langchain.llms.symblai_nebula import Nebula
 
 
 def test_symblai_nebula_call() -> None:
     """Test valid call to Nebula."""
-    conversation = """Speaker 1: Thank you for calling ABC, company.Speaker 1: My name 
-is Mary.Speaker 1: How may I help you?Speaker 2: Today?Speaker 1: All right, 
-Madam.Speaker 1: I really apologize for this inconvenient.Speaker 1: I will be happy 
-to assist you in this matter.Speaker 1: Could you please offer me Yuri your account 
-number?Speaker 1: Alright Madam, thank you very much.Speaker 1: Let me check that 
-for confirmation.Speaker 1: Did you say 534 00 365?Speaker 2: 48?Speaker 1: Very good 
-man.Speaker 1: Now for verification purposes, can I please get your full?Speaker 
-2: Name?Speaker 1: Alright, thank you.Speaker 1: Very much.Speaker 1: Madam.Speaker 
-1: Can I, please get your birthdate now?Speaker 1: I am sorry madam.Speaker 1: I 
-didn't make this clear is for verification.Speaker 1: Purposes is the company 
-request.Speaker 1: The system requires me, your name, your complete name and your 
-date of.Speaker 2: Birth.Speaker 2: Alright, thank you very much madam.Speaker 1: 
-All right.Speaker 1: Thank you very much, Madam.Speaker 1: Thank you for that 
-information.Speaker 1: Let me check what happens.Speaker 2: Here.Speaker 1: So 
-according to our data space them, you did pay your last bill last August the 12, 
-which was two days ago in one of our Affiliated payment centers.Speaker 1: So, at the 
-moment you currently, We have zero balance.Speaker 1: So however, the bill that you 
-received was generated a week before you made the pavement, this is reason why you 
-already make this payment, have not been reflected yet.Speaker 1: So what we do in 
-this case, you just simply disregard the amount indicated in the field and you 
-continue to enjoy our service man.Speaker 1: Sure, Madam.Speaker 1: And I am sure 
-you need your cell phone for everything for life, right?Speaker 1: So I really 
-apologize for this inconvenience.Speaker 1: And let me tell you that delays in the 
-bill is usually caused by delays in our Courier Service.Speaker 1: That is to say 
-that it'''s a problem, not with the company, but with a courier service, For a more 
-updated, feel of your account, you can visit our website and log into your account, 
-and they'''re in the system.Speaker 1: On the website, you are going to have the 
-possibility to pay the bill.Speaker 1: That is more.Speaker 2: Updated.Speaker 2: 
-Of course, Madam I can definitely assist you with that.Speaker 2: Once you have, 
-you want to see your bill updated, please go to www.hsn BC campus, any.com after 
-that.Speaker 2: You will see in the tale.Speaker 1: All right corner.Speaker 1: So 
-you're going to see a pay now button.Speaker 1: Please click on the pay now button 
-and the serve.Speaker 1: The system is going to ask you for personal 
-information.Speaker 1: Such as your first name, your ID account, your the number of 
-your account, your email address, and your phone number once you complete this personal 
-information."""
-    llm = Nebula(
-        conversation=conversation,
-    )
+    conversation = """Sam: Good morning, team! Let's keep this standup concise. 
+    We'll go in the usual order: what you did yesterday, 
+    what you plan to do today, and any blockers. Alex, kick us off.
+Alex: Morning! Yesterday, I wrapped up the UI for the user dashboard. 
+The new charts and widgets are now responsive. 
+I also had a sync with the design team to ensure the final touchups are in 
+line with the brand guidelines. Today, I'll start integrating the frontend with 
+the new API endpoints Rhea was working on. 
+The only blocker is waiting for some final API documentation, 
+but I guess Rhea can update on that.
+Rhea: Hey, all! Yep, about the API documentation - I completed the majority of
+ the backend work for user data retrieval yesterday. 
+ The endpoints are mostly set up, but I need to do a bit more testing today. 
+ I'll finalize the API documentation by noon, so that should unblock Alex. 
+ After that, I’ll be working on optimizing the database queries 
+ for faster data fetching. No other blockers on my end.
+Sam: Great, thanks Rhea. Do reach out if you need any testing assistance
+ or if there are any hitches with the database. 
+ Now, my update: Yesterday, I coordinated with the client to get clarity 
+ on some feature requirements. Today, I'll be updating our project roadmap 
+ and timelines based on their feedback. Additionally, I'll be sitting with 
+ the QA team in the afternoon for preliminary testing. 
+ Blocker: I might need both of you to be available for a quick call 
+ in case the client wants to discuss the changes live.
+Alex: Sounds good, Sam. Just let us know a little in advance for the call.
+Rhea: Agreed. We can make time for that.
+Sam: Perfect! Let's keep the momentum going. Reach out if there are any 
+sudden issues or support needed. Have a productive day!
+Alex: You too.
+Rhea: Thanks, bye!"""
+    llm = Nebula(nebula_api_key="<your_api_key>")
 
-    template = """Identify the {count} main objectives or goals mentioned in this 
-context concisely in less points. Emphasize on key intents."""
-    prompt = PromptTemplate.from_template(template)
+    instruction = """Identify the main objectives mentioned in this 
+conversation."""
+    prompt = PromptTemplate.from_template("{instruction}\n{conversation}")
 
     llm_chain = LLMChain(prompt=prompt, llm=llm)
-    output = llm_chain.run(count="five")
-
+    output = llm_chain.run(instruction=instruction, conversation=conversation)
     assert isinstance(output, str)
diff --git a/libs/langchain/tests/integration_tests/llms/test_titan_takeoff.py b/libs/langchain/tests/integration_tests/llms/test_titan_takeoff.py
new file mode 100644
index 00000000000..640fd4feb9a
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/llms/test_titan_takeoff.py
@@ -0,0 +1,18 @@
+"""Test Titan Takeoff wrapper."""
+
+
+import responses
+
+from langchain.llms.titan_takeoff import TitanTakeoff
+
+
+@responses.activate
+def test_titan_takeoff_call() -> None:
+    """Test valid call to Titan Takeoff."""
+    url = "http://localhost:8000/generate"
+    responses.add(responses.POST, url, json={"message": "2 + 2 is 4"}, status=200)
+
+    # response = requests.post(url)
+    llm = TitanTakeoff()
+    output = llm("What is 2 + 2?")
+    assert isinstance(output, str)
diff --git a/libs/langchain/tests/unit_tests/memory/chat_message_histories/test_zep.py b/libs/langchain/tests/integration_tests/memory/chat_message_histories/test_zep.py
similarity index 79%
rename from libs/langchain/tests/unit_tests/memory/chat_message_histories/test_zep.py
rename to libs/langchain/tests/integration_tests/memory/chat_message_histories/test_zep.py
index 9eda5d929c7..e2ffd7ac78f 100644
--- a/libs/langchain/tests/unit_tests/memory/chat_message_histories/test_zep.py
+++ b/libs/langchain/tests/integration_tests/memory/chat_message_histories/test_zep.py
@@ -14,6 +14,9 @@ if TYPE_CHECKING:
 @pytest.mark.requires("zep_python")
 def zep_chat(mocker: MockerFixture) -> ZepChatMessageHistory:
     mock_zep_client: ZepClient = mocker.patch("zep_python.ZepClient", autospec=True)
+    mock_zep_client.memory = mocker.patch(
+        "zep_python.memory.client.MemoryClient", autospec=True
+    )
     zep_chat: ZepChatMessageHistory = ZepChatMessageHistory(
         "test_session", "http://localhost:8000"
     )
@@ -34,7 +37,7 @@ def test_messages(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> Non
             Message(content="message2", role="human", metadata={"key2": "value2"}),
         ],
     )
-    zep_chat.zep_client.get_memory.return_value = mock_memory  # type: ignore
+    zep_chat.zep_client.memory.get_memory.return_value = mock_memory  # type: ignore
 
     result = zep_chat.messages
 
@@ -49,25 +52,25 @@ def test_add_user_message(
     mocker: MockerFixture, zep_chat: ZepChatMessageHistory
 ) -> None:
     zep_chat.add_user_message("test message")
-    zep_chat.zep_client.add_memory.assert_called_once()  # type: ignore
+    zep_chat.zep_client.memory.add_memory.assert_called_once()  # type: ignore
 
 
 @pytest.mark.requires("zep_python")
 def test_add_ai_message(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> None:
     zep_chat.add_ai_message("test message")
-    zep_chat.zep_client.add_memory.assert_called_once()  # type: ignore
+    zep_chat.zep_client.memory.add_memory.assert_called_once()  # type: ignore
 
 
 @pytest.mark.requires("zep_python")
 def test_append(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> None:
     zep_chat.add_message(AIMessage(content="test message"))
-    zep_chat.zep_client.add_memory.assert_called_once()  # type: ignore
+    zep_chat.zep_client.memory.add_memory.assert_called_once()  # type: ignore
 
 
 @pytest.mark.requires("zep_python")
 def test_search(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> None:
     zep_chat.search("test query")
-    zep_chat.zep_client.search_memory.assert_called_once_with(  # type: ignore
+    zep_chat.zep_client.memory.search_memory.assert_called_once_with(  # type: ignore
         "test_session", mocker.ANY, limit=None
     )
 
@@ -75,6 +78,6 @@ def test_search(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> None:
 @pytest.mark.requires("zep_python")
 def test_clear(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> None:
     zep_chat.clear()
-    zep_chat.zep_client.delete_memory.assert_called_once_with(  # type: ignore
+    zep_chat.zep_client.memory.delete_memory.assert_called_once_with(  # type: ignore
         "test_session"
     )
diff --git a/libs/langchain/tests/integration_tests/memory/test_rockset.py b/libs/langchain/tests/integration_tests/memory/test_rockset.py
index e1cb50d05f4..2d6ef5dbdba 100644
--- a/libs/langchain/tests/integration_tests/memory/test_rockset.py
+++ b/libs/langchain/tests/integration_tests/memory/test_rockset.py
@@ -8,8 +8,6 @@ and ROCKSET_REGION environment variables set.
 import json
 import os
 
-from rockset import DevRegions, Regions, RocksetClient
-
 from langchain.memory import ConversationBufferMemory
 from langchain.memory.chat_message_histories import RocksetChatMessageHistory
 from langchain.schema.messages import _message_to_dict
@@ -23,6 +21,8 @@ class TestRockset:
 
     @classmethod
     def setup_class(cls) -> None:
+        from rockset import DevRegions, Regions, RocksetClient
+
         assert os.environ.get("ROCKSET_API_KEY") is not None
         assert os.environ.get("ROCKSET_REGION") is not None
 
diff --git a/libs/langchain/tests/integration_tests/retrievers/docarray/fixtures.py b/libs/langchain/tests/integration_tests/retrievers/docarray/fixtures.py
index 520f3e54e5a..951a38a6657 100644
--- a/libs/langchain/tests/integration_tests/retrievers/docarray/fixtures.py
+++ b/libs/langchain/tests/integration_tests/retrievers/docarray/fixtures.py
@@ -5,7 +5,8 @@ from typing import TYPE_CHECKING, Any, Dict, Generator, Tuple
 
 import numpy as np
 import pytest
-from pydantic import Field
+
+from langchain.pydantic_v1 import Field
 
 if TYPE_CHECKING:
     from docarray.index import (
diff --git a/libs/langchain/tests/unit_tests/retrievers/test_zep.py b/libs/langchain/tests/integration_tests/retrievers/test_zep.py
similarity index 91%
rename from libs/langchain/tests/unit_tests/retrievers/test_zep.py
rename to libs/langchain/tests/integration_tests/retrievers/test_zep.py
index 23e34a2319a..621ca0dd8ba 100644
--- a/libs/langchain/tests/unit_tests/retrievers/test_zep.py
+++ b/libs/langchain/tests/integration_tests/retrievers/test_zep.py
@@ -58,10 +58,13 @@ def zep_retriever(
     mocker: MockerFixture, search_results: List[MemorySearchResult]
 ) -> ZepRetriever:
     mock_zep_client: ZepClient = mocker.patch("zep_python.ZepClient", autospec=True)
-    mock_zep_client.search_memory.return_value = copy.deepcopy(  # type: ignore
+    mock_zep_client.memory = mocker.patch(
+        "zep_python.memory.client.MemoryClient", autospec=True
+    )
+    mock_zep_client.memory.search_memory.return_value = copy.deepcopy(  # type: ignore
         search_results
     )
-    mock_zep_client.asearch_memory.return_value = copy.deepcopy(  # type: ignore
+    mock_zep_client.memory.asearch_memory.return_value = copy.deepcopy(  # type: ignore
         search_results
     )
     zep = ZepRetriever(session_id="123", url="http://localhost:8000")
diff --git a/libs/langchain/tests/integration_tests/storage/__init__.py b/libs/langchain/tests/integration_tests/storage/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/langchain/tests/integration_tests/storage/test_redis.py b/libs/langchain/tests/integration_tests/storage/test_redis.py
new file mode 100644
index 00000000000..109c8b1f5ea
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/storage/test_redis.py
@@ -0,0 +1,97 @@
+"""Implement integration tests for Redis storage."""
+import os
+import typing
+import uuid
+
+import pytest
+
+from langchain.storage.redis import RedisStore
+
+if typing.TYPE_CHECKING:
+    from redis import Redis
+
+pytest.importorskip("redis")
+
+
+@pytest.fixture
+def redis_client() -> Redis:
+    """Yield redis client."""
+    import redis
+
+    # Using standard port, but protecting against accidental data loss
+    # by requiring a password.
+    # This fixture flushes the database!
+    # The only role of the password is to prevent users from accidentally
+    # deleting their data.
+    # The password should establish the identity of the server being.
+    port = 6379
+    password = os.environ.get("REDIS_PASSWORD") or str(uuid.uuid4())
+    client = redis.Redis(host="localhost", port=port, password=password, db=0)
+    try:
+        client.ping()
+    except redis.exceptions.ConnectionError:
+        pytest.skip(
+            "Redis server is not running or is not accessible. "
+            "Verify that credentials are correct. "
+        )
+    # ATTENTION: This will delete all keys in the database!
+    client.flushdb()
+    return client
+
+
+def test_mget(redis_client: Redis) -> None:
+    """Test mget method."""
+    store = RedisStore(client=redis_client, ttl=None)
+    keys = ["key1", "key2"]
+    redis_client.mset({"key1": b"value1", "key2": b"value2"})
+    result = store.mget(keys)
+    assert result == [b"value1", b"value2"]
+
+
+def test_mset(redis_client: Redis) -> None:
+    """Test that multiple keys can be set."""
+    store = RedisStore(client=redis_client, ttl=None)
+    key_value_pairs = [("key1", b"value1"), ("key2", b"value2")]
+    store.mset(key_value_pairs)
+    result = redis_client.mget(["key1", "key2"])
+    assert result == [b"value1", b"value2"]
+
+
+def test_mdelete(redis_client: Redis) -> None:
+    """Test that deletion works as expected."""
+    store = RedisStore(client=redis_client, ttl=None)
+    keys = ["key1", "key2"]
+    redis_client.mset({"key1": b"value1", "key2": b"value2"})
+    store.mdelete(keys)
+    result = redis_client.mget(keys)
+    assert result == [None, None]
+
+
+def test_yield_keys(redis_client: Redis) -> None:
+    store = RedisStore(client=redis_client, ttl=None)
+    redis_client.mset({"key1": b"value1", "key2": b"value2"})
+    assert sorted(store.yield_keys()) == ["key1", "key2"]
+    assert sorted(store.yield_keys(prefix="key*")) == ["key1", "key2"]
+    assert sorted(store.yield_keys(prefix="lang*")) == []
+
+
+def test_namespace(redis_client: Redis) -> None:
+    """Test that a namespace is prepended to all keys properly."""
+    store = RedisStore(client=redis_client, ttl=None, namespace="meow")
+    key_value_pairs = [("key1", b"value1"), ("key2", b"value2")]
+    store.mset(key_value_pairs)
+
+    assert sorted(redis_client.scan_iter("*")) == [
+        b"meow/key1",
+        b"meow/key2",
+    ]
+
+    store.mdelete(["key1"])
+
+    assert sorted(redis_client.scan_iter("*")) == [
+        b"meow/key2",
+    ]
+
+    assert list(store.yield_keys()) == ["key2"]
+    assert list(store.yield_keys(prefix="key*")) == ["key2"]
+    assert list(store.yield_keys(prefix="key1")) == []
diff --git a/libs/langchain/tests/integration_tests/utilities/test_arxiv.py b/libs/langchain/tests/integration_tests/utilities/test_arxiv.py
index 0fc2fd6deba..17dd1337496 100644
--- a/libs/langchain/tests/integration_tests/utilities/test_arxiv.py
+++ b/libs/langchain/tests/integration_tests/utilities/test_arxiv.py
@@ -5,6 +5,7 @@ import pytest
 
 from langchain.agents.load_tools import load_tools
 from langchain.schema import Document
+from langchain.tools import ArxivQueryRun
 from langchain.tools.base import BaseTool
 from langchain.utilities import ArxivAPIWrapper
 
@@ -81,7 +82,7 @@ def test_load_returns_unlimited_doc_content_chars() -> None:
     doc_content_chars_max = None
     api_client = ArxivAPIWrapper(doc_content_chars_max=doc_content_chars_max)
     docs = api_client.load("1605.08386")
-    assert len(docs[0].page_content) == 54337
+    assert len(docs[0].page_content) == pytest.approx(54338, rel=1e-2)
 
 
 def test_load_returns_full_set_of_metadata() -> None:
@@ -120,7 +121,7 @@ def test_load_arxiv_from_universal_entry_with_params() -> None:
         "load_all_available_meta": True,
     }
     arxiv_tool = _load_arxiv_from_universal_entry(**params)
-    assert isinstance(arxiv_tool, ArxivAPIWrapper)
+    assert isinstance(arxiv_tool, ArxivQueryRun)
     wp = arxiv_tool.api_wrapper
     assert wp.top_k_results == 1, "failed to assert top_k_results"
     assert wp.load_max_docs == 10, "failed to assert load_max_docs"
diff --git a/libs/langchain/tests/integration_tests/utilities/test_tensorflow_datasets.py b/libs/langchain/tests/integration_tests/utilities/test_tensorflow_datasets.py
index de2d572b18c..811fec0611e 100644
--- a/libs/langchain/tests/integration_tests/utilities/test_tensorflow_datasets.py
+++ b/libs/langchain/tests/integration_tests/utilities/test_tensorflow_datasets.py
@@ -1,16 +1,16 @@
 """Integration tests for the TensorFlow Dataset client."""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
 
 import pytest
-import tensorflow as tf
-from pydantic.error_wrappers import ValidationError
 
+from langchain.pydantic_v1 import ValidationError
 from langchain.schema.document import Document
 from langchain.utilities.tensorflow_datasets import TensorflowDatasets
 
-# adding tensorflow and tensorflow_datasets to pyproject.toml is not working
-# these tests can be tested in isolation only
-tensorflow = pytest.importorskip("tensorflow")
-tensorflow_datasets = pytest.importorskip("tensorflow_datasets")
+if TYPE_CHECKING:
+    import tensorflow as tf  # noqa: E402
 
 
 def decode_to_str(item: tf.Tensor) -> str:
diff --git a/libs/langchain/tests/integration_tests/vectorstores/cassettes/test_elasticsearch/TestElasticsearch.test_custom_index_add_documents.yaml b/libs/langchain/tests/integration_tests/vectorstores/cassettes/test_elasticsearch/TestElasticsearch.test_custom_index_add_documents.yaml
index df6940d988d..46094ad565e 100644
--- a/libs/langchain/tests/integration_tests/vectorstores/cassettes/test_elasticsearch/TestElasticsearch.test_custom_index_add_documents.yaml
+++ b/libs/langchain/tests/integration_tests/vectorstores/cassettes/test_elasticsearch/TestElasticsearch.test_custom_index_add_documents.yaml
@@ -27,13 +27,402 @@ interactions:
       304, 2225, 67329, 977, 323, 80744, 8032, 18, 60, 71923, 617, 264, 18702, 315,
       2761, 14991, 294, 4351, 645, 430, 36236, 872, 6930, 505, 5674, 323, 79383, 304,
       5369, 311, 18899, 872, 15962, 30295, 13, 2435, 617, 12387, 7437, 315, 8454,
-      481, 18311, 13, 220, 26778, 9606, 527, 72627, 56217, 11, 902, 527, 44304, 430,
-      527, 520, 279, 1948, 315, 872, 3691, 8957, 13, 8593, 10507, 2997, 279, 52835,
-      44892, 11, 6437, 44892, 11, 2294, 4251, 44892, 11, 296, 29886, 44892, 11, 270,
-      86524, 44892, 11, 323, 24354, 2025, 44892, 13], [2059, 7341, 527, 10791, 555,
-      12966, 369, 44892, 13339, 477, 44892, 1913, 19724, 13, 9176, 44892, 22673, 527,
-      21699, 555, 3823, 7640, 13, 8876, 220, 4468, 15, 11, 44892, 22673, 617, 1027,
-      11293, 555, 220, 6028, 13689, 10213, 505, 927, 69, 11218, 13]], "encoding_format":
+      481, 18311, 382, 35862, 9606, 527, 72627, 56217, 11, 902, 527, 44304, 430, 527,
+      520, 279, 1948, 315, 872, 3691, 8957, 13, 8593, 10507, 2997, 279, 52835, 44892,
+      11, 6437, 44892, 11, 2294, 4251, 44892, 11, 296, 29886, 44892, 11, 270, 86524,
+      44892, 11, 323, 24354, 2025, 44892, 13], [2059, 7341, 527, 10791, 555, 12966,
+      369, 44892, 13339, 477, 44892, 1913, 19724, 13, 9176, 44892, 22673, 527, 21699,
+      555, 3823, 7640, 13, 8876, 220, 4468, 15, 11, 44892, 22673, 617, 1027, 11293,
+      555, 220, 6028, 13689, 10213, 505, 927, 69, 11218, 13]], "model": "text-embedding-ada-002",
+      "encoding_format": "base64"}'
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '2706'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - User-Agent-DUMMY
+      X-OpenAI-Client-User-Agent:
+      - X-OpenAI-Client-User-Agent-DUMMY
+      authorization:
+      - authorization-DUMMY
+    method: POST
+    uri: https://api.openai.com/v1/embeddings
+  response:
+    body:
+      string: !!binary |
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diff --git a/libs/langchain/tests/integration_tests/vectorstores/cassettes/test_elasticsearch/TestElasticsearch.test_default_index_from_documents.yaml b/libs/langchain/tests/integration_tests/vectorstores/cassettes/test_elasticsearch/TestElasticsearch.test_default_index_from_documents.yaml
index 43322f3536f..dc70f418ab1 100644
--- a/libs/langchain/tests/integration_tests/vectorstores/cassettes/test_elasticsearch/TestElasticsearch.test_default_index_from_documents.yaml
+++ b/libs/langchain/tests/integration_tests/vectorstores/cassettes/test_elasticsearch/TestElasticsearch.test_default_index_from_documents.yaml
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-
-        dWx0 495
-
-        c3Ry 496
-
-        Li4= 497
-
-        Iiw= 498
-
-        IHlvdQ== 499
-
-        eXBl 500
-
-        cGw= 501
-
-        IG5ldw== 502
-
-        IGo= 503
-
-        ICAgICAgICAgICAgICAgICAgIA== 504
-
-        IGZyb20= 505
-
-        IGV4 506
-
-        IE8= 507
-
-        MjA= 508
-
-        bGQ= 509
-
-        IFs= 510
-
-        b2M= 511
-
-        Ogo= 512
-
-        IHNl 513
-
-        IGxl 514
-
-        LS0tLS0tLS0= 515
-
-        LnM= 516
-
-        ewo= 517
-
-        Jyw= 518
-
-        YW50 519
-
-        IGF0 520
-
-        YXNl 521
-
-        LmM= 522
-
-        IGNo 523
-
-        PC8= 524
-
-        YXZl 525
-
-        YW5n 526
-
-        IGFyZQ== 527
-
-        IGludA== 528
-
-        4oCZ 529
-
-        X3Q= 530
-
-        ZXJ0 531
-
-        aWFs 532
-
-        YWN0 533
-
-        fQo= 534
-
-        aXZl 535
-
-        b2Rl 536
-
-        b3N0 537
-
-        IGNsYXNz 538
-
-        IG5vdA== 539
-
-        b2c= 540
-
-        b3Jk 541
-
-        YWx1ZQ== 542
-
-        YWxs 543
-
-        ZmY= 544
-
-        KCk7Cg== 545
-
-        b250 546
-
-        aW1l 547
-
-        YXJl 548
-
-        IFU= 549
-
-        IHBy 550
-
-        IDo= 551
-
-        aWVz 552
-
-        aXpl 553
-
-        dXJl 554
-
-        IGJ5 555
-
-        aXJl 556
-
-        IH0KCg== 557
-
-        LnA= 558
-
-        IHNo 559
-
-        aWNl 560
-
-        YXN0 561
-
-        cHRpb24= 562
-
-        dHJpbmc= 563
-
-        b2s= 564
-
-        X18= 565
-
-        Y2w= 566
-
-        IyM= 567
-
-        IGhl 568
-
-        YXJk 569
-
-        KS4= 570
-
-        IEA= 571
-
-        aWV3 572
-
-        CQkJ 573
-
-        IHdhcw== 574
-
-        aXA= 575
-
-        dGhpcw== 576
-
-        IHU= 577
-
-        IFRoZQ== 578
-
-        aWRl 579
-
-        YWNl 580
-
-        aWI= 581
-
-        YWM= 582
-
-        cm91 583
-
-        IHdl 584
-
-        amVjdA== 585
-
-        IHB1YmxpYw== 586
-
-        YWs= 587
-
-        dmU= 588
-
-        YXRo 589
-
-        b2lk 590
-
-        ID0+ 591
-
-        dXN0 592
-
-        cXVl 593
-
-        IHJlcw== 594
-
-        KSk= 595
-
-        J3M= 596
-
-        IGs= 597
-
-        YW5z 598
-
-        eXN0 599
-
-        dW5jdGlvbg== 600
-
-        KioqKioqKio= 601
-
-        IGk= 602
-
-        IHVz 603
-
-        cHA= 604
-
-        MTA= 605
-
-        b25l 606
-
-        YWls 607
-
-        PT09PQ== 608
-
-        bmFtZQ== 609
-
-        IHN0cg== 610
-
-        IC8= 611
-
-        ICY= 612
-
-        YWNo 613
-
-        ZGl2 614
-
-        eXN0ZW0= 615
-
-        ZWxs 616
-
-        IGhhdmU= 617
-
-        ZXJy 618
-
-        b3VsZA== 619
-
-        dWxs 620
-
-        cG9u 621
-
-        IEo= 622
-
-        X3A= 623
-
-        ID09 624
-
-        aWdu 625
-
-        U3Q= 626
-
-        Lgo= 627
-
-        IHBs 628
-
-        KTsKCg== 629
-
-        Zm9ybQ== 630
-
-        cHV0 631
-
-        b3VudA== 632
-
-        fQoK 633
-
-        ZGQ= 634
-
-        aXRl 635
-
-        IGdldA== 636
-
-        cnI= 637
-
-        b21l 638
-
-        IOKA 639
-
-        YXJhbQ== 640
-
-        Y2M= 641
-
-        ICov 642
-
-        RVI= 643
-
-        SW4= 644
-
-        bGVz 645
-
-        X3M= 646
-
-        b25n 647
-
-        aWU= 648
-
-        IGNhbg== 649
-
-        IFY= 650
-
-        ZXJ2 651
-
-        cHI= 652
-
-        IHVu 653
-
-        cm93 654
-
-        YmVy 655
-
-        IGRv 656
-
-        bGw= 657
-
-        IGVs 658
-
-        IHNlbGY= 659
-
-        YXRlZA== 660
-
-        YXJ5 661
-
-        IC4= 662
-
-        J10= 663
-
-        dWQ= 664
-
-        IGVu 665
-
-        IFRo 666
-
-        ICAgICAgICAgICAgICAgICAgICAgICA= 667
-
-        dGU= 668
-
-        X2M= 669
-
-        dWN0 670
-
-        IGFi 671
-
-        b3Jr 672
-
-        LmdldA== 673
-
-        ICM= 674
-
-        YXc= 675
-
-        cmVzcw== 676
-
-        b2I= 677
-
-        TmFtZQ== 678
-
-        MjAx 679
-
-        YXBw 680
-
-        Wyc= 681
-
-        IGFsbA== 682
-
-        b3J5 683
-
-        aXRpb24= 684
-
-        YW5jZQ== 685
-
-        ZWFy 686
-
-        IGNvbnQ= 687
-
-        dmVudA== 688
-
-        aWE= 689
-
-        IHdpbGw= 690
-
-        SU4= 691
-
-        ICAgICAgICAg 692
-
-        cmV0dXJu 693
-
-        IDwv 694
-
-        ZGF0YQ== 695
-
-        KQoK 696
-
-        UmU= 697
-
-        cGxl 698
-
-        aWxk 699
-
-        dGhlcg== 700
-
-        IHlvdXI= 701
-
-        Igo= 702
-
-        KCQ= 703
-
-        IG91dA== 704
-
-        KSw= 705
-
-        IGhhcw== 706
-
-        U3RyaW5n 707
-
-        c28= 708
-
-        IHVw 709
-
-        YXg= 710
-
-        IGRlZg== 711
-
-        IGJv 712
-
-        Z2U= 713
-
-        YWxzZQ== 714
-
-        T04= 715
-
-        cGVy 716
-
-        MTI= 717
-
-        aWNo 718
-
-        IGJ1dA== 719
-
-        IAo= 720
-
-        IF8= 721
-
-        X20= 722
-
-        YWRk 723
-
-        cXVlc3Q= 724
-
-        b2RlbA== 725
-
-        c2VsZg== 726
-
-        ZXJ5 727
-
-        ZnQ= 728
-
-        ZW5z 729
-
-        Ly8vLw== 730
-
-        YWtl 731
-
-        LkM= 732
-
-        IGdv 733
-
-        IGZ1bmN0aW9u 734
-
-        IEs= 735
-
-        aXZhdGU= 736
-
-        IGlt 737
-
-        IGNvbnN0 738
-
-        LnQ= 739
-
-        ICovCg== 740
-
-        KTsNCg== 741
-
-        IHZvaWQ= 742
-
-        IHNldA== 743
-
-        IFN5c3RlbQ== 744
-
-        Y3Jp 745
-
-        KCkK 746
-
-        bGk= 747
-
-        CWlm 748
-
-        Lm0= 749
-
-        YWxseQ== 750
-
-        c2V0 751
-
-        ZXA= 752
-
-        4oCZcw== 753
-
-        Ym8= 754
-
-        ZGVm 755
-
-        JywK 756
-
-        IG1l 757
-
-        ICE= 758
-
-        YXRjaA== 759
-
-        Ij4= 760
-
-        IiwK 761
-
-        ZWM= 762
-
-        IElu 763
-
-        cGg= 764
-
-        IHw= 765
-
-        X2Y= 766
-
-        IHZhcg== 767
-
-        ZW5jZQ== 768
-
-        SWQ= 769
-
-        cmVl 770
-
-        aW5r 771
-
-        bGVjdA== 772
-
-        dWc= 773
-
-        ZXRo 774
-
-        IGVsc2U= 775
-
-        LS0tLS0tLS0tLS0tLS0tLQ== 776
-
-        MTk= 777
-
-        Y29udA== 778
-
-        IHNv 779
-
-        YXRpYw== 780
-
-        IGxv 781
-
-        cHJv 782
-
-        dG9u 783
-
-        c3M= 784
-
-        b3du 785
-
-        YWJlbA== 786
-
-        b2ludA== 787
-
-        b3Vz 788
-
-        ZWxk 789
-
-        U1Q= 790
-
-        VGhl 791
-
-        ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICA= 792
-
-        UkU= 793
-
-        Ijo= 794
-
-        b2xvcg== 795
-
-        dHA= 796
-
-        ZWc= 797
-
-        a2V5 798
-
-        dWRl 799
-
-        IFN0 800
-
-        b3VuZA== 801
-
-        IGFy 802
-
-        Iik7Cg== 803
-
-        ZW5lcg== 804
-
-        c2Vy 805
-
-        MTE= 806
-
-        YmplY3Q= 807
-
-        ZXNzYWdl 808
-
-        ZmVy 809
-
-        IG1vcmU= 810
-
-        YXRpb25z 811
-
-        ZW50cw== 812
-
-        IGhpcw== 813
-
-        IHRoZXk= 814
-
-        LlM= 815
-
-        IFk= 816
-
-        dXNl 817
-
-        bmU= 818
-
-        aXNo 819
-
-        b2xk 820
-
-        X2Q= 821
-
-        aW8= 822
-
-        aWVsZA== 823
-
-        IHBlcg== 824
-
-        Q29udA== 825
-
-        aW5ncw== 826
-
-        IyMjIw== 827
-
-        IGRhdGE= 828
-
-        IHNh 829
-
-        ZWY= 830
-
-        Zm8= 831
-
-        IG9uZQ== 832
-
-        ZW5n 833
-
-        IGRpcw== 834
-
-        QVQ= 835
-
-        IG5hbWU= 836
-
-        IHRydWU= 837
-
-        dmFs 838
-
-        bGVk 839
-
-        LmY= 840
-
-        IG5l 841
-
-        IGVuZA== 842
-
-        MzI= 843
-
-        LlQ= 844
-
-        MTY= 845
-
-        Y3Jl 846
-
-        YXJr 847
-
-        bG9n 848
-
-        RXg= 849
-
-        ZXJyb3I= 850
-
-        X2lk 851
-
-        dXJyZQ== 852
-
-        YW5nZQ== 853
-
-        IG51bGw= 854
-
-        cnJheQ== 855
-
-        IG15 856
-
-        cGFu 857
-
-        aWN0 858
-
-        YXRvcg== 859
-
-        Vmlldw== 860
-
-        TGlzdA== 861
-
-        CXJldHVybg== 862
-
-        4oCd 863
-
-        IHByZQ== 864
-
-        IHg= 865
-
-        Y2x1ZGU= 866
-
-        YXJn 867
-
-        MTU= 868
-
-        b3Y= 869
-
-        Lmg= 870
-
-        ID4= 871
-
-        IHRoZWly 872
-
-        Jyk= 873
-
-        aXJzdA== 874
-
-        aWNr 875
-
-        Z2g= 876
-
-        TEU= 877
-
-        T1I= 878
-
-        IHByaXZhdGU= 879
-
-        dGVt 880
-
-        DQoNCg== 881
-
-        dXNlcg== 882
-
-        ICk= 883
-
-        Y29t 884
-
-        LkE= 885
-
-        IjsK 886
-
-        IGlk 887
-
-        cmVhZA== 888
-
-        IHdobw== 889
-
-        X2I= 890
-
-        Ij4K 891
-
-        IHRpbWU= 892
-
-        IG1hbg== 893
-
-        cnk= 894
-
-        PT09PT09PT0= 895
-
-        cm91cA== 896
-
-        cm9w 897
-
-        cHVibGlj 898
-
-        dmVs 899
-
-        dW1iZXI= 900
-
-        Ymxl 901
-
-        IHdoaWNo 902
-
-        KioqKioqKioqKioqKioqKg== 903
-
-        IGFueQ== 904
-
-        IGZhbHNl 905
-
-        d2U= 906
-
-        IHZhbHVl 907
-
-        IGxp 908
-
-        Iik= 909
-
-        bmRlcg== 910
-
-        Z3I= 911
-
-        IG5v 912
-
-        cGFyYW0= 913
-
-        MjU= 914
-
-        Zmln 915
-
-        LmNvbQ== 916
-
-        IGFwcA== 917
-
-        X2w= 918
-
-        aW9ucw== 919
-
-        LkQ= 920
-
-        IENo 921
-
-        IGFib3V0 922
-
-        IGFkZA== 923
-
-        IHN1 924
-
-        IHN0cmluZw== 925
-
-        SUQ= 926
-
-        IG92ZXI= 927
-
-        c3RyaW5n 928
-
-        Lmw= 929
-
-        b3VyY2U= 930
-
-        MDAw 931
-
-        X0M= 932
-
-        XQo= 933
-
-        IHF1 934
-
-        IFN0cmluZw== 935
-
-        Y2E= 936
-
-        U0U= 937
-
-        IHJv 938
-
-        c2g= 939
-
-        dWFs 940
-
-        VHlwZQ== 941
-
-        c29u 942
-
-        bmV3 943
-
-        ZXJu 944
-
-        IGFn 945
-
-        QVI= 946
-
-        XTsK 947
-
-        XS4= 948
-
-        ID8= 949
-
-        aWNhbA== 950
-
-        IGRlcw== 951
-
-        dXRo 952
-
-        aXg= 953
-
-        YXlz 954
-
-        IHR5cGU= 955
-
-        J3Q= 956
-
-        YXVsdA== 957
-
-        IGludGVy 958
-
-        dmFy 959
-
-        LmI= 960
-
-        IHBhcnQ= 961
-
-        LmQ= 962
-
-        dXJyZW50 963
-
-        SVQ= 964
-
-        RU4= 965
-
-        MzA= 966
-
-        ZW5j 967
-
-        KGY= 968
-
-        cmE= 969
-
-        dmFsdWU= 970
-
-        Y2hv 971
-
-        MTg= 972
-
-        dXR0b24= 973
-
-        b3Nl 974
-
-        MTQ= 975
-
-        ICE9 976
-
-        YXRlcg== 977
-
-        w6k= 978
-
-        cmVhdGU= 979
-
-        b2xs 980
-
-        cG9z 981
-
-        eWxl 982
-
-        bmc= 983
-
-        QUw= 984
-
-        dXNpbmc= 985
-
-        YW1lcw== 986
-
-        IHsNCg== 987
-
-        YXRlcw== 988
-
-        ZWx5 989
-
-        IHdvcms= 990
-
-        IGVt 991
-
-        aW5hbA== 992
-
-        IHNw 993
-
-        IHdoZW4= 994
-
-        LnNldA== 995
-
-        ICAgICAg 996
-
-        KToK 997
-
-        dG8= 998
-
-        cXVpcmU= 999
-
-        aW5kb3c= 1000
-
-        bGVtZW50 1001
-
-        cGVjdA== 1002
-
-        YXNo 1003
-
-        W2k= 1004
-
-        IHVzZQ== 1005
-
-        LkY= 1006
-
-        cGVj 1007
-
-        IGFk 1008
-
-        b3Zl 1009
-
-        Y2VwdGlvbg== 1010
-
-        ZW5ndGg= 1011
-
-        aW5jbHVkZQ== 1012
-
-        YWRlcg== 1013
-
-        ICAgICAgICAgICAgICAgICAgICAgICAgICAg 1014
-
-        YXR1cw== 1015
-
-        VGg= 1016
-
-        aXRsZQ== 1017
-
-        cml0 1018
-
-        dm9pZA== 1019
-
-        KCku 1020
-
-        KAo= 1021
-
-        IG9mZg== 1022
-
-        IG90aGVy 1023
-
-        ICYm 1024
-
-        JzsK 1025
-
-        bXM= 1026
-
-        IGJlZW4= 1027
-
-        IHRl 1028
-
-        bWw= 1029
-
-        Y28= 1030
-
-        bmM= 1031
-
-        MTM= 1032
-
-        ZXJ2aWNl 1033
-
-        ICU= 1034
-
-        KioK 1035
-
-        YW5u 1036
-
-        YWRl 1037
-
-        CgoKCg== 1038
-
-        bG9jaw== 1039
-
-        Y29uc3Q= 1040
-
-        MTAw 1041
-
-        cG9uc2U= 1042
-
-        IHN1cA== 1043
-
-        Kys= 1044
-
-        ZGF0ZQ== 1045
-
-        IGFjYw== 1046
-
-        IGhhZA== 1047
-
-        IGJ1 1048
-
-        MjAw 1049
-
-        IFJl 1050
-
-        IHdlcmU= 1051
-
-        IGZpbGU= 1052
-
-        IHdvdWxk 1053
-
-        IOKAnA== 1054
-
-        dmVu 1055
-
-        aXNz 1056
-
-        IG91cg== 1057
-
-        Y2xhc3M= 1058
-
-        cmF3 1059
-
-        IHllYXI= 1060
-
-        RGF0YQ== 1061
-
-        IHZhbA== 1062
-
-        IHNvbWU= 1063
-
-        ZnRlcg== 1064
-
-        eXM= 1065
-
-        IC8vLw== 1066
-
-        cm91bmQ= 1067
-
-        dmlldw== 1068
-
-        IHBl 1069
-
-        IHRoZXJl 1070
-
-        IHNhaWQ= 1071
-
-        ZHU= 1072
-
-        b2Y= 1073
-
-        bGluZQ== 1074
-
-        Lyo= 1075
-
-        ZHVjdA== 1076
-
-        IGhlcg== 1077
-
-        ICAgICAgICAgICAgIA== 1078
-
-        UmVz 1079
-
-        IGNv 1080
-
-        IGNvbW0= 1081
-
-        aXNl 1082
-
-        bWlu 1083
-
-        ICAgIAo= 1084
-
-        I2luY2x1ZGU= 1085
-
-        ZXRob2Q= 1086
-
-        LlA= 1087
-
-        dXRl 1088
-
-        IGFzcw== 1089
-
-        SW50 1090
-
-        YXNr 1091
-
-        bG9j 1092
-
-        IGxpa2U= 1093
-
-        b2R5 1094
-
-        IGxldA== 1095
-
-        bG9hZA== 1096
-
-        IGFt 1097
-
-        cm9s 1098
-
-        IGdy 1099
-
-        eXA= 1100
-
-        IGFsc28= 1101
-
-        IEl0 1102
-
-        dXJs 1103
-
-        aWZpYw== 1104
-
-        b3Jz 1105
-
-        X1A= 1106
-
-        X24= 1107
-
-        aWdo 1108
-
-        IHRoYW4= 1109
-
-        Q29t 1110
-
-        QU4= 1111
-
-        VUw= 1112
-
-        YXRpbmc= 1113
-
-        MTc= 1114
-
-        IFRoaXM= 1115
-
-        cmVm 1116
-
-        X1M= 1117
-
-        IHN0YXRpYw== 1118
-
-        cm9sbA== 1119
-
-        IGp1c3Q= 1120
-
-        IHJlc3VsdA== 1121
-
-        aWFu 1122
-
-        aWR0aA== 1123
-
-        IHRoZW0= 1124
-
-        KSk7Cg== 1125
-
-        ZGVy 1126
-
-        cmVhaw== 1127
-
-        Q29u 1128
-
-        Oi8v 1129
-
-        dWxl 1130
-
-        Li4u 1131
-
-        YXJjaA== 1132
-
-        ZW1lbnQ= 1133
-
-        IDw8 1134
-
-        NTA= 1135
-
-        dXNo 1136
-
-        ZW5zZQ== 1137
-
-        YXJy 1138
-
-        IGludG8= 1139
-
-        Y2Vzcw== 1140
-
-        YW1w 1141
-
-        aWVk 1142
-
-        dW1lbnQ= 1143
-
-        IFw= 1144
-
-        XSw= 1145
-
-        d28= 1146
-
-        YWxz 1147
-
-        IHdoYXQ= 1148
-
-        YW5j 1149
-
-        VmFsdWU= 1150
-
-        PSc= 1151
-
-        b2x1bQ== 1152
-
-        IHBvcw== 1153
-
-        YWdlcw== 1154
-
-        YXllcg== 1155
-
-        IHNj 1156
-
-        dWVz 1157
-
-        IikK 1158
-
-        X1Q= 1159
-
-        IGxpc3Q= 1160
-
-        KHM= 1161
-
-        IGNhc2U= 1162
-
-        Q2g= 1163
-
-        CQkJCQk= 1164
-
-        Ly8vLy8vLy8= 1165
-
-        cG9uZW50 1166
-
-        IHo= 1167
-
-        IGtu 1168
-
-        bGV0 1169
-
-        REU= 1170
-
-        cmVk 1171
-
-        IGZl 1172
-
-        IH0sCg== 1173
-
-        ICw= 1174
-
-        KHQ= 1175
-
-        IGZpcnN0 1176
-
-        Jyk7Cg== 1177
-
-        d29yZA== 1178
-
-        IGltcG9ydA== 1179
-
-        IGFjdA== 1180
-
-        IGNoYXI= 1181
-
-        Q1Q= 1182
-
-        IFRy 1183
-
-        b3BsZQ== 1184
-
-        PXs= 1185
-
-        CWY= 1186
-
-        MjQ= 1187
-
-        aWVudA== 1188
-
-        Y2VudA== 1189
-
-        Lmo= 1190
-
-        bGVjdGlvbg== 1191
-
-        KSkK 1192
-
-        IG9ubHk= 1193
-
-        IHByaW50 1194
-
-        bWVy 1195
-
-        Llc= 1196
-
-        b2Nr 1197
-
-        IC0t 1198
-
-        VGV4dA== 1199
-
-        IG9w 1200
-
-        YW5r 1201
-
-        IGl0cw== 1202
-
-        IGJhY2s= 1203
-
-        WyI= 1204
-
-        IG5lZWQ= 1205
-
-        IGNs 1206
-
-        IHN1Yg== 1207
-
-        IGxh 1208
-
-        KCg= 1209
-
-        LiI= 1210
-
-        T2JqZWN0 1211
-
-        IHN0YXJ0 1212
-
-        ZmlsZQ== 1213
-
-        KHNlbGY= 1214
-
-        bmVy 1215
-
-        ZXk= 1216
-
-        IHVzZXI= 1217
-
-        IGVudA== 1218
-
-        IENvbQ== 1219
-
-        aXRz 1220
-
-        IENvbg== 1221
-
-        b3VibGU= 1222
-
-        b3dlcg== 1223
-
-        aXRlbQ== 1224
-
-        dmVyeQ== 1225
-
-        IFdl 1226
-
-        NjQ= 1227
-
-        bGljaw== 1228
-
-        IFE= 1229
-
-        cGhw 1230
-
-        dHRw 1231
-
-        Jzo= 1232
-
-        aWNz 1233
-
-        IHVuZGVy 1234
-
-        ICoK 1235
-
-        Lkw= 1236
-
-        KTs= 1237
-
-        aWNlcw== 1238
-
-        IHJlZw== 1239
-
-        KQ0K 1240
-
-        CXB1YmxpYw== 1241
-
-        U1M= 1242
-
-        IHRoZW4= 1243
-
-        cmVhdA== 1244
-
-        aW91cw== 1245
-
-        Lkc= 1246
-
-        ZWs= 1247
-
-        aXJlY3Q= 1248
-
-        aGVjaw== 1249
-
-        Y3JpcHQ= 1250
-
-        bmluZw== 1251
-
-        IFVu 1252
-
-        IG1heQ== 1253
-
-        IFdo 1254
-
-        Qm8= 1255
-
-        SXRlbQ== 1256
-
-        c3RydWN0 1257
-
-        LnN0 1258
-
-        cmVhbQ== 1259
-
-        aWJsZQ== 1260
-
-        bG9hdA== 1261
-
-        IG9yZw== 1262
-
-        dW5k 1263
-
-        c3Vt 1264
-
-        X2lu 1265
-
-        Li4v 1266
-
-        X00= 1267
-
-        IGhvdw== 1268
-
-        cml0ZQ== 1269
-
-        Jwo= 1270
-
-        VG8= 1271
-
-        NDA= 1272
-
-        d3c= 1273
-
-        IHBlb3BsZQ== 1274
-
-        aW5kZXg= 1275
-
-        Lm4= 1276
-
-        aHR0cA== 1277
-
-        KG0= 1278
-
-        ZWN0b3I= 1279
-
-        IGluZA== 1280
-
-        IGphdg== 1281
-
-        XSwK 1282
-
-        IEhl 1283
-
-        X3N0 1284
-
-        ZnVs 1285
-
-        b2xl 1286
-
-        KXsK 1287
-
-        IHNob3VsZA== 1288
-
-        b3B5 1289
-
-        ZWxw 1290
-
-        aWVy 1291
-
-        X25hbWU= 1292
-
-        ZXJzb24= 1293
-
-        SU9O 1294
-
-        b3Rl 1295
-
-        IHRlc3Q= 1296
-
-        IGJldA== 1297
-
-        cnJvcg== 1298
-
-        dWxhcg== 1299
-
-        44A= 1300
-
-        INA= 1301
-
-        YnM= 1302
-
-        dGluZw== 1303
-
-        IG1ha2U= 1304
-
-        VHI= 1305
-
-        IGFmdGVy 1306
-
-        YXJnZXQ= 1307
-
-        Uk8= 1308
-
-        b2x1bW4= 1309
-
-        cmM= 1310
-
-        X3Jl 1311
-
-        ZGVmaW5l 1312
-
-        MjI= 1313
-
-        IHJpZ2h0 1314
-
-        cmlnaHQ= 1315
-
-        ZGF5 1316
-
-        IGxvbmc= 1317
-
-        W10= 1318
-
-        KHA= 1319
-
-        dGQ= 1320
-
-        Y29uZA== 1321
-
-        IFBybw== 1322
-
-        IHJlbQ== 1323
-
-        cHRpb25z 1324
-
-        dmlk 1325
-
-        Lmc= 1326
-
-        IGV4dA== 1327
-
-        IF9f 1328
-
-        JykK 1329
-
-        cGFjZQ== 1330
-
-        bXA= 1331
-
-        IG1pbg== 1332
-
-        c3RhbmNl 1333
-
-        YWly 1334
-
-        YWN0aW9u 1335
-
-        d2g= 1336
-
-        dHlwZQ== 1337
-
-        dXRpbA== 1338
-
-        YWl0 1339
-
-        PD8= 1340
-
-        SUM= 1341
-
-        dGV4dA== 1342
-
-        IHBo 1343
-
-        IGZs 1344
-
-        Lk0= 1345
-
-        Y2Nlc3M= 1346
-
-        YnI= 1347
-
-        Zm9yZQ== 1348
-
-        ZXJzaW9u 1349
-
-        KSwK 1350
-
-        LnJl 1351
-
-        YXRlZw== 1352
-
-        IGxvYw== 1353
-
-        aW5z 1354
-
-        LXM= 1355
-
-        dHJpYg== 1356
-
-        IEludA== 1357
-
-        IGFycmF5 1358
-
-        LCI= 1359
-
-        UHJv 1360
-
-        KGM= 1361
-
-        ZXNzaW9u 1362
-
-        PgoK 1363
-
-        IHNoZQ== 1364
-
-        Il0= 1365
-
-        YXBo 1366
-
-        IGV4cA== 1367
-
-        ZXJ0eQ== 1368
-
-        IFNl 1369
-
-        IHBhcg== 1370
-
-        dW5j 1371
-
-        RVQ= 1372
-
-        IHJlYWQ= 1373
-
-        cHJpbnQ= 1374
-
-        IHJlbA== 1375
-
-        IGZvcm0= 1376
-
-        IGRy 1377
-
-        RXhjZXB0aW9u 1378
-
-        aW5wdXQ= 1379
-
-        IHRyYW5z 1380
-
-        IyMjIyMjIyM= 1381
-
-        b3JkZXI= 1382
-
-        Qnk= 1383
-
-        IGF3 1384
-
-        aXRpZXM= 1385
-
-        dWZm 1386
-
-        cGxheQ== 1387
-
-        LmFkZA== 1388
-
-        IOKAkw== 1389
-
-        IHdhbnQ= 1390
-
-        IGNvbXA= 1391
-
-        bWVudHM= 1392
-
-        IHx8 1393
-
-        YXo= 1394
-
-        YmU= 1395
-
-        IG51bWJlcg== 1396
-
-        IHJlcXVpcmU= 1397
-
-        IEV4 1398
-
-        NjA= 1399
-
-        IGNvbA== 1400
-
-        IGtleQ== 1401
-
-        ZW1iZXI= 1402
-
-        IHR3bw== 1403
-
-        IHNpemU= 1404
-
-        IHdoZXJl 1405
-
-        VVQ= 1406
-
-        cmVzdWx0 1407
-
-        ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIA== 1408
-
-        b3VnaA== 1409
-
-        b3JsZA== 1410
-
-        b29k 1411
-
-        dWNo 1412
-
-        YXRpdmU= 1413
-
-        Z2Vy 1414
-
-        YXJlbnQ= 1415
-
-        IC8q 1416
-
-        IGFyZw== 1417
-
-        IHdoaWxl 1418
-
-        MjM= 1419
-
-        KHRoaXM= 1420
-
-        IHJlYw== 1421
-
-        IGRpZg== 1422
-
-        U3RhdGU= 1423
-
-        IHNwZWM= 1424
-
-        cmlkZQ== 1425
-
-        X0Y= 1426
-
-        IGxvb2s= 1427
-
-        QU0= 1428
-
-        aWxpdHk= 1429
-
-        ZXRlcg== 1430
-
-        4oCZdA== 1431
-
-        CgoK 1432
-
-        YXlvdXQ= 1433
-
-        LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0= 1434
-
-        YWdlcg== 1435
-
-        IGNvdWxk 1436
-
-        IGJy 1437
-
-        ZW5kcw== 1438
-
-        dXJlcw== 1439
-
-        IGtub3c= 1440
-
-        ZXRz 1441
-
-        IElm 1442
-
-        IFNo 1443
-
-        Lnc= 1444
-
-        YmFjaw== 1445
-
-        IHNlcg== 1446
-
-        ICs9 1447
-
-        IGZy 1448
-
-        KCkpOwo= 1449
-
-        IGhhbmQ= 1450
-
-        SW5k 1451
-
-        VUxM 1452
-
-        SW0= 1453
-
-        KCk7Cgo= 1454
-
-        IG1vc3Q= 1455
-
-        IHRyeQ== 1456
-
-        IG5vdw== 1457
-
-        cm91Z2g= 1458
-
-        Pg0K 1459
-
-        YWNrYWdl 1460
-
-        IGhpbQ== 1461
-
-        Ll8= 1462
-
-        aWZ5 1463
-
-        IGJyZWFr 1464
-
-        ICk7Cg== 1465
-
-        cmVu 1466
-
-        I2RlZmluZQ== 1467
-
-        aXR0 1468
-
-        IGFw 1469
-
-        CWM= 1470
-
-        KG4= 1471
-
-        IFlvdQ== 1472
-
-        OgoK 1473
-
-        LW0= 1474
-
-        IGV2ZXJ5 1475
-
-        dXN0b20= 1476
-
-        bGllbnQ= 1477
-
-        b2N1bWVudA== 1478
-
-        Y3JpcHRpb24= 1479
-
-        RXJyb3I= 1480
-
-        LWI= 1481
-
-        0L4= 1482
-
-        XVs= 1483
-
-        OTk= 1484
-
-        dHJhbnM= 1485
-
-        IHBvaW50 1486
-
-        IHN0ZA== 1487
-
-        IGZpbA== 1488
-
-        VGltZQ== 1489
-
-        ODA= 1490
-
-        IG1vZA== 1491
-
-        IC0+ 1492
-
-        IGVycm9y 1493
-
-        YWg= 1494
-
-        IHRleHQ= 1495
-
-        cm9sbGVy 1496
-
-        bG9zZQ== 1497
-
-        cWw= 1498
-
-        IHBvbA== 1499
-
-        Pjwv 1500
-
-        IHNob3c= 1501
-
-        VXNlcg== 1502
-
-        YXNlZA== 1503
-
-        IHsKCg== 1504
-
-        IGZpbmQ= 1505
-
-        0LA= 1506
-
-        RUQ= 1507
-
-        c3Bhbg== 1508
-
-        ZW51 1509
-
-        IGN1cnJlbnQ= 1510
-
-        IHVzZWQ= 1511
-
-        Y2VwdA== 1512
-
-        Y2x1ZA== 1513
-
-        IHBsYXk= 1514
-
-        IGxvZw== 1515
-
-        dXRpb24= 1516
-
-        Zmw= 1517
-
-        IHNlZQ== 1518
-
-        aW5kb3dz 1519
-
-        IGhlbHA= 1520
-
-        IHRoZXNl 1521
-
-        IHBhc3M= 1522
-
-        IGRvd24= 1523
-
-        IGV2ZW4= 1524
-
-        YXNvbg== 1525
-
-        dWlsZA== 1526
-
-        ZnJvbQ== 1527
-
-        KGQ= 1528
-
-        IGJs 1529
-
-        bGFiZWw= 1530
-
-        ZWxzZQ== 1531
-
-        0LU= 1532
-
-        ICgh 1533
-
-        aXplZA== 1534
-
-        KCks 1535
-
-        IG9i 1536
-
-        IGl0ZW0= 1537
-
-        dW1w 1538
-
-        VVI= 1539
-
-        b3Ju 1540
-
-        IGRvbg== 1541
-
-        U2U= 1542
-
-        bWFu 1543
-
-        Mjc= 1544
-
-        YW1wbGU= 1545
-
-        dG4= 1546
-
-        PT09PT09PT09PT09PT09PQ== 1547
-
-        SGU= 1548
-
-        Z3JhbQ== 1549
-
-        IGRpZA== 1550
-
-        d24= 1551
-
-        X2g= 1552
-
-        aXZlcg== 1553
-
-        IHNt 1554
-
-        IHRocm91Z2g= 1555
-
-        IEFu 1556
-
-        Y2hl 1557
-
-        IGludg== 1558
-
-        b3VzZQ== 1559
-
-        IGVz 1560
-
-        IE5ldw== 1561
-
-        ZXhwb3J0 1562
-
-        bWFyeQ== 1563
-
-        dXRv 1564
-
-        bGVy 1565
-
-        IGxhc3Q= 1566
-
-        IGV2ZW50 1567
-
-        dHJ5 1568
-
-        77w= 1569
-
-        aWx5 1570
-
-        aWduZWQ= 1571
-
-        aW5lcw== 1572
-
-        b2xsb3c= 1573
-
-        aWNlbnNl 1574
-
-        c29sZQ== 1575
-
-        bGVhcg== 1576
-
-        KGludA== 1577
-
-        IGFnYWlu 1578
-
-        IGhpZ2g= 1579
-
-        aHRtbA== 1580
-
-        SW5kZXg= 1581
-
-        dXRob3I= 1582
-
-        IC8qKgo= 1583
-
-        IGxpbmU= 1584
-
-        RXZlbnQ= 1585
-
-        X0Q= 1586
-
-        IGRvZXM= 1587
-
-        aXRpYWw= 1588
-
-        IGNy 1589
-
-        YXJz 1590
-
-        Mjg= 1591
-
-        IHRlbQ== 1592
-
-        Y2F1c2U= 1593
-
-        ZmFjZQ== 1594
-
-        IGA= 1595
-
-        X0E= 1596
-
-        QnV0dG9u 1597
-
-        YXR1cmU= 1598
-
-        ZWN0ZWQ= 1599
-
-        RVM= 1600
-
-        aXN0ZXI= 1601
-
-        CQo= 1602
-
-        IGJlZm9yZQ== 1603
-
-        YWxl 1604
-
-        b3RoZXI= 1605
-
-        IGJlY2F1c2U= 1606
-
-        cm9pZA== 1607
-
-        IGVk 1608
-
-        aWs= 1609
-
-        cmVn 1610
-
-        IERl 1611
-
-        IGRpc3Q= 1612
-
-        fSwK 1613
-
-        IHN0YXRl 1614
-
-        IGNvbnM= 1615
-
-        cmludA== 1616
-
-        YXR0 1617
-
-        IGhlcmU= 1618
-
-        aW5lZA== 1619
-
-        IGZpbmFs 1620
-
-        ICIi 1621
-
-        S2V5 1622
-
-        TE8= 1623
-
-        IGRlbA== 1624
-
-        cHR5 1625
-
-        dGhpbmc= 1626
-
-        MjY= 1627
-
-        IEFuZA== 1628
-
-        IHJ1bg== 1629
-
-        IFg= 1630
-
-        eW0= 1631
-
-        LmFwcA== 1632
-
-        IHZlcnk= 1633
-
-        Y2Vz 1634
-
-        X04= 1635
-
-        YXJlZA== 1636
-
-        d2FyZA== 1637
-
-        bGlzdA== 1638
-
-        aXRlZA== 1639
-
-        b2xvZw== 1640
-
-        aXRjaA== 1641
-
-        Qm94 1642
-
-        aWZl 1643
-
-        MzM= 1644
-
-        IGFj 1645
-
-        IG1vZGVs 1646
-
-        IG1vbg== 1647
-
-        IHdheQ== 1648
-
-        bGV0ZQ== 1649
-
-        IGNhbGw= 1650
-
-        IGF0dA== 1651
-
-        IGNhbA== 1652
-
-        dmVydA== 1653
-
-        IGRlYw== 1654
-
-        bGVhc2U= 1655
-
-        b3Vu 1656
-
-        IH0pOwo= 1657
-
-        ZnI= 1658
-
-        Zm9ybWF0aW9u 1659
-
-        ZXRhaWw= 1660
-
-        IG51bQ== 1661
-
-        YWo= 1662
-
-        cXVlcnk= 1663
-
-        IHdlbGw= 1664
-
-        IG9iamVjdA== 1665
-
-        IEFz 1666
-
-        IHllYXJz 1667
-
-        Q29sb3I= 1668
-
-        SVM= 1669
-
-        IGRlZmF1bHQ= 1670
-
-        V2g= 1671
-
-        IGlucw== 1672
-
-        YWludA== 1673
-
-        IGphdmE= 1674
-
-        IHNpbQ== 1675
-
-        IEFy 1676
-
-        bW9u 1677
-
-        dGls 1678
-
-        KCk7DQo= 1679
-
-        KTo= 1680
-
-        U2V0 1681
-
-        Mjk= 1682
-
-        YXR0ZXI= 1683
-
-        IHZpZXc= 1684
-
-        IHByZXM= 1685
-
-        YXJyYXk= 1686
-
-        V2U= 1687
-
-        QXQ= 1688
-
-        IGJlbA== 1689
-
-        IG1hbnk= 1690
-
-        MjE= 1691
-
-        TWFu 1692
-
-        ZW5kZXI= 1693
-
-        IGJlaW5n 1694
-
-        IGdvb2Q= 1695
-
-        CQkJCQkJ 1696
-
-        YXRpb25hbA== 1697
-
-        d2FyZQ== 1698
-
-        LmxvZw== 1699
-
-        ew0K 1700
-
-        IHVzaW5n 1701
-
-        X0I= 1702
-
-        IDo9 1703
-
-        X3c= 1704
-
-        aXN0cw== 1705
-
-        bGlzaA== 1706
-
-        IHN0dWQ= 1707
-
-        IEFs 1708
-
-        IGd1 1709
-
-        Y29uZmln 1710
-
-        dXJpbmc= 1711
-
-        dGltZQ== 1712
-
-        b2tlbg== 1713
-
-        YW1lc3BhY2U= 1714
-
-        IHJlcXVlc3Q= 1715
-
-        IGNoaWxk 1716
-
-        IMM= 1717
-
-        bG9i 1718
-
-        IHBhcmFt 1719
-
-        IH0NCg== 1720
-
-        MDE= 1721
-
-        IGVjaG8= 1722
-
-        ZnVuY3Rpb24= 1723
-
-        KioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKio= 1724
-
-        cHM= 1725
-
-        RWxlbWVudA== 1726
-
-        YWxr 1727
-
-        bGljYXRpb24= 1728
-
-        Ynk= 1729
-
-        U2l6ZQ== 1730
-
-        cmF3aW5n 1731
-
-        IHBlcnNvbg== 1732
-
-        ICAgICAgICAgICAgICAgICA= 1733
-
-        XG4= 1734
-
-        b2JqZWN0 1735
-
-        aW5jZQ== 1736
-
-        RW4= 1737
-
-        RmlsZQ== 1738
-
-        dWY= 1739
-
-        ZmZlY3Q= 1740
-
-        QUM= 1741
-
-        IHN0eWxl 1742
-
-        c3VtbWFyeQ== 1743
-
-        IHF1ZQ== 1744
-
-        X3I= 1745
-
-        ICgk 1746
-
-        TW9kZWw= 1747
-
-        aWRlbnQ= 1748
-
-        IG1ldGhvZA== 1749
-
-        SUw= 1750
-
-        b3R0 1751
-
-        bGVzcw== 1752
-
-        SU5H 1753
-
-        ICgp 1754
-
-        IGV4cGVjdA== 1755
-
-        eW5j 1756
-
-        cGFja2FnZQ== 1757
-
-        MzU= 1758
-
-        dXJz 1759
-
-        IHByb3Q= 1760
-
-        Li8= 1761
-
-        cHJl 1762
-
-        ICkK 1763
-
-        bWE= 1764
-
-        IHN1cg== 1765
-
-        IGZvdW5k 1766
-
-        SW5mbw== 1767
-
-        cGFy 1768
-
-        aW1lcw== 1769
-
-        LmU= 1770
-
-        YWlucw== 1771
-
-        IHBvc3Q= 1772
-
-        LWQ= 1773
-
-        NDU= 1774
-
-        b2xlYW4= 1775
-
-        IHNs 1776
-
-        UEU= 1777
-
-        IHN1Y2g= 1778
-
-        c2VsZWN0 1779
-
-        YWluZXI= 1780
-
-        IHRoaW5r 1781
-
-        IGRpZmZlcg== 1782
-
-        LnI= 1783
-
-        LyoqCg== 1784
-
-        RkY= 1785
-
-        b29s 1786
-
-        cGxhdGU= 1787
-
-        cXVhbA== 1788
-
-        IEZvcg== 1789
-
-        IG11Y2g= 1790
-
-        dWM= 1791
-
-        KG5ldw== 1792
-
-        b2R1bGU= 1793
-
-        IHNvbQ== 1794
-
-        IGh0dHA= 1795
-
-        IExpc3Q= 1796
-
-        IGNvdW50 1797
-
-        IGluc3Q= 1798
-
-        Y2hhcg== 1799
-
-        bWl0 1800
-
-        Lmlk 1801
-
-        YWtpbmc= 1802
-
-        IGdlbmVy 1803
-
-        cHg= 1804
-
-        dmljZQ== 1805
-
-        Mzc= 1806
-
-        X2RhdGE= 1807
-
-        IE5VTEw= 1808
-
-        fQ0K 1809
-
-        aWRk 1810
-
-        44CC 1811
-
-        IG1lZA== 1812
-
-        b3Jn 1813
-
-        aWRlcg== 1814
-
-        YWNoZQ== 1815
-
-        d29yaw== 1816
-
-        IGNoZWNr 1817
-
-        d2Vlbg== 1818
-
-        ICgo 1819
-
-        dGhl 1820
-
-        YW50cw== 1821
-
-        Pjw= 1822
-
-        LkI= 1823
-
-        LWM= 1824
-
-        IG9wZW4= 1825
-
-        IGVzdA== 1826
-
-        ICAgICAgICAK 1827
-
-        IG5leHQ= 1828
-
-        SU0= 1829
-
-        0YI= 1830
-
-        T1Q= 1831
-
-        w7M= 1832
-
-        IGZvbGxvdw== 1833
-
-        Y29udGVudA== 1834
-
-        ICAgICAgICAgICAg 1835
-
-        IGluY2x1ZA== 1836
-
-        SEU= 1837
-
-        IFJlcw== 1838
-
-        IGhyZWY= 1839
-
-        0Lg= 1840
-
-        IGNhcg== 1841
-
-        eXBlcw== 1842
-
-        aW1hZ2U= 1843
-
-        VW4= 1844
-
-        IGJvb2w= 1845
-
-        QUQ= 1846
-
-        IGdhbWU= 1847
-
-        LkZvcm0= 1848
-
-        cm93cw== 1849
-
-        Ki8= 1850
-
-        dmVsb3A= 1851
-
-        LkRyYXdpbmc= 1852
-
-        IHBhdGg= 1853
-
-        aXNpb24= 1854
-
-        IGVhY2g= 1855
-
-        IFBs 1856
-
-        X3R5cGU= 1857
-
-        UGF0aA== 1858
-
-        bmVjdGlvbg== 1859
-
-        IGF2 1860
-
-        Jyku 1861
-
-        IHN1cHBvcnQ= 1862
-
-        RU5U 1863
-
-        cmVt 1864
-
-        Iiku 1865
-
-        IG93bg== 1866
-
-        IGNvcg== 1867
-
-        Y291bnQ= 1868
-
-        bWlzcw== 1869
-
-        dWFsbHk= 1870
-
-        IG1lbQ== 1871
-
-        c3Rk 1872
-
-        aWVuY2U= 1873
-
-        c2VhcmNo 1874
-
-        IgoK 1875
-
-        Rm9ybQ== 1876
-
-        IHNleA== 1877
-
-        ZW5hbWU= 1878
-
-        IHNpZ24= 1879
-
-        IGV0 1880
-
-        ICAgICAgICAgIA== 1881
-
-        Jywn 1882
-
-        IEFwcA== 1883
-
-        IHRob3Nl 1884
-
-        b2Zm 1885
-
-        IGVycg== 1886
-
-        IHN5c3RlbQ== 1887
-
-        IGJlc3Q= 1888
-
-        Y29kZQ== 1889
-
-        IHNhbWU= 1890
-
-        IGRp 1891
-
-        dXNz 1892
-
-        IGNyZWF0ZQ== 1893
-
-        YXRoZXI= 1894
-
-        QXJyYXk= 1895
-
-        Lmlu 1896
-
-        ZmU= 1897
-
-        U2VydmljZQ== 1898
-
-        VU4= 1899
-
-        YXRz 1900
-
-        IFo= 1901
-
-        YWx0aA== 1902
-
-        IG1hZGU= 1903
-
-        dHJ1ZQ== 1904
-
-        QUI= 1905
-
-        IG1hcms= 1906
-
-        cmlk 1907
-
-        aWZpZWQ= 1908
-
-        LA0K 1909
-
-        eW4= 1910
-
-        cHJlc3M= 1911
-
-        IGdyb3Vw 1912
-
-        IGZpbg== 1913
-
-        IExpY2Vuc2U= 1914
-
-        RmllbGQ= 1915
-
-        ZWdlcg== 1916
-
-        IHdvcmxk 1917
-
-        aW5lc3M= 1918
-
-        dHk= 1919
-
-        IHByb2Nlc3M= 1920
-
-        KGI= 1921
-
-        IGNyZQ== 1922
-
-        YXJu 1923
-
-        aXZlcw== 1924
-
-        IG1haW4= 1925
-
-        aWRlbw== 1926
-
-        MzY= 1927
-
-        X2c= 1928
-
-        QUc= 1929
-
-        dmFsaWQ= 1930
-
-        aW1n 1931
-
-        UEk= 1932
-
-        IGNvbG9y 1933
-
-        IHJlcG9ydA== 1934
-
-        IHRha2U= 1935
-
-        cmli 1936
-
-        T00= 1937
-
-        IGRheQ== 1938
-
-        UmVxdWVzdA== 1939
-
-        IHNr 1940
-
-        YmVycw== 1941
-
-        CXM= 1942
-
-        LkFkZA== 1943
-
-        b290 1944
-
-        SW1hZ2U= 1945
-
-        IGNvbXBsZQ== 1946
-
-        b2xsZWN0aW9u 1947
-
-        IHRvcA== 1948
-
-        IGZyZWU= 1949
-
-        QVM= 1950
-
-        RGU= 1951
-
-        IE9u 1952
-
-        SUc= 1953
-
-        OTA= 1954
-
-        ZXRh 1955
-
-        RGF0ZQ== 1956
-
-        IGFjdGlvbg== 1957
-
-        MzQ= 1958
-
-        T3Zlcg== 1959
-
-        aXRvcg== 1960
-
-        ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICA= 1961
-
-        bm90 1962
-
-        IGluZGV4 1963
-
-        aGVy 1964
-
-        aWNvbg== 1965
-
-        T24= 1966
-
-        Ow0KDQo= 1967
-
-        aXZpdHk= 1968
-
-        bWFuZA== 1969
-
-        LldpbmRvd3M= 1970
-
-        T0w= 1971
-
-        IHJlYWw= 1972
-
-        IG1heA== 1973
-
-        bGFuZA== 1974
-
-        Li4uLg== 1975
-
-        cmFwaA== 1976
-
-        IGJ1aWxk 1977
-
-        bGVn 1978
-
-        YXNzd29yZA== 1979
-
-        PwoK 1980
-
-        4oCm 1981
-
-        b29r 1982
-
-        dWNr 1983
-
-        IG1lc3NhZ2U= 1984
-
-        dGVzdA== 1985
-
-        aXZlcnM= 1986
-
-        Mzg= 1987
-
-        IGlucHV0 1988
-
-        IGFydA== 1989
-
-        IGJldHdlZW4= 1990
-
-        R2V0 1991
-
-        ZW50ZXI= 1992
-
-        Z3JvdW5k 1993
-
-        ZW5l 1994
-
-        w6E= 1995
-
-        Lmxlbmd0aA== 1996
-
-        Tm9kZQ== 1997
-
-        KGk= 1998
-
-        Q2xhc3M= 1999
-
-        Zm9y 2000
-
-        IOKAlA== 2001
-
-        dGVu 2002
-
-        b2lu 2003
-
-        IGtl 2004
-
-        dWk= 2005
-
-        IElO 2006
-
-        IHRhYmxl 2007
-
-        c3Vi 2008
-
-        IExl 2009
-
-        IGhlYWQ= 2010
-
-        IG11c3Q= 2011
-
-        Ly8vLy8vLy8vLy8vLy8vLw== 2012
-
-        LnV0aWw= 2013
-
-        Q29udGV4dA== 2014
-
-        IG9yZGVy 2015
-
-        IG1vdg== 2016
-
-        b3Zlcg== 2017
-
-        IGNvbnRpbg== 2018
-
-        IHNheQ== 2019
-
-        c3RhdGlj 2020
-
-        LlRleHQ= 2021
-
-        IGNsYXNzTmFtZQ== 2022
-
-        cGFueQ== 2023
-
-        IHRlcg== 2024
-
-        aGVhZA== 2025
-
-        cmc= 2026
-
-        IHByb2R1Y3Q= 2027
-
-        VGhpcw== 2028
-
-        LuKAnQ== 2029
-
-        IEJ1dA== 2030
-
-        NzA= 2031
-
-        bG95 2032
-
-        IGRvdWJsZQ== 2033
-
-        c2c= 2034
-
-        IHBsYWNl 2035
-
-        Lng= 2036
-
-        bWVzc2FnZQ== 2037
-
-        IGluZm9ybWF0aW9u 2038
-
-        cHJpdmF0ZQ== 2039
-
-        IG9wZXI= 2040
-
-        Y2Vk 2041
-
-        ZGI= 2042
-
-        Ij48Lw== 2043
-
-        UGFyYW0= 2044
-
-        aWNsZQ== 2045
-
-        IHdlZWs= 2046
-
-        IHByb3A= 2047
-
-        dGFibGU= 2048
-
-        aWRnZXQ= 2049
-
-        cGxhY2U= 2050
-
-        UHJvcA== 2051
-
-        IEFsbA== 2052
-
-        ZWxz 2053
-
-        Ym94 2054
-
-        LgoKCgo= 2055
-
-        LlI= 2056
-
-        IFRv 2057
-
-        aXRlcg== 2058
-
-        U2g= 2059
-
-        dXJhdGlvbg== 2060
-
-        b2xkZXI= 2061
-
-        X2xpc3Q= 2062
-
-        Y29tZQ== 2063
-
-        IHN3 2064
-
-        aXphdGlvbg== 2065
-
-        CWZvcg== 2066
-
-        Ymw= 2067
-
-        IHByb2dyYW0= 2068
-
-        KGU= 2069
-
-        YXBl 2070
-
-        Y2hlY2s= 2071
-
-        LkZvcm1z 2072
-
-        IHVuZA== 2073
-
-        YXRlZ29yeQ== 2074
-
-        NzU= 2075
-
-        YWdz 2076
-
-        IHJlc3BvbnNl 2077
-
-        VVM= 2078
-
-        cmVxdWVzdA== 2079
-
-        IHN0cnVjdA== 2080
-
-        ZXNjcmlwdGlvbg== 2081
-
-        IGNvZGU= 2082
-
-        X0g= 2083
-
-        dWZmZXI= 2084
-
-        IHdpdGhvdXQ= 2085
-
-        bG9iYWw= 2086
-
-        TWFuYWdlcg== 2087
-
-        aWx0ZXI= 2088
-
-        UE8= 2089
-
-        CXRoaXM= 2090
-
-        b3B0aW9u 2091
-
-        IHNvbA== 2092
-
-        ID09PQ== 2093
-
-        YWtlcw== 2094
-
-        Q29udHJvbGxlcg== 2095
-
-        NDQ= 2096
-
-        TWVzc2FnZQ== 2097
-
-        IHJlZg== 2098
-
-        ZXZlcg== 2099
-
-        IFNv 2100
-
-        YWluaW5n 2101
-
-        LmFwcGVuZA== 2102
-
-        IHN0aWxs 2103
-
-        IHByb3ZpZA== 2104
-
-        IGFzc2VydA== 2105
-
-        bWVk 2106
-
-        IGNhcA== 2107
-
-        dXNpbmVzcw== 2108
-
-        IHJlcA== 2109
-
-        dGluZ3M= 2110
-
-        dmVk 2111
-
-        Lk4= 2112
-
-        YXBp 2113
-
-        T0Q= 2114
-
-        IGZpZWxk 2115
-
-        aXZlbg== 2116
-
-        b3Rv 2117
-
-        4oCc 2118
-
-        Y29s 2119
-
-        KHg= 2120
-
-        Z2h0 2121
-
-        UmVzdWx0 2122
-
-        Q29kZQ== 2123
-
-        Lmlz 2124
-
-        bGluaw== 2125
-
-        IGNvdXI= 2126
-
-        QW4= 2127
-
-        IHRlYW0= 2128
-
-        CWludA== 2129
-
-        aWZ0 2130
-
-        NTU= 2131
-
-        IHNlY29uZA== 2132
-
-        IGdvaW5n 2133
-
-        IHJhbmdl 2134
-
-        X0U= 2135
-
-        bmVzcw== 2136
-
-        Mzk= 2137
-
-        IGZhbQ== 2138
-
-        IG5pbA== 2139
-
-        IENvbnQ= 2140
-
-        YWlsYWJsZQ== 2141
-
-        dXRlcw== 2142
-
-        YXRhYg== 2143
-
-        IGZhY3Q= 2144
-
-        IHZpcw== 2145
-
-        KCY= 2146
-
-        IEFO 2147
-
-        MzE= 2148
-
-        QWw= 2149
-
-        dGl0bGU= 2150
-
-        IGFuZHJvaWQ= 2151
-
-        Q0U= 2152
-
-        XCI= 2153
-
-        aXJ0 2154
-
-        IHdyaXQ= 2155
-
-        0L0= 2156
-
-        CW0= 2157
-
-        ZnR3YXJl 2158
-
-        b25k 2159
-
-        IHJldA== 2160
-
-        b3NpdGlvbg== 2161
-
-        IGhvbWU= 2162
-
-        IGxlZnQ= 2163
-
-        YXJncw== 2164
-
-        bWVyaWM= 2165
-
-        NDg= 2166
-
-        IGRpcmVjdA== 2167
-
-        b2Np 2168
-
-        UGw= 2169
-
-        QXM= 2170
-
-        cmV0 2171
-
-        YWRv 2172
-
-        T2Y= 2173
-
-        Y2hu 2174
-
-        IEdldA== 2175
-
-        ZWU= 2176
-
-        cm9zcw== 2177
-
-        KCk7 2178
-
-        X19fXw== 2179
-
-        LnBo 2180
-
-        SXQ= 2181
-
-        b3V0ZQ== 2182
-
-        IGV4cGVy 2183
-
-        Y2hvb2w= 2184
-
-        d3d3 2185
-
-        fSw= 2186
-
-        IGFsbG93 2187
-
-        IMI= 2188
-
-        KCkp 2189
-
-        c2l6ZQ== 2190
-
-        aXNt 2191
-
-        YWk= 2192
-
-        dHJhY3Q= 2193
-
-        YW5l 2194
-
-        Li4uCgo= 2195
-
-        Y29udGV4dA== 2196
-
-        IGJlZw== 2197
-
-        Q0g= 2198
-
-        IHBhZ2U= 2199
-
-        aGlw 2200
-
-        bm8= 2201
-
-        Y29yZQ== 2202
-
-        c3A= 2203
-
-        IGRpZmZlcmVudA== 2204
-
-        aWFibGU= 2205
-
-        IE1l 2206
-
-        X0lO 2207
-
-        YnV0dG9u 2208
-
-        IElz 2209
-
-        ZXJ2aWNlcw== 2210
-
-        IGNh 2211
-
-        IGFyb3VuZA== 2212
-
-        QXBw 2213
-
-        cmF0aW9u 2214
-
-        IHJlY2U= 2215
-
-        IHJlYWxseQ== 2216
-
-        IGltYWdl 2217
-
-        IHRhcmdldA== 2218
-
-        IGRlcA== 2219
-
-        b3B5cmlnaHQ= 2220
-
-        dHJh 2221
-
-        aW5nbGU= 2222
-
-        aXRhbA== 2223
-
-        TGF5b3V0 2224
-
-        IGJvdGg= 2225
-
-        T3ZlcnJpZGU= 2226
-
-        YXJt 2227
-
-        PT4= 2228
-
-        YXRlcmlhbA== 2229
-
-        aWxlZA== 2230
-
-        IHB1dA== 2231
-
-        UXU= 2232
-
-        0YA= 2233
-
-        dW5n 2234
-
-        bWFw 2235
-
-        CQkJCQkJCQk= 2236
-
-        IGxldmVs 2237
-
-        Q29tcG9uZW50 2238
-
-        Ym9vaw== 2239
-
-        Y3JlZW4= 2240
-
-        X1JF 2241
-
-        IGNvbmZpZw== 2242
-
-        44E= 2243
-
-        T3I= 2244
-
-        LmRhdGE= 2245
-
-        IGRvY3VtZW50 2246
-
-        Iiwi 2247
-
-        dHJpYnV0ZQ== 2248
-
-        dXg= 2249
-
-        TG9n 2250
-
-        ZmVyZW5jZQ== 2251
-
-        cG9zdA== 2252
-
-        X2U= 2253
-
-        IGxvY2Fs 2254
-
-        YW5kb20= 2255
-
-        YXNzZXJ0 2256
-
-        VmFs 2257
-
-        bGVjdGVk 2258
-
-        aW5h 2259
-
-        YXRhYmFzZQ== 2260
-
-        QWRk 2261
-
-        IGNvbnRlbnQ= 2262
-
-        LnByaW50 2263
-
-        c2lnbmVk 2264
-
-        cmlj 2265
-
-        LiIKCg== 2266
-
-        IGZh 2267
-
-        IQoK 2268
-
-        LWY= 2269
-
-        aXZlZA== 2270
-
-        IHF1ZXN0 2271
-
-        LmV4 2272
-
-        IGZsb2F0 2273
-
-        IGRldmVsb3A= 2274
-
-        0L7Q 2275
-
-        TWFw 2276
-
-        YWRpbmc= 2277
-
-        IHBvc3M= 2278
-
-        VUU= 2279
-
-        bmFtZXNwYWNl 2280
-
-        X08= 2281
-
-        CWI= 2282
-
-        LkdldA== 2283
-
-        Pig= 2284
-
-        anNvbg== 2285
-
-        ZXRhaWxz 2286
-
-        NjY= 2287
-
-        IHRvbw== 2288
-
-        IGV4dGVuZHM= 2289
-
-        IE5vbmU= 2290
-
-        IGZvcmU= 2291
-
-        KFN0cmluZw== 2292
-
-        Zm9ybWF0 2293
-
-        IGdyZWF0 2294
-
-        aW50ZXI= 2295
-
-        Y2FsZQ== 2296
-
-        0YE= 2297
-
-        cm9u 2298
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-
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-
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-
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-
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-
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-
-        bGluZw== 2785
-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
-        SVI= 2871
-
-        IHJvdw== 2872
-
-        IHNlZW0= 2873
-
-        IHE= 2874
-
-        IHNob3J0 2875
-
-        IE5vdA== 2876
-
-        aXBw 2877
-
-        R3JvdXA= 2878
-
-        c2VjdGlvbg== 2879
-
-        bWF4 2880
-
-        aXJs 2881
-
-        IG92ZXJyaWRl 2882
-
-        IGNvbXBhbnk= 2883
-
-        IGRvbmU= 2884
-
-        Iik7DQo= 2885
-
-        IGdyZQ== 2886
-
-        LlJl 2887
-
-        IGJlbGll 2888
-
-        cmlzdA== 2889
-
-        IGhlYWx0aA== 2890
-
-        QU5U 2891
-
-        KCkKCg== 2892
-
-        IEJl 2893
-
-        LnZhbHVl 2894
-
-        IEdy 2895
-
-        b3R0b20= 2896
-
-        IGFyZ3M= 2897
-
-        UFQ= 2898
-
-        c3RhdHVz 2899
-
-        ZnVuYw== 2900
-
-        dW1lbnRz 2901
-
-        LWg= 2902
-
-        TnVtYmVy 2903
-
-        Og0K 2904
-
-        IExvZw== 2905
-
-        ZXJ2ZXI= 2906
-
-        ICksCg== 2907
-
-        YW1lbnQ= 2908
-
-        IG9iag== 2909
-
-        aW5j 2910
-
-        IGNoaWxkcmVu 2911
-
-        aWN5 2912
-
-        SVo= 2913
-
-        YW5kcw== 2914
-
-        YWJseQ== 2915
-
-        IGRpc3RyaWI= 2916
-
-        IGN1cg== 2917
-
-        ZXJpYWw= 2918
-
-        IGRheXM= 2919
-
-        cmVhdGVk 2920
-
-        cmVjdA== 2921
-
-        LWw= 2922
-
-        aXJt 2923
-
-        aWRkZW4= 2924
-
-        b21i 2925
-
-        IGluaXRpYWw= 2926
-
-        Lmpz 2927
-
-        IOI= 2928
-
-        UXVlcnk= 2929
-
-        IG9ubGluZQ== 2930
-
-        aW1hbA== 2931
-
-        LmNvbg== 2932
-
-        YXU= 2933
-
-        VXJs 2934
-
-        Y29udHJvbA== 2935
-
-        aXJlY3Rpb24= 2936
-
-        IGluc3RhbmNl 2937
-
-        T1JU 2938
-
-        IEZy 2939
-
-        d2hlcmU= 2940
-
-        IGphdmF4 2941
-
-        IG9yZ2Fu 2942
-
-        YXB0ZXI= 2943
-
-        IHJlYXNvbg== 2944
-
-        b3B0aW9ucw== 2945
-
-        NTk= 2946
-
-        IE1hcg== 2947
-
-        KGE= 2948
-
-        IHdpdGhpbg== 2949
-
-        LuKAnQoK 2950
-
-        T0RF 2951
-
-        X0RF 2952
-
-        YWRtaW4= 2953
-
-        ZW5kZWQ= 2954
-
-        IGRlc2lnbg== 2955
-
-        IERhdGE= 2956
-
-        dW5l 2957
-
-        IEZpbGU= 2958
-
-        cm9vdA== 2959
-
-        IGNlbnQ= 2960
-
-        IGFycg== 2961
-
-        X2FkZA== 2962
-
-        bGVu 2963
-
-        cGFnZQ== 2964
-
-        LCc= 2965
-
-        X3N0cg== 2966
-
-        IGJybw== 2967
-
-        YWJpbGl0eQ== 2968
-
-        b3V0aA== 2969
-
-        NTg= 2970
-
-        L2M= 2971
-
-        cG9zZQ== 2972
-
-        aXJ0dWFs 2973
-
-        ZWFyY2g= 2974
-
-        X3VybA== 2975
-
-        YXJnaW4= 2976
-
-        SHR0cA== 2977
-
-        IHNjaG9vbA== 2978
-
-        YXZh 2979
-
-        IGNvbnNpZGVy 2980
-
-        LmxhYmVs 2981
-
-        IEFycmF5 2982
-
-        NDI= 2983
-
-        d2Vi 2984
-
-        b3B0 2985
-
-        LnByaW50bG4= 2986
-
-        dWxhdGlvbg== 2987
-
-        IGZ1bmM= 2988
-
-        UEw= 2989
-
-        ICJc 2990
-
-        IFRleHQ= 2991
-
-        YWN0b3J5 2992
-
-        KGZ1bmN0aW9u 2993
-
-        bnVsbA== 2994
-
-        IGVuZw== 2995
-
-        ZG93bg== 2996
-
-        IGluY2x1ZGU= 2997
-
-        IEVu 2998
-
-        IERy 2999
-
-        IGRi 3000
-
-        ISE= 3001
-
-        c2lkZQ== 3002
-
-        IGluaXQ= 3003
-
-        cXVpcmVk 3004
-
-        IFNoZQ== 3005
-
-        Q29sdW1u 3006
-
-        cmVhY3Q= 3007
-
-        IGFubg== 3008
-
-        IHN0b3A= 3009
-
-        IGxhdGVy 3010
-
-        IFRoYXQ= 3011
-
-        ZW50aW9u 3012
-
-        ZGY= 3013
-
-        VUc= 3014
-
-        SUxF 3015
-
-        IGNsaWVudA== 3016
-
-        cmFmdA== 3017
-
-        ZmZlcg== 3018
-
-        UE9TVA== 3019
-
-        ZWxwZXI= 3020
-
-        IGxvdmU= 3021
-
-        cXVvdGU= 3022
-
-        b3Vk 3023
-
-        IGpzb24= 3024
-
-        IGFibGU= 3025
-
-        IG1lbg== 3026
-
-        QVg= 3027
-
-        IENvcHlyaWdodA== 3028
-
-        w7Y= 3029
-
-        YXZpZw== 3030
-
-        cmVx 3031
-
-        Q2xpZW50 3032
-
-        fSk7Cg== 3033
-
-        LkNvbQ== 3034
-
-        ZXJj 3035
-
-        aWx0 3036
-
-        cGVjaWFs 3037
-
-        X2NvbQ== 3038
-
-        cm9vbQ== 3039
-
-        Lk5hbWU= 3040
-
-        IGdpdmU= 3041
-
-        YW1i 3042
-
-        aWtl 3043
-
-        IGNvbmRpdGlvbg== 3044
-
-        Y2xpZW50 3045
-
-        YXRvcnM= 3046
-
-        OiI= 3047
-
-        IGNvcHk= 3048
-
-        dXR1cmU= 3049
-
-        aXZlcnNpdHk= 3050
-
-        ZXJuYWw= 3051
-
-        e3s= 3052
-
-        IENhbg== 3053
-
-        b3VuYw== 3054
-
-        ZG8= 3055
-
-        IG9jYw== 3056
-
-        IGFwcHJv 3057
-
-        dGhlcnM= 3058
-
-        emU= 3059
-
-        IGVpdGhlcg== 3060
-
-        IEZs 3061
-
-        IGltcG9ydGFudA== 3062
-
-        IGxlYWQ= 3063
-
-        YXR0cg== 3064
-
-        QVJU 3065
-
-        RXF1YWw= 3066
-
-        IGRh 3067
-
-        ZXRjaA== 3068
-
-        ZW50aXR5 3069
-
-        IGZhbWlseQ== 3070
-
-        YWRkaW5n 3071
-
-        IG9wdGlvbg== 3072
-
-        IGV4aXN0 3073
-
-        aWNh 3074
-
-        IE9iamVjdA== 3075
-
-        Njk= 3076
-
-        J3Zl 3077
-
-        dmVycw== 3078
-
-        aXRpb25hbA== 3079
-
-        Njc= 3080
-
-        b3V0cHV0 3081
-
-        IFRydWU= 3082
-
-        IE9G 3083
-
-        X3RpbWU= 3084
-
-        IG9mZmVy 3085
-
-        IH0pOwoK 3086
-
-        SEVS 3087
-
-        ZWdpbg== 3088
-
-        IiI= 3089
-
-        IHdhdGVy 3090
-
-        IGNoZQ== 3091
-
-        IE15 3092
-
-        b3JlZA== 3093
-
-        IHN0ZXA= 3094
-
-        YW5jZXM= 3095
-
-        Q0s= 3096
-
-        QVk= 3097
-
-        4Lg= 3098
-
-        c3RydWN0aW9u 3099
-
-        KEM= 3100
-
-        MzAw 3101
-
-        b3VjaA== 3102
-
-        U3RyZWFt 3103
-
-        YWN0aXZl 3104
-
-        YW1h 3105
-
-        RW50aXR5 3106
-
-        cHJvZHVjdA== 3107
-
-        KCl7Cg== 3108
-
-        IGdvdmVybm1lbnQ= 3109
-
-        IElE 3110
-
-        YWpvcg== 3111
-
-        QW5k 3112
-
-        IGRpc3BsYXk= 3113
-
-        0Ls= 3114
-
-        IHRpbWVz 3115
-
-        IGZvdXI= 3116
-
-        IGZhcg== 3117
-
-        IHByZXNlbnQ= 3118
-
-        IE5T 3119
-
-        IFwK 3120
-
-        dWVzdA== 3121
-
-        IGJhcw== 3122
-
-        ZWNobw== 3123
-
-        Y2hpbGQ= 3124
-
-        aWZpZXI= 3125
-
-        SGFuZGxlcg== 3126
-
-        IGxpYg== 3127
-
-        UHJvcGVydHk= 3128
-
-        dHJhbnNsYXRpb24= 3129
-
-        IHJvb20= 3130
-
-        IG9uY2U= 3131
-
-        IFtd 3132
-
-        Y2VudGVy 3133
-
-        PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0= 3134
-
-        IHJlc3VsdHM= 3135
-
-        IGNvbnRpbnVl 3136
-
-        IHRhbGs= 3137
-
-        X2dldA== 3138
-
-        IGdyb3c= 3139
-
-        LnN3 3140
-
-        ZWI= 3141
-
-        IFB1YmxpYw== 3142
-
-        T1A= 3143
-
-        ZWN1dGU= 3144
-
-        b2xz 3145
-
-        ICoq 3146
-
-        Iik7Cgo= 3147
-
-        IG1hc3M= 3148
-
-        dXJlZA== 3149
-
-        LmNsYXNz 3150
-
-        b21pYw== 3151
-
-        IG1lYW4= 3152
-
-        aXBz 3153
-
-        IGF1dA== 3154
-
-        KTsNCg0K 3155
-
-        IHVudGls 3156
-
-        IG1hcmtldA== 3157
-
-        IGFyZWE= 3158
-
-        dWl0 3159
-
-        IGxlbmd0aA== 3160
-
-        IFdpdGg= 3161
-
-        c3RydWN0b3I= 3162
-
-        ZXZlbnQ= 3163
-
-        Ij48 3164
-
-        IFNw 3165
-
-        SVY= 3166
-
-        IG11cw== 3167
-
-        aWZm 3168
-
-        IGtpbmQ= 3169
-
-        YXV0aG9y 3170
-
-        b3VuZHM= 3171
-
-        bWI= 3172
-
-        X2tleQ== 3173
-
-        NDE= 3174
-
-        d2lkdGg= 3175
-
-        cG9zaXRvcnk= 3176
-
-        IGxpZ2h0 3177
-
-        dWs= 3178
-
-        Um93 3179
-
-        b2hu 3180
-
-        YWxm 3181
-
-        dmlyb25tZW50 3182
-
-        YXBwZXI= 3183
-
-        b2xsZWN0aW9ucw== 3184
-
-        IHNpZGU= 3185
-
-        X2luZm8= 3186
-
-        IGV4YW1wbGU= 3187
-
-        aW1hcnk= 3188
-
-        IHdy 3189
-
-        IGNhbXA= 3190
-
-        Y3JpYmU= 3191
-
-        MjU1 3192
-
-        Ii8= 3193
-
-        IG1pc3M= 3194
-
-        d2F5 3195
-
-        IGJhc2Vk 3196
-
-        IHBsYW4= 3197
-
-        Vmlz 3198
-
-        b21haW4= 3199
-
-        dW5r 3200
-
-        IGF3YXk= 3201
-
-        VVA= 3202
-
-        PFQ= 3203
-
-        T1M= 3204
-
-        aW9k 3205
-
-        IE1vbg== 3206
-
-        4oCZcmU= 3207
-
-        IGxpaw== 3208
-
-        w6c= 3209
-
-        aXZlbHk= 3210
-
-        LnY= 3211
-
-        aW1lcg== 3212
-
-        aXplcg== 3213
-
-        U3Vi 3214
-
-        IGJ1dHRvbg== 3215
-
-        IFVw 3216
-
-        IGV4cGVyaWVuY2U= 3217
-
-        Q0w= 3218
-
-        IHJlbmRlcg== 3219
-
-        X3ZhbHVl 3220
-
-        IG5lYXI= 3221
-
-        VVJM 3222
-
-        YWx0 3223
-
-        IGNvdW50cnk= 3224
-
-        aWJpbGl0eQ== 3225
-
-        NTc= 3226
-
-        KCksCg== 3227
-
-        ZWFk 3228
-
-        IGF1dGhvcg== 3229
-
-        IHNwZWNpZmlj 3230
-
-        YmFzZQ== 3231
-
-        KG5hbWU= 3232
-
-        b25lcw== 3233
-
-        IERv 3234
-
-        IGFsb25n 3235
-
-        eWVhcg== 3236
-
-        IGV4cHJlc3M= 3237
-
-        Lic= 3238
-
-        ZW52 3239
-
-        IGJlZ2lu 3240
-
-        IHNvZnR3YXJl 3241
-
-        IGltcA== 3242
-
-        IHdpbg== 3243
-
-        w7Nu 3244
-
-        IHRoaW5n 3245
-
-        VHJhbnM= 3246
-
-        IFRIRQ== 3247
-
-        IDw/ 3248
-
-        IHdoeQ== 3249
-
-        IGRvZXNu 3250
-
-        aWo= 3251
-
-        Z2luZw== 3252
-
-        CWc= 3253
-
-        IHNpbmdsZQ== 3254
-
-        b2Zmc2V0 3255
-
-        YXJuaW5n 3256
-
-        b2dyYXBo 3257
-
-        bGV5 3258
-
-        X2NvdW50 3259
-
-        IGFuYWw= 3260
-
-        Y3JlYXRl 3261
-
-        L20= 3262
-
-        IFJlZw== 3263
-
-        OTg= 3264
-
-        dW5jaA== 3265
-
-        PSQ= 3266
-
-        aXNr 3267
-
-        IHJpZ2h0cw== 3268
-
-        KE0= 3269
-
-        ICIiIgo= 3270
-
-        YXBlcg== 3271
-
-        Lm1vZGVs 3272
-
-        IHBv 3273
-
-        ZW1wdHk= 3274
-
-        YXJ0bWVudA== 3275
-
-        IGFudA== 3276
-
-        IFdoZW4= 3277
-
-        IHdvbWVu 3278
-
-        IEVk 3279
-
-        IHNlYXNvbg== 3280
-
-        IGRlc3Q= 3281
-
-        w6M= 3282
-
-        KGg= 3283
-
-        IHBvc3NpYmxl 3284
-
-        IHNldmVy 3285
-
-        IGJ0bg== 3286
-
-        IGRpZG4= 3287
-
-        IHNlbnQ= 3288
-
-        IGVuYw== 3289
-
-        IGNvbW1hbmQ= 3290
-
-        IF0sCg== 3291
-
-        X3g= 3292
-
-        IHJlY2VudA== 3293
-
-        b2x1dGlvbg== 3294
-
-        dmVjdG9y 3295
-
-        IEJ5 3296
-
-        IE1heQ== 3297
-
-        IEFjdA== 3298
-
-        u78= 3299
-
-        IG1vbmV5 3300
-
-        SU5U 3301
-
-        YnNpdGU= 3302
-
-        CXA= 3303
-
-        Lg0K 3304
-
-        77u/ 3305
-
-        c2w= 3306
-
-        YXR0ZXJu 3307
-
-        IENsYXNz 3308
-
-        IHRvbGQ= 3309
-
-        dWRpbw== 3310
-
-        Y3VycmVudA== 3311
-
-        IGVxdQ== 3312
-
-        IGF1dG8= 3313
-
-        IFN0YXRl 3314
-
-        ZGE= 3315
-
-        bXNn 3316
-
-        KSk7Cgo= 3317
-
-        IHdvcmtpbmc= 3318
-
-        IHF1ZXJ5 3319
-
-        IEJy 3320
-
-        IHdpbmRvdw== 3321
-
-        YXV0aA== 3322
-
-        b25seQ== 3323
-
-        CXQ= 3324
-
-        IGxlYXN0 3325
-
-        YWdu 3326
-
-        IGV4cGw= 3327
-
-        aXR0ZXI= 3328
-
-        YXJpbmc= 3329
-
-        IGNvbHVtbg== 3330
-
-        IEdlbmVyYWw= 3331
-
-        Ijoi 3332
-
-        ZXJhbA== 3333
-
-        cmlvcg== 3334
-
-        IHJlY29yZA== 3335
-
-        SUI= 3336
-
-        RVg= 3337
-
-        IGRhdA== 3338
-
-        IG1ha2luZw== 3339
-
-        dWVk 3340
-
-        IENhcg== 3341
-
-        ZW1w 3342
-
-        Ii4= 3343
-
-        IE1lZA== 3344
-
-        IGNsb3Nl 3345
-
-        IHBlcmNlbnQ= 3346
-
-        IHBhc3Q= 3347
-
-        KGc= 3348
-
-        Oig= 3349
-
-        IHdyaXRl 3350
-
-        IG1vdmU= 3351
-
-        IHBhdA== 3352
-
-        Q29udHJvbA== 3353
-
-        LlRv 3354
-
-        IHZp 3355
-
-        Ki8K 3356
-
-        aW5hdGU= 3357
-
-        J2xs 3358
-
-        YWdlZA== 3359
-
-        TnVsbA== 3360
-
-        IHNwZWNpYWw= 3361
-
-        SVpF 3362
-
-        IGNpdHk= 3363
-
-        LyoK 3364
-
-        IEVuZw== 3365
-
-        aXhlZA== 3366
-
-        aW5hcnk= 3367
-
-        cHk= 3368
-
-        IGVmZg== 3369
-
-        YXJpbw== 3370
-
-        IHRlbGw= 3371
-
-        YXZvcg== 3372
-
-        IHNlbGVjdA== 3373
-
-        bGV2ZWw= 3374
-
-        aW11bQ== 3375
-
-        b3Blcg== 3376
-
-        QnVpbGRlcg== 3377
-
-        SVA= 3378
-
-        JyksCg== 3379
-
-        ZXNj 3380
-
-        IGZvbnQ= 3381
-
-        IjsKCg== 3382
-
-        IEFt 3383
-
-        aXNoZWQ= 3384
-
-        aWxscw== 3385
-
-        SW50ZXI= 3386
-
-        T1c= 3387
-
-        IGNvdXJzZQ== 3388
-
-        IGxhdGU= 3389
-
-        aWRkbGU= 3390
-
-        NDM= 3391
-
-        IGFtb3VudA== 3392
-
-        IGFzeW5j 3393
-
-        aW5v 3394
-
-        Y3Vs 3395
-
-        IOw= 3396
-
-        YW5kbGU= 3397
-
-        X3VzZXI= 3398
-
-        IGJlbg== 3399
-
-        IENhbA== 3400
-
-        ICRf 3401
-
-        IFJlcA== 3402
-
-        IGVub3VnaA== 3403
-
-        VG9rZW4= 3404
-
-        LnVzZXI= 3405
-
-        KGo= 3406
-
-        U2M= 3407
-
-        V2lkdGg= 3408
-
-        bm93 3409
-
-        YXRmb3Jt 3410
-
-        IGxvb2tpbmc= 3411
-
-        IGhvbGQ= 3412
-
-        TW9kdWxl 3413
-
-        SVRZ 3414
-
-        dm8= 3415
-
-        aXNvbg== 3416
-
-        LkRhdGE= 3417
-
-        eWM= 3418
-
-        IHBvdA== 3419
-
-        IFRydW1w 3420
-
-        aWR1YWw= 3421
-
-        aWRlcw== 3422
-
-        cnQ= 3423
-
-        IHByb3BlcnR5 3424
-
-        ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIA== 3425
-
-        YW1ld29yaw== 3426
-
-        Z28= 3427
-
-        IGxvdw== 3428
-
-        IHBhcmE= 3429
-
-        IHByaWNl 3430
-
-        dXJ5 3431
-
-        IHRvZGF5 3432
-
-        cm95 3433
-
-        ICcv 3434
-
-        IHBvbGl0 3435
-
-        ICcn 3436
-
-        eW1i 3437
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-        IGRlbg== 3453
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-
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-        b2N1cw== 3466
-
-        IERF 3467
-
-        IE1pbg== 3468
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-        IG9wdA== 3469
-
-        CWJyZWFr 3470
-
-        dW1lcg== 3471
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-
-        dGhlbg== 3473
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-        IGRldA== 3474
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-        IHJldmlldw== 3477
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-        KCcv 3478
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-        bW92ZQ== 3479
-
-        IHN3aXRjaA== 3480
-
-        RVJU 3481
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-        cGF0Y2g= 3482
-
-        YW5ub3Q= 3483
-
-        44I= 3484
-
-        IGFib3Zl 3485
-
-        aXRpdmU= 3486
-
-        NTY= 3487
-
-        IHF1ZXN0aW9u 3488
-
-        IFF1 3489
-
-        44CCCgo= 3490
-
-        Z2xl 3491
-
-        IHdvcmQ= 3492
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-        IHByb3ZpZGU= 3493
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-        IFJldHVybg== 3494
-
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-
-        dXN0cg== 3497
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-        IHB1Ymxpc2g= 3498
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-
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-        IENPTg== 3501
-
-        LWlu 3502
-
-        YWxsYmFjaw== 3503
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-        IGNvdmVy 3504
-
-        XFw= 3505
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-        Y29sb3I= 3506
-
-        IElT 3507
-
-        IHdoZXRoZXI= 3508
-
-        aW1hdGU= 3509
-
-        aXNj 3510
-
-        QmFy 3511
-
-        IGRpdg== 3512
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-        QmU= 3513
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-        b3Vybg== 3514
-
-        IGhhdmluZw== 3515
-
-        bGVt 3516
-
-        cGxheWVy 3517
-
-        YWJz 3518
-
-        YW1lcmE= 3519
-
-        bmV5 3520
-
-        IGV4Yw== 3521
-
-        Z2V0aGVy 3522
-
-        cGxpZWQ= 3523
-
-        YW8= 3524
-
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-
-        ICsr 3526
-
-        aXBl 3527
-
-        c2hvdw== 3528
-
-        L2Q= 3529
-
-        Wzo= 3530
-
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-
-        bGV2 3532
-
-        X0lE 3533
-
-        OTc= 3534
-
-        cmFyeQ== 3535
-
-        YWRlcw== 3536
-
-        X3Nl 3537
-
-        YXVzZQ== 3538
-
-        IGVtcGxveQ== 3539
-
-        ICovDQo= 3540
-
-        IGZyZQ== 3541
-
-        ICdA 3542
-
-        IGNvbXBsZXQ= 3543
-
-        IGxhcmdl 3544
-
-        cmFs 3545
-
-        XHg= 3546
-
-        IGZhYw== 3547
-
-        PFN0cmluZw== 3548
-
-        IGNyZWF0ZWQ= 3549
-
-        dXBlcg== 3550
-
-        LnN0YXRl 3551
-
-        IGhvc3Q= 3552
-
-        ZW5lcmlj 3553
-
-        L2I= 3554
-
-        KCE= 3555
-
-        d2hpbGU= 3556
-
-        aWFz 3557
-
-        QlVH 3558
-
-        ICk7Cgo= 3559
-
-        IHJvbGU= 3560
-
-        UmVn 3561
-
-        IENvbG9y 3562
-
-        U3RhcnQ= 3563
-
-        IHBvcm4= 3564
-
-        dG9w 3565
-
-        IHdlYg== 3566
-
-        IGRldg== 3567
-
-        IGRlYWw= 3568
-
-        KyspCg== 3569
-
-        SW50ZWdlcg== 3570
-
-        cG9zaXRpb24= 3571
-
-        Lm9u 3572
-
-        ICgi 3573
-
-        5Lg= 3574
-
-        IHByb2JsZW0= 3575
-
-        c3Y= 3576
-
-        IHByZXNz 3577
-
-        QUJMRQ== 3578
-
-        QVRJT04= 3579
-
-        IFNlZQ== 3580
-
-        YW5jaA== 3581
-
-        IHRob3VnaA== 3582
-
-        bGVlcA== 3583
-
-        IDwhLS0= 3584
-
-        IHBvaW50cw== 3585
-
-        ICAgICAgICAgICAgICAgICAgICAgICAgIA== 3586
-
-        Lko= 3587
-
-        IDo6 3588
-
-        cHRy 3589
-
-        REI= 3590
-
-        Kys7Cg== 3591
-
-        LnBuZw== 3592
-
-        bm9kZQ== 3593
-
-        c29mdA== 3594
-
-        cG9uZA== 3595
-
-        IGV2ZXI= 3596
-
-        LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLQ==
-        3597
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-        TWVudQ== 3598
-
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-
-        IHNlcnZpY2Vz 3600
-
-        cGc= 3601
-
-        fSkK 3602
-
-        cGFyYW1z 3603
-
-        IGFjdHVhbGx5 3604
-
-        ICIv 3605
-
-        RW1wdHk= 3606
-
-        TWV0aG9k 3607
-
-        IGlkZW50 3608
-
-        dW5pYw== 3609
-
-        IG1pbGxpb24= 3610
-
-        IGFmZg== 3611
-
-        c3R5bGU= 3612
-
-        IGNvbmM= 3613
-
-        aW9z 3614
-
-        aWdubWVudA== 3615
-
-        VUxU 3616
-
-        UHI= 3617
-
-        IjsNCg== 3618
-
-        IHVuZGVyc3RhbmQ= 3619
-
-        dWFyeQ== 3620
-
-        IGhhcHBlbg== 3621
-
-        IHNlcnZlcg== 3622
-
-        IENv 3623
-
-        U0M= 3624
-
-        IGxlcw== 3625
-
-        IGZpbGVz 3626
-
-        R3JpZA== 3627
-
-        c3Fs 3628
-
-        IG9mdGVu 3629
-
-        IGluZm8= 3630
-
-        X3Ry 3631
-
-        c3Jj 3632
-
-        b255 3633
-
-        IHNwYWNl 3634
-
-        dW1i 3635
-
-        IHBhc3N3b3Jk 3636
-
-        IHN0b3Jl 3637
-
-        LAoK 3638
-
-        IFdoYXQ= 3639
-
-        Z2Vk 3640
-
-        IEZhbHNl 3641
-
-        VXM= 3642
-
-        c3dlcg== 3643
-
-        X2luZGV4 3644
-
-        IGZvcm1hdA== 3645
-
-        bW9zdA== 3646
-
-        c20= 3647
-
-        TmV3 3648
-
-        IGRldGFpbHM= 3649
-
-        IHByb2I= 3650
-
-        IEFORA== 3651
-
-        KCkNCg== 3652
-
-        aWxhcg== 3653
-
-        ICR7 3654
-
-        cnlwdA== 3655
-
-        LkNvbGxlY3Rpb25z 3656
-
-        JHRoaXM= 3657
-
-        IEZyZWU= 3658
-
-        X29m 3659
-
-        KGZhbHNl 3660
-
-        ZGF0ZWQ= 3661
-
-        ID4+ 3662
-
-        IGZhY2U= 3663
-
-        Q1RJT04= 3664
-
-        IHNhdmU= 3665
-
-        IHR5cA== 3666
-
-        ZGV2 3667
-
-        KCIj 3668
-
-        QUdF 3669
-
-        Y29udGFpbmVy 3670
-
-        ZWRpdA== 3671
-
-        UUw= 3672
-
-        IGl0ZW1z 3673
-
-        IHNvY2lhbA== 3674
-
-        aWVu 3675
-
-        IFJlYWN0 3676
-
-        KS4KCg== 3677
-
-        IG1hcg== 3678
-
-        IHJlZHU= 3679
-
-        IFJF 3680
-
-        LnB1dA== 3681
-
-        IG1ham9y 3682
-
-        Q2VsbA== 3683
-
-        bmV4dA== 3684
-
-        IGV4cGVjdGVk 3685
-
-        IHlldA== 3686
-
-        IGluZGl2 3687
-
-        dHJpYnV0ZXM= 3688
-
-        YXRpcw== 3689
-
-        YW1lZA== 3690
-
-        IGZvb2Q= 3691
-
-        U291cmNl 3692
-
-        KHN0cmluZw== 3693
-
-        ICsK 3694
-
-        aXRlcw== 3695
-
-        ZHI= 3696
-
-        IG1lbWJlcnM= 3697
-
-        IGNvbWI= 3698
-
-        aXRlbXM= 3699
-
-        IFBlcg== 3700
-
-        VEg= 3701
-
-        PVRydWU= 3702
-
-        IGJhcg== 3703
-
-        X1NF 3704
-
-        Y29tbQ== 3705
-
-        KHc= 3706
-
-        KQoKCg== 3707
-
-        IHNlbmQ= 3708
-
-        IGluYw== 3709
-
-        dW5zaWduZWQ= 3710
-
-        RkE= 3711
-
-        IHBhcmFtcw== 3712
-
-        YXBwaW5n 3713
-
-        cm9z 3714
-
-        dWdpbg== 3715
-
-        ZmE= 3716
-
-        IGNvbm5lY3Rpb24= 3717
-
-        IH07Cgo= 3718
-
-        IGJlY29tZQ== 3719
-
-        TW9kZQ== 3720
-
-        IGV2 3721
-
-        IGRpZmY= 3722
-
-        IFVuaXRlZA== 3723
-
-        SGVpZ2h0 3724
-
-        ZnVsbHk= 3725
-
-        aW1hZ2Vz 3726
-
-        IG1ha2Vz 3727
-
-        IGdsb2JhbA== 3728
-
-        IGNvbnRhY3Q= 3729
-
-        JzoK 3730
-
-        IGFicw== 3731
-
-        0LDQ 3732
-
-        ZmxvYXQ= 3733
-
-        IGV4Y2VwdA== 3734
-
-        IFBvbA== 3735
-
-        Q2hpbGQ= 3736
-
-        dHlw 3737
-
-        IGNlcnRhaW4= 3738
-
-        acOzbg== 3739
-
-        T1VU 3740
-
-        IGltcHJv 3741
-
-        aWxlcw== 3742
-
-        IC0tPgo= 3743
-
-        IFBhcnQ= 3744
-
-        dmFsdWVz 3745
-
-        b3Nz 3746
-
-        Lyoq 3747
-
-        aWxpdA== 3748
-
-        IEV2ZW50 3749
-
-        Y3VyaXR5 3750
-
-        c3Rlcg== 3751
-
-        IGNoYXJhY3Rlcg== 3752
-
-        MTk4 3753
-
-        IG5ld3M= 3754
-
-        ICIs 3755
-
-        IGRldmljZQ== 3756
-
-        Y2Vs 3757
-
-        bG9naW4= 3758
-
-        aGVldA== 3759
-
-        RGVmYXVsdA== 3760
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-        CSA= 3762
-
-        Y2xpY2s= 3763
-
-        KHZhbHVl 3764
-
-        IEFi 3765
-
-        IHByZXZpb3Vz 3766
-
-        RVJST1I= 3767
-
-        b2NhbA== 3768
-
-        IG1hdGVyaWFs 3769
-
-        IGJlbG93 3770
-
-        IENocmlzdA== 3771
-
-        IG1lZGlh 3772
-
-        Y292ZXI= 3773
-
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-
-        IGZhaWw= 3775
-
-        IGJsYWNr 3776
-
-        IGNvbXBvbmVudA== 3777
-
-        IEFtZXJpY2Fu 3778
-
-        IGFkZGVk 3779
-
-        IGJ1eQ== 3780
-
-        c3RpdA== 3781
-
-        IGNhbWU= 3782
-
-        IGRlbGV0ZQ== 3783
-
-        cHJvcGVydHk= 3784
-
-        b2Rpbmc= 3785
-
-        IGNhcmQ= 3786
-
-        cm9wcw== 3787
-
-        IGh0dHBz 3788
-
-        IHJvb3Q= 3789
-
-        IGhhbmRsZQ== 3790
-
-        Q0M= 3791
-
-        QmFjaw== 3792
-
-        ZW1wbGF0ZQ== 3793
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-        IGdldHRpbmc= 3794
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-
-        bWFpbA== 3796
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-
-        LmFzc2VydA== 3798
-
-        IERlYw== 3799
-
-        KHRydWU= 3800
-
-        IGNvbXB1dA== 3801
-
-        IGNsYWlt 3802
-
-        Jz0+ 3803
-
-        IFN1Yg== 3804
-
-        IGFpcg== 3805
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-        b3Bz 3806
-
-        bmF2 3807
-
-        ZW1lbnRz 3808
-
-        KGlk 3809
-
-        IGVudGVy 3810
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-
-        IGRvaW5n 3815
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-        IFJlZA== 3816
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-
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-        IHdhdGNo 3821
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-        IGh1bWFu 3823
-
-        IGRhbQ== 3824
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-        cGVuZA== 3825
-
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-
-        IHRheA== 3827
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-        IGdpcmw= 3828
-
-        cmVldA== 3829
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-        IGJveA== 3830
-
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-
-        KHY= 3832
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-
-        IGludGVyZmFjZQ== 3834
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-        IG1zZw== 3835
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-        ZmVjdA== 3836
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-        IHRyYWNr 3839
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-        Jyk7Cgo= 3840
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-        amU= 3841
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-        aXN0cg== 3843
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-        KFM= 3844
-
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-
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-
-        aXR0ZWQ= 3847
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-
-        Kik= 3849
-
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-
-        IGFwcGxpY2F0aW9u 3851
-
-        YW55 3852
-
-        LnN3aW5n 3853
-
-        IHBhY2s= 3854
-
-        XHU= 3855
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-        IHByYWN0 3856
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-        IHNlY3Rpb24= 3857
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-        IHVuc2lnbmVk 3859
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-        0YM= 3865
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-        w6U= 3870
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-
-        IHRocm93cw== 3872
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-        LnZpZXc= 3877
-
-        IHRlcm1z 3878
-
-        ZnJhbWV3b3Jr 3879
-
-        LXI= 3880
-
-        IGFwcGx5 3881
-
-        IHNlc3Npb24= 3882
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-
-        dWdnZXN0 3884
-
-        IG90aGVycw== 3885
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-
-        IGZ1bmQ= 3887
-
-        SW5pdA== 3888
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-        X18o 3889
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-        ZW5zb3I= 3890
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-
-        IHNldmVyYWw= 3892
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-        aWk= 3893
-
-        W2o= 3894
-
-        SU8= 3895
-
-        IHRlbXBsYXRl 3896
-
-        UG9zaXRpb24= 3897
-
-        IGVjb24= 3898
-
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-
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-
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-
-        bWFpbg== 3902
-
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-
-        aW1lbnQ= 3904
-
-        UmVj 3905
-
-        bW0= 3906
-
-        IFVuaXZlcnNpdHk= 3907
-
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-
-        ICAgICAgICAgICAgICAgICAgICA= 3909
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-
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-
-        IGVzYw== 3920
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-
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-
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-
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-
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-
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-
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-
-        IG1vZGU= 3941
-
-        ICc8 3942
-
-        X2FycmF5 3943
-
-        IGF4 3944
-
-        J107Cg== 3945
-
-        aXJlcw== 3946
-
-        VGhlcmU= 3947
-
-        dWdodA== 3948
-
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-
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-
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-
-        IHRvb2s= 3952
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-
-        IH19 3954
-
-        ID8+Cg== 3955
-
-        IHByb2R1Y3Rz 3956
-
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-
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-
-        LnBhdGg= 3960
-
-        JwoK 3961
-
-        IFBvc3Q= 3962
-
-        YXZlbA== 3963
-
-        KDo= 3964
-
-        MTUw 3965
-
-        IG5lZWRz 3966
-
-        IGtub3du 3967
-
-        Rmw= 3968
-
-        IGV4ZWM= 3969
-
-        IHNlZW4= 3970
-
-        NTE= 3971
-
-        dW1l 3972
-
-        IGJvcmRlcg== 3973
-
-        IGxpdmU= 3974
-
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-
-        UGVy 3976
-
-        IHZhcmlhYmxl 3977
-
-        aWV0 3978
-
-        IERlZg== 3979
-
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-
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-
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-
-        Zmlyc3Q= 3983
-
-        IHByb3ZpZGVk 3984
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-        Ly8vLy8vLy8vLy8vLy8vLy8vLy8vLy8vLy8vLy8vLy8= 3985
-
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-
-        IGhvcGU= 3987
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-        YXV0bw== 3989
-
-        ZmluZA== 3990
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-        X3N0cmluZw== 3991
-
-        YnRu 3992
-
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-        4169
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-
-        IGFnbw== 4227
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-
-        IEFOWQ== 4230
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-        X2No 4231
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-        IERlcA== 4241
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-        IHBvcg== 4247
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-
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-
-        IFtdCg== 4260
-
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-        bm93bg== 4262
-
-        IHZvdA== 4263
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-        IE5PVA== 4276
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-        IEJyaXQ= 5567
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-        L3Y= 5574
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-        LnNob3c= 5577
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-
-        Y29tbW9u 5581
-
-        aW1hdGlvbg== 5582
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-        IGNvdXJ0 5590
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-
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-        ICQoJyM= 5601
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-
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-        IFdpbmRvd3M= 5632
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-        ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIA==
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-
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-
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-
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-
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-        LS0tCg== 11192
-
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-
-        MDA3 11194
-
-        IFJ1bnRpbWU= 11195
-
-        IHdvcnJ5 11196
-
-        IFNwZWM= 11197
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-
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-
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-
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-
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-
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-
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-
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-
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-        IGVmZmljaWVudA== 11297
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
-        KCc8 11394
-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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-
-        IGV4ZWN1dGlvbg== 11572
-
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-
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-        CWE= 11575
-
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-
-        LkZvbnRTdHlsZQ== 11577
-
-        IGFib3J0 11578
-
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-
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-
-        IGRw 11581
-
-        IH19PC8= 11582
-
-        IE1h 11583
-
-        MjE0 11584
-
-        LlJvd3M= 11585
-
-        IFBldA== 11586
-
-        JSk= 11587
-
-        cmFuZA== 11588
-
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-
-        UnVsZQ== 11590
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-        IGhlbA== 11591
-
-        MDIx 11592
-
-        UklURQ== 11593
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-        IHJhdGlv 11595
-
-        IENPTkRJVElPTlM= 11596
-
-        b3NvcGg= 11597
-
-        IElM 11598
-
-        IGFkdmVudA== 11599
-
-        Y2Fw 11600
-
-        Ozwv 11601
-
-        IFVTQg== 11602
-
-        RHJpdmVy 11603
-
-        IG91cnM= 11604
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-
-        Lks= 11606
-
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-
-        LnE= 11608
-
-        CXN0cg== 11609
-
-        L2NvbW1vbg== 11610
-
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-        IFBERg== 11612
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-
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-        IFF1ZXJ5 11615
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-        LnJlc3BvbnNl 11616
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-
-        IHByb2Nlc3Nlcw== 11618
-
-        PEludGVnZXI= 11619
-
-        aW1v 11620
-
-        IGFkZHM= 11621
-
-        IGVudGlyZWx5 11622
-
-        IHdhc2g= 11623
-
-        LyoqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKg==
-        11624
-
-        IGFuaW1hdGVk 11625
-
-        IHByb2ZpdA== 11626
-
-        ZW5jaW5n 11627
-
-        L1M= 11628
-
-        IFN5bQ== 11629
-
-        IG1hbnVhbA== 11630
-
-        RG93bmxvYWQ= 11631
-
-        ICghJA== 11632
-
-        IG1vdGlvbg== 11633
-
-        d2VicGFjaw== 11634
-
-        LWJvdHRvbQ== 11635
-
-        IGdyYXR1aXQ= 11636
-
-        UEc= 11637
-
-        KDos 11638
-
-        IGVyYQ== 11639
-
-        IGhv 11640
-
-        IEppbQ== 11641
-
-        cXVpcg== 11642
-
-        IEJBU0lT 11643
-
-        w6Fu 11644
-
-        REVS 11645
-
-        IGV4cGVuc2l2ZQ== 11646
-
-        X2Nv 11647
-
-        Qm91bmRz 11648
-
-        V2VsbA== 11649
-
-        IERlbW9jcmF0aWM= 11650
-
-        IOKGkg== 11651
-
-        LlJlbQ== 11652
-
-        X1NZ 11653
-
-        bmFtZXM= 11654
-
-        IFZp 11655
-
-        IGlzaW5zdGFuY2U= 11656
-
-        XCI+ 11657
-
-        ICo9 11658
-
-        IFBT 11659
-
-        IGRhbmdlcm91cw== 11660
-
-        W3A= 11661
-
-        T01F 11662
-
-        T3RoZXI= 11663
-
-        IFN0cmluZ0J1aWxkZXI= 11664
-
-        UG9pbnRz 11665
-
-        aGVhZGluZw== 11666
-
-        IGN1cnJlbmN5 11667
-
-        IHBlcmNlbnRhZ2U= 11668
-
-        X0FQSQ== 11669
-
-        IGNsYXNzaWM= 11670
-
-        dGhlYWQ= 11671
-
-        IE1P 11672
-
-        RkU= 11673
-
-        SWR4 11674
-
-        YXdhaXQ= 11675
-
-        IMOo 11676
-
-        IGFjY2lkZW50 11677
-
-        IHZhcmlhbnQ= 11678
-
-        IG15c3Q= 11679
-
-        IExhbmQ= 11680
-
-        IEJyZQ== 11681
-
-        IGhhcm0= 11682
-
-        IEFjYw== 11683
-
-        IGNoYXJnZWQ= 11684
-
-        aW9uZXM= 11685
-
-        VmlzaWJpbGl0eQ== 11686
-
-        YXJyeQ== 11687
-
-        IExhbmd1YWdl 11688
-
-        IHdhbGtpbmc= 11689
-
-        Ii4KCg== 11690
-
-        aWZlcg== 11691
-
-        IGxlYWRlcnNoaXA= 11692
-
-        LkZyb20= 11693
-
-        eW5hbQ== 11694
-
-        IHRpbWVzdGFtcA== 11695
-
-        aXB0 11696
-
-        IEhhcw== 11697
-
-        UkVGRVI= 11698
-
-        IEl0cw== 11699
-
-        IGxpc3RlbmVy 11700
-
-        VVRF 11701
-
-        MjEz 11702
-
-        X2Rlc2NyaXB0aW9u 11703
-
-        IGV4cGVyaWVuY2Vz 11704
-
-        IGNyZWF0ZXM= 11705
-
-        UlM= 11706
-
-        Y2FydA== 11707
-
-        YmxhY2s= 11708
-
-        IGNob2ljZXM= 11709
-
-        d2Fy 11710
-
-        NzUw 11711
-
-        ICcnJw== 11712
-
-        IG9yZGVyZWQ= 11713
-
-        IGV2ZW5pbmc= 11714
-
-        IHBpbA== 11715
-
-        IHR1bg== 11716
-
-        IEJhZA== 11717
-
-        KGFwcA== 11718
-
-        cmFuZG9t 11719
-
-        IGV4cGxpY2l0 11720
-
-        IGFycml2ZWQ= 11721
-
-        IGZseQ== 11722
-
-        IGVjb25vbQ== 11723
-
-        LW1haWw= 11724
-
-        IGxpc3Rz 11725
-
-        IGFyY2hpdGVjdA== 11726
-
-        MjM0 11727
-
-        IFBheQ== 11728
-
-        IGRz 11729
-
-        IFNvbA== 11730
-
-        IHZlaGljbGVz 11731
-
-        SHo= 11732
-
-        LWNvbQ== 11733
-
-        IGtpbmc= 11734
-
-        X2VxdWFs 11735
-
-        IEhlbHA= 11736
-
-        IGFidXNl 11737
-
-        NDgw 11738
-
-        MTY5 11739
-
-        LS07Cg== 11740
-
-        IGV4dHI= 11741
-
-        IGNoZW1pY2Fs 11742
-
-        5L8= 11743
-
-        IG9yaWVudA== 11744
-
-        IGJyZWF0aA== 11745
-
-        IFNwYWNl 11746
-
-        KGVsZW1lbnQ= 11747
-
-        d2FpdA== 11748
-
-        REVE 11749
-
-        aWdtYQ== 11750
-
-        IGVudHI= 11751
-
-        IHNvYg== 11752
-
-        LW5hbWU= 11753
-
-        IGFmZmVjdGVk 11754
-
-        aWth 11755
-
-        IGNvYWw= 11756
-
-        X3dvcms= 11757
-
-        IGh1bmRyZWRz 11758
-
-        IHBvbGl0aWNz 11759
-
-        c3ViamVjdA== 11760
-
-        IGNvbnN1bWVy 11761
-
-        QU5HRQ== 11762
-
-        IHJlcGVhdGVk 11763
-
-        U2VuZA== 11764
-
-        ICNb 11765
-
-        IHByb3RvY29s 11766
-
-        IGxlYWRz 11767
-
-        dXNldW0= 11768
-
-        RXZlcnk= 11769
-
-        ODA4 11770
-
-        MTc0 11771
-
-        SW1wb3J0 11772
-
-        KGNvdW50 11773
-
-        IGNoYWxsZW5nZXM= 11774
-
-        IG5vdmVs 11775
-
-        IGRlcGFydA== 11776
-
-        Yml0cw== 11777
-
-        LkN1cnJlbnQ= 11778
-
-        IGAkew== 11779
-
-        b3Rpbmc= 11780
-
-        KFw= 11781
-
-        IGNyZWF0aXZl 11782
-
-        IGJ1ZmY= 11783
-
-        IGludHJvZHVjZWQ= 11784
-
-        dXNpYw== 11785
-
-        bW9kdWxlcw== 11786
-
-        QXJl 11787
-
-        LWRvYw== 11788
-
-        bGFuZ3VhZ2U= 11789
-
-        X2NhY2hl 11790
-
-        IHRvZA== 11791
-
-        Pz48Lw== 11792
-
-        b21ldGhpbmc= 11793
-
-        IGh1bg== 11794
-
-        5bo= 11795
-
-        YXRlcnM= 11796
-
-        SW50ZW50 11797
-
-        IGltcGxlbWVudGVk 11798
-
-        IENhc2U= 11799
-
-        Q2hpbGRyZW4= 11800
-
-        IG5vdGlmaWNhdGlvbg== 11801
-
-        UmVuZGVyZXI= 11802
-
-        V3JhcHBlcg== 11803
-
-        T2JqZWN0cw== 11804
-
-        dGw= 11805
-
-        LkNvbnRhaW5z 11806
-
-        UGx1Z2lu 11807
-
-        LnJvdw== 11808
-
-        IGZvcmc= 11809
-
-        IHBlcm1pdA== 11810
-
-        IHRhcmdldHM= 11811
-
-        IElG 11812
-
-        IHRpcA== 11813
-
-        c2V4 11814
-
-        IHN1cHBvcnRz 11815
-
-        IGZvbGQ= 11816
-
-        cGhvdG8= 11817
-
-        fSwNCg== 11818
-
-        IGdvb2dsZQ== 11819
-
-        JCgnIw== 11820
-
-        IHNoYXJpbmc= 11821
-
-        IGdvb2Rz 11822
-
-        dnM= 11823
-
-        IERhbg== 11824
-
-        UmF0ZQ== 11825
-
-        IE1hcnRpbg== 11826
-
-        IG1hbm5lcg== 11827
-
-        bGll 11828
-
-        LlRoZQ== 11829
-
-        SW50ZXJuYWw= 11830
-
-        IENPTlRS 11831
-
-        TW9jaw== 11832
-
-        UklHSFQ= 11833
-
-        ICd7 11834
-
-        IGNvbnRyb2xz 11835
-
-        TWF0 11836
-
-        IG1hbmQ= 11837
-
-        IGV4dGVuZGVk 11838
-
-        T2s= 11839
-
-        IGVtYmVk 11840
-
-        IHBsYW5ldA== 11841
-
-        IE5vbg== 11842
-
-        LWNo 11843
-
-        KSIs 11844
-
-        ZXBhcg== 11845
-
-        IGJlbGlldmVk 11846
-
-        IEVudmlyb25tZW50 11847
-
-        IEZyaWVuZA== 11848
-
-        LXJlcw== 11849
-
-        IGhhbmRsaW5n 11850
-
-        bmlj 11851
-
-        LWxldmVs 11852
-
-        c2NyaQ== 11853
-
-        WG1s 11854
-
-        QkU= 11855
-
-        dW5nZW4= 11856
-
-        IGFsdGVy 11857
-
-        W2lkeA== 11858
-
-        UG9w 11859
-
-        Y2Ft 11860
-
-        ICgoKA== 11861
-
-        IHNoaXBwaW5n 11862
-
-        IGJhdHRlcnk= 11863
-
-        aWRkbGV3YXJl 11864
-
-        TUM= 11865
-
-        IGltcGw= 11866
-
-        b3RhdGlvbg== 11867
-
-        IExhYg== 11868
-
-        PGZvcm0= 11869
-
-        CW5hbWU= 11870
-
-        IEdhbWVz 11871
-
-        cmF5 11872
-
-        RXh0cmE= 11873
-
-        VHdv 11874
-
-        KHBsYXllcg== 11875
-
-        IExlcw== 11876
-
-        wrA= 11877
-
-        IGNoYXJzZXQ= 11878
-
-        IGpvdXJuZXk= 11879
-
-        ZXRpbmc= 11880
-
-        5pg= 11881
-
-        4pQ= 11882
-
-        55So 11883
-
-        IGRpbg== 11884
-
-        IHBlcm1hbg== 11885
-
-        IHNvbHZl 11886
-
-        IGxhdW5jaGVk 11887
-
-        IG5pbmU= 11888
-
-        IHNlbmRpbmc= 11889
-
-        IHRlbGxpbmc= 11890
-
-        LnBhc3N3b3Jk 11891
-
-        IE1hdHJpeA== 11892
-
-        ZXJpYw== 11893
-
-        IGdyYWI= 11894
-
-        LnU= 11895
-
-        IExpYnJhcnk= 11896
-
-        IGRlYnQ= 11897
-
-        SU5L 11898
-
-        LmZpbmRWaWV3QnlJZA== 11899
-
-        IGZyZXF1ZW5jeQ== 11900
-
-        LmFk 11901
-
-        X1RFU1Q= 11902
-
-        IG5lZ290 11903
-
-        IEFmcmljYW4= 11904
-
-        c2VuZGVy 11905
-
-        xaE= 11906
-
-        R2xvYmFs 11907
-
-        MTcz 11908
-
-        IGV4cGVydHM= 11909
-
-        KyspDQo= 11910
-
-        IGRlcGVuZGluZw== 11911
-
-        Z3JheQ== 11912
-
-        IGp1ZGdl 11913
-
-        IHNlbnRlbmNl 11914
-
-        bG9zdXJl 11915
-
-        QWM= 11916
-
-        IHRyYWNl 11917
-
-        RWRnZQ== 11918
-
-        IGZyaWVuZGx5 11919
-
-        IGNvbmNlcm5lZA== 11920
-
-        YmxvZw== 11921
-
-        IGNsYWltZWQ= 11922
-
-        fSc= 11923
-
-        aW50ZWdlcg== 11924
-
-        X3RyZWU= 11925
-
-        CWNvbnRpbnVl 11926
-
-        eGk= 11927
-
-        IGFjY2VwdGVk 11928
-
-        X29uZQ== 11929
-
-        IEVkdWNhdGlvbg== 11930
-
-        dWJsaXNoZWQ= 11931
-
-        Z29u 11932
-
-        YXBwb2ludA== 11933
-
-        b3V0cw== 11934
-
-        IG1pbmluZw== 11935
-
-        IHNvbmdz 11936
-
-        IGhlcnNlbGY= 11937
-
-        IGdyYW50ZWQ= 11938
-
-        IHBhc3Npb24= 11939
-
-        IExha2U= 11940
-
-        IGxvYW4= 11941
-
-        dWVudA== 11942
-
-        Y2hhbnQ= 11943
-
-        IGRldGFpbGVk 11944
-
-        ZXhjZXB0 11945
-
-        X2NtZA== 11946
-
-        IEhF 11947
-
-        UmVsYXRlZA== 11948
-
-        enQ= 11949
-
-        J30sCg== 11950
-
-        IHNwZWNpZmljYWxseQ== 11951
-
-        U3RhdGlj 11952
-
-        IGNhcnJpZWQ= 11953
-
-        QU5T 11954
-
-        XCI6 11955
-
-        Q3JlYXRlZA== 11956
-
-        IGN1bA== 11957
-
-        XS0= 11958
-
-        X2FwaQ== 11959
-
-        RlA= 11960
-
-        IHNpdHRpbmc= 11961
-
-        ICIiKQ== 11962
-
-        CWdvdG8= 11963
-
-        IEVxdQ== 11964
-
-        IGFzc2F1bHQ= 11965
-
-        a2lucw== 11966
-
-        YW5jZXI= 11967
-
-        b2dlbg== 11968
-
-        IHZvdGVycw== 11969
-
-        IFByb3Q= 11970
-
-        RGVzY3JpcHRvcg== 11971
-
-        44O8 11972
-
-        LkFzc2VydA== 11973
-
-        YnNpdGVz 11974
-
-        b3N0ZXI= 11975
-
-        LW1lbnU= 11976
-
-        IGFybXM= 11977
-
-        LkNsaWVudA== 11978
-
-        LmJhY2tncm91bmQ= 11979
-
-        YXZpdHk= 11980
-
-        IHZ1bA== 11981
-
-        X01BU0s= 11982
-
-        IGhvdXNpbmc= 11983
-
-        IGJlYXI= 11984
-
-        X2l0ZXI= 11985
-
-        cGlyZWQ= 11986
-
-        IG1hcmtldHM= 11987
-
-        IFN0dWRlbnQ= 11988
-
-        IHRpY2tldA== 11989
-
-        IG1pbGxpb25z 11990
-
-        ZmxhdGVy 11991
-
-        KT0= 11992
-
-        IHJlY292ZXI= 11993
-
-        IEZvcmNl 11994
-
-        IEJvdGg= 11995
-
-        IHZpY3RpbQ== 11996
-
-        IERpc2M= 11997
-
-        cmVwb3J0 11998
-
-        IGZvdXJ0aA== 11999
-
-        IEFzc2VtYmx5 12000
-
-        L3VzZXI= 12001
-
-        TnVsbE9y 12002
-
-        dGV4dGFyZWE= 12003
-
-        IGF0aA== 12004
-
-        IChb 12005
-
-        IGNoYW5uZWxz 12006
-
-        IEp1c3RpY2U= 12007
-
-        Y2hvaWNl 12008
-
-        TE9CQUw= 12009
-
-        ZXhlYw== 12010
-
-        ZW1hbGU= 12011
-
-        IGVsZW0= 12012
-
-        X2xl 12013
-
-        IHJlc3BvbnNpYmlsaXR5 12014
-
-        IFR3 12015
-
-        SUNBVElPTg== 12016
-
-        IGVsc2VpZg== 12017
-
-        IGZv 12018
-
-        YXN0cw== 12019
-
-        IHRyZWF0ZWQ= 12020
-
-        c2Vu 12021
-
-        IFZpY3Q= 12022
-
-        c3VtZXI= 12023
-
-        X0JBU0U= 12024
-
-        IGFzdA== 12025
-
-        Pnt7 12026
-
-        IFJlc291cmNl 12027
-
-        IFN0YW5kYXJk 12028
-
-        IFByZW0= 12029
-
-        dXBkYXRlZA== 12030
-
-        aXZhbGVudA== 12031
-
-        IGFzc2V0cw== 12032
-
-        X3RlbXA= 12033
-
-        IGludGVyZXN0cw== 12034
-
-        IGhhcmR3YXJl 12035
-
-        IFJvbQ== 12036
-
-        IFNoYXJl 12037
-
-        ICcnCg== 12038
-
-        ICos 12039
-
-        IFRha2U= 12040
-
-        IEltYWdlcw== 12041
-
-        X0NIRUNL 12042
-
-        KHR5cGVvZg== 12043
-
-        IEp1bg== 12044
-
-        XDxe 12045
-
-        IGxpcXU= 12046
-
-        IHdvcnN0 12047
-
-        eW1ib2xz 12048
-
-        CQkJICAg 12049
-
-        IGRyaXZlcnM= 12050
-
-        IERvY3VtZW50 12051
-
-        ZW5v 12052
-
-        IFRlY2hub2xvZ3k= 12053
-
-        IGFwcHJvdmVk 12054
-
-        dW1wcw== 12055
-
-        IHNub3c= 12056
-
-        Zm9ybWFuY2U= 12057
-
-        X0FTU0VSVA== 12058
-
-        dWl0cw== 12059
-
-        MjA3 12060
-
-        2YY= 12061
-
-        IGRpZmZlcmVuY2Vz 12062
-
-        LlZpc2libGU= 12063
-
-        CQkJDQo= 12064
-
-        IFBz 12065
-
-        X2ZldGNo 12066
-
-        IHRvZG8= 12067
-
-        LicsCg== 12068
-
-        IHNlbA== 12069
-
-        dXJlcnM= 12070
-
-        aW52YWxpZA== 12071
-
-        IHR3ZWV0 12072
-
-        VkVM 12073
-
-        IHJlc2VhcmNoZXJz 12074
-
-        IHNwcmludGY= 12075
-
-        IFJP 12076
-
-        IHBlbA== 12077
-
-        LlRyYW5z 12078
-
-        IGlsbGVnYWw= 12079
-
-        ZGlhbG9n 12080
-
-        c21hcnR5 12081
-
-        bGc= 12082
-
-        X01JTg== 12083
-
-        IGhlcm8= 12084
-
-        ZmluYWw= 12085
-
-        IHBw 12086
-
-        Lkxl 12087
-
-        IGNp 12088
-
-        CVJU 12089
-
-        IHN1Z2dlc3RlZA== 12090
-
-        cGRm 12091
-
-        YWNoaW5n 12092
-
-        IFJv 12093
-
-        IFByb3BlcnRpZXM= 12094
-
-        IFNp 12095
-
-        IGJ1eWluZw== 12096
-
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-
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-        IG9idmlvdXNseQ== 14224
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-
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-        LmNvbmY= 14263
-
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-        IENQVQ== 14266
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-
-        IHBlcmZlY3RseQ== 14268
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-
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-
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-
-        IFNjcmVlbg== 14275
-
-        5p0= 14276
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-        IHJlZHVjdGlvbg== 14278
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-        LkdldENvbXBvbmVudA== 14279
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-
-        IGN1cmw= 14284
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-
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-
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-        IG1hbnVmYWN0dXJlcg== 14290
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-        Q29sdW1ucw== 14292
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-
-        IHNsb3dseQ== 14297
-
-        IFRvd24= 14298
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-
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-
-        X3ByZWZpeA== 14301
-
-        IEFsZXJ0 14302
-
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-
-        w6Ry 14304
-
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-
-        IE1E 14306
-
-        IGNvdXJzZXM= 14307
-
-        YXRoYW4= 14308
-
-        6Zk= 14309
-
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-
-        IFNFUg== 14311
-
-        ZXNpZ24= 14312
-
-        QWRkcg== 14313
-
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-
-        KCIuLw== 14315
-
-        XX0= 14316
-
-        LmZvbnQ= 14317
-
-        IEluc3RhZ3JhbQ== 14318
-
-        IEJvcmRlcg== 14319
-
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-
-        IGhhbGw= 14321
-
-        IHJ1bQ== 14322
-
-        X2JpdA== 14323
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-
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-
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-
-        X3JlZ2lzdGVy 14327
-
-        KENvbnRleHQ= 14328
-
-        IG9wcG9zaXRl 14329
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-        Um9vbQ== 14330
-
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-
-        0LDQvdC4 14332
-
-        IGVuam95ZWQ= 14333
-
-        X3J1bg== 14334
-
-        Q2xlYXI= 14335
-
-        4oCY 14336
-
-        IEZvcmQ= 14337
-
-        b25pYw== 14338
-
-        b3N0ZW4= 14339
-
-        Il0p 14340
-
-        X2F1dGg= 14341
-
-        Ly8NCg== 14342
-
-        IHN1ZmZpY2llbnQ= 14343
-
-        TEVT 14344
-
-        IHBoZW4= 14345
-
-        IG9o 14346
-
-        X2Nzdg== 14347
-
-        IHJvdXRpbmU= 14348
-
-        LkFyZUVxdWFs 14349
-
-        YXlsb3I= 14350
-
-        IGJhc2tldA== 14351
-
-        X0NPTU0= 14352
-
-        cnlwdGVk 14353
-
-        U2lt 14354
-
-        IFNob3A= 14355
-
-        IHN0dWRpbw== 14356
-
-        YXRvcw== 14357
-
-        KFc= 14358
-
-        W3N0cmluZw== 14359
-
-        w6R0 14360
-
-        b2dh 14361
-
-        IHNocg== 14362
-
-        IHNpY2s= 14363
-
-        QW5vdGhlcg== 14364
-
-        IGRvb3Jz 14365
-
-        X05F 14366
-
-        IFRIUkVF 14367
-
-        Lm9yZGVy 14368
-
-        cmF6aWw= 14369
-
-        IG1hcHM= 14370
-
-        X1RSVUU= 14371
-
-        dHJhbnNsYXRl 14372
-
-        IG5lYXJieQ== 14373
-
-        MjY1 14374
-
-        IG5hY2g= 14375
-
-        TE9BVA== 14376
-
-        YmF0Y2g= 14377
-
-        MjI5 14378
-
-        IGx1eA== 14379
-
-        YXNoZXM= 14380
-
-        YW5nZXJz 14381
-
-        4oCm4oCm 14382
-
-        X0VWRU5U 14383
-
-        X1VQ 14384
-
-        IGFjdHM= 14385
-
-        aW52 14386
-
-        X01FVEhPRA== 14387
-
-        Y2Npb24= 14388
-
-        IHJldGFpbg== 14389
-
-        dXRjaA== 14390
-
-        INCx 14391
-
-        IGtub3dpbmc= 14392
-
-        IHJlcHJlc2VudGluZw== 14393
-
-        Tk9U 14394
-
-        cG5n 14395
-
-        Q29udHJhY3Q= 14396
-
-        IHRyaWNr 14397
-
-        IEVkaXRpb24= 14398
-
-        dXBsaWNhdGU= 14399
-
-        IGNvbnRyb2xsZWQ= 14400
-
-        Y2Zn 14401
-
-        amF2YXNjcmlwdA== 14402
-
-        IG1pbGs= 14403
-
-        V2hpdGU= 14404
-
-        U2VxdWVuY2U= 14405
-
-        YXdh 14406
-
-        IGRpc2N1c3NlZA== 14407
-
-        NTAx 14408
-
-        IEJ1c2g= 14409
-
-        IFlFUw== 14410
-
-        LmZhY3Rvcnk= 14411
-
-        dGFncw== 14412
-
-        IHRhY3Q= 14413
-
-        IHNpZA== 14414
-
-        JCQ= 14415
-
-        IEVudW0= 14416
-
-        Mjc1 14417
-
-        IGZyYW1lcw== 14418
-
-        fSk7 14419
-
-        IHJlZ3Vs 14420
-
-        J107DQo= 14421
-
-        UmVnaW9u 14422
-
-        MzIx 14423
-
-        ZmZm 14424
-
-        IGNybw== 14425
-
-        KGNvbQ== 14426
-
-        PSIr 14427
-
-        U3R1ZGVudA== 14428
-
-        IGRpc2FwcG9pbnQ= 14429
-
-        UkVTVUxU 14430
-
-        Q291bnRlcg== 14431
-
-        IGJ1dHRlcg== 14432
-
-        IEhh 14433
-
-        IERpZ2l0YWw= 14434
-
-        IGJpZA== 14435
-
-        Ij57ew== 14436
-
-        aW5nZXJz 14437
-
-        IENvdW50cnk= 14438
-
-        X3RwbA== 14439
-
-        Il0pCg== 14440
-
-        L2s= 14441
-
-        ZGF0aW5n 14442
-
-        OiM= 14443
-
-        IERBVEE= 14444
-
-        eW5jaHJvbg== 14445
-
-        X2JvZHk= 14446
-
-        b2xseXdvb2Q= 14447
-
-        IHZhbG9y 14448
-
-        aXBpZW50 14449
-
-        b2Z0 14450
-
-        VUJM 14451
-
-        ZG9jcw== 14452
-
-        IHN5bmNocm9u 14453
-
-        IGZvcm1lZA== 14454
-
-        cnVwdGlvbg== 14455
-
-        IGxpc3Rh 14456
-
-        UmVxdWVzdE1hcHBpbmc= 14457
-
-        IHZpbGxhZ2U= 14458
-
-        IGtub2Nr 14459
-
-        b2Nz 14460
-
-        Ins= 14461
-
-        X2ZsYWdz 14462
-
-        IHRyYW5zYWN0aW9ucw== 14463
-
-        IGhhYml0 14464
-
-        IEpl 14465
-
-        ZWRlbg== 14466
-
-        IGFpcmNyYWZ0 14467
-
-        aXJr 14468
-
-        IEFC 14469
-
-        IGZhaXJseQ== 14470
-
-        LmludGVy 14471
-
-        LkFjdA== 14472
-
-        IGluc3RydW1lbnQ= 14473
-
-        cmVtb3ZlQ2xhc3M= 14474
-
-        LmNvbW1hbmQ= 14475
-
-        0Yk= 14476
-
-        CW1lbQ== 14477
-
-        KG1pbg== 14478
-
-        IG90 14479
-
-        IGNvbGxl 14480
-
-        PXM= 14481
-
-        dGltZW91dA== 14482
-
-        IGlkcw== 14483
-
-        IE1hdGNo 14484
-
-        aWpu 14485
-
-        emVybw== 14486
-
-        NDEw 14487
-
-        IG5ldHdvcmtz 14488
-
-        Lmdvdg== 14489
-
-        IGludGVs 14490
-
-        IHNlY3Rpb25z 14491
-
-        b3V0aW5l 14492
-
-        KGNtZA== 14493
-
-        KGRpcg== 14494
-
-        IExJQUJJTElUWQ== 14495
-
-        IEJsb2c= 14496
-
-        IGJyaWRnZQ== 14497
-
-        MzA4 14498
-
-        IENW 14499
-
-        Y29udmVydA== 14500
-
-        ICIpCg== 14501
-
-        IEJlcm4= 14502
-
-        X1BP 14503
-
-        ZXZhbA== 14504
-
-        KHNldA== 14505
-
-        dG9vbA== 14506
-
-        IHBheW1lbnRz 14507
-
-        QmVoYXZpb3Vy 14508
-
-        IGNvbmNyZXRl 14509
-
-        IGVsaWc= 14510
-
-        IGFjY2VsZXI= 14511
-
-        IGhvbGU= 14512
-
-        X28= 14513
-
-        VEVHRVI= 14514
-
-        IGdyYXBoaWNz 14515
-
-        T3du 14516
-
-        Rm9ybWF0dGVy 14517
-
-        b25kZXI= 14518
-
-        IHBhY2thZ2Vz 14519
-
-        L2E= 14520
-
-        IEtub3c= 14521
-
-        T3JEZWZhdWx0 14522
-
-        IGR1dHk= 14523
-
-        V2FpdA== 14524
-
-        0L3QsA== 14525
-
-        X3JlY29yZA== 14526
-
-        W3Q= 14527
-
-        TWVzaA== 14528
-
-        IG9uZ29pbmc= 14529
-
-        LmJlYW5z 14530
-
-        IHRhbg== 14531
-
-        IGludGVycHJldA== 14532
-
-        YXN0ZXJz 14533
-
-        UVVBTA== 14534
-
-        IGxlZ3M= 14535
-
-        XFJlcXVlc3Q= 14536
-
-        LWZpbGU= 14537
-
-        X211dGV4 14538
-
-        IFNhaW50 14539
-
-        Ly8j 14540
-
-        IHByb2hpYg== 14541
-
-        KGluZm8= 14542
-
-        Oj0= 14543
-
-        bGludXg= 14544
-
-        IGJsbw== 14545
-
-        b3RpYw== 14546
-
-        CWZpbmFs 14547
-
-        X2V4cA== 14548
-
-        IFN0b3A= 14549
-
-        YXBpbmc= 14550
-
-        KHNhdmVk 14551
-
-        X3B1c2g= 14552
-
-        IGVhc2U= 14553
-
-        X0ZS 14554
-
-        cG9uc2l2ZQ== 14555
-
-        c3RyY21w 14556
-
-        OgoKCgo= 14557
-
-        5Lu2 14558
-
-        b2xp 14559
-
-        IGV4dHJlbWU= 14560
-
-        IHByb2Zlc3Nvcg== 14561
-
-        SW1hZ2Vz 14562
-
-        LklPRXhjZXB0aW9u 14563
-
-        IGFkZHJlc3Nlcw== 14564
-
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-
-        IGluY29ycG9y 14566
-
-        IHVzZUVmZmVjdA== 14567
-
-        X09G 14568
-
-        IERh 14569
-
-        bm9tYnJl 14570
-
-        SVJTVA== 14571
-
-        IGRpc2NyaW0= 14572
-
-        IGNvbXBlbnM= 14573
-
-        Z3JlZ2F0ZQ== 14574
-
-        YW5jZWxs 14575
-
-        YWNoZXM= 14576
-
-        IENyaXRlcmlh 14577
-
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-
-        RGVzdHJveQ== 14579
-
-        IHNlY29uZGFyeQ== 14580
-
-        V2F0Y2g= 14581
-
-        IFNlbQ== 14582
-
-        IE1jQw== 14583
-
-        IGFjYWRlbWlj 14584
-
-        VXBwZXI= 14585
-
-        Ojp+ 14586
-
-        dXRyYWw= 14587
-
-        IERvZw== 14588
-
-        YWRlZA== 14589
-
-        MjM3 14590
-
-        VmFsaWRhdG9y 14591
-
-        IGRlcml2ZWQ= 14592
-
-        IHNldFRpbWVvdXQ= 14593
-
-        IEtlbg== 14594
-
-        IHR5cGljYWw= 14595
-
-        IEJvYg== 14596
-
-        IGJvdW5kcw== 14597
-
-        IFNlYXNvbg== 14598
-
-        IGNyYXp5 14599
-
-        ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg 14600
-
-        LXJvdXRlcg== 14601
-
-        aXR0ZXN0 14602
-
-        IE1pcg== 14603
-
-        IGVtb3Rpb25hbA== 14604
-
-        LHY= 14605
-
-        Y24= 14606
-
-        L3N0 14607
-
-        5b0= 14608
-
-        b25vbQ== 14609
-
-        IGRlY2xhcmVk 14610
-
-        Pi4= 14611
-
-        YWlsaW5n 14612
-
-        IC8qPDw8 14613
-
-        IG5vcm1hbGx5 14614
-
-        KE1l 14615
-
-        ZXZpbg== 14616
-
-        bGlrZWx5 14617
-
-        IHBvaW50ZWQ= 14618
-
-        IFN0YWNr 14619
-
-        IHdhbGxz 14620
-
-        LlZlY3Rvcg== 14621
-
-        bWVhbg== 14622
-
-        XV0K 14623
-
-        IGxpc3RlbmluZw== 14624
-
-        YWR2 14625
-
-        IHN3YXA= 14626
-
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-
-        2Ko= 14628
-
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-
-        dWxz 14630
-
-        PG9wdGlvbg== 14631
-
-        bm90YXRpb25z 14632
-
-        IGVtYWlscw== 14633
-
-        IFVrcg== 14634
-
-        YXN0YQ== 14635
-
-        IFRodXM= 14636
-
-        IFN0b25l 14637
-
-        IGFwcGVhbA== 14638
-
-        LuKAmQ== 14639
-
-        IHJlZ3VsYXRpb25z 14640
-
-        UHJlZmVyZW5jZXM= 14641
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-
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-
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-
-        IHRlY2hub2xvZ2llcw== 14645
-
-        IHBhcmFncmFwaA== 14646
-
-        IG5lY2Vzc2FyaWx5 14647
-
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-
-        LmVhY2g= 14650
-
-        PGZsb2F0 14651
-
-        cmVzYQ== 14652
-
-        IHVuZGVyc3Q= 14653
-
-        IGZpbmdlcg== 14654
-
-        cHJlc3NlZA== 14655
-
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-
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-
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-
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-
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-
-        IHdlaWdodHM= 14661
-
-        IFJvbg== 14662
-
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-
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-
-        LS0tLS0tLS0tLQo= 14665
-
-        cGVyaW1lbnQ= 14666
-
-        IHRvU3RyaW5n 14667
-
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-
-        IENhbWVyYQ== 14669
-
-        IQoKCgo= 14670
-
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-
-        UHJlZml4 14672
-
-        IGluc3RpdHV0aW9ucw== 14673
-
-        OmludA== 14674
-
-        IGV4cG9zdXJl 14675
-
-        cGF0dGVybg== 14676
-
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-
-        Lm51bWJlcg== 14678
-
-        cmVkaWVudA== 14679
-
-        QXJndW1lbnRFeGNlcHRpb24= 14680
-
-        IENoaWVm 14681
-
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-
-        IGVsZWN0cm9uaWM= 14683
-
-        cm9uZw== 14684
-
-        ZXJk 14685
-
-        c3BOZXQ= 14686
-
-        cmFpdA== 14687
-
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-
-        IE9oaW8= 14689
-
-        Q29udHJvbGxlcnM= 14690
-
-        IGNvbnRpbnVpbmc= 14691
-
-        IFRlbXBsYXRl 14692
-
-        IEV0aA== 14693
-
-        c3o= 14694
-
-        L2Vudg== 14695
-
-        RW52 14696
-
-        JS4= 14697
-
-        YXJ0ZXJz 14698
-
-        KSgo 14699
-
-        IFRBQkxF 14700
-
-        IMOu 14701
-
-        cGVyYXR1cmU= 14702
-
-        cHJvZ3Jlc3M= 14703
-
-        UHJlcw== 14704
-
-        6rA= 14705
-
-        aW1wbGVtZW50YXRpb24= 14706
-
-        IGJpZW4= 14707
-
-        IHN0cmVldHM= 14708
-
-        X01TRw== 14709
-
-        TmV3cw== 14710
-
-        IyMj 14711
-
-        Oi8= 14712
-
-        IGN1dHRpbmc= 14713
-
-        eEI= 14714
-
-        cmVzc2Vk 14715
-
-        X0VOQUJMRQ== 14716
-
-        bGFi 14717
-
-        IGNhdXNpbmc= 14718
-
-        XSkpOwo= 14719
-
-        YnJh 14720
-
-        eEZGRkY= 14721
-
-        aWxseQ== 14722
-
-        cGxldGlvbg== 14723
-
-        d2lsbA== 14724
-
-        X2Jhcg== 14725
-
-        IHN0cnVjdHVyZXM= 14726
-
-        IEltcA== 14727
-
-        24w= 14728
-
-        IDw+ 14729
-
-        IC0tLS0tLS0tLS0tLS0tLS0= 14730
-
-        X0JVRkZFUg== 14731
-
-        LmRpcg== 14732
-
-        IHBsYWlu 14733
-
-        IHBlZXI= 14734
-
-        MjQ5 14735
-
-        Z2c= 14736
-
-        b2ludHM= 14737
-
-        IHNvbWV3aGF0 14738
-
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-
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-
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-
-        b3JkaW5hdG9y 17900
-
-        IGVpbmVt 17901
-
-        Q2Fubm90 17902
-
-        IGFybWVk 17903
-
-        CVY= 17904
-
-        5LiK 17905
-
-        LkZsYXQ= 17906
-
-        IFNlcA== 17907
-
-        IFN1YmplY3Q= 17908
-
-        X2ZvbnQ= 17909
-
-        IGNoYXJhY3RlcmlzdGljcw== 17910
-
-        RG9uZQ== 17911
-
-        ZWxu 17912
-
-        IyMjIyMjIyMjIyMj 17913
-
-        UE9T 17914
-
-        IGRlbnNpdHk= 17915
-
-        IFBsYXRmb3Jt 17916
-
-        LWl0ZW1z 17917
-
-        IG92ZXJz 17918
-
-        IHB1c2hpbmc= 17919
-
-        56Q= 17920
-
-        LkNvbm5lY3Rpb24= 17921
-
-        X3Rlcm0= 17922
-
-        IGluaXRpYWxpemF0aW9u 17923
-
-        X19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX18= 17924
-
-        56w= 17925
-
-        LmRvY3VtZW50 17926
-
-        bGVzaA== 17927
-
-        CWRvY3VtZW50 17928
-
-        IFBpbg== 17929
-
-        w6dh 17930
-
-        IGRlZmluaXRpb25z 17931
-
-        LlBhdGg= 17932
-
-        X1dSSVRF 17933
-
-        IAkK 17934
-
-        Pz4KCg== 17935
-
-        IHRlcnJpYmxl 17936
-
-        YmVhbg== 17937
-
-        aWNrZXRz 17938
-
-        IFNW 17939
-
-        QnV5 17940
-
-        KHRhc2s= 17941
-
-        IHJlZ2ltZQ== 17942
-
-        Z29vZ2xl 17943
-
-        IGNyYWNr 17944
-
-        LnZpc2l0 17945
-
-        TlVN 17946
-
-        ZW5lcmd5 17947
-
-        IHN0cnVjaw== 17948
-
-        X3NhbXBsZQ== 17949
-
-        LnBheWxvYWQ= 17950
-
-        IHJldmlz 17951
-
-        IFNjZW5l 17952
-
-        IHBn 17953
-
-        IGJyZWFrZmFzdA== 17954
-
-        VVJSRU5U 17955
-
-        LmNoYXJBdA== 17956
-
-        X2V4Y2VwdGlvbg== 17957
-
-        IEFudG9u 17958
-
-        IGd1aWRlbGluZXM= 17959
-
-        IGV4aGF1c3Q= 17960
-
-        IEZpbmFuY2lhbA== 17961
-
-        IGluZGVudA== 17962
-
-        IGRlc2t0b3A= 17963
-
-        SGlkZGVu 17964
-
-        RmFpbHVyZQ== 17965
-
-        IHByaW5jaXBsZQ== 17966
-
-        IGl2 17967
-
-        IHNla3M= 17968
-
-        bmV0d29yaw== 17969
-
-        IG51bWJlck9m 17970
-
-        IEFsYmVydA== 17971
-
-        CWxvbmc= 17972
-
-        ODAx 17973
-
-        LC4= 17974
-
-        IHplcm9z 17975
-
-        ZmFkZQ== 17976
-
-        IFR5cA== 17977
-
-        IFRlcm0= 17978
-
-        IEFydHM= 17979
-
-        LkFwcGxpY2F0aW9u 17980
-
-        IGJlaGFsZg== 17981
-
-        5oi3 17982
-
-        IG1lcmU= 17983
-
-        KGAkew== 17984
-
-        IGF3YXJlbmVzcw== 17985
-
-        ZWxwZXJz 17986
-
-        ZmxpeA== 17987
-
-        IHdlaWdo 17988
-
-        IGVzdGltYXRlcw== 17989
-
-        LmNoaWxk 17990
-
-        L08= 17991
-
-        IEJpdG1hcA== 17992
-
-        LmJvdHRvbQ== 17993
-
-        ICoqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioq
-        17994
-
-        RXhwZWN0 17995
-
-        ZW50bw== 17996
-
-        IEZvcnVt 17997
-
-        dmVyYWw= 17998
-
-        IGphaWw= 17999
-
-        IGFiaWxpdGllcw== 18000
-
-        IEhPTEQ= 18001
-
-        IENpdA== 18002
-
-        IGR5bmFt 18003
-
-        IGdyYXk= 18004
-
-        CQkJCQkJCQkJCQkJCQ== 18005
-
-        Lm5leHRJbnQ= 18006
-
-        YW50bHk= 18007
-
-        IEFSSVNJTkc= 18008
-
-        KHByaXZhdGU= 18009
-
-        IHJlamVjdGVk 18010
-
-        IE5pYw== 18011
-
-        IGxlYXRoZXI= 18012
-
-        PXsK 18013
-
-        YWx5dGljcw== 18014
-
-        dGhldGlj 18015
-
-        LlRvcA== 18016
-
-        Mzcz 18017
-
-        LlBhZ2U= 18018
-
-        PXtg 18019
-
-        IDsNCg== 18020
-
-        ZGVwdGg= 18021
-
-        bWFubg== 18022
-
-        V0Q= 18023
-
-        IFNvbQ== 18024
-
-        LlJpZ2h0 18025
-
-        ICl9Cg== 18026
-
-        IHRyYWl0 18027
-
-        w5c= 18028
-
-        aWFj 18029
-
-        IHJ2 18030
-
-        U2FtcGxl 18031
-
-        LlhtbA== 18032
-
-        b3BwZWQ= 18033
-
-        INGE 18034
-
-        bGlzdHM= 18035
-
-        IHRlYXI= 18036
-
-        aXZlcnNhcnk= 18037
-
-        LmNvbGxlY3Rpb24= 18038
-
-        IENvbnN0aXR1dGlvbg== 18039
-
-        IEh0dHBSZXNwb25zZQ== 18040
-
-        IGJyaWxs 18041
-
-        IFByb20= 18042
-
-        aG92ZXI= 18043
-
-        MzY2 18044
-
-        IE1pYW1p 18045
-
-        IGFyZ3Vl 18046
-
-        X2Zsb2F0 18047
-
-        NTA0 18048
-
-        IOOC 18049
-
-        IG5hdA== 18050
-
-        IFRhbA== 18051
-
-        IGludGVncmF0aW9u 18052
-
-        KGN1cg== 18053
-
-        IHJlbW92aW5n 18054
-
-        IGNvZWZm 18055
-
-        IFRob3VnaA== 18056
-
-        IGZvcmVjYXN0 18057
-
-        NDA4 18058
-
-        IFZlZ2Fz 18059
-
-        U2l0ZQ== 18060
-
-        MzQ2 18061
-
-        IHRyYWI= 18062
-
-        IEhlbnJ5 18063
-
-        LWk= 18064
-
-        IGludm9sdmVz 18065
-
-        QlQ= 18066
-
-        IHNsbw== 18067
-
-        SW52b2tl 18068
-
-        IGx1Y2t5 18069
-
-        MDI1 18070
-
-        cmF0 18071
-
-        ID8K 18072
-
-        IGhhbmRsZWQ= 18073
-
-        KGZk 18074
-
-        Y29udGVudHM= 18075
-
-        IE9GRg== 18076
-
-        UkY= 18077
-
-        IHN0eQ== 18078
-
-        IE1vdG9y 18079
-
-        dGVyeQ== 18080
-
-        dGF4 18081
-
-        TUFQ 18082
-
-        IE1ycw== 18083
-
-        IHBob25lcw== 18084
-
-        IFVJVmlldw== 18085
-
-        IikpKTsK 18086
-
-        KGRldg== 18087
-
-        IElyaXNo 18088
-
-        MDE5 18089
-
-        IHdz 18090
-
-        REk= 18091
-
-        X09GRlNFVA== 18092
-
-        IEV2ZW50cw== 18093
-
-        IHN0YWdlcw== 18094
-
-        IH0vLw== 18095
-
-        IGhhYmVu 18096
-
-        U1RBTkNF 18097
-
-        IFNpbg== 18098
-
-        IE1vbmV5 18099
-
-        KHRvcA== 18100
-
-        IGFwcG9pbnRtZW50 18101
-
-        VkVSU0lPTg== 18102
-
-        bWV0YWRhdGE= 18103
-
-        X2NvbW1lbnQ= 18104
-
-        IGNvbGxlYWd1ZXM= 18105
-
-        bWFwcw== 18106
-
-        4pg= 18107
-
-        CgkK 18108
-
-        KGFs 18109
-
-        X3JlcQ== 18110
-
-        IGZ1dA== 18111
-
-        IGFyY2hpdGVjdHVyZQ== 18112
-
-        MzUx 18113
-
-        IFdIRVRIRVI= 18114
-
-        ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIA== 18115
-
-        X3NjcmVlbg== 18116
-
-        IHN0eWxlVXJscw== 18117
-
-        IG1vbnN0ZXI= 18118
-
-        LnVw 18119
-
-        cGhpYQ== 18120
-
-        IHByb2Nlc3Nvcg== 18121
-
-        IFRlcnI= 18122
-
-        PScs 18123
-
-        IE1hbnVmYWN0 18124
-
-        IE5U 18125
-
-        a2Vs 18126
-
-        aWJlcm4= 18127
-
-        CWZpbGU= 18128
-
-        QWxp 18129
-
-        cmllbnRhdGlvbg== 18130
-
-        IC8vIQ== 18131
-
-        YXBvcmU= 18132
-
-        YW5lb3Vz 18133
-
-        IENyZWF0 18134
-
-        Zm9sZGVy 18135
-
-        NDE1 18136
-
-        IGhheQ== 18137
-
-        U3VwcHJlc3M= 18138
-
-        KGxlZnQ= 18139
-
-        IGV1cm8= 18140
-
-        IGRpc2NsYWltZXI= 18141
-
-        dXN0cnk= 18142
-
-        c2hpcHM= 18143
-
-        X2Zk 18144
-
-        IEZh 18145
-
-        X2luc2VydA== 18146
-
-        IHJvbA== 18147
-
-        aWZ0aW5n 18148
-
-        IENvbW1lbnRz 18149
-
-        X2Jy 18150
-
-        IGxvc3Nlcw== 18151
-
-        IEFkZGVk 18152
-
-        Y2hhcmc= 18153
-
-        INC/0L4= 18154
-
-        X3N5c3RlbQ== 18155
-
-        IFNvbWV0aW1lcw== 18156
-
-        IFNwYWlu 18157
-
-        KGdyb3Vw 18158
-
-        aWFsaXM= 18159
-
-        IGRvbGxhcg== 18160
-
-        IEFyZ3M= 18161
-
-        NDk5 18162
-
-        Mjk3 18163
-
-        cXVpcmVz 18164
-
-        IFRlbg== 18165
-
-        LnNjc3M= 18166
-
-        IHN1cnZpdmU= 18167
-
-        dXNhZ2U= 18168
-
-        IGp1bg== 18169
-
-        aW1pdGVy 18170
-
-        77yBCgo= 18171
-
-        IGZpZnRo 18172
-
-        dG9nZ2xl 18173
-
-        IGRlY2xpbmU= 18174
-
-        KCQi 18175
-
-        KExvbmc= 18176
-
-        aW5nZQ== 18177
-
-        IHBpbG90 18178
-
-        LWxpZ2h0 18179
-
-        LXJhZGl1cw== 18180
-
-        IHBvZGNhc3Q= 18181
-
-        IG5hdHVyYWxseQ== 18182
-
-        UGFnZXM= 18183
-
-        5Li6 18184
-
-        IERlc3BpdGU= 18185
-
-        IGxpZ2h0aW5n 18186
-
-        IGNyYXRl 18187
-
-        IEJpbmFyeQ== 18188
-
-        IHJlZHVjaW5n 18189
-
-        IGVsZWc= 18190
-
-        IE1vdXNl 18191
-
-        IFRlc3RCZWQ= 18192
-
-        IGJlZm9yZUVhY2g= 18193
-
-        X0FSUkFZ 18194
-
-        UmVkaXJlY3Q= 18195
-
-        MzI5 18196
-
-        IGZsb29k 18197
-
-        IHNoaXBz 18198
-
-        MzYz 18199
-
-        IGVsZWN0cmljaXR5 18200
-
-        KSoo 18201
-
-        6rg= 18202
-
-        IFZpZXQ= 18203
-
-        aGVybw== 18204
-
-        IGRpYQ== 18205
-
-        IEtlbnQ= 18206
-
-        aGVhcnQ= 18207
-
-        IHRocmVhdHM= 18208
-
-        X2FjYw== 18209
-
-        IHN5bWJvbHM= 18210
-
-        aXNjaGVu 18211
-
-        X2luc3Q= 18212
-
-        Q3JpdGVyaW9u 18213
-
-        IFRJTQ== 18214
-
-        LkhlaWdodA== 18215
-
-        NTgw 18216
-
-        IOKAmQ== 18217
-
-        KCk7CgoK 18218
-
-        UHJvZHVjdHM= 18219
-
-        X1NQ 18220
-
-        IEN5 18221
-
-        IGRlcGVuZGVudA== 18222
-
-        ZXN0ZQ== 18223
-
-        IGRhdG9z 18224
-
-        ZGl0 18225
-
-        0LDQsg== 18226
-
-        SUdOQUw= 18227
-
-        IGxlc3Nvbg== 18228
-
-        Ij4n 18229
-
-        IENvdmVy 18230
-
-        IEhvcGU= 18231
-
-        IFRpbWVy 18232
-
-        IGRhZA== 18233
-
-        dmlkZXJz 18234
-
-        IFBob3Q= 18235
-
-        Lz8= 18236
-
-        cm9weQ== 18237
-
-        b21pbmc= 18238
-
-        YXNpb24= 18239
-
-        IFwo 18240
-
-        IEVU 18241
-
-        IFJlYWRpbmc= 18242
-
-        IGVwaXNvZGVz 18243
-
-        bG0= 18244
-
-        NDIx 18245
-
-        ZWNoYQ== 18246
-
-        IG5ldXJv 18247
-
-        ODIw 18248
-
-        IGhhcm1vbg== 18249
-
-        IGxpYmVyYWw= 18250
-
-        LWluZA== 18251
-
-        Mzkz 18252
-
-        REFUQQ== 18253
-
-        IGV2ZXJ5ZGF5 18254
-
-        IGRpdmlkZWQ= 18255
-
-        IEFjdGl2ZVJlY29yZA== 18256
-
-        ZmlndXJl 18257
-
-        VUE= 18258
-
-        5Lk= 18259
-
-        cmllbmRseQ== 18260
-
-        dGVjaA== 18261
-
-        NjAx 18262
-
-        LmdhbWVPYmplY3Q= 18263
-
-        0LjRgtGM 18264
-
-        Mzc0 18265
-
-        IG1vb24= 18266
-
-        ZnRpbWU= 18267
-
-        IG5vY2g= 18268
-
-        IFRPUlQ= 18269
-
-        IFZN 18270
-
-        LmluaXRpYWw= 18271
-
-        KGNoaWxk 18272
-
-        IG11c2ljYWw= 18273
-
-        IG9j 18274
-
-        YmFz 18275
-
-        IEhheQ== 18276
-
-        MzYx 18277
-
-        X2xvbmc= 18278
-
-        IG1lbXNldA== 18279
-
-        aWxleQ== 18280
-
-        YWRlbHBoaWE= 18281
-
-        U1Y= 18282
-
-        cm9hdA== 18283
-
-        X3R4 18284
-
-        IGxvbg== 18285
-
-        IG5nT25Jbml0 18286
-
-        YnA= 18287
-
-        IEdvbGRlbg== 18288
-
-        QUNIRQ== 18289
-
-        IHdvcnJpZWQ= 18290
-
-        YXpp 18291
-
-        RWFy 18292
-
-        VGFrZQ== 18293
-
-        KGZw 18294
-
-        YnVyZ2g= 18295
-
-        X0RhdGE= 18296
-
-        Z3Jlcw== 18297
-
-        IE9udA== 18298
-
-        cHVz 18299
-
-        IHRyYW5zcGFyZW50 18300
-
-        IHBvY2tldA== 18301
-
-        IHJhbQ== 18302
-
-        aWdyYXRpb25z 18303
-
-        Lg0KDQo= 18304
-
-        IFso 18305
-
-        IGFkb3B0ZWQ= 18306
-
-        IHJlcG9ydGVkbHk= 18307
-
-        IERyZWFt 18308
-
-        IH0pKTsK 18309
-
-        bG9zaW5n 18310
-
-        IHRlZXRo 18311
-
-        IEJvb2tz 18312
-
-        Iiwm 18313
-
-        ZW5ueQ== 18314
-
-        TEVNRU5U 18315
-
-        IGdlbA== 18316
-
-        IFBsYW50 18317
-
-        NDM3 18318
-
-        IeKAnQ== 18319
-
-        Lmhvc3Q= 18320
-
-        IFJlcGx5 18321
-
-        Mzc2 18322
-
-        cmVuZ3Ro 18323
-
-        IHJlY29nbml0aW9u 18324
-
-        IH19Pgo= 18325
-
-        TEE= 18326
-
-        IG1pcnJvcg== 18327
-
-        IGFzc2lzdGFudA== 18328
-
-        KGRldmljZQ== 18329
-
-        IHNwaXJpdHVhbA== 18330
-
-        YnVpbGRlcg== 18331
-
-        wqc= 18332
-
-        IG91dHI= 18333
-
-        IHR0 18334
-
-        IFBFUg== 18335
-
-        IHJhZGljYWw= 18336
-
-        TWV0aG9kcw== 18337
-
-        IHBhY2U= 18338
-
-        dWR5 18339
-
-        IGd1dA== 18340
-
-        IEdyZWVr 18341
-
-        IG5vbmF0b21pYw== 18342
-
-        IFBhcGVy 18343
-
-        X0dQSU8= 18344
-
-        IG9ic3Q= 18345
-
-        LkFk 18346
-
-        dmlyb25tZW50cw== 18347
-
-        IFNvdg== 18348
-
-        MzU2 18349
-
-        KGNvbg== 18350
-
-        IFRyYW5zYWN0aW9u 18351
-
-        LmFzc2lnbg== 18352
-
-        CWNhdGNo 18353
-
-        ZWx0ZXI= 18354
-
-        IGJpdGNvaW4= 18355
-
-        X0dS 18356
-
-        IDw/PQ== 18357
-
-        X2xhbmc= 18358
-
-        7J2E 18359
-
-        QnJvd3Nlcg== 18360
-
-        IGNvbnNpZGVyYXRpb24= 18361
-
-        IEV4ZWN1dGl2ZQ== 18362
-
-        6Ze0 18363
-
-        O1w= 18364
-
-        IEpTT05PYmplY3Q= 18365
-
-        IEJlbGw= 18366
-
-        IHNwb2tlc21hbg== 18367
-
-        fn5+fn5+fn4= 18368
-
-        b2NrZXk= 18369
-
-        IEdybw== 18370
-
-        IEF3 18371
-
-        Q29uc3RyYWludA== 18372
-
-        IFByYWN0 18373
-
-        IEV2ZXI= 18374
-
-        cHJpbQ== 18375
-
-        OnsK 18376
-
-        X2lt 18377
-
-        UE4= 18378
-
-        TWlsbGlz 18379
-
-        VU1FTlQ= 18380
-
-        IGJhZ3M= 18381
-
-        w6Vy 18382
-
-        QU5ORUw= 18383
-
-        MzU0 18384
-
-        IGlj 18385
-
-        IHRyYW5zcG9ydGF0aW9u 18386
-
-        IFNhdWRp 18387
-
-        aGFuZGxlcg== 18388
-
-        RHJhZw== 18389
-
-        IGhk 18390
-
-        Y29sbGFwc2U= 18391
-
-        X1BI 18392
-
-        IHVi 18393
-
-        QVJN 18394
-
-        IEFQUA== 18395
-
-        IHRvbmlnaHQ= 18396
-
-        IGRpbmluZw== 18397
-
-        UmVjb2du 18398
-
-        IGJj 18399
-
-        aWd0 18400
-
-        KG51bWJlcg== 18401
-
-        Qm9vdA== 18402
-
-        IGVsc2V3aGVyZQ== 18403
-
-        IGFycm93 18404
-
-        YXJnYQ== 18405
-
-        IGRlbGljaW91cw== 18406
-
-        IFNO 18407
-
-        V1I= 18408
-
-        VmFsaWRhdGU= 18409
-
-        IFF1YWxpdHk= 18410
-
-        KGVtYWls 18411
-
-        IGludGVycHJl 18412
-
-        aWdhdGlvbg== 18413
-
-        IGNob2NvbGF0ZQ== 18414
-
-        NTI1 18415
-
-        X2VkZ2U= 18416
-
-        IHN0b3Bz 18417
-
-        OmZ1bmN0aW9u 18418
-
-        KXw= 18419
-
-        IHRoYWk= 18420
-
-        IExvYWRpbmc= 18421
-
-        U3Rvcnk= 18422
-
-        VHJpZ2dlcg== 18423
-
-        YnJhbmNo 18424
-
-        IHRk 18425
-
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-        Tm9tYnJl 19468
-
-        X21vbnRo 19469
-
-        xrA= 19470
-
-        LXN0YXJ0 19471
-
-        eHlnZW4= 19472
-
-        CWZ0 19473
-
-        aWFnbm9zdGljcw== 19474
-
-        IE1hdHRoZXc= 19475
-
-        IGNvbmNlcHRz 19476
-
-        IGNvbnN0cg== 19477
-
-        LlN0YXRl 19478
-
-        0LjQvQ== 19479
-
-        Tm92 19480
-
-        zrE= 19481
-
-        IFBhbmVs 19482
-
-        5Liq 19483
-
-        Y29tcGFyZQ== 19484
-
-        PigpCg== 19485
-
-        IGFwcGx5aW5n 19486
-
-        IHByb21pc2Vk 19487
-
-        IG94 19488
-
-        bmNpYQ== 19489
-
-        IFZhbGlkYXRpb24= 19490
-
-        b3J0cw== 19491
-
-        X2N1cg== 19492
-
-        ZWxlY3Q= 19493
-
-        ZXll 19494
-
-        KERhdGE= 19495
-
-        IHJlcG9ydGVy 19496
-
-        IEJ1ZmY= 19497
-
-        Mzk1 19498
-
-        IHNy 19499
-
-        ICI7 19500
-
-        aWNreQ== 19501
-
-        IHRlbXBvcg== 19502
-
-        U04= 19503
-
-        IHJlc2lkZW50 19504
-
-        cGlyZXM= 19505
-
-        eXNpY2Fs 19506
-
-        IGVuZG9yc2U= 19507
-
-        IFNvbmc= 19508
-
-        aXNFbXB0eQ== 19509
-
-        bGVldA== 19510
-
-        X3V0aWw= 19511
-
-        IGRpc3Rpbmd1 19512
-
-        IFRhbGs= 19513
-
-        IE1vdA== 19514
-
-        KGRlZmF1bHQ= 19515
-
-        LkFyZw== 19516
-
-        Z29yaXRobXM= 19517
-
-        X3dvcmRz 19518
-
-        aW1tZXI= 19519
-
-        X3Jlc2V0 19520
-
-        ZmFtaWx5 19521
-
-        V1c= 19522
-
-        IHNhdmluZ3M= 19523
-
-        IOKAnQ== 19524
-
-        X2VuYWJsZQ== 19525
-
-        c2lkZWJhcg== 19526
-
-        UnVubmluZw== 19527
-
-        IGFsaQ== 19528
-
-        IHRlc3RpbQ== 19529
-
-        IHdhcm5pbmdz 19530
-
-        IENoZW0= 19531
-
-        IEV4aXQ= 19532
-
-        IGZvdW5kZXI= 19533
-
-        cGVjdG9y 19534
-
-        IHJt 19535
-
-        X2RhdGFzZXQ= 19536
-
-        IERhcw== 19537
-
-        IGhhbg== 19538
-
-        R2V0dHk= 19539
-
-        w6Fs 19540
-
-        IG55 19541
-
-        IHBvdmVydHk= 19542
-
-        IHJlc3VsdGVk 19543
-
-        LmJ5 19544
-
-        IFZpc2l0 19545
-
-        IG9idGFpbmluZw== 19546
-
-        LycuJA== 19547
-
-        ICAgICAgICAgICAK 19548
-
-        c2hhbGw= 19549
-
-        X0xFRlQ= 19550
-
-        VUlJbWFnZQ== 19551
-
-        X05hbWU= 19552
-
-        aGF2ZQ== 19553
-
-        IE5vYg== 19554
-
-        bHI= 19555
-
-        LWZvb3Rlcg== 19556
-
-        IG5ha2Vk 19557
-
-        IEdhcmRlbg== 19558
-
-        XEZhY2FkZXM= 19559
-
-        IGdyYWR1YXRl 19560
-
-        NDE3 19561
-
-        IGZyYW5jaGlzZQ== 19562
-
-        cGxhbmU= 19563
-
-        IGNvbnRyaWJ1dGlvbnM= 19564
-
-        IHN0cmluZ1dpdGg= 19565
-
-        IGNyeXB0bw== 19566
-
-        IG1vdmVtZW50cw== 19567
-
-        YXRoZXJz 19568
-
-        IGxpZmV0aW1l 19569
-
-        IGNvbW11bmljYXRl 19570
-
-        amFy 19571
-
-        IEZyYWdtZW50 19572
-
-        X0lG 19573
-
-        IE5hdnk= 19574
-
-        IEZpZ3VyZQ== 19575
-
-        IHNpbXVsYXRpb24= 19576
-
-        X3N0b3A= 19577
-
-        IHJlcG9ydGVycw== 19578
-
-        IHZlcnN1cw== 19579
-
-        YWph 19580
-
-        IM6x 19581
-
-        IGdvdmVybm9y 19582
-
-        TGlzdEl0ZW0= 19583
-
-        IHNlYWxlZA== 19584
-
-        LkJhY2tncm91bmQ= 19585
-
-        ZWRp 19586
-
-        YXNoaW5n 19587
-
-        IGxpcA== 19588
-
-        IElo 19589
-
-        bWVyZ2U= 19590
-
-        IG5lYw== 19591
-
-        MDI0 19592
-
-        ZWxvY2l0eQ== 19593
-
-        QVRFRw== 19594
-
-        IHNlZWRz 19595
-
-        IGZsb2F0aW5n 19596
-
-        NzAx 19597
-
-        X0ZB 19598
-
-        d2Fsaw== 19599
-
-        CXVzZXI= 19600
-
-        X2RlcHRo 19601
-
-        IHdhZ2U= 19602
-
-        QGFwcA== 19603
-
-        Tmls 19604
-
-        KFsi 19605
-
-        KHZlY3Rvcg== 19606
-
-        IHNlY3JldGFyeQ== 19607
-
-        NDYx 19608
-
-        IGpQYW5lbA== 19609
-
-        dmV6 19610
-
-        wqDCoMKgwqA= 19611
-
-        ZGlyZWN0aW9u 19612
-
-        IEVQ 19613
-
-        IGh1bnQ= 19614
-
-        Mzk2 19615
-
-        SnNvblByb3BlcnR5 19616
-
-        IFBPUlQ= 19617
-
-        XSIs 19618
-
-        0LDQvw== 19619
-
-        IEZvcmVpZ24= 19620
-
-        cGFuaWM= 19621
-
-        IHRyaWFscw== 19622
-
-        IEFsZQ== 19623
-
-        IHJ1cmFs 19624
-
-        LXZhbHVl 19625
-
-        YXV0aG9yaXplZA== 19626
-
-        IFNjb3RsYW5k 19627
-
-        LmRyb3A= 19628
-
-        IE1U 19629
-
-        57E= 19630
-
-        Mzkx 19631
-
-        cm93dGg= 19632
-
-        NTE1 19633
-
-        RmlsZVBhdGg= 19634
-
-        IHJlY2FsbA== 19635
-
-        aWZsZQ== 19636
-
-        IGNlbA== 19637
-
-        IFNFTEVDVA== 19638
-
-        a24= 19639
-
-        X2Nhc2U= 19640
-
-        IGNyb3A= 19641
-
-        NTQz 19642
-
-        c3VyZQ== 19643
-
-        cG90 19644
-
-        SUNT 19645
-
-        IHN0ZW0= 19646
-
-        IGluZHVzdHJpZXM= 19647
-
-        UHV0 19648
-
-        IGFiZXI= 19649
-
-        cm9hZGNhc3Q= 19650
-
-        SWNvbnM= 19651
-
-        KSIpCg== 19652
-
-        5oiQ5Yqf 19653
-
-        Z3Vp 19654
-
-        IGFzc3VtZWQ= 19655
-
-        IHJ4 19656
-
-        RUE= 19657
-
-        6Kc= 19658
-
-        RUxM 19659
-
-        IGRvc2U= 19660
-
-        IGluZQ== 19661
-
-        IGRlZXBlcg== 19662
-
-        bGlkZXI= 19663
-
-        IG9yZGluYXJ5 19664
-
-        IGdvbGY= 19665
-
-        NjA1 19666
-
-        X0lNQUdF 19667
-
-        IE5BTUU= 19668
-
-        KG1vZHVsZQ== 19669
-
-        IGF0b20= 19670
-
-        IGJlbHQ= 19671
-
-        IG9mZmljZXM= 19672
-
-        NTA2 19673
-
-        YmV0YQ== 19674
-
-        IHBoaWxvc29waHk= 19675
-
-        KEpTT04= 19676
-
-        LWZpZWxk 19677
-
-        IGludHJvZHVjZQ== 19678
-
-        IGNvbnZlbmllbmNl 19679
-
-        b3B0aW0= 19680
-
-        PiIK 19681
-
-        YXRoeQ== 19682
-
-        IGVtcGxveWVy 19683
-
-        cXVhdGU= 19684
-
-        IGVkaXRlZA== 19685
-
-        QXJndW1lbnRz 19686
-
-        IE5hdGlvbnM= 19687
-
-        X18p 19688
-
-        IG5vc2U= 19689
-
-        IFNhbXBsZQ== 19690
-
-        JykKCgo= 19691
-
-        IGNha2U= 19692
-
-        LmdldEF0dHJpYnV0ZQ== 19693
-
-        SEQ= 19694
-
-        Mzky 19695
-
-        TW9kaWZpZWQ= 19696
-
-        NDQ1 19697
-
-        IHByZWRpY3RlZA== 19698
-
-        xYQ= 19699
-
-        YW5pZQ== 19700
-
-        U29ycnk= 19701
-
-        KGRvYw== 19702
-
-        d2luZA== 19703
-
-        aWV2ZQ== 19704
-
-        IHByb3Zpc2lvbnM= 19705
-
-        QVRFUg== 19706
-
-        T1RF 19707
-
-        TVk= 19708
-
-        LkF1dG93aXJlZA== 19709
-
-        IEJhdGg= 19710
-
-        NDIz 19711
-
-        LkJvb2xlYW4= 19712
-
-        IGJhY2tlbmQ= 19713
-
-        Lk1vdXNl 19714
-
-        YXRlcmFs 19715
-
-        cGFwZXI= 19716
-
-        Q29uc3Q= 19717
-
-        IFZS 19718
-
-        X2VudGl0eQ== 19719
-
-        X0NUUkw= 19720
-
-        IFByb3RlY3Rpb24= 19721
-
-        IEdN 19722
-
-        IFN0dWR5 19723
-
-        IHNvdXA= 19724
-
-        b3RpbWU= 19725
-
-        J3VzZQ== 19726
-
-        XSI= 19727
-
-        L3VzZXJz 19728
-
-        YXVn 19729
-
-        IEhvbmc= 19730
-
-        X25vcm0= 19731
-
-        44Go 19732
-
-        IHNlY3Jl 19733
-
-        KEJ1aWxk 19734
-
-        IENvbnRyYWN0 19735
-
-        b2xhcw== 19736
-
-        IHNhdWNl 19737
-
-        IGFnZ3Jlc3NpdmU= 19738
-
-        IHJhY2lhbA== 19739
-
-        Y2hhcmFjdGVy 19740
-
-        QEA= 19741
-
-        IGNvbXBpbGU= 19742
-
-        IFZvaWQ= 19743
-
-        X3JlbQ== 19744
-
-        X21lbW9yeQ== 19745
-
-        MzQ4 19746
-
-        a2s= 19747
-
-        IG1pYw== 19748
-
-        U2FtZQ== 19749
-
-        VXRpbGl0eQ== 19750
-
-        IEh0bWw= 19751
-
-        IFhtbA== 19752
-
-        UmVhZHk= 19753
-
-        IGdhbGw= 19754
-
-        IGFsbGVnZWRseQ== 19755
-
-        CQkJCSAgIA== 19756
-
-        IE1ldGFs 19757
-
-        IFBlcnNvbmFs 19758
-
-        IGJvcmRlclJhZGl1cw== 19759
-
-        cnhqcw== 19760
-
-        b2JqZWN0cw== 19761
-
-        IHdhbnRpbmc= 19762
-
-        IGJvd2w= 19763
-
-        dmVuZG9y 19764
-
-        b2Zmc2V0b2Y= 19765
-
-        IFJz 19766
-
-        IFJhdGluZw== 19767
-
-        IHJhbGx5 19768
-
-        X05PREU= 19769
-
-        NDE4 19770
-
-        IE1peA== 19771
-
-        IGFkdmVydGlz 19772
-
-        NDg1 19773
-
-        NjY3 19774
-
-        IG5hcnJhdGl2ZQ== 19775
-
-        c2Fs 19776
-
-        IG1j 19777
-
-        U0Vycm9y 19778
-
-        IGZpbmdlcnM= 19779
-
-        IGFjY29tcGFueQ== 19780
-
-        IHRpcmVk 19781
-
-        IHN0cmlkZQ== 19782
-
-        IGd1aQ== 19783
-
-        ZWxpc3Q= 19784
-
-        TG9jYWxl 19785
-
-        IHJlbGVhc2Vz 19786
-
-        aWtpbmc= 19787
-
-        IGFuZ2Vy 19788
-
-        KSkpCgo= 19789
-
-        YWxsZXN0 19790
-
-        U3VtbWFyeQ== 19791
-
-        KE8= 19792
-
-        KGZvcg== 19793
-
-        IGJhc2tldGJhbGw= 19794
-
-        IHJvYWRz 19795
-
-        IEluc3RhbGw= 19796
-
-        IEZhYg== 19797
-
-        aXRtYXA= 19798
-
-        NDc1 19799
-
-        ICkpCg== 19800
-
-        IGludGVyc2VjdGlvbg== 19801
-
-        aWdoYm9y 19802
-
-        IEJyeQ== 19803
-
-        IEhFUkU= 19804
-
-        U29mdHdhcmU= 19805
-
-        ZWxmYXJl 19806
-
-        YWNz 19807
-
-        NjIy 19808
-
-        IHRyYWlsZXI= 19809
-
-        LmdldENsYXNz 19810
-
-        Y2hhcnM= 19811
-
-        IHJlZ3VsYXRpb24= 19812
-
-        IHJlZmVycw== 19813
-
-        IGRlc3RydWN0aW9u 19814
-
-        IGNvbnRpbnVvdXM= 19815
-
-        IEF1c3Rpbg== 19816
-
-        6aI= 19817
-
-        YWthbg== 19818
-
-        LndpbmRvdw== 19819
-
-        IFRlbXBsYXRlcw== 19820
-
-        IGFic2VuY2U= 19821
-
-        Om4= 19822
-
-        IGRpc29yZGVy 19823
-
-        Zmxhc2g= 19824
-
-        IGRlbGV0 19825
-
-        Ym9hcmRz 19826
-
-        ICAJ 19827
-
-        Uk9Q 19828
-
-        ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIA==
-        19829
-
-        IGFjcXU= 19830
-
-        IGxhd3N1aXQ= 19831
-
-        IFJldmlld3M= 19832
-
-        IGdhcmFnZQ== 19833
-
-        dGltZXI= 19834
-
-        IGVq 19835
-
-        IFJlY3RhbmdsZQ== 19836
-
-        IGZsb3dlcnM= 19837
-
-        Mzk4 19838
-
-        aWxzdA== 19839
-
-        IEluc3RhbmNl 19840
-
-        U3VwZXI= 19841
-
-        ZGV0 19842
-
-        ZGlzcG9zaW5n 19843
-
-        IEVT 19844
-
-        IElD 19845
-
-        dmVyZQ== 19846
-
-        U2s= 19847
-
-        X2NoYW5uZWxz 19848
-
-        cHV0ZWQ= 19849
-
-        L251bGw= 19850
-
-        bm5lbg== 19851
-
-        NDMx 19852
-
-        IEdhbGxlcnk= 19853
-
-        X2dsb2JhbA== 19854
-
-        QXV0aGVudGljYXRpb24= 19855
-
-        IFJhbms= 19856
-
-        IGJsb2NrZWQ= 19857
-
-        IGNhbG0= 19858
-
-        bWFya2V0 19859
-
-        CXZhbA== 19860
-
-        IGF1Zw== 19861
-
-        cGVyaW9k 19862
-
-        IENvbnN0YW50 19863
-
-        ID8+Ij4K 19864
-
-        IGxvYmJ5 19865
-
-        cGFs 19866
-
-        Mzc5 19867
-
-        IHNpbms= 19868
-
-        NTA4 19869
-
-        aWFo 19870
-
-        0KE= 19871
-
-        dXJuYW1l 19872
-
-        IGNvbnZlcg== 19873
-
-        IGludmVzdGlnYXRl 19874
-
-        Q2hyaXN0 19875
-
-        SHVi 19876
-
-        IElORA== 19877
-
-        IFBlZA== 19878
-
-        dXJhcw== 19879
-
-        CXVybA== 19880
-
-        IFRybw== 19881
-
-        IHByZWZlcmVuY2Vz 19882
-
-        IGd1YXJhbnRlZWQ= 19883
-
-        YAoK 19884
-
-        IHBvcnRpb25z 19885
-
-        IGV2YWx1 19886
-
-        Jz48Lw== 19887
-
-        KCl7Cgo= 19888
-
-        ZW5jb2RlZA== 19889
-
-        emlsbGE= 19890
-
-        LkNsYXNz 19891
-
-        ICpf 19892
-
-        Xyc= 19893
-
-        IHZpZXdlZA== 19894
-
-        IFBoaWxhZGVscGhpYQ== 19895
-
-        LnJvd3M= 19896
-
-        QWRkZWQ= 19897
-
-        IFRvdWNo 19898
-
-        ODQw 19899
-
-        LmRlbGVnYXRl 19900
-
-        cXVlZXpl 19901
-
-        c2xpZGU= 19902
-
-        IFNlbmlvcg== 19903
-
-        KHRhZw== 19904
-
-        IGludGVydmlld3M= 19905
-
-        IHN1YQ== 19906
-
-        YXRhcw== 19907
-
-        QAoK 19908
-
-        ZGlzdGFuY2U= 19909
-
-        IHNlaW4= 19910
-
-        bGF0ZXN0 19911
-
-        IFByaW5jZQ== 19912
-
-        IGx1eHVyeQ== 19913
-
-        IHJlZnI= 19914
-
-        IEtpdGNoZW4= 19915
-
-        0YQ= 19916
-
-        KGF0 19917
-
-        RmluYWw= 19918
-
-        w7xjaw== 19919
-
-        X3plcm8= 19920
-
-        IEFCQw== 19921
-
-        IE1hbmNoZXN0ZXI= 19922
-
-        IGNvdw== 19923
-
-        Q09M 19924
-
-        X05VTUJFUg== 19925
-
-        Y2hhbmdlcw== 19926
-
-        Z2VuZXJhdGU= 19927
-
-        LlByaW50Zg== 19928
-
-        MzY5 19929
-
-        c2hhcmU= 19930
-
-        U3RvY2s= 19931
-
-        IFBU 19932
-
-        QW5pbQ== 19933
-
-        YW5nYQ== 19934
-
-        IGln 19935
-
-        dXBsb2Fkcw== 19936
-
-        IHBhY2tlZA== 19937
-
-        IH1dOwo= 19938
-
-        KHNlbmRlcg== 19939
-
-        IFdpcmU= 19940
-
-        aXNvbnM= 19941
-
-        IHBsYXlvZmY= 19942
-
-        XEU= 19943
-
-        NjA4 19944
-
-        L1I= 19945
-
-        IGhlYWRlZA== 19946
-
-        QWxwaGE= 19947
-
-        KG9yZGVy 19948
-
-        IG9wcG9uZW50cw== 19949
-
-        YWNrc29u 19950
-
-        X21lbWJlcg== 19951
-
-        VHVybg== 19952
-
-        IFNvdmlldA== 19953
-
-        7JeQ 19954
-
-        YXVnZQ== 19955
-
-        NDQ4 19956
-
-        IGluY29taW5n 19957
-
-        IGphaw== 19958
-
-        LWdhbWU= 19959
-
-        IE1hbGU= 19960
-
-        IE1vbnRo 19961
-
-        U3RhZ2U= 19962
-
-        LmV4ZQ== 19963
-
-        T3duUHJvcGVydHk= 19964
-
-        LnNldEl0ZW0= 19965
-
-        IGRj 19966
-
-        5L2c 19967
-
-        IGJydXQ= 19968
-
-        IGF0dGVtcHRpbmc= 19969
-
-        Lmxlbg== 19970
-
-        IGp1ZGdtZW50 19971
-
-        IHNhYg== 19972
-
-        IGNhZA== 19973
-
-        IEl0ZW1z 19974
-
-        Y29tZm9ydA== 19975
-
-        ZWxpemU= 19976
-
-        L2xvZw== 19977
-
-        IGVudHJlcHJlbmU= 19978
-
-        IGNvbXBpbGVy 19979
-
-        X3ZhbGlkYXRpb24= 19980
-
-        cmV2aWV3 19981
-
-        IHRleHRCb3g= 19982
-
-        IGZyYWN0aW9u 19983
-
-        IEJhbA== 19984
-
-        PjsKCg== 19985
-
-        LkF1dG9TY2FsZU1vZGU= 19986
-
-        IGNhdHM= 19987
-
-        NDY1 19988
-
-        IHJlZ2lzdHJ5 19989
-
-        dWx1cw== 19990
-
-        Rkk= 19991
-
-        cGF5bG9hZA== 19992
-
-        LXNlYXJjaA== 19993
-
-        IHN0YXlpbmc= 19994
-
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-
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-        UHJldmlvdXM= 21994
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-
-        b2xsYXI= 21999
-
-        MDI4 22000
-
-        X2V2YWw= 22001
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-        CU5hbWU= 22002
-
-        KHRyZWU= 22003
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-
-        NTcx 22005
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-        KV4= 30876
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-        Qk9BUkQ= 30877
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-        Ki8s 30878
-
-        IGd1bHA= 30879
-
-        IEJlbm4= 30880
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-        IGRlY2tz 30881
-
-        LnN0YXR1c0NvZGU= 30882
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-        IGh1Zw== 30884
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-        dWd1 30885
-
-        IHBsZWQ= 30886
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-        LCIl 30887
-
-        aGFwZQ== 30888
-
-        INC30LDQvw== 30889
-
-        IE1haW5l 30890
-
-        LnJlYWw= 30891
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-        IGRhbGFt 30892
-
-        IE1pbm9y 30893
-
-        LkZsb2F0 30894
-
-        ZGlzcA== 30895
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-        IHRs 30896
-
-        IGVuY291bnQ= 30897
-
-        PT4k 30898
-
-        IGZn 30899
-
-        dGVlcw== 30900
-
-        IFJlY29tbQ== 30901
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-        w6Rs 30902
-
-        IGNoZW1pc3RyeQ== 30903
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-        T0lE 30905
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-        IGZvcmV4 30906
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-        IEFwcGVuZA== 30907
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-        IHsq 30908
-
-        IFN1cHBseQ== 30909
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-        Q0dGbG9hdA== 30910
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-        KGJs 30911
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-        IGF0ZQ== 30912
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-        YWRvcmE= 30913
-
-        IGd1c3Q= 30914
-
-        QXNzb2Np 30915
-
-        Pi4K 30916
-
-        RkVUQ0g= 30917
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-        LnNlcmlhbA== 30918
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-        d2lkZ2V0cw== 30919
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-        aWVmcw== 30921
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-        IFByZWQ= 30924
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-        2K0= 30925
-
-        5LqL 30926
-
-        dWJlcm5ldGVz 30927
-
-        IExhdXJh 30928
-
-        IGxhYmVsZWQ= 30929
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-        SGlnaGxpZ2h0 30930
-
-        IGFubm95aW5n 30931
-
-        L3VwZGF0ZQ== 30932
-
-        KGRlc2NyaXB0aW9u 30933
-
-        IGludGltaWQ= 30934
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-        JGM= 30935
-
-        IikpKQo= 30936
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-        LkFQ 30937
-
-        IFtdKg== 30938
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-        IEVYSVQ= 30939
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-        Lkhvc3Q= 30940
-
-        IE9QRU4= 30941
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-        LnNlbmRNZXNzYWdl 30942
-
-        X2NhbWVyYQ== 30943
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-        X3RpbGU= 30944
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-        b25vbW91cw== 30946
-
-        IGRpc2Fkdg== 30947
-
-        IG5hYXI= 30948
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-        aW5kZXhPZg== 30949
-
-        IFBQ 30950
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-        LnByb3RvY29s 30951
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-        QUZF 30952
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-        IyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMj 30954
-
-        dW1iYWk= 30955
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-        LnN0YXRz 30956
-
-        IEdF 30957
-
-        IGll 30958
-
-        IFNURA== 30959
-
-        IE1hbm4= 30960
-
-        LnJlZmxlY3Q= 30961
-
-        S0I= 30962
-
-        IGRpdmU= 30963
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-        Lndhdg== 30964
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-        LyotLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t
-        30965
-
-        L3NldHRpbmdz 30966
-
-        LmxpZmVjeWNsZQ== 30967
-
-        IGRhdWdodGVycw== 30968
-
-        b3J1cw== 30969
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-        dWJlcg== 30970
-
-        TklORw== 30971
-
-        c3RyaQ== 30972
-
-        IFRpcA== 30973
-
-        IHpu 30974
-
-        IHN3aXRjaGVk 30975
-
-        aW5ldA== 30976
-
-        dWZmeQ== 30977
-
-        IFRyYW5zcG9ydGF0aW9u 30978
-
-        KGNvbmY= 30979
-
-        ZnJpY2E= 30980
-
-        IFhM 30981
-
-        IExlYWQ= 30982
-
-        X3BlcmNlbnQ= 30983
-
-        PE1hcA== 30984
-
-        IHRocnVzdA== 30985
-
-        b3Ji 30986
-
-        aWtr 30987
-
-        IHRyYXVtYQ== 30988
-
-        QWNjZXNzb3I= 30989
-
-        IEZpdA== 30990
-
-        IFN0cmluZ0J1ZmZlcg== 30991
-
-        ZXhwbA== 30992
-
-        KHNjcmVlbg== 30993
-
-        IGF1ZGllbmNlcw== 30994
-
-        IE9QVElPTg== 30995
-
-        X3JvdW5k 30996
-
-        W25vZGU= 30997
-
-        YmVo 30998
-
-        LT5fXw== 30999
-
-        cGVybWlzc2lvbnM= 31000
-
-        IERldGVybWluZQ== 31001
-
-        Lk1hbg== 31002
-
-        IGFkdmFuY2Vz 31003
-
-        LklucHV0U3RyZWFt 31004
-
-        IHN0cm9uZ2VzdA== 31005
-
-        IGVCYXk= 31006
-
-        ICMt 31007
-
-        IGRpcm5hbWU= 31008
-
-        IFNNUw== 31009
-
-        IG1lZGljYXRpb25z 31010
-
-        IGFtZW5kZWQ= 31011
-
-        IGNodXJjaGVz 31012
-
-        IEltcGVyaWFs 31013
-
-        JHJvdw== 31014
-
-        IE1hZGlzb24= 31015
-
-        IEluc3A= 31016
-
-        IGFmZmFpcg== 31017
-
-        IHBzeWNob2xvZ3k= 31018
-
-        dmg= 31019
-
-        IHNldmVyaXR5 31020
-
-        4oCQ 31021
-
-        IHN0cmlwcw== 31022
-
-        QUg= 31023
-
-        dmVydGlzaW5n 31024
-
-        IGNvbnNl 31025
-
-        SU1BR0U= 31026
-
-        IFN0YXRz 31027
-
-        CXNj 31028
-
-        LkN1cnNvcg== 31029
-
-        IGZyZWV6ZQ== 31030
-
-        c3Nvbg== 31031
-
-        KHhtbA== 31032
-
-        IFN1c2Fu 31033
-
-        LnRpbGU= 31034
-
-        ZWRlZA== 31035
-
-        ICAgIAkJCQ== 31036
-
-        dWVsbGU= 31037
-
-        IE1pdGNoZWxs 31038
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-        YmFzZWQ= 31039
-
-        T3BlcmFuZA== 31040
-
-        veaVsA== 31041
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-        b3VuY2Vz 31044
-
-        aWxkbw== 31045
-
-        LmV4ZWN1dGVRdWVyeQ== 31046
-
-        IGFwcHJvYWNoaW5n 31047
-
-        IFNldmVu 31048
-
-        IG51dHM= 31049
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-        IHJpYw== 31050
-
-        YXNzaWdubWVudA== 31051
-
-        IGNhbGN1bGF0b3I= 31052
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-        IE11cnBoeQ== 31053
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-        IEJvdQ== 31054
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-        7YQ= 31055
-
-        IGJ1dHQ= 31056
-
-        IHRpY2tz 31057
-
-        UHJvamVjdHM= 31058
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-        aWxpYg== 31059
-
-        LnRleHRDb2xvcg== 31060
-
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-
-        X2xvZ28= 31062
-
-        KHRlbXBsYXRl 31063
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-        IElOSVQ= 31064
-
-        IGltYWdlVmlldw== 31065
-
-        c2NyaXB0aW9ucw== 31066
-
-        T1JJVFk= 31067
-
-        Q29uc3VtZXI= 31068
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-        IHVucHJlY2VkZW50ZWQ= 31069
-
-        IHRvdXJpc3Q= 31070
-
-        IGJyb24= 31071
-
-        IGNvbnRyYWN0b3I= 31072
-
-        IGxpY2VuY2U= 31073
-
-        IE5hbQ== 31074
-
-        5q8= 31075
-
-        KHRyYW5zZm9ybQ== 31076
-
-        X0FUVA== 31077
-
-        UHJlZg== 31078
-
-        IEdhbQ== 31079
-
-        IHZlc3NlbHM= 31080
-
-        IGhhdg== 31081
-
-        TGF0ZXI= 31082
-
-        LlRvTG93ZXI= 31083
-
-        IHVybHM= 31084
-
-        IGJyZWFrZG93bg== 31085
-
-        IHBlbmFsdGllcw== 31086
-
-        IGZvc3Rlcg== 31087
-
-        IFVF 31088
-
-        IGNsdWU= 31089
-
-        Y29tZWQ= 31090
-
-        5ZCN56ew 31091
-
-        LW1haW4= 31092
-
-        IHB0cw== 31093
-
-        IGNvdW50ZWQ= 31094
-
-        aWN0cw== 31095
-
-        L3Bvc3Q= 31096
-
-        IGdldGF0dHI= 31097
-
-        IHBpbmc= 31098
-
-        QU5DRUw= 31099
-
-        IHBlYw== 31100
-
-        0YXQvtC0 31101
-
-        YW50b20= 31102
-
-        IEJsdWVwcmludA== 31103
-
-        IEV2ZW50RW1pdHRlcg== 31104
-
-        IGzDpA== 31105
-
-        5rI= 31106
-
-        IHN0cmF3 31107
-
-        KGNvbXA= 31108
-
-        J3VuZQ== 31109
-
-        Pk4= 31110
-
-        LWNsaWVudA== 31111
-
-        ZXNNb2R1bGU= 31112
-
-        LWJhc2U= 31113
-
-        IHJldHJlYXQ= 31114
-
-        X3NpbXBsZQ== 31115
-
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-
-        ZmVl 31117
-
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-
-        Q29udHJvbEl0ZW0= 31119
-
-        IHN1YnNjcmliZXJz 31120
-
-        cGxlYXNl 31121
-
-        IEVmZg== 31122
-
-        IHBvdW5k 31123
-
-        IEJ5dGVz 31124
-
-        IFRlYQ== 31125
-
-        X2FjdGl2aXR5 31126
-
-        IG1heGlt 31127
-
-        IG9wY29kZQ== 31128
-
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-
-        LmNvbnN0YW50 31130
-
-        O30= 31131
-
-        b21icmVz 31132
-
-        IGNhcmVlcnM= 31133
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-        KS4KCgoK 31134
-
-        IHNwcmVhZGluZw== 31135
-
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-        IE9yZA== 31137
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-
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-
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-
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-        IFBH 31144
-
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-
-        Ym9s 31146
-
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-
-        X01FTU9SWQ== 31148
-
-        IFVJQWxlcnQ= 31149
-
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-
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-
-        IExvZ28= 31152
-
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-
-        W2luZA== 31154
-
-        IGFsdG9nZXRoZXI= 31155
-
-        IHdvbmRlcmVk 31156
-
-        IGVzcGVy 31157
-
-        IExpYmVyYWw= 31158
-
-        IG9zcw== 31159
-
-        IGVsaXQ= 31160
-
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-
-        b2RveA== 31162
-
-        X21lbnRpb25z 31163
-
-        IERvdWdsYXM= 31164
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-
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-        IGluaXRXaXRoRnJhbWU= 31167
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-        LmJsb2c= 31168
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-
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-
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-
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-
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-
-        QVRBTA== 31174
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-
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-
-        IGh5cG90aGVzaXM= 31178
-
-        IGNhdGE= 31179
-
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-
-        IENsYXI= 31181
-
-        IHByZWRlY2Vzcw== 31182
-
-        IHNpdHVhdGVk 31183
-
-        LXdvcmxk 31184
-
-        KSkv 31185
-
-        IGhlYWRsaW5lcw== 31186
-
-        LnN0YXQ= 31187
-
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-
-        c3BhdGg= 31189
-
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-
-        IFNlcnZsZXRFeGNlcHRpb24= 31191
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-        U3Vu 31192
-
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-
-        IERpcg== 31194
-
-        44O744O744O7 31195
-
-        X2Nvb3Jk 31196
-
-        IE9wdGlt 31197
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-
-        LmJpdA== 31199
-
-        WFhY 31200
-
-        IHRvZGFz 31201
-
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-
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-        aW1pcg== 31204
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-        IHBvbGl0aWNhbGx5 31205
-
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-
-        IHRyYWRlZA== 31207
-
-        IHt7JA== 31208
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-
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-
-        IHdhcmVob3VzZQ== 31212
-
-        dG9kYXk= 31213
-
-        LEw= 31214
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-        b3Jw 31215
-
-        PHNlY3Rpb24= 31216
-
-        LWJy 31217
-
-        eW1l 31218
-
-        IFVzZXJTZXJ2aWNl 31219
-
-        IGxpYmVydHk= 31220
-
-        IG1vbWVudG8= 31221
-
-        KEltYWdl 31222
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-        PHNpemU= 31223
-
-        U2No 31224
-
-        IGpvZw== 31225
-
-        aW9sb2d5 31226
-
-        YXJlbnRseQ== 31227
-
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-
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-
-        IG1hbmE= 31231
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-
-        QnVpbGRpbmc= 31233
-
-        c3RhaXJz 31234
-
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-
-        IHNlY3Q= 31237
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-        IHNpZ2g= 31238
-
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-
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-
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-
-        IENvcnBz 31242
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-
-        IEtheQ== 31245
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-
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-
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-
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-
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-
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-
-        IGdlc3R1cmU= 31257
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-        IG9iamV0 31260
-
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-
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-
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-
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-
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-
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-
-        cHY= 31268
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-
-        IG1hc3Nhc2pl 31270
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-        IHByYWN0aXRpb24= 31271
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-
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-        0Jg= 31274
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-
-        IGNvdW50aWVz 31276
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-        Z3Vlc3Q= 31277
-
-        ZmFu 31278
-
-        IHdvcmRlbg== 31279
-
-        LmRp 31280
-
-        0L3QsNGH 31281
-
-        wr8= 31282
-
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-
-        IHNob3Jl 31284
-
-        IGfDtg== 31285
-
-        IHJlcGFpcnM= 31286
-
-        IGhlbHBlcnM= 31287
-
-        IGNlbnRlcmVk 31288
-
-        T0xMT1c= 31289
-
-        IG1hcFN0YXRlVG9Qcm9wcw== 31290
-
-        IGNlbnRz 31291
-
-        PEE= 31292
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-        IGV4cGVjdGF0aW9u 31293
-
-        T2N0b2Jlcg== 31294
-
-        IGJnY29sb3I= 31295
-
-        Y2FsZXM= 31296
-
-        LkNPTg== 31297
-
-        IFZlbA== 31298
-
-        IGNyeWluZw== 31299
-
-        LXNlYXNvbg== 31300
-
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-
-        X0xPQ0FUSU9O 31302
-
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-
-        b21pbmF0b3I= 31306
-
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-
-        IGV0aGljYWw= 31308
-
-        aGFzaHRhZ3M= 31309
-
-        ZW1wbG8= 31310
-
-        IG7Dum1lcm8= 31311
-
-        KGFjdGl2aXR5 31312
-
-        LlN0b3A= 31313
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-        LnN0cmZ0aW1l 31314
-
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-
-        IHRvZQ== 31316
-
-        CU5vZGU= 31317
-
-        IikNCg0K 31318
-
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-
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-
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-
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-
-        IGpzb25PYmplY3Q= 31327
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-
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-
-        IHdpbA== 31331
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-        IHNsb3Bl 31332
-
-        IG3DpQ== 31333
-
-        IElyYXFp 31334
-
-        IG9yZ2FuaXpl 31335
-
-        CWpRdWVyeQ== 31336
-
-        SFVE 31337
-
-        c2hpbmU= 31338
-
-        Lndl 31339
-
-        IFNraWxscw== 31340
-
-        cG9uc29y 31341
-
-        IGNvbmNsdXNpb25z 31342
-
-        IHJlZm9ybXM= 31343
-
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-
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-
-        IGZvZw== 31349
-
-        IEhP 31350
-
-        IEZyaWVk 31351
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-        IGluZXZpdGFibGU= 31352
-
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-
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-
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-
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-
-        IGNvbm5lY3Rpdml0eQ== 31357
-
-        LnR3aWc= 31358
-
-        IEt5bGU= 31359
-
-        KGRzdA== 31360
-
-        LVNo 31361
-
-        IFN0dWRpb3M= 31362
-
-        KExldmVs 31363
-
-        LmpldA== 31364
-
-        X1BST1RP 31365
-
-        LWRlY29yYXRpb24= 31366
-
-        T1RIRVI= 31367
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-        IHJlYWRpbHk= 31368
-
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-
-        IG11bHRpcGx5 31370
-
-        IExJQg== 31371
-
-        YXJtZWQ= 31372
-
-        IHNvb25lcg== 31373
-
-        5oQ= 31374
-
-        X0VT 31375
-
-        IGZvc3NpbA== 31376
-
-        IEFuYw== 31377
-
-        4oCcVGhpcw== 31378
-
-        bG9kYXNo 31379
-
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-
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-
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-
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-
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-
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-        IGVuZXI= 38556
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-
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-
-        IFJhdGVk 38560
-
-        IGdsdWU= 38561
-
-        L2Jsb2c= 38562
-
-        IGdyZW4= 38563
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-
-        LkNI 38565
-
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-        IFBSSU1BUlk= 38567
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-
-        Lk1JTg== 38570
-
-        IFRoZWF0ZXI= 38571
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-        6ZI= 38572
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-        YXRlZ29yaWU= 38573
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-        5q61 38574
-
-        IGFwcGV0aXRl 38575
-
-        c3F1YXJl 38576
-
-        IEFsZXhhbmQ= 38577
-
-        LlVzZXJJZA== 38578
-
-        X2d0 38579
-
-        X2VudGVy 38580
-
-        IGdyYWR1YXRlcw== 38581
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-        RnJhZ21lbnRNYW5hZ2Vy 38582
-
-        QXV0aG9yaXpl 38583
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-        LU5MUw== 38584
-
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-
-        IHRyaXVtcGg= 38586
-
-        dXN0aW5n 38587
-
-        X1BBUkFNUw== 38588
-
-        Q2hhcmFjdGVycw== 38589
-
-        KDosOiw= 38590
-
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-
-        TUh6 38592
-
-        IHdhc2hlZA== 38593
-
-        IHVuY2xl 38594
-
-        U3RldmU= 38595
-
-        YXJkb3du 38596
-
-        PHN0ZGlv 38597
-
-        X3Rlcm1z 38598
-
-        IE1BUg== 38599
-
-        IGhvc2U= 38600
-
-        dWN1cw== 38601
-
-        IENsYWlt 38602
-
-        IFJhbXM= 38603
-
-        IG1vZGVsQnVpbGRlcg== 38604
-
-        IG7DqQ== 38605
-
-        dXNlcklE 38606
-
-        PWpzb24= 38607
-
-        LlJlc3BvbnNlV3JpdGVy 38608
-
-        mOiupA== 38609
-
-        IGdydXBv 38610
-
-        LWl0 38611
-
-        IEtP 38612
-
-        LU1haWw= 38613
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-        IGNvbmZlcmVuY2Vz 38614
-
-        SUZB 38615
-
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-
-        IHByb25vdW5jZWQ= 38617
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-        IGFuY2VzdG9ycw== 38618
-
-        IFRSQUNF 38619
-
-        IEdlRm9yY2U= 38620
-
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-
-        cGVsbA== 38622
-
-        ZW1vamk= 38623
-
-        INmI 38624
-
-        R2VucmU= 38625
-
-        IGNvbmNlbnRyYXRlZA== 38626
-
-        amFuZw== 38627
-
-        TU9URQ== 38628
-
-        IFpvb20= 38629
-
-        dG9vbGJhcg== 38630
-
-        IHV0dGVybHk= 38631
-
-        IGVuY29tcGFzcw== 38632
-
-        IFNvY2Nlcg== 38633
-
-        IGV1cm9wZQ== 38634
-
-        LWFpcg== 38635
-
-        LmFuaW0= 38636
-
-        X0NUTA== 38637
-
-        aGVyZW50 38638
-
-        cmV4 38639
-
-        aW50ZXJhY3RpdmU= 38640
-
-        44Gn44GZ 38641
-
-        IEthcw== 38642
-
-        IGRlc3BlcmF0ZWx5 38643
-
-        KGFy 38644
-
-        IGJpaw== 38645
-
-        IHRyYXZlcnNl 38646
-
-        ZXVycw== 38647
-
-        UmVjeWNsZXJWaWV3 38648
-
-        IE1hcmdhcmV0 38649
-
-        IGhvcGVmdWw= 38650
-
-        IE1pZw== 38651
-
-        X01FTUJFUg== 38652
-
-        cmVjZWl2ZXI= 38653
-
-        TWF0Y2hlcg== 38654
-
-        ZGVwZW5kZW50 38655
-
-        IGV4Y2VsbGVuY2U= 38656
-
-        0LDQtg== 38657
-
-        TE9T 38658
-
-        QXNwZWN0 38659
-
-        IGFkYWxhaA== 38660
-
-        IEVjb25vbXk= 38661
-
-        dWxvdXNseQ== 38662
-
-        IGV2YWx1YXRpbmc= 38663
-
-        IGRldmlhdGlvbg== 38664
-
-        ZXh0ZXI= 38665
-
-        L2RhdA== 38666
-
-        Q29scw== 38667
-
-        IFBva2Vy 38668
-
-        Ym9hcmRpbmc= 38669
-
-        LkNoaWxkcmVu 38670
-
-        QU5HTEU= 38671
-
-        w68= 38672
-
-        IFlvZ2E= 38673
-
-        IGhhdGVk 38674
-
-        QWRhbQ== 38675
-
-        IEZDQw== 38676
-
-        SU1BTA== 38677
-
-        IGZhaW50 38678
-
-        X0RJU1BMQVk= 38679
-
-        IGV2b2x2ZQ== 38680
-
-        IGZyaWRnZQ== 38681
-
-        IHLDqWc= 38682
-
-        IGVtb3Rpb25hbGx5 38683
-
-        4oCcSWY= 38684
-
-        YXdlaQ== 38685
-
-        ZXJlc2E= 38686
-
-        Jywi 38687
-
-        QkVHSU4= 38688
-
-        IFZBUkNIQVI= 38689
-
-        IHhp 38690
-
-        ZmFjdG9y 38691
-
-        dHo= 38692
-
-        X3BoYXNl 38693
-
-        U0VR 38694
-
-        KHJhbmQ= 38695
-
-        IG1hdGhlbWF0aWNz 38696
-
-        IGNvbnRleHRz 38697
-
-        LWFj 38698
-
-        IEZJRw== 38699
-
-        IENhcHRpb24= 38700
-
-        IFdhaXRGb3I= 38701
-
-        LXdlc3Q= 38702
-
-        IGZpcmVmaWdodA== 38703
-
-        X0xFRA== 38704
-
-        ZWN0aW9ucw== 38705
-
-        CXRocm93cw== 38706
-
-        IFRha2Vz 38707
-
-        b2JyZQ== 38708
-
-        IEF2YXRhcg== 38709
-
-        IElubm92YXRpb24= 38710
-
-        IGNhbGlicmF0aW9u 38711
-
-        OnRoaXM= 38712
-
-        X2VuY29kaW5n 38713
-
-        IGNhbGN1bGF0aW5n 38714
-
-        ICMjIyMjIyMjIyMjIyMjIyM= 38715
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-        IFByb2dyYW1z 38716
-
-        IEhJR0g= 38717
-
-        LmNvbmZpZ3VyZVRlc3RpbmdNb2R1bGU= 38718
-
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-
-        X0RCRw== 38720
-
-        Il0sDQo= 38721
-
-        0LDQsQ== 38722
-
-        IHNpbWlsYXJpdHk= 38723
-
-        IHByemV6 38724
-
-        IEZpcm0= 38725
-
-        IG1pc3VuZGVy 38726
-
-        IE1vdmluZw== 38727
-
-        IE1PVg== 38728
-
-        IHJlYWN0b3I= 38729
-
-        UmVxdWVzdGVk 38730
-
-        ZXhwZWN0cw== 38731
-
-        IGVyZWN0 38732
-
-        bGljaHQ= 38733
-
-        b3VsZGVy 38734
-
-        SURHRVQ= 38735
-
-        IGRldmls 38736
-
-        IHByb2dyYW1tZXM= 38737
-
-        IENvbW1vbk1vZHVsZQ== 38738
-
-        ICInIg== 38739
-
-        KEF1dGg= 38740
-
-        44CC77yM 38741
-
-        IFN0YXRlZnVsV2lkZ2V0 38742
-
-        6K6h 38743
-
-        L29wZW4= 38744
-
-        aW5hbGx5 38745
-
-        LlJvdW5k 38746
-
-        IFdpc2g= 38747
-
-        IGh1bWFuaXRhcmlhbg== 38748
-
-        QWNjZXNzVG9rZW4= 38749
-
-        IFNPQw== 38750
-
-        IHBva2Vtb24= 38751
-
-        IHZhcG9y 38752
-
-        X2FkZGVk 38753
-
-        CUdldA== 38754
-
-        c3BlbGw= 38755
-
-        IEluaXRpYXRpdmU= 38756
-
-        IEhFTA== 38757
-
-        YWlycm8= 38758
-
-        YmxlZA== 38759
-
-        INCx0Ys= 38760
-
-        IHNlbnNpYmxl 38761
-
-        IEx1YQ== 38762
-
-        fCgK 38763
-
-        IGZpeHR1cmVz 38764
-
-        IG9yZ2FzbQ== 38765
-
-        Q3V0 38766
-
-        dWt0 38767
-
-        Z3Vl 38768
-
-        IGNyZWRpYmlsaXR5 38769
-
-        OmltYWdl 38770
-
-        IENQUA== 38771
-
-        LnNu 38772
-
-        KGRlc2M= 38773
-
-        IFJlaWQ= 38774
-
-        LWRlZ3JlZQ== 38775
-
-        X3NvdW5k 38776
-
-        Q2xvbmU= 38777
-
-        4buZ 38778
-
-        YWtzaQ== 38779
-
-        PiR7 38780
-
-        X2NvbmZpcm1hdGlvbg== 38781
-
-        IHRyb3BoeQ== 38782
-
-        V29ya3M= 38783
-
-        IEVsZWN0cm9uaWNz 38784
-
-        IE1lZGl0ZXJyYW5lYW4= 38785
-
-        X21ldHJpY3M= 38786
-
-        IGFubm91bmNpbmc= 38787
-
-        IERBWQ== 38788
-
-        X3Byb3Rv 38789
-
-        IHBlYXI= 38790
-
-        YmFzZVVybA== 38791
-
-        CQkJCQkJCQkK 38792
-
-        IGNvb3JkaW5hdGlvbg== 38793
-
-        Ok4= 38794
-
-        LmFuaW1hdGU= 38795
-
-        IENvdHRvbg== 38796
-
-        X2hpdA== 38797
-
-        4pw= 38798
-
-        IGpldHp0 38799
-
-        aWZ0ZXI= 38800
-
-        KGZpZWxkcw== 38801
-
-        b3dubG9hZA== 38802
-
-        aWZpY2FjaW9u 38803
-
-        LmN1ZGE= 38804
-
-        IExpdQ== 38805
-
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-
-        IEFjZQ== 38807
-
-        0YDQsNC8 38808
-
-        IFN1cGVybWFu 38809
-
-        IEdhcmNpYQ== 38810
-
-        IGFycmVzdHM= 38811
-
-        YWdhcg== 38812
-
-        IHt9KQ== 38813
-
-        IG1hY3Jvcw== 38814
-
-        cm91cGU= 38815
-
-        w6p0cmU= 38816
-
-        IHR3aXN0ZWQ= 38817
-
-        c3RydW1lbnRz 38818
-
-        Xygi 38819
-
-        X3ZlcnRpY2Vz 38820
-
-        IFRyYW5zaXRpb24= 38821
-
-        0LjQug== 38822
-
-        W21heA== 38823
-
-        bWluZA== 38824
-
-        IGFjY2Vzc1Rva2Vu 38825
-
-        IHVubGU= 38826
-
-        bXVz 38827
-
-        Y29w 38828
-
-        IEZhY3Rvcg== 38829
-
-        IGNvbmNlZA== 38830
-
-        IHJldHI= 38831
-
-        LmxpbmFsZw== 38832
-
-        LXNsaWRlcg== 38833
-
-        b2Js 38834
-
-        X1N0YXRpY0ZpZWxkcw== 38835
-
-        IHpvbWJpZQ== 38836
-
-        c2VsbGluZw== 38837
-
-        IGNoYXA= 38838
-
-        IHNoYWtpbmc= 38839
-
-        IFRyYW5zbGF0ZQ== 38840
-
-        IEFtc3RlcmRhbQ== 38841
-
-        IEVUSA== 38842
-
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-
-        a2Q= 38844
-
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-
-        IHByZWNlZGluZw== 38846
-
-        IHByaXg= 38847
-
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-
-        X21vZGlmaWVk 38849
-
-        YXJkd2FyZQ== 38850
-
-        ID8+Ij4= 38851
-
-        IERX 38852
-
-        YCR7 38853
-
-        ID8+Ij48Pw== 38854
-
-        dXllbg== 38855
-
-        IGRvbm5h 38856
-
-        IHhzaQ== 38857
-
-        ICQiew== 38858
-
-        IERyYXdpbmc= 38859
-
-        LG5pbA== 38860
-
-        IG9uZGVy 38861
-
-        Qkc= 38862
-
-        T2JzZXJ2 38863
-
-        IGNvbnNpZGVyYXRpb25z 38864
-
-        Ym9hdA== 38865
-
-        IEJhbmtz 38866
-
-        IGluZGljdA== 38867
-
-        LEk= 38868
-
-        IEJsdQ== 38869
-
-        KHZlcnNpb24= 38870
-
-        Y2xpZW50ZQ== 38871
-
-        b2xhbg== 38872
-
-        TEVTUw== 38873
-
-        YXNzZXJ0U2FtZQ== 38874
-
-        X3ZvaWQ= 38875
-
-        IFdBUw== 38876
-
-        CWVudW0= 38877
-
-        IG1peGVy 38878
-
-        RVc= 38879
-
-        YWZmZQ== 38880
-
-        IGJsb3dqb2I= 38881
-
-        dGV4dEZpZWxk 38882
-
-        IGltbWVuc2U= 38883
-
-        X3JlcG8= 38884
-
-        IGdsb2JhbHM= 38885
-
-        YW50YWdlcw== 38886
-
-        LnRvZGF5 38887
-
-        VGh1cnNkYXk= 38888
-
-        IEJyaWc= 38889
-
-        e30pCg== 38890
-
-        IEltYWdpbmU= 38891
-
-        KEdQSU8= 38892
-
-        IGVzdG8= 38893
-
-        IFByb3ZpbmNl 38894
-
-        IE1lbnRhbA== 38895
-
-        X2NlbGxz 38896
-
-        IEp1bGlhbg== 38897
-
-        LlNjcmVlbg== 38898
-
-        IGNhbmRsZQ== 38899
-
-        IG1vbmRl 38900
-
-        IHZlcmc= 38901
-
-        aXRlcmFscw== 38902
-
-        LWxheW91dA== 38903
-
-        R3Vlc3Q= 38904
-
-        IHZpbmQ= 38905
-
-        IEVjaG8= 38906
-
-        Jyl9 38907
-
-        IG1hbm4= 38908
-
-        X0JPT0xFQU4= 38909
-
-        aGFw 38910
-
-        IG5pZ2h0bWFyZQ== 38911
-
-        VUdI 38912
-
-        IG5vbmV0aGVsZXNz 38913
-
-        IGF0aGU= 38914
-
-        IEhvbGxhbmQ= 38915
-
-        IEJvcm4= 38916
-
-        XE9STQ== 38917
-
-        YW51dA== 38918
-
-        X2xldmVscw== 38919
-
-        IHBldGl0ZQ== 38920
-
-        LWFydA== 38921
-
-        X1NIT1c= 38922
-
-        bnVtYmVyT2Y= 38923
-
-        X3RodW1ibmFpbA== 38924
-
-        YW1pbnM= 38925
-
-        IERlZmluZXM= 38926
-
-        ICI9 38927
-
-        LlN0YXR1c0NvZGU= 38928
-
-        IGRpZ25pdHk= 38929
-
-        IEJpa2U= 38930
-
-        Lk5ld0xpbmU= 38931
-
-        IEdsYXM= 38932
-
-        KGxvZ2dlcg== 38933
-
-        IGNhdGNoZXM= 38934
-
-        dm90ZXM= 38935
-
-        IGV4YW1pbmluZw== 38936
-
-        L3JlZ2lzdGVy 38937
-
-        IHNwZWNpZnlpbmc= 38938
-
-        X2ZpeGVk 38939
-
-        IGRyYXdpbmdz 38940
-
-        VGhyZXNob2xk 38941
-
-        QXg= 38942
-
-        IEFyY2hpdGVjdHVyZQ== 38943
-
-        KHBpZA== 38944
-
-        V2lyZQ== 38945
-
-        KGNvbnQ= 38946
-
-        bGFuZQ== 38947
-
-        TGlzdHM= 38948
-
-        IHNwcmludA== 38949
-
-        IGdyYW5kZmF0aGVy 38950
-
-        X0FH 38951
-
-        IHNjaGVkdWxpbmc= 38952
-
-        Q0xVUw== 38953
-
-        YXR1cml0eQ== 38954
-
-        IGxvY2tpbmc= 38955
-
-        W3NpemU= 38956
-
-        X3N0eWxlcw== 38957
-
-        IHdi 38958
-
-        LS0+Cgo= 38959
-
-        IHNwaW5uaW5n 38960
-
-        X3BlbmRpbmc= 38961
-
-        TWF0Y2hlcnM= 38962
-
-        LktleXM= 38963
-
-        IFBW 38964
-
-        ZW51cw== 38965
-
-        YW50aXM= 38966
-
-        IGRpc2NhcmQ= 38967
-
-        IGhhdWw= 38968
-
-        IGVtcGly 38969
-
-        IHBhdGh3YXk= 38970
-
-        IG9haw== 38971
-
-        0LzQtdC9 38972
-
-        LWluZHVjZWQ= 38973
-
-        IGltcGFpcg== 38974
-
-        IENhbGdhcnk= 38975
-
-        LmlzSGlkZGVu 38976
-
-        ZHo= 38977
-
-        X2luY2x1ZGU= 38978
-
-        IGdt 38979
-
-        ICcoJw== 38980
-
-        UFk= 38981
-
-        dWdnZXN0aW9ucw== 38982
-
-        IGNvbW1vZGl0eQ== 38983
-
-        Y3Jv 38984
-
-        L3N1Yg== 38985
-
-        IGdldEluc3RhbmNl 38986
-
-        IExlZ2FjeQ== 38987
-
-        IEtpbA== 38988
-
-        QmFs 38989
-
-        KHNob3J0 38990
-
-        SW5mb3Jt 38991
-
-        K3g= 38992
-
-        KnI= 38993
-
-        IEhvcGVmdWxseQ== 38994
-
-        b3JhdGU= 38995
-
-        IG1hY2hlbg== 38996
-
-        IHRyZWF0eQ== 38997
-
-        IE9yaQ== 38998
-
-        LnB1YmxpYw== 38999
-
-        LWhvcml6b250YWw= 39000
-
-        IHRhY3RpYw== 39001
-
-        IGJvcmQ= 39002
-
-        d2FyZXM= 39003
-
-        IGFtbW8= 39004
-
-        IExpc3Rz 39005
-
-        IGVxdWF0aW9ucw== 39006
-
-        L2hlcg== 39007
-
-        IE5TVw== 39008
-
-        Qm91bmRpbmc= 39009
-
-        X0NvbGxlY3Rpb25z 39010
-
-        IGF2YWls 39011
-
-        LkRyb3BEb3du 39012
-
-        6LA= 39013
-
-        IGho 39014
-
-        IGzDoA== 39015
-
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-        IGltYWdlcnk= 41545
-
-        Y29uYw== 41546
-
-        X21pc3Npbmc= 41547
-
-        IHN1cnBsdXM= 41548
-
-        S1M= 41549
-
-        CVJUSE9PSw== 41550
-
-        VGVsbA== 41551
-
-        cml1bQ== 41552
-
-        IFJhZGl1cw== 41553
-
-        cmlrYQ== 41554
-
-        bG9zaW9u 41555
-
-        IEhlcm4= 41556
-
-        R2FtbWE= 41557
-
-        IEZlZQ== 41558
-
-        IE5hbWVk 41559
-
-        IENhbnlvbg== 41560
-
-        IEpTT05BcnJheQ== 41561
-
-        IHp3ZWk= 41562
-
-        IFNTSA== 41563
-
-        IHNlcnZhbnQ= 41564
-
-        Y29hbA== 41565
-
-        IGRlbnlpbmc= 41566
-
-        IHNwbGl0cw== 41567
-
-        SW5jb3JyZWN0 41568
-
-        IHRveA== 41569
-
-        IEFuYWx5c3Q= 41570
-
-        IGFjY3JlZA== 41571
-
-        dWJsZQ== 41572
-
-        IHd0 41573
-
-        IFRyaWFs 41574
-
-        LmV4dGVuc2lvbg== 41575
-
-        IENhcmVlcg== 41576
-
-        IHNlY3VyaW5n 41577
-
-        IExpbA== 41578
-
-        IHByb2plY3Rpb25z 41579
-
-        IHllYXN0 41580
-
-        TWFkZQ== 41581
-
-        IGZvdW5kYXRpb25z 41582
-
-        YWNpZmlj 41583
-
-        LnZvbHVtZQ== 41584
-
-        IG1pcnJvcnM= 41585
-
-        IyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyM=
-        41586
-
-        IHZpb2xhdGU= 41587
-
-        YXJzZXJz 41588
-
-        IHNvY2lv 41589
-
-        IHRraW50ZXI= 41590
-
-        IExJTks= 41591
-
-        LmdldFNpemU= 41592
-
-        IFdob2xl 41593
-
-        KXZpZXdEaWRMb2Fk 41594
-
-        CWRvbmU= 41595
-
-        dWRlYXU= 41596
-
-        XCI+PC8= 41597
-
-        QW5kcmV3 41598
-
-        ZXJi 41599
-
-        IGbDtg== 41600
-
-        LmNsdXN0ZXI= 41601
-
-        IGRpc2NvdXJzZQ== 41602
-
-        X0RFRklO 41603
-
-        IHB1ZWRlbg== 41604
-
-        IExPVw== 41605
-
-        LmF2 41606
-
-        IHByZWNh 41607
-
-        IHF1bw== 41608
-
-        IHZlbG9j 41609
-
-        LCcn 41610
-
-        IHh5eg== 41611
-
-        CXBhZGRpbmc= 41612
-
-        IHRvbWF0b2Vz 41613
-
-        IEJlbnQ= 41614
-
-        X2N1cnI= 41615
-
-        TlNEYXRl 41616
-
-        IGdldEN1cnJlbnQ= 41617
-
-        IFtg 41618
-
-        V2VkbmVzZGF5 41619
-
-        LkJhcg== 41620
-
-        IFZvdXM= 41621
-
-        aW56 41622
-
-        IFF1aW5u 41623
-
-        ZXhjZWw= 41624
-
-        ZG9z 41625
-
-        IG91dGRhdGVk 41626
-
-        T1VUSA== 41627
-
-        IE1ha2Vy 41628
-
-        ZXBlbmRlbmN5 41629
-
-        IGR1bGw= 41630
-
-        IFdpbm4= 41631
-
-        b2dl 41632
-
-        Y2xhdmU= 41633
-
-        IG5vdmE= 41634
-
-        IGF2YWw= 41635
-
-        Q2FwdA== 41636
-
-        IFNwb3RpZnk= 41637
-
-        IGp1bA== 41638
-
-        KXRhYmxlVmlldw== 41639
-
-        IGZpbGVuYW1lcw== 41640
-
-        IGVza29ydA== 41641
-
-        5ZGo 41642
-
-        IHNrZXc= 41643
-
-        dGVyaW9y 41644
-
-        IGZpbmFuYw== 41645
-
-        IHRhYmxh 41646
-
-        IFVJQg== 41647
-
-        ICgpOg== 41648
-
-        IERvY2tlcg== 41649
-
-        cGVyY2VudGFnZQ== 41650
-
-        TWVldA== 41651
-
-        aWNoaQ== 41652
-
-        IGludGVyaW0= 41653
-
-        ICc9Jw== 41654
-
-        LkpTT05PYmplY3Q= 41655
-
-        KGZpZA== 41656
-
-        IGRvd250 41657
-
-        IHRyYW5zaWVudA== 41658
-
-        IFN0ZXBo 41659
-
-        IGlnbm9yYW5jZQ== 41660
-
-        IENvZGVz 41661
-
-        PScnLA== 41662
-
-        IElDRQ== 41663
-
-        IHRyYW5xdQ== 41664
-
-        IEV4dGVuZGVk 41665
-
-        IG11bmQ= 41666
-
-        IEhPTUU= 41667
-
-        IGtpbG9tZXRlcnM= 41668
-
-        IGltYWdlbg== 41669
-
-        b3V4 41670
-
-        KHN6 41671
-
-        WW91bmc= 41672
-
-        dWZmZWQ= 41673
-
-        IFdha2U= 41674
-
-        IGFpZGU= 41675
-
-        UFJPQw== 41676
-
-        IFJhdA== 41677
-
-        IExpdGg= 41678
-
-        YmFydA== 41679
-
-        IEFycmFuZ2U= 41680
-
-        cHJvbXB0 41681
-
-        0KM= 41682
-
-        KGN0 41683
-
-        IEludGVydmFs 41684
-
-        ZGVwdA== 41685
-
-        RGFuaWVs 41686
-
-        IGZpbGxz 41687
-
-        LnRlbnNvcg== 41688
-
-        KHRyaW0= 41689
-
-        IGplYWxvdXM= 41690
-
-        RmVi 41691
-
-        XENvbW1vbg== 41692
-
-        IGFtZW5kbWVudHM= 41693
-
-        X29wZXJhdG9y 41694
-
-        X2N1c3RvbWl6ZQ== 41695
-
-        IF1d 41696
-
-        IGJu 41697
-
-        IGRpc2FwcG9pbnRtZW50 41698
-
-        IG1pbGxlbm4= 41699
-
-        LndoZW4= 41700
-
-        IG9iZXk= 41701
-
-        IG9mZmVuZGVycw== 41702
-
-        V2lsZA== 41703
-
-        IGNlbGxGb3I= 41704
-
-        IGFwcGFyYXR1cw== 41705
-
-        LmFmdGVy 41706
-
-        IEVQUw== 41707
-
-        IGFkb3JhYmxl 41708
-
-        b3BlcmFuZA== 41709
-
-        KGxpc3RlbmVy 41710
-
-        dmVhbA== 41711
-
-        ICko 41712
-
-        IGNhcmRpb3Zhc2N1bGFy 41713
-
-        dXBsaWNhdGVz 41714
-
-        cmlzdG9s 41715
-
-        IHJlZnVzZXM= 41716
-
-        KFFXaWRnZXQ= 41717
-
-        IGVsZW1lbnRv 41718
-
-        TnVtYmVyT2Y= 41719
-
-        LmRlbGF5 41720
-
-        Lmdyb3Vwcw== 41721
-
-        Ij4nKw== 41722
-
-        5Z2A 41723
-
-        YWNlbmN5 41724
-
-        KFVSTA== 41725
-
-        X2hhbGY= 41726
-
-        PWw= 41727
-
-        IGxpc3RWaWV3 41728
-
-        KHNlY3Rpb24= 41729
-
-        LnRvQXJyYXk= 41730
-
-        Ky8= 41731
-
-        IFJvZHJpZ3Vleg== 41732
-
-        aXN0cmVhbQ== 41733
-
-        IGVsaWdpYmlsaXR5 41734
-
-        Ojot 41735
-
-        Lm5ld0luc3RhbmNl 41736
-
-        UEI= 41737
-
-        IEFzc2V0cw== 41738
-
-        IENvbXBvc2l0ZQ== 41739
-
-        IExhYnM= 41740
-
-        IEhhbWFz 41741
-
-        KyspOwo= 41742
-
-        IGJsaw== 41743
-
-        IE5lbw== 41744
-
-        THVj 41745
-
-        QGxvZ2lu 41746
-
-        IHVuYXdhcmU= 41747
-
-        Lm1ldA== 41748
-
-        X1JFTEVBU0U= 41749
-
-        KFNU 41750
-
-        QU1JTA== 41751
-
-        cmlrZQ== 41752
-
-        ICgpewo= 41753
-
-        KHNwcmludGY= 41754
-
-        IEFjY291bnRz 41755
-
-        IFZJRVc= 41756
-
-        IEFq 41757
-
-        44Kw 41758
-
-        IHdoaXNr 41759
-
-        IGlkaQ== 41760
-
-        IHJvZGU= 41761
-
-        IGlobg== 41762
-
-        IEVsZW1lbnRhcnk= 41763
-
-        UXR5 41764
-
-        IGludHJpZ3Vpbmc= 41765
-
-        IOWk 41766
-
-        Sm9icw== 41767
-
-        CW9mZnNldA== 41768
-
-        IEFobWVk 41769
-
-        IFRhbGliYW4= 41770
-
-        IOiOt+WPlg== 41771
-
-        IGluamVjdGVk 41772
-
-        LkF1dGhlbnRpY2F0aW9u 41773
-
-        X2xpbmVhcg== 41774
-
-        LkRlY2ltYWw= 41775
-
-        IGFwcGxlcw== 41776
-
-        IHNoYXJlaG9sZGVycw== 41777
-
-        IGJha2Vk 41778
-
-        LmRpZmY= 41779
-
-        IEVkZGll 41780
-
-        b2tlcnM= 41781
-
-        IGNvbmZyb250ZWQ= 41782
-
-        dm9pY2Vz 41783
-
-        IHR1cw== 41784
-
-        IFNwaW4= 41785
-
-        Tk9ERQ== 41786
-
-        X1Vu 41787
-
-        Q1RY 41788
-
-        L2dvb2dsZQ== 41789
-
-        VGVtcGVyYXR1cmU= 41790
-
-        ICcnKS4= 41791
-
-        IG1hZ25pZmljZW50 41792
-
-        IHN0YXJ0SW5kZXg= 41793
-
-        c2VtYmxlcw== 41794
-
-        QW55b25l 41795
-
-        ems= 41796
-
-        ZWhlbg== 41797
-
-        IERhbWU= 41798
-
-        LnN0cmljdA== 41799
-
-        IHJlcGxhY2Vz 41800
-
-        IGxpbmViYWNr 41801
-
-        IHB1c2hlcw== 41802
-
-        IGNoZWVr 41803
-
-        IFNoaQ== 41804
-
-        X0JZVEVT 41805
-
-        UkVB 41806
-
-        4bqjbg== 41807
-
-        X0NPTk5FQ1RJT04= 41808
-
-        R2F0ZXdheQ== 41809
-
-        IFRyYXZpcw== 41810
-
-        IEFY 41811
-
-        IEJhc2ljYWxseQ== 41812
-
-        IFVwZ3JhZGU= 41813
-
-        4Ko= 41814
-
-        dGhlbWVz 41815
-
-        ZXJtbw== 41816
-
-        a29y 41817
-
-        RmVtYWxl 41818
-
-        X2F0dGFjaA== 41819
-
-        IOyCrOyaqQ== 41820
-
-        IHBveg== 41821
-
-        PT09PT09PT09PT09PT0K 41822
-
-        KHN5bWJvbA== 41823
-
-        IFNlY3Rvcg== 41824
-
-        X18pCgo= 41825
-
-        X3BhZGRpbmc= 41826
-
-        77yaIg== 41827
-
-        IGZhYnM= 41828
-
-        IHJhbmdlZA== 41829
-
-        c2V0TmFtZQ== 41830
-
-        IHBlcnJvcg== 41831
-
-        4pc= 41832
-
-        IEZpbGVSZWFkZXI= 41833
-
-        IGZ1bGZpbGxlZA== 41834
-
-        X0N1cnJlbnQ= 41835
-
-        IGRvbWluYXRl 41836
-
-        IHNtdWdn 41837
-
-        UG9zdE1hcHBpbmc= 41838
-
-        X2ZvcmNl 41839
-
-        IGJsb2M= 41840
-
-        IEdpYW50 41841
-
-        KHZpZGVv 41842
-
-        IENV 41843
-
-        U3lzdGVtU2VydmljZQ== 41844
-
-        IGVsZg== 41845
-
-        IGtvbnRha3Q= 41846
-
-        66o= 41847
-
-        a2Vlcw== 41848
-
-        Z3Rr 41849
-
-        IHBhcmFtSW50 41850
-
-        IG1hcmt1cA== 41851
-
-        dWFsZXM= 41852
-
-        IGFjY291bnRlZA== 41853
-
-        IGdhbmdiYW5n 41854
-
-        UllQVA== 41855
-
-        IFdyb25n 41856
-
-        IGNyZWRpdGVk 41857
-
-        IE1FU1NBR0U= 41858
-
-        IGZsYXdz 41859
-
-        IGJidw== 41860
-
-        IG1ldGFib2xpYw== 41861
-
-        IE9FTQ== 41862
-
-        L2V2ZW50 41863
-
-        KENvbGxlY3RvcnM= 41864
-
-        bW9udG9u 41865
-
-        YXBwZWFy 41866
-
-        IG9wdGVk 41867
-
-        IGNoZWF0 41868
-
-        IGRhdg== 41869
-
-        IFByb2NlZWQ= 41870
-
-        IOq4 41871
-
-        YW5rZWQ= 41872
-
-        0LjQtw== 41873
-
-        YW5zaw== 41874
-
-        IEhhbmc= 41875
-
-        IENsZXI= 41876
-
-        IGRpc2d1 41877
-
-        IGNtYXA= 41878
-
-        LmNsanM= 41879
-
-        IGF1bWVudA== 41880
-
-        bGV6 41881
-
-        IEpvaW5lZA== 41882
-
-        X3JlY2VpdmVk 41883
-
-        IGFlcmlhbA== 41884
-
-        b3RlbA== 41885
-
-        IGdyZWV0 41886
-
-        InM= 41887
-
-        IEdlbmVzaXM= 41888
-
-        IENhbGlm 41889
-
-        cGFuaW9u 41890
-
-        IHRhaWxvcmVk 41891
-
-        bWFwcGluZw== 41892
-
-        YW5kRXhwZWN0 41893
-
-        LnRyYWNr 41894
-
-        YXRvbXk= 41895
-
-        IE93 41896
-
-        dWxsYWg= 41897
-
-        Llllcw== 41898
-
-        IFNpbXBsZU5hbWU= 41899
-
-        ZGJo 41900
-
-        J2Vu 41901
-
-        IG5vbnNlbnNl 41902
-
-        IHBoaWxvc29waGljYWw= 41903
-
-        KGdldENvbnRleHQ= 41904
-
-        IGlzc28= 41905
-
-        IEFDRQ== 41906
-
-        c3RhcnREYXRl 41907
-
-        IGLEmWQ= 41908
-
-        IEFVVEhPUg== 41909
-
-        IEdsb2Jl 41910
-
-        IGluc2VjdHM= 41911
-
-        X0Fs 41912
-
-        dXNoaW5n 41913
-
-        6K6w 41914
-
-        L0hvbWU= 41915
-
-        IExvY2FsRGF0ZQ== 41916
-
-        bmVlZGVk 41917
-
-        aGVzaXZl 41918
-
-        IGlsbHVzaW9u 41919
-
-        5LqM 41920
-
-        IHRyYXQ= 41921
-
-        eG8= 41922
-
-        L2RldGFpbA== 41923
-
-        X01BVENI 41924
-
-        IGJyb2FkYmFuZA== 41925
-
-        IHdhbA== 41926
-
-        IElsbGVnYWxTdGF0ZUV4Y2VwdGlvbg== 41927
-
-        SVJFQ1RJT04= 41928
-
-        IG5vcnRoZWFzdA== 41929
-
-        ZXNpdW0= 41930
-
-        IENsaWVudGU= 41931
-
-        dWxhbmNl 41932
-
-        bnR5 41933
-
-        IHRlY24= 41934
-
-        RGV2aWNlcw== 41935
-
-        IGdyYWlucw== 41936
-
-        IE9n 41937
-
-        IFNFTA== 41938
-
-        dWRpYW50 41939
-
-        ICsrOwo= 41940
-
-        IGV4cGxhbmF0aW9ucw== 41941
-
-        b2Njbw== 41942
-
-        IGRpZXRz 41943
-
-        IGNvaG9ydA== 41944
-
-        KGNvbnRyb2xsZXI= 41945
-
-        Lkl0ZXJhdG9y 41946
-
-        LXJpY2g= 41947
-
-        cm9jZXNz 41948
-
-        R0Q= 41949
-
-        IGNhcmJvaHlkcg== 41950
-
-        IGZyaWVk 41951
-
-        IEVtcGxveW1lbnQ= 41952
-
-        7J6l 41953
-
-        IExlb25hcmQ= 41954
-
-        XyR7 41955
-
-        cXVhcmVz 41956
-
-        IGNvbXBhbmlvbnM= 41957
-
-        IHBhcmlz 41958
-
-        IHN0aW11bGF0aW9u 41959
-
-        IFpvbw== 41960
-
-        IHJlbGV2YW5jZQ== 41961
-
-        IENvbG91cg== 41962
-
-        IHNwZWFy 41963
-
-        b3Rpb25hbA== 41964
-
-        IExpdGU= 41965
-
-        IEtvc3Rlbg== 41966
-
-        IMOz 41967
-
-        X2F0dGFjaG1lbnQ= 41968
-
-        b3JwaGlj 41969
-
-        IGRhbWl0 41970
-
-        IGRsZw== 41971
-
-        IHRocml2ZQ== 41972
-
-        Q0hBTkdF 41973
-
-        IEFwcGFyZW50bHk= 41974
-
-        IGF0dWFs 41975
-
-        IHJvb3RlZA== 41976
-
-        KGltYWdlcw== 41977
-
-        YXdp 41978
-
-        YXJpYXQ= 41979
-
-        IGNoZXJyeQ== 41980
-
-        U1RBVElD 41981
-
-        bW50 41982
-
-        IFVzZXJJZA== 41983
-
-        aWxsZXQ= 41984
-
-        IEhpc3Bhbmlj 41985
-
-        IG5haw== 41986
-
-        IGNlbnRybw== 41987
-
-        IGRpbXM= 41988
-
-        X2luaXRpYWxpemU= 41989
-
-        xLFr 41990
-
-        IENlbnRlcnM= 41991
-
-        UkVO 41992
-
-        IGV2b2x1dGlvbmFyeQ== 41993
-
-        IFRvcGljcw== 41994
-
-        X2RhbWFnZQ== 41995
-
-        ZW1lcg== 41996
-
-        IHJ1bmQ= 41997
-
-        IHB1bmlzaGVk 41998
-
-        IGN1Ymlj 41999
-
-        ZmFpcg== 42000
-
-        W107Cgo= 42001
-
-        IGluc3RhbnRpYXRl 42002
-
-        IG92ZXJzZWU= 42003
-
-        LWRlbGV0ZQ== 42004
-
-        dW50ZWVy 42005
-
-        c3RhcnRUaW1l 42006
-
-        IFBpcGVsaW5l 42007
-
-        X0dBTUU= 42008
-
-        IENpcg== 42009
-
-        CU51bGw= 42010
-
-        LkZvcm1hdHRpbmc= 42011
-
-        dWN1bWJlcg== 42012
-
-        IFJpZGU= 42013
-
-        IHpvbw== 42014
-
-        IGNoZWNrZXI= 42015
-
-        5ZCM 42016
-
-        PUM= 42017
-
-        IGdyaXQ= 42018
-
-        Iik7Ly8= 42019
-
-        X3h5 42020
-
-        IERlY2xhcmF0aW9u 42021
-
-        IGNhbGxhYmxl 42022
-
-        Rm9v 42023
-
-        IExpc3RJdGVt 42024
-
-        IGluYWNjdXI= 42025
-
-        bWxpbg== 42026
-
-        CURhdGE= 42027
-
-        IGV2b2x2aW5n 42028
-
-        YXdhbg== 42029
-
-        IGNhZmU= 42030
-
-        Zm9saw== 42031
-
-        X0lEWA== 42032
-
-        IEFueXRoaW5n 42033
-
-        IFBhbGVzdGluZQ== 42034
-
-        IEdyaWRWaWV3 42035
-
-        IGNvbG9ueQ== 42036
-
-        IEdlcm1hbnM= 42037
-
-        KCs= 42038
-
-        LnBpZA== 42039
-
-        LmpzeA== 42040
-
-        IFN1cGVyaW9y 42041
-
-        Q2hyaXN0aWFu 42042
-
-        IExlY3Q= 42043
-
-        CUdhbWU= 42044
-
-        IGluc3RydW1lbnRhbA== 42045
-
-        QW5pbWF0aW9ucw== 42046
-
-        0LTQsNC7 42047
-
-        IE1vc2Vz 42048
-
-        CQkNCgkJDQo= 42049
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-        IGVsbQ== 43305
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-        IGNhbm9uaWNhbA== 43553
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-        IHN1Y2Nlc3Nlcw== 48188
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-        IFN1cmdlcnk= 48190
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-        IGNvdWdhcg== 48191
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-        IGN1cnNv 48192
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-
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-
-        IHpt 48229
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-        IGFkYXB0aXZl 48232
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-        IG11c2hyb29tcw== 48251
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-        IGludGVycA== 48371
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-
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-
-        IExBU1Q= 48395
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-        IHdlbGxz 48396
-
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-
-        X1JPTEU= 48398
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-        IERhbmdlcg== 48399
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-        IFJlc29sdmU= 48402
-
-        IFJvb21z 48403
-
-        X1JH 48404
-
-        IFFU 48405
-
-        b29w 48406
-
-        IEhlYXA= 48407
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-
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-
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-
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-        IGhpbmQ= 48419
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-
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-
-        X3BvbA== 48423
-
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-
-        IGluc2Vy 48425
-
-        KH4= 48426
-
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-
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-
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-
-        IG1pdHQ= 48432
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-
-        IGJ1Y2tz 48434
-
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-
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-
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-
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-        U3RkU3RyaW5n 48441
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-
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-
-        LWVkZ2U= 48448
-
-        IG1hcnM= 48449
-
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-
-        IGVtcGF0aHk= 48451
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-        IGNvbGxpc2lvbnM= 48453
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-        IGNhcnRl 48454
-
-        IFRlaWw= 48455
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-        IE1QTA== 48456
-
-        IHBvcm7DtA== 48457
-
-        IGFpcmxpbmVz 48458
-
-        QXdz 48459
-
-        TnM= 48460
-
-        IFNwYXdu 48461
-
-        KHVzZQ== 48462
-
-        6buY6K6k 48463
-
-        IHlhY2M= 48464
-
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-
-        IGNvbmZlc3M= 48466
-
-        IHBlcXVl 48467
-
-        cmFnZQ== 48468
-
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-
-        L2RhdGF0YWJsZXM= 48470
-
-        IFNob3dlcg== 48471
-
-        X18v 48472
-
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-
-        IGJ1c2Nhcg== 48474
-
-        IEhhdXM= 48475
-
-        aXphw6fDo28= 48476
-
-        X2VudGl0aWVz 48477
-
-        lYw= 48478
-
-        mow= 48479
-
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-
-        dmlydA== 48481
-
-        LWNoZXZyb24= 48482
-
-        KFJlc3VsdA== 48483
-
-        Y2FrZQ== 48484
-
-        Q09NRQ== 48485
-
-        IHByb2hpYml0 48486
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-        IENoZXNz 48487
-
-        IGJlYXVjb3Vw 48488
-
-        INGH0YLQvg== 48489
-
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-
-        IElL 48491
-
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-
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-
-        IHNsZWVr 48494
-
-        IFNwZWNpZnk= 48495
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-
-        xZ90 48497
-
-        IFVzZXJOYW1l 48498
-
-        CVZhbHVl 48499
-
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-
-        IGV4Y2hhbmdlZA== 48501
-
-        aXBzaXM= 48502
-
-        LnJlbGF0ZWQ= 48503
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-        IFNlaXRl 48504
-
-        X0JBUg== 48505
-
-        IExlbQ== 48506
-
-        IFdBVENI 48507
-
-        IENsaWVudHM= 48508
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-        IC4q 48509
-
-        IEVhcmw= 48510
-
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-        IGZvcmVpZ25lcnM= 48512
-
-        IHN0cmVuZ3RoZW5pbmc= 48513
-
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-
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-
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-
-        IGNhbGN1bGF0ZXM= 48517
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-
-        IGN6YXM= 48519
-
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-
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-
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-
-        IF57Cg== 48523
-
-        LlBvcA== 48524
-
-        IERlc2lnbmVk 48525
-
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-
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-
-        IEd1aWRlbGluZXM= 48528
-
-        aXBsaW5l 48529
-
-        IGNhY2hpbmc= 48530
-
-        LlJlYWRlcg== 48531
-
-        X2V4dGVybmFs 48532
-
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-
-        IFdlZWtlbmQ= 48534
-
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-
-        IEJlaQ== 48536
-
-        IHsqfQ== 48537
-
-        IFJ1ZA== 48538
-
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-
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-
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-
-        IHByZXBhcmVz 48542
-
-        IHNlcmlhbGl6YXRpb24= 48543
-
-        ZXdhdGVy 48544
-
-        IGFkYw== 48545
-
-        OgoKCgoKCg== 48546
-
-        UmVmZXI= 48547
-
-        IHNjYW5uZWQ= 48548
-
-        fX0KCg== 48549
-
-        IEZ1bA== 48550
-
-        IHRvdXJpbmc= 48551
-
-        44OD44Kv 48552
-
-        Pigo 48553
-
-        c3VydmV5 48554
-
-        IO2Y 48555
-
-        Li4uJykK 48556
-
-        IERpdmlkZXI= 48557
-
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-
-        X0NBTkNFTA== 48559
-
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-
-        c3Rpbg== 48561
-
-        IEhlYXRo 48562
-
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-        IEludHI= 61894
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-        IG5vb3A= 61929
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-
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-
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-
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-        KX19Ig== 71887
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-
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diff --git a/libs/langchain/tests/integration_tests/vectorstores/docker-compose/elasticsearch.yml b/libs/langchain/tests/integration_tests/vectorstores/docker-compose/elasticsearch.yml
index 5060f082930..6d27d63a5d5 100644
--- a/libs/langchain/tests/integration_tests/vectorstores/docker-compose/elasticsearch.yml
+++ b/libs/langchain/tests/integration_tests/vectorstores/docker-compose/elasticsearch.yml
@@ -2,7 +2,7 @@ version: "3"
 
 services:
   elasticsearch:
-    image: docker.elastic.co/elasticsearch/elasticsearch:8.7.0 # https://www.docker.elastic.co/r/elasticsearch/elasticsearch
+    image: docker.elastic.co/elasticsearch/elasticsearch:8.9.0 # https://www.docker.elastic.co/r/elasticsearch/elasticsearch
     environment:
       - discovery.type=single-node
       - xpack.security.enabled=false # security has been disabled, so no login or password is required.
@@ -10,17 +10,25 @@ services:
     ports:
       - "9200:9200"
     healthcheck:
-      test: [ "CMD-SHELL", "curl --silent --fail http://localhost:9200/_cluster/health || exit 1" ]
+      test:
+        [
+          "CMD-SHELL",
+          "curl --silent --fail http://localhost:9200/_cluster/health || exit 1",
+        ]
       interval: 10s
       retries: 60
 
   kibana:
-    image: docker.elastic.co/kibana/kibana:8.7.0
+    image: docker.elastic.co/kibana/kibana:8.9.0
     environment:
       - ELASTICSEARCH_URL=http://elasticsearch:9200
     ports:
       - "5601:5601"
     healthcheck:
-      test: [ "CMD-SHELL", "curl --silent --fail http://localhost:5601/login || exit 1" ]
+      test:
+        [
+          "CMD-SHELL",
+          "curl --silent --fail http://localhost:5601/login || exit 1",
+        ]
       interval: 10s
       retries: 60
diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_bagel.py b/libs/langchain/tests/integration_tests/vectorstores/test_bagel.py
new file mode 100644
index 00000000000..0a268dd0ae2
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/vectorstores/test_bagel.py
@@ -0,0 +1,169 @@
+from langchain.docstore.document import Document
+from langchain.vectorstores import Bagel
+from tests.integration_tests.vectorstores.fake_embeddings import (
+    FakeEmbeddings,
+)
+
+
+def test_similarity_search() -> None:
+    """Test similarity search"""
+    from bagel.config import Settings
+
+    setting = Settings(
+        bagel_api_impl="rest",
+        bagel_server_host="api.bageldb.ai",
+    )
+    bagel = Bagel(client_settings=setting)
+    bagel.add_texts(texts=["hello bagel", "hello langchain"])
+    result = bagel.similarity_search(query="bagel", k=1)
+    assert result == [Document(page_content="hello bagel")]
+    bagel.delete_cluster()
+
+
+def test_bagel() -> None:
+    """Test from_texts"""
+    texts = ["hello bagel", "hello langchain"]
+    txt_search = Bagel.from_texts(cluster_name="testing", texts=texts)
+    output = txt_search.similarity_search("hello bagel", k=1)
+    assert output == [Document(page_content="hello bagel")]
+    txt_search.delete_cluster()
+
+
+def test_with_metadatas() -> None:
+    """Test end to end construction and search."""
+    texts = ["hello bagel", "hello langchain"]
+    metadatas = [{"metadata": str(i)} for i in range(len(texts))]
+    txt_search = Bagel.from_texts(
+        cluster_name="testing",
+        texts=texts,
+        metadatas=metadatas,
+    )
+    output = txt_search.similarity_search("hello bagel", k=1)
+    assert output == [Document(page_content="hello bagel", metadata={"metadata": "0"})]
+    txt_search.delete_cluster()
+
+
+def test_with_metadatas_with_scores() -> None:
+    """Test end to end construction and scored search."""
+    texts = ["hello bagel", "hello langchain"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    txt_search = Bagel.from_texts(
+        cluster_name="testing", texts=texts, metadatas=metadatas
+    )
+    output = txt_search.similarity_search_with_score("hello bagel", k=1)
+    assert output == [
+        (Document(page_content="hello bagel", metadata={"page": "0"}), 0.0)
+    ]
+    txt_search.delete_cluster()
+
+
+def test_with_metadatas_with_scores_using_vector() -> None:
+    """Test end to end construction and scored search, using embedding vector."""
+    texts = ["hello bagel", "hello langchain"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    embeddings = [[1.1, 2.3, 3.2], [0.3, 0.3, 0.1]]
+
+    vector_search = Bagel.from_texts(
+        cluster_name="testing_vector",
+        texts=texts,
+        metadatas=metadatas,
+        text_embeddings=embeddings,
+    )
+
+    embedded_query = [1.1, 2.3, 3.2]
+    output = vector_search.similarity_search_by_vector_with_relevance_scores(
+        query_embeddings=embedded_query, k=1
+    )
+    assert output == [
+        (Document(page_content="hello bagel", metadata={"page": "0"}), 0.0)
+    ]
+    vector_search.delete_cluster()
+
+
+def test_with_metadatas_with_scores_using_vector_embe() -> None:
+    """Test end to end construction and scored search, using embedding vector."""
+    texts = ["hello bagel", "hello langchain"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    embedding_function = FakeEmbeddings()
+
+    vector_search = Bagel.from_texts(
+        cluster_name="testing_vector_embedding1",
+        texts=texts,
+        metadatas=metadatas,
+        embedding=embedding_function,
+    )
+    embedded_query = embedding_function.embed_query("hello bagel")
+    output = vector_search.similarity_search_by_vector_with_relevance_scores(
+        query_embeddings=embedded_query, k=1
+    )
+    assert output == [
+        (Document(page_content="hello bagel", metadata={"page": "0"}), 0.0)
+    ]
+    vector_search.delete_cluster()
+
+
+def test_search_filter() -> None:
+    """Test end to end construction and search with metadata filtering."""
+    texts = ["hello bagel", "hello langchain"]
+    metadatas = [{"first_letter": text[0]} for text in texts]
+    txt_search = Bagel.from_texts(
+        cluster_name="testing",
+        texts=texts,
+        metadatas=metadatas,
+    )
+    output = txt_search.similarity_search("bagel", k=1, where={"first_letter": "h"})
+    assert output == [
+        Document(page_content="hello bagel", metadata={"first_letter": "h"})
+    ]
+    output = txt_search.similarity_search("langchain", k=1, where={"first_letter": "h"})
+    assert output == [
+        Document(page_content="hello langchain", metadata={"first_letter": "h"})
+    ]
+    txt_search.delete_cluster()
+
+
+def test_search_filter_with_scores() -> None:
+    texts = ["hello bagel", "this is langchain"]
+    metadatas = [{"source": "notion"}, {"source": "google"}]
+    txt_search = Bagel.from_texts(
+        cluster_name="testing",
+        texts=texts,
+        metadatas=metadatas,
+    )
+    output = txt_search.similarity_search_with_score(
+        "hello bagel", k=1, where={"source": "notion"}
+    )
+    assert output == [
+        (Document(page_content="hello bagel", metadata={"source": "notion"}), 0.0)
+    ]
+    txt_search.delete_cluster()
+
+
+def test_with_include_parameter() -> None:
+    """Test end to end construction and include parameter."""
+    texts = ["hello bagel", "this is langchain"]
+    docsearch = Bagel.from_texts(cluster_name="testing", texts=texts)
+    output = docsearch.get(include=["embeddings"])
+    assert output["embeddings"] is not None
+    output = docsearch.get()
+    assert output["embeddings"] is None
+    docsearch.delete_cluster()
+
+
+def test_bagel_update_document() -> None:
+    """Test the update_document function in the Bagel class."""
+    initial_content = "bagel"
+    document_id = "doc1"
+    original_doc = Document(page_content=initial_content, metadata={"page": "0"})
+
+    docsearch = Bagel.from_documents(
+        cluster_name="testing_docs",
+        documents=[original_doc],
+        ids=[document_id],
+    )
+
+    updated_content = "updated bagel doc"
+    updated_doc = Document(page_content=updated_content, metadata={"page": "0"})
+    docsearch.update_document(document_id=document_id, document=updated_doc)
+    output = docsearch.similarity_search(updated_content, k=1)
+    assert output == [Document(page_content=updated_content, metadata={"page": "0"})]
diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_dashvector.py b/libs/langchain/tests/integration_tests/vectorstores/test_dashvector.py
new file mode 100644
index 00000000000..e01a12073e3
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/vectorstores/test_dashvector.py
@@ -0,0 +1,75 @@
+from time import sleep
+
+from langchain.schema import Document
+from langchain.vectorstores import DashVector
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+texts = ["foo", "bar", "baz"]
+ids = ["1", "2", "3"]
+
+
+def test_dashvector_from_texts() -> None:
+    dashvector = DashVector.from_texts(
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        ids=ids,
+    )
+
+    # the vector insert operation is async by design, we wait here a bit for the
+    # insertion to complete.
+    sleep(0.5)
+    output = dashvector.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+def test_dashvector_with_text_with_metadatas() -> None:
+    metadatas = [{"meta": i} for i in range(len(texts))]
+    dashvector = DashVector.from_texts(
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        metadatas=metadatas,
+        ids=ids,
+    )
+
+    # the vector insert operation is async by design, we wait here a bit for the
+    # insertion to complete.
+    sleep(0.5)
+    output = dashvector.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo", metadata={"meta": 0})]
+
+
+def test_dashvector_search_with_filter() -> None:
+    metadatas = [{"meta": i} for i in range(len(texts))]
+    dashvector = DashVector.from_texts(
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        metadatas=metadatas,
+        ids=ids,
+    )
+
+    # the vector insert operation is async by design, we wait here a bit for the
+    # insertion to complete.
+    sleep(0.5)
+    output = dashvector.similarity_search("foo", filter="meta=2")
+    assert output == [Document(page_content="baz", metadata={"meta": 2})]
+
+
+def test_dashvector_search_with_scores() -> None:
+    dashvector = DashVector.from_texts(
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        ids=ids,
+    )
+
+    # the vector insert operation is async by design, we wait here a bit for the
+    # insertion to complete.
+    sleep(0.5)
+    output = dashvector.similarity_search_with_relevance_scores("foo")
+    docs, scores = zip(*output)
+
+    assert scores[0] < scores[1] < scores[2]
+    assert list(docs) == [
+        Document(page_content="foo"),
+        Document(page_content="bar"),
+        Document(page_content="baz"),
+    ]
diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_elastic_vector_search.py b/libs/langchain/tests/integration_tests/vectorstores/test_elastic_vector_search.py
new file mode 100644
index 00000000000..b5188f5b99b
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/vectorstores/test_elastic_vector_search.py
@@ -0,0 +1,160 @@
+"""Test ElasticSearch functionality."""
+import logging
+import os
+import uuid
+from typing import Generator, List, Union
+
+import pytest
+
+from langchain.docstore.document import Document
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+logging.basicConfig(level=logging.DEBUG)
+
+"""
+cd tests/integration_tests/vectorstores/docker-compose
+docker-compose -f elasticsearch.yml up
+"""
+
+
+class TestElasticsearch:
+    @classmethod
+    def setup_class(cls) -> None:
+        if not os.getenv("OPENAI_API_KEY"):
+            raise ValueError("OPENAI_API_KEY environment variable is not set")
+
+    @pytest.fixture(scope="class", autouse=True)
+    def elasticsearch_url(self) -> Union[str, Generator[str, None, None]]:
+        """Return the elasticsearch url."""
+        from elasticsearch import Elasticsearch
+
+        url = "http://localhost:9200"
+        yield url
+        es = Elasticsearch(hosts=url)
+
+        # Clear all indexes
+        index_names = es.indices.get(index="_all").keys()
+        for index_name in index_names:
+            # print(index_name)
+            es.indices.delete(index=index_name)
+
+    def test_similarity_search_without_metadata(self, elasticsearch_url: str) -> None:
+        """Test end to end construction and search without metadata."""
+        texts = ["foo", "bar", "baz"]
+        docsearch = ElasticVectorSearch.from_texts(
+            texts, FakeEmbeddings(), elasticsearch_url=elasticsearch_url
+        )
+        output = docsearch.similarity_search("foo", k=1)
+        assert output == [Document(page_content="foo")]
+
+    @pytest.mark.skip(
+        reason="Docker build has no ssl certs. Enable this test when testing with ssl."
+    )
+    def test_similarity_search_with_ssl_verify(self, elasticsearch_url: str) -> None:
+        """Test end to end construction and search with ssl verify."""
+        ssl_verify = {
+            "verify_certs": True,
+            "basic_auth": ("ES_USER", "ES_PASSWORD"),
+            "ca_certs": "ES_CA_CERTS_PATH",
+        }
+        texts = ["foo", "bar", "baz"]
+        docsearch = ElasticVectorSearch.from_texts(
+            texts,
+            FakeEmbeddings(),
+            elasticsearch_url="http://localhost:9200",
+            ssl_verify=ssl_verify,
+        )
+        output = docsearch.similarity_search("foo", k=1)
+        assert output == [Document(page_content="foo")]
+
+    def test_similarity_search_with_metadata(self, elasticsearch_url: str) -> None:
+        """Test end to end construction and search with metadata."""
+        texts = ["foo", "bar", "baz"]
+        metadatas = [{"page": i} for i in range(len(texts))]
+        docsearch = ElasticVectorSearch.from_texts(
+            texts,
+            FakeEmbeddings(),
+            metadatas=metadatas,
+            elasticsearch_url=elasticsearch_url,
+        )
+        output = docsearch.similarity_search("foo", k=1)
+        assert output == [Document(page_content="foo", metadata={"page": 0})]
+
+    @pytest.mark.vcr(ignore_localhost=True)
+    def test_default_index_from_documents(
+        self,
+        documents: List[Document],
+        embedding_openai: OpenAIEmbeddings,
+        elasticsearch_url: str,
+    ) -> None:
+        """This test checks the construction of a default
+        ElasticSearch index using the 'from_documents'."""
+
+        elastic_vector_search = ElasticVectorSearch.from_documents(
+            documents=documents,
+            embedding=embedding_openai,
+            elasticsearch_url=elasticsearch_url,
+        )
+
+        search_result = elastic_vector_search.similarity_search("sharks")
+
+        assert len(search_result) != 0
+
+    @pytest.mark.vcr(ignore_localhost=True)
+    def test_custom_index_from_documents(
+        self,
+        documents: List[Document],
+        embedding_openai: OpenAIEmbeddings,
+        elasticsearch_url: str,
+    ) -> None:
+        """This test checks the construction of a custom
+        ElasticSearch index using the 'from_documents'."""
+        from elasticsearch import Elasticsearch
+
+        index_name = f"custom_index_{uuid.uuid4().hex}"
+        elastic_vector_search = ElasticVectorSearch.from_documents(
+            documents=documents,
+            embedding=embedding_openai,
+            elasticsearch_url=elasticsearch_url,
+            index_name=index_name,
+        )
+        es = Elasticsearch(hosts=elasticsearch_url)
+        index_names = es.indices.get(index="_all").keys()
+        assert index_name in index_names
+
+        search_result = elastic_vector_search.similarity_search("sharks")
+
+        assert len(search_result) != 0
+
+    @pytest.mark.vcr(ignore_localhost=True)
+    def test_custom_index_add_documents(
+        self,
+        documents: List[Document],
+        embedding_openai: OpenAIEmbeddings,
+        elasticsearch_url: str,
+    ) -> None:
+        """This test checks the construction of a custom
+        ElasticSearch index using the 'add_documents'."""
+        from elasticsearch import Elasticsearch
+
+        index_name = f"custom_index_{uuid.uuid4().hex}"
+        elastic_vector_search = ElasticVectorSearch(
+            embedding=embedding_openai,
+            elasticsearch_url=elasticsearch_url,
+            index_name=index_name,
+        )
+        es = Elasticsearch(hosts=elasticsearch_url)
+        elastic_vector_search.add_documents(documents)
+
+        index_names = es.indices.get(index="_all").keys()
+        assert index_name in index_names
+
+        search_result = elastic_vector_search.similarity_search("sharks")
+
+        assert len(search_result) != 0
+
+    def test_custom_index_add_documents_to_exists_store(self) -> None:
+        # TODO: implement it
+        pass
diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_elasticsearch.py b/libs/langchain/tests/integration_tests/vectorstores/test_elasticsearch.py
index db87f0bc5a2..79b42ddd029 100644
--- a/libs/langchain/tests/integration_tests/vectorstores/test_elasticsearch.py
+++ b/libs/langchain/tests/integration_tests/vectorstores/test_elasticsearch.py
@@ -7,17 +7,38 @@ from typing import Generator, List, Union
 import pytest
 
 from langchain.docstore.document import Document
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch
-from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+from langchain.vectorstores.elasticsearch import ElasticsearchStore
+from tests.integration_tests.vectorstores.fake_embeddings import (
+    ConsistentFakeEmbeddings,
+    FakeEmbeddings,
+)
 
 logging.basicConfig(level=logging.DEBUG)
 
 """
 cd tests/integration_tests/vectorstores/docker-compose
 docker-compose -f elasticsearch.yml up
+
+By default runs against local docker instance of Elasticsearch.
+To run against Elastic Cloud, set the following environment variables:
+- ES_CLOUD_ID
+- ES_USERNAME
+- ES_PASSWORD
+
+Some of the tests require the following models to be deployed in the ML Node:
+- elser (can be downloaded and deployed through Kibana and trained models UI)
+- sentence-transformers__all-minilm-l6-v2 (can be deployed 
+  through API, loaded via eland)
+
+These tests that require the models to be deployed are skipped by default. 
+Enable them by adding the model name to the modelsDeployed list below.
 """
 
+modelsDeployed: List[str] = [
+    # "elser",
+    # "sentence-transformers__all-minilm-l6-v2",
+]
+
 
 class TestElasticsearch:
     @classmethod
@@ -26,135 +47,530 @@ class TestElasticsearch:
             raise ValueError("OPENAI_API_KEY environment variable is not set")
 
     @pytest.fixture(scope="class", autouse=True)
-    def elasticsearch_url(self) -> Union[str, Generator[str, None, None]]:
-        """Return the elasticsearch url."""
+    def elasticsearch_connection(self) -> Union[dict, Generator[dict, None, None]]:
+        # Running this integration test with Elastic Cloud
+        # Required for in-stack inference testing (ELSER + model_id)
         from elasticsearch import Elasticsearch
 
-        url = "http://localhost:9200"
-        yield url
-        es = Elasticsearch(hosts=url)
+        es_url = os.environ.get("ES_URL", "http://localhost:9200")
+        cloud_id = os.environ.get("ES_CLOUD_ID")
+        es_username = os.environ.get("ES_USERNAME", "elastic")
+        es_password = os.environ.get("ES_PASSWORD", "changeme")
+
+        if cloud_id:
+            yield {
+                "es_cloud_id": cloud_id,
+                "es_user": es_username,
+                "es_password": es_password,
+            }
+            es = Elasticsearch(cloud_id=cloud_id, basic_auth=(es_username, es_password))
+
+        else:
+            # Running this integration test with local docker instance
+            yield {
+                "es_url": es_url,
+            }
+            es = Elasticsearch(hosts=es_url)
 
         # Clear all indexes
         index_names = es.indices.get(index="_all").keys()
         for index_name in index_names:
-            # print(index_name)
-            es.indices.delete(index=index_name)
+            if index_name.startswith("test_"):
+                es.indices.delete(index=index_name)
+        es.indices.refresh(index="_all")
 
-    def test_similarity_search_without_metadata(self, elasticsearch_url: str) -> None:
+        # clear all test pipelines
+        try:
+            response = es.ingest.get_pipeline(id="test_*,*_sparse_embedding")
+
+            for pipeline_id, _ in response.items():
+                try:
+                    es.ingest.delete_pipeline(id=pipeline_id)
+                    print(f"Deleted pipeline: {pipeline_id}")
+                except Exception as e:
+                    print(f"Pipeline error: {e}")
+        except Exception:
+            pass
+
+    @pytest.fixture(scope="function")
+    def index_name(self) -> str:
+        """Return the index name."""
+        return f"test_{uuid.uuid4().hex}"
+
+    def test_similarity_search_without_metadata(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
         """Test end to end construction and search without metadata."""
-        texts = ["foo", "bar", "baz"]
-        docsearch = ElasticVectorSearch.from_texts(
-            texts, FakeEmbeddings(), elasticsearch_url=elasticsearch_url
-        )
-        output = docsearch.similarity_search("foo", k=1)
-        assert output == [Document(page_content="foo")]
 
-    def test_similarity_search_with_ssl_verify(self, elasticsearch_url: str) -> None:
-        """Test end to end construction and search with ssl verify."""
-        ssl_verify = {
-            "verify_certs": True,
-            "basic_auth": ("ES_USER", "ES_PASSWORD"),
-            "ca_certs": "ES_CA_CERTS_PATH",
-        }
+        def assert_query(query_body: dict, query: str) -> dict:
+            assert query_body == {
+                "knn": {
+                    "field": "vector",
+                    "filter": [],
+                    "k": 1,
+                    "num_candidates": 50,
+                    "query_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
+                }
+            }
+            return query_body
+
         texts = ["foo", "bar", "baz"]
-        docsearch = ElasticVectorSearch.from_texts(
+        print(elasticsearch_connection)
+        docsearch = ElasticsearchStore.from_texts(
             texts,
             FakeEmbeddings(),
-            elasticsearch_url=elasticsearch_url,
-            ssl_verify=ssl_verify,
+            **elasticsearch_connection,
+            index_name=index_name,
         )
-        output = docsearch.similarity_search("foo", k=1)
+        output = docsearch.similarity_search("foo", k=1, custom_query=assert_query)
         assert output == [Document(page_content="foo")]
 
-    def test_similarity_search_with_metadata(self, elasticsearch_url: str) -> None:
+    @pytest.mark.asyncio
+    async def test_similarity_search_without_metadat_async(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
+        """Test end to end construction and search without metadata."""
+        texts = ["foo", "bar", "baz"]
+        print(elasticsearch_connection)
+        docsearch = ElasticsearchStore.from_texts(
+            texts,
+            FakeEmbeddings(),
+            **elasticsearch_connection,
+            index_name=index_name,
+        )
+        output = await docsearch.asimilarity_search("foo", k=1)
+        assert output == [Document(page_content="foo")]
+
+    def test_similarity_search_with_metadata(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
         """Test end to end construction and search with metadata."""
         texts = ["foo", "bar", "baz"]
         metadatas = [{"page": i} for i in range(len(texts))]
-        docsearch = ElasticVectorSearch.from_texts(
+        docsearch = ElasticsearchStore.from_texts(
             texts,
-            FakeEmbeddings(),
+            ConsistentFakeEmbeddings(),
             metadatas=metadatas,
-            elasticsearch_url=elasticsearch_url,
+            **elasticsearch_connection,
+            index_name=index_name,
         )
+
         output = docsearch.similarity_search("foo", k=1)
         assert output == [Document(page_content="foo", metadata={"page": 0})]
 
-    @pytest.mark.vcr(ignore_localhost=True)
-    def test_default_index_from_documents(
-        self,
-        documents: List[Document],
-        embedding_openai: OpenAIEmbeddings,
-        elasticsearch_url: str,
+        output = docsearch.similarity_search("bar", k=1)
+        assert output == [Document(page_content="bar", metadata={"page": 1})]
+
+    def test_similarity_search_with_filter(
+        self, elasticsearch_connection: dict, index_name: str
     ) -> None:
-        """This test checks the construction of a default
-        ElasticSearch index using the 'from_documents'."""
-
-        elastic_vector_search = ElasticVectorSearch.from_documents(
-            documents=documents,
-            embedding=embedding_openai,
-            elasticsearch_url=elasticsearch_url,
-        )
-
-        search_result = elastic_vector_search.similarity_search("sharks")
-
-        print(search_result)
-        assert len(search_result) != 0
-
-    @pytest.mark.vcr(ignore_localhost=True)
-    def test_custom_index_from_documents(
-        self,
-        documents: List[Document],
-        embedding_openai: OpenAIEmbeddings,
-        elasticsearch_url: str,
-    ) -> None:
-        """This test checks the construction of a custom
-        ElasticSearch index using the 'from_documents'."""
-        from elasticsearch import Elasticsearch
-
-        index_name = f"custom_index_{uuid.uuid4().hex}"
-        elastic_vector_search = ElasticVectorSearch.from_documents(
-            documents=documents,
-            embedding=embedding_openai,
-            elasticsearch_url=elasticsearch_url,
+        """Test end to end construction and search with metadata."""
+        texts = ["foo", "foo", "foo"]
+        metadatas = [{"page": i} for i in range(len(texts))]
+        docsearch = ElasticsearchStore.from_texts(
+            texts,
+            FakeEmbeddings(),
+            metadatas=metadatas,
+            **elasticsearch_connection,
             index_name=index_name,
         )
-        es = Elasticsearch(hosts=elasticsearch_url)
-        index_names = es.indices.get(index="_all").keys()
-        assert index_name in index_names
 
-        search_result = elastic_vector_search.similarity_search("sharks")
-        print(search_result)
+        def assert_query(query_body: dict, query: str) -> dict:
+            assert query_body == {
+                "knn": {
+                    "field": "vector",
+                    "filter": [{"term": {"metadata.page": "1"}}],
+                    "k": 3,
+                    "num_candidates": 50,
+                    "query_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
+                }
+            }
+            return query_body
 
-        assert len(search_result) != 0
+        output = docsearch.similarity_search(
+            query="foo",
+            k=3,
+            filter=[{"term": {"metadata.page": "1"}}],
+            custom_query=assert_query,
+        )
+        assert output == [Document(page_content="foo", metadata={"page": 1})]
 
-    @pytest.mark.vcr(ignore_localhost=True)
-    def test_custom_index_add_documents(
-        self,
-        documents: List[Document],
-        embedding_openai: OpenAIEmbeddings,
-        elasticsearch_url: str,
+    def test_similarity_search_exact_search(
+        self, elasticsearch_connection: dict, index_name: str
     ) -> None:
-        """This test checks the construction of a custom
-        ElasticSearch index using the 'add_documents'."""
-        from elasticsearch import Elasticsearch
+        """Test end to end construction and search with metadata."""
+        texts = ["foo", "bar", "baz"]
+        docsearch = ElasticsearchStore.from_texts(
+            texts,
+            FakeEmbeddings(),
+            **elasticsearch_connection,
+            index_name=index_name,
+            strategy=ElasticsearchStore.ExactRetrievalStrategy(),
+        )
 
-        index_name = f"custom_index_{uuid.uuid4().hex}"
-        elastic_vector_search = ElasticVectorSearch(
-            embedding=embedding_openai,
-            elasticsearch_url=elasticsearch_url,
+        expected_query = {
+            "query": {
+                "script_score": {
+                    "query": {"match_all": {}},
+                    "script": {
+                        "source": "cosineSimilarity(params.query_vector, 'vector') + 1.0",  # noqa: E501
+                        "params": {
+                            "query_vector": [
+                                1.0,
+                                1.0,
+                                1.0,
+                                1.0,
+                                1.0,
+                                1.0,
+                                1.0,
+                                1.0,
+                                1.0,
+                                0.0,
+                            ]
+                        },
+                    },
+                }
+            }
+        }
+
+        def assert_query(query_body: dict, query: str) -> dict:
+            assert query_body == expected_query
+            return query_body
+
+        output = docsearch.similarity_search("foo", k=1, custom_query=assert_query)
+        assert output == [Document(page_content="foo")]
+
+    def test_similarity_search_exact_search_with_filter(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
+        """Test end to end construction and search with metadata."""
+        texts = ["foo", "bar", "baz"]
+        metadatas = [{"page": i} for i in range(len(texts))]
+        docsearch = ElasticsearchStore.from_texts(
+            texts,
+            FakeEmbeddings(),
+            **elasticsearch_connection,
+            index_name=index_name,
+            metadatas=metadatas,
+            strategy=ElasticsearchStore.ExactRetrievalStrategy(),
+        )
+
+        def assert_query(query_body: dict, query: str) -> dict:
+            expected_query = {
+                "query": {
+                    "script_score": {
+                        "query": {"bool": {"filter": [{"term": {"metadata.page": 0}}]}},
+                        "script": {
+                            "source": "cosineSimilarity(params.query_vector, 'vector') + 1.0",  # noqa: E501
+                            "params": {
+                                "query_vector": [
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    0.0,
+                                ]
+                            },
+                        },
+                    }
+                }
+            }
+            assert query_body == expected_query
+            return query_body
+
+        output = docsearch.similarity_search(
+            "foo",
+            k=1,
+            custom_query=assert_query,
+            filter=[{"term": {"metadata.page": 0}}],
+        )
+        assert output == [Document(page_content="foo", metadata={"page": 0})]
+
+    def test_similarity_search_exact_search_distance_dot_product(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
+        """Test end to end construction and search with metadata."""
+        texts = ["foo", "bar", "baz"]
+        docsearch = ElasticsearchStore.from_texts(
+            texts,
+            FakeEmbeddings(),
+            **elasticsearch_connection,
+            index_name=index_name,
+            strategy=ElasticsearchStore.ExactRetrievalStrategy(),
+            distance_strategy="DOT_PRODUCT",
+        )
+
+        def assert_query(query_body: dict, query: str) -> dict:
+            assert query_body == {
+                "query": {
+                    "script_score": {
+                        "query": {"match_all": {}},
+                        "script": {
+                            "source": """
+            double value = dotProduct(params.query_vector, 'vector');
+            return sigmoid(1, Math.E, -value); 
+            """,
+                            "params": {
+                                "query_vector": [
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    1.0,
+                                    0.0,
+                                ]
+                            },
+                        },
+                    }
+                }
+            }
+            return query_body
+
+        output = docsearch.similarity_search("foo", k=1, custom_query=assert_query)
+        assert output == [Document(page_content="foo")]
+
+    def test_similarity_search_exact_search_unknown_distance_strategy(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
+        """Test end to end construction and search with unknown distance strategy."""
+
+        with pytest.raises(KeyError):
+            texts = ["foo", "bar", "baz"]
+            ElasticsearchStore.from_texts(
+                texts,
+                FakeEmbeddings(),
+                **elasticsearch_connection,
+                index_name=index_name,
+                strategy=ElasticsearchStore.ExactRetrievalStrategy(),
+                distance_strategy="NOT_A_STRATEGY",
+            )
+
+    def test_similarity_search_approx_with_hybrid_search(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
+        """Test end to end construction and search with metadata."""
+        texts = ["foo", "bar", "baz"]
+        docsearch = ElasticsearchStore.from_texts(
+            texts,
+            FakeEmbeddings(),
+            **elasticsearch_connection,
+            index_name=index_name,
+            strategy=ElasticsearchStore.ApproxRetrievalStrategy(hybrid=True),
+        )
+
+        def assert_query(query_body: dict, query: str) -> dict:
+            assert query_body == {
+                "knn": {
+                    "field": "vector",
+                    "filter": [],
+                    "k": 1,
+                    "num_candidates": 50,
+                    "query_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
+                },
+                "query": {
+                    "bool": {
+                        "filter": [],
+                        "must": [{"match": {"text": {"query": "foo"}}}],
+                    }
+                },
+                "rank": {"rrf": {}},
+            }
+            return query_body
+
+        output = docsearch.similarity_search("foo", k=1, custom_query=assert_query)
+        assert output == [Document(page_content="foo")]
+
+    def test_similarity_search_approx_with_custom_query_fn(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
+        """test that custom query function is called
+        with the query string and query body"""
+
+        def my_custom_query(query_body: dict, query: str) -> dict:
+            assert query == "foo"
+            assert query_body == {
+                "knn": {
+                    "field": "vector",
+                    "filter": [],
+                    "k": 1,
+                    "num_candidates": 50,
+                    "query_vector": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
+                }
+            }
+            return {"query": {"match": {"text": {"query": "bar"}}}}
+
+        """Test end to end construction and search with metadata."""
+        texts = ["foo", "bar", "baz"]
+        docsearch = ElasticsearchStore.from_texts(
+            texts, FakeEmbeddings(), **elasticsearch_connection, index_name=index_name
+        )
+        output = docsearch.similarity_search("foo", k=1, custom_query=my_custom_query)
+        assert output == [Document(page_content="bar")]
+
+    @pytest.mark.skipif(
+        "sentence-transformers__all-minilm-l6-v2" not in modelsDeployed,
+        reason="Sentence Transformers model not deployed in ML Node, skipping test",
+    )
+    def test_similarity_search_with_approx_infer_instack(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
+        """test end to end with approx retrieval strategy and inference in-stack"""
+        docsearch = ElasticsearchStore(
+            index_name=index_name,
+            strategy=ElasticsearchStore.ApproxRetrievalStrategy(
+                query_model_id="sentence-transformers__all-minilm-l6-v2"
+            ),
+            query_field="text_field",
+            vector_query_field="vector_query_field.predicted_value",
+            **elasticsearch_connection,
+        )
+
+        # setting up the pipeline for inference
+        docsearch.client.ingest.put_pipeline(
+            id="test_pipeline",
+            processors=[
+                {
+                    "inference": {
+                        "model_id": "sentence-transformers__all-minilm-l6-v2",
+                        "field_map": {"query_field": "text_field"},
+                        "target_field": "vector_query_field",
+                    }
+                }
+            ],
+        )
+
+        # creating a new index with the pipeline,
+        # not relying on langchain to create the index
+        docsearch.client.indices.create(
+            index=index_name,
+            mappings={
+                "properties": {
+                    "text_field": {"type": "text"},
+                    "vector_query_field": {
+                        "properties": {
+                            "predicted_value": {
+                                "type": "dense_vector",
+                                "dims": 384,
+                                "index": True,
+                                "similarity": "l2_norm",
+                            }
+                        }
+                    },
+                }
+            },
+            settings={"index": {"default_pipeline": "pipeline"}},
+        )
+
+        # adding documents to the index
+        texts = ["foo", "bar", "baz"]
+
+        for i, text in enumerate(texts):
+            docsearch.client.create(
+                index=index_name,
+                id=str(i),
+                document={"text_field": text, "metadata": {}},
+            )
+
+        def assert_query(query_body: dict, query: str) -> dict:
+            assert query_body == {
+                "knn": {
+                    "filter": [],
+                    "field": "vector_query_field.predicted_value",
+                    "k": 1,
+                    "num_candidates": 50,
+                    "query_vector_builder": {
+                        "text_embedding": {
+                            "model_id": "sentence-transformers__all-minilm-l6-v2",
+                            "model_text": "foo",
+                        }
+                    },
+                }
+            }
+            return query_body
+
+        output = docsearch.similarity_search("foo", k=1, custom_query=assert_query)
+        assert output == [Document(page_content="foo")]
+
+        output = docsearch.similarity_search("bar", k=1)
+        assert output == [Document(page_content="bar")]
+
+    @pytest.mark.skipif(
+        "elser" not in modelsDeployed,
+        reason="ELSER not deployed in ML Node, skipping test",
+    )
+    def test_similarity_search_with_sparse_infer_instack(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
+        """test end to end with sparse retrieval strategy and inference in-stack"""
+        texts = ["foo", "bar", "baz"]
+        docsearch = ElasticsearchStore.from_texts(
+            texts,
+            **elasticsearch_connection,
+            index_name=index_name,
+            strategy=ElasticsearchStore.SparseVectorRetrievalStrategy(),
+        )
+        output = docsearch.similarity_search("foo", k=1)
+        assert output == [Document(page_content="foo")]
+
+    def test_elasticsearch_with_relevance_score(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
+        """Test to make sure the relevance score is scaled to 0-1."""
+        texts = ["foo", "bar", "baz"]
+        metadatas = [{"page": str(i)} for i in range(len(texts))]
+        embeddings = FakeEmbeddings()
+
+        docsearch = ElasticsearchStore.from_texts(
+            index_name=index_name,
+            texts=texts,
+            embedding=embeddings,
+            metadatas=metadatas,
+            **elasticsearch_connection,
+        )
+
+        embedded_query = embeddings.embed_query("foo")
+        output = docsearch.similarity_search_by_vector_with_relevance_scores(
+            embedding=embedded_query, k=1
+        )
+        assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)]
+
+    def test_elasticsearch_delete_ids(
+        self, elasticsearch_connection: dict, index_name: str
+    ) -> None:
+        """Test delete methods from vector store."""
+        texts = ["foo", "bar", "baz", "gni"]
+        metadatas = [{"page": i} for i in range(len(texts))]
+        docsearch = ElasticsearchStore(
+            embedding=ConsistentFakeEmbeddings(),
+            **elasticsearch_connection,
             index_name=index_name,
         )
-        es = Elasticsearch(hosts=elasticsearch_url)
-        elastic_vector_search.add_documents(documents)
 
-        index_names = es.indices.get(index="_all").keys()
-        assert index_name in index_names
+        ids = docsearch.add_texts(texts, metadatas)
+        output = docsearch.similarity_search("foo", k=10)
+        assert len(output) == 4
 
-        search_result = elastic_vector_search.similarity_search("sharks")
-        print(search_result)
+        docsearch.delete(ids[1:3])
+        output = docsearch.similarity_search("foo", k=10)
+        assert len(output) == 2
 
-        assert len(search_result) != 0
+        docsearch.delete(["not-existing"])
+        output = docsearch.similarity_search("foo", k=10)
+        assert len(output) == 2
 
-    def test_custom_index_add_documents_to_exists_store(self) -> None:
-        # TODO: implement it
-        pass
+        docsearch.delete([ids[0]])
+        output = docsearch.similarity_search("foo", k=10)
+        assert len(output) == 1
+
+        docsearch.delete([ids[3]])
+        output = docsearch.similarity_search("gni", k=10)
+        assert len(output) == 0
diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_vectara.py b/libs/langchain/tests/integration_tests/vectorstores/test_vectara.py
index 2d725f88a37..57338e7f994 100644
--- a/libs/langchain/tests/integration_tests/vectorstores/test_vectara.py
+++ b/libs/langchain/tests/integration_tests/vectorstores/test_vectara.py
@@ -86,7 +86,6 @@ def test_vectara_from_files() -> None:
         n_sentence_context=0,
         filter="doc.test_num = 2",
     )
-    print(output)
     assert output[0].page_content == (
         "By the commonly adopted machine learning tradition "
         "(e.g., Chapter 28 in Murphy, 2012; Deng and Li, 2013), it may be natural "
@@ -94,3 +93,24 @@ def test_vectara_from_files() -> None:
         "(e.g., DNNs) and deep probabilistic generative models (e.g., DBN, Deep "
         "Boltzmann Machine (DBM))."
     )
+
+    # finally do a similarity search to see if all works okay
+    output = docsearch.similarity_search(
+        "By the commonly adopted machine learning tradition",
+        k=1,
+        n_sentence_context=1,
+        filter="doc.test_num = 2",
+    )
+    print(output[0].page_content)
+    assert output[0].page_content == (
+        """\
+Note the use of “hybrid” in 3) above is different from that used sometimes in the literature, \
+which for example refers to the hybrid systems for speech recognition feeding the output probabilities of a neural network into an HMM \
+(Bengio et al., 1991; Bourlard and Morgan, 1993; Morgan, 2012). \
+By the commonly adopted machine learning tradition (e.g., Chapter 28 in Murphy, 2012; Deng and Li, 2013), \
+it may be natural to just classify deep learning techniques into deep discriminative models (e.g., DNNs) \
+and deep probabilistic generative models (e.g., DBN, Deep Boltzmann Machine (DBM)). \
+This classification scheme, however, misses a key insight gained in deep learning research about how generative \
+models can greatly improve the training of DNNs and other deep discriminative models via better regularization.\
+"""  # noqa: E501
+    )
diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_zep.py b/libs/langchain/tests/integration_tests/vectorstores/test_zep.py
new file mode 100644
index 00000000000..b424ab9398d
--- /dev/null
+++ b/libs/langchain/tests/integration_tests/vectorstores/test_zep.py
@@ -0,0 +1,238 @@
+# mypy: disable-error-code=attr-defined
+import copy
+from random import random
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+from uuid import uuid4
+
+import pytest
+from pytest_mock import MockerFixture
+
+from langchain.schema import Document
+from langchain.vectorstores import ZepVectorStore
+from langchain.vectorstores.zep import CollectionConfig
+
+if TYPE_CHECKING:
+    from zep_python.document import Document as ZepDocument
+    from zep_python.document import DocumentCollection
+
+VECTOR_DIMS = 5
+
+
+def gen_vector() -> List[float]:
+    return [random() for _ in range(VECTOR_DIMS)]
+
+
+def gen_mock_zep_document(
+    collection_name: str,
+    embedding_dimensions: Optional[int] = None,
+) -> "ZepDocument":
+    from zep_python.document import Document as ZepDocument
+
+    embedding = (
+        [random() for _ in range(embedding_dimensions)]
+        if embedding_dimensions
+        else None
+    )
+
+    return ZepDocument(
+        uuid=str(uuid4()),
+        collection_name=collection_name,
+        content="Test Document",
+        embedding=embedding,
+        metadata={"key": "value"},
+    )
+
+
+@pytest.fixture
+def texts_metadatas() -> Dict[str, Any]:
+    return {
+        "texts": ["Test Document" for _ in range(2)],
+        "metadatas": [{"key": "value"} for _ in range(2)],
+    }
+
+
+@pytest.fixture
+def mock_documents() -> List[Document]:
+    return [
+        Document(
+            page_content="Test Document",
+            metadata={"key": "value"},
+        )
+        for _ in range(2)
+    ]
+
+
+@pytest.fixture
+def texts_metadatas_as_zep_documents() -> List["ZepDocument"]:
+    from zep_python.document import Document as ZepDocument
+
+    return [
+        ZepDocument(
+            content="Test Document",
+            metadata={"key": "value"},
+        )
+        for _ in range(2)
+    ]
+
+
+@pytest.fixture
+def search_results() -> List["ZepDocument"]:
+    return [
+        gen_mock_zep_document(
+            collection_name="test_collection", embedding_dimensions=VECTOR_DIMS
+        )
+        for _ in range(2)
+    ]
+
+
+@pytest.fixture
+def search_results_with_query_embedding() -> Tuple[List["ZepDocument"], List[float]]:
+    return_count = 2
+    return [
+        gen_mock_zep_document(
+            collection_name="test_collection", embedding_dimensions=VECTOR_DIMS
+        )
+        for _ in range(return_count)
+    ], gen_vector()
+
+
+@pytest.fixture
+def mock_collection_config() -> CollectionConfig:
+    return CollectionConfig(
+        name="test_collection",
+        description="Test Collection",
+        metadata={"key": "value"},
+        embedding_dimensions=VECTOR_DIMS,
+        is_auto_embedded=True,
+    )
+
+
+@pytest.fixture
+@pytest.mark.requires("zep_python")
+def mock_collection(
+    mocker: MockerFixture,
+    mock_collection_config: CollectionConfig,
+    search_results: List[Document],
+    search_results_with_query_embedding: Tuple[List[Document], List[float]],
+) -> "DocumentCollection":
+    from zep_python.document import DocumentCollection
+
+    mock_collection: DocumentCollection = mocker.patch(
+        "zep_python.document.collections.DocumentCollection", autospec=True
+    )
+    mock_collection.search.return_value = copy.deepcopy(search_results)
+    mock_collection.asearch.return_value = copy.deepcopy(search_results)
+
+    temp_value = copy.deepcopy(search_results_with_query_embedding)
+    mock_collection.search_return_query_vector.return_value = copy.deepcopy(temp_value)
+    mock_collection.asearch_return_query_vector.return_value = copy.deepcopy(temp_value)
+
+    mock_collection.name = mock_collection_config.name
+    mock_collection.is_auto_embedded = mock_collection_config.is_auto_embedded
+    mock_collection.embedding_dimensions = mock_collection_config.embedding_dimensions
+
+    return mock_collection
+
+
+@pytest.fixture
+@pytest.mark.requires("zep_python")
+def zep_vectorstore(
+    mocker: MockerFixture,
+    mock_collection: "DocumentCollection",
+    mock_collection_config: CollectionConfig,
+) -> ZepVectorStore:
+    mock_document_client = mocker.patch(
+        "zep_python.document.client.DocumentClient", autospec=True
+    )
+    mock_document_client.get_collection.return_value = mock_collection
+    mock_client = mocker.patch("zep_python.ZepClient", autospec=True)
+    mock_client.return_value.document = mock_document_client
+
+    vs = ZepVectorStore(
+        mock_collection_config.name,
+        "http://localhost:8080",
+        api_key="test",
+        config=mock_collection_config,
+    )
+    return vs
+
+
+@pytest.mark.requires("zep_python")
+def test_from_texts(
+    zep_vectorstore: ZepVectorStore,
+    mock_collection_config: CollectionConfig,
+    mock_collection: "DocumentCollection",
+    texts_metadatas: Dict[str, Any],
+    texts_metadatas_as_zep_documents: List["ZepDocument"],
+) -> None:
+    vs = zep_vectorstore.from_texts(
+        **texts_metadatas,
+        collection_name=mock_collection_config.name,
+        api_url="http://localhost:8000"
+    )
+
+    vs._collection.add_documents.assert_called_once_with(  # type: ignore
+        texts_metadatas_as_zep_documents
+    )
+
+
+@pytest.mark.requires("zep_python")
+def test_add_documents(
+    zep_vectorstore: ZepVectorStore,
+    mock_collection: "DocumentCollection",
+    mock_documents: List[Document],
+    texts_metadatas_as_zep_documents: List["ZepDocument"],
+) -> None:
+    zep_vectorstore.add_documents(mock_documents)
+
+    mock_collection.add_documents.assert_called_once_with(  # type: ignore
+        texts_metadatas_as_zep_documents
+    )
+
+
+@pytest.mark.requires("zep_python")
+@pytest.mark.asyncio
+async def test_asearch_similarity(
+    zep_vectorstore: ZepVectorStore,
+) -> None:
+    r = await zep_vectorstore.asearch(
+        query="Test Document", search_type="similarity", k=2
+    )
+
+    assert len(r) == 2
+    assert r[0].page_content == "Test Document"
+    assert r[0].metadata == {"key": "value"}
+
+
+@pytest.mark.requires("zep_python")
+@pytest.mark.asyncio
+async def test_asearch_mmr(
+    zep_vectorstore: ZepVectorStore,
+) -> None:
+    r = await zep_vectorstore.asearch(query="Test Document", search_type="mmr", k=1)
+
+    assert len(r) == 1
+    assert r[0].page_content == "Test Document"
+    assert r[0].metadata == {"key": "value"}
+
+
+@pytest.mark.requires("zep_python")
+def test_search_similarity(
+    zep_vectorstore: ZepVectorStore,
+) -> None:
+    r = zep_vectorstore.search(query="Test Document", search_type="similarity", k=2)
+
+    assert len(r) == 2
+    assert r[0].page_content == "Test Document"
+    assert r[0].metadata == {"key": "value"}
+
+
+@pytest.mark.requires("zep_python")
+def test_search_mmr(
+    zep_vectorstore: ZepVectorStore,
+) -> None:
+    r = zep_vectorstore.search(query="Test Document", search_type="mmr", k=1)
+
+    assert len(r) == 1
+    assert r[0].page_content == "Test Document"
+    assert r[0].metadata == {"key": "value"}
diff --git a/libs/langchain/tests/mock_servers/robot/server.py b/libs/langchain/tests/mock_servers/robot/server.py
index 5af50d3f753..40a6aa609cc 100644
--- a/libs/langchain/tests/mock_servers/robot/server.py
+++ b/libs/langchain/tests/mock_servers/robot/server.py
@@ -7,7 +7,8 @@ import uvicorn
 from fastapi import FastAPI, HTTPException, Query
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.openapi.utils import get_openapi
-from pydantic import BaseModel, Field
+
+from langchain.pydantic_v1 import BaseModel, Field
 
 PORT = 7289
 
diff --git a/libs/langchain/tests/unit_tests/_api/test_deprecation.py b/libs/langchain/tests/unit_tests/_api/test_deprecation.py
index ef16c38223d..7b6b359cbf4 100644
--- a/libs/langchain/tests/unit_tests/_api/test_deprecation.py
+++ b/libs/langchain/tests/unit_tests/_api/test_deprecation.py
@@ -2,9 +2,9 @@ import warnings
 from typing import Any, Dict
 
 import pytest
-from pydantic import BaseModel
 
 from langchain._api.deprecation import _warn_deprecated, deprecated
+from langchain.pydantic_v1 import BaseModel
 
 
 @pytest.mark.parametrize(
@@ -115,7 +115,9 @@ def test_deprecated_function() -> None:
             "and will be removed in 3.0.0"
         )
 
-        assert deprecated_function.__doc__ == "[*Deprecated*]  original doc"
+        doc = deprecated_function.__doc__
+        assert isinstance(doc, str)
+        assert doc.startswith("[*Deprecated*]  original doc")
 
 
 def test_deprecated_method() -> None:
@@ -131,7 +133,9 @@ def test_deprecated_method() -> None:
             "LangChain 2.0.0 and will be removed in 3.0.0"
         )
 
-        assert obj.deprecated_method.__doc__ == "[*Deprecated*]  original doc"
+        doc = obj.deprecated_method.__doc__
+        assert isinstance(doc, str)
+        assert doc.startswith("[*Deprecated*]  original doc")
 
 
 def test_deprecated_classmethod() -> None:
@@ -145,10 +149,10 @@ def test_deprecated_classmethod() -> None:
             "The function `deprecated_classmethod` was deprecated in "
             "LangChain 2.0.0 and will be removed in 3.0.0"
         )
-        assert (
-            ClassWithDeprecatedMethods.deprecated_classmethod.__doc__
-            == "[*Deprecated*]  original doc"
-        )
+
+        doc = ClassWithDeprecatedMethods.deprecated_classmethod.__doc__
+        assert isinstance(doc, str)
+        assert doc.startswith("[*Deprecated*]  original doc")
 
 
 def test_deprecated_staticmethod() -> None:
@@ -166,10 +170,9 @@ def test_deprecated_staticmethod() -> None:
             "The function `deprecated_staticmethod` was deprecated in "
             "LangChain 2.0.0 and will be removed in 3.0.0"
         )
-        assert (
-            ClassWithDeprecatedMethods.deprecated_staticmethod.__doc__
-            == "[*Deprecated*]  original doc"
-        )
+        doc = ClassWithDeprecatedMethods.deprecated_staticmethod.__doc__
+        assert isinstance(doc, str)
+        assert doc.startswith("[*Deprecated*]  original doc")
 
 
 def test_deprecated_property() -> None:
@@ -187,10 +190,9 @@ def test_deprecated_property() -> None:
             "The function `deprecated_property` was deprecated in "
             "LangChain 2.0.0 and will be removed in 3.0.0"
         )
-        assert (
-            ClassWithDeprecatedMethods.deprecated_property.__doc__
-            == "[*Deprecated*]  original doc"
-        )
+        doc = ClassWithDeprecatedMethods.deprecated_property.__doc__
+        assert isinstance(doc, str)
+        assert doc.startswith("[*Deprecated*]  original doc")
 
 
 def test_whole_class_deprecation() -> None:
@@ -249,4 +251,6 @@ def test_deprecated_method_pydantic() -> None:
             "LangChain 2.0.0 and will be removed in 3.0.0"
         )
 
-        assert obj.deprecated_method.__doc__ == "[*Deprecated*]  original doc"
+        doc = obj.deprecated_method.__doc__
+        assert isinstance(doc, str)
+        assert doc.startswith("[*Deprecated*]  original doc")
diff --git a/libs/langchain/tests/unit_tests/agents/test_structured_chat.py b/libs/langchain/tests/unit_tests/agents/test_structured_chat.py
new file mode 100644
index 00000000000..356c3d87e2d
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/agents/test_structured_chat.py
@@ -0,0 +1,47 @@
+"""Unittests for langchain.agents.chat package."""
+from typing import Tuple
+
+from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser
+from langchain.schema import AgentAction
+
+output_parser = StructuredChatOutputParser()
+
+
+def get_action_and_input(text: str) -> Tuple[str, str]:
+    output = output_parser.parse(text)
+    if isinstance(output, AgentAction):
+        return output.tool, str(output.tool_input)
+    else:
+        return "Final Answer", output.return_values["output"]
+
+
+def test_parse_with_language() -> None:
+    llm_output = """I can use the `foo` tool to achieve the goal.
+
+    Action:
+    ```json
+    {
+      "action": "foo",
+      "action_input": "bar"
+    }
+    ```
+    """
+    action, action_input = get_action_and_input(llm_output)
+    assert action == "foo"
+    assert action_input == "bar"
+
+
+def test_parse_without_language() -> None:
+    llm_output = """I can use the `foo` tool to achieve the goal.
+
+    Action:
+    ```
+    {
+      "action": "foo",
+      "action_input": "bar"
+    }
+    ```
+    """
+    action, action_input = get_action_and_input(llm_output)
+    assert action == "foo"
+    assert action_input == "bar"
diff --git a/libs/langchain/tests/unit_tests/callbacks/fake_callback_handler.py b/libs/langchain/tests/unit_tests/callbacks/fake_callback_handler.py
index f4819c6930e..91f0c09bd18 100644
--- a/libs/langchain/tests/unit_tests/callbacks/fake_callback_handler.py
+++ b/libs/langchain/tests/unit_tests/callbacks/fake_callback_handler.py
@@ -3,9 +3,8 @@ from itertools import chain
 from typing import Any, Dict, List, Optional, Union
 from uuid import UUID
 
-from pydantic import BaseModel
-
 from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
+from langchain.pydantic_v1 import BaseModel
 from langchain.schema.messages import BaseMessage
 
 
diff --git a/libs/langchain/tests/unit_tests/callbacks/test_openai_info.py b/libs/langchain/tests/unit_tests/callbacks/test_openai_info.py
index 1fa62a74adc..4580ccd4883 100644
--- a/libs/langchain/tests/unit_tests/callbacks/test_openai_info.py
+++ b/libs/langchain/tests/unit_tests/callbacks/test_openai_info.py
@@ -1,3 +1,6 @@
+from unittest.mock import MagicMock
+from uuid import uuid4
+
 import pytest
 
 from langchain.callbacks import OpenAICallbackHandler
@@ -124,3 +127,7 @@ def test_on_llm_end_no_cost_invalid_model(
     )
     handler.on_llm_end(response)
     assert handler.total_cost == 0
+
+
+def test_on_retry_works(handler: OpenAICallbackHandler) -> None:
+    handler.on_retry(MagicMock(), run_id=uuid4())
diff --git a/libs/langchain/tests/unit_tests/chains/test_combine_documents.py b/libs/langchain/tests/unit_tests/chains/test_combine_documents.py
index df2212588e6..a970c33cd4d 100644
--- a/libs/langchain/tests/unit_tests/chains/test_combine_documents.py
+++ b/libs/langchain/tests/unit_tests/chains/test_combine_documents.py
@@ -9,8 +9,10 @@ from langchain.chains.combine_documents.reduce import (
     _collapse_docs,
     _split_list_of_docs,
 )
+from langchain.chains.qa_with_sources import load_qa_with_sources_chain
 from langchain.docstore.document import Document
 from langchain.schema import format_document
+from tests.unit_tests.llms.fake_llm import FakeLLM
 
 
 def _fake_docs_len_func(docs: List[Document]) -> int:
@@ -21,6 +23,11 @@ def _fake_combine_docs_func(docs: List[Document], **kwargs: Any) -> str:
     return "".join([d.page_content for d in docs])
 
 
+def test_multiple_input_keys() -> None:
+    chain = load_qa_with_sources_chain(FakeLLM(), chain_type="stuff")
+    assert chain.input_keys == ["input_documents", "question"]
+
+
 def test__split_list_long_single_doc() -> None:
     """Test splitting of a long single doc."""
     docs = [Document(page_content="foo" * 100)]
diff --git a/libs/langchain/tests/unit_tests/chains/test_qa_with_sources.py b/libs/langchain/tests/unit_tests/chains/test_qa_with_sources.py
new file mode 100644
index 00000000000..e69d9b5cd11
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/chains/test_qa_with_sources.py
@@ -0,0 +1,71 @@
+import pytest
+
+from langchain.chains.qa_with_sources.base import QAWithSourcesChain
+from tests.unit_tests.llms.fake_llm import FakeLLM
+
+
+@pytest.mark.parametrize(
+    "text,answer,sources",
+    [
+        (
+            "This Agreement is governed by English law.\nSOURCES: 28-pl",
+            "This Agreement is governed by English law.\n",
+            "28-pl",
+        ),
+        (
+            "This Agreement is governed by English law.\n"
+            "SOURCES: 28-pl\n\n"
+            "QUESTION: Which state/country's law governs the interpretation of the "
+            "contract?\n"
+            "FINAL ANSWER: This Agreement is governed by English law.\n"
+            "SOURCES: 28-pl",
+            "This Agreement is governed by English law.\n",
+            "28-pl",
+        ),
+        (
+            "The president did not mention Michael Jackson in the provided content.\n"
+            "SOURCES: \n\n"
+            "Note: Since the content provided does not contain any information about "
+            "Michael Jackson, there are no sources to cite for this specific question.",
+            "The president did not mention Michael Jackson in the provided content.\n",
+            "",
+        ),
+        # The following text was generated by gpt-3.5-turbo
+        (
+            "To diagnose the problem, please answer the following questions and send "
+            "them in one message to IT:\nA1. Are you connected to the office network? "
+            "VPN will not work from the office network.\nA2. Are you sure about your "
+            "login/password?\nA3. Are you using any other VPN (e.g. from a client)?\n"
+            "A4. When was the last time you used the company VPN?\n"
+            "SOURCES: 1\n\n"
+            "ALTERNATIVE OPTION: Another option is to run the VPN in CLI, but keep in "
+            "mind that DNS settings may not work and there may be a need for manual "
+            "modification of the local resolver or /etc/hosts and/or ~/.ssh/config "
+            "files to be able to connect to machines in the company. With the "
+            "appropriate packages installed, the only thing needed to establish "
+            "a connection is to run the command:\nsudo openvpn --config config.ovpn"
+            "\n\nWe will be asked for a username and password - provide the login "
+            "details, the same ones that have been used so far for VPN connection, "
+            "connecting to the company's WiFi, or printers (in the Warsaw office)."
+            "\n\nFinally, just use the VPN connection.\n"
+            "SOURCES: 2\n\n"
+            "ALTERNATIVE OPTION (for Windows): Download the"
+            "OpenVPN client application version 2.6 or newer from the official "
+            "website: https://openvpn.net/community-downloads/\n"
+            "SOURCES: 3",
+            "To diagnose the problem, please answer the following questions and send "
+            "them in one message to IT:\nA1. Are you connected to the office network? "
+            "VPN will not work from the office network.\nA2. Are you sure about your "
+            "login/password?\nA3. Are you using any other VPN (e.g. from a client)?\n"
+            "A4. When was the last time you used the company VPN?\n",
+            "1",
+        ),
+    ],
+)
+def test_spliting_answer_into_answer_and_sources(
+    text: str, answer: str, sources: str
+) -> None:
+    qa_chain = QAWithSourcesChain.from_llm(FakeLLM())
+    generated_answer, generated_sources = qa_chain._split_sources(text)
+    assert generated_answer == answer
+    assert generated_sources == sources
diff --git a/libs/langchain/tests/unit_tests/chat_models/test_ernie.py b/libs/langchain/tests/unit_tests/chat_models/test_ernie.py
new file mode 100644
index 00000000000..06eb1019615
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/chat_models/test_ernie.py
@@ -0,0 +1,16 @@
+from langchain.chat_models.ernie import _convert_message_to_dict
+from langchain.schema.messages import AIMessage, HumanMessage
+
+
+def test__convert_dict_to_message_human() -> None:
+    message = HumanMessage(content="foo")
+    result = _convert_message_to_dict(message)
+    expected_output = {"role": "user", "content": "foo"}
+    assert result == expected_output
+
+
+def test__convert_dict_to_message_ai() -> None:
+    message = AIMessage(content="foo")
+    result = _convert_message_to_dict(message)
+    expected_output = {"role": "assistant", "content": "foo"}
+    assert result == expected_output
diff --git a/libs/langchain/tests/unit_tests/chat_models/test_openai.py b/libs/langchain/tests/unit_tests/chat_models/test_openai.py
index 95ecbe9e5b7..b417d82e947 100644
--- a/libs/langchain/tests/unit_tests/chat_models/test_openai.py
+++ b/libs/langchain/tests/unit_tests/chat_models/test_openai.py
@@ -5,9 +5,9 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 
+from langchain.adapters.openai import convert_dict_to_message
 from langchain.chat_models.openai import (
     ChatOpenAI,
-    _convert_dict_to_message,
 )
 from langchain.schema.messages import (
     AIMessage,
@@ -20,7 +20,7 @@ from langchain.schema.messages import (
 def test_function_message_dict_to_function_message() -> None:
     content = json.dumps({"result": "Example #1"})
     name = "test_function"
-    result = _convert_dict_to_message(
+    result = convert_dict_to_message(
         {
             "role": "function",
             "name": name,
@@ -34,21 +34,21 @@ def test_function_message_dict_to_function_message() -> None:
 
 def test__convert_dict_to_message_human() -> None:
     message = {"role": "user", "content": "foo"}
-    result = _convert_dict_to_message(message)
+    result = convert_dict_to_message(message)
     expected_output = HumanMessage(content="foo")
     assert result == expected_output
 
 
 def test__convert_dict_to_message_ai() -> None:
     message = {"role": "assistant", "content": "foo"}
-    result = _convert_dict_to_message(message)
+    result = convert_dict_to_message(message)
     expected_output = AIMessage(content="foo")
     assert result == expected_output
 
 
 def test__convert_dict_to_message_system() -> None:
     message = {"role": "system", "content": "foo"}
-    result = _convert_dict_to_message(message)
+    result = convert_dict_to_message(message)
     expected_output = SystemMessage(content="foo")
     assert result == expected_output
 
diff --git a/libs/langchain/tests/unit_tests/document_loaders/blob_loaders/test_filesystem_blob_loader.py b/libs/langchain/tests/unit_tests/document_loaders/blob_loaders/test_filesystem_blob_loader.py
index 0c40bc08b18..2544f9a141d 100644
--- a/libs/langchain/tests/unit_tests/document_loaders/blob_loaders/test_filesystem_blob_loader.py
+++ b/libs/langchain/tests/unit_tests/document_loaders/blob_loaders/test_filesystem_blob_loader.py
@@ -2,7 +2,7 @@
 import os
 import tempfile
 from pathlib import Path
-from typing import Generator, Sequence
+from typing import Generator
 
 import pytest
 
@@ -42,56 +42,98 @@ def toy_dir() -> Generator[Path, None, None]:
         yield Path(temp_dir)
 
 
-@pytest.mark.parametrize(
-    "glob, suffixes, relative_filenames",
-    [
-        (
-            "**/[!.]*",
-            None,
-            [
-                "test.html",
-                "test.txt",
-                "some_dir/nested_file.txt",
-                "some_dir/other_dir/more_nested.txt",
-            ],
-        ),
-        ("*", None, ["test.html", "test.txt", ".hidden_file"]),
-        ("**/*.html", None, ["test.html"]),
-        ("*/*.txt", None, ["some_dir/nested_file.txt"]),
-        (
-            "**/*.txt",
-            None,
-            [
-                "test.txt",
-                "some_dir/nested_file.txt",
-                "some_dir/other_dir/more_nested.txt",
-            ],
-        ),
-        (
-            "**/*",
-            [".txt"],
-            [
-                "test.txt",
-                "some_dir/nested_file.txt",
-                "some_dir/other_dir/more_nested.txt",
-            ],
-        ),
-        ("meeeeeeow", None, []),
-        ("*", [".html", ".txt"], ["test.html", "test.txt"]),
-    ],
-)
-def test_file_names_exist(
-    toy_dir: str,
-    glob: str,
-    suffixes: Sequence[str],
-    relative_filenames: Sequence[str],
-) -> None:
+_TEST_CASES = [
+    {
+        "glob": "**/[!.]*",
+        "suffixes": None,
+        "exclude": (),
+        "relative_filenames": [
+            "test.html",
+            "test.txt",
+            "some_dir/nested_file.txt",
+            "some_dir/other_dir/more_nested.txt",
+        ],
+    },
+    {
+        "glob": "*",
+        "suffixes": None,
+        "exclude": (),
+        "relative_filenames": ["test.html", "test.txt", ".hidden_file"],
+    },
+    {
+        "glob": "**/*.html",
+        "suffixes": None,
+        "exclude": (),
+        "relative_filenames": ["test.html"],
+    },
+    {
+        "glob": "*/*.txt",
+        "suffixes": None,
+        "exclude": (),
+        "relative_filenames": ["some_dir/nested_file.txt"],
+    },
+    {
+        "glob": "**/*.txt",
+        "suffixes": None,
+        "exclude": (),
+        "relative_filenames": [
+            "test.txt",
+            "some_dir/nested_file.txt",
+            "some_dir/other_dir/more_nested.txt",
+        ],
+    },
+    {
+        "glob": "**/*",
+        "suffixes": [".txt"],
+        "exclude": (),
+        "relative_filenames": [
+            "test.txt",
+            "some_dir/nested_file.txt",
+            "some_dir/other_dir/more_nested.txt",
+        ],
+    },
+    {
+        "glob": "meeeeeeow",
+        "suffixes": None,
+        "exclude": (),
+        "relative_filenames": [],
+    },
+    {
+        "glob": "*",
+        "suffixes": [".html", ".txt"],
+        "exclude": (),
+        "relative_filenames": ["test.html", "test.txt"],
+    },
+    # Using exclude patterns
+    {
+        "glob": "**/*",
+        "suffixes": [".txt"],
+        "exclude": ("some_dir/*",),
+        "relative_filenames": ["test.txt", "some_dir/other_dir/more_nested.txt"],
+    },
+    # Using 2 exclude patterns, one of which is recursive
+    {
+        "glob": "**/*",
+        "suffixes": None,
+        "exclude": ("**/*.txt", ".hidden*"),
+        "relative_filenames": ["test.html"],
+    },
+]
+
+
+@pytest.mark.parametrize("params", _TEST_CASES)
+def test_file_names_exist(toy_dir: str, params: dict) -> None:
     """Verify that the file names exist."""
 
-    loader = FileSystemBlobLoader(toy_dir, glob=glob, suffixes=suffixes)
-    blobs = list(loader.yield_blobs())
+    glob_pattern = params["glob"]
+    suffixes = params["suffixes"]
+    exclude = params["exclude"]
+    relative_filenames = params["relative_filenames"]
 
-    assert loader.count_matching_files() == len(relative_filenames)
+    loader = FileSystemBlobLoader(
+        toy_dir, glob=glob_pattern, suffixes=suffixes, exclude=exclude
+    )
+    blobs = list(loader.yield_blobs())
 
     file_names = sorted(str(blob.path) for blob in blobs)
 
@@ -101,6 +143,7 @@ def test_file_names_exist(
     )
 
     assert file_names == expected_filenames
+    assert loader.count_matching_files() == len(relative_filenames)
 
 
 @pytest.mark.requires("tqdm")
diff --git a/libs/langchain/tests/unit_tests/document_loaders/test_arcgis_loader.py b/libs/langchain/tests/unit_tests/document_loaders/test_arcgis_loader.py
new file mode 100644
index 00000000000..a2f7d05e3ee
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/document_loaders/test_arcgis_loader.py
@@ -0,0 +1,48 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from langchain.document_loaders import ArcGISLoader
+
+
+@pytest.fixture
+def arcgis_mocks(mock_feature_layer, mock_gis):  # type: ignore
+    sys_modules = {
+        "arcgis": MagicMock(),
+        "arcgis.features.FeatureLayer": mock_feature_layer,
+        "arcgis.gis.GIS": mock_gis,
+    }
+    with patch.dict("sys.modules", sys_modules):
+        yield
+
+
+@pytest.fixture
+def mock_feature_layer():  # type: ignore
+    feature_layer = MagicMock()
+    feature_layer.query.return_value = [
+        MagicMock(as_dict={"attributes": {"field": "value"}})
+    ]
+    feature_layer.url = "https://example.com/layer_url"
+    feature_layer.properties = {
+        "description": "<html><body>Some HTML content</body></html>",
+        "name": "test",
+    }
+    return feature_layer
+
+
+@pytest.fixture
+def mock_gis():  # type: ignore
+    gis = MagicMock()
+    gis.content.get.return_value = MagicMock(description="Item description")
+    return gis
+
+
+def test_lazy_load(arcgis_mocks, mock_feature_layer, mock_gis):  # type: ignore
+    loader = ArcGISLoader(layer=mock_feature_layer, gis=mock_gis)
+    loader.BEAUTIFULSOUP = None
+
+    documents = list(loader.lazy_load())
+
+    assert len(documents) == 1
+    assert documents[0].metadata["url"] == "https://example.com/layer_url"
+    # Add more assertions based on your expected behavior
diff --git a/libs/langchain/tests/unit_tests/embeddings/test_caching.py b/libs/langchain/tests/unit_tests/embeddings/test_caching.py
new file mode 100644
index 00000000000..0b2a1096db7
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/embeddings/test_caching.py
@@ -0,0 +1,49 @@
+"""Embeddings tests."""
+from typing import List
+
+import pytest
+
+from langchain.embeddings import CacheBackedEmbeddings
+from langchain.embeddings.base import Embeddings
+from langchain.storage.in_memory import InMemoryStore
+
+
+class MockEmbeddings(Embeddings):
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        # Simulate embedding documents
+        embeddings: List[List[float]] = []
+        for text in texts:
+            embeddings.append([len(text), len(text) + 1])
+        return embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        # Simulate embedding a query
+        return [5.0, 6.0]
+
+
+@pytest.fixture
+def cache_embeddings() -> CacheBackedEmbeddings:
+    """Create a cache backed embeddings."""
+    store = InMemoryStore()
+    embeddings = MockEmbeddings()
+    return CacheBackedEmbeddings.from_bytes_store(
+        embeddings, store, namespace="test_namespace"
+    )
+
+
+def test_embed_documents(cache_embeddings: CacheBackedEmbeddings) -> None:
+    texts = ["1", "22", "a", "333"]
+    vectors = cache_embeddings.embed_documents(texts)
+    expected_vectors: List[List[float]] = [[1, 2.0], [2.0, 3.0], [1.0, 2.0], [3.0, 4.0]]
+    assert vectors == expected_vectors
+    keys = list(cache_embeddings.document_embedding_store.yield_keys())
+    assert len(keys) == 4
+    # UUID is expected to be the same for the same text
+    assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12"
+
+
+def test_embed_query(cache_embeddings: CacheBackedEmbeddings) -> None:
+    text = "query_text"
+    vector = cache_embeddings.embed_query(text)
+    expected_vector = [5.0, 6.0]
+    assert vector == expected_vector
diff --git a/libs/langchain/tests/unit_tests/evaluation/agents/test_eval_chain.py b/libs/langchain/tests/unit_tests/evaluation/agents/test_eval_chain.py
index 5acb93baa51..6a625821eb2 100644
--- a/libs/langchain/tests/unit_tests/evaluation/agents/test_eval_chain.py
+++ b/libs/langchain/tests/unit_tests/evaluation/agents/test_eval_chain.py
@@ -3,7 +3,6 @@
 from typing import Any, Dict, List, Optional, Tuple
 
 import pytest
-from pydantic import Field
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.evaluation.agents.trajectory_eval_chain import (
@@ -11,6 +10,7 @@ from langchain.evaluation.agents.trajectory_eval_chain import (
     TrajectoryEvalChain,
     TrajectoryOutputParser,
 )
+from langchain.pydantic_v1 import Field
 from langchain.schema import AgentAction, BaseMessage, OutputParserException
 from langchain.tools.base import tool
 from tests.unit_tests.llms.fake_chat_model import FakeChatModel
diff --git a/libs/langchain/tests/unit_tests/evaluation/parsing/__init__.py b/libs/langchain/tests/unit_tests/evaluation/parsing/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/langchain/tests/unit_tests/evaluation/parsing/test_base.py b/libs/langchain/tests/unit_tests/evaluation/parsing/test_base.py
new file mode 100644
index 00000000000..9a10224f160
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/evaluation/parsing/test_base.py
@@ -0,0 +1,177 @@
+import random
+
+import pytest
+
+from langchain.evaluation.parsing.base import (
+    JsonEqualityEvaluator,
+    JsonValidityEvaluator,
+)
+
+
+@pytest.fixture
+def json_validity_evaluator() -> JsonValidityEvaluator:
+    return JsonValidityEvaluator()
+
+
+def test_json_validity_evaluator_requires_input(
+    json_validity_evaluator: JsonValidityEvaluator,
+) -> None:
+    assert json_validity_evaluator.requires_input is False
+
+
+def test_json_validity_evaluator_requires_reference(
+    json_validity_evaluator: JsonValidityEvaluator,
+) -> None:
+    assert json_validity_evaluator.requires_reference is False
+
+
+def test_json_validity_evaluator_evaluation_name(
+    json_validity_evaluator: JsonValidityEvaluator,
+) -> None:
+    assert json_validity_evaluator.evaluation_name == "json_validity"
+
+
+def test_json_validity_evaluator_evaluate_valid_json(
+    json_validity_evaluator: JsonValidityEvaluator,
+) -> None:
+    prediction = '{"name": "John", "age": 30, "city": "New York"}'
+    result = json_validity_evaluator.evaluate_strings(prediction=prediction)
+    assert result == {"score": 1}
+
+
+def test_json_validity_evaluator_evaluate_invalid_json(
+    json_validity_evaluator: JsonValidityEvaluator,
+) -> None:
+    prediction = '{"name": "John", "age": 30, "city": "New York",}'
+    result = json_validity_evaluator.evaluate_strings(prediction=prediction)
+    assert result["score"] == 0
+    assert result["reasoning"].startswith(
+        "Expecting property name enclosed in double quotes"
+    )
+
+
+@pytest.fixture
+def json_equality_evaluator() -> JsonEqualityEvaluator:
+    return JsonEqualityEvaluator()
+
+
+def test_json_equality_evaluator_requires_input(
+    json_equality_evaluator: JsonEqualityEvaluator,
+) -> None:
+    assert json_equality_evaluator.requires_input is False
+
+
+def test_json_equality_evaluator_requires_reference(
+    json_equality_evaluator: JsonEqualityEvaluator,
+) -> None:
+    assert json_equality_evaluator.requires_reference is True
+
+
+def test_json_equality_evaluator_evaluation_name(
+    json_equality_evaluator: JsonEqualityEvaluator,
+) -> None:
+    assert json_equality_evaluator.evaluation_name == "json_equality"
+
+
+def test_json_equality_evaluator_parse_json(
+    json_equality_evaluator: JsonEqualityEvaluator,
+) -> None:
+    string = '{"a": 1}'
+    result = json_equality_evaluator._parse_json(string)
+    assert result == {"a": 1}
+
+
+def test_json_equality_evaluator_evaluate_strings_equal(
+    json_equality_evaluator: JsonEqualityEvaluator,
+) -> None:
+    prediction = '{"a": 1}'
+    reference = '{"a": 1}'
+    result = json_equality_evaluator.evaluate_strings(
+        prediction=prediction, reference=reference
+    )
+    assert result == {"score": True}
+
+
+def test_json_equality_evaluator_evaluate_strings_not_equal(
+    json_equality_evaluator: JsonEqualityEvaluator,
+) -> None:
+    prediction = '{"a": 1}'
+    reference = '{"a": 2}'
+    result = json_equality_evaluator.evaluate_strings(
+        prediction=prediction, reference=reference
+    )
+    assert result == {"score": False}
+
+
+def test_json_equality_evaluator_evaluate_strings_custom_operator_equal() -> None:
+    def operator(x: dict, y: dict) -> bool:
+        return x["a"] == y["a"]
+
+    evaluator = JsonEqualityEvaluator(operator=operator)
+    prediction = '{"a": 1, "b": 2}'
+    reference = '{"a": 1, "c": 3}'
+    result = evaluator.evaluate_strings(prediction=prediction, reference=reference)
+    assert result == {"score": True}
+
+
+def test_json_equality_evaluator_evaluate_strings_custom_operator_not_equal() -> None:
+    def operator(x: dict, y: dict) -> bool:
+        return x["a"] == y["a"]
+
+    evaluator = JsonEqualityEvaluator(operator=operator)
+    prediction = '{"a": 1}'
+    reference = '{"a": 2}'
+    result = evaluator.evaluate_strings(prediction=prediction, reference=reference)
+    assert result == {"score": False}
+
+
+def test_json_equality_evaluator_evaluate_lists_permutation_invariant() -> None:
+    evaluator = JsonEqualityEvaluator()
+    prediction = '[{"a": 1, "b": 2}, {"a": 2, "b": 3}]'
+    reference = '[{"a": 2, "b": 3}, {"a": 1, "b": 2}]'
+    result = evaluator.evaluate_strings(prediction=prediction, reference=reference)
+    assert result == {"score": True}
+
+    prediction = '[{"a": 1, "b": 2}, {"a": 2, "b": 3}]'
+    reference = '[{"a": 2, "b": 3}, {"a": 1, "b": 4}]'
+    result = evaluator.evaluate_strings(prediction=prediction, reference=reference)
+    assert result == {"score": False}
+
+    prediction = '[{"a": 1, "b": 2}, {"a": 2, "b": 3}]'
+    reference = '[{"a": 2, "b": 3}]'
+    result = evaluator.evaluate_strings(prediction=prediction, reference=reference)
+    assert result == {"score": False}
+
+    prediction = '[{"a": 1, "b": 2}, {"a": 2, "b": 3}]'
+    reference = '[{"a": 2, "b": 3}, {"a": 1, "b": 2}, {"a": 3, "b": 4}]'
+    result = evaluator.evaluate_strings(prediction=prediction, reference=reference)
+    assert result == {"score": False}
+
+    prediction = '[{"a": 1, "b": 2}, {"a": 2, "b": 3}]'
+    reference = '[{"a": 2, "b": 3}, {"b": 2,"a": 1}, {"a": 3, "b": 4}]'
+    result = evaluator.evaluate_strings(prediction=reference, reference=prediction)
+    assert result == {"score": False}
+
+    # Limit tests
+    prediction = (
+        "[" + ",".join([f'{{"a": {i}, "b": {i+1}}}' for i in range(1000)]) + "]"
+    )
+    rlist = [f'{{"a": {i}, "b": {i+1}}}' for i in range(1000)]
+    random.shuffle(rlist)
+    reference = "[" + ",".join(rlist) + "]"
+    result = evaluator.evaluate_strings(prediction=prediction, reference=reference)
+    assert result == {"score": True}
+
+    prediction = (
+        "[" + ",".join([f'{{"b": {i+1}, "a": {i}}}' for i in range(1000)]) + "]"
+    )
+    reference = (
+        "["
+        + ",".join(
+            [f'{{"a": {i+1}, "b": {i+2}}}' for i in range(999)]
+            + ['{"a": 1000, "b": 1001}']
+        )
+        + "]"
+    )
+    result = evaluator.evaluate_strings(prediction=prediction, reference=reference)
+    assert result == {"score": False}
diff --git a/libs/langchain/tests/unit_tests/evaluation/test_loading.py b/libs/langchain/tests/unit_tests/evaluation/test_loading.py
index 11715e92503..8b1e5938919 100644
--- a/libs/langchain/tests/unit_tests/evaluation/test_loading.py
+++ b/libs/langchain/tests/unit_tests/evaluation/test_loading.py
@@ -40,6 +40,7 @@ def test_load_evaluators(evaluator_type: EvaluatorType) -> None:
             EvaluatorType.LABELED_CRITERIA,
             EvaluatorType.LABELED_PAIRWISE_STRING,
         ],
+        [EvaluatorType.JSON_EQUALITY],
     ],
 )
 def test_eval_chain_requires_references(evaluator_types: List[EvaluatorType]) -> None:
diff --git a/libs/langchain/tests/unit_tests/llms/fake_llm.py b/libs/langchain/tests/unit_tests/llms/fake_llm.py
index e7fc0d6dec8..c92df7bf1c4 100644
--- a/libs/langchain/tests/unit_tests/llms/fake_llm.py
+++ b/libs/langchain/tests/unit_tests/llms/fake_llm.py
@@ -1,10 +1,9 @@
 """Fake LLM wrapper for testing purposes."""
 from typing import Any, Dict, List, Mapping, Optional, cast
 
-from pydantic import validator
-
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.llms.base import LLM
+from langchain.pydantic_v1 import validator
 
 
 class FakeLLM(LLM):
diff --git a/libs/langchain/tests/unit_tests/output_parsers/test_pydantic_parser.py b/libs/langchain/tests/unit_tests/output_parsers/test_pydantic_parser.py
index 8c12049960a..2e75860f19b 100644
--- a/libs/langchain/tests/unit_tests/output_parsers/test_pydantic_parser.py
+++ b/libs/langchain/tests/unit_tests/output_parsers/test_pydantic_parser.py
@@ -2,9 +2,8 @@
 from enum import Enum
 from typing import Optional
 
-from pydantic import BaseModel, Field
-
 from langchain.output_parsers.pydantic import PydanticOutputParser
+from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema import OutputParserException
 
 
diff --git a/libs/langchain/tests/unit_tests/prompts/test_chat.py b/libs/langchain/tests/unit_tests/prompts/test_chat.py
index 517741151d9..7b089c687c6 100644
--- a/libs/langchain/tests/unit_tests/prompts/test_chat.py
+++ b/libs/langchain/tests/unit_tests/prompts/test_chat.py
@@ -282,6 +282,42 @@ def test_convert_to_message(
     assert _convert_to_message(args) == expected
 
 
+def test_chat_prompt_template_indexing() -> None:
+    message1 = SystemMessage(content="foo")
+    message2 = HumanMessage(content="bar")
+    message3 = HumanMessage(content="baz")
+    template = ChatPromptTemplate.from_messages([message1, message2, message3])
+    assert template[0] == message1
+    assert template[1] == message2
+
+    # Slice starting from index 1
+    slice_template = template[1:]
+    assert slice_template[0] == message2
+    assert len(slice_template) == 2
+
+
+def test_chat_prompt_template_append_and_extend() -> None:
+    """Test append and extend methods of ChatPromptTemplate."""
+    message1 = SystemMessage(content="foo")
+    message2 = HumanMessage(content="bar")
+    message3 = HumanMessage(content="baz")
+    template = ChatPromptTemplate.from_messages([message1])
+    template.append(message2)
+    template.append(message3)
+    assert len(template) == 3
+    template.extend([message2, message3])
+    assert len(template) == 5
+    assert template.messages == [
+        message1,
+        message2,
+        message3,
+        message2,
+        message3,
+    ]
+    template.append(("system", "hello!"))
+    assert template[-1] == SystemMessagePromptTemplate.from_template("hello!")
+
+
 def test_convert_to_message_is_strict() -> None:
     """Verify that _convert_to_message is strict."""
     with pytest.raises(ValueError):
diff --git a/libs/langchain/tests/unit_tests/retrievers/self_query/test_elasticsearch.py b/libs/langchain/tests/unit_tests/retrievers/self_query/test_elasticsearch.py
new file mode 100644
index 00000000000..924bb1a8c9b
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/retrievers/self_query/test_elasticsearch.py
@@ -0,0 +1,220 @@
+from typing import Dict, Tuple
+
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+    StructuredQuery,
+)
+from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator
+
+DEFAULT_TRANSLATOR = ElasticsearchTranslator()
+
+
+def test_visit_comparison() -> None:
+    comp = Comparison(comparator=Comparator.EQ, attribute="foo", value="1")
+    expected = {"term": {"metadata.foo.keyword": "1"}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_comparison_range_gt() -> None:
+    comp = Comparison(comparator=Comparator.GT, attribute="foo", value=1)
+    expected = {"range": {"metadata.foo": {"gt": 1}}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_comparison_range_gte() -> None:
+    comp = Comparison(comparator=Comparator.GTE, attribute="foo", value=1)
+    expected = {"range": {"metadata.foo": {"gte": 1}}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_comparison_range_lt() -> None:
+    comp = Comparison(comparator=Comparator.LT, attribute="foo", value=1)
+    expected = {"range": {"metadata.foo": {"lt": 1}}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_comparison_range_lte() -> None:
+    comp = Comparison(comparator=Comparator.LTE, attribute="foo", value=1)
+    expected = {"range": {"metadata.foo": {"lte": 1}}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_comparison_range_match() -> None:
+    comp = Comparison(comparator=Comparator.CONTAIN, attribute="foo", value="1")
+    expected = {"match": {"metadata.foo": "1"}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_comparison_range_like() -> None:
+    comp = Comparison(comparator=Comparator.LIKE, attribute="foo", value="bar")
+    expected = {"fuzzy": {"metadata.foo": {"value": "bar", "fuzziness": "AUTO"}}}
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+    assert expected == actual
+
+
+def test_visit_operation() -> None:
+    op = Operation(
+        operator=Operator.AND,
+        arguments=[
+            Comparison(comparator=Comparator.EQ, attribute="foo", value=2),
+            Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
+        ],
+    )
+    expected = {
+        "bool": {
+            "must": [
+                {"term": {"metadata.foo": 2}},
+                {"term": {"metadata.bar.keyword": "baz"}},
+            ]
+        }
+    }
+    actual = DEFAULT_TRANSLATOR.visit_operation(op)
+    assert expected == actual
+
+
+def test_visit_operation_or() -> None:
+    op = Operation(
+        operator=Operator.OR,
+        arguments=[
+            Comparison(comparator=Comparator.EQ, attribute="foo", value=2),
+            Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
+        ],
+    )
+    expected = {
+        "bool": {
+            "should": [
+                {"term": {"metadata.foo": 2}},
+                {"term": {"metadata.bar.keyword": "baz"}},
+            ]
+        }
+    }
+    actual = DEFAULT_TRANSLATOR.visit_operation(op)
+    assert expected == actual
+
+
+def test_visit_operation_not() -> None:
+    op = Operation(
+        operator=Operator.NOT,
+        arguments=[
+            Comparison(comparator=Comparator.EQ, attribute="foo", value=2),
+            Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
+        ],
+    )
+    expected = {
+        "bool": {
+            "must_not": [
+                {"term": {"metadata.foo": 2}},
+                {"term": {"metadata.bar.keyword": "baz"}},
+            ]
+        }
+    }
+    actual = DEFAULT_TRANSLATOR.visit_operation(op)
+    assert expected == actual
+
+
+def test_visit_structured_query() -> None:
+    query = "What is the capital of France?"
+
+    structured_query = StructuredQuery(query=query, filter=None, limit=None)
+    expected: Tuple[str, Dict] = (query, {})
+    actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
+    assert expected == actual
+
+
+def test_visit_structured_query_filter() -> None:
+    query = "What is the capital of France?"
+    comp = Comparison(comparator=Comparator.EQ, attribute="foo", value="1")
+    structured_query = StructuredQuery(query=query, filter=comp, limit=None)
+    expected = (
+        query,
+        {"filter": [{"term": {"metadata.foo.keyword": "1"}}]},
+    )
+    actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
+    assert expected == actual
+
+
+def test_visit_structured_query_filter_and() -> None:
+    query = "What is the capital of France?"
+    op = Operation(
+        operator=Operator.AND,
+        arguments=[
+            Comparison(comparator=Comparator.EQ, attribute="foo", value=2),
+            Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
+        ],
+    )
+    structured_query = StructuredQuery(query=query, filter=op, limit=None)
+    expected = (
+        query,
+        {
+            "filter": [
+                {
+                    "bool": {
+                        "must": [
+                            {"term": {"metadata.foo": 2}},
+                            {"term": {"metadata.bar.keyword": "baz"}},
+                        ]
+                    }
+                }
+            ]
+        },
+    )
+    actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
+    assert expected == actual
+
+
+def test_visit_structured_query_complex() -> None:
+    query = "What is the capital of France?"
+    op = Operation(
+        operator=Operator.AND,
+        arguments=[
+            Comparison(comparator=Comparator.EQ, attribute="foo", value=2),
+            Operation(
+                operator=Operator.OR,
+                arguments=[
+                    Comparison(comparator=Comparator.LT, attribute="bar", value=1),
+                    Comparison(comparator=Comparator.LIKE, attribute="bar", value="10"),
+                ],
+            ),
+        ],
+    )
+    structured_query = StructuredQuery(query=query, filter=op, limit=None)
+    expected = (
+        query,
+        {
+            "filter": [
+                {
+                    "bool": {
+                        "must": [
+                            {"term": {"metadata.foo": 2}},
+                            {
+                                "bool": {
+                                    "should": [
+                                        {"range": {"metadata.bar": {"lt": 1}}},
+                                        {
+                                            "fuzzy": {
+                                                "metadata.bar": {
+                                                    "value": "10",
+                                                    "fuzziness": "AUTO",
+                                                }
+                                            }
+                                        },
+                                    ]
+                                }
+                            },
+                        ]
+                    }
+                }
+            ]
+        },
+    )
+    actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
+    assert expected == actual
diff --git a/libs/langchain/tests/unit_tests/retrievers/test_svm.py b/libs/langchain/tests/unit_tests/retrievers/test_svm.py
index 2f52fc38cd5..491be75a6cf 100644
--- a/libs/langchain/tests/unit_tests/retrievers/test_svm.py
+++ b/libs/langchain/tests/unit_tests/retrievers/test_svm.py
@@ -25,3 +25,18 @@ class TestSVMRetriever:
             documents=input_docs, embeddings=FakeEmbeddings(size=100)
         )
         assert len(svm_retriever.texts) == 3
+
+    @pytest.mark.requires("sklearn")
+    def test_metadata_persists(self) -> None:
+        input_docs = [
+            Document(page_content="I have a pen.", metadata={"foo": "bar"}),
+            Document(page_content="How about you?", metadata={"foo": "baz"}),
+            Document(page_content="I have a bag.", metadata={"foo": "qux"}),
+        ]
+        svm_retriever = SVMRetriever.from_documents(
+            documents=input_docs, embeddings=FakeEmbeddings(size=100)
+        )
+        query = "Have anything?"
+        output_docs = svm_retriever.get_relevant_documents(query=query)
+        for doc in output_docs:
+            assert "foo" in doc.metadata
diff --git a/libs/langchain/tests/unit_tests/schema/__snapshots__/test_runnable.ambr b/libs/langchain/tests/unit_tests/schema/__snapshots__/test_runnable.ambr
index fcedca63c3c..2d2872c1479 100644
--- a/libs/langchain/tests/unit_tests/schema/__snapshots__/test_runnable.ambr
+++ b/libs/langchain/tests/unit_tests/schema/__snapshots__/test_runnable.ambr
@@ -129,6 +129,7 @@
           "langchain",
           "schema",
           "runnable",
+          "base",
           "RunnableLambda"
         ]
       },
@@ -344,6 +345,7 @@
             "langchain",
             "schema",
             "runnable",
+            "base",
             "RunnableLambda"
           ]
         },
@@ -447,9 +449,260 @@
 # ---
 # name: test_combining_sequences.3
   list([
-    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.chain: 'chain'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.llm: 'llm'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.chain: 'chain'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'RunnableLambda']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'foobar'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nicer assistant.', additional_kwargs={}), HumanMessage(content='foobar', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.llm: 'llm'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='baz, qux', additional_kwargs={}, example=False)}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
+    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'foobar'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nicer assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'foobar', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='baz, qux', additional_kwargs={}, example=False)}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
   ])
 # ---
+# name: test_llm_with_fallbacks[llm_chain_with_fallbacks]
+  '''
+  {
+    "lc": 1,
+    "type": "constructor",
+    "id": [
+      "langchain",
+      "schema",
+      "runnable",
+      "RunnableSequence"
+    ],
+    "kwargs": {
+      "first": {
+        "lc": 1,
+        "type": "constructor",
+        "id": [
+          "langchain",
+          "schema",
+          "runnable",
+          "RunnableMap"
+        ],
+        "kwargs": {
+          "steps": {
+            "buz": {
+              "lc": 1,
+              "type": "not_implemented",
+              "id": [
+                "langchain",
+                "schema",
+                "runnable",
+                "base",
+                "RunnableLambda"
+              ]
+            }
+          }
+        }
+      },
+      "last": {
+        "lc": 1,
+        "type": "constructor",
+        "id": [
+          "langchain",
+          "schema",
+          "runnable",
+          "RunnableWithFallbacks"
+        ],
+        "kwargs": {
+          "runnable": {
+            "lc": 1,
+            "type": "constructor",
+            "id": [
+              "langchain",
+              "schema",
+              "runnable",
+              "RunnableSequence"
+            ],
+            "kwargs": {
+              "first": {
+                "lc": 1,
+                "type": "constructor",
+                "id": [
+                  "langchain",
+                  "prompts",
+                  "prompt",
+                  "PromptTemplate"
+                ],
+                "kwargs": {
+                  "input_variables": [
+                    "buz"
+                  ],
+                  "template": "what did baz say to {buz}",
+                  "template_format": "f-string",
+                  "partial_variables": {}
+                }
+              },
+              "last": {
+                "lc": 1,
+                "type": "not_implemented",
+                "id": [
+                  "langchain",
+                  "llms",
+                  "fake",
+                  "FakeListLLM"
+                ]
+              }
+            }
+          },
+          "fallbacks": [
+            {
+              "lc": 1,
+              "type": "constructor",
+              "id": [
+                "langchain",
+                "schema",
+                "runnable",
+                "RunnableSequence"
+              ],
+              "kwargs": {
+                "first": {
+                  "lc": 1,
+                  "type": "constructor",
+                  "id": [
+                    "langchain",
+                    "prompts",
+                    "prompt",
+                    "PromptTemplate"
+                  ],
+                  "kwargs": {
+                    "input_variables": [
+                      "buz"
+                    ],
+                    "template": "what did baz say to {buz}",
+                    "template_format": "f-string",
+                    "partial_variables": {}
+                  }
+                },
+                "last": {
+                  "lc": 1,
+                  "type": "not_implemented",
+                  "id": [
+                    "langchain",
+                    "llms",
+                    "fake",
+                    "FakeListLLM"
+                  ]
+                }
+              }
+            }
+          ],
+          "exceptions_to_handle": [
+            {
+              "lc": 1,
+              "type": "not_implemented",
+              "id": [
+                "builtins",
+                "Exception"
+              ]
+            }
+          ]
+        }
+      }
+    }
+  }
+  '''
+# ---
+# name: test_llm_with_fallbacks[llm_with_fallbacks]
+  '''
+  {
+    "lc": 1,
+    "type": "constructor",
+    "id": [
+      "langchain",
+      "schema",
+      "runnable",
+      "RunnableWithFallbacks"
+    ],
+    "kwargs": {
+      "runnable": {
+        "lc": 1,
+        "type": "not_implemented",
+        "id": [
+          "langchain",
+          "llms",
+          "fake",
+          "FakeListLLM"
+        ]
+      },
+      "fallbacks": [
+        {
+          "lc": 1,
+          "type": "not_implemented",
+          "id": [
+            "langchain",
+            "llms",
+            "fake",
+            "FakeListLLM"
+          ]
+        }
+      ],
+      "exceptions_to_handle": [
+        {
+          "lc": 1,
+          "type": "not_implemented",
+          "id": [
+            "builtins",
+            "Exception"
+          ]
+        }
+      ]
+    }
+  }
+  '''
+# ---
+# name: test_llm_with_fallbacks[llm_with_multi_fallbacks]
+  '''
+  {
+    "lc": 1,
+    "type": "constructor",
+    "id": [
+      "langchain",
+      "schema",
+      "runnable",
+      "RunnableWithFallbacks"
+    ],
+    "kwargs": {
+      "runnable": {
+        "lc": 1,
+        "type": "not_implemented",
+        "id": [
+          "langchain",
+          "llms",
+          "fake",
+          "FakeListLLM"
+        ]
+      },
+      "fallbacks": [
+        {
+          "lc": 1,
+          "type": "not_implemented",
+          "id": [
+            "langchain",
+            "llms",
+            "fake",
+            "FakeListLLM"
+          ]
+        },
+        {
+          "lc": 1,
+          "type": "not_implemented",
+          "id": [
+            "langchain",
+            "llms",
+            "fake",
+            "FakeListLLM"
+          ]
+        }
+      ],
+      "exceptions_to_handle": [
+        {
+          "lc": 1,
+          "type": "not_implemented",
+          "id": [
+            "builtins",
+            "Exception"
+          ]
+        }
+      ]
+    }
+  }
+  '''
+# ---
 # name: test_prompt_with_chat_model
   '''
   {
@@ -550,7 +803,7 @@
 # ---
 # name: test_prompt_with_chat_model.1
   list([
-    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.chain: 'chain'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, example=False)}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.llm: 'llm'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
+    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, example=False)}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
   ])
 # ---
 # name: test_prompt_with_chat_model_and_parser
@@ -666,7 +919,7 @@
 # ---
 # name: test_prompt_with_chat_model_and_parser.1
   list([
-    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.chain: 'chain'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.llm: 'llm'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
+    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
   ])
 # ---
 # name: test_prompt_with_llm
@@ -769,13 +1022,13 @@
 # ---
 # name: test_prompt_with_llm.1
   list([
-    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.chain: 'chain'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.llm: 'llm'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
+    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
   ])
 # ---
 # name: test_prompt_with_llm.2
   list([
-    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.chain: 'chain'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'bar'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your name?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.llm: 'llm'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bar', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
-    Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.chain: 'chain'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}), HumanMessage(content='What is your favorite color?', additional_kwargs={}, example=False)])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type=<RunTypeEnum.llm: 'llm'>, end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
+    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'bar'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bar', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
+    Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}]}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your favorite color?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]),
   ])
 # ---
 # name: test_router_runnable
@@ -808,6 +1061,7 @@
                 "langchain",
                 "schema",
                 "runnable",
+                "base",
                 "RunnableLambda"
               ]
             },
@@ -829,6 +1083,7 @@
                       "langchain",
                       "schema",
                       "runnable",
+                      "base",
                       "RunnableLambda"
                     ]
                   }
@@ -1045,6 +1300,7 @@
                     "langchain",
                     "schema",
                     "runnable",
+                    "base",
                     "RunnableLambda"
                   ]
                 }
@@ -1067,6 +1323,7 @@
                     "langchain",
                     "schema",
                     "runnable",
+                    "base",
                     "RunnableLambda"
                   ]
                 },
@@ -1087,6 +1344,7 @@
                 "langchain",
                 "schema",
                 "runnable",
+                "base",
                 "RunnableLambda"
               ]
             }
@@ -1285,6 +1543,7 @@
             "langchain",
             "schema",
             "runnable",
+            "base",
             "RunnableLambda"
           ]
         }
@@ -1419,6 +1678,7 @@
             "langchain",
             "schema",
             "runnable",
+            "base",
             "RunnableLambda"
           ]
         }
@@ -1478,6 +1738,7 @@
                 "langchain",
                 "schema",
                 "runnable",
+                "base",
                 "RunnableLambda"
               ]
             }
diff --git a/libs/langchain/tests/unit_tests/schema/test_runnable.py b/libs/langchain/tests/unit_tests/schema/test_runnable.py
index 4ae6eb1cf2f..c0cae4d9bdf 100644
--- a/libs/langchain/tests/unit_tests/schema/test_runnable.py
+++ b/libs/langchain/tests/unit_tests/schema/test_runnable.py
@@ -250,7 +250,9 @@ async def test_prompt_with_chat_model(
             HumanMessage(content="What is your name?"),
         ]
     )
+
     assert tracer.runs == snapshot
+
     mocker.stop(prompt_spy)
     mocker.stop(chat_spy)
 
@@ -442,6 +444,7 @@ def test_prompt_with_chat_model_and_parser(
         ]
     )
     assert parser_spy.call_args.args[1] == AIMessage(content="foo, bar")
+
     assert tracer.runs == snapshot
 
 
@@ -500,6 +503,7 @@ def test_combining_sequences(
     assert combined_chain.invoke(
         {"question": "What is your name?"}, dict(callbacks=[tracer])
     ) == ["baz", "qux"]
+
     assert tracer.runs == snapshot
 
 
@@ -784,6 +788,13 @@ def test_deep_stream() -> None:
     assert len(chunks) == len("foo-lish")
     assert "".join(chunks) == "foo-lish"
 
+    chunks = []
+    for chunk in (chain | RunnablePassthrough()).stream({"question": "What up"}):
+        chunks.append(chunk)
+
+    assert len(chunks) == len("foo-lish")
+    assert "".join(chunks) == "foo-lish"
+
 
 @pytest.mark.asyncio
 async def test_deep_astream() -> None:
@@ -804,6 +815,13 @@ async def test_deep_astream() -> None:
     assert len(chunks) == len("foo-lish")
     assert "".join(chunks) == "foo-lish"
 
+    chunks = []
+    async for chunk in (chain | RunnablePassthrough()).astream({"question": "What up"}):
+        chunks.append(chunk)
+
+    assert len(chunks) == len("foo-lish")
+    assert "".join(chunks) == "foo-lish"
+
 
 @pytest.fixture()
 def llm_with_fallbacks() -> RunnableWithFallbacks:
@@ -839,7 +857,7 @@ def llm_chain_with_fallbacks() -> RunnableSequence:
 )
 @pytest.mark.asyncio
 async def test_llm_with_fallbacks(
-    runnable: RunnableWithFallbacks, request: Any
+    runnable: RunnableWithFallbacks, request: Any, snapshot: SnapshotAssertion
 ) -> None:
     runnable = request.getfixturevalue(runnable)
     assert runnable.invoke("hello") == "bar"
@@ -848,3 +866,4 @@ async def test_llm_with_fallbacks(
     assert await runnable.ainvoke("hello") == "bar"
     assert await runnable.abatch(["hi", "hey", "bye"]) == ["bar"] * 3
     assert list(await runnable.ainvoke("hello")) == list("bar")
+    assert dumps(runnable, pretty=True) == snapshot
diff --git a/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py b/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py
new file mode 100644
index 00000000000..7f9b103d33a
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py
@@ -0,0 +1,27 @@
+"""Tests for the string run evaluator."""
+
+from unittest.mock import MagicMock
+
+from langchain.evaluation import criteria
+from langchain.smith.evaluation.string_run_evaluator import (
+    ChainStringRunMapper,
+    StringRunEvaluatorChain,
+)
+from tests.unit_tests.llms import fake_llm
+
+
+def test_evaluate_run() -> None:
+    run_mapper = ChainStringRunMapper()
+    example_mapper = MagicMock()
+    string_evaluator = criteria.CriteriaEvalChain.from_llm(fake_llm.FakeLLM())
+    evaluator = StringRunEvaluatorChain(
+        run_mapper=run_mapper,
+        example_mapper=example_mapper,
+        name="test_evaluator",
+        string_evaluator=string_evaluator,
+    )
+    run = MagicMock()
+    example = MagicMock()
+    res = evaluator.evaluate_run(run, example)
+    assert res.comment.startswith("Error evaluating run ")
+    assert res.key == string_evaluator.evaluation_name
diff --git a/libs/langchain/tests/unit_tests/storage/test_redis.py b/libs/langchain/tests/unit_tests/storage/test_redis.py
new file mode 100644
index 00000000000..ff882ed8df8
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/storage/test_redis.py
@@ -0,0 +1,11 @@
+"""Light weight unit test that attempts to import RedisStore.
+
+The actual code is tested in integration tests.
+
+This test is intended to catch errors in the import process.
+"""
+
+
+def test_import_storage() -> None:
+    """Attempt to import storage modules."""
+    from langchain.storage.redis import RedisStore  # noqa
diff --git a/libs/langchain/tests/unit_tests/test_dependencies.py b/libs/langchain/tests/unit_tests/test_dependencies.py
index 85c6efdfd1a..e446485cacf 100644
--- a/libs/langchain/tests/unit_tests/test_dependencies.py
+++ b/libs/langchain/tests/unit_tests/test_dependencies.py
@@ -41,7 +41,6 @@ def test_required_dependencies(poetry_conf: Mapping[str, Any]) -> None:
         "langsmith",
         "numexpr",
         "numpy",
-        "openapi-schema-pydantic",
         "pydantic",
         "python",
         "requests",
diff --git a/libs/langchain/tests/unit_tests/test_schema.py b/libs/langchain/tests/unit_tests/test_schema.py
index facbf935074..3fa2be84e12 100644
--- a/libs/langchain/tests/unit_tests/test_schema.py
+++ b/libs/langchain/tests/unit_tests/test_schema.py
@@ -13,9 +13,10 @@ from langchain.schema.messages import (
 
 
 class TestGetBufferString(unittest.TestCase):
-    human_msg: HumanMessage = HumanMessage(content="human")
-    ai_msg: AIMessage = AIMessage(content="ai")
-    sys_msg: SystemMessage = SystemMessage(content="sys")
+    def setUp(self) -> None:
+        self.human_msg = HumanMessage(content="human")
+        self.ai_msg = AIMessage(content="ai")
+        self.sys_msg = SystemMessage(content="sys")
 
     def test_empty_input(self) -> None:
         self.assertEqual(get_buffer_string([]), "")
@@ -58,20 +59,14 @@ class TestGetBufferString(unittest.TestCase):
         )
 
 
-class TestMessageDictConversion(unittest.TestCase):
-    human_msg: HumanMessage = HumanMessage(
-        content="human", additional_kwargs={"key": "value"}
-    )
-    ai_msg: AIMessage = AIMessage(content="ai")
-    sys_msg: SystemMessage = SystemMessage(content="sys")
+def test_multiple_msg() -> None:
+    human_msg = HumanMessage(content="human", additional_kwargs={"key": "value"})
+    ai_msg = AIMessage(content="ai")
+    sys_msg = SystemMessage(content="sys")
 
-    def test_multiple_msg(self) -> None:
-        msgs = [
-            self.human_msg,
-            self.ai_msg,
-            self.sys_msg,
-        ]
-        self.assertEqual(
-            messages_from_dict(messages_to_dict(msgs)),
-            msgs,
-        )
+    msgs = [
+        human_msg,
+        ai_msg,
+        sys_msg,
+    ]
+    assert messages_from_dict(messages_to_dict(msgs)) == msgs
diff --git a/libs/langchain/tests/unit_tests/tools/openapi/test_api_models.py b/libs/langchain/tests/unit_tests/tools/openapi/test_api_models.py
index 945309aafad..a68bc2787d1 100644
--- a/libs/langchain/tests/unit_tests/tools/openapi/test_api_models.py
+++ b/libs/langchain/tests/unit_tests/tools/openapi/test_api_models.py
@@ -4,16 +4,20 @@ import os
 from pathlib import Path
 from typing import Iterable, List, Tuple
 
+import pytest
+
+# Keep at top of file to ensure that pydantic test can be skipped before
+# pydantic v1 related imports are attempted by openapi_schema_pydantic.
+from langchain.pydantic_v1 import _PYDANTIC_MAJOR_VERSION
+
+if _PYDANTIC_MAJOR_VERSION != 1:
+    pytest.skip(
+        f"Pydantic major version {_PYDANTIC_MAJOR_VERSION} is not supported.",
+        allow_module_level=True,
+    )
+
 import pytest
 import yaml
-from openapi_schema_pydantic import (
-    Components,
-    Info,
-    MediaType,
-    Reference,
-    RequestBody,
-    Schema,
-)
 
 from langchain.tools.openapi.utils.api_models import (
     APIOperation,
@@ -74,30 +78,38 @@ def http_paths_and_methods() -> List[Tuple[str, OpenAPISpec, str, str]]:
     return http_paths_and_methods
 
 
-@pytest.mark.parametrize(
-    "spec_name, spec, path, method",
-    http_paths_and_methods(),
-)
-def test_parse_api_operations(
-    spec_name: str, spec: OpenAPISpec, path: str, method: str
-) -> None:
+@pytest.mark.requires("openapi_schema_pydantic")
+def test_parse_api_operations() -> None:
     """Test the APIOperation class."""
-    try:
-        APIOperation.from_openapi_spec(spec, path, method)
-    except Exception as e:
-        raise AssertionError(f"Error processing {spec_name}: {e} ") from e
+    for spec_name, spec, path, method in http_paths_and_methods():
+        try:
+            APIOperation.from_openapi_spec(spec, path, method)
+        except Exception as e:
+            raise AssertionError(f"Error processing {spec_name}: {e} ") from e
 
 
+@pytest.mark.requires("openapi_schema_pydantic")
 @pytest.fixture
 def raw_spec() -> OpenAPISpec:
     """Return a raw OpenAPI spec."""
+    from openapi_schema_pydantic import Info
+
     return OpenAPISpec(
         info=Info(title="Test API", version="1.0.0"),
     )
 
 
+@pytest.mark.requires("openapi_schema_pydantic")
 def test_api_request_body_from_request_body_with_ref(raw_spec: OpenAPISpec) -> None:
     """Test instantiating APIRequestBody from RequestBody with a reference."""
+    from openapi_schema_pydantic import (
+        Components,
+        MediaType,
+        Reference,
+        RequestBody,
+        Schema,
+    )
+
     raw_spec.components = Components(
         schemas={
             "Foo": Schema(
@@ -128,8 +140,15 @@ def test_api_request_body_from_request_body_with_ref(raw_spec: OpenAPISpec) -> N
     assert api_request_body.media_type == "application/json"
 
 
+@pytest.mark.requires("openapi_schema_pydantic")
 def test_api_request_body_from_request_body_with_schema(raw_spec: OpenAPISpec) -> None:
     """Test instantiating APIRequestBody from RequestBody with a schema."""
+    from openapi_schema_pydantic import (
+        MediaType,
+        RequestBody,
+        Schema,
+    )
+
     request_body = RequestBody(
         content={
             "application/json": MediaType(
@@ -152,7 +171,14 @@ def test_api_request_body_from_request_body_with_schema(raw_spec: OpenAPISpec) -
     assert api_request_body.media_type == "application/json"
 
 
+@pytest.mark.requires("openapi_schema_pydantic")
 def test_api_request_body_property_from_schema(raw_spec: OpenAPISpec) -> None:
+    from openapi_schema_pydantic import (
+        Components,
+        Reference,
+        Schema,
+    )
+
     raw_spec.components = Components(
         schemas={
             "Bar": Schema(
diff --git a/libs/langchain/tests/unit_tests/tools/test_base.py b/libs/langchain/tests/unit_tests/tools/test_base.py
index c42ae202d0f..a66953a0669 100644
--- a/libs/langchain/tests/unit_tests/tools/test_base.py
+++ b/libs/langchain/tests/unit_tests/tools/test_base.py
@@ -6,13 +6,13 @@ from functools import partial
 from typing import Any, List, Optional, Type, Union
 
 import pytest
-from pydantic import BaseModel
 
 from langchain.agents.tools import Tool, tool
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForToolRun,
     CallbackManagerForToolRun,
 )
+from langchain.pydantic_v1 import BaseModel
 from langchain.tools.base import (
     BaseTool,
     SchemaAnnotationError,
@@ -43,9 +43,9 @@ class _MockSchema(BaseModel):
 
 
 class _MockStructuredTool(BaseTool):
-    name = "structured_api"
+    name: str = "structured_api"
     args_schema: Type[BaseModel] = _MockSchema
-    description = "A Structured Tool"
+    description: str = "A Structured Tool"
 
     def _run(self, arg1: int, arg2: bool, arg3: Optional[dict] = None) -> str:
         return f"{arg1} {arg2} {arg3}"
@@ -64,34 +64,15 @@ def test_structured_args() -> None:
     assert structured_api.run(args) == expected_result
 
 
-def test_unannotated_base_tool_raises_error() -> None:
-    """Test that a BaseTool without type hints raises an exception.""" ""
-    with pytest.raises(SchemaAnnotationError):
-
-        class _UnAnnotatedTool(BaseTool):
-            name = "structured_api"
-            # This would silently be ignored without the custom metaclass
-            args_schema = _MockSchema
-            description = "A Structured Tool"
-
-            def _run(self, arg1: int, arg2: bool, arg3: Optional[dict] = None) -> str:
-                return f"{arg1} {arg2} {arg3}"
-
-            async def _arun(
-                self, arg1: int, arg2: bool, arg3: Optional[dict] = None
-            ) -> str:
-                raise NotImplementedError
-
-
 def test_misannotated_base_tool_raises_error() -> None:
     """Test that a BaseTool with the incorrect typehint raises an exception.""" ""
     with pytest.raises(SchemaAnnotationError):
 
         class _MisAnnotatedTool(BaseTool):
-            name = "structured_api"
+            name: str = "structured_api"
             # This would silently be ignored without the custom metaclass
             args_schema: BaseModel = _MockSchema  # type: ignore
-            description = "A Structured Tool"
+            description: str = "A Structured Tool"
 
             def _run(self, arg1: int, arg2: bool, arg3: Optional[dict] = None) -> str:
                 return f"{arg1} {arg2} {arg3}"
@@ -106,9 +87,9 @@ def test_forward_ref_annotated_base_tool_accepted() -> None:
     """Test that a using forward ref annotation syntax is accepted.""" ""
 
     class _ForwardRefAnnotatedTool(BaseTool):
-        name = "structured_api"
+        name: str = "structured_api"
         args_schema: "Type[BaseModel]" = _MockSchema
-        description = "A Structured Tool"
+        description: str = "A Structured Tool"
 
         def _run(self, arg1: int, arg2: bool, arg3: Optional[dict] = None) -> str:
             return f"{arg1} {arg2} {arg3}"
@@ -123,9 +104,9 @@ def test_subclass_annotated_base_tool_accepted() -> None:
     """Test BaseTool child w/ custom schema isn't overwritten."""
 
     class _ForwardRefAnnotatedTool(BaseTool):
-        name = "structured_api"
+        name: str = "structured_api"
         args_schema: Type[_MockSchema] = _MockSchema
-        description = "A Structured Tool"
+        description: str = "A Structured Tool"
 
         def _run(self, arg1: int, arg2: bool, arg3: Optional[dict] = None) -> str:
             return f"{arg1} {arg2} {arg3}"
@@ -173,8 +154,8 @@ def test_decorated_function_schema_equivalent() -> None:
 
 def test_args_kwargs_filtered() -> None:
     class _SingleArgToolWithKwargs(BaseTool):
-        name = "single_arg_tool"
-        description = "A  single arged tool with kwargs"
+        name: str = "single_arg_tool"
+        description: str = "A  single arged tool with kwargs"
 
         def _run(
             self,
@@ -196,8 +177,8 @@ def test_args_kwargs_filtered() -> None:
     assert tool.is_single_input
 
     class _VarArgToolWithKwargs(BaseTool):
-        name = "single_arg_tool"
-        description = "A single arged tool with kwargs"
+        name: str = "single_arg_tool"
+        description: str = "A single arged tool with kwargs"
 
         def _run(
             self,
@@ -288,8 +269,8 @@ def test_base_tool_inheritance_base_schema() -> None:
     """Test schema is correctly inferred when inheriting from BaseTool."""
 
     class _MockSimpleTool(BaseTool):
-        name = "simple_tool"
-        description = "A Simple Tool"
+        name: str = "simple_tool"
+        description: str = "A Simple Tool"
 
         def _run(self, tool_input: str) -> str:
             return f"{tool_input}"
@@ -612,8 +593,8 @@ async def test_create_async_tool() -> None:
 
 
 class _FakeExceptionTool(BaseTool):
-    name = "exception"
-    description = "an exception-throwing tool"
+    name: str = "exception"
+    description: str = "an exception-throwing tool"
     exception: Exception = ToolException()
 
     def _run(self) -> str:
diff --git a/libs/langchain/tests/unit_tests/tools/test_json.py b/libs/langchain/tests/unit_tests/tools/test_json.py
index 36a96595e03..b677b1577d3 100644
--- a/libs/langchain/tests/unit_tests/tools/test_json.py
+++ b/libs/langchain/tests/unit_tests/tools/test_json.py
@@ -30,6 +30,10 @@ def test_json_spec_value() -> None:
     assert spec.value('data["baz"]') == "{'test': {'foo': [1, 2, 3]}}"
     assert spec.value('data["baz"]["test"]') == "{'foo': [1, 2, 3]}"
     assert spec.value('data["baz"]["test"]["foo"]') == "[1, 2, 3]"
+    assert spec.value("data['foo']") == "bar"
+    assert spec.value("data['baz']") == "{'test': {'foo': [1, 2, 3]}}"
+    assert spec.value("data['baz']['test']") == "{'foo': [1, 2, 3]}"
+    assert spec.value("data['baz']['test']['foo']") == "[1, 2, 3]"
 
 
 def test_json_spec_value_max_length() -> None:
diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_faiss.py b/libs/langchain/tests/unit_tests/vectorstores/test_faiss.py
similarity index 90%
rename from libs/langchain/tests/integration_tests/vectorstores/test_faiss.py
rename to libs/langchain/tests/unit_tests/vectorstores/test_faiss.py
index d2b4f71ded8..ff74dea302c 100644
--- a/libs/langchain/tests/integration_tests/vectorstores/test_faiss.py
+++ b/libs/langchain/tests/unit_tests/vectorstores/test_faiss.py
@@ -7,11 +7,12 @@ import pytest
 
 from langchain.docstore.document import Document
 from langchain.docstore.in_memory import InMemoryDocstore
-from langchain.docstore.wikipedia import Wikipedia
 from langchain.vectorstores.faiss import FAISS
 from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+from tests.unit_tests.agents.test_react import FakeDocstore
 
 
+@pytest.mark.requires("faiss")
 def test_faiss() -> None:
     """Test end to end construction and search."""
     texts = ["foo", "bar", "baz"]
@@ -29,6 +30,7 @@ def test_faiss() -> None:
     assert output == [Document(page_content="foo")]
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_vector_sim() -> None:
     """Test vector similarity."""
     texts = ["foo", "bar", "baz"]
@@ -47,6 +49,7 @@ def test_faiss_vector_sim() -> None:
     assert output == [Document(page_content="foo")]
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_vector_sim_with_score_threshold() -> None:
     """Test vector similarity."""
     texts = ["foo", "bar", "baz"]
@@ -65,6 +68,7 @@ def test_faiss_vector_sim_with_score_threshold() -> None:
     assert output == [Document(page_content="foo")]
 
 
+@pytest.mark.requires("faiss")
 def test_similarity_search_with_score_by_vector() -> None:
     """Test vector similarity with score by vector."""
     texts = ["foo", "bar", "baz"]
@@ -84,6 +88,7 @@ def test_similarity_search_with_score_by_vector() -> None:
     assert output[0][0] == Document(page_content="foo")
 
 
+@pytest.mark.requires("faiss")
 def test_similarity_search_with_score_by_vector_with_score_threshold() -> None:
     """Test vector similarity with score by vector."""
     texts = ["foo", "bar", "baz"]
@@ -108,6 +113,7 @@ def test_similarity_search_with_score_by_vector_with_score_threshold() -> None:
     assert output[0][1] < 0.2
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_mmr() -> None:
     texts = ["foo", "foo", "fou", "foy"]
     docsearch = FAISS.from_texts(texts, FakeEmbeddings())
@@ -122,6 +128,7 @@ def test_faiss_mmr() -> None:
     assert output[1][0] != Document(page_content="foo")
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_mmr_with_metadatas() -> None:
     texts = ["foo", "foo", "fou", "foy"]
     metadatas = [{"page": i} for i in range(len(texts))]
@@ -136,6 +143,7 @@ def test_faiss_mmr_with_metadatas() -> None:
     assert output[1][0] != Document(page_content="foo", metadata={"page": 0})
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_mmr_with_metadatas_and_filter() -> None:
     texts = ["foo", "foo", "fou", "foy"]
     metadatas = [{"page": i} for i in range(len(texts))]
@@ -149,6 +157,7 @@ def test_faiss_mmr_with_metadatas_and_filter() -> None:
     assert output[0][1] == 0.0
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_mmr_with_metadatas_and_list_filter() -> None:
     texts = ["foo", "foo", "fou", "foy"]
     metadatas = [{"page": i} if i <= 3 else {"page": 3} for i in range(len(texts))]
@@ -163,6 +172,7 @@ def test_faiss_mmr_with_metadatas_and_list_filter() -> None:
     assert output[1][0] != Document(page_content="foo", metadata={"page": 0})
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_with_metadatas() -> None:
     """Test end to end construction and search."""
     texts = ["foo", "bar", "baz"]
@@ -186,6 +196,7 @@ def test_faiss_with_metadatas() -> None:
     assert output == [Document(page_content="foo", metadata={"page": 0})]
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_with_metadatas_and_filter() -> None:
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
@@ -208,6 +219,7 @@ def test_faiss_with_metadatas_and_filter() -> None:
     assert output == [Document(page_content="bar", metadata={"page": 1})]
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_with_metadatas_and_list_filter() -> None:
     texts = ["foo", "bar", "baz", "foo", "qux"]
     metadatas = [{"page": i} if i <= 3 else {"page": 3} for i in range(len(texts))]
@@ -236,6 +248,7 @@ def test_faiss_with_metadatas_and_list_filter() -> None:
     assert output == [Document(page_content="foo", metadata={"page": 0})]
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_search_not_found() -> None:
     """Test what happens when document is not found."""
     texts = ["foo", "bar", "baz"]
@@ -246,6 +259,7 @@ def test_faiss_search_not_found() -> None:
         docsearch.similarity_search("foo")
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_add_texts() -> None:
     """Test end to end adding of texts."""
     # Create initial doc store.
@@ -257,13 +271,15 @@ def test_faiss_add_texts() -> None:
     assert output == [Document(page_content="foo"), Document(page_content="foo")]
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_add_texts_not_supported() -> None:
     """Test adding of texts to a docstore that doesn't support it."""
-    docsearch = FAISS(FakeEmbeddings().embed_query, None, Wikipedia(), {})
+    docsearch = FAISS(FakeEmbeddings().embed_query, None, FakeDocstore(), {})
     with pytest.raises(ValueError):
         docsearch.add_texts(["foo"])
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_local_save_load() -> None:
     """Test end to end serialization."""
     texts = ["foo", "bar", "baz"]
@@ -275,6 +291,7 @@ def test_faiss_local_save_load() -> None:
     assert new_docsearch.index is not None
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_similarity_search_with_relevance_scores() -> None:
     """Test the similarity search with normalized similarities."""
     texts = ["foo", "bar", "baz"]
@@ -289,6 +306,7 @@ def test_faiss_similarity_search_with_relevance_scores() -> None:
     assert score == 1.0
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_similarity_search_with_relevance_scores_with_threshold() -> None:
     """Test the similarity search with normalized similarities with score threshold."""
     texts = ["foo", "bar", "baz"]
@@ -306,6 +324,7 @@ def test_faiss_similarity_search_with_relevance_scores_with_threshold() -> None:
     assert score == 1.0
 
 
+@pytest.mark.requires("faiss")
 def test_faiss_invalid_normalize_fn() -> None:
     """Test the similarity search with normalized similarities."""
     texts = ["foo", "bar", "baz"]
@@ -316,12 +335,22 @@ def test_faiss_invalid_normalize_fn() -> None:
         docsearch.similarity_search_with_relevance_scores("foo", k=1)
 
 
+@pytest.mark.requires("faiss")
 def test_missing_normalize_score_fn() -> None:
     """Test doesn't perform similarity search without a valid distance strategy."""
+    texts = ["foo", "bar", "baz"]
+    faiss_instance = FAISS.from_texts(texts, FakeEmbeddings(), distance_strategy="fake")
     with pytest.raises(ValueError):
-        texts = ["foo", "bar", "baz"]
-        faiss_instance = FAISS.from_texts(
-            texts, FakeEmbeddings(), distance_strategy="fake"
-        )
-
         faiss_instance.similarity_search_with_relevance_scores("foo", k=2)
+
+
+@pytest.mark.requires("faiss")
+def test_delete() -> None:
+    """Test the similarity search with normalized similarities."""
+    ids = ["a", "b", "c"]
+    docsearch = FAISS.from_texts(["foo", "bar", "baz"], FakeEmbeddings(), ids=ids)
+    docsearch.delete(ids[1:2])
+
+    result = docsearch.similarity_search("bar", k=2)
+    assert sorted([d.page_content for d in result]) == ["baz", "foo"]
+    assert docsearch.index_to_docstore_id == {0: ids[0], 1: ids[2]}
diff --git a/libs/langchain/tests/unit_tests/vectorstores/test_utils.py b/libs/langchain/tests/unit_tests/vectorstores/test_utils.py
index 6ad76e424d2..907ed2cde30 100644
--- a/libs/langchain/tests/unit_tests/vectorstores/test_utils.py
+++ b/libs/langchain/tests/unit_tests/vectorstores/test_utils.py
@@ -1,7 +1,11 @@
 """Test vector store utility functions."""
 import numpy as np
 
-from langchain.vectorstores.utils import maximal_marginal_relevance
+from langchain.docstore.document import Document
+from langchain.vectorstores.utils import (
+    filter_complex_metadata,
+    maximal_marginal_relevance,
+)
 
 
 def test_maximal_marginal_relevance_lambda_zero() -> None:
@@ -52,3 +56,70 @@ def test_maximal_marginal_relevance_query_dim() -> None:
     first = maximal_marginal_relevance(query_embedding, embedding_list)
     second = maximal_marginal_relevance(query_embedding_2d, embedding_list)
     assert first == second
+
+
+def test_filter_list_metadata() -> None:
+    documents = [
+        Document(
+            page_content="",
+            metadata={
+                "key1": "this is a string!",
+                "key2": ["a", "list", "of", "strings"],
+            },
+        ),
+        Document(
+            page_content="",
+            metadata={
+                "key1": "this is another string!",
+                "key2": {"foo"},
+            },
+        ),
+        Document(
+            page_content="",
+            metadata={
+                "key1": "this is another string!",
+                "key2": {"foo": "bar"},
+            },
+        ),
+        Document(
+            page_content="",
+            metadata={
+                "key1": "this is another string!",
+                "key2": True,
+            },
+        ),
+        Document(
+            page_content="",
+            metadata={
+                "key1": "this is another string!",
+                "key2": 1,
+            },
+        ),
+        Document(
+            page_content="",
+            metadata={
+                "key1": "this is another string!",
+                "key2": 1.0,
+            },
+        ),
+        Document(
+            page_content="",
+            metadata={
+                "key1": "this is another string!",
+                "key2": "foo",
+            },
+        ),
+    ]
+
+    updated_documents = filter_complex_metadata(documents)
+    filtered_metadata = [doc.metadata for doc in updated_documents]
+
+    assert filtered_metadata == [
+        {"key1": "this is a string!"},
+        {"key1": "this is another string!"},
+        {"key1": "this is another string!"},
+        {"key1": "this is another string!", "key2": True},
+        {"key1": "this is another string!", "key2": 1},
+        {"key1": "this is another string!", "key2": 1.0},
+        {"key1": "this is another string!", "key2": "foo"},
+    ]
diff --git a/poetry.lock b/poetry.lock
index be71e6b2562..7599ad353e8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,10 +1,9 @@
-# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
 
 [[package]]
 name = "aiohttp"
 version = "3.8.5"
 description = "Async http client/server framework (asyncio)"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -113,7 +112,6 @@ speedups = ["Brotli", "aiodns", "cchardet"]
 name = "aiosignal"
 version = "1.3.1"
 description = "aiosignal: a list of registered asynchronous callbacks"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -128,7 +126,6 @@ frozenlist = ">=1.1.0"
 name = "alabaster"
 version = "0.7.13"
 description = "A configurable sidebar-enabled Sphinx theme"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -140,7 +137,6 @@ files = [
 name = "anyio"
 version = "3.7.1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -162,7 +158,6 @@ trio = ["trio (<0.22)"]
 name = "appnope"
 version = "0.1.3"
 description = "Disable App Nap on macOS >= 10.9"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -174,7 +169,6 @@ files = [
 name = "argon2-cffi"
 version = "21.3.0"
 description = "The secure Argon2 password hashing algorithm."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -194,7 +188,6 @@ tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
 name = "argon2-cffi-bindings"
 version = "21.2.0"
 description = "Low-level CFFI bindings for Argon2"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -232,7 +225,6 @@ tests = ["pytest"]
 name = "arrow"
 version = "1.2.3"
 description = "Better dates & times for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -247,7 +239,6 @@ python-dateutil = ">=2.7.0"
 name = "asttokens"
 version = "2.2.1"
 description = "Annotate AST trees with source code positions"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -265,7 +256,6 @@ test = ["astroid", "pytest"]
 name = "async-lru"
 version = "2.0.3"
 description = "Simple LRU cache for asyncio"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -280,7 +270,6 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
 name = "async-timeout"
 version = "4.0.2"
 description = "Timeout context manager for asyncio programs"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -292,7 +281,6 @@ files = [
 name = "attrs"
 version = "23.1.0"
 description = "Classes Without Boilerplate"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -311,7 +299,6 @@ tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pyte
 name = "autodoc-pydantic"
 version = "1.9.0"
 description = "Seamlessly integrate pydantic models in your Sphinx documentation."
-category = "dev"
 optional = false
 python-versions = ">=3.7.1,<4.0.0"
 files = [
@@ -333,7 +320,6 @@ test = ["coverage (>=7,<8)", "pytest (>=7,<8)"]
 name = "babel"
 version = "2.12.1"
 description = "Internationalization utilities"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -348,7 +334,6 @@ pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
 name = "backcall"
 version = "0.2.0"
 description = "Specifications for callback functions passed in to an API"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -360,7 +345,6 @@ files = [
 name = "beautifulsoup4"
 version = "4.12.2"
 description = "Screen-scraping library"
-category = "dev"
 optional = false
 python-versions = ">=3.6.0"
 files = [
@@ -379,7 +363,6 @@ lxml = ["lxml"]
 name = "black"
 version = "23.7.0"
 description = "The uncompromising code formatter."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -428,7 +411,6 @@ uvloop = ["uvloop (>=0.15.2)"]
 name = "bleach"
 version = "6.0.0"
 description = "An easy safelist-based HTML-sanitizing tool."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -447,7 +429,6 @@ css = ["tinycss2 (>=1.1.0,<1.2)"]
 name = "certifi"
 version = "2023.5.7"
 description = "Python package for providing Mozilla's CA Bundle."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -459,7 +440,6 @@ files = [
 name = "cffi"
 version = "1.15.1"
 description = "Foreign Function Interface for Python calling C code."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -536,7 +516,6 @@ pycparser = "*"
 name = "charset-normalizer"
 version = "3.2.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
-category = "dev"
 optional = false
 python-versions = ">=3.7.0"
 files = [
@@ -621,7 +600,6 @@ files = [
 name = "click"
 version = "8.1.6"
 description = "Composable command line interface toolkit"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -636,7 +614,6 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 name = "codespell"
 version = "2.2.5"
 description = "Codespell"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -654,7 +631,6 @@ types = ["chardet (>=5.1.0)", "mypy", "pytest", "pytest-cov", "pytest-dependency
 name = "colorama"
 version = "0.4.6"
 description = "Cross-platform colored terminal text."
-category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 files = [
@@ -666,7 +642,6 @@ files = [
 name = "comm"
 version = "0.1.3"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -686,7 +661,6 @@ typing = ["mypy (>=0.990)"]
 name = "dataclasses-json"
 version = "0.5.9"
 description = "Easily serialize dataclasses to and from JSON"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -706,7 +680,6 @@ dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest (
 name = "debugpy"
 version = "1.6.7"
 description = "An implementation of the Debug Adapter Protocol for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -734,7 +707,6 @@ files = [
 name = "decorator"
 version = "5.1.1"
 description = "Decorators for Humans"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -746,7 +718,6 @@ files = [
 name = "defusedxml"
 version = "0.7.1"
 description = "XML bomb protection for Python stdlib modules"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
@@ -758,7 +729,6 @@ files = [
 name = "dnspython"
 version = "2.4.0"
 description = "DNS toolkit"
-category = "dev"
 optional = false
 python-versions = ">=3.8,<4.0"
 files = [
@@ -782,7 +752,6 @@ wmi = ["wmi (>=1.5.1,<2.0.0)"]
 name = "docutils"
 version = "0.17.1"
 description = "Docutils -- Python Documentation Utilities"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
@@ -794,7 +763,6 @@ files = [
 name = "entrypoints"
 version = "0.4"
 description = "Discover and load entry points from installed packages."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -806,7 +774,6 @@ files = [
 name = "exceptiongroup"
 version = "1.1.2"
 description = "Backport of PEP 654 (exception groups)"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -821,7 +788,6 @@ test = ["pytest (>=6)"]
 name = "executing"
 version = "1.2.0"
 description = "Get the currently executing AST node of a frame, and other information"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -836,7 +802,6 @@ tests = ["asttokens", "littleutils", "pytest", "rich"]
 name = "fastcore"
 version = "1.4.2"
 description = "Python supercharged for fastai development"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -855,7 +820,6 @@ dev = ["matplotlib", "nbdev (>=0.2.39)", "numpy", "pandas", "pillow", "torch"]
 name = "fastjsonschema"
 version = "2.17.1"
 description = "Fastest Python implementation of JSON schema"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -870,7 +834,6 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 name = "fastrelease"
 version = "0.1.17"
 description = "Simplified releases using GitHub Issues"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -889,7 +852,6 @@ pyyaml = "*"
 name = "fqdn"
 version = "1.5.1"
 description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
 files = [
@@ -901,7 +863,6 @@ files = [
 name = "frozenlist"
 version = "1.4.0"
 description = "A list-like structure which implements collections.abc.MutableSequence"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -972,7 +933,6 @@ files = [
 name = "ghapi"
 version = "0.1.22"
 description = "A python client for the GitHub API"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -992,7 +952,6 @@ dev = ["jsonref"]
 name = "greenlet"
 version = "2.0.2"
 description = "Lightweight in-process concurrent programming"
-category = "dev"
 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*"
 files = [
@@ -1066,7 +1025,6 @@ test = ["objgraph", "psutil"]
 name = "h11"
 version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1078,7 +1036,6 @@ files = [
 name = "httpcore"
 version = "0.17.3"
 description = "A minimal low-level HTTP client."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1090,17 +1047,16 @@ files = [
 anyio = ">=3.0,<5.0"
 certifi = "*"
 h11 = ">=0.13,<0.15"
-sniffio = ">=1.0.0,<2.0.0"
+sniffio = "==1.*"
 
 [package.extras]
 http2 = ["h2 (>=3,<5)"]
-socks = ["socksio (>=1.0.0,<2.0.0)"]
+socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "idna"
 version = "3.4"
 description = "Internationalized Domain Names in Applications (IDNA)"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -1112,7 +1068,6 @@ files = [
 name = "imagesize"
 version = "1.4.1"
 description = "Getting image size from png/jpeg/jpeg2000/gif file"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
@@ -1124,7 +1079,6 @@ files = [
 name = "importlib-metadata"
 version = "6.8.0"
 description = "Read metadata from Python packages"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1144,7 +1098,6 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs
 name = "importlib-resources"
 version = "6.0.0"
 description = "Read resources from Python packages"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1163,7 +1116,6 @@ testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)",
 name = "ipykernel"
 version = "6.24.0"
 description = "IPython Kernel for Jupyter"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1177,7 +1129,7 @@ comm = ">=0.1.1"
 debugpy = ">=1.6.5"
 ipython = ">=7.23.1"
 jupyter-client = ">=6.1.12"
-jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 matplotlib-inline = ">=0.1"
 nest-asyncio = "*"
 packaging = "*"
@@ -1197,7 +1149,6 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
 name = "ipython"
 version = "8.12.2"
 description = "IPython: Productive Interactive Computing"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1237,7 +1188,6 @@ test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pa
 name = "ipython-genutils"
 version = "0.2.0"
 description = "Vestigial utilities from IPython"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -1249,7 +1199,6 @@ files = [
 name = "ipywidgets"
 version = "8.0.7"
 description = "Jupyter interactive widgets"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1271,7 +1220,6 @@ test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
 name = "isoduration"
 version = "20.11.0"
 description = "Operations with ISO 8601 durations"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1286,7 +1234,6 @@ arrow = ">=0.15.0"
 name = "jedi"
 version = "0.18.2"
 description = "An autocompletion tool for Python that can be used for text editors."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -1306,7 +1253,6 @@ testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 name = "jinja2"
 version = "3.1.2"
 description = "A very fast and expressive template engine."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1324,7 +1270,6 @@ i18n = ["Babel (>=2.7)"]
 name = "json5"
 version = "0.9.14"
 description = "A Python implementation of the JSON5 data format."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -1339,7 +1284,6 @@ dev = ["hypothesis"]
 name = "jsonpointer"
 version = "2.4"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
 files = [
@@ -1350,7 +1294,6 @@ files = [
 name = "jsonschema"
 version = "4.18.4"
 description = "An implementation of JSON Schema validation for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1382,7 +1325,6 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 name = "jsonschema-specifications"
 version = "2023.7.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1398,7 +1340,6 @@ referencing = ">=0.28.0"
 name = "jupyter"
 version = "1.0.0"
 description = "Jupyter metapackage. Install all the Jupyter components in one go."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -1419,7 +1360,6 @@ qtconsole = "*"
 name = "jupyter-cache"
 version = "0.6.1"
 description = "A defined interface for working with a cache of jupyter notebooks."
-category = "dev"
 optional = false
 python-versions = "~=3.8"
 files = [
@@ -1447,7 +1387,6 @@ testing = ["coverage", "ipykernel", "jupytext", "matplotlib", "nbdime", "nbforma
 name = "jupyter-client"
 version = "7.4.9"
 description = "Jupyter protocol implementation and client libraries"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1472,7 +1411,6 @@ test = ["codecov", "coverage", "ipykernel (>=6.12)", "ipython", "mypy", "pre-com
 name = "jupyter-console"
 version = "6.6.3"
 description = "Jupyter terminal console"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1484,7 +1422,7 @@ files = [
 ipykernel = ">=6.14"
 ipython = "*"
 jupyter-client = ">=7.0.0"
-jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 prompt-toolkit = ">=3.0.30"
 pygments = "*"
 pyzmq = ">=17"
@@ -1497,7 +1435,6 @@ test = ["flaky", "pexpect", "pytest"]
 name = "jupyter-core"
 version = "5.3.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1518,7 +1455,6 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 name = "jupyter-events"
 version = "0.6.3"
 description = "Jupyter Event System library"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1543,7 +1479,6 @@ test = ["click", "coverage", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=
 name = "jupyter-lsp"
 version = "2.2.0"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1559,7 +1494,6 @@ jupyter-server = ">=1.1.2"
 name = "jupyter-server"
 version = "2.7.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1572,7 +1506,7 @@ anyio = ">=3.1.0"
 argon2-cffi = "*"
 jinja2 = "*"
 jupyter-client = ">=7.4.4"
-jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 jupyter-events = ">=0.6.0"
 jupyter-server-terminals = "*"
 nbconvert = ">=6.4.4"
@@ -1596,7 +1530,6 @@ test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-sc
 name = "jupyter-server-terminals"
 version = "0.4.4"
 description = "A Jupyter Server Extension Providing Terminals."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1616,7 +1549,6 @@ test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov",
 name = "jupyterlab"
 version = "4.0.3"
 description = "JupyterLab computational environment"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1650,7 +1582,6 @@ test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-cons
 name = "jupyterlab-pygments"
 version = "0.2.2"
 description = "Pygments theme using JupyterLab CSS variables"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1662,7 +1593,6 @@ files = [
 name = "jupyterlab-server"
 version = "2.23.0"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1689,7 +1619,6 @@ test = ["hatch", "ipykernel", "jupyterlab-server[openapi]", "openapi-spec-valida
 name = "jupyterlab-widgets"
 version = "3.0.8"
 description = "Jupyter interactive widgets for JupyterLab"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1699,9 +1628,8 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.0.251"
+version = "0.0.265"
 description = "Building applications with LLMs through composability"
-category = "dev"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = []
@@ -1711,26 +1639,26 @@ develop = true
 aiohttp = "^3.8.3"
 async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
 dataclasses-json = "^0.5.7"
-langsmith = "~0.0.11"
+langsmith = "~0.0.21"
 numexpr = "^2.8.4"
 numpy = "^1"
 openapi-schema-pydantic = "^1.2"
-pydantic = "^1"
+pydantic = ">=1,<3"
 PyYAML = ">=5.3"
 requests = "^2"
 SQLAlchemy = ">=1.4,<3"
 tenacity = "^8.1.0"
 
 [package.extras]
-all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "amadeus (>=8.1.0)", "anthropic (>=0.3,<0.4)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "awadb (>=0.3.9,<0.4.0)", "azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clarifai (>=9.1.0)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=4,<5)", "deeplake (>=3.6.8,<4.0.0)", "docarray[hnswlib] (>=0.32.0,<0.33.0)", "duckduckgo-search (>=3.8.3,<4.0.0)", "elasticsearch (>=8,<9)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-auth (>=2.18.1,<3.0.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jina (>=3.14,<4.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lancedb (>=0.1,<0.2)", "langkit (>=0.0.6,<0.1.0)", "lark (>=1.1.5,<2.0.0)", "libdeeplake (>=0.0.60,<0.0.61)", "librosa (>=0.10.0.post2,<0.11.0)", "lxml (>=4.9.2,<5.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "marqo (>=0.11.0,<0.12.0)", "momento (>=1.5.0,<2.0.0)", "nebula3-python (>=3.4.0,<4.0.0)", "neo4j (>=5.8.1,<6.0.0)", "networkx (>=2.6.3,<3.0.0)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "octoai-sdk (>=0.1.1,<0.2.0)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "opensearch-py (>=2.0.0,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pymongo (>=4.3.3,<5.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "python-arango (>=7.5.9,<8.0.0)", "pyvespa (>=0.33.0,<0.34.0)", "qdrant-client (>=1.3.1,<2.0.0)", "rdflib (>=6.3.2,<7.0.0)", "redis (>=4,<5)", "requests-toolbelt (>=1.0.0,<2.0.0)", "sentence-transformers (>=2,<3)", "singlestoredb (>=0.7.1,<0.8.0)", "spacy (>=3,<4)", "steamship (>=2.16.9,<3.0.0)", "tensorflow-text (>=2.11.0,<3.0.0)", "tigrisdb (>=1.0.0b6,<2.0.0)", "tiktoken (>=0.3.2,<0.4.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)", "xinference (>=0.0.6,<0.0.7)"]
+all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "amadeus (>=8.1.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "awadb (>=0.3.9,<0.4.0)", "azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clarifai (>=9.1.0)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=4,<5)", "deeplake (>=3.6.8,<4.0.0)", "docarray[hnswlib] (>=0.32.0,<0.33.0)", "duckduckgo-search (>=3.8.3,<4.0.0)", "elasticsearch (>=8,<9)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-auth (>=2.18.1,<3.0.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lancedb (>=0.1,<0.2)", "langkit (>=0.0.6,<0.1.0)", "lark (>=1.1.5,<2.0.0)", "libdeeplake (>=0.0.60,<0.0.61)", "librosa (>=0.10.0.post2,<0.11.0)", "lxml (>=4.9.2,<5.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "marqo (>=0.11.0,<0.12.0)", "momento (>=1.5.0,<2.0.0)", "nebula3-python (>=3.4.0,<4.0.0)", "neo4j (>=5.8.1,<6.0.0)", "networkx (>=2.6.3,<3.0.0)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "opensearch-py (>=2.0.0,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pymongo (>=4.3.3,<5.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "python-arango (>=7.5.9,<8.0.0)", "pyvespa (>=0.33.0,<0.34.0)", "qdrant-client (>=1.3.1,<2.0.0)", "rdflib (>=6.3.2,<7.0.0)", "redis (>=4,<5)", "requests-toolbelt (>=1.0.0,<2.0.0)", "sentence-transformers (>=2,<3)", "singlestoredb (>=0.7.1,<0.8.0)", "tensorflow-text (>=2.11.0,<3.0.0)", "tigrisdb (>=1.0.0b6,<2.0.0)", "tiktoken (>=0.3.2,<0.4.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
 azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b6)", "openai (>=0,<1)"]
 clarifai = ["clarifai (>=9.1.0)"]
 cohere = ["cohere (>=4,<5)"]
 docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
 embeddings = ["sentence-transformers (>=2,<3)"]
-extended-testing = ["atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.0.7,<0.0.8)", "chardet (>=5.1.0,<6.0.0)", "esprima (>=4.0.1,<5.0.0)", "feedparser (>=6.0.10,<7.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "openai (>=0,<1)", "openai (>=0,<1)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tqdm (>=4.48.0)", "xinference (>=0.0.6,<0.0.7)", "zep-python (>=0.32)"]
+extended-testing = ["amazon-textract-caller (<2)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.0.7,<0.0.8)", "chardet (>=5.1.0,<6.0.0)", "esprima (>=4.0.1,<5.0.0)", "feedparser (>=6.0.10,<7.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "openai (>=0,<1)", "openai (>=0,<1)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tqdm (>=4.48.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
 javascript = ["esprima (>=4.0.1,<5.0.0)"]
-llms = ["anthropic (>=0.3,<0.4)", "clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (>=0,<1)", "openllm (>=0.1.19)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)", "xinference (>=0.0.6,<0.0.7)"]
+llms = ["clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (>=0,<1)", "openllm (>=0.1.19)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
 openai = ["openai (>=0,<1)", "tiktoken (>=0.3.2,<0.4.0)"]
 qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"]
 text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
@@ -1741,25 +1669,23 @@ url = "libs/langchain"
 
 [[package]]
 name = "langsmith"
-version = "0.0.12"
+version = "0.0.22"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
-category = "dev"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langsmith-0.0.12-py3-none-any.whl", hash = "sha256:d7a4360d1984b57689c6470ffcef79dcb217dd7129bdfd3bcfcb22dc058787f6"},
-    {file = "langsmith-0.0.12.tar.gz", hash = "sha256:5abf30ac4ebb6b68955bea617669585e80a6fe9def90664503d5fbd6d095e91e"},
+    {file = "langsmith-0.0.22-py3-none-any.whl", hash = "sha256:1bc94a2e5bfa355ca15d9e658c2c2d04c8cc45c61892a1be08a7c3b40f2fd3f4"},
+    {file = "langsmith-0.0.22.tar.gz", hash = "sha256:5726c7841294db2a9e5863e20718878d16e28722bdaf3169a278ff3bda2f0be7"},
 ]
 
 [package.dependencies]
-pydantic = ">=1,<2"
+pydantic = ">=1,<3"
 requests = ">=2,<3"
 
 [[package]]
 name = "linkchecker"
 version = "10.2.1"
 description = "check links in web documents or full websites"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1776,7 +1702,6 @@ requests = ">=2.20"
 name = "livereload"
 version = "2.6.3"
 description = "Python LiveReload is an awesome tool for web developers"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -1792,7 +1717,6 @@ tornado = {version = "*", markers = "python_version > \"2.7\""}
 name = "markdown-it-py"
 version = "2.2.0"
 description = "Python port of markdown-it. Markdown parsing, done right!"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1817,7 +1741,6 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 name = "markupsafe"
 version = "2.1.3"
 description = "Safely add untrusted strings to HTML/XML markup."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1877,7 +1800,6 @@ files = [
 name = "marshmallow"
 version = "3.20.1"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1898,7 +1820,6 @@ tests = ["pytest", "pytz", "simplejson"]
 name = "marshmallow-enum"
 version = "1.5.1"
 description = "Enum field for Marshmallow"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -1913,7 +1834,6 @@ marshmallow = ">=2.0.0"
 name = "matplotlib-inline"
 version = "0.1.6"
 description = "Inline Matplotlib backend for Jupyter"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -1928,7 +1848,6 @@ traitlets = "*"
 name = "mdit-py-plugins"
 version = "0.3.5"
 description = "Collection of plugins for markdown-it-py"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1948,7 +1867,6 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 name = "mdurl"
 version = "0.1.2"
 description = "Markdown URL utilities"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1960,7 +1878,6 @@ files = [
 name = "mistune"
 version = "3.0.1"
 description = "A sane and fast Markdown parser with useful plugins and renderers"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1972,7 +1889,6 @@ files = [
 name = "multidict"
 version = "6.0.4"
 description = "multidict implementation"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2056,7 +1972,6 @@ files = [
 name = "mypy-extensions"
 version = "1.0.0"
 description = "Type system extensions for programs checked with the mypy type checker."
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -2068,7 +1983,6 @@ files = [
 name = "myst-nb"
 version = "0.17.2"
 description = "A Jupyter Notebook Sphinx reader built on top of the MyST markdown parser."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2097,7 +2011,6 @@ testing = ["beautifulsoup4", "coverage (>=6.4,<8.0)", "ipykernel (>=5.5,<6.0)",
 name = "myst-parser"
 version = "0.18.1"
 description = "An extended commonmark compliant parser, with bridges to docutils & sphinx."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2124,7 +2037,6 @@ testing = ["beautifulsoup4", "coverage[toml]", "pytest (>=6,<7)", "pytest-cov",
 name = "nbclient"
 version = "0.7.4"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
-category = "dev"
 optional = false
 python-versions = ">=3.7.0"
 files = [
@@ -2134,7 +2046,7 @@ files = [
 
 [package.dependencies]
 jupyter-client = ">=6.1.12"
-jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 nbformat = ">=5.1"
 traitlets = ">=5.3"
 
@@ -2147,7 +2059,6 @@ test = ["flaky", "ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "p
 name = "nbconvert"
 version = "7.7.2"
 description = "Converting Jupyter Notebooks"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -2186,7 +2097,6 @@ webpdf = ["playwright"]
 name = "nbdev"
 version = "1.2.0"
 description = "Writing a library entirely in notebooks"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2211,7 +2121,6 @@ pyyaml = "*"
 name = "nbdoc"
 version = "0.0.82"
 description = "Generate beautiful, testable documentation with Jupyter Notebooks"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -2232,7 +2141,6 @@ pip = "*"
 name = "nbformat"
 version = "5.9.1"
 description = "The Jupyter Notebook format"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -2254,7 +2162,6 @@ test = ["pep440", "pre-commit", "pytest", "testpath"]
 name = "nbsphinx"
 version = "0.8.12"
 description = "Jupyter Notebook Tools for Sphinx"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2274,7 +2181,6 @@ traitlets = ">=5"
 name = "nest-asyncio"
 version = "1.5.6"
 description = "Patch asyncio to allow nested event loops"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -2286,7 +2192,6 @@ files = [
 name = "notebook"
 version = "7.0.0"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -2311,7 +2216,6 @@ test = ["ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[tes
 name = "notebook-shim"
 version = "0.2.3"
 description = "A shim layer for notebook traits and config"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2329,7 +2233,6 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 name = "numexpr"
 version = "2.8.4"
 description = "Fast numerical expression evaluator for NumPy"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2372,7 +2275,6 @@ numpy = ">=1.13.3"
 name = "numpy"
 version = "1.24.4"
 description = "Fundamental package for array computing in Python"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -2410,7 +2312,6 @@ files = [
 name = "numpydoc"
 version = "1.2"
 description = "Sphinx extension to support docstrings in Numpy format"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2429,7 +2330,6 @@ testing = ["matplotlib", "pytest", "pytest-cov"]
 name = "openapi-schema-pydantic"
 version = "1.2.4"
 description = "OpenAPI (v3) specification schema as pydantic class"
-category = "dev"
 optional = false
 python-versions = ">=3.6.1"
 files = [
@@ -2444,7 +2344,6 @@ pydantic = ">=1.8.2"
 name = "overrides"
 version = "7.3.1"
 description = "A decorator to automatically detect mismatch when overriding a method."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2456,7 +2355,6 @@ files = [
 name = "packaging"
 version = "23.1"
 description = "Core utilities for Python packages"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2468,7 +2366,6 @@ files = [
 name = "pandocfilters"
 version = "1.5.0"
 description = "Utilities for writing pandoc filters in python"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
@@ -2480,7 +2377,6 @@ files = [
 name = "parso"
 version = "0.8.3"
 description = "A Python Parser"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2496,7 +2392,6 @@ testing = ["docopt", "pytest (<6.0.0)"]
 name = "pathspec"
 version = "0.11.1"
 description = "Utility library for gitignore style pattern matching of file paths."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2508,7 +2403,6 @@ files = [
 name = "pexpect"
 version = "4.8.0"
 description = "Pexpect allows easy control of interactive console applications."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -2523,7 +2417,6 @@ ptyprocess = ">=0.5"
 name = "pickleshare"
 version = "0.7.5"
 description = "Tiny 'shelve'-like database with concurrency support"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -2535,7 +2428,6 @@ files = [
 name = "pip"
 version = "23.2"
 description = "The PyPA recommended tool for installing Python packages."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2547,7 +2439,6 @@ files = [
 name = "pkgutil-resolve-name"
 version = "1.3.10"
 description = "Resolve a name to an object."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2559,7 +2450,6 @@ files = [
 name = "platformdirs"
 version = "3.9.1"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2575,7 +2465,6 @@ test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.3.1)", "pytest-
 name = "prometheus-client"
 version = "0.17.1"
 description = "Python client for the Prometheus monitoring system."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2590,7 +2479,6 @@ twisted = ["twisted"]
 name = "prompt-toolkit"
 version = "3.0.39"
 description = "Library for building powerful interactive command lines in Python"
-category = "dev"
 optional = false
 python-versions = ">=3.7.0"
 files = [
@@ -2605,7 +2493,6 @@ wcwidth = "*"
 name = "psutil"
 version = "5.9.5"
 description = "Cross-platform lib for process and system monitoring in Python."
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
@@ -2632,7 +2519,6 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
 name = "ptyprocess"
 version = "0.7.0"
 description = "Run a subprocess in a pseudo terminal"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -2644,7 +2530,6 @@ files = [
 name = "pure-eval"
 version = "0.2.2"
 description = "Safely evaluate AST nodes without side effects"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -2659,7 +2544,6 @@ tests = ["pytest"]
 name = "pycparser"
 version = "2.21"
 description = "C parser in Python"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
@@ -2671,7 +2555,6 @@ files = [
 name = "pydantic"
 version = "1.10.11"
 description = "Data validation and settings management using python type hints"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2724,7 +2607,6 @@ email = ["email-validator (>=1.0.3)"]
 name = "pydata-sphinx-theme"
 version = "0.8.1"
 description = "Bootstrap-based Sphinx theme from the PyData community"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2748,7 +2630,6 @@ test = ["pydata-sphinx-theme[doc]", "pytest"]
 name = "pygments"
 version = "2.15.1"
 description = "Pygments is a syntax highlighting package written in Python."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2763,7 +2644,6 @@ plugins = ["importlib-metadata"]
 name = "python-dateutil"
 version = "2.8.2"
 description = "Extensions to the standard Python datetime module"
-category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
@@ -2778,7 +2658,6 @@ six = ">=1.5"
 name = "python-json-logger"
 version = "2.0.7"
 description = "A python library adding a json log formatter"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2790,7 +2669,6 @@ files = [
 name = "pytz"
 version = "2023.3"
 description = "World timezone definitions, modern and historical"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -2802,7 +2680,6 @@ files = [
 name = "pywin32"
 version = "306"
 description = "Python for Window Extensions"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -2826,7 +2703,6 @@ files = [
 name = "pywinpty"
 version = "2.0.11"
 description = "Pseudo terminal support for Windows from Python."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -2841,7 +2717,6 @@ files = [
 name = "pyyaml"
 version = "6.0.1"
 description = "YAML parser and emitter for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2891,7 +2766,6 @@ files = [
 name = "pyzmq"
 version = "25.1.0"
 description = "Python bindings for 0MQ"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2981,7 +2855,6 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 name = "qtconsole"
 version = "5.4.3"
 description = "Jupyter Qt console"
-category = "dev"
 optional = false
 python-versions = ">= 3.7"
 files = [
@@ -3008,7 +2881,6 @@ test = ["flaky", "pytest", "pytest-qt"]
 name = "qtpy"
 version = "2.3.1"
 description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3026,7 +2898,6 @@ test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
 name = "referencing"
 version = "0.30.0"
 description = "JSON Referencing + Python"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -3042,7 +2913,6 @@ rpds-py = ">=0.7.0"
 name = "requests"
 version = "2.31.0"
 description = "Python HTTP for Humans."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3064,7 +2934,6 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 name = "rfc3339-validator"
 version = "0.1.4"
 description = "A pure python RFC3339 validator"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
@@ -3079,7 +2948,6 @@ six = "*"
 name = "rfc3986-validator"
 version = "0.1.1"
 description = "Pure python rfc3986 validator"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
@@ -3091,7 +2959,6 @@ files = [
 name = "rpds-py"
 version = "0.9.2"
 description = "Python bindings to Rust's persistent data structures (rpds)"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -3198,7 +3065,6 @@ files = [
 name = "send2trash"
 version = "1.8.2"
 description = "Send file to trash natively under Mac OS X, Windows and Linux"
-category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 files = [
@@ -3215,7 +3081,6 @@ win32 = ["pywin32"]
 name = "six"
 version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
-category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 files = [
@@ -3227,7 +3092,6 @@ files = [
 name = "sniffio"
 version = "1.3.0"
 description = "Sniff out which async library your code is running under"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3239,7 +3103,6 @@ files = [
 name = "snowballstemmer"
 version = "2.2.0"
 description = "This package provides 29 stemmers for 28 languages generated from Snowball algorithms."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -3251,7 +3114,6 @@ files = [
 name = "soupsieve"
 version = "2.4.1"
 description = "A modern CSS selector implementation for Beautiful Soup."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3263,7 +3125,6 @@ files = [
 name = "sphinx"
 version = "4.5.0"
 description = "Python documentation generator"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -3299,7 +3160,6 @@ test = ["cython", "html5lib", "pytest", "pytest-cov", "typed-ast"]
 name = "sphinx-autobuild"
 version = "2021.3.14"
 description = "Rebuild Sphinx documentation on changes, with live-reload in the browser."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -3319,7 +3179,6 @@ test = ["pytest", "pytest-cov"]
 name = "sphinx-book-theme"
 version = "0.3.3"
 description = "A clean book theme for scientific explanations and documentation with Sphinx"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3341,7 +3200,6 @@ test = ["beautifulsoup4 (>=4.6.1,<5)", "coverage", "myst-nb (>=0.13.2,<0.14.0)",
 name = "sphinx-copybutton"
 version = "0.5.2"
 description = "Add a copy button to each of your code cells."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3360,7 +3218,6 @@ rtd = ["ipython", "myst-nb", "sphinx", "sphinx-book-theme", "sphinx-examples"]
 name = "sphinx-panels"
 version = "0.6.0"
 description = "A sphinx extension for creating panels in a grid layout."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -3382,7 +3239,6 @@ themes = ["myst-parser (>=0.12.9,<0.13.0)", "pydata-sphinx-theme (>=0.4.0,<0.5.0
 name = "sphinx-rtd-theme"
 version = "1.2.2"
 description = "Read the Docs theme for Sphinx"
-category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
@@ -3402,7 +3258,6 @@ dev = ["bump2version", "sphinxcontrib-httpdomain", "transifex-client", "wheel"]
 name = "sphinx-typlog-theme"
 version = "0.8.0"
 description = "A typlog Sphinx theme"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -3417,7 +3272,6 @@ dev = ["livereload", "sphinx"]
 name = "sphinxcontrib-applehelp"
 version = "1.0.4"
 description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -3433,7 +3287,6 @@ test = ["pytest"]
 name = "sphinxcontrib-devhelp"
 version = "1.0.2"
 description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp document."
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -3449,7 +3302,6 @@ test = ["pytest"]
 name = "sphinxcontrib-htmlhelp"
 version = "2.0.1"
 description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -3465,7 +3317,6 @@ test = ["html5lib", "pytest"]
 name = "sphinxcontrib-jquery"
 version = "4.1"
 description = "Extension to include jQuery on newer Sphinx releases"
-category = "dev"
 optional = false
 python-versions = ">=2.7"
 files = [
@@ -3480,7 +3331,6 @@ Sphinx = ">=1.8"
 name = "sphinxcontrib-jsmath"
 version = "1.0.1"
 description = "A sphinx extension which renders display math in HTML via JavaScript"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -3495,7 +3345,6 @@ test = ["flake8", "mypy", "pytest"]
 name = "sphinxcontrib-qthelp"
 version = "1.0.3"
 description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp document."
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -3511,7 +3360,6 @@ test = ["pytest"]
 name = "sphinxcontrib-serializinghtml"
 version = "1.1.5"
 description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)."
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -3527,7 +3375,6 @@ test = ["pytest"]
 name = "sqlalchemy"
 version = "2.0.19"
 description = "Database Abstraction Library"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3575,7 +3422,7 @@ files = [
 ]
 
 [package.dependencies]
-greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""}
+greenlet = {version = "!=0.4.17", markers = "platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_machine == \"AMD64\" or platform_machine == \"amd64\" or platform_machine == \"x86_64\" or platform_machine == \"ppc64le\" or platform_machine == \"aarch64\""}
 typing-extensions = ">=4.2.0"
 
 [package.extras]
@@ -3606,7 +3453,6 @@ sqlcipher = ["sqlcipher3-binary"]
 name = "stack-data"
 version = "0.6.2"
 description = "Extract data from python stack frames and tracebacks for informative displays"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -3626,7 +3472,6 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 name = "tabulate"
 version = "0.9.0"
 description = "Pretty-print tabular data"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3641,7 +3486,6 @@ widechars = ["wcwidth"]
 name = "tenacity"
 version = "8.2.2"
 description = "Retry code until it succeeds"
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -3656,7 +3500,6 @@ doc = ["reno", "sphinx", "tornado (>=4.5)"]
 name = "terminado"
 version = "0.17.1"
 description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3677,7 +3520,6 @@ test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
 name = "tinycss2"
 version = "1.2.1"
 description = "A tiny CSS parser"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3696,7 +3538,6 @@ test = ["flake8", "isort", "pytest"]
 name = "tokenize-rt"
 version = "5.1.0"
 description = "A wrapper around the stdlib `tokenize` which roundtrips."
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -3708,7 +3549,6 @@ files = [
 name = "toml"
 version = "0.10.2"
 description = "Python Library for Tom's Obvious, Minimal Language"
-category = "dev"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 files = [
@@ -3720,7 +3560,6 @@ files = [
 name = "tomli"
 version = "2.0.1"
 description = "A lil' TOML parser"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3732,7 +3571,6 @@ files = [
 name = "tornado"
 version = "6.3.2"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
-category = "dev"
 optional = false
 python-versions = ">= 3.8"
 files = [
@@ -3753,7 +3591,6 @@ files = [
 name = "traitlets"
 version = "5.9.0"
 description = "Traitlets Python configuration system"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3769,7 +3606,6 @@ test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
 name = "typing-extensions"
 version = "4.7.1"
 description = "Backported and Experimental Type Hints for Python 3.7+"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3781,7 +3617,6 @@ files = [
 name = "typing-inspect"
 version = "0.9.0"
 description = "Runtime inspection utilities for typing module."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -3797,7 +3632,6 @@ typing-extensions = ">=3.7.4"
 name = "uri-template"
 version = "1.3.0"
 description = "RFC 6570 URI Template Processor"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3812,7 +3646,6 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 name = "urllib3"
 version = "2.0.4"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3830,7 +3663,6 @@ zstd = ["zstandard (>=0.18.0)"]
 name = "wcwidth"
 version = "0.2.6"
 description = "Measures the displayed width of unicode strings in a terminal"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -3842,7 +3674,6 @@ files = [
 name = "webcolors"
 version = "1.13"
 description = "A library for working with the color formats defined by HTML and CSS."
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3858,7 +3689,6 @@ tests = ["pytest", "pytest-cov"]
 name = "webencodings"
 version = "0.5.1"
 description = "Character encoding aliases for legacy web content"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -3870,7 +3700,6 @@ files = [
 name = "websocket-client"
 version = "1.6.1"
 description = "WebSocket client for Python with low level API options"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3887,7 +3716,6 @@ test = ["websockets"]
 name = "widgetsnbextension"
 version = "4.0.8"
 description = "Jupyter interactive widgets for Jupyter Notebook"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3899,7 +3727,6 @@ files = [
 name = "yarl"
 version = "1.9.2"
 description = "Yet another URL library"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3987,7 +3814,6 @@ multidict = ">=4.0"
 name = "zipp"
 version = "3.16.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
-category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [