pinecone: release 0.2.0.dev1 (#26386)

TODO: update `aiohttp` >= 3.10.

.github/scripts/check_diff.py

@@ -16,6 +16,10 @@ LANGCHAIN_DIRS = [
     "libs/experimental",
 ]
 
+# for 0.3rc, we are ignoring core dependents
+# in order to be able to get CI to pass for individual PRs.
+IGNORE_CORE_DEPENDENTS = True
+
 # ignored partners are removed from dependents
 # but still run if directly edited
 IGNORED_PARTNERS = [
@@ -23,9 +27,6 @@ IGNORED_PARTNERS = [
     # specifically in huggingface jobs
     # https://github.com/langchain-ai/langchain/issues/25558
     "huggingface",
-    # remove ai21 because of breaking changes in sdk version 2.14.0
-    # that have not been fixed yet
-    "ai21",
 ]
 
 
@@ -105,9 +106,9 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     if dir_ == "libs/core":
         return [
             {"working-directory": dir_, "python-version": f"3.{v}"}
-            for v in range(8, 13)
+            for v in range(9, 13)
         ]
-    min_python = "3.8"
+    min_python = "3.9"
     max_python = "3.12"
 
     # custom logic for specific directories
@@ -187,6 +188,9 @@ if __name__ == "__main__":
             # for extended testing
             found = False
             for dir_ in LANGCHAIN_DIRS:
+                if dir_ == "libs/core" and IGNORE_CORE_DEPENDENTS:
+                    dirs_to_run["extended-test"].add(dir_)
+                    continue
                 if file.startswith(dir_):
                     found = True
                 if found:
@@ -198,7 +202,6 @@ if __name__ == "__main__":
             dirs_to_run["test"].add("libs/partners/mistralai")
             dirs_to_run["test"].add("libs/partners/openai")
             dirs_to_run["test"].add("libs/partners/anthropic")
-            dirs_to_run["test"].add("libs/partners/ai21")
             dirs_to_run["test"].add("libs/partners/fireworks")
             dirs_to_run["test"].add("libs/partners/groq")
 

.github/scripts/check_prerelease_dependencies.py

@@ -11,7 +11,7 @@ if __name__ == "__main__":
 
     # see if we're releasing an rc
     version = toml_data["tool"]["poetry"]["version"]
-    releasing_rc = "rc" in version
+    releasing_rc = "rc" in version or "dev" in version
 
     # if not, iterate through dependencies and make sure none allow prereleases
     if not releasing_rc:

.github/scripts/get_min_versions.py

@@ -15,6 +15,7 @@ MIN_VERSION_LIBS = [
     "langchain",
     "langchain-text-splitters",
     "SQLAlchemy",
+    "pydantic",
 ]
 
 SKIP_IF_PULL_REQUEST = ["langchain-core"]

.github/workflows/_dependencies.yml

@@ -1,114 +0,0 @@
-name: dependencies
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-      langchain-location:
-        required: false
-        type: string
-        description: "Relative path to the langchain library folder"
-      python-version:
-        required: true
-        type: string
-        description: "Python version to use"
-
-env:
-  POETRY_VERSION: "1.7.1"
-
-jobs:
-  build:
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-    runs-on: ubuntu-latest
-    name: dependency checks ${{ inputs.python-version }}
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ inputs.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: pydantic-cross-compat
-
-      - name: Install dependencies
-        shell: bash
-        run: poetry install
-
-      - name: Check imports with base dependencies
-        shell: bash
-        run: poetry run make check_imports
-
-      - name: Install test dependencies
-        shell: bash
-        run: poetry install --with test
-
-      - name: Install langchain editable
-        working-directory: ${{ inputs.working-directory }}
-        if: ${{ inputs.langchain-location }}
-        env:
-          LANGCHAIN_LOCATION: ${{ inputs.langchain-location }}
-        run: |
-          poetry run pip install -e "$LANGCHAIN_LOCATION"
-
-      - name: Install the opposite major version of pydantic
-        # If normal tests use pydantic v1, here we'll use v2, and vice versa.
-        shell: bash
-        # airbyte currently doesn't support pydantic v2
-        if: ${{ !startsWith(inputs.working-directory, 'libs/partners/airbyte') }}
-        run: |
-          # Determine the major part of pydantic version
-          REGULAR_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1)
-
-          if [[ "$REGULAR_VERSION" == "1" ]]; then
-            PYDANTIC_DEP=">=2.1,<3"
-            TEST_WITH_VERSION="2"
-          elif [[ "$REGULAR_VERSION" == "2" ]]; then
-            PYDANTIC_DEP="<2"
-            TEST_WITH_VERSION="1"
-          else
-            echo "Unexpected pydantic major version '$REGULAR_VERSION', cannot determine which version to use for cross-compatibility test."
-            exit 1
-          fi
-
-          # Install via `pip` instead of `poetry add` to avoid changing lockfile,
-          # which would prevent caching from working: the cache would get saved
-          # to a different key than where it gets loaded from.
-          poetry run pip install "pydantic${PYDANTIC_DEP}"
-
-          # Ensure that the correct pydantic is installed now.
-          echo "Checking pydantic version... Expecting ${TEST_WITH_VERSION}"
-
-          # Determine the major part of pydantic version
-          CURRENT_VERSION=$(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 [[ "$CURRENT_VERSION" != "$TEST_WITH_VERSION" ]]; then
-            echo "Error: expected pydantic version ${CURRENT_VERSION} to have been installed, but found: ${TEST_WITH_VERSION}"
-            exit 1
-          fi
-          echo "Found pydantic version ${CURRENT_VERSION}, as expected"
-      - name: Run pydantic compatibility tests
-        # airbyte currently doesn't support pydantic v2
-        if: ${{ !startsWith(inputs.working-directory, 'libs/partners/airbyte') }}
-        shell: bash
-        run: make test
-
-      - name: Ensure the tests did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/_integration_test.yml

@@ -67,6 +67,7 @@ jobs:
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
           GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
+          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}

.github/workflows/_release.yml

@@ -273,6 +273,7 @@ jobs:
           GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}

.github/workflows/check_diffs.yml

@@ -46,6 +46,7 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.lint) }}
+      fail-fast: false
     uses: ./.github/workflows/_lint.yml
     with:
       working-directory: ${{ matrix.job-configs.working-directory }}
@@ -59,6 +60,7 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.test) }}
+      fail-fast: false
     uses: ./.github/workflows/_test.yml
     with:
       working-directory: ${{ matrix.job-configs.working-directory }}
@@ -71,6 +73,7 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.test-doc-imports) }}
+      fail-fast: false
     uses: ./.github/workflows/_test_doc_imports.yml
     secrets: inherit
     with:
@@ -83,25 +86,13 @@ jobs:
     strategy:
       matrix:
         job-configs: ${{ fromJson(needs.build.outputs.compile-integration-tests) }}
+      fail-fast: false
     uses: ./.github/workflows/_compile_integration_test.yml
     with:
       working-directory: ${{ matrix.job-configs.working-directory }}
       python-version: ${{ matrix.job-configs.python-version }}
     secrets: inherit
 
-  dependencies:
-    name: cd ${{ matrix.job-configs.working-directory }}
-    needs: [ build ]
-    if: ${{ needs.build.outputs.dependencies != '[]' }}
-    strategy:
-      matrix:
-        job-configs: ${{ fromJson(needs.build.outputs.dependencies) }}
-    uses: ./.github/workflows/_dependencies.yml
-    with:
-      working-directory: ${{ matrix.job-configs.working-directory }}
-      python-version: ${{ matrix.job-configs.python-version }}
-    secrets: inherit
-
   extended-tests:
     name: "cd ${{ matrix.job-configs.working-directory }} / make extended_tests #${{ matrix.job-configs.python-version }}"
     needs: [ build ]
@@ -110,6 +101,7 @@ jobs:
       matrix:
         # note different variable for extended test dirs
         job-configs: ${{ fromJson(needs.build.outputs.extended-tests) }}
+      fail-fast: false
     runs-on: ubuntu-latest
     defaults:
       run:
@@ -149,7 +141,7 @@ jobs:
           echo "$STATUS" | grep 'nothing to commit, working tree clean'
   ci_success:
     name: "CI Success"
-    needs: [build, lint, test, compile-integration-tests, dependencies, extended-tests, test-doc-imports]
+    needs: [build, lint, test, compile-integration-tests, extended-tests, test-doc-imports]
     if: |
       always()
     runs-on: ubuntu-latest

.github/workflows/codespell.yml

@@ -5,7 +5,6 @@ on:
   push:
     branches: [master, v0.1]
   pull_request:
-    branches: [master, v0.1]
 
 permissions:
   contents: read

.github/workflows/scheduled_test.yml

@@ -17,16 +17,14 @@ jobs:
       fail-fast: false
       matrix:
         python-version:
-          - "3.8"
+          - "3.9"
           - "3.11"
         working-directory:
           - "libs/partners/openai"
           - "libs/partners/anthropic"
-          - "libs/partners/ai21"
           - "libs/partners/fireworks"
           - "libs/partners/groq"
           - "libs/partners/mistralai"
-          - "libs/partners/together"
           - "libs/partners/google-vertexai"
           - "libs/partners/google-genai"
           - "libs/partners/aws"
@@ -90,11 +88,10 @@ jobs:
           AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
           AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
           AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
-          AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
-          TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
           COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}

Makefile

@@ -36,7 +36,6 @@ api_docs_build:
 API_PKG ?= text-splitters
 
 api_docs_quick_preview:
-	poetry run pip install "pydantic<2"
 	poetry run python docs/api_reference/create_api_rst.py $(API_PKG)
 	cd docs/api_reference && poetry run make html
 	poetry run python docs/api_reference/scripts/custom_formatter.py docs/api_reference/_build/html/

cookbook/README.md

@@ -4,6 +4,8 @@ Example code for building applications with LangChain, with an emphasis on more
 
 Notebook | Description
 :- | :-
+[agent_fireworks_ai_langchain_mongodb.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/agent_fireworks_ai_langchain_mongodb.ipynb) | Build an AI Agent With Memory Using MongoDB, LangChain and FireWorksAI.
+[mongodb-langchain-cache-memory.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/mongodb-langchain-cache-memory.ipynb) | Build a RAG Application with Semantic Cache Using MongoDB and LangChain.
 [LLaMA2_sql_chat.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/LLaMA2_sql_chat.ipynb) | Build a chat application that interacts with a SQL database using an open source llm (llama2), specifically demonstrated on an SQLite database containing rosters.
 [Semi_Structured_RAG.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_Structured_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data, including text and tables, using unstructured for parsing, multi-vector retriever for storing, and lcel for implementing chains.
 [Semi_structured_and_multi_moda...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using unstructured for parsing, multi-vector retriever for storage and retrieval, and lcel for implementing chains.

cookbook/code-analysis-deeplake.ipynb

@@ -90,7 +90,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "# Please manually enter OpenAI Key"
    ]
   },

cookbook/cql_agent.ipynb

@@ -38,7 +38,7 @@
    "source": [
     "Connection is via `cassio` using `auto=True` parameter, and the notebook uses OpenAI. You should create a `.env` file accordingly.\n",
     "\n",
-    "For Casssandra, set:\n",
+    "For Cassandra, set:\n",
     "```bash\n",
     "CASSANDRA_CONTACT_POINTS\n",
     "CASSANDRA_USERNAME\n",

docs/Makefile

@@ -33,8 +33,8 @@ install-py-deps:
 	python3 -m venv .venv
 	$(PYTHON) -m pip install --upgrade pip
 	$(PYTHON) -m pip install --upgrade uv
-	$(PYTHON) -m uv pip install -r vercel_requirements.txt
-	$(PYTHON) -m uv pip install --editable $(PARTNER_DEPS_LIST)
+	$(PYTHON) -m uv pip install --pre -r vercel_requirements.txt
+	$(PYTHON) -m uv pip install --pre --editable $(PARTNER_DEPS_LIST)
 
 generate-files:
 	mkdir -p $(INTERMEDIATE_DIR)
@@ -73,6 +73,8 @@ append-related:
 generate-references:
 	$(PYTHON) scripts/generate_api_reference_links.py --docs_dir $(OUTPUT_NEW_DOCS_DIR)
 
+update-md: generate-files md-sync
+
 build: install-py-deps generate-files copy-infra render md-sync append-related
 
 vercel-build: install-vercel-deps build generate-references

docs/api_reference/requirements.txt

@@ -1,5 +1,5 @@
-autodoc_pydantic>=1,<2
-sphinx<=7
+autodoc_pydantic>=2,<3
+sphinx>=8,<9
 myst-parser>=3
 sphinx-autobuild>=2024
 pydata-sphinx-theme>=0.15
@@ -8,4 +8,4 @@ myst-nb>=1.1.1
 pyyaml
 sphinx-design
 sphinx-copybutton
-beautifulsoup4
+beautifulsoup4

docs/api_reference/scripts/custom_formatter.py

@@ -17,7 +17,10 @@ def process_toc_h3_elements(html_content: str) -> str:
 
     # Process each element
     for element in toc_h3_elements:
-        element = element.a.code.span
+        try:
+            element = element.a.code.span
+        except Exception:
+            continue
         # Get the text content of the element
         content = element.get_text()
 

docs/api_reference/templates/pydantic.rst

@@ -15,7 +15,7 @@
     :member-order: groupwise
     :show-inheritance: True
     :special-members: __call__
-    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace
+    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, model_construct, model_copy, model_dump, model_dump_json, model_parametrized_name, model_post_init, model_rebuild, model_validate, model_validate_json, model_validate_strings, model_extra, model_fields_set, model_json_schema
 
 
     {% block attributes %}

docs/api_reference/templates/runnable_pydantic.rst

@@ -15,7 +15,7 @@
     :member-order: groupwise
     :show-inheritance: True
     :special-members: __call__
-    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign, as_tool
+    :exclude-members: construct, copy, dict, from_orm, parse_file, parse_obj, parse_raw, schema, schema_json, update_forward_refs, validate, json, is_lc_serializable, to_json_not_implemented, lc_secrets, lc_attributes, lc_id, get_lc_namespace, astream_log, transform, atransform, get_output_schema, get_prompts, config_schema, map, pick, pipe, with_listeners, with_alisteners, with_config, with_fallbacks, with_types, with_retry, InputType, OutputType, config_specs, output_schema, get_input_schema, get_graph, get_name, input_schema, name, bind, assign, as_tool, get_config_jsonschema, get_input_jsonschema, get_output_jsonschema, model_construct, model_copy, model_dump, model_dump_json, model_parametrized_name, model_post_init, model_rebuild, model_validate, model_validate_json, model_validate_strings, to_json, model_extra, model_fields_set, model_json_schema
 
     .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
 

docs/docs/additional_resources/arxiv_references.mdx

@@ -5,51 +5,89 @@ This page contains `arXiv` papers referenced in the LangChain Documentation, API
  Templates, and Cookbooks.
 
 From the opposite direction, scientists use `LangChain` in research and reference it in the research papers. 
-Here you find papers that reference:
-- [LangChain](https://arxiv.org/search/?query=langchain&searchtype=all&source=header)
-- [LangGraph](https://arxiv.org/search/?query=langgraph&searchtype=all&source=header)
-- [LangSmith](https://arxiv.org/search/?query=langsmith&searchtype=all&source=header)
+
+`arXiv` papers with references to:
+ [LangChain](https://arxiv.org/search/?query=langchain&searchtype=all&source=header) | [LangGraph](https://arxiv.org/search/?query=langgraph&searchtype=all&source=header) | [LangSmith](https://arxiv.org/search/?query=langsmith&searchtype=all&source=header)
 
 ## Summary
 
 | arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation|
 |------------------|---------|-------------------|------------------------|
-| `2402.03620v1` [Self-Discover: Large Language Models Self-Compose Reasoning Structures](http://arxiv.org/abs/2402.03620v1) | Pei Zhou, Jay Pujara, Xiang Ren,  et al. | 2024-02-06 | `Cookbook:` [self-discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
-| `2401.18059v1` [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](http://arxiv.org/abs/2401.18059v1) | Parth Sarthi, Salman Abdullah, Aditi Tuli,  et al. | 2024-01-31 | `Cookbook:` [RAPTOR](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
-| `2401.15884v2` [Corrective Retrieval Augmented Generation](http://arxiv.org/abs/2401.15884v2) | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al. | 2024-01-29 | `Cookbook:` [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
-| `2401.04088v1` [Mixtral of Experts](http://arxiv.org/abs/2401.04088v1) | Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux,  et al. | 2024-01-08 | `Cookbook:` [together_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
-| `2312.06648v2` [Dense X Retrieval: What Retrieval Granularity Should We Use?](http://arxiv.org/abs/2312.06648v2) | Tong Chen, Hongwei Wang, Sihao Chen,  et al. | 2023-12-11 | `Template:` [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
-| `2311.09210v1` [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](http://arxiv.org/abs/2311.09210v1) | Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al. | 2023-11-15 | `Template:` [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
-| `2310.11511v1` [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](http://arxiv.org/abs/2310.11511v1) | Akari Asai, Zeqiu Wu, Yizhong Wang,  et al. | 2023-10-17 | `Cookbook:` [langgraph_self_rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
-| `2310.06117v2` [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](http://arxiv.org/abs/2310.06117v2) | Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al. | 2023-10-09 | `Template:` [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting), `Cookbook:` [stepback-qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
-| `2307.09288v2` [Llama 2: Open Foundation and Fine-Tuned Chat Models](http://arxiv.org/abs/2307.09288v2) | Hugo Touvron, Louis Martin, Kevin Stone,  et al. | 2023-07-18 | `Cookbook:` [Semi_Structured_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
-| `2305.14283v3` [Query Rewriting for Retrieval-Augmented Large Language Models](http://arxiv.org/abs/2305.14283v3) | Xinbei Ma, Yeyun Gong, Pengcheng He,  et al. | 2023-05-23 | `Template:` [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read), `Cookbook:` [rewrite](https://github.com/langchain-ai/langchain/blob/master/cookbook/rewrite.ipynb)
-| `2305.08291v1` [Large Language Model Guided Tree-of-Thought](http://arxiv.org/abs/2305.08291v1) | Jieyi Long | 2023-05-15 | `API:` [langchain_experimental.tot](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.tot), `Cookbook:` [tree_of_thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
-| `2305.04091v3` [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](http://arxiv.org/abs/2305.04091v3) | Lei Wang, Wanyu Xu, Yihuai Lan,  et al. | 2023-05-06 | `Cookbook:` [plan_and_execute_agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
-| `2305.02156v1` [Zero-Shot Listwise Document Reranking with a Large Language Model](http://arxiv.org/abs/2305.02156v1) | Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al. | 2023-05-03 | `API:` [langchain...LLMListwiseRerank](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
-| `2304.08485v2` [Visual Instruction Tuning](http://arxiv.org/abs/2304.08485v2) | Haotian Liu, Chunyuan Li, Qingyang Wu,  et al. | 2023-04-17 | `Cookbook:` [Semi_structured_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb), [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb)
-| `2304.03442v2` [Generative Agents: Interactive Simulacra of Human Behavior](http://arxiv.org/abs/2304.03442v2) | Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai,  et al. | 2023-04-07 | `Cookbook:` [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb), [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb)
-| `2303.17760v2` [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](http://arxiv.org/abs/2303.17760v2) | Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani,  et al. | 2023-03-31 | `Cookbook:` [camel_role_playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
-| `2303.17580v4` [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](http://arxiv.org/abs/2303.17580v4) | Yongliang Shen, Kaitao Song, Xu Tan,  et al. | 2023-03-30 | `API:` [langchain_experimental.autonomous_agents](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.autonomous_agents), `Cookbook:` [hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
-| `2301.10226v4` [A Watermark for Large Language Models](http://arxiv.org/abs/2301.10226v4) | John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al. | 2023-01-24 | `API:` [langchain_community...OCIModelDeploymentTGI](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/langchain_community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
-| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin,  et al. | 2022-12-20 | `API:` [langchain...HypotheticalDocumentEmbedder](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde), `Cookbook:` [hypothetical_document_embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
-| `2212.07425v3` [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](http://arxiv.org/abs/2212.07425v3) | Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al. | 2022-12-12 | `API:` [langchain_experimental.fallacy_removal](https://python.langchain.com/v0.2/api_reference//arxiv/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
-| `2211.13892v2` [Complementary Explanations for Effective In-Context Learning](http://arxiv.org/abs/2211.13892v2) | Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al. | 2022-11-25 | `API:` [langchain_core...MaxMarginalRelevanceExampleSelector](https://python.langchain.com/v0.2/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
-| `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou,  et al. | 2022-11-18 | `API:` [langchain_experimental.pal_chain](https://python.langchain.com/v0.2/api_reference//python/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://python.langchain.com/v0.2/api_reference/experimental/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), `Cookbook:` [program_aided_language_model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
-| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al. | 2022-10-06 | `Docs:` [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), `API:` [langchain...TrajectoryEvalChain](https://python.langchain.com/v0.2/api_reference/langchain/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain), [langchain...create_react_agent](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent)
-| `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al. | 2022-09-22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
-| `2205.13147v4` [Matryoshka Representation Learning](http://arxiv.org/abs/2205.13147v4) | Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al. | 2022-05-26 | `Docs:` [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
-| `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022-05-25 | `API:` [langchain_community...LaserEmbeddings](https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
-| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022-03-15 | `API:` [langchain_community...SQLDatabase](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
-| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher,  et al. | 2022-02-01 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
-| `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy,  et al. | 2021-02-26 | `API:` [langchain_experimental.open_clip](https://python.langchain.com/v0.2/api_reference//arxiv/experimental_api_reference.html#module-langchain_experimental.open_clip)
-| `1909.05858v2` [CTRL: A Conditional Transformer Language Model for Controllable Generation](http://arxiv.org/abs/1909.05858v2) | Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al. | 2019-09-11 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+| `2403.14403v2` [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](http://arxiv.org/abs/2403.14403v2) | Soyeong Jeong, Jinheon Baek, Sukmin Cho,  et al. | 2024‑03‑21 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `2402.03620v1` [Self-Discover: Large Language Models Self-Compose Reasoning Structures](http://arxiv.org/abs/2402.03620v1) | Pei Zhou, Jay Pujara, Xiang Ren,  et al. | 2024‑02‑06 | `Cookbook:` [Self-Discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
+| `2402.03367v2` [RAG-Fusion: a New Take on Retrieval-Augmented Generation](http://arxiv.org/abs/2402.03367v2) | Zackary Rackauckas | 2024‑01‑31 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `2401.18059v1` [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](http://arxiv.org/abs/2401.18059v1) | Parth Sarthi, Salman Abdullah, Aditi Tuli,  et al. | 2024‑01‑31 | `Cookbook:` [Raptor](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
+| `2401.15884v2` [Corrective Retrieval Augmented Generation](http://arxiv.org/abs/2401.15884v2) | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al. | 2024‑01‑29 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `Cookbook:` [Langgraph Crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
+| `2401.08500v1` [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](http://arxiv.org/abs/2401.08500v1) | Tal Ridnik, Dedy Kredo, Itamar Friedman | 2024‑01‑16 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `2401.04088v1` [Mixtral of Experts](http://arxiv.org/abs/2401.04088v1) | Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux,  et al. | 2024‑01‑08 | `Cookbook:` [Together Ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
+| `2312.06648v2` [Dense X Retrieval: What Retrieval Granularity Should We Use?](http://arxiv.org/abs/2312.06648v2) | Tong Chen, Hongwei Wang, Sihao Chen,  et al. | 2023‑12‑11 | `Template:` [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
+| `2311.09210v1` [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](http://arxiv.org/abs/2311.09210v1) | Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al. | 2023‑11‑15 | `Template:` [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
+| `2310.11511v1` [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](http://arxiv.org/abs/2310.11511v1) | Akari Asai, Zeqiu Wu, Yizhong Wang,  et al. | 2023‑10‑17 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `Cookbook:` [Langgraph Self Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
+| `2310.06117v2` [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](http://arxiv.org/abs/2310.06117v2) | Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al. | 2023‑10‑09 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `Template:` [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting), `Cookbook:` [Stepback-Qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
+| `2307.15337v3` [Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation](http://arxiv.org/abs/2307.15337v3) | Xuefei Ning, Zinan Lin, Zixuan Zhou,  et al. | 2023‑07‑28 | `Template:` [skeleton-of-thought](https://python.langchain.com/docs/templates/skeleton-of-thought)
+| `2307.09288v2` [Llama 2: Open Foundation and Fine-Tuned Chat Models](http://arxiv.org/abs/2307.09288v2) | Hugo Touvron, Louis Martin, Kevin Stone,  et al. | 2023‑07‑18 | `Cookbook:` [Semi Structured Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
+| `2307.03172v3` [Lost in the Middle: How Language Models Use Long Contexts](http://arxiv.org/abs/2307.03172v3) | Nelson F. Liu, Kevin Lin, John Hewitt,  et al. | 2023‑07‑06 | `Docs:` [docs/how_to/long_context_reorder](https://python.langchain.com/v0.2/docs/how_to/long_context_reorder)
+| `2305.14283v3` [Query Rewriting for Retrieval-Augmented Large Language Models](http://arxiv.org/abs/2305.14283v3) | Xinbei Ma, Yeyun Gong, Pengcheng He,  et al. | 2023‑05‑23 | `Template:` [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read), `Cookbook:` [Rewrite](https://github.com/langchain-ai/langchain/blob/master/cookbook/rewrite.ipynb)
+| `2305.08291v1` [Large Language Model Guided Tree-of-Thought](http://arxiv.org/abs/2305.08291v1) | Jieyi Long | 2023‑05‑15 | `API:` [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot), `Cookbook:` [Tree Of Thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
+| `2305.04091v3` [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](http://arxiv.org/abs/2305.04091v3) | Lei Wang, Wanyu Xu, Yihuai Lan,  et al. | 2023‑05‑06 | `Cookbook:` [Plan And Execute Agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
+| `2305.02156v1` [Zero-Shot Listwise Document Reranking with a Large Language Model](http://arxiv.org/abs/2305.02156v1) | Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al. | 2023‑05‑03 | `Docs:` [docs/how_to/contextual_compression](https://python.langchain.com/v0.2/docs/how_to/contextual_compression), `API:` [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
+| `2304.08485v2` [Visual Instruction Tuning](http://arxiv.org/abs/2304.08485v2) | Haotian Liu, Chunyuan Li, Qingyang Wu,  et al. | 2023‑04‑17 | `Cookbook:` [Semi Structured Multi Modal Rag Llama2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb), [Semi Structured And Multi Modal Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb)
+| `2304.03442v2` [Generative Agents: Interactive Simulacra of Human Behavior](http://arxiv.org/abs/2304.03442v2) | Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai,  et al. | 2023‑04‑07 | `Cookbook:` [Generative Agents Interactive Simulacra Of Human Behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb), [Multiagent Bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb)
+| `2303.17760v2` [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](http://arxiv.org/abs/2303.17760v2) | Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani,  et al. | 2023‑03‑31 | `Cookbook:` [Camel Role Playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
+| `2303.17580v4` [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](http://arxiv.org/abs/2303.17580v4) | Yongliang Shen, Kaitao Song, Xu Tan,  et al. | 2023‑03‑30 | `API:` [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents), `Cookbook:` [Hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
+| `2301.10226v4` [A Watermark for Large Language Models](http://arxiv.org/abs/2301.10226v4) | John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al. | 2023‑01‑24 | `API:` [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
+| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin,  et al. | 2022‑12‑20 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `API:` [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde), `Cookbook:` [Hypothetical Document Embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
+| `2212.08073v1` [Constitutional AI: Harmlessness from AI Feedback](http://arxiv.org/abs/2212.08073v1) | Yuntao Bai, Saurav Kadavath, Sandipan Kundu,  et al. | 2022‑12‑15 | `Docs:` [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/v0.2/docs/versions/migrating_chains/constitutional_chain)
+| `2212.07425v3` [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](http://arxiv.org/abs/2212.07425v3) | Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al. | 2022‑12‑12 | `API:` [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
+| `2211.13892v2` [Complementary Explanations for Effective In-Context Learning](http://arxiv.org/abs/2211.13892v2) | Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al. | 2022‑11‑25 | `API:` [langchain_core...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
+| `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou,  et al. | 2022‑11‑18 | `API:` [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), `Cookbook:` [Program Aided Language Model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
+| `2210.11934v2` [An Analysis of Fusion Functions for Hybrid Retrieval](http://arxiv.org/abs/2210.11934v2) | Sebastian Bruch, Siyu Gai, Amir Ingber | 2022‑10‑21 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al. | 2022‑10‑06 | `Docs:` [docs/integrations/tools/ionic_shopping](https://python.langchain.com/v0.2/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/v0.2/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `API:` [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
+| `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al. | 2022‑09‑22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/v0.2/docs/integrations/providers/activeloop_deeplake)
+| `2205.13147v4` [Matryoshka Representation Learning](http://arxiv.org/abs/2205.13147v4) | Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al. | 2022‑05‑26 | `Docs:` [docs/integrations/providers/snowflake](https://python.langchain.com/v0.2/docs/integrations/providers/snowflake)
+| `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022‑05‑25 | `API:` [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
+| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022‑03‑15 | `Docs:` [docs/tutorials/sql_qa](https://python.langchain.com/v0.2/docs/tutorials/sql_qa), `API:` [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
+| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher,  et al. | 2022‑02‑01 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
+| `2112.01488v3` [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](http://arxiv.org/abs/2112.01488v3) | Keshav Santhanam, Omar Khattab, Jon Saad-Falcon,  et al. | 2021‑12‑02 | `Docs:` [docs/integrations/retrievers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/v0.2/docs/integrations/providers/dspy)
+| `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy,  et al. | 2021‑02‑26 | `API:` [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
+| `2005.14165v4` [Language Models are Few-Shot Learners](http://arxiv.org/abs/2005.14165v4) | Tom B. Brown, Benjamin Mann, Nick Ryder,  et al. | 2020‑05‑28 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `2005.11401v4` [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](http://arxiv.org/abs/2005.11401v4) | Patrick Lewis, Ethan Perez, Aleksandra Piktus,  et al. | 2020‑05‑22 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `1909.05858v2` [CTRL: A Conditional Transformer Language Model for Controllable Generation](http://arxiv.org/abs/1909.05858v2) | Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al. | 2019‑09‑11 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
 
+## Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
+
+- **Authors:** Soyeong Jeong, Jinheon Baek, Sukmin Cho,  et al.
+- **arXiv id:** [2403.14403v2](http://arxiv.org/abs/2403.14403v2)  **Published Date:** 2024-03-21
+- **LangChain:**
+
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+
+**Abstract:** Retrieval-Augmented Large Language Models (LLMs), which incorporate the
+non-parametric knowledge from external knowledge bases into LLMs, have emerged
+as a promising approach to enhancing response accuracy in several tasks, such
+as Question-Answering (QA). However, even though there are various approaches
+dealing with queries of different complexities, they either handle simple
+queries with unnecessary computational overhead or fail to adequately address
+complex multi-step queries; yet, not all user requests fall into only one of
+the simple or complex categories. In this work, we propose a novel adaptive QA
+framework, that can dynamically select the most suitable strategy for
+(retrieval-augmented) LLMs from the simplest to the most sophisticated ones
+based on the query complexity. Also, this selection process is operationalized
+with a classifier, which is a smaller LM trained to predict the complexity
+level of incoming queries with automatically collected labels, obtained from
+actual predicted outcomes of models and inherent inductive biases in datasets.
+This approach offers a balanced strategy, seamlessly adapting between the
+iterative and single-step retrieval-augmented LLMs, as well as the no-retrieval
+methods, in response to a range of query complexities. We validate our model on
+a set of open-domain QA datasets, covering multiple query complexities, and
+show that ours enhances the overall efficiency and accuracy of QA systems,
+compared to relevant baselines including the adaptive retrieval approaches.
+Code is available at: https://github.com/starsuzi/Adaptive-RAG.
+                
 ## Self-Discover: Large Language Models Self-Compose Reasoning Structures
 
-- **arXiv id:** [2402.03620v1](http://arxiv.org/abs/2402.03620v1)  **Published Date:** 2024-02-06
-- **Title:** Self-Discover: Large Language Models Self-Compose Reasoning Structures
 - **Authors:** Pei Zhou, Jay Pujara, Xiang Ren,  et al.
+- **arXiv id:** [2402.03620v1](http://arxiv.org/abs/2402.03620v1)  **Published Date:** 2024-02-06
 - **LangChain:**
 
    - **Cookbook:** [self-discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
@@ -69,11 +107,33 @@ the self-discovered reasoning structures are universally applicable across
 model families: from PaLM 2-L to GPT-4, and from GPT-4 to Llama2, and share
 commonalities with human reasoning patterns.
                 
+## RAG-Fusion: a New Take on Retrieval-Augmented Generation
+
+- **Authors:** Zackary Rackauckas
+- **arXiv id:** [2402.03367v2](http://arxiv.org/abs/2402.03367v2)  **Published Date:** 2024-01-31
+- **LangChain:**
+
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+
+**Abstract:** Infineon has identified a need for engineers, account managers, and customers
+to rapidly obtain product information. This problem is traditionally addressed
+with retrieval-augmented generation (RAG) chatbots, but in this study, I
+evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion
+combines RAG and reciprocal rank fusion (RRF) by generating multiple queries,
+reranking them with reciprocal scores and fusing the documents and scores.
+Through manually evaluating answers on accuracy, relevance, and
+comprehensiveness, I found that RAG-Fusion was able to provide accurate and
+comprehensive answers due to the generated queries contextualizing the original
+query from various perspectives. However, some answers strayed off topic when
+the generated queries' relevance to the original query is insufficient. This
+research marks significant progress in artificial intelligence (AI) and natural
+language processing (NLP) applications and demonstrates transformations in a
+global and multi-industry context.
+                
 ## RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
 
-- **arXiv id:** [2401.18059v1](http://arxiv.org/abs/2401.18059v1)  **Published Date:** 2024-01-31
-- **Title:** RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
 - **Authors:** Parth Sarthi, Salman Abdullah, Aditi Tuli,  et al.
+- **arXiv id:** [2401.18059v1](http://arxiv.org/abs/2401.18059v1)  **Published Date:** 2024-01-31
 - **LangChain:**
 
    - **Cookbook:** [RAPTOR](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
@@ -95,11 +155,11 @@ benchmark by 20% in absolute accuracy.
                 
 ## Corrective Retrieval Augmented Generation
 
-- **arXiv id:** [2401.15884v2](http://arxiv.org/abs/2401.15884v2)  **Published Date:** 2024-01-29
-- **Title:** Corrective Retrieval Augmented Generation
 - **Authors:** Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al.
+- **arXiv id:** [2401.15884v2](http://arxiv.org/abs/2401.15884v2)  **Published Date:** 2024-01-29
 - **LangChain:**
 
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
    - **Cookbook:** [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
 
 **Abstract:** Large language models (LLMs) inevitably exhibit hallucinations since the
@@ -121,11 +181,36 @@ RAG-based approaches. Experiments on four datasets covering short- and
 long-form generation tasks show that CRAG can significantly improve the
 performance of RAG-based approaches.
                 
+## Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
+
+- **Authors:** Tal Ridnik, Dedy Kredo, Itamar Friedman
+- **arXiv id:** [2401.08500v1](http://arxiv.org/abs/2401.08500v1)  **Published Date:** 2024-01-16
+- **LangChain:**
+
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+
+**Abstract:** Code generation problems differ from common natural language problems - they
+require matching the exact syntax of the target language, identifying happy
+paths and edge cases, paying attention to numerous small details in the problem
+spec, and addressing other code-specific issues and requirements. Hence, many
+of the optimizations and tricks that have been successful in natural language
+generation may not be effective for code tasks. In this work, we propose a new
+approach to code generation by LLMs, which we call AlphaCodium - a test-based,
+multi-stage, code-oriented iterative flow, that improves the performances of
+LLMs on code problems. We tested AlphaCodium on a challenging code generation
+dataset called CodeContests, which includes competitive programming problems
+from platforms such as Codeforces. The proposed flow consistently and
+significantly improves results. On the validation set, for example, GPT-4
+accuracy (pass@5) increased from 19% with a single well-designed direct prompt
+to 44% with the AlphaCodium flow. Many of the principles and best practices
+acquired in this work, we believe, are broadly applicable to general code
+generation tasks. Full implementation is available at:
+https://github.com/Codium-ai/AlphaCodium
+                
 ## Mixtral of Experts
 
-- **arXiv id:** [2401.04088v1](http://arxiv.org/abs/2401.04088v1)  **Published Date:** 2024-01-08
-- **Title:** Mixtral of Experts
 - **Authors:** Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux,  et al.
+- **arXiv id:** [2401.04088v1](http://arxiv.org/abs/2401.04088v1)  **Published Date:** 2024-01-08
 - **LangChain:**
 
    - **Cookbook:** [together_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
@@ -147,9 +232,8 @@ the base and instruct models are released under the Apache 2.0 license.
                 
 ## Dense X Retrieval: What Retrieval Granularity Should We Use?
 
-- **arXiv id:** [2312.06648v2](http://arxiv.org/abs/2312.06648v2)  **Published Date:** 2023-12-11
-- **Title:** Dense X Retrieval: What Retrieval Granularity Should We Use?
 - **Authors:** Tong Chen, Hongwei Wang, Sihao Chen,  et al.
+- **arXiv id:** [2312.06648v2](http://arxiv.org/abs/2312.06648v2)  **Published Date:** 2023-12-11
 - **LangChain:**
 
    - **Template:** [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
@@ -174,9 +258,8 @@ information.
                 
 ## Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
 
-- **arXiv id:** [2311.09210v1](http://arxiv.org/abs/2311.09210v1)  **Published Date:** 2023-11-15
-- **Title:** Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
 - **Authors:** Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al.
+- **arXiv id:** [2311.09210v1](http://arxiv.org/abs/2311.09210v1)  **Published Date:** 2023-11-15
 - **LangChain:**
 
    - **Template:** [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
@@ -206,11 +289,11 @@ outside the pre-training knowledge scope.
                 
 ## Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
 
-- **arXiv id:** [2310.11511v1](http://arxiv.org/abs/2310.11511v1)  **Published Date:** 2023-10-17
-- **Title:** Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
 - **Authors:** Akari Asai, Zeqiu Wu, Yizhong Wang,  et al.
+- **arXiv id:** [2310.11511v1](http://arxiv.org/abs/2310.11511v1)  **Published Date:** 2023-10-17
 - **LangChain:**
 
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
    - **Cookbook:** [langgraph_self_rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
 
 **Abstract:** Despite their remarkable capabilities, large language models (LLMs) often
@@ -237,11 +320,11 @@ to these models.
                 
 ## Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
 
-- **arXiv id:** [2310.06117v2](http://arxiv.org/abs/2310.06117v2)  **Published Date:** 2023-10-09
-- **Title:** Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
 - **Authors:** Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al.
+- **arXiv id:** [2310.06117v2](http://arxiv.org/abs/2310.06117v2)  **Published Date:** 2023-10-09
 - **LangChain:**
 
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
    - **Template:** [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting)
    - **Cookbook:** [stepback-qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
 
@@ -256,11 +339,31 @@ including STEM, Knowledge QA, and Multi-Hop Reasoning. For instance, Step-Back
 Prompting improves PaLM-2L performance on MMLU (Physics and Chemistry) by 7%
 and 11% respectively, TimeQA by 27%, and MuSiQue by 7%.
                 
+## Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation
+
+- **Authors:** Xuefei Ning, Zinan Lin, Zixuan Zhou,  et al.
+- **arXiv id:** [2307.15337v3](http://arxiv.org/abs/2307.15337v3)  **Published Date:** 2023-07-28
+- **LangChain:**
+
+   - **Template:** [skeleton-of-thought](https://python.langchain.com/docs/templates/skeleton-of-thought)
+
+**Abstract:** This work aims at decreasing the end-to-end generation latency of large
+language models (LLMs). One of the major causes of the high generation latency
+is the sequential decoding approach adopted by almost all state-of-the-art
+LLMs. In this work, motivated by the thinking and writing process of humans, we
+propose Skeleton-of-Thought (SoT), which first guides LLMs to generate the
+skeleton of the answer, and then conducts parallel API calls or batched
+decoding to complete the contents of each skeleton point in parallel. Not only
+does SoT provide considerable speed-ups across 12 LLMs, but it can also
+potentially improve the answer quality on several question categories. SoT is
+an initial attempt at data-centric optimization for inference efficiency, and
+showcases the potential of eliciting high-quality answers by explicitly
+planning the answer structure in language.
+                
 ## Llama 2: Open Foundation and Fine-Tuned Chat Models
 
-- **arXiv id:** [2307.09288v2](http://arxiv.org/abs/2307.09288v2)  **Published Date:** 2023-07-18
-- **Title:** Llama 2: Open Foundation and Fine-Tuned Chat Models
 - **Authors:** Hugo Touvron, Louis Martin, Kevin Stone,  et al.
+- **arXiv id:** [2307.09288v2](http://arxiv.org/abs/2307.09288v2)  **Published Date:** 2023-07-18
 - **LangChain:**
 
    - **Cookbook:** [Semi_Structured_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
@@ -275,11 +378,32 @@ detailed description of our approach to fine-tuning and safety improvements of
 Llama 2-Chat in order to enable the community to build on our work and
 contribute to the responsible development of LLMs.
                 
+## Lost in the Middle: How Language Models Use Long Contexts
+
+- **Authors:** Nelson F. Liu, Kevin Lin, John Hewitt,  et al.
+- **arXiv id:** [2307.03172v3](http://arxiv.org/abs/2307.03172v3)  **Published Date:** 2023-07-06
+- **LangChain:**
+
+   - **Documentation:** [docs/how_to/long_context_reorder](https://python.langchain.com/v0.2/docs/how_to/long_context_reorder)
+
+**Abstract:** While recent language models have the ability to take long contexts as input,
+relatively little is known about how well they use longer context. We analyze
+the performance of language models on two tasks that require identifying
+relevant information in their input contexts: multi-document question answering
+and key-value retrieval. We find that performance can degrade significantly
+when changing the position of relevant information, indicating that current
+language models do not robustly make use of information in long input contexts.
+In particular, we observe that performance is often highest when relevant
+information occurs at the beginning or end of the input context, and
+significantly degrades when models must access relevant information in the
+middle of long contexts, even for explicitly long-context models. Our analysis
+provides a better understanding of how language models use their input context
+and provides new evaluation protocols for future long-context language models.
+                
 ## Query Rewriting for Retrieval-Augmented Large Language Models
 
-- **arXiv id:** [2305.14283v3](http://arxiv.org/abs/2305.14283v3)  **Published Date:** 2023-05-23
-- **Title:** Query Rewriting for Retrieval-Augmented Large Language Models
 - **Authors:** Xinbei Ma, Yeyun Gong, Pengcheng He,  et al.
+- **arXiv id:** [2305.14283v3](http://arxiv.org/abs/2305.14283v3)  **Published Date:** 2023-05-23
 - **LangChain:**
 
    - **Template:** [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read)
@@ -305,12 +429,11 @@ for retrieval-augmented LLM.
                 
 ## Large Language Model Guided Tree-of-Thought
 
-- **arXiv id:** [2305.08291v1](http://arxiv.org/abs/2305.08291v1)  **Published Date:** 2023-05-15
-- **Title:** Large Language Model Guided Tree-of-Thought
 - **Authors:** Jieyi Long
+- **arXiv id:** [2305.08291v1](http://arxiv.org/abs/2305.08291v1)  **Published Date:** 2023-05-15
 - **LangChain:**
 
-   - **API Reference:** [langchain_experimental.tot](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.tot)
+   - **API Reference:** [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot)
    - **Cookbook:** [tree_of_thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
 
 **Abstract:** In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel
@@ -333,9 +456,8 @@ implementation of the ToT-based Sudoku solver is available on GitHub:
                 
 ## Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
 
-- **arXiv id:** [2305.04091v3](http://arxiv.org/abs/2305.04091v3)  **Published Date:** 2023-05-06
-- **Title:** Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
 - **Authors:** Lei Wang, Wanyu Xu, Yihuai Lan,  et al.
+- **arXiv id:** [2305.04091v3](http://arxiv.org/abs/2305.04091v3)  **Published Date:** 2023-05-06
 - **LangChain:**
 
    - **Cookbook:** [plan_and_execute_agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
@@ -364,12 +486,12 @@ https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting.
                 
 ## Zero-Shot Listwise Document Reranking with a Large Language Model
 
-- **arXiv id:** [2305.02156v1](http://arxiv.org/abs/2305.02156v1)  **Published Date:** 2023-05-03
-- **Title:** Zero-Shot Listwise Document Reranking with a Large Language Model
 - **Authors:** Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al.
+- **arXiv id:** [2305.02156v1](http://arxiv.org/abs/2305.02156v1)  **Published Date:** 2023-05-03
 - **LangChain:**
 
-   - **API Reference:** [langchain...LLMListwiseRerank](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
+   - **Documentation:** [docs/how_to/contextual_compression](https://python.langchain.com/v0.2/docs/how_to/contextual_compression)
+   - **API Reference:** [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
 
 **Abstract:** Supervised ranking methods based on bi-encoder or cross-encoder architectures
 have shown success in multi-stage text ranking tasks, but they require large
@@ -388,12 +510,11 @@ with results showing its potential to generalize across different languages.
                 
 ## Visual Instruction Tuning
 
-- **arXiv id:** [2304.08485v2](http://arxiv.org/abs/2304.08485v2)  **Published Date:** 2023-04-17
-- **Title:** Visual Instruction Tuning
 - **Authors:** Haotian Liu, Chunyuan Li, Qingyang Wu,  et al.
+- **arXiv id:** [2304.08485v2](http://arxiv.org/abs/2304.08485v2)  **Published Date:** 2023-04-17
 - **LangChain:**
 
-   - **Cookbook:** [Semi_structured_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb), [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb)
+   - **Cookbook:** [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb), [Semi_structured_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb)
 
 **Abstract:** Instruction tuning large language models (LLMs) using machine-generated
 instruction-following data has improved zero-shot capabilities on new tasks,
@@ -413,12 +534,11 @@ publicly available.
                 
 ## Generative Agents: Interactive Simulacra of Human Behavior
 
-- **arXiv id:** [2304.03442v2](http://arxiv.org/abs/2304.03442v2)  **Published Date:** 2023-04-07
-- **Title:** Generative Agents: Interactive Simulacra of Human Behavior
 - **Authors:** Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai,  et al.
+- **arXiv id:** [2304.03442v2](http://arxiv.org/abs/2304.03442v2)  **Published Date:** 2023-04-07
 - **LangChain:**
 
-   - **Cookbook:** [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb), [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb)
+   - **Cookbook:** [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb), [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb)
 
 **Abstract:** Believable proxies of human behavior can empower interactive applications
 ranging from immersive environments to rehearsal spaces for interpersonal
@@ -447,9 +567,8 @@ interaction patterns for enabling believable simulations of human behavior.
                 
 ## CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
 
-- **arXiv id:** [2303.17760v2](http://arxiv.org/abs/2303.17760v2)  **Published Date:** 2023-03-31
-- **Title:** CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
 - **Authors:** Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani,  et al.
+- **arXiv id:** [2303.17760v2](http://arxiv.org/abs/2303.17760v2)  **Published Date:** 2023-03-31
 - **LangChain:**
 
    - **Cookbook:** [camel_role_playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
@@ -475,12 +594,11 @@ agents and beyond: https://github.com/camel-ai/camel.
                 
 ## HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
 
-- **arXiv id:** [2303.17580v4](http://arxiv.org/abs/2303.17580v4)  **Published Date:** 2023-03-30
-- **Title:** HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
 - **Authors:** Yongliang Shen, Kaitao Song, Xu Tan,  et al.
+- **arXiv id:** [2303.17580v4](http://arxiv.org/abs/2303.17580v4)  **Published Date:** 2023-03-30
 - **LangChain:**
 
-   - **API Reference:** [langchain_experimental.autonomous_agents](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.autonomous_agents)
+   - **API Reference:** [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents)
    - **Cookbook:** [hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
 
 **Abstract:** Solving complicated AI tasks with different domains and modalities is a key
@@ -505,12 +623,11 @@ realization of artificial general intelligence.
                 
 ## A Watermark for Large Language Models
 
-- **arXiv id:** [2301.10226v4](http://arxiv.org/abs/2301.10226v4)  **Published Date:** 2023-01-24
-- **Title:** A Watermark for Large Language Models
 - **Authors:** John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al.
+- **arXiv id:** [2301.10226v4](http://arxiv.org/abs/2301.10226v4)  **Published Date:** 2023-01-24
 - **LangChain:**
 
-   - **API Reference:** [langchain_community...OCIModelDeploymentTGI](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/langchain_community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+   - **API Reference:** [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
 
 **Abstract:** Potential harms of large language models can be mitigated by watermarking
 model output, i.e., embedding signals into generated text that are invisible to
@@ -528,12 +645,12 @@ family, and discuss robustness and security.
                 
 ## Precise Zero-Shot Dense Retrieval without Relevance Labels
 
-- **arXiv id:** [2212.10496v1](http://arxiv.org/abs/2212.10496v1)  **Published Date:** 2022-12-20
-- **Title:** Precise Zero-Shot Dense Retrieval without Relevance Labels
 - **Authors:** Luyu Gao, Xueguang Ma, Jimmy Lin,  et al.
+- **arXiv id:** [2212.10496v1](http://arxiv.org/abs/2212.10496v1)  **Published Date:** 2022-12-20
 - **LangChain:**
 
-   - **API Reference:** [langchain...HypotheticalDocumentEmbedder](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **API Reference:** [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
    - **Template:** [hyde](https://python.langchain.com/docs/templates/hyde)
    - **Cookbook:** [hypothetical_document_embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
 
@@ -555,14 +672,40 @@ state-of-the-art unsupervised dense retriever Contriever and shows strong
 performance comparable to fine-tuned retrievers, across various tasks (e.g. web
 search, QA, fact verification) and languages~(e.g. sw, ko, ja).
                 
-## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
+## Constitutional AI: Harmlessness from AI Feedback
 
-- **arXiv id:** [2212.07425v3](http://arxiv.org/abs/2212.07425v3)  **Published Date:** 2022-12-12
-- **Title:** Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
-- **Authors:** Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al.
+- **Authors:** Yuntao Bai, Saurav Kadavath, Sandipan Kundu,  et al.
+- **arXiv id:** [2212.08073v1](http://arxiv.org/abs/2212.08073v1)  **Published Date:** 2022-12-15
 - **LangChain:**
 
-   - **API Reference:** [langchain_experimental.fallacy_removal](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.fallacy_removal)
+   - **Documentation:** [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/v0.2/docs/versions/migrating_chains/constitutional_chain)
+
+**Abstract:** As AI systems become more capable, we would like to enlist their help to
+supervise other AIs. We experiment with methods for training a harmless AI
+assistant through self-improvement, without any human labels identifying
+harmful outputs. The only human oversight is provided through a list of rules
+or principles, and so we refer to the method as 'Constitutional AI'. The
+process involves both a supervised learning and a reinforcement learning phase.
+In the supervised phase we sample from an initial model, then generate
+self-critiques and revisions, and then finetune the original model on revised
+responses. In the RL phase, we sample from the finetuned model, use a model to
+evaluate which of the two samples is better, and then train a preference model
+from this dataset of AI preferences. We then train with RL using the preference
+model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a
+result we are able to train a harmless but non-evasive AI assistant that
+engages with harmful queries by explaining its objections to them. Both the SL
+and RL methods can leverage chain-of-thought style reasoning to improve the
+human-judged performance and transparency of AI decision making. These methods
+make it possible to control AI behavior more precisely and with far fewer human
+labels.
+                
+## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
+
+- **Authors:** Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al.
+- **arXiv id:** [2212.07425v3](http://arxiv.org/abs/2212.07425v3)  **Published Date:** 2022-12-12
+- **LangChain:**
+
+   - **API Reference:** [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
 
 **Abstract:** The spread of misinformation, propaganda, and flawed argumentation has been
 amplified in the Internet era. Given the volume of data and the subtlety of
@@ -588,12 +731,11 @@ further work on logical fallacy identification.
                 
 ## Complementary Explanations for Effective In-Context Learning
 
-- **arXiv id:** [2211.13892v2](http://arxiv.org/abs/2211.13892v2)  **Published Date:** 2022-11-25
-- **Title:** Complementary Explanations for Effective In-Context Learning
 - **Authors:** Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al.
+- **arXiv id:** [2211.13892v2](http://arxiv.org/abs/2211.13892v2)  **Published Date:** 2022-11-25
 - **LangChain:**
 
-   - **API Reference:** [langchain_core...MaxMarginalRelevanceExampleSelector](https://python.langchain.com/v0.2/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
+   - **API Reference:** [langchain_core...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
 
 **Abstract:** Large language models (LLMs) have exhibited remarkable capabilities in
 learning from explanations in prompts, but there has been limited understanding
@@ -614,12 +756,11 @@ performance across three real-world tasks on multiple LLMs.
                 
 ## PAL: Program-aided Language Models
 
-- **arXiv id:** [2211.10435v2](http://arxiv.org/abs/2211.10435v2)  **Published Date:** 2022-11-18
-- **Title:** PAL: Program-aided Language Models
 - **Authors:** Luyu Gao, Aman Madaan, Shuyan Zhou,  et al.
+- **arXiv id:** [2211.10435v2](http://arxiv.org/abs/2211.10435v2)  **Published Date:** 2022-11-18
 - **LangChain:**
 
-   - **API Reference:** [langchain_experimental.pal_chain](https://python.langchain.com/v0.2/api_reference//python/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://python.langchain.com/v0.2/api_reference/experimental/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain)
+   - **API Reference:** [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain)
    - **Cookbook:** [program_aided_language_model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
 
 **Abstract:** Large language models (LLMs) have recently demonstrated an impressive ability
@@ -645,15 +786,33 @@ accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B
 which uses chain-of-thought by absolute 15% top-1. Our code and data are
 publicly available at http://reasonwithpal.com/ .
                 
-## ReAct: Synergizing Reasoning and Acting in Language Models
+## An Analysis of Fusion Functions for Hybrid Retrieval
 
-- **arXiv id:** [2210.03629v3](http://arxiv.org/abs/2210.03629v3)  **Published Date:** 2022-10-06
-- **Title:** ReAct: Synergizing Reasoning and Acting in Language Models
-- **Authors:** Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al.
+- **Authors:** Sebastian Bruch, Siyu Gai, Amir Ingber
+- **arXiv id:** [2210.11934v2](http://arxiv.org/abs/2210.11934v2)  **Published Date:** 2022-10-21
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping)
-   - **API Reference:** [langchain...TrajectoryEvalChain](https://python.langchain.com/v0.2/api_reference/langchain/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain), [langchain...create_react_agent](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+
+**Abstract:** We study hybrid search in text retrieval where lexical and semantic search
+are fused together with the intuition that the two are complementary in how
+they model relevance. In particular, we examine fusion by a convex combination
+(CC) of lexical and semantic scores, as well as the Reciprocal Rank Fusion
+(RRF) method, and identify their advantages and potential pitfalls. Contrary to
+existing studies, we find RRF to be sensitive to its parameters; that the
+learning of a CC fusion is generally agnostic to the choice of score
+normalization; that CC outperforms RRF in in-domain and out-of-domain settings;
+and finally, that CC is sample efficient, requiring only a small set of
+training examples to tune its only parameter to a target domain.
+                
+## ReAct: Synergizing Reasoning and Acting in Language Models
+
+- **Authors:** Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al.
+- **arXiv id:** [2210.03629v3](http://arxiv.org/abs/2210.03629v3)  **Published Date:** 2022-10-06
+- **LangChain:**
+
+   - **Documentation:** [docs/integrations/tools/ionic_shopping](https://python.langchain.com/v0.2/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/v0.2/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **API Reference:** [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
 
 **Abstract:** While large language models (LLMs) have demonstrated impressive capabilities
 across tasks in language understanding and interactive decision making, their
@@ -680,12 +839,11 @@ Project site with code: https://react-lm.github.io
                 
 ## Deep Lake: a Lakehouse for Deep Learning
 
-- **arXiv id:** [2209.10785v2](http://arxiv.org/abs/2209.10785v2)  **Published Date:** 2022-09-22
-- **Title:** Deep Lake: a Lakehouse for Deep Learning
 - **Authors:** Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al.
+- **arXiv id:** [2209.10785v2](http://arxiv.org/abs/2209.10785v2)  **Published Date:** 2022-09-22
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
+   - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/v0.2/docs/integrations/providers/activeloop_deeplake)
 
 **Abstract:** Traditional data lakes provide critical data infrastructure for analytical
 workloads by enabling time travel, running SQL queries, ingesting data with
@@ -706,12 +864,11 @@ TensorFlow, JAX, and integrate with numerous MLOps tools.
                 
 ## Matryoshka Representation Learning
 
-- **arXiv id:** [2205.13147v4](http://arxiv.org/abs/2205.13147v4)  **Published Date:** 2022-05-26
-- **Title:** Matryoshka Representation Learning
 - **Authors:** Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al.
+- **arXiv id:** [2205.13147v4](http://arxiv.org/abs/2205.13147v4)  **Published Date:** 2022-05-26
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
+   - **Documentation:** [docs/integrations/providers/snowflake](https://python.langchain.com/v0.2/docs/integrations/providers/snowflake)
 
 **Abstract:** Learned representations are a central component in modern ML systems, serving
 a multitude of downstream tasks. When training such representations, it is
@@ -738,12 +895,11 @@ are open-sourced at https://github.com/RAIVNLab/MRL.
                 
 ## Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
 
-- **arXiv id:** [2205.12654v1](http://arxiv.org/abs/2205.12654v1)  **Published Date:** 2022-05-25
-- **Title:** Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
 - **Authors:** Kevin Heffernan, Onur Çelebi, Holger Schwenk
+- **arXiv id:** [2205.12654v1](http://arxiv.org/abs/2205.12654v1)  **Published Date:** 2022-05-25
 - **LangChain:**
 
-   - **API Reference:** [langchain_community...LaserEmbeddings](https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
+   - **API Reference:** [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
 
 **Abstract:** Scaling multilingual representation learning beyond the hundred most frequent
 languages is challenging, in particular to cover the long tail of low-resource
@@ -765,12 +921,12 @@ encoders, mine bitexts, and validate the bitexts by training NMT systems.
                 
 ## Evaluating the Text-to-SQL Capabilities of Large Language Models
 
-- **arXiv id:** [2204.00498v1](http://arxiv.org/abs/2204.00498v1)  **Published Date:** 2022-03-15
-- **Title:** Evaluating the Text-to-SQL Capabilities of Large Language Models
 - **Authors:** Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau
+- **arXiv id:** [2204.00498v1](http://arxiv.org/abs/2204.00498v1)  **Published Date:** 2022-03-15
 - **LangChain:**
 
-   - **API Reference:** [langchain_community...SQLDatabase](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
+   - **Documentation:** [docs/tutorials/sql_qa](https://python.langchain.com/v0.2/docs/tutorials/sql_qa)
+   - **API Reference:** [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
 
 **Abstract:** We perform an empirical evaluation of Text-to-SQL capabilities of the Codex
 language model. We find that, without any finetuning, Codex is a strong
@@ -782,12 +938,11 @@ few-shot examples.
                 
 ## Locally Typical Sampling
 
-- **arXiv id:** [2202.00666v5](http://arxiv.org/abs/2202.00666v5)  **Published Date:** 2022-02-01
-- **Title:** Locally Typical Sampling
 - **Authors:** Clara Meister, Tiago Pimentel, Gian Wiher,  et al.
+- **arXiv id:** [2202.00666v5](http://arxiv.org/abs/2202.00666v5)  **Published Date:** 2022-02-01
 - **LangChain:**
 
-   - **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+   - **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
 
 **Abstract:** Today's probabilistic language generators fall short when it comes to
 producing coherent and fluent text despite the fact that the underlying models
@@ -810,14 +965,35 @@ locally typical sampling offers competitive performance (in both abstractive
 summarization and story generation) in terms of quality while consistently
 reducing degenerate repetitions.
                 
-## Learning Transferable Visual Models From Natural Language Supervision
+## ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
 
-- **arXiv id:** [2103.00020v1](http://arxiv.org/abs/2103.00020v1)  **Published Date:** 2021-02-26
-- **Title:** Learning Transferable Visual Models From Natural Language Supervision
-- **Authors:** Alec Radford, Jong Wook Kim, Chris Hallacy,  et al.
+- **Authors:** Keshav Santhanam, Omar Khattab, Jon Saad-Falcon,  et al.
+- **arXiv id:** [2112.01488v3](http://arxiv.org/abs/2112.01488v3)  **Published Date:** 2021-12-02
 - **LangChain:**
 
-   - **API Reference:** [langchain_experimental.open_clip](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.open_clip)
+   - **Documentation:** [docs/integrations/retrievers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/v0.2/docs/integrations/providers/dspy)
+
+**Abstract:** Neural information retrieval (IR) has greatly advanced search and other
+knowledge-intensive language tasks. While many neural IR methods encode queries
+and documents into single-vector representations, late interaction models
+produce multi-vector representations at the granularity of each token and
+decompose relevance modeling into scalable token-level computations. This
+decomposition has been shown to make late interaction more effective, but it
+inflates the space footprint of these models by an order of magnitude. In this
+work, we introduce ColBERTv2, a retriever that couples an aggressive residual
+compression mechanism with a denoised supervision strategy to simultaneously
+improve the quality and space footprint of late interaction. We evaluate
+ColBERTv2 across a wide range of benchmarks, establishing state-of-the-art
+quality within and outside the training domain while reducing the space
+footprint of late interaction models by 6--10$\times$.
+                
+## Learning Transferable Visual Models From Natural Language Supervision
+
+- **Authors:** Alec Radford, Jong Wook Kim, Chris Hallacy,  et al.
+- **arXiv id:** [2103.00020v1](http://arxiv.org/abs/2103.00020v1)  **Published Date:** 2021-02-26
+- **LangChain:**
+
+   - **API Reference:** [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
 
 **Abstract:** State-of-the-art computer vision systems are trained to predict a fixed set
 of predetermined object categories. This restricted form of supervision limits
@@ -840,14 +1016,77 @@ zero-shot without needing to use any of the 1.28 million training examples it
 was trained on. We release our code and pre-trained model weights at
 https://github.com/OpenAI/CLIP.
                 
-## CTRL: A Conditional Transformer Language Model for Controllable Generation
+## Language Models are Few-Shot Learners
 
-- **arXiv id:** [1909.05858v2](http://arxiv.org/abs/1909.05858v2)  **Published Date:** 2019-09-11
-- **Title:** CTRL: A Conditional Transformer Language Model for Controllable Generation
-- **Authors:** Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al.
+- **Authors:** Tom B. Brown, Benjamin Mann, Nick Ryder,  et al.
+- **arXiv id:** [2005.14165v4](http://arxiv.org/abs/2005.14165v4)  **Published Date:** 2020-05-28
 - **LangChain:**
 
-   - **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/langchain_community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+
+**Abstract:** Recent work has demonstrated substantial gains on many NLP tasks and
+benchmarks by pre-training on a large corpus of text followed by fine-tuning on
+a specific task. While typically task-agnostic in architecture, this method
+still requires task-specific fine-tuning datasets of thousands or tens of
+thousands of examples. By contrast, humans can generally perform a new language
+task from only a few examples or from simple instructions - something which
+current NLP systems still largely struggle to do. Here we show that scaling up
+language models greatly improves task-agnostic, few-shot performance, sometimes
+even reaching competitiveness with prior state-of-the-art fine-tuning
+approaches. Specifically, we train GPT-3, an autoregressive language model with
+175 billion parameters, 10x more than any previous non-sparse language model,
+and test its performance in the few-shot setting. For all tasks, GPT-3 is
+applied without any gradient updates or fine-tuning, with tasks and few-shot
+demonstrations specified purely via text interaction with the model. GPT-3
+achieves strong performance on many NLP datasets, including translation,
+question-answering, and cloze tasks, as well as several tasks that require
+on-the-fly reasoning or domain adaptation, such as unscrambling words, using a
+novel word in a sentence, or performing 3-digit arithmetic. At the same time,
+we also identify some datasets where GPT-3's few-shot learning still struggles,
+as well as some datasets where GPT-3 faces methodological issues related to
+training on large web corpora. Finally, we find that GPT-3 can generate samples
+of news articles which human evaluators have difficulty distinguishing from
+articles written by humans. We discuss broader societal impacts of this finding
+and of GPT-3 in general.
+                
+## Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
+
+- **Authors:** Patrick Lewis, Ethan Perez, Aleksandra Piktus,  et al.
+- **arXiv id:** [2005.11401v4](http://arxiv.org/abs/2005.11401v4)  **Published Date:** 2020-05-22
+- **LangChain:**
+
+   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+
+**Abstract:** Large pre-trained language models have been shown to store factual knowledge
+in their parameters, and achieve state-of-the-art results when fine-tuned on
+downstream NLP tasks. However, their ability to access and precisely manipulate
+knowledge is still limited, and hence on knowledge-intensive tasks, their
+performance lags behind task-specific architectures. Additionally, providing
+provenance for their decisions and updating their world knowledge remain open
+research problems. Pre-trained models with a differentiable access mechanism to
+explicit non-parametric memory can overcome this issue, but have so far been
+only investigated for extractive downstream tasks. We explore a general-purpose
+fine-tuning recipe for retrieval-augmented generation (RAG) -- models which
+combine pre-trained parametric and non-parametric memory for language
+generation. We introduce RAG models where the parametric memory is a
+pre-trained seq2seq model and the non-parametric memory is a dense vector index
+of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG
+formulations, one which conditions on the same retrieved passages across the
+whole generated sequence, the other can use different passages per token. We
+fine-tune and evaluate our models on a wide range of knowledge-intensive NLP
+tasks and set the state-of-the-art on three open domain QA tasks, outperforming
+parametric seq2seq models and task-specific retrieve-and-extract architectures.
+For language generation tasks, we find that RAG models generate more specific,
+diverse and factual language than a state-of-the-art parametric-only seq2seq
+baseline.
+                
+## CTRL: A Conditional Transformer Language Model for Controllable Generation
+
+- **Authors:** Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al.
+- **arXiv id:** [1909.05858v2](http://arxiv.org/abs/1909.05858v2)  **Published Date:** 2019-09-11
+- **LangChain:**
+
+   - **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
 
 **Abstract:** Large-scale language models show promising text generation capabilities, but
 users cannot easily control particular aspects of the generated text. We

docs/docs/concepts.mdx

@@ -15,11 +15,6 @@ The interfaces for core components like LLMs, vector stores, retrievers and more
 No third party integrations are defined here.
 The dependencies are kept purposefully very lightweight.
 
-### Partner packages
-
-While the long tail of integrations are in `langchain-community`, we split popular integrations into their own packages (e.g. `langchain-openai`, `langchain-anthropic`, etc).
-This was done in order to improve support for these important integrations.
-
 ### `langchain`
 
 The main `langchain` package contains chains, agents, and retrieval strategies that make up an application's cognitive architecture.
@@ -33,6 +28,11 @@ Key partner packages are separated out (see below).
 This contains all integrations for various components (LLMs, vector stores, retrievers).
 All dependencies in this package are optional to keep the package as lightweight as possible.
 
+### Partner packages
+
+While the long tail of integrations is in `langchain-community`, we split popular integrations into their own packages (e.g. `langchain-openai`, `langchain-anthropic`, etc).
+This was done in order to improve support for these important integrations.
+
 ### [`langgraph`](https://langchain-ai.github.io/langgraph)
 
 `langgraph` is an extension of `langchain` aimed at
@@ -61,28 +61,28 @@ A developer platform that lets you debug, test, evaluate, and monitor LLM applic
 ## LangChain Expression Language (LCEL)
 <span data-heading-keywords="lcel"></span>
 
-LangChain Expression Language, or LCEL, is a declarative way to chain LangChain components.
+`LangChain Expression Language`, or `LCEL`, is a declarative way to chain LangChain components.
 LCEL was designed from day 1 to **support putting prototypes in production, with no code changes**, from the simplest “prompt + LLM” chain to the most complex chains (we’ve seen folks successfully run LCEL chains with 100s of steps in production). To highlight a few of the reasons you might want to use LCEL:
 
-**First-class streaming support**
+- **First-class streaming support:**
 When you build your chains with LCEL you get the best possible time-to-first-token (time elapsed until the first chunk of output comes out). For some chains this means eg. we stream tokens straight from an LLM to a streaming output parser, and you get back parsed, incremental chunks of output at the same rate as the LLM provider outputs the raw tokens.
 
-**Async support**
+- **Async support:**
 Any chain built with LCEL can be called both with the synchronous API (eg. in your Jupyter notebook while prototyping) as well as with the asynchronous API (eg. in a [LangServe](/docs/langserve/) server). This enables using the same code for prototypes and in production, with great performance, and the ability to handle many concurrent requests in the same server.
 
-**Optimized parallel execution**
+- **Optimized parallel execution:**
 Whenever your LCEL chains have steps that can be executed in parallel (eg if you fetch documents from multiple retrievers) we automatically do it, both in the sync and the async interfaces, for the smallest possible latency.
 
-**Retries and fallbacks**
+- **Retries and fallbacks:**
 Configure retries and fallbacks for any part of your LCEL chain. This is a great way to make your chains more reliable at scale. We’re currently working on adding streaming support for retries/fallbacks, so you can get the added reliability without any latency cost.
 
-**Access intermediate results**
+- **Access intermediate results:**
 For more complex chains it’s often very useful to access the results of intermediate steps even before the final output is produced. This can be used to let end-users know something is happening, or even just to debug your chain. You can stream intermediate results, and it’s available on every [LangServe](/docs/langserve) server.
 
-**Input and output schemas**
+- **Input and output schemas**
 Input and output schemas give every LCEL chain Pydantic and JSONSchema schemas inferred from the structure of your chain. This can be used for validation of inputs and outputs, and is an integral part of LangServe.
 
-[**Seamless LangSmith tracing**](https://docs.smith.langchain.com)
+- [**Seamless LangSmith tracing**](https://docs.smith.langchain.com)
 As your chains get more and more complex, it becomes increasingly important to understand what exactly is happening at every step.
 With LCEL, **all** steps are automatically logged to [LangSmith](https://docs.smith.langchain.com/) for maximum observability and debuggability.
 
@@ -186,7 +186,7 @@ For a full list of LangChain model providers with multimodal models, [check out
 <span data-heading-keywords="llm,llms"></span>
 
 :::caution
-Pure text-in/text-out LLMs tend to be older or lower-level. Many popular models are best used as [chat completion models](/docs/concepts/#chat-models),
+Pure text-in/text-out LLMs tend to be older or lower-level. Many new popular models are best used as [chat completion models](/docs/concepts/#chat-models),
 even for non-chat use cases.
 
 You are probably looking for [the section above instead](/docs/concepts/#chat-models).
@@ -201,7 +201,7 @@ When messages are passed in as input, they will be formatted into a string under
 
 LangChain does not host any LLMs, rather we rely on third party integrations.
 
-For specifics on how to use LLMs, see the [relevant how-to guides here](/docs/how_to/#llms).
+For specifics on how to use LLMs, see the [how-to guides](/docs/how_to/#llms).
 
 ### Messages
 
@@ -215,7 +215,7 @@ LangChain has different message classes for different roles.
 The `content` property describes the content of the message.
 This can be a few different things:
 
-- A string (most models deal this type of content)
+- A string (most models deal with this type of content)
 - A List of dictionaries (this is used for multimodal input, where the dictionary contains information about that input type and that input location)
 
 Optionally, messages can have a `name` property which allows for differentiating between multiple speakers with the same role.
@@ -365,24 +365,18 @@ See documentation for that [here](/docs/concepts/#function-tool-calling).
 
 :::
 
-Responsible for taking the output of a model and transforming it to a more suitable format for downstream tasks.
+`Output parser` is responsible for taking the output of a model and transforming it to a more suitable format for downstream tasks.
 Useful when you are using LLMs to generate structured data, or to normalize output from chat models and LLMs.
 
 LangChain has lots of different types of output parsers. This is a list of output parsers LangChain supports. The table below has various pieces of information:
 
-**Name**: The name of the output parser
-
-**Supports Streaming**: Whether the output parser supports streaming.
-
-**Has Format Instructions**: Whether the output parser has format instructions. This is generally available except when (a) the desired schema is not specified in the prompt but rather in other parameters (like OpenAI function calling), or (b) when the OutputParser wraps another OutputParser.
-
-**Calls LLM**: Whether this output parser itself calls an LLM. This is usually only done by output parsers that attempt to correct misformatted output.
-
-**Input Type**: Expected input type. Most output parsers work on both strings and messages, but some (like OpenAI Functions) need a message with specific kwargs.
-
-**Output Type**: The output type of the object returned by the parser.
-
-**Description**: Our commentary on this output parser and when to use it.
+- **Name**: The name of the output parser
+- **Supports Streaming**: Whether the output parser supports streaming.
+- **Has Format Instructions**: Whether the output parser has format instructions. This is generally available except when (a) the desired schema is not specified in the prompt but rather in other parameters (like OpenAI function calling), or (b) when the OutputParser wraps another OutputParser.
+- **Calls LLM**: Whether this output parser itself calls an LLM. This is usually only done by output parsers that attempt to correct misformatted output.
+- **Input Type**: Expected input type. Most output parsers work on both strings and messages, but some (like OpenAI Functions) need a message with specific kwargs.
+- **Output Type**: The output type of the object returned by the parser.
+- **Description**: Our commentary on this output parser and when to use it.
 
 | Name            | Supports Streaming | Has Format Instructions       | Calls LLM | Input Type                       | Output Type          | Description                                                                                                                                                                                                                                              |
 |-----------------|--------------------|-------------------------------|-----------|----------------------------------|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
@@ -534,10 +528,10 @@ Tools are needed whenever you want a model to control parts of your code or call
 
 A tool consists of:
 
-1. The name of the tool.
-2. A description of what the tool does.
-3. A JSON schema defining the inputs to the tool.
-4. A function (and, optionally, an async variant of the function).
+1. The `name` of the tool.
+2. A `description` of what the tool does.
+3. A `JSON schema` defining the inputs to the tool.
+4. A `function` (and, optionally, an async variant of the function).
 
 When a tool is bound to a model, the name, description and JSON schema are provided as context to the model.
 Given a list of tools and a set of instructions, a model can request to call one or more tools with specific inputs.
@@ -650,14 +644,14 @@ The results of those actions can then be fed back into the agent and it determin
 [LangGraph](https://github.com/langchain-ai/langgraph) is an extension of LangChain specifically aimed at creating highly controllable and customizable agents.
 Please check out that documentation for a more in depth overview of agent concepts.
 
-There is a legacy agent concept in LangChain that we are moving towards deprecating: `AgentExecutor`.
+There is a legacy `agent` concept in LangChain that we are moving towards deprecating: `AgentExecutor`.
 AgentExecutor was essentially a runtime for agents.
 It was a great place to get started, however, it was not flexible enough as you started to have more customized agents.
 In order to solve that we built LangGraph to be this flexible, highly-controllable runtime.
 
 If you are still using AgentExecutor, do not fear: we still have a guide on [how to use AgentExecutor](/docs/how_to/agent_executor).
 It is recommended, however, that you start to transition to LangGraph.
-In order to assist in this we have put together a [transition guide on how to do so](/docs/how_to/migrate_agent).
+In order to assist in this, we have put together a [transition guide on how to do so](/docs/how_to/migrate_agent).
 
 #### ReAct agents
 <span data-heading-keywords="react,react agent"></span>
@@ -743,7 +737,7 @@ callbacks to any child objects.
 :::important Async in Python<=3.10
 
 Any `RunnableLambda`, a `RunnableGenerator`, or `Tool` that invokes other runnables
-and is running async in python<=3.10, will have to propagate callbacks to child
+and is running `async` in python<=3.10, will have to propagate callbacks to child
 objects manually. This is because LangChain cannot automatically propagate
 callbacks to child objects in this case.
 
@@ -873,7 +867,7 @@ Furthermore, using tokens can also improve efficiency, since the model processes
 ### Function/tool calling
 
 :::info
-We use the term tool calling interchangeably with function calling. Although
+We use the term `tool calling` interchangeably with `function calling`. Although
 function calling is sometimes meant to refer to invocations of a single function,
 we treat all models as though they can return multiple tool or function calls in
 each message.
@@ -951,7 +945,7 @@ Here's an example:
 ```python
 from typing import Optional
 
-from langchain_core.pydantic_v1 import BaseModel, Field
+from pydantic import BaseModel, Field
 
 
 class Joke(BaseModel):
@@ -968,7 +962,6 @@ structured_llm.invoke("Tell me a joke about cats")
 
 ```
 Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=None)
-
 ```
 
 We recommend this method as a starting point when working with structured output:
@@ -1069,7 +1062,7 @@ a `tool_calls` field containing `args` that match the desired shape.
 There are several acceptable formats you can use to bind tools to a model in LangChain. Here's one example:
 
 ```python
-from langchain_core.pydantic_v1 import BaseModel, Field
+from pydantic import BaseModel, Field
 from langchain_openai import ChatOpenAI
 
 class ResponseFormatter(BaseModel):
@@ -1107,7 +1100,11 @@ For a full list of model providers that support tool calling, [see this table](/
 
 ### Few-shot prompting
 
-One of the most effective ways to improve model performance is to give a model examples of what you want it to do. The technique of adding example inputs and expected outputs to a model prompt is known as "few-shot prompting". There are a few things to think about when doing few-shot prompting:
+One of the most effective ways to improve model performance is to give a model examples of 
+what you want it to do. The technique of adding example inputs and expected outputs 
+to a model prompt is known as "few-shot prompting". The technique is based on the
+[Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) paper.
+There are a few things to think about when doing few-shot prompting:
 
 1. How are examples generated?
 2. How many examples are in each prompt?
@@ -1182,8 +1179,10 @@ You can see a case study of how Anthropic and OpenAI respond to different few-sh
 
 ### Retrieval
 
-LLMs are trained on a large but fixed dataset, limiting their ability to reason over private or recent information. Fine-tuning an LLM with specific facts is one way to mitigate this, but is often [poorly suited for factual recall](https://www.anyscale.com/blog/fine-tuning-is-for-form-not-facts) and [can be costly](https://www.glean.com/blog/how-to-build-an-ai-assistant-for-the-enterprise). 
-Retrieval is the process of providing relevant information to an LLM to improve its response for a given input. Retrieval augmented generation (RAG) is the process of grounding the LLM generation (output) using the retrieved information.
+LLMs are trained on a large but fixed dataset, limiting their ability to reason over private or recent information. 
+Fine-tuning an LLM with specific facts is one way to mitigate this, but is often [poorly suited for factual recall](https://www.anyscale.com/blog/fine-tuning-is-for-form-not-facts) and [can be costly](https://www.glean.com/blog/how-to-build-an-ai-assistant-for-the-enterprise). 
+`Retrieval` is the process of providing relevant information to an LLM to improve its response for a given input. 
+`Retrieval augmented generation` (`RAG`) [paper](https://arxiv.org/abs/2005.11401) is the process of grounding the LLM generation (output) using the retrieved information.
 
 :::tip
 
@@ -1203,12 +1202,12 @@ First, consider the user input(s) to your RAG system. Ideally, a RAG system can
 **Using an LLM to review and optionally modify the input is the central idea behind query translation.** This serves as a general buffer, optimizing raw user inputs for your retrieval system. 
 For example, this can be as simple as extracting keywords or as complex as generating multiple sub-questions for a complex query.
 
-| Name          | When to use | Description |
-|---------------|-------------|-------------|
+| Name          | When to use | Description                                                                                                                                                                                                                                                                            |
+|---------------|-------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | [Multi-query](/docs/how_to/MultiQueryRetriever/)   | When you need to cover multiple perspectives of a question. | Rewrite the user question from multiple perspectives, retrieve documents for each rewritten question, return the unique documents for all queries. |
-| [Decomposition](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb) | When a question can be broken down into smaller subproblems. | Decompose a question into a set of subproblems / questions, which can either be solved sequentially (use the answer from first + retrieval to answer the second) or in parallel (consolidate each answer into final answer). |
-| [Step-back](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb)     | When a higher-level conceptual understanding is required. | First prompt the LLM to ask a generic step-back question about higher-level concepts or principles, and retrieve relevant facts about them. Use this grounding to help answer the user question. |
-| [HyDE](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb)          | If you have challenges retrieving relevant documents using the raw user inputs. | Use an LLM to convert questions into hypothetical documents that answer the question. Use the embedded hypothetical documents to retrieve real documents with the premise that doc-doc similarity search can produce more relevant matches. |
+| [Decomposition](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb) | When a question can be broken down into smaller subproblems. | Decompose a question into a set of subproblems / questions, which can either be solved sequentially (use the answer from first + retrieval to answer the second) or in parallel (consolidate each answer into final answer).                                                           |
+| [Step-back](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb)     | When a higher-level conceptual understanding is required. | First prompt the LLM to ask a generic step-back question about higher-level concepts or principles, and retrieve relevant facts about them. Use this grounding to help answer the user question. [Paper](https://arxiv.org/pdf/2310.06117).                                            |
+| [HyDE](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb)          | If you have challenges retrieving relevant documents using the raw user inputs. | Use an LLM to convert questions into hypothetical documents that answer the question. Use the embedded hypothetical documents to retrieve real documents with the premise that doc-doc similarity search can produce more relevant matches. [Paper](https://arxiv.org/abs/2212.10496). |
 
 :::tip
 
@@ -1282,11 +1281,11 @@ Fifth, consider ways to improve the quality of your similarity search itself. Em
 
 There are some additional tricks to improve the quality of your retrieval. Embeddings excel at capturing semantic information, but may struggle with keyword-based queries. Many [vector stores](/docs/integrations/retrievers/pinecone_hybrid_search/) offer built-in [hybrid-search](https://docs.pinecone.io/guides/data/understanding-hybrid-search) to combine keyword and semantic similarity, which marries the benefits of both approaches. Furthermore, many vector stores have [maximal marginal relevance](https://python.langchain.com/v0.1/docs/modules/model_io/prompts/example_selectors/mmr/), which attempts to diversify the results of a search to avoid returning similar and redundant documents. 
 
-| Name              | When to use                                              | Description |
-|-------------------|----------------------------------------------------------|-------------|
-| [ColBERT](/docs/integrations/providers/ragatouille/#using-colbert-as-a-reranker)           | When higher granularity embeddings are needed.           | ColBERT uses contextually influenced embeddings for each token in the document and query to get a granular query-document similarity score. |
-| [Hybrid search](/docs/integrations/retrievers/pinecone_hybrid_search/)     | When combining keyword-based and semantic similarity.    | Hybrid search combines keyword and semantic similarity, marrying the benefits of both approaches. |
-| [Maximal Marginal Relevance (MMR)](/docs/integrations/vectorstores/pinecone/#maximal-marginal-relevance-searches) | When needing to diversify search results. | MMR attempts to diversify the results of a search to avoid returning similar and redundant documents. |
+| Name              | When to use                                              | Description                                                                                                                                                                            |
+|-------------------|----------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [ColBERT](/docs/integrations/providers/ragatouille/#using-colbert-as-a-reranker)           | When higher granularity embeddings are needed.           | ColBERT uses contextually influenced embeddings for each token in the document and query to get a granular query-document similarity score. [Paper](https://arxiv.org/abs/2112.01488). |
+| [Hybrid search](/docs/integrations/retrievers/pinecone_hybrid_search/)     | When combining keyword-based and semantic similarity.    | Hybrid search combines keyword and semantic similarity, marrying the benefits of both approaches. [Paper](https://arxiv.org/abs/2210.11934).                                                                               |
+| [Maximal Marginal Relevance (MMR)](/docs/integrations/vectorstores/pinecone/#maximal-marginal-relevance-searches) | When needing to diversify search results. | MMR attempts to diversify the results of a search to avoid returning similar and redundant documents.                                                                                  |
 
 :::tip
 
@@ -1306,7 +1305,7 @@ Sixth, consider ways to filter or rank retrieved documents. This is very useful
 
 :::tip
 
-See our RAG from Scratch video on [RAG-Fusion](https://youtu.be/77qELPbNgxA?feature=shared), on approach for post-processing across multiple queries:  Rewrite the user question from multiple perspectives, retrieve documents for each rewritten question, and combine the ranks of multiple search result lists to produce a single, unified ranking with [Reciprocal Rank Fusion (RRF)](https://towardsdatascience.com/forget-rag-the-future-is-rag-fusion-1147298d8ad1).
+See our RAG from Scratch video on [RAG-Fusion](https://youtu.be/77qELPbNgxA?feature=shared) ([paper](https://arxiv.org/abs/2402.03367)), on approach for post-processing across multiple queries:  Rewrite the user question from multiple perspectives, retrieve documents for each rewritten question, and combine the ranks of multiple search result lists to produce a single, unified ranking with [Reciprocal Rank Fusion (RRF)](https://towardsdatascience.com/forget-rag-the-future-is-rag-fusion-1147298d8ad1).
 
 :::
 

docs/docs/contributing/faq.mdx

@@ -24,3 +24,16 @@ for more information.
 Notably, Github doesn't allow this setting to be enabled for forks in **organizations** ([issue](https://github.com/orgs/community/discussions/5634)).
 If you are working in an organization, we recommend submitting your PR from a personal
 fork in order to enable this setting.
+
+### Why hasn't my PR been reviewed?
+
+Please reference our [Review Process](/docs/contributing/review_process/).
+
+### Why was my PR closed?
+
+Please reference our [Review Process](/docs/contributing/review_process/).
+
+### I think my PR was closed in a way that didn't follow the review process. What should I do?
+
+Tag `@efriis` in the PR comments referencing the portion of the review
+process that you believe was not followed. We'll take a look!

docs/docs/contributing/review_process.mdx

@@ -0,0 +1,95 @@
+# Review Process
+
+## Overview
+
+This document outlines the process used by the LangChain maintainers for reviewing pull requests (PRs). The primary objective of this process is to enhance the LangChain developer experience.
+
+## Review Statuses
+
+We categorize PRs using three main statuses, which are marked as project item statuses in the right sidebar and can be viewed in detail [here](https://github.com/orgs/langchain-ai/projects/12/views/1).
+
+- **Triage**: 
+  - Initial status for all newly submitted PRs.
+  - Requires a maintainer to categorize it into one of the other statuses.
+
+- **Needs Support**:
+  - PRs that require community feedback or additional input before moving forward.
+  - Automatically promoted to the backlog if it receives 5 upvotes.
+  - An auto-comment is generated when this status is applied, explaining the flow and the upvote requirement.
+  - If the PR remains in this status for 25 days, it will be marked as “stale” via auto-comment.
+  - PRs will be auto-closed after 30 days if no further action is taken.
+
+- **In Review**:
+  - PRs that are actively under review by our team.
+  - These are regularly reviewed and monitored.
+
+**Note:** A PR may only have one status at a time.
+
+**Note:** You may notice 3 additional statuses of Done, Closed, and Internal that
+are external to this lifecycle. Done and Closed PRs have been merged or closed,
+respectively. Internal is for PRs submitted by core maintainers, and these PRs are owned
+by the submitter.
+
+## Review Guidelines
+
+1. **PRs that touch /libs/core**:
+   - PRs that directly impact core code and are likely to affect end users.
+   - **Triage Guideline**: most PRs should either go straight to `In Review` or closed.
+   - These PRs are given top priority and are reviewed the fastest.
+   - PRs that don't have a **concise** descriptions of their motivation (either in PR summary of in a linked issue) are likely to be closed without an in-depth review. Please do not generate verbose PR descriptions with an LLM.
+   - PRs that don't have unit tests are likely to be closed.
+   - Feature requests should first be opened as a GitHub issue and discussed with the LangChain maintainers. Large PRs submitted without prior discussion are likely to be closed.
+
+2. **PRs that touch /libs/langchain**:
+   - High-impact PRs that are closely related to core PRs but slightly lower in priority.
+   - **Triage Guideline**: most PRs should either go straight to `In Review` or closed.
+   - These are reviewed and closed aggressively, similar to core PRs.
+   - New feature requests should be discussed with the core maintainer team beforehand in an issue.
+
+3. **PRs that touch /libs/partners/****:
+   - PRs involving integration packages.
+   - **Triage Guideline**: most PRs should either go straight to `In Review` or closed.
+   - The review may be conducted by our team or handed off to the partner's development team, depending on the PR's content.
+   - We maintain communication lines with most partner dev teams to facilitate this process.
+
+4. **Community PRs**:
+   - Most community PRs will get an initial status of "needs support".
+   - **Triage Guideline**: most PRs should go to `Needs support`. Bugfixes on high-traffic integrations should go straight to `In review`.
+   - **Triage Guideline**: all new features and integrations should go to `Needs support` and will be closed if they do not get enough support (measured by upvotes or comments).
+   - PRs in the `Needs Support` status for 20 days are marked as “stale” and will be closed after 30 days if no action is taken.
+
+5. **Documentation PRs**:
+   - PRs that touch the documentation content in docs/docs.
+   - **Triage Guideline**:
+      - PRs that fix typos or small errors in a single file and pass CI should go straight to `In Review`.
+      - PRs that make changes that have been discussed and agreed upon in an issue should go straight to `In Review`.
+      - PRs that add new pages or change the structure of the documentation should go to `Needs Support`.
+   - We strive to standardize documentation formats to streamline the review process.
+   - CI jobs run against documentation to ensure adherence to standards, automating much of the review.
+
+6. **PRs must be in English**:
+   - PRs that are not in English will be closed without review.
+   - This is to ensure that all maintainers can review the PRs effectively.
+
+## How to see a PR's status
+
+See screenshot:
+
+![PR Status](/img/review_process_status.png)
+
+*To see the status of all open PRs, please visit the [LangChain Project Board](https://github.com/orgs/langchain-ai/projects/12/views/2).*
+
+## Review Prioritization
+
+Our goal is to provide the best possible development experience by focusing on making software that:
+
+- Works: Works as intended (is bug-free).
+- Is useful: Improves LLM app development with components that work off-the-shelf and runtimes that simplify app building.
+- Is easy: Is intuitive to use and well-documented.
+
+We believe this process reflects our priorities and are open to feedback if you feel it does not.
+
+## Github Discussion
+
+We welcome your feedback on this process. Please feel free to add a comment in 
+[this GitHub Discussion](https://github.com/langchain-ai/langchain/discussions/25920).

docs/docs/how_to/MultiQueryRetriever.ipynb

@@ -18,8 +18,23 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "994d6c74",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:08:00.190093Z",
+     "iopub.status.busy": "2024-09-10T20:08:00.189665Z",
+     "iopub.status.idle": "2024-09-10T20:08:05.438015Z",
+     "shell.execute_reply": "2024-09-10T20:08:05.437685Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
+     ]
+    }
+   ],
    "source": [
     "# Build a sample vectorDB\n",
     "from langchain_chroma import Chroma\n",
@@ -54,7 +69,14 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "edbca101",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:08:05.439930Z",
+     "iopub.status.busy": "2024-09-10T20:08:05.439810Z",
+     "iopub.status.idle": "2024-09-10T20:08:05.553766Z",
+     "shell.execute_reply": "2024-09-10T20:08:05.553520Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain.retrievers.multi_query import MultiQueryRetriever\n",
@@ -71,7 +93,14 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "9e6d3b69",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:08:05.555359Z",
+     "iopub.status.busy": "2024-09-10T20:08:05.555262Z",
+     "iopub.status.idle": "2024-09-10T20:08:05.557046Z",
+     "shell.execute_reply": "2024-09-10T20:08:05.556825Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Set logging for the queries\n",
@@ -85,13 +114,20 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "bc93dc2b-9407-48b0-9f9a-338247e7eb69",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:08:05.558176Z",
+     "iopub.status.busy": "2024-09-10T20:08:05.558100Z",
+     "iopub.status.idle": "2024-09-10T20:08:07.250342Z",
+     "shell.execute_reply": "2024-09-10T20:08:07.249711Z"
+    }
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. How can Task Decomposition be achieved through different methods?', '2. What strategies are commonly used for Task Decomposition?', '3. What are the various techniques for breaking down tasks in Task Decomposition?']\n"
+      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. How can Task Decomposition be achieved through different methods?', '2. What strategies are commonly used for Task Decomposition?', '3. What are the various ways to break down tasks in Task Decomposition?']\n"
      ]
     },
     {
@@ -137,14 +173,21 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "d9afb0ca",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:08:07.253875Z",
+     "iopub.status.busy": "2024-09-10T20:08:07.253600Z",
+     "iopub.status.idle": "2024-09-10T20:08:07.277848Z",
+     "shell.execute_reply": "2024-09-10T20:08:07.277487Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import List\n",
     "\n",
     "from langchain_core.output_parsers import BaseOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "# Output parser will split the LLM result into a list of queries\n",
@@ -180,13 +223,20 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "59c75c56-dbd7-4887-b9ba-0b5b21069f51",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:08:07.280001Z",
+     "iopub.status.busy": "2024-09-10T20:08:07.279861Z",
+     "iopub.status.idle": "2024-09-10T20:08:09.579525Z",
+     "shell.execute_reply": "2024-09-10T20:08:09.578837Z"
+    }
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. Can you provide insights on regression from the course material?', '2. How is regression discussed in the course content?', '3. What information does the course offer about regression?', '4. In what way is regression covered in the course?', '5. What are the teachings of the course regarding regression?']\n"
+      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. Can you provide insights on regression from the course material?', '2. How is regression discussed in the course content?', '3. What information does the course offer regarding regression?', '4. In what way is regression covered in the course?', \"5. What are the course's teachings on regression?\"]\n"
      ]
     },
     {
@@ -228,7 +278,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/agent_executor.ipynb

@@ -461,7 +461,7 @@
    "id": "f8014c9d",
    "metadata": {},
    "source": [
-    "Now, we can initalize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/#agents).\n",
+    "Now, we can initialize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/#agents).\n",
     "\n",
     "Note that we are passing in the `model`, not `model_with_tools`. That is because `create_tool_calling_agent` will call `.bind_tools` for us under the hood."
    ]

docs/docs/how_to/assign.ipynb

@@ -45,7 +45,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/binding.ipynb

@@ -49,7 +49,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {
@@ -183,7 +184,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_z0OU2CytqENVrRTI6T8DkI3u', 'function': {'arguments': '{\"location\": \"San Francisco, CA\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}, {'id': 'call_ft96IJBh0cMKkQWrZjNg4bsw', 'function': {'arguments': '{\"location\": \"New York, NY\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}, {'id': 'call_tfbtGgCLmuBuWgZLvpPwvUMH', 'function': {'arguments': '{\"location\": \"Los Angeles, CA\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 84, 'prompt_tokens': 85, 'total_tokens': 169}, 'model_name': 'gpt-3.5-turbo-1106', 'system_fingerprint': 'fp_77a673219d', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d57ad5fa-b52a-4822-bc3e-74f838697e18-0', tool_calls=[{'name': 'get_current_weather', 'args': {'location': 'San Francisco, CA', 'unit': 'celsius'}, 'id': 'call_z0OU2CytqENVrRTI6T8DkI3u'}, {'name': 'get_current_weather', 'args': {'location': 'New York, NY', 'unit': 'celsius'}, 'id': 'call_ft96IJBh0cMKkQWrZjNg4bsw'}, {'name': 'get_current_weather', 'args': {'location': 'Los Angeles, CA', 'unit': 'celsius'}, 'id': 'call_tfbtGgCLmuBuWgZLvpPwvUMH'}])"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_z0OU2CytqENVrRTI6T8DkI3u', 'function': {'arguments': '{\"location\": \"San Francisco, CA\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}, {'id': 'call_ft96IJBh0cMKkQWrZjNg4bsw', 'function': {'arguments': '{\"location\": \"New York, NY\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}, {'id': 'call_tfbtGgCLmuBuWgZLvpPwvUMH', 'function': {'arguments': '{\"location\": \"Los Angeles, CA\", \"unit\": \"celsius\"}', 'name': 'get_current_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 84, 'prompt_tokens': 85, 'total_tokens': 169}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_77a673219d', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d57ad5fa-b52a-4822-bc3e-74f838697e18-0', tool_calls=[{'name': 'get_current_weather', 'args': {'location': 'San Francisco, CA', 'unit': 'celsius'}, 'id': 'call_z0OU2CytqENVrRTI6T8DkI3u'}, {'name': 'get_current_weather', 'args': {'location': 'New York, NY', 'unit': 'celsius'}, 'id': 'call_ft96IJBh0cMKkQWrZjNg4bsw'}, {'name': 'get_current_weather', 'args': {'location': 'Los Angeles, CA', 'unit': 'celsius'}, 'id': 'call_tfbtGgCLmuBuWgZLvpPwvUMH'}])"
       ]
      },
      "execution_count": 5,
@@ -192,7 +193,7 @@
     }
    ],
    "source": [
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo-1106\").bind(tools=tools)\n",
+    "model = ChatOpenAI(model=\"gpt-4o-mini\").bind(tools=tools)\n",
     "model.invoke(\"What's the weather in SF, NYC and LA?\")"
    ]
   },

docs/docs/how_to/chat_model_caching.ipynb

@@ -50,7 +50,8 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
     "llm = ChatOpenAI()"
    ]

docs/docs/how_to/chat_models_universal_init.ipynb

@@ -26,10 +26,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "165b0de6-9ae3-4e3d-aa98-4fc8a97c4a06",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:22:32.858670Z",
+     "iopub.status.busy": "2024-09-10T20:22:32.858278Z",
+     "iopub.status.idle": "2024-09-10T20:22:33.009452Z",
+     "shell.execute_reply": "2024-09-10T20:22:33.007022Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zsh:1: 0.2.8 not found\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install -qU langchain>=0.2.8 langchain-openai langchain-anthropic langchain-google-vertexai"
    ]
@@ -44,19 +66,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "79e14913-803c-4382-9009-5c6af3d75d35",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:22:33.015729Z",
+     "iopub.status.busy": "2024-09-10T20:22:33.015241Z",
+     "iopub.status.idle": "2024-09-10T20:22:39.391716Z",
+     "shell.execute_reply": "2024-09-10T20:22:39.390438Z"
+    }
+   },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/4j/2rz3865x6qg07tx43146py8h0000gn/T/ipykernel_95293/571506279.py:4: LangChainBetaWarning: The function `init_chat_model` is in beta. It is actively being worked on, so the API may change.\n",
+      "  gpt_4o = init_chat_model(\"gpt-4o\", model_provider=\"openai\", temperature=0)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPT-4o: I'm an AI created by OpenAI, and I don't have a personal name. How can I assist you today?\n",
+      "\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "GPT-4o: I'm an AI created by OpenAI, and I don't have a personal name. You can call me Assistant! How can I help you today?\n",
-      "\n",
       "Claude Opus: My name is Claude. It's nice to meet you!\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gemini 1.5: I am a large language model, trained by Google. \n",
       "\n",
-      "Gemini 1.5: I am a large language model, trained by Google. I do not have a name. \n",
+      "I don't have a name like a person does. You can call me Bard if you like! 😊 \n",
       "\n",
       "\n"
      ]
@@ -94,9 +145,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "0378ccc6-95bc-4d50-be50-fccc193f0a71",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:22:39.396908Z",
+     "iopub.status.busy": "2024-09-10T20:22:39.396563Z",
+     "iopub.status.idle": "2024-09-10T20:22:39.444959Z",
+     "shell.execute_reply": "2024-09-10T20:22:39.444646Z"
+    }
+   },
    "outputs": [],
    "source": [
     "gpt_4o = init_chat_model(\"gpt-4o\", temperature=0)\n",
@@ -116,17 +174,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "6c037f27-12d7-4e83-811e-4245c0e3ba58",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:22:39.446901Z",
+     "iopub.status.busy": "2024-09-10T20:22:39.446773Z",
+     "iopub.status.idle": "2024-09-10T20:22:40.301906Z",
+     "shell.execute_reply": "2024-09-10T20:22:40.300918Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any other name you prefer! How can I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 37, 'prompt_tokens': 11, 'total_tokens': 48}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d576307f90', 'finish_reason': 'stop', 'logprobs': None}, id='run-5428ab5c-b5c0-46de-9946-5d4ca40dbdc8-0', usage_metadata={'input_tokens': 11, 'output_tokens': 37, 'total_tokens': 48})"
+       "AIMessage(content=\"I'm an AI created by OpenAI, and I don't have a personal name. How can I assist you today?\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 11, 'total_tokens': 34}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_25624ae3a5', 'finish_reason': 'stop', 'logprobs': None}, id='run-b41df187-4627-490d-af3c-1c96282d3eb0-0', usage_metadata={'input_tokens': 11, 'output_tokens': 23, 'total_tokens': 34})"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -141,17 +206,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "321e3036-abd2-4e1f-bcc6-606efd036954",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:22:40.316030Z",
+     "iopub.status.busy": "2024-09-10T20:22:40.315628Z",
+     "iopub.status.idle": "2024-09-10T20:22:41.199134Z",
+     "shell.execute_reply": "2024-09-10T20:22:41.198173Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"My name is Claude. It's nice to meet you!\", response_metadata={'id': 'msg_012XvotUJ3kGLXJUWKBVxJUi', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-1ad1eefe-f1c6-4244-8bc6-90e2cb7ee554-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})"
+       "AIMessage(content=\"My name is Claude. It's nice to meet you!\", additional_kwargs={}, response_metadata={'id': 'msg_01Fx9P74A7syoFkwE73CdMMY', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-a0fd2bbd-3b7e-46bf-8d69-a48c7e60b03c-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -174,17 +246,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "814a2289-d0db-401e-b555-d5116112b413",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:22:41.203346Z",
+     "iopub.status.busy": "2024-09-10T20:22:41.203004Z",
+     "iopub.status.idle": "2024-09-10T20:22:41.891450Z",
+     "shell.execute_reply": "2024-09-10T20:22:41.890539Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any other name you prefer! How can I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 37, 'prompt_tokens': 11, 'total_tokens': 48}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_ce0793330f', 'finish_reason': 'stop', 'logprobs': None}, id='run-3923e328-7715-4cd6-b215-98e4b6bf7c9d-0', usage_metadata={'input_tokens': 11, 'output_tokens': 37, 'total_tokens': 48})"
+       "AIMessage(content=\"I'm an AI created by OpenAI, and I don't have a personal name. How can I assist you today?\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 11, 'total_tokens': 34}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_25624ae3a5', 'finish_reason': 'stop', 'logprobs': None}, id='run-3380f977-4b89-4f44-bc02-b64043b3166f-0', usage_metadata={'input_tokens': 11, 'output_tokens': 23, 'total_tokens': 34})"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -202,17 +281,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "6c8755ba-c001-4f5a-a497-be3f1db83244",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:22:41.896413Z",
+     "iopub.status.busy": "2024-09-10T20:22:41.895967Z",
+     "iopub.status.idle": "2024-09-10T20:22:42.767565Z",
+     "shell.execute_reply": "2024-09-10T20:22:42.766619Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"My name is Claude. It's nice to meet you!\", response_metadata={'id': 'msg_01RyYR64DoMPNCfHeNnroMXm', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-22446159-3723-43e6-88df-b84797e7751d-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})"
+       "AIMessage(content=\"My name is Claude. It's nice to meet you!\", additional_kwargs={}, response_metadata={'id': 'msg_01EFKSWpmsn2PSYPQa4cNHWb', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 11, 'output_tokens': 15}}, id='run-3c58f47c-41b9-4e56-92e7-fb9602e3787c-0', usage_metadata={'input_tokens': 11, 'output_tokens': 15, 'total_tokens': 26})"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -242,28 +328,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "067dabee-1050-4110-ae24-c48eba01e13b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:22:42.771941Z",
+     "iopub.status.busy": "2024-09-10T20:22:42.771606Z",
+     "iopub.status.idle": "2024-09-10T20:22:43.909206Z",
+     "shell.execute_reply": "2024-09-10T20:22:43.908496Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[{'name': 'GetPopulation',\n",
        "  'args': {'location': 'Los Angeles, CA'},\n",
-       "  'id': 'call_sYT3PFMufHGWJD32Hi2CTNUP'},\n",
+       "  'id': 'call_Ga9m8FAArIyEjItHmztPYA22',\n",
+       "  'type': 'tool_call'},\n",
        " {'name': 'GetPopulation',\n",
        "  'args': {'location': 'New York, NY'},\n",
-       "  'id': 'call_j1qjhxRnD3ffQmRyqjlI1Lnk'}]"
+       "  'id': 'call_jh2dEvBaAHRaw5JUDthOs7rt',\n",
+       "  'type': 'tool_call'}]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class GetWeather(BaseModel):\n",
@@ -288,22 +383,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "e57dfe9f-cd24-4e37-9ce9-ccf8daf78f89",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:22:43.912746Z",
+     "iopub.status.busy": "2024-09-10T20:22:43.912447Z",
+     "iopub.status.idle": "2024-09-10T20:22:46.437049Z",
+     "shell.execute_reply": "2024-09-10T20:22:46.436093Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[{'name': 'GetPopulation',\n",
        "  'args': {'location': 'Los Angeles, CA'},\n",
-       "  'id': 'toolu_01CxEHxKtVbLBrvzFS7GQ5xR'},\n",
+       "  'id': 'toolu_01JMufPf4F4t2zLj7miFeqXp',\n",
+       "  'type': 'tool_call'},\n",
        " {'name': 'GetPopulation',\n",
        "  'args': {'location': 'New York City, NY'},\n",
-       "  'id': 'toolu_013A79qt5toWSsKunFBDZd5S'}]"
+       "  'id': 'toolu_01RQBHcE8kEEbYTuuS8WqY1u',\n",
+       "  'type': 'tool_call'}]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/docs/how_to/chat_token_usage_tracking.ipynb

@@ -71,7 +71,7 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
     "openai_response = llm.invoke(\"hello\")\n",
     "openai_response.usage_metadata"
    ]
@@ -182,13 +182,13 @@
       "content=' you' id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623'\n",
       "content=' today' id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623'\n",
       "content='?' id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623'\n",
-      "content='' response_metadata={'finish_reason': 'stop', 'model_name': 'gpt-3.5-turbo-0125'} id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623'\n",
+      "content='' response_metadata={'finish_reason': 'stop', 'model_name': 'gpt-4o-mini'} id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623'\n",
       "content='' id='run-adb20c31-60c7-43a2-99b2-d4a53ca5f623' usage_metadata={'input_tokens': 8, 'output_tokens': 9, 'total_tokens': 17}\n"
      ]
     }
    ],
    "source": [
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
     "\n",
     "aggregate = None\n",
     "for chunk in llm.stream(\"hello\", stream_usage=True):\n",
@@ -252,7 +252,7 @@
       "content=' you' id='run-8e758550-94b0-4cca-a298-57482793c25d'\n",
       "content=' today' id='run-8e758550-94b0-4cca-a298-57482793c25d'\n",
       "content='?' id='run-8e758550-94b0-4cca-a298-57482793c25d'\n",
-      "content='' response_metadata={'finish_reason': 'stop', 'model_name': 'gpt-3.5-turbo-0125'} id='run-8e758550-94b0-4cca-a298-57482793c25d'\n"
+      "content='' response_metadata={'finish_reason': 'stop', 'model_name': 'gpt-4o-mini'} id='run-8e758550-94b0-4cca-a298-57482793c25d'\n"
      ]
     }
    ],
@@ -289,7 +289,7 @@
     }
    ],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Joke(BaseModel):\n",
@@ -300,7 +300,7 @@
     "\n",
     "\n",
     "llm = ChatOpenAI(\n",
-    "    model=\"gpt-3.5-turbo-0125\",\n",
+    "    model=\"gpt-4o-mini\",\n",
     "    stream_usage=True,\n",
     ")\n",
     "# Under the hood, .with_structured_output binds tools to the\n",
@@ -362,7 +362,7 @@
     "from langchain_community.callbacks.manager import get_openai_callback\n",
     "\n",
     "llm = ChatOpenAI(\n",
-    "    model=\"gpt-3.5-turbo-0125\",\n",
+    "    model=\"gpt-4o-mini\",\n",
     "    temperature=0,\n",
     "    stream_usage=True,\n",
     ")\n",

docs/docs/how_to/chatbots_memory.ipynb

@@ -77,7 +77,7 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "chat = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")"
+    "chat = ChatOpenAI(model=\"gpt-4o-mini\")"
    ]
   },
   {
@@ -191,7 +191,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='You just asked me to translate the sentence \"I love programming\" from English to French.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 61, 'total_tokens': 79}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5cbb21c2-9c30-4031-8ea8-bfc497989535-0', usage_metadata={'input_tokens': 61, 'output_tokens': 18, 'total_tokens': 79})"
+       "AIMessage(content='You just asked me to translate the sentence \"I love programming\" from English to French.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 61, 'total_tokens': 79}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5cbb21c2-9c30-4031-8ea8-bfc497989535-0', usage_metadata={'input_tokens': 61, 'output_tokens': 18, 'total_tokens': 79})"
       ]
      },
      "execution_count": 5,
@@ -312,7 +312,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='\"J\\'adore la programmation.\"', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 39, 'total_tokens': 48}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-648b0822-b0bb-47a2-8e7d-7d34744be8f2-0', usage_metadata={'input_tokens': 39, 'output_tokens': 9, 'total_tokens': 48})"
+       "AIMessage(content='\"J\\'adore la programmation.\"', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 39, 'total_tokens': 48}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-648b0822-b0bb-47a2-8e7d-7d34744be8f2-0', usage_metadata={'input_tokens': 39, 'output_tokens': 9, 'total_tokens': 48})"
       ]
      },
      "execution_count": 8,
@@ -342,7 +342,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='You asked me to translate the sentence \"I love programming\" from English to French.', response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 63, 'total_tokens': 80}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5950435c-1dc2-43a6-836f-f989fd62c95e-0', usage_metadata={'input_tokens': 63, 'output_tokens': 17, 'total_tokens': 80})"
+       "AIMessage(content='You asked me to translate the sentence \"I love programming\" from English to French.', response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 63, 'total_tokens': 80}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5950435c-1dc2-43a6-836f-f989fd62c95e-0', usage_metadata={'input_tokens': 63, 'output_tokens': 17, 'total_tokens': 80})"
       ]
      },
      "execution_count": 9,
@@ -421,7 +421,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Your name is Nemo.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 66, 'total_tokens': 72}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f8aabef8-631a-4238-a39b-701e881fbe47-0', usage_metadata={'input_tokens': 66, 'output_tokens': 6, 'total_tokens': 72})"
+       "AIMessage(content='Your name is Nemo.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 66, 'total_tokens': 72}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f8aabef8-631a-4238-a39b-701e881fbe47-0', usage_metadata={'input_tokens': 66, 'output_tokens': 6, 'total_tokens': 72})"
       ]
      },
      "execution_count": 22,
@@ -501,7 +501,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='P. Sherman is a fictional character from the animated movie \"Finding Nemo\" who lives at 42 Wallaby Way, Sydney.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 53, 'total_tokens': 80}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5642ef3a-fdbe-43cf-a575-d1785976a1b9-0', usage_metadata={'input_tokens': 53, 'output_tokens': 27, 'total_tokens': 80})"
+       "AIMessage(content='P. Sherman is a fictional character from the animated movie \"Finding Nemo\" who lives at 42 Wallaby Way, Sydney.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 53, 'total_tokens': 80}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5642ef3a-fdbe-43cf-a575-d1785976a1b9-0', usage_metadata={'input_tokens': 53, 'output_tokens': 27, 'total_tokens': 80})"
       ]
      },
      "execution_count": 24,
@@ -529,9 +529,9 @@
        " HumanMessage(content='How are you today?'),\n",
        " AIMessage(content='Fine thanks!'),\n",
        " HumanMessage(content=\"What's my name?\"),\n",
-       " AIMessage(content='Your name is Nemo.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 66, 'total_tokens': 72}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f8aabef8-631a-4238-a39b-701e881fbe47-0', usage_metadata={'input_tokens': 66, 'output_tokens': 6, 'total_tokens': 72}),\n",
+       " AIMessage(content='Your name is Nemo.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 66, 'total_tokens': 72}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f8aabef8-631a-4238-a39b-701e881fbe47-0', usage_metadata={'input_tokens': 66, 'output_tokens': 6, 'total_tokens': 72}),\n",
        " HumanMessage(content='Where does P. Sherman live?'),\n",
-       " AIMessage(content='P. Sherman is a fictional character from the animated movie \"Finding Nemo\" who lives at 42 Wallaby Way, Sydney.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 53, 'total_tokens': 80}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5642ef3a-fdbe-43cf-a575-d1785976a1b9-0', usage_metadata={'input_tokens': 53, 'output_tokens': 27, 'total_tokens': 80})]"
+       " AIMessage(content='P. Sherman is a fictional character from the animated movie \"Finding Nemo\" who lives at 42 Wallaby Way, Sydney.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 53, 'total_tokens': 80}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5642ef3a-fdbe-43cf-a575-d1785976a1b9-0', usage_metadata={'input_tokens': 53, 'output_tokens': 27, 'total_tokens': 80})]"
       ]
      },
      "execution_count": 25,
@@ -565,7 +565,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"I'm sorry, but I don't have access to your personal information, so I don't know your name. How else may I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 31, 'prompt_tokens': 74, 'total_tokens': 105}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0ab03495-1f7c-4151-9070-56d2d1c565ff-0', usage_metadata={'input_tokens': 74, 'output_tokens': 31, 'total_tokens': 105})"
+       "AIMessage(content=\"I'm sorry, but I don't have access to your personal information, so I don't know your name. How else may I assist you today?\", response_metadata={'token_usage': {'completion_tokens': 31, 'prompt_tokens': 74, 'total_tokens': 105}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0ab03495-1f7c-4151-9070-56d2d1c565ff-0', usage_metadata={'input_tokens': 74, 'output_tokens': 31, 'total_tokens': 105})"
       ]
      },
      "execution_count": 27,

docs/docs/how_to/chatbots_retrieval.ipynb

@@ -71,7 +71,7 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "chat = ChatOpenAI(model=\"gpt-3.5-turbo-1106\", temperature=0.2)"
+    "chat = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.2)"
    ]
   },
   {

docs/docs/how_to/chatbots_tools.ipynb

@@ -70,7 +70,7 @@
     "\n",
     "# Choose the LLM that will drive the agent\n",
     "# Only certain models support this\n",
-    "chat = ChatOpenAI(model=\"gpt-3.5-turbo-1106\", temperature=0)"
+    "chat = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]
   },
   {

docs/docs/how_to/configure.ipynb

@@ -58,7 +58,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {
@@ -281,7 +282,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
+    "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/contextual_compression.ipynb

@@ -258,7 +258,7 @@
     "from langchain.retrievers.document_compressors import LLMListwiseRerank\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "\n",
     "_filter = LLMListwiseRerank.from_llm(llm, top_n=1)\n",
     "compression_retriever = ContextualCompressionRetriever(\n",

docs/docs/how_to/convert_runnable_to_tool.ipynb

@@ -190,7 +190,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class GSchema(BaseModel):\n",
@@ -285,7 +285,7 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]
   },
   {
@@ -362,11 +362,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_W8cnfOjwqEn4cFcg19LN9mYD', 'function': {'arguments': '{\"__arg1\":\"dogs\"}', 'name': 'pet_info_retriever'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 60, 'total_tokens': 79}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d7f81de9-1fb7-4caf-81ed-16dcdb0b2ab4-0', tool_calls=[{'name': 'pet_info_retriever', 'args': {'__arg1': 'dogs'}, 'id': 'call_W8cnfOjwqEn4cFcg19LN9mYD'}], usage_metadata={'input_tokens': 60, 'output_tokens': 19, 'total_tokens': 79})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_W8cnfOjwqEn4cFcg19LN9mYD', 'function': {'arguments': '{\"__arg1\":\"dogs\"}', 'name': 'pet_info_retriever'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 60, 'total_tokens': 79}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d7f81de9-1fb7-4caf-81ed-16dcdb0b2ab4-0', tool_calls=[{'name': 'pet_info_retriever', 'args': {'__arg1': 'dogs'}, 'id': 'call_W8cnfOjwqEn4cFcg19LN9mYD'}], usage_metadata={'input_tokens': 60, 'output_tokens': 19, 'total_tokens': 79})]}}\n",
       "----\n",
       "{'tools': {'messages': [ToolMessage(content=\"[Document(id='86f835fe-4bbe-4ec6-aeb4-489a8b541707', page_content='Dogs are great companions, known for their loyalty and friendliness.')]\", name='pet_info_retriever', tool_call_id='call_W8cnfOjwqEn4cFcg19LN9mYD')]}}\n",
       "----\n",
-      "{'agent': {'messages': [AIMessage(content='Dogs are known for being great companions, known for their loyalty and friendliness.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 134, 'total_tokens': 152}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-9ca5847a-a5eb-44c0-a774-84cc2c5bbc5b-0', usage_metadata={'input_tokens': 134, 'output_tokens': 18, 'total_tokens': 152})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='Dogs are known for being great companions, known for their loyalty and friendliness.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 134, 'total_tokens': 152}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-9ca5847a-a5eb-44c0-a774-84cc2c5bbc5b-0', usage_metadata={'input_tokens': 134, 'output_tokens': 18, 'total_tokens': 152})]}}\n",
       "----\n"
      ]
     }
@@ -497,11 +497,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_17iLPWvOD23zqwd1QVQ00Y63', 'function': {'arguments': '{\"question\":\"What are dogs known for according to pirates?\",\"answer_style\":\"quote\"}', 'name': 'pet_expert'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 28, 'prompt_tokens': 59, 'total_tokens': 87}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-7fef44f3-7bba-4e63-8c51-2ad9c5e65e2e-0', tool_calls=[{'name': 'pet_expert', 'args': {'question': 'What are dogs known for according to pirates?', 'answer_style': 'quote'}, 'id': 'call_17iLPWvOD23zqwd1QVQ00Y63'}], usage_metadata={'input_tokens': 59, 'output_tokens': 28, 'total_tokens': 87})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_17iLPWvOD23zqwd1QVQ00Y63', 'function': {'arguments': '{\"question\":\"What are dogs known for according to pirates?\",\"answer_style\":\"quote\"}', 'name': 'pet_expert'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 28, 'prompt_tokens': 59, 'total_tokens': 87}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-7fef44f3-7bba-4e63-8c51-2ad9c5e65e2e-0', tool_calls=[{'name': 'pet_expert', 'args': {'question': 'What are dogs known for according to pirates?', 'answer_style': 'quote'}, 'id': 'call_17iLPWvOD23zqwd1QVQ00Y63'}], usage_metadata={'input_tokens': 59, 'output_tokens': 28, 'total_tokens': 87})]}}\n",
       "----\n",
       "{'tools': {'messages': [ToolMessage(content='\"Dogs are known for their loyalty and friendliness, making them great companions for pirates on long sea voyages.\"', name='pet_expert', tool_call_id='call_17iLPWvOD23zqwd1QVQ00Y63')]}}\n",
       "----\n",
-      "{'agent': {'messages': [AIMessage(content='According to pirates, dogs are known for their loyalty and friendliness, making them great companions for pirates on long sea voyages.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 119, 'total_tokens': 146}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5a30edc3-7be0-4743-b980-ca2f8cad9b8d-0', usage_metadata={'input_tokens': 119, 'output_tokens': 27, 'total_tokens': 146})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='According to pirates, dogs are known for their loyalty and friendliness, making them great companions for pirates on long sea voyages.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 119, 'total_tokens': 146}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5a30edc3-7be0-4743-b980-ca2f8cad9b8d-0', usage_metadata={'input_tokens': 119, 'output_tokens': 27, 'total_tokens': 146})]}}\n",
       "----\n"
      ]
     }

docs/docs/how_to/custom_tools.ipynb

@@ -9,12 +9,12 @@
     "\n",
     "When constructing an agent, you will need to provide it with a list of `Tool`s that it can use. Besides the actual function that is called, the Tool consists of several components:\n",
     "\n",
-    "| Attribute       | Type                      | Description                                                                                                      |\n",
-    "|-----------------|---------------------------|------------------------------------------------------------------------------------------------------------------|\n",
-    "| name          | str                     | Must be unique within a set of tools provided to an LLM or agent.                                           |\n",
-    "| description   | str                     | Describes what the tool does. Used as context by the LLM or agent.                                       |\n",
-    "| args_schema   | Pydantic BaseModel      | Optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters |\n",
-    "| return_direct   | boolean      | Only relevant for agents. When True, after invoking the given tool, the agent will stop and return the result direcly to the user.  |\n",
+    "| Attribute     | Type                            | Description                                                                                                                                                                    |\n",
+    "|---------------|---------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
+    "| name          | str                             | Must be unique within a set of tools provided to an LLM or agent.                                                                                                              |\n",
+    "| description   | str                             | Describes what the tool does. Used as context by the LLM or agent.                                                                                                             |\n",
+    "| args_schema   | pydantic.BaseModel | Optional but recommended, and required if using callback handlers. It can be used to provide more information (e.g., few-shot examples) or validation for expected parameters. |\n",
+    "| return_direct | boolean                         | Only relevant for agents. When True, after invoking the given tool, the agent will stop and return the result direcly to the user.                                             |\n",
     "\n",
     "LangChain supports the creation of tools from:\n",
     "\n",
@@ -48,7 +48,14 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "cc7005cd-072f-4d37-8453-6297468e5192",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:52.645451Z",
+     "iopub.status.busy": "2024-09-10T20:25:52.645081Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.030958Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.030669Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -88,7 +95,14 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "0c0991db-b997-4611-be37-4346e660506b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.032544Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.032420Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.035349Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.035123Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.tools import tool\n",
@@ -112,22 +126,29 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "5626423f-053e-4a66-adca-1d794d835397",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.036658Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.036574Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.041154Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.040964Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'title': 'multiply_by_maxSchema',\n",
-       " 'description': 'Multiply a by the maximum of b.',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'a': {'title': 'A',\n",
-       "   'description': 'scale factor',\n",
+       "{'description': 'Multiply a by the maximum of b.',\n",
+       " 'properties': {'a': {'description': 'scale factor',\n",
+       "   'title': 'A',\n",
        "   'type': 'string'},\n",
-       "  'b': {'title': 'B',\n",
-       "   'description': 'list of ints over which to take maximum',\n",
-       "   'type': 'array',\n",
-       "   'items': {'type': 'integer'}}},\n",
-       " 'required': ['a', 'b']}"
+       "  'b': {'description': 'list of ints over which to take maximum',\n",
+       "   'items': {'type': 'integer'},\n",
+       "   'title': 'B',\n",
+       "   'type': 'array'}},\n",
+       " 'required': ['a', 'b'],\n",
+       " 'title': 'multiply_by_maxSchema',\n",
+       " 'type': 'object'}"
       ]
      },
      "execution_count": 3,
@@ -163,7 +184,14 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "9216d03a-f6ea-4216-b7e1-0661823a4c0b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.042516Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.042427Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.045217Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.045010Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -171,13 +199,13 @@
      "text": [
       "multiplication-tool\n",
       "Multiply two numbers.\n",
-      "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n",
+      "{'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}\n",
       "True\n"
      ]
     }
    ],
    "source": [
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class CalculatorInput(BaseModel):\n",
@@ -218,19 +246,26 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "336f5538-956e-47d5-9bde-b732559f9e61",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.046526Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.046456Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.050045Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.049836Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'title': 'fooSchema',\n",
-       " 'description': 'The foo.',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'bar': {'title': 'Bar',\n",
-       "   'description': 'The bar.',\n",
+       "{'description': 'The foo.',\n",
+       " 'properties': {'bar': {'description': 'The bar.',\n",
+       "   'title': 'Bar',\n",
        "   'type': 'string'},\n",
-       "  'baz': {'title': 'Baz', 'description': 'The baz.', 'type': 'integer'}},\n",
-       " 'required': ['bar', 'baz']}"
+       "  'baz': {'description': 'The baz.', 'title': 'Baz', 'type': 'integer'}},\n",
+       " 'required': ['bar', 'baz'],\n",
+       " 'title': 'fooSchema',\n",
+       " 'type': 'object'}"
       ]
      },
      "execution_count": 5,
@@ -277,7 +312,14 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "564fbe6f-11df-402d-b135-ef6ff25e1e63",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.051302Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.051218Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.059704Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.059490Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -320,7 +362,14 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "6bc055d4-1fbe-4db5-8881-9c382eba6b1b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.060971Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.060883Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.064615Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.064408Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -329,7 +378,7 @@
       "6\n",
       "Calculator\n",
       "multiply numbers\n",
-      "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n"
+      "{'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}\n"
      ]
     }
    ],
@@ -373,17 +422,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "8ef593c5-cf72-4c10-bfc9-7d21874a0c24",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.065797Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.065733Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.130458Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.130229Z"
+    }
+   },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/4j/2rz3865x6qg07tx43146py8h0000gn/T/ipykernel_95770/2548361071.py:14: LangChainBetaWarning: This API is in beta and may change in the future.\n",
+      "  as_tool = chain.as_tool(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "{'answer_style': {'title': 'Answer Style', 'type': 'string'}}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -428,19 +492,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "1dad8f8e",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.131904Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.131803Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.136797Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.136563Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import Optional, Type\n",
     "\n",
-    "from langchain.pydantic_v1 import BaseModel\n",
     "from langchain_core.callbacks import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
+    "from pydantic import BaseModel\n",
     "\n",
     "\n",
     "class CalculatorInput(BaseModel):\n",
@@ -448,9 +519,11 @@
     "    b: int = Field(description=\"second number\")\n",
     "\n",
     "\n",
+    "# Note: It's important that every field has type hints. BaseTool is a\n",
+    "# Pydantic class and not having type hints can lead to unexpected behavior.\n",
     "class CustomCalculatorTool(BaseTool):\n",
-    "    name = \"Calculator\"\n",
-    "    description = \"useful for when you need to answer questions about math\"\n",
+    "    name: str = \"Calculator\"\n",
+    "    description: str = \"useful for when you need to answer questions about math\"\n",
     "    args_schema: Type[BaseModel] = CalculatorInput\n",
     "    return_direct: bool = True\n",
     "\n",
@@ -477,9 +550,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "bb551c33",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.138074Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.138007Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.141360Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.141158Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -487,7 +567,7 @@
      "text": [
       "Calculator\n",
       "useful for when you need to answer questions about math\n",
-      "{'a': {'title': 'A', 'description': 'first number', 'type': 'integer'}, 'b': {'title': 'B', 'description': 'second number', 'type': 'integer'}}\n",
+      "{'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}\n",
       "True\n",
       "6\n",
       "6\n"
@@ -528,9 +608,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "6615cb77-fd4c-4676-8965-f92cc71d4944",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.142587Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.142504Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.147205Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.146995Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -560,9 +647,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "bb2af583-eadd-41f4-a645-bf8748bd3dcd",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.148383Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.148307Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.152684Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.152486Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -605,9 +699,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "4ad0932c-8610-4278-8c57-f9218f654c8a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.153849Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.153773Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.158312Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.158130Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -650,9 +751,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "7094c0e8-6192-4870-a942-aad5b5ae48fd",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.159440Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.159364Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.160922Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.160712Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.tools import ToolException\n",
@@ -673,9 +781,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "b4d22022-b105-4ccc-a15b-412cb9ea3097",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.162046Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.161968Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.165236Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.165052Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -683,7 +798,7 @@
        "'Error: There is no city by the name of foobar.'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -707,9 +822,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "3fad1728-d367-4e1b-9b54-3172981271cf",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.166372Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.166294Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.169739Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.169553Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -717,7 +839,7 @@
        "\"There is no such city, but it's probably above 0K there!\""
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -741,9 +863,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "ebfe7c1f-318d-4e58-99e1-f31e69473c46",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.170937Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.170859Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.174498Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.174304Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -751,7 +880,7 @@
        "'The following errors occurred during tool execution: `Error: There is no city by the name of foobar.`'"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -791,9 +920,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 18,
    "id": "14905425-0334-43a0-9de9-5bcf622ede0e",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.175683Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.175605Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.178798Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.178601Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import random\n",
@@ -820,9 +956,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
    "id": "0f2e1528-404b-46e6-b87c-f0957c4b9217",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.179881Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.179807Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.182100Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.181940Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -830,7 +973,7 @@
        "'Successfully generated array of 10 random ints in [0, 9].'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -849,17 +992,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 20,
    "id": "cc197777-26eb-46b3-a83b-c2ce116c6311",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.183238Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.183170Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.185752Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.185567Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ToolMessage(content='Successfully generated array of 10 random ints in [0, 9].', name='generate_random_ints', tool_call_id='123', artifact=[1, 4, 2, 5, 3, 9, 0, 4, 7, 7])"
+       "ToolMessage(content='Successfully generated array of 10 random ints in [0, 9].', name='generate_random_ints', tool_call_id='123', artifact=[4, 8, 2, 4, 1, 0, 9, 5, 8, 1])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -885,9 +1035,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 21,
    "id": "fe1a09d1-378b-4b91-bb5e-0697c3d7eb92",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.186884Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.186803Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.190718Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.190494Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.tools import BaseTool\n",
@@ -917,17 +1074,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 22,
    "id": "8c3d16f6-1c4a-48ab-b05a-38547c592e79",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:25:53.191872Z",
+     "iopub.status.busy": "2024-09-10T20:25:53.191794Z",
+     "iopub.status.idle": "2024-09-10T20:25:53.194396Z",
+     "shell.execute_reply": "2024-09-10T20:25:53.194184Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ToolMessage(content='Generated 3 floats in [0.1, 3.3333], rounded to 4 decimals.', name='generate_random_floats', tool_call_id='123', artifact=[1.4277, 0.7578, 2.4871])"
+       "ToolMessage(content='Generated 3 floats in [0.1, 3.3333], rounded to 4 decimals.', name='generate_random_floats', tool_call_id='123', artifact=[1.5566, 0.5134, 2.7914])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/docs/how_to/document_loader_pdf.ipynb

@@ -90,7 +90,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
    ]
   },
   {

docs/docs/how_to/embed_text.mdx

@@ -8,7 +8,7 @@ The Embeddings class is a class designed for interfacing with text embedding mod
 
 Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.
 
-The base Embeddings class in LangChain provides two methods: one for embedding documents and one for embedding a query. The former, `.embed_documents`, takes as input multiple texts, while the latter, `.embed_query`, takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself). 
+The base Embeddings class in LangChain provides two methods: one for embedding documents and one for embedding a query. The former, `.embed_documents`, takes as input multiple texts, while the latter, `.embed_query`, takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).
 `.embed_query` will return a list of floats, whereas `.embed_documents` returns a list of lists of floats.
 
 ## Get started
@@ -94,15 +94,6 @@ from langchain_huggingface import HuggingFaceEmbeddings
 
 embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
 ```
-
-You can also leave the `model_name` blank to use the default [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) model.
-
-```python
-from langchain_huggingface import HuggingFaceEmbeddings
-
-embeddings_model = HuggingFaceEmbeddings()
-```
-
   </TabItem>
 </Tabs>
 

docs/docs/how_to/extraction_examples.ipynb

@@ -29,9 +29,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "89579144-bcb3-490a-8036-86a0a6bcd56b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:26:41.780410Z",
+     "iopub.status.busy": "2024-09-10T20:26:41.780102Z",
+     "iopub.status.idle": "2024-09-10T20:26:42.147112Z",
+     "shell.execute_reply": "2024-09-10T20:26:42.146838Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
@@ -67,17 +74,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "610c3025-ea63-4cd7-88bd-c8cbcb4d8a3f",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:26:42.148746Z",
+     "iopub.status.busy": "2024-09-10T20:26:42.148621Z",
+     "iopub.status.idle": "2024-09-10T20:26:42.162044Z",
+     "shell.execute_reply": "2024-09-10T20:26:42.161794Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ChatPromptValue(messages=[SystemMessage(content=\"You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value.\"), HumanMessage(content='testing 1 2 3'), HumanMessage(content='this is some text')])"
+       "ChatPromptValue(messages=[SystemMessage(content=\"You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value.\", additional_kwargs={}, response_metadata={}), HumanMessage(content='testing 1 2 3', additional_kwargs={}, response_metadata={}), HumanMessage(content='this is some text', additional_kwargs={}, response_metadata={})])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -104,15 +118,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "d875a49a-d2cb-4b9e-b5bf-41073bc3905c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:26:42.163477Z",
+     "iopub.status.busy": "2024-09-10T20:26:42.163391Z",
+     "iopub.status.idle": "2024-09-10T20:26:42.324449Z",
+     "shell.execute_reply": "2024-09-10T20:26:42.324206Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -162,9 +183,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "08356810-77ce-4e68-99d9-faa0326f2cee",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:26:42.326100Z",
+     "iopub.status.busy": "2024-09-10T20:26:42.326016Z",
+     "iopub.status.idle": "2024-09-10T20:26:42.329260Z",
+     "shell.execute_reply": "2024-09-10T20:26:42.329014Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import uuid\n",
@@ -177,7 +205,7 @@
     "    SystemMessage,\n",
     "    ToolMessage,\n",
     ")\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Example(TypedDict):\n",
@@ -238,9 +266,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "7f59a745-5c81-4011-a4c5-a33ec1eca7ef",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:26:42.330580Z",
+     "iopub.status.busy": "2024-09-10T20:26:42.330488Z",
+     "iopub.status.idle": "2024-09-10T20:26:42.332813Z",
+     "shell.execute_reply": "2024-09-10T20:26:42.332598Z"
+    }
+   },
    "outputs": [],
    "source": [
     "examples = [\n",
@@ -273,22 +308,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "976bb7b8-09c4-4a3e-80df-49a483705c08",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:26:42.333955Z",
+     "iopub.status.busy": "2024-09-10T20:26:42.333876Z",
+     "iopub.status.idle": "2024-09-10T20:26:42.336841Z",
+     "shell.execute_reply": "2024-09-10T20:26:42.336635Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "system: content=\"You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value.\"\n",
-      "human: content=\"The ocean is vast and blue. It's more than 20,000 feet deep. There are many fish in it.\"\n",
-      "ai: content='' tool_calls=[{'name': 'Person', 'args': {'name': None, 'hair_color': None, 'height_in_meters': None}, 'id': 'b843ba77-4c9c-48ef-92a4-54e534f24521'}]\n",
-      "tool: content='You have correctly called this tool.' tool_call_id='b843ba77-4c9c-48ef-92a4-54e534f24521'\n",
-      "human: content='Fiona traveled far from France to Spain.'\n",
-      "ai: content='' tool_calls=[{'name': 'Person', 'args': {'name': 'Fiona', 'hair_color': None, 'height_in_meters': None}, 'id': '46f00d6b-50e5-4482-9406-b07bb10340f6'}]\n",
-      "tool: content='You have correctly called this tool.' tool_call_id='46f00d6b-50e5-4482-9406-b07bb10340f6'\n",
-      "human: content='this is some text'\n"
+      "system: content=\"You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value.\" additional_kwargs={} response_metadata={}\n",
+      "human: content=\"The ocean is vast and blue. It's more than 20,000 feet deep. There are many fish in it.\" additional_kwargs={} response_metadata={}\n",
+      "ai: content='' additional_kwargs={} response_metadata={} tool_calls=[{'name': 'Data', 'args': {'people': []}, 'id': '240159b1-1405-4107-a07c-3c6b91b3d5b7', 'type': 'tool_call'}]\n",
+      "tool: content='You have correctly called this tool.' tool_call_id='240159b1-1405-4107-a07c-3c6b91b3d5b7'\n",
+      "human: content='Fiona traveled far from France to Spain.' additional_kwargs={} response_metadata={}\n",
+      "ai: content='' additional_kwargs={} response_metadata={} tool_calls=[{'name': 'Data', 'args': {'people': [{'name': 'Fiona', 'hair_color': None, 'height_in_meters': None}]}, 'id': '3fc521e4-d1d2-4c20-bf40-e3d72f1068da', 'type': 'tool_call'}]\n",
+      "tool: content='You have correctly called this tool.' tool_call_id='3fc521e4-d1d2-4c20-bf40-e3d72f1068da'\n",
+      "human: content='this is some text' additional_kwargs={} response_metadata={}\n"
      ]
     }
    ],
@@ -320,9 +362,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "df2e1ee1-69e8-4c4d-b349-95f2e320317b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:26:42.338001Z",
+     "iopub.status.busy": "2024-09-10T20:26:42.337915Z",
+     "iopub.status.idle": "2024-09-10T20:26:42.349121Z",
+     "shell.execute_reply": "2024-09-10T20:26:42.348908Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# | output: false\n",
@@ -343,9 +392,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "dbfea43d-769b-42e9-a76f-ce722f7d6f93",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:26:42.350335Z",
+     "iopub.status.busy": "2024-09-10T20:26:42.350264Z",
+     "iopub.status.idle": "2024-09-10T20:26:42.424894Z",
+     "shell.execute_reply": "2024-09-10T20:26:42.424623Z"
+    }
+   },
    "outputs": [],
    "source": [
     "runnable = prompt | llm.with_structured_output(\n",
@@ -367,18 +423,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "66545cab-af2a-40a4-9dc9-b4110458b7d3",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:26:42.426258Z",
+     "iopub.status.busy": "2024-09-10T20:26:42.426187Z",
+     "iopub.status.idle": "2024-09-10T20:26:46.151633Z",
+     "shell.execute_reply": "2024-09-10T20:26:46.150690Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "people=[Person(name='earth', hair_color='null', height_in_meters='null')]\n",
-      "people=[Person(name='earth', hair_color='null', height_in_meters='null')]\n",
-      "people=[]\n",
-      "people=[Person(name='earth', hair_color='null', height_in_meters='null')]\n",
+      "people=[]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "people=[]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "people=[]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "people=[]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "people=[]\n"
      ]
     }
@@ -401,18 +488,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "1c09d805-ec16-4123-aef9-6a5b59499b5c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:26:46.155346Z",
+     "iopub.status.busy": "2024-09-10T20:26:46.155110Z",
+     "iopub.status.idle": "2024-09-10T20:26:51.810359Z",
+     "shell.execute_reply": "2024-09-10T20:26:51.809636Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "people=[]\n",
-      "people=[]\n",
-      "people=[]\n",
-      "people=[]\n",
+      "people=[]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "people=[]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "people=[]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "people=[]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "people=[]\n"
      ]
     }
@@ -435,9 +553,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "a9b7a762-1b75-4f9f-b9d9-6732dd05802c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:26:51.813309Z",
+     "iopub.status.busy": "2024-09-10T20:26:51.813150Z",
+     "iopub.status.idle": "2024-09-10T20:26:53.474153Z",
+     "shell.execute_reply": "2024-09-10T20:26:53.473522Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -445,7 +570,7 @@
        "Data(people=[Person(name='Harrison', hair_color='black', height_in_meters=None)])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,7 +601,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/extraction_long_text.ipynb

@@ -23,16 +23,56 @@
    "id": "57969139-ad0a-487e-97d8-cb30e2af9742",
    "metadata": {},
    "source": [
-    "## Set up\n",
+    "## Setup\n",
     "\n",
-    "We need some example data! Let's download an article about [cars from wikipedia](https://en.wikipedia.org/wiki/Car) and load it as a LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html)."
+    "First we'll install the dependencies needed for this guide:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "84460db2-36e1-4037-bfa6-2a11883c2ba5",
+   "id": "a3b4d838-5be4-4207-8a4a-9ef5624c48f2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:19.850767Z",
+     "iopub.status.busy": "2024-09-10T20:35:19.850427Z",
+     "iopub.status.idle": "2024-09-10T20:35:21.432233Z",
+     "shell.execute_reply": "2024-09-10T20:35:21.431606Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -qU langchain-community lxml faiss-cpu langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac000b03-33fc-414f-8f2c-3850df621a35",
    "metadata": {},
+   "source": [
+    "Now we need some example data! Let's download an article about [cars from wikipedia](https://en.wikipedia.org/wiki/Car) and load it as a LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "84460db2-36e1-4037-bfa6-2a11883c2ba5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:21.434882Z",
+     "iopub.status.busy": "2024-09-10T20:35:21.434571Z",
+     "iopub.status.idle": "2024-09-10T20:35:22.214545Z",
+     "shell.execute_reply": "2024-09-10T20:35:22.214253Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import re\n",
@@ -55,15 +95,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "fcb6917b-123d-4630-a0ce-ed8b293d482d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:22.216143Z",
+     "iopub.status.busy": "2024-09-10T20:35:22.216039Z",
+     "iopub.status.idle": "2024-09-10T20:35:22.218117Z",
+     "shell.execute_reply": "2024-09-10T20:35:22.217854Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "79174\n"
+      "80427\n"
      ]
     }
    ],
@@ -87,13 +134,20 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "a3b288ed-87a6-4af0-aac8-20921dc370d4",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:22.219468Z",
+     "iopub.status.busy": "2024-09-10T20:35:22.219395Z",
+     "iopub.status.idle": "2024-09-10T20:35:22.340594Z",
+     "shell.execute_reply": "2024-09-10T20:35:22.340319Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class KeyDevelopment(BaseModel):\n",
@@ -156,7 +210,14 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "109f4f05-d0ff-431d-93d9-8f5aa34979a6",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:22.342277Z",
+     "iopub.status.busy": "2024-09-10T20:35:22.342171Z",
+     "iopub.status.idle": "2024-09-10T20:35:22.532302Z",
+     "shell.execute_reply": "2024-09-10T20:35:22.532034Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# | output: false\n",
@@ -171,7 +232,14 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "aa4ae224-6d3d-4fe2-b210-7db19a9fe580",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:22.533795Z",
+     "iopub.status.busy": "2024-09-10T20:35:22.533708Z",
+     "iopub.status.idle": "2024-09-10T20:35:22.610573Z",
+     "shell.execute_reply": "2024-09-10T20:35:22.610307Z"
+    }
+   },
    "outputs": [],
    "source": [
     "extractor = prompt | llm.with_structured_output(\n",
@@ -194,7 +262,14 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "27b8a373-14b3-45ea-8bf5-9749122ad927",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:22.612123Z",
+     "iopub.status.busy": "2024-09-10T20:35:22.612052Z",
+     "iopub.status.idle": "2024-09-10T20:35:22.753493Z",
+     "shell.execute_reply": "2024-09-10T20:35:22.753179Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_text_splitters import TokenTextSplitter\n",
@@ -227,7 +302,14 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "6ba766b5-8d6c-48e6-8d69-f391a66b65d2",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:22.755067Z",
+     "iopub.status.busy": "2024-09-10T20:35:22.754987Z",
+     "iopub.status.idle": "2024-09-10T20:35:36.691130Z",
+     "shell.execute_reply": "2024-09-10T20:35:36.690500Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Limit just to the first 3 chunks\n",
@@ -254,21 +336,27 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "c3f77470-ce6c-477f-8957-650913218632",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:36.694799Z",
+     "iopub.status.busy": "2024-09-10T20:35:36.694458Z",
+     "iopub.status.idle": "2024-09-10T20:35:36.701416Z",
+     "shell.execute_reply": "2024-09-10T20:35:36.700993Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[KeyDevelopment(year=1966, description='The Toyota Corolla began production, becoming the best-selling series of automobile in history.', evidence='The Toyota Corolla, which has been in production since 1966, is the best-selling series of automobile in history.'),\n",
-       " KeyDevelopment(year=1769, description='Nicolas-Joseph Cugnot built the first steam-powered road vehicle.', evidence='The French inventor Nicolas-Joseph Cugnot built the first steam-powered road vehicle in 1769.'),\n",
-       " KeyDevelopment(year=1808, description='François Isaac de Rivaz designed and constructed the first internal combustion-powered automobile.', evidence='the Swiss inventor François Isaac de Rivaz designed and constructed the first internal combustion-powered automobile in 1808.'),\n",
-       " KeyDevelopment(year=1886, description='Carl Benz patented his Benz Patent-Motorwagen, inventing the modern car.', evidence='The modern car—a practical, marketable automobile for everyday use—was invented in 1886, when the German inventor Carl Benz patented his Benz Patent-Motorwagen.'),\n",
-       " KeyDevelopment(year=1908, description='Ford Model T, one of the first cars affordable by the masses, began production.', evidence='One of the first cars affordable by the masses was the Ford Model T, begun in 1908, an American car manufactured by the Ford Motor Company.'),\n",
-       " KeyDevelopment(year=1888, description=\"Bertha Benz undertook the first road trip by car to prove the road-worthiness of her husband's invention.\", evidence=\"In August 1888, Bertha Benz, the wife of Carl Benz, undertook the first road trip by car, to prove the road-worthiness of her husband's invention.\"),\n",
+       "[KeyDevelopment(year=1769, description='Nicolas-Joseph Cugnot built the first full-scale, self-propelled mechanical vehicle, a steam-powered tricycle.', evidence='Nicolas-Joseph Cugnot is widely credited with building the first full-scale, self-propelled mechanical vehicle in about 1769; he created a steam-powered tricycle.'),\n",
+       " KeyDevelopment(year=1807, description=\"Nicéphore Niépce and his brother Claude created what was probably the world's first internal combustion engine.\", evidence=\"In 1807, Nicéphore Niépce and his brother Claude created what was probably the world's first internal combustion engine (which they called a Pyréolophore), but installed it in a boat on the river Saone in France.\"),\n",
+       " KeyDevelopment(year=1886, description='Carl Benz patented the Benz Patent-Motorwagen, marking the birth of the modern car.', evidence='In November 1881, French inventor Gustave Trouvé demonstrated a three-wheeled car powered by electricity at the International Exposition of Electricity. Although several other German engineers (including Gottlieb Daimler, Wilhelm Maybach, and Siegfried Marcus) were working on cars at about the same time, the year 1886 is regarded as the birth year of the modern car—a practical, marketable automobile for everyday use—when the German Carl Benz patented his Benz Patent-Motorwagen; he is generally acknowledged as the inventor of the car.'),\n",
+       " KeyDevelopment(year=1886, description='Carl Benz began promotion of his vehicle, marking the introduction of the first commercially available automobile.', evidence='Benz began promotion of the vehicle on 3 July 1886.'),\n",
+       " KeyDevelopment(year=1888, description=\"Bertha Benz undertook the first road trip by car to prove the road-worthiness of her husband's invention.\", evidence=\"In August 1888, Bertha Benz, the wife and business partner of Carl Benz, undertook the first road trip by car, to prove the road-worthiness of her husband's invention.\"),\n",
        " KeyDevelopment(year=1896, description='Benz designed and patented the first internal-combustion flat engine, called boxermotor.', evidence='In 1896, Benz designed and patented the first internal-combustion flat engine, called boxermotor.'),\n",
-       " KeyDevelopment(year=1897, description='Nesselsdorfer Wagenbau produced the Präsident automobil, one of the first factory-made cars in the world.', evidence='The first motor car in central Europe and one of the first factory-made cars in the world, was produced by Czech company Nesselsdorfer Wagenbau (later renamed to Tatra) in 1897, the Präsident automobil.'),\n",
-       " KeyDevelopment(year=1890, description='Daimler Motoren Gesellschaft (DMG) was founded by Daimler and Maybach in Cannstatt.', evidence='Daimler and Maybach founded Daimler Motoren Gesellschaft (DMG) in Cannstatt in 1890.'),\n",
-       " KeyDevelopment(year=1891, description='Auguste Doriot and Louis Rigoulot completed the longest trip by a petrol-driven vehicle with a Daimler powered Peugeot Type 3.', evidence='In 1891, Auguste Doriot and his Peugeot colleague Louis Rigoulot completed the longest trip by a petrol-driven vehicle when their self-designed and built Daimler powered Peugeot Type 3 completed 2,100 kilometres (1,300 mi) from Valentigney to Paris and Brest and back again.')]"
+       " KeyDevelopment(year=1897, description='The first motor car in central Europe and one of the first factory-made cars in the world, the Präsident automobil, was produced by Nesselsdorfer Wagenbau.', evidence='The first motor car in central Europe and one of the first factory-made cars in the world, was produced by Czech company Nesselsdorfer Wagenbau (later renamed to Tatra) in 1897, the Präsident automobil.'),\n",
+       " KeyDevelopment(year=1901, description='Ransom Olds started large-scale, production-line manufacturing of affordable cars at his Oldsmobile factory in Lansing, Michigan.', evidence='Large-scale, production-line manufacturing of affordable cars was started by Ransom Olds in 1901 at his Oldsmobile factory in Lansing, Michigan.'),\n",
+       " KeyDevelopment(year=1913, description=\"Henry Ford introduced the world's first moving assembly line for cars at the Highland Park Ford Plant.\", evidence=\"This concept was greatly expanded by Henry Ford, beginning in 1913 with the world's first moving assembly line for cars at the Highland Park Ford Plant.\")]"
       ]
      },
      "execution_count": 9,
@@ -315,7 +403,14 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "aaf37c82-625b-4fa1-8e88-73303f08ac16",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:36.703897Z",
+     "iopub.status.busy": "2024-09-10T20:35:36.703718Z",
+     "iopub.status.idle": "2024-09-10T20:35:38.451523Z",
+     "shell.execute_reply": "2024-09-10T20:35:38.450925Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_community.vectorstores import FAISS\n",
@@ -344,7 +439,14 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "47aad00b-7013-4f7f-a1b0-02ef269093bf",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:38.455094Z",
+     "iopub.status.busy": "2024-09-10T20:35:38.454851Z",
+     "iopub.status.idle": "2024-09-10T20:35:38.458315Z",
+     "shell.execute_reply": "2024-09-10T20:35:38.457940Z"
+    }
+   },
    "outputs": [],
    "source": [
     "rag_extractor = {\n",
@@ -356,7 +458,14 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "68f2de01-0cd8-456e-a959-db236189d41b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:38.460115Z",
+     "iopub.status.busy": "2024-09-10T20:35:38.459949Z",
+     "iopub.status.idle": "2024-09-10T20:35:43.195532Z",
+     "shell.execute_reply": "2024-09-10T20:35:43.194254Z"
+    }
+   },
    "outputs": [],
    "source": [
     "results = rag_extractor.invoke(\"Key developments associated with cars\")"
@@ -366,15 +475,21 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "1788e2d6-77bb-417f-827c-eb96c035164e",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:43.200497Z",
+     "iopub.status.busy": "2024-09-10T20:35:43.200037Z",
+     "iopub.status.idle": "2024-09-10T20:35:43.206773Z",
+     "shell.execute_reply": "2024-09-10T20:35:43.205426Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "year=1869 description='Mary Ward became one of the first documented car fatalities in Parsonstown, Ireland.' evidence='Mary Ward became one of the first documented car fatalities in 1869 in Parsonstown, Ireland,'\n",
-      "year=1899 description=\"Henry Bliss one of the US's first pedestrian car casualties in New York City.\" evidence=\"Henry Bliss one of the US's first pedestrian car casualties in 1899 in New York City.\"\n",
-      "year=2030 description='All fossil fuel vehicles will be banned in Amsterdam.' evidence='all fossil fuel vehicles will be banned in Amsterdam from 2030.'\n"
+      "year=2006 description='Car-sharing services in the US experienced double-digit growth in revenue and membership.' evidence='in the US, some car-sharing services have experienced double-digit growth in revenue and membership growth between 2006 and 2007.'\n",
+      "year=2020 description='56 million cars were manufactured worldwide, with China producing the most.' evidence='In 2020, there were 56 million cars manufactured worldwide, down from 67 million the previous year. The automotive industry in China produces by far the most (20 million in 2020).'\n"
      ]
     }
    ],
@@ -416,7 +531,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/extraction_parse.ipynb

@@ -27,9 +27,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "25487939-8713-4ec7-b774-e4a761ac8298",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:44.442501Z",
+     "iopub.status.busy": "2024-09-10T20:35:44.442044Z",
+     "iopub.status.idle": "2024-09-10T20:35:44.872217Z",
+     "shell.execute_reply": "2024-09-10T20:35:44.871897Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# | output: false\n",
@@ -62,16 +69,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "497eb023-c043-443d-ac62-2d4ea85fe1b0",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:44.873979Z",
+     "iopub.status.busy": "2024-09-10T20:35:44.873840Z",
+     "iopub.status.idle": "2024-09-10T20:35:44.878966Z",
+     "shell.execute_reply": "2024-09-10T20:35:44.878718Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
+    "from pydantic import BaseModel, Field, validator\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -114,9 +128,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "20b99ffb-a114-49a9-a7be-154c525f8ada",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:44.880355Z",
+     "iopub.status.busy": "2024-09-10T20:35:44.880277Z",
+     "iopub.status.idle": "2024-09-10T20:35:44.881834Z",
+     "shell.execute_reply": "2024-09-10T20:35:44.881601Z"
+    }
+   },
    "outputs": [],
    "source": [
     "query = \"Anna is 23 years old and she is 6 feet tall\""
@@ -124,9 +145,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "4f3a66ce-de19-4571-9e54-67504ae3fba7",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:44.883138Z",
+     "iopub.status.busy": "2024-09-10T20:35:44.883049Z",
+     "iopub.status.idle": "2024-09-10T20:35:44.885139Z",
+     "shell.execute_reply": "2024-09-10T20:35:44.884801Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -140,7 +168,7 @@
       "\n",
       "Here is the output schema:\n",
       "```\n",
-      "{\"description\": \"Identifying information about all people in a text.\", \"properties\": {\"people\": {\"title\": \"People\", \"type\": \"array\", \"items\": {\"$ref\": \"#/definitions/Person\"}}}, \"required\": [\"people\"], \"definitions\": {\"Person\": {\"title\": \"Person\", \"description\": \"Information about a person.\", \"type\": \"object\", \"properties\": {\"name\": {\"title\": \"Name\", \"description\": \"The name of the person\", \"type\": \"string\"}, \"height_in_meters\": {\"title\": \"Height In Meters\", \"description\": \"The height of the person expressed in meters.\", \"type\": \"number\"}}, \"required\": [\"name\", \"height_in_meters\"]}}}\n",
+      "{\"$defs\": {\"Person\": {\"description\": \"Information about a person.\", \"properties\": {\"name\": {\"description\": \"The name of the person\", \"title\": \"Name\", \"type\": \"string\"}, \"height_in_meters\": {\"description\": \"The height of the person expressed in meters.\", \"title\": \"Height In Meters\", \"type\": \"number\"}}, \"required\": [\"name\", \"height_in_meters\"], \"title\": \"Person\", \"type\": \"object\"}}, \"description\": \"Identifying information about all people in a text.\", \"properties\": {\"people\": {\"items\": {\"$ref\": \"#/$defs/Person\"}, \"title\": \"People\", \"type\": \"array\"}}, \"required\": [\"people\"]}\n",
       "```\n",
       "Human: Anna is 23 years old and she is 6 feet tall\n"
      ]
@@ -160,9 +188,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "7e0041eb-37dc-4384-9fe3-6dd8c356371e",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:44.886765Z",
+     "iopub.status.busy": "2024-09-10T20:35:44.886675Z",
+     "iopub.status.idle": "2024-09-10T20:35:46.835960Z",
+     "shell.execute_reply": "2024-09-10T20:35:46.835282Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -170,7 +205,7 @@
        "People(people=[Person(name='Anna', height_in_meters=1.83)])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,9 +244,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "b1f11912-c1bb-4a2a-a482-79bf3996961f",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:46.839577Z",
+     "iopub.status.busy": "2024-09-10T20:35:46.839233Z",
+     "iopub.status.idle": "2024-09-10T20:35:46.849663Z",
+     "shell.execute_reply": "2024-09-10T20:35:46.849177Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import json\n",
@@ -221,7 +263,7 @@
     "from langchain_anthropic.chat_models import ChatAnthropic\n",
     "from langchain_core.messages import AIMessage\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
+    "from pydantic import BaseModel, Field, validator\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -279,16 +321,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "9260d5e8-3b6c-4639-9f3b-fb2f90239e4b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:46.851870Z",
+     "iopub.status.busy": "2024-09-10T20:35:46.851698Z",
+     "iopub.status.idle": "2024-09-10T20:35:46.854786Z",
+     "shell.execute_reply": "2024-09-10T20:35:46.854424Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "System: Answer the user query. Output your answer as JSON that  matches the given schema: ```json\n",
-      "{'title': 'People', 'description': 'Identifying information about all people in a text.', 'type': 'object', 'properties': {'people': {'title': 'People', 'type': 'array', 'items': {'$ref': '#/definitions/Person'}}}, 'required': ['people'], 'definitions': {'Person': {'title': 'Person', 'description': 'Information about a person.', 'type': 'object', 'properties': {'name': {'title': 'Name', 'description': 'The name of the person', 'type': 'string'}, 'height_in_meters': {'title': 'Height In Meters', 'description': 'The height of the person expressed in meters.', 'type': 'number'}}, 'required': ['name', 'height_in_meters']}}}\n",
+      "{'$defs': {'Person': {'description': 'Information about a person.', 'properties': {'name': {'description': 'The name of the person', 'title': 'Name', 'type': 'string'}, 'height_in_meters': {'description': 'The height of the person expressed in meters.', 'title': 'Height In Meters', 'type': 'number'}}, 'required': ['name', 'height_in_meters'], 'title': 'Person', 'type': 'object'}}, 'description': 'Identifying information about all people in a text.', 'properties': {'people': {'items': {'$ref': '#/$defs/Person'}, 'title': 'People', 'type': 'array'}}, 'required': ['people'], 'title': 'People', 'type': 'object'}\n",
       "```. Make sure to wrap the answer in ```json and ``` tags\n",
       "Human: Anna is 23 years old and she is 6 feet tall\n"
      ]
@@ -301,17 +350,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "c523301d-ae0e-45e3-b195-7fd28c67a5c4",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-10T20:35:46.856945Z",
+     "iopub.status.busy": "2024-09-10T20:35:46.856769Z",
+     "iopub.status.idle": "2024-09-10T20:35:48.373728Z",
+     "shell.execute_reply": "2024-09-10T20:35:48.373079Z"
+    }
+   },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/bagatur/langchain/.venv/lib/python3.11/site-packages/pydantic/_internal/_fields.py:201: UserWarning: Field name \"schema\" in \"PromptInput\" shadows an attribute in parent \"BaseModel\"\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "[{'people': [{'name': 'Anna', 'height_in_meters': 1.83}]}]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -349,7 +413,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/fallbacks.ipynb

@@ -90,7 +90,7 @@
    "outputs": [],
    "source": [
     "# Note that we set max_retries = 0 to avoid retrying on RateLimits, etc\n",
-    "openai_llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", max_retries=0)\n",
+    "openai_llm = ChatOpenAI(model=\"gpt-4o-mini\", max_retries=0)\n",
     "anthropic_llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\n",
     "llm = openai_llm.with_fallbacks([anthropic_llm])"
    ]

docs/docs/how_to/few_shot_examples_chat.ipynb

@@ -66,7 +66,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {
@@ -86,7 +87,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='The expression \"2 🦜 9\" is not a standard mathematical operation or equation. It appears to be a combination of the number 2 and the parrot emoji 🦜 followed by the number 9. It does not have a specific mathematical meaning.', response_metadata={'token_usage': {'completion_tokens': 54, 'prompt_tokens': 17, 'total_tokens': 71}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-aad12dda-5c47-4a1e-9949-6fe94e03242a-0', usage_metadata={'input_tokens': 17, 'output_tokens': 54, 'total_tokens': 71})"
+       "AIMessage(content='The expression \"2 🦜 9\" is not a standard mathematical operation or equation. It appears to be a combination of the number 2 and the parrot emoji 🦜 followed by the number 9. It does not have a specific mathematical meaning.', response_metadata={'token_usage': {'completion_tokens': 54, 'prompt_tokens': 17, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-aad12dda-5c47-4a1e-9949-6fe94e03242a-0', usage_metadata={'input_tokens': 17, 'output_tokens': 54, 'total_tokens': 71})"
       ]
      },
      "execution_count": 4,
@@ -97,7 +98,7 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0.0)\n",
+    "model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.0)\n",
     "\n",
     "model.invoke(\"What is 2 🦜 9?\")"
    ]
@@ -212,7 +213,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='11', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5ec4e051-262f-408e-ad00-3f2ebeb561c3-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})"
+       "AIMessage(content='11', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5ec4e051-262f-408e-ad00-3f2ebeb561c3-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})"
       ]
      },
      "execution_count": 8,
@@ -418,7 +419,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='6', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d1863e5e-17cd-4e9d-bf7a-b9f118747a65-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})"
+       "AIMessage(content='6', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 60, 'total_tokens': 61}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d1863e5e-17cd-4e9d-bf7a-b9f118747a65-0', usage_metadata={'input_tokens': 60, 'output_tokens': 1, 'total_tokens': 61})"
       ]
      },
      "execution_count": 13,
@@ -427,7 +428,7 @@
     }
    ],
    "source": [
-    "chain = final_prompt | ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0.0)\n",
+    "chain = final_prompt | ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.0)\n",
     "\n",
     "chain.invoke({\"input\": \"What's 3 🦜 3?\"})"
    ]

docs/docs/how_to/function_calling.ipynb

@@ -136,7 +136,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "# Note that the docstrings here are crucial, as they will be passed along\n",
@@ -191,7 +191,7 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]
   },
   {
@@ -696,7 +696,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/functions.ipynb

@@ -54,7 +54,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/graph_constructing.ipynb

@@ -24,7 +24,7 @@
     "\n",
     "## Architecture\n",
     "\n",
-    "At a high-level, the steps of constructing a knowledge are from text are:\n",
+    "At a high-level, the steps of constructing a knowledge graph from text are:\n",
     "\n",
     "1. **Extracting structured information from text**: Model is used to extract structured graph information from text.\n",
     "2. **Storing into graph database**: Storing the extracted structured graph information into a graph database enables downstream RAG applications\n",

docs/docs/how_to/graph_mapping.ipynb

@@ -163,8 +163,8 @@
     "from typing import List, Optional\n",
     "\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",

docs/docs/how_to/graph_semantic.ipynb

@@ -177,14 +177,15 @@
    "source": [
     "from typing import Optional, Type\n",
     "\n",
-    "# Import things that are needed generically\n",
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.callbacks import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
     "\n",
+    "# Import things that are needed generically\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
     "description_query = \"\"\"\n",
     "MATCH (m:Movie|Person)\n",
     "WHERE m.title CONTAINS $candidate OR m.name CONTAINS $candidate\n",
@@ -226,14 +227,15 @@
    "source": [
     "from typing import Optional, Type\n",
     "\n",
-    "# Import things that are needed generically\n",
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.callbacks import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
     "\n",
+    "# Import things that are needed generically\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
     "\n",
     "class InformationInput(BaseModel):\n",
     "    entity: str = Field(description=\"movie or a person mentioned in the question\")\n",

docs/docs/how_to/installation.mdx

@@ -9,7 +9,7 @@ functionality to install.
 
 ## Official release
 
-To install the main LangChain package, run:
+To install the main `langchain` package, run:
 
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
@@ -26,8 +26,7 @@ import CodeBlock from "@theme/CodeBlock";
 
 While this package acts as a sane starting point to using LangChain,
 much of the value of LangChain comes when integrating it with various model providers, datastores, etc.
-By default, the dependencies needed to do that are NOT installed. You will need to install the dependencies for specific integrations separately.
-We'll show how to do that in the next sections of this guide.
+By default, the dependencies needed to do that are NOT installed. You will need to install the dependencies for specific integrations separately, which we show below.
 
 ## Ecosystem packages
 
@@ -41,14 +40,6 @@ When installing a package, you do not need to explicitly install that package's
 However, you may choose to if you are using a feature only available in a certain version of that dependency.
 If you do, you should make sure that the installed or pinned version is compatible with any other integration packages you use.
 
-### From source
-
-If you want to install from source, you can do so by cloning the repo and be sure that the directory is `PATH/TO/REPO/langchain/libs/langchain` running:
-
-```bash
-pip install -e .
-```
-
 ### LangChain core
 The `langchain-core` package contains base abstractions that the rest of the LangChain ecosystem uses, along with the LangChain Expression Language. It is automatically installed by `langchain`, but can also be used separately. Install with:
 
@@ -56,8 +47,18 @@ The `langchain-core` package contains base abstractions that the rest of the Lan
 pip install langchain-core
 ```
 
-### LangChain community
-The `langchain-community` package contains third-party integrations. Install with:
+### Integration packages
+
+Certain integrations like OpenAI and Anthropic have their own packages.
+Any integrations that require their own package will be documented as such in the [Integration docs](/docs/integrations/platforms/).
+You can see a list of all integration packages in the [API reference](https://api.python.langchain.com) under the "Partner libs" dropdown.
+To install one of these run:
+
+```bash
+pip install langchain-openai
+```
+
+Any integrations that haven't been split out into their own packages will live in the `langchain-community` package. Install with:
 
 ```bash
 pip install langchain-community
@@ -89,7 +90,7 @@ pip install "langserve[all]"
 ```
 for both client and server dependencies. Or `pip install "langserve[client]"` for client code, and `pip install "langserve[server]"` for server code.
 
-## LangChain CLI
+### LangChain CLI
 The LangChain CLI is useful for working with LangChain templates and other LangServe projects.
 Install with:
 
@@ -105,3 +106,13 @@ If you are not using LangChain, you can install it with:
 ```bash
 pip install langsmith
 ```
+
+### From source
+
+If you want to install a package from source, you can do so by cloning the [main LangChain repo](https://github.com/langchain-ai/langchain), enter the directory of the package you want to install `PATH/TO/REPO/langchain/libs/{package}`, and run:
+
+```bash
+pip install -e .
+```
+
+LangGraph, LangSmith SDK, and certain integration packages live outside the main LangChain repo. You can see [all repos here](https://github.com/langchain-ai).

docs/docs/how_to/llm_caching.ipynb

@@ -25,7 +25,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "# Please manually enter OpenAI Key"
    ]
   },

docs/docs/how_to/logprobs.ipynb

@@ -94,7 +94,7 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\").bind(logprobs=True)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\").bind(logprobs=True)\n",
     "\n",
     "msg = llm.invoke((\"human\", \"how are you today\"))\n",
     "\n",

docs/docs/how_to/merge_message_runs.ipynb

@@ -11,12 +11,30 @@
     "\n",
     "The `merge_message_runs` utility makes it easy to merge consecutive messages of the same type.\n",
     "\n",
+    "### Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "198ce37f-4466-45a2-8878-d75cd01a5d23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-core langchain-anthropic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5c3ca6e-e5b3-4151-8307-9101713a20ae",
+   "metadata": {},
+   "source": [
     "## Basic usage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "1a215bbb-c05c-40b0-a6fd-d94884d517df",
    "metadata": {},
    "outputs": [
@@ -24,11 +42,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "SystemMessage(content=\"you're a good assistant.\\nyou always respond with a joke.\")\n",
+      "SystemMessage(content=\"you're a good assistant.\\nyou always respond with a joke.\", additional_kwargs={}, response_metadata={})\n",
       "\n",
-      "HumanMessage(content=[{'type': 'text', 'text': \"i wonder why it's called langchain\"}, 'and who is harrison chasing anyways'])\n",
+      "HumanMessage(content=[{'type': 'text', 'text': \"i wonder why it's called langchain\"}, 'and who is harrison chasing anyways'], additional_kwargs={}, response_metadata={})\n",
       "\n",
-      "AIMessage(content='Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!\\nWhy, he\\'s probably chasing after the last cup of coffee in the office!')\n"
+      "AIMessage(content='Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!\\nWhy, he\\'s probably chasing after the last cup of coffee in the office!', additional_kwargs={}, response_metadata={})\n"
      ]
     }
    ],
@@ -63,38 +81,6 @@
     "Notice that if the contents of one of the messages to merge is a list of content blocks then the merged message will have a list of content blocks. And if both messages to merge have string contents then those are concatenated with a newline character."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "11f7e8d3",
-   "metadata": {},
-   "source": [
-    "The `merge_message_runs` utility also works with messages composed together using the overloaded `+` operation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b51855c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "messages = (\n",
-    "    SystemMessage(\"you're a good assistant.\")\n",
-    "    + SystemMessage(\"you always respond with a joke.\")\n",
-    "    + HumanMessage([{\"type\": \"text\", \"text\": \"i wonder why it's called langchain\"}])\n",
-    "    + HumanMessage(\"and who is harrison chasing anyways\")\n",
-    "    + AIMessage(\n",
-    "        'Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!'\n",
-    "    )\n",
-    "    + AIMessage(\n",
-    "        \"Why, he's probably chasing after the last cup of coffee in the office!\"\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "merged = merge_message_runs(messages)\n",
-    "print(\"\\n\\n\".join([repr(x) for x in merged]))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "1b2eee74-71c8-4168-b968-bca580c25d18",
@@ -107,23 +93,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "id": "6d5a0283-11f8-435b-b27b-7b18f7693592",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=[], response_metadata={'id': 'msg_01D6R8Naum57q8qBau9vLBUX', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 84, 'output_tokens': 3}}, id='run-ac0c465b-b54f-4b8b-9295-e5951250d653-0', usage_metadata={'input_tokens': 84, 'output_tokens': 3, 'total_tokens': 87})"
+       "AIMessage(content=[], additional_kwargs={}, response_metadata={'id': 'msg_01KNGUMTuzBVfwNouLDpUMwf', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 84, 'output_tokens': 3}}, id='run-b908b198-9c24-450b-9749-9d4a8182937b-0', usage_metadata={'input_tokens': 84, 'output_tokens': 3, 'total_tokens': 87})"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# pip install -U langchain-anthropic\n",
+    "%pip install -qU langchain-anthropic\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "\n",
     "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)\n",
@@ -146,19 +139,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "460817a6-c327-429d-958e-181a8c46059c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[SystemMessage(content=\"you're a good assistant.\\nyou always respond with a joke.\"),\n",
-       " HumanMessage(content=[{'type': 'text', 'text': \"i wonder why it's called langchain\"}, 'and who is harrison chasing anyways']),\n",
-       " AIMessage(content='Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!\\nWhy, he\\'s probably chasing after the last cup of coffee in the office!')]"
+       "[SystemMessage(content=\"you're a good assistant.\\nyou always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
+       " HumanMessage(content=[{'type': 'text', 'text': \"i wonder why it's called langchain\"}, 'and who is harrison chasing anyways'], additional_kwargs={}, response_metadata={}),\n",
+       " AIMessage(content='Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!\\nWhy, he\\'s probably chasing after the last cup of coffee in the office!', additional_kwargs={}, response_metadata={})]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -167,6 +160,53 @@
     "merger.invoke(messages)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4178837d-b155-492d-9404-d567accc1fa0",
+   "metadata": {},
+   "source": [
+    "`merge_message_runs` can also be placed after a prompt:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "620530ab-ed05-4899-b984-bfa4cd738465",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='A convergent series is an infinite series whose partial sums approach a finite value as more terms are added. In other words, the sequence of partial sums has a limit.\\n\\nMore formally, an infinite series Σ an (where an are the terms of the series) is said to be convergent if the sequence of partial sums:\\n\\nS1 = a1\\nS2 = a1 + a2  \\nS3 = a1 + a2 + a3\\n...\\nSn = a1 + a2 + a3 + ... + an\\n...\\n\\nconverges to some finite number S as n goes to infinity. We write:\\n\\nlim n→∞ Sn = S\\n\\nThe finite number S is called the sum of the convergent infinite series.\\n\\nIf the sequence of partial sums does not approach any finite limit, the infinite series is said to be divergent.\\n\\nSome key properties:\\n- A series converges if and only if the sequence of its partial sums is a Cauchy sequence.\\n- Absolute/conditional convergence criteria help determine if a given series converges.\\n- Convergent series have many important applications in mathematics, physics, engineering etc.', additional_kwargs={}, response_metadata={'id': 'msg_01MfV6y2hep7ZNvDz24A36U4', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 29, 'output_tokens': 267}}, id='run-9d925f58-021e-4bd0-94fc-f8f5e91010a4-0', usage_metadata={'input_tokens': 29, 'output_tokens': 267, 'total_tokens': 296})"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\"system\", \"You're great a {skill}\"),\n",
+    "        (\"system\", \"You're also great at explaining things\"),\n",
+    "        (\"human\", \"{query}\"),\n",
+    "    ]\n",
+    ")\n",
+    "chain = prompt | merger | llm\n",
+    "chain.invoke({\"skill\": \"math\", \"query\": \"what's the definition of a convergent series\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51ba533a-43c7-4e5f-bd91-a4ec23ceeb34",
+   "metadata": {},
+   "source": [
+    "LangSmith Trace: https://smith.langchain.com/public/432150b6-9909-40a7-8ae7-944b7e657438/r/f4ad5fb2-4d38-42a6-b780-25f62617d53f"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4548d916-ce21-4dc6-8f19-eedb8003ace6",
@@ -194,7 +234,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/migrate_agent.ipynb

@@ -65,9 +65,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API key:\\n\")"
    ]
   },
   {
@@ -82,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "1e425fea-2796-4b99-bee6-9a6ffe73f756",
    "metadata": {},
    "outputs": [],
@@ -115,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "03ea357c-9c36-4464-b2cc-27bd150e1554",
    "metadata": {},
    "outputs": [
@@ -126,7 +128,7 @@
        " 'output': 'The value of `magic_function(3)` is 5.'}"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -162,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "53a3737a-d167-4255-89bf-20ac37f89a3e",
    "metadata": {},
    "outputs": [
@@ -173,7 +175,7 @@
        " 'output': 'The value of `magic_function(3)` is 5.'}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -193,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "74ecebe3-512e-409c-a661-bdd5b0a2b782",
    "metadata": {},
    "outputs": [
@@ -201,10 +203,10 @@
      "data": {
       "text/plain": [
        "{'input': 'Pardon?',\n",
-       " 'output': 'The value you get when you apply `magic_function` to the input 3 is 5.'}"
+       " 'output': 'The value returned by `magic_function` when the input is 3 is 5.'}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -243,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "a9a11ccd-75e2-4c11-844d-a34870b0ff91",
    "metadata": {},
    "outputs": [
@@ -254,7 +256,7 @@
        " 'output': 'El valor de `magic_function(3)` es 5.'}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -295,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "a9486805-676a-4d19-a5c4-08b41b172989",
    "metadata": {},
    "outputs": [],
@@ -324,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "d369ab45-0c82-45f4-9d3e-8efb8dd47e2c",
    "metadata": {},
    "outputs": [
@@ -332,7 +334,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'input': 'what is the value of magic_function(3)?', 'output': 'El valor de magic_function(3) es 5. ¡Pandamonium!'}\n"
+      "{'input': 'what is the value of magic_function(3)?', 'output': 'The value of magic_function(3) is 5. ¡Pandamonium!'}\n"
      ]
     }
    ],
@@ -386,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "b97beba5-8f74-430c-9399-91b77c8fa15c",
    "metadata": {},
    "outputs": [
@@ -394,11 +396,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hi Polly! The output of the magic function for the input 3 is 5.\n",
+      "Hi Polly! The output of applying the magic function to the input 3 is 5.\n",
       "---\n",
-      "Yes, your name is Polly!\n",
+      "Yes, you mentioned your name is Polly.\n",
       "---\n",
-      "The output of the magic function for the input 3 is 5.\n"
+      "The output of applying the magic function to the input 3 is 5.\n"
      ]
     }
    ],
@@ -476,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "baca3dc6-678b-4509-9275-2fd653102898",
    "metadata": {},
    "outputs": [
@@ -484,16 +486,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hi Polly! The output of the magic_function for the input of 3 is 5.\n",
+      "Hi Polly! The output of applying the magic function to the input 3 is 5.\n",
       "---\n",
       "Yes, your name is Polly!\n",
       "---\n",
-      "The output of the magic_function for the input of 3 was 5.\n"
+      "The output of applying the magic function to the input 3 was 5.\n"
      ]
     }
    ],
    "source": [
-    "from langgraph.checkpoint import MemorySaver  # an in-memory checkpointer\n",
+    "from langgraph.checkpoint.memory import MemorySaver  # an in-memory checkpointer\n",
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
     "system_message = \"You are a helpful assistant.\"\n",
@@ -544,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "e62843c4-1107-41f0-a50b-aea256e28053",
    "metadata": {},
    "outputs": [
@@ -552,8 +554,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'actions': [ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-5664e138-7085-4da7-a49e-5656a87b8d78', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_1exy0rScfPmo4fy27FbQ5qJ2')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-5664e138-7085-4da7-a49e-5656a87b8d78', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'index': 0, 'type': 'tool_call_chunk'}])]}\n",
-      "{'steps': [AgentStep(action=ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-5664e138-7085-4da7-a49e-5656a87b8d78', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_1exy0rScfPmo4fy27FbQ5qJ2', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_1exy0rScfPmo4fy27FbQ5qJ2'), observation=5)], 'messages': [FunctionMessage(content='5', name='magic_function')]}\n",
+      "{'actions': [ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_gNzQT96XWoyZqVl1jI1yMnjy', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_c9aa9c0491'}, id='run-dc7ce17d-02fd-4fdb-be82-7c902410b6b7', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_gNzQT96XWoyZqVl1jI1yMnjy', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_gNzQT96XWoyZqVl1jI1yMnjy', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_gNzQT96XWoyZqVl1jI1yMnjy')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_gNzQT96XWoyZqVl1jI1yMnjy', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_c9aa9c0491'}, id='run-dc7ce17d-02fd-4fdb-be82-7c902410b6b7', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_gNzQT96XWoyZqVl1jI1yMnjy', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_gNzQT96XWoyZqVl1jI1yMnjy', 'index': 0, 'type': 'tool_call_chunk'}])]}\n",
+      "{'steps': [AgentStep(action=ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_gNzQT96XWoyZqVl1jI1yMnjy', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_c9aa9c0491'}, id='run-dc7ce17d-02fd-4fdb-be82-7c902410b6b7', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_gNzQT96XWoyZqVl1jI1yMnjy', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_gNzQT96XWoyZqVl1jI1yMnjy', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_gNzQT96XWoyZqVl1jI1yMnjy'), observation=5)], 'messages': [FunctionMessage(content='5', name='magic_function')]}\n",
       "{'output': 'The value of `magic_function(3)` is 5.', 'messages': [AIMessage(content='The value of `magic_function(3)` is 5.')]}\n"
      ]
     }
@@ -604,7 +606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "076ebc85-f804-4093-a25a-a16334c9898e",
    "metadata": {},
    "outputs": [
@@ -612,9 +614,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_my9rzFSKR4T1yYKwCsfbZB8A', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 61, 'total_tokens': 75}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_bc2a86f5f5', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-dd705555-8fae-4fb1-a033-5d99a23e3c22-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_my9rzFSKR4T1yYKwCsfbZB8A', 'type': 'tool_call'}], usage_metadata={'input_tokens': 61, 'output_tokens': 14, 'total_tokens': 75})]}}\n",
-      "{'tools': {'messages': [ToolMessage(content='5', name='magic_function', tool_call_id='call_my9rzFSKR4T1yYKwCsfbZB8A')]}}\n",
-      "{'agent': {'messages': [AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 84, 'total_tokens': 98}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'stop', 'logprobs': None}, id='run-698cad05-8cb2-4d08-8c2a-881e354f6cc7-0', usage_metadata={'input_tokens': 84, 'output_tokens': 14, 'total_tokens': 98})]}}\n"
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_I0nztlIcc0e9ry5dn53YLZUM', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 61, 'total_tokens': 75}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-5f9bd87d-3692-4d13-8d27-1859e13e2156-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_I0nztlIcc0e9ry5dn53YLZUM', 'type': 'tool_call'}], usage_metadata={'input_tokens': 61, 'output_tokens': 14, 'total_tokens': 75})]}}\n",
+      "{'tools': {'messages': [ToolMessage(content='5', name='magic_function', tool_call_id='call_I0nztlIcc0e9ry5dn53YLZUM')]}}\n",
+      "{'agent': {'messages': [AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 84, 'total_tokens': 98}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'stop', 'logprobs': None}, id='run-f6015ca6-93e5-45e8-8b28-b3f0a8d203dc-0', usage_metadata={'input_tokens': 84, 'output_tokens': 14, 'total_tokens': 98})]}}\n"
      ]
     }
    ],
@@ -654,7 +656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "id": "a2f720f3-c121-4be2-b498-92c16bb44b0a",
    "metadata": {},
    "outputs": [
@@ -662,7 +664,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_uPZ2D1Bo5mdED3gwgaeWURrf', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518'}, id='run-a792db4a-278d-4090-82ae-904a30eada93', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_uPZ2D1Bo5mdED3gwgaeWURrf', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_uPZ2D1Bo5mdED3gwgaeWURrf', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_uPZ2D1Bo5mdED3gwgaeWURrf'), 5)]\n"
+      "[(ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_wjaAyTjI2LSYOq7C8QZYSxEs', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_c9aa9c0491'}, id='run-99e06b70-1ef6-4761-834b-87b6c5252e20', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_wjaAyTjI2LSYOq7C8QZYSxEs', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_wjaAyTjI2LSYOq7C8QZYSxEs', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_wjaAyTjI2LSYOq7C8QZYSxEs'), 5)]\n"
      ]
     }
    ],
@@ -684,20 +686,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "id": "ef23117a-5ccb-42ce-80c3-ea49a9d3a942",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'messages': [HumanMessage(content='what is the value of magic_function(3)?', id='cd7d0f49-a0e0-425a-b2b0-603a716058ed'),\n",
-       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_VfZ9287DuybOSrBsQH5X12xf', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a1e965cd-bf61-44f9-aec1-8aaecb80955f-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_VfZ9287DuybOSrBsQH5X12xf', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}),\n",
-       "  ToolMessage(content='5', name='magic_function', id='20d5c2fe-a5d8-47fa-9e04-5282642e2039', tool_call_id='call_VfZ9287DuybOSrBsQH5X12xf'),\n",
-       "  AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 78, 'total_tokens': 92}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'stop', 'logprobs': None}, id='run-abf9341c-ef41-4157-935d-a3be5dfa2f41-0', usage_metadata={'input_tokens': 78, 'output_tokens': 14, 'total_tokens': 92})]}"
+       "{'messages': [HumanMessage(content='what is the value of magic_function(3)?', id='2d369331-8052-4167-bd85-9f6d8ad021ae'),\n",
+       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_oXiSQSe6WeWj7XIKXxZrO2IC', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-297e7fc9-726f-46a0-8c67-dc28ed1724d0-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_oXiSQSe6WeWj7XIKXxZrO2IC', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}),\n",
+       "  ToolMessage(content='5', name='magic_function', id='46370faf-9598-423c-b94b-aca8cb4f035d', tool_call_id='call_oXiSQSe6WeWj7XIKXxZrO2IC'),\n",
+       "  AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 78, 'total_tokens': 92}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'stop', 'logprobs': None}, id='run-f48efaff-0c2c-4632-bbf9-7ee626f73d02-0', usage_metadata={'input_tokens': 78, 'output_tokens': 14, 'total_tokens': 92})]}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -757,7 +759,7 @@
       "Invoking: `magic_function` with `{'input': '3'}`\n",
       "\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3mParece que hubo un error al intentar calcular el valor de la función mágica. ¿Te gustaría que lo intente de nuevo?\u001b[0m\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3mHubo un error al intentar obtener el valor de `magic_function(3)`. ¿Podrías intentarlo de nuevo o proporcionar más detalles?\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -766,7 +768,7 @@
      "data": {
       "text/plain": [
        "{'input': 'what is the value of magic_function(3)?',\n",
-       " 'output': 'Parece que hubo un error al intentar calcular el valor de la función mágica. ¿Te gustaría que lo intente de nuevo?'}"
+       " 'output': 'Hubo un error al intentar obtener el valor de `magic_function(3)`. ¿Podrías intentarlo de nuevo o proporcionar más detalles?'}"
       ]
      },
      "execution_count": 17,
@@ -819,12 +821,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "content='what is the value of magic_function(3)?' id='74e2d5e8-2b59-4820-979c-8d11ecfc14c2'\n",
-      "content='' additional_kwargs={'tool_calls': [{'id': 'call_ihtrH6IG95pDXpKluIwAgi3J', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-5a35e465-8a08-43dd-ac8b-4a76dcace305-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_ihtrH6IG95pDXpKluIwAgi3J', 'type': 'tool_call'}] usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}\n",
-      "content='Sorry, there was an error. Please try again.' name='magic_function' id='8c37c19b-3586-46b1-aab9-a045786801a2' tool_call_id='call_ihtrH6IG95pDXpKluIwAgi3J'\n",
-      "content='It seems there was an error in processing the request. Let me try again.' additional_kwargs={'tool_calls': [{'id': 'call_iF0vYWAd6rfely0cXSqdMOnF', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 31, 'prompt_tokens': 88, 'total_tokens': 119}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-eb88ec77-d492-43a5-a5dd-4cefef9a6920-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_iF0vYWAd6rfely0cXSqdMOnF', 'type': 'tool_call'}] usage_metadata={'input_tokens': 88, 'output_tokens': 31, 'total_tokens': 119}\n",
-      "content='Sorry, there was an error. Please try again.' name='magic_function' id='c9ff261f-a0f1-4c92-a9f2-cd749f62d911' tool_call_id='call_iF0vYWAd6rfely0cXSqdMOnF'\n",
-      "content='I am currently unable to process the request with the input \"3\" for the `magic_function`. If you have any other questions or need assistance with something else, please let me know!' response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 141, 'total_tokens': 180}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'stop', 'logprobs': None} id='run-d42508aa-f286-4b57-80fb-f8a76736d470-0' usage_metadata={'input_tokens': 141, 'output_tokens': 39, 'total_tokens': 180}\n"
+      "content='what is the value of magic_function(3)?' id='fe74bb30-45b8-4a40-a5ed-fd6678da5428'\n",
+      "content='' additional_kwargs={'tool_calls': [{'id': 'call_TNKfNy6fgZNdJAvHUMXwtp8f', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-dad8bfc1-477c-40d2-9016-243d25c0dd13-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_TNKfNy6fgZNdJAvHUMXwtp8f', 'type': 'tool_call'}] usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}\n",
+      "content='Sorry, there was an error. Please try again.' name='magic_function' id='653226e0-3187-40be-a774-4c7c2612239e' tool_call_id='call_TNKfNy6fgZNdJAvHUMXwtp8f'\n",
+      "content='It looks like there was an issue with processing the request. Let me try that again.' additional_kwargs={'tool_calls': [{'id': 'call_K0wJ8fQLYGv8fYXY1Uo5U5sG', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 33, 'prompt_tokens': 88, 'total_tokens': 121}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-d4c85437-6625-4e57-81f9-86de6842be7b-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_K0wJ8fQLYGv8fYXY1Uo5U5sG', 'type': 'tool_call'}] usage_metadata={'input_tokens': 88, 'output_tokens': 33, 'total_tokens': 121}\n",
+      "content='Sorry, there was an error. Please try again.' name='magic_function' id='9b530d03-95df-401e-bb4f-5cada1195033' tool_call_id='call_K0wJ8fQLYGv8fYXY1Uo5U5sG'\n",
+      "content='It seems that there is a persistent issue with processing the request. Let me attempt it one more time.' additional_kwargs={'tool_calls': [{'id': 'call_7ECwwNBDo4SH56oczErZJVRT', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 36, 'prompt_tokens': 143, 'total_tokens': 179}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-9f3f651e-a641-4112-99ed-d1ac11169582-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_7ECwwNBDo4SH56oczErZJVRT', 'type': 'tool_call'}] usage_metadata={'input_tokens': 143, 'output_tokens': 36, 'total_tokens': 179}\n",
+      "content='Sorry, there was an error. Please try again.' name='magic_function' id='e4cd152b-4eb1-47df-ac76-f88e79adbe19' tool_call_id='call_7ECwwNBDo4SH56oczErZJVRT'\n",
+      "content=\"It seems there is a consistent issue with processing the request for the magic function. Let's try using a different approach to resolve this.\" additional_kwargs={'tool_calls': [{'id': 'call_DMAL0UwBRijzuPjCTSwR2r17', 'function': {'arguments': '{\"input\":\"three\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 41, 'prompt_tokens': 201, 'total_tokens': 242}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_c9aa9c0491', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-cd9f4e5c-f881-462c-abe3-890e73f46a01-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 'three'}, 'id': 'call_DMAL0UwBRijzuPjCTSwR2r17', 'type': 'tool_call'}] usage_metadata={'input_tokens': 201, 'output_tokens': 41, 'total_tokens': 242}\n",
+      "{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
      ]
     }
    ],
@@ -939,9 +944,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_FKiTkTd0Ffd4rkYSzERprf1M', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b842f7b6-ec10-40f8-8c0e-baa220b77e91-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_FKiTkTd0Ffd4rkYSzERprf1M', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_o8Ym0u9UfzArhIm1lV7O0CXF', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d9faf125-1ff8-4de2-a75b-97e07d28dc4d-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_o8Ym0u9UfzArhIm1lV7O0CXF', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69})]}}\n",
       "------\n",
-      "{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
+      "{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to a step timeout.'}\n"
      ]
     }
    ],
@@ -957,7 +962,7 @@
     "        print(chunk)\n",
     "        print(\"------\")\n",
     "except TimeoutError:\n",
-    "    print({\"input\": query, \"output\": \"Agent stopped due to max iterations.\"})"
+    "    print({\"input\": query, \"output\": \"Agent stopped due to a step timeout.\"})"
    ]
   },
   {
@@ -978,7 +983,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_WoOB8juagB08xrP38twYlYKR', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73dee47e-30ab-42c9-bb0c-6f227cac96cd-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_WoOB8juagB08xrP38twYlYKR', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69})]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_gsGzyhyvR25iNV6W9VR2TIdQ', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-9ad8f834-06c5-41cf-9eec-6b7e0f5e777e-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_gsGzyhyvR25iNV6W9VR2TIdQ', 'type': 'tool_call'}], usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69})]}}\n",
       "------\n",
       "Task Cancelled.\n"
      ]
@@ -1089,10 +1094,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "content='what is the value of magic_function(3)?' id='4fa7fbe5-758c-47a3-9268-717665d10680'\n",
-      "content='' additional_kwargs={'tool_calls': [{'id': 'call_ujE0IQBbIQnxcF9gsZXQfdhF', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-65d689aa-baee-4342-a5d2-048feefab418-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_ujE0IQBbIQnxcF9gsZXQfdhF', 'type': 'tool_call'}] usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}\n",
-      "content='Sorry there was an error, please try again.' name='magic_function' id='ef8ddf1d-9ad7-4ac0-b784-b673c4d94bbd' tool_call_id='call_ujE0IQBbIQnxcF9gsZXQfdhF'\n",
-      "content='It seems there was an issue with the previous attempt. Let me try that again.' additional_kwargs={'tool_calls': [{'id': 'call_GcsAfCFUHJ50BN2IOWnwTbQ7', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 32, 'prompt_tokens': 87, 'total_tokens': 119}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_4e2b2da518', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-54527c4b-8ff0-4ee8-8abf-224886bd222e-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_GcsAfCFUHJ50BN2IOWnwTbQ7', 'type': 'tool_call'}] usage_metadata={'input_tokens': 87, 'output_tokens': 32, 'total_tokens': 119}\n",
+      "content='what is the value of magic_function(3)?' id='6487a942-0a9a-4e8a-9556-553a45fa9c5a'\n",
+      "content='' additional_kwargs={'tool_calls': [{'id': 'call_pe5KVY5No9iT4JWqrm5MwL1D', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 55, 'total_tokens': 69}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-04147325-fb72-462a-a1d9-6aa4e86e3d8a-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_pe5KVY5No9iT4JWqrm5MwL1D', 'type': 'tool_call'}] usage_metadata={'input_tokens': 55, 'output_tokens': 14, 'total_tokens': 69}\n",
+      "content='Sorry there was an error, please try again.' name='magic_function' id='bc0bf58f-7c6c-42ed-a96d-a2afa79f16a9' tool_call_id='call_pe5KVY5No9iT4JWqrm5MwL1D'\n",
+      "content=\"It seems there was an issue with processing the request. I'll try again.\" additional_kwargs={'tool_calls': [{'id': 'call_5rV7k3g7oW38bD9KUTsSxK8l', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 30, 'prompt_tokens': 87, 'total_tokens': 117}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3aa7262c27', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-6e43ffd4-fb6f-4222-8503-a50ae268c0be-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_5rV7k3g7oW38bD9KUTsSxK8l', 'type': 'tool_call'}] usage_metadata={'input_tokens': 87, 'output_tokens': 30, 'total_tokens': 117}\n",
       "{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
      ]
     }
@@ -1322,7 +1327,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/multi_vector.ipynb

@@ -440,7 +440,7 @@
    "source": [
     "from typing import List\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class HypotheticalQuestions(BaseModel):\n",

docs/docs/how_to/output_parser_fixing.ipynb

@@ -24,8 +24,8 @@
     "from typing import List\n",
     "\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
-    "from langchain_openai import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field"
    ]
   },
   {

docs/docs/how_to/output_parser_json.ipynb

@@ -47,7 +47,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {
@@ -71,8 +72,8 @@
    "source": [
     "from langchain_core.output_parsers import JsonOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "model = ChatOpenAI(temperature=0)\n",
     "\n",

docs/docs/how_to/output_parser_retry.ipynb

@@ -20,8 +20,8 @@
     "from langchain.output_parsers import OutputFixingParser\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
-    "from langchain_openai import ChatOpenAI, OpenAI"
+    "from langchain_openai import ChatOpenAI, OpenAI\n",
+    "from pydantic import BaseModel, Field"
    ]
   },
   {

docs/docs/how_to/output_parser_structured.ipynb

@@ -35,17 +35,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "id": "1594b2bf-2a6f-47bb-9a81-38930f8e606b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')"
+       "Joke(setup='Why did the tomato turn red?', punchline='Because it saw the salad dressing!')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -53,8 +53,8 @@
    "source": [
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
     "from langchain_openai import OpenAI\n",
+    "from pydantic import BaseModel, Field, model_validator\n",
     "\n",
     "model = OpenAI(model_name=\"gpt-3.5-turbo-instruct\", temperature=0.0)\n",
     "\n",
@@ -65,11 +65,13 @@
     "    punchline: str = Field(description=\"answer to resolve the joke\")\n",
     "\n",
     "    # You can add custom validation logic easily with Pydantic.\n",
-    "    @validator(\"setup\")\n",
-    "    def question_ends_with_question_mark(cls, field):\n",
-    "        if field[-1] != \"?\":\n",
+    "    @model_validator(mode=\"before\")\n",
+    "    @classmethod\n",
+    "    def question_ends_with_question_mark(cls, values: dict) -> dict:\n",
+    "        setup = values[\"setup\"]\n",
+    "        if setup[-1] != \"?\":\n",
     "            raise ValueError(\"Badly formed question!\")\n",
-    "        return field\n",
+    "        return values\n",
     "\n",
     "\n",
     "# Set up a parser + inject instructions into the prompt template.\n",
@@ -239,9 +241,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-311",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-311"
   },
   "language_info": {
    "codemirror_mode": {
@@ -253,7 +255,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/output_parser_xml.ipynb

@@ -41,7 +41,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
+    "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/output_parser_yaml.ipynb

@@ -39,7 +39,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {
@@ -70,8 +71,8 @@
    "source": [
     "from langchain.output_parsers import YamlOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_openai import ChatOpenAI\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "# Define your desired data structure.\n",

docs/docs/how_to/parallel.ipynb

@@ -60,7 +60,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/passthrough.ipynb

@@ -46,7 +46,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()"
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/how_to/qa_citations.ipynb

@@ -269,7 +269,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class CitedAnswer(BaseModel):\n",

docs/docs/how_to/query_constructing_filters.ipynb

@@ -24,9 +24,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "id": "8ca446a0",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:32:35.834087Z",
+     "iopub.status.busy": "2024-09-11T02:32:35.833763Z",
+     "iopub.status.idle": "2024-09-11T02:32:36.588973Z",
+     "shell.execute_reply": "2024-09-11T02:32:36.588677Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import Optional\n",
@@ -40,7 +47,7 @@
     ")\n",
     "from langchain_community.query_constructors.chroma import ChromaTranslator\n",
     "from langchain_community.query_constructors.elasticsearch import ElasticsearchTranslator\n",
-    "from langchain_core.pydantic_v1 import BaseModel"
+    "from pydantic import BaseModel"
    ]
   },
   {
@@ -53,9 +60,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
    "id": "64055006",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:32:36.590665Z",
+     "iopub.status.busy": "2024-09-11T02:32:36.590527Z",
+     "iopub.status.idle": "2024-09-11T02:32:36.592985Z",
+     "shell.execute_reply": "2024-09-11T02:32:36.592763Z"
+    }
+   },
    "outputs": [],
    "source": [
     "class Search(BaseModel):\n",
@@ -66,9 +80,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "id": "44eb6d98",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:32:36.594147Z",
+     "iopub.status.busy": "2024-09-11T02:32:36.594072Z",
+     "iopub.status.idle": "2024-09-11T02:32:36.595777Z",
+     "shell.execute_reply": "2024-09-11T02:32:36.595563Z"
+    }
+   },
    "outputs": [],
    "source": [
     "search_query = Search(query=\"RAG\", start_year=2022, author=\"LangChain\")"
@@ -76,9 +97,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "id": "e8ba6705",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:32:36.596902Z",
+     "iopub.status.busy": "2024-09-11T02:32:36.596824Z",
+     "iopub.status.idle": "2024-09-11T02:32:36.598805Z",
+     "shell.execute_reply": "2024-09-11T02:32:36.598629Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def construct_comparisons(query: Search):\n",
@@ -104,9 +132,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 5,
    "id": "6a79c9da",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:32:36.599989Z",
+     "iopub.status.busy": "2024-09-11T02:32:36.599909Z",
+     "iopub.status.idle": "2024-09-11T02:32:36.601521Z",
+     "shell.execute_reply": "2024-09-11T02:32:36.601306Z"
+    }
+   },
    "outputs": [],
    "source": [
     "comparisons = construct_comparisons(search_query)"
@@ -114,9 +149,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 6,
    "id": "2d0e9689",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:32:36.602688Z",
+     "iopub.status.busy": "2024-09-11T02:32:36.602603Z",
+     "iopub.status.idle": "2024-09-11T02:32:36.604171Z",
+     "shell.execute_reply": "2024-09-11T02:32:36.603981Z"
+    }
+   },
    "outputs": [],
    "source": [
     "_filter = Operation(operator=Operator.AND, arguments=comparisons)"
@@ -124,9 +166,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "id": "e4c0b2ce",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:32:36.605267Z",
+     "iopub.status.busy": "2024-09-11T02:32:36.605190Z",
+     "iopub.status.idle": "2024-09-11T02:32:36.607993Z",
+     "shell.execute_reply": "2024-09-11T02:32:36.607796Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -135,7 +184,7 @@
        "   {'term': {'metadata.author.keyword': 'LangChain'}}]}}"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -146,9 +195,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 8,
    "id": "d75455ae",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:32:36.609091Z",
+     "iopub.status.busy": "2024-09-11T02:32:36.609012Z",
+     "iopub.status.idle": "2024-09-11T02:32:36.611075Z",
+     "shell.execute_reply": "2024-09-11T02:32:36.610869Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -156,7 +212,7 @@
        "{'$and': [{'start_year': {'$gt': 2022}}, {'author': {'$eq': 'LangChain'}}]}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -182,7 +238,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_few_shot.ipynb

@@ -35,7 +35,14 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:48.329739Z",
+     "iopub.status.busy": "2024-09-11T02:33:48.329033Z",
+     "iopub.status.idle": "2024-09-11T02:33:48.334555Z",
+     "shell.execute_reply": "2024-09-11T02:33:48.334086Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# %pip install -qU langchain-core langchain-openai"
@@ -53,15 +60,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "40e2979e-a818-4b96-ac25-039336f94319",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:48.337140Z",
+     "iopub.status.busy": "2024-09-11T02:33:48.336958Z",
+     "iopub.status.idle": "2024-09-11T02:33:48.342671Z",
+     "shell.execute_reply": "2024-09-11T02:33:48.342281Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
     "# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
@@ -80,14 +95,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 3,
    "id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:48.345004Z",
+     "iopub.status.busy": "2024-09-11T02:33:48.344838Z",
+     "iopub.status.idle": "2024-09-11T02:33:48.413166Z",
+     "shell.execute_reply": "2024-09-11T02:33:48.412908Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "sub_queries_description = \"\"\"\\\n",
     "If the original question contains multiple distinct sub-questions, \\\n",
@@ -121,9 +143,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 4,
    "id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:48.414805Z",
+     "iopub.status.busy": "2024-09-11T02:33:48.414700Z",
+     "iopub.status.idle": "2024-09-11T02:33:49.023858Z",
+     "shell.execute_reply": "2024-09-11T02:33:49.023547Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
@@ -143,7 +172,7 @@
     "        (\"human\", \"{question}\"),\n",
     "    ]\n",
     ")\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm = llm.with_structured_output(Search)\n",
     "query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
    ]
@@ -158,17 +187,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 5,
    "id": "0bcfce06-6f0c-4f9d-a1fc-dc29342d2aae",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:49.025536Z",
+     "iopub.status.busy": "2024-09-11T02:33:49.025437Z",
+     "iopub.status.idle": "2024-09-11T02:33:50.170550Z",
+     "shell.execute_reply": "2024-09-11T02:33:50.169835Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='web voyager vs reflection agents', sub_queries=['difference between web voyager and reflection agents', 'do web voyager and reflection agents use langgraph'], publish_year=None)"
+       "Search(query='difference between web voyager and reflection agents', sub_queries=['what is web voyager', 'what are reflection agents', 'do both web voyager and reflection agents use langgraph?'], publish_year=None)"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -193,9 +229,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 6,
    "id": "15b4923d-a08e-452d-8889-9a09a57d1095",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:50.180367Z",
+     "iopub.status.busy": "2024-09-11T02:33:50.173961Z",
+     "iopub.status.idle": "2024-09-11T02:33:50.186703Z",
+     "shell.execute_reply": "2024-09-11T02:33:50.186090Z"
+    }
+   },
    "outputs": [],
    "source": [
     "examples = []"
@@ -203,9 +246,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 7,
    "id": "da5330e6-827a-40e5-982b-b23b6286b758",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:50.189822Z",
+     "iopub.status.busy": "2024-09-11T02:33:50.189617Z",
+     "iopub.status.idle": "2024-09-11T02:33:50.195116Z",
+     "shell.execute_reply": "2024-09-11T02:33:50.194617Z"
+    }
+   },
    "outputs": [],
    "source": [
     "question = \"What's chat langchain, is it a langchain template?\"\n",
@@ -218,9 +268,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 8,
    "id": "580e857a-27df-4ecf-a19c-458dc9244ec8",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:50.198178Z",
+     "iopub.status.busy": "2024-09-11T02:33:50.198002Z",
+     "iopub.status.idle": "2024-09-11T02:33:50.204115Z",
+     "shell.execute_reply": "2024-09-11T02:33:50.202534Z"
+    }
+   },
    "outputs": [],
    "source": [
     "question = \"How to build multi-agent system and stream intermediate steps from it\"\n",
@@ -238,9 +295,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 9,
    "id": "fa63310d-69e3-4701-825c-fbb01f8a5a16",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:50.207416Z",
+     "iopub.status.busy": "2024-09-11T02:33:50.207196Z",
+     "iopub.status.idle": "2024-09-11T02:33:50.212484Z",
+     "shell.execute_reply": "2024-09-11T02:33:50.211974Z"
+    }
+   },
    "outputs": [],
    "source": [
     "question = \"LangChain agents vs LangGraph?\"\n",
@@ -266,9 +330,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 10,
    "id": "68b03709-9a60-4acf-b96c-cafe1056c6f3",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:50.215540Z",
+     "iopub.status.busy": "2024-09-11T02:33:50.215250Z",
+     "iopub.status.idle": "2024-09-11T02:33:50.224108Z",
+     "shell.execute_reply": "2024-09-11T02:33:50.223490Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import uuid\n",
@@ -313,9 +384,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 11,
    "id": "d9bf9f87-3e6b-4fc2-957b-949b077fab54",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:50.227215Z",
+     "iopub.status.busy": "2024-09-11T02:33:50.226993Z",
+     "iopub.status.idle": "2024-09-11T02:33:50.231333Z",
+     "shell.execute_reply": "2024-09-11T02:33:50.230742Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.prompts import MessagesPlaceholder\n",
@@ -329,17 +407,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 12,
    "id": "e565ccb0-3530-4782-b56b-d1f6d0a8e559",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:33:50.233833Z",
+     "iopub.status.busy": "2024-09-11T02:33:50.233646Z",
+     "iopub.status.idle": "2024-09-11T02:33:51.318133Z",
+     "shell.execute_reply": "2024-09-11T02:33:51.317640Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='Difference between web voyager and reflection agents, do they both use LangGraph?', sub_queries=['What is Web Voyager', 'What are Reflection agents', 'Do Web Voyager and Reflection agents use LangGraph'], publish_year=None)"
+       "Search(query=\"What's the difference between web voyager and reflection agents? Do both use langgraph?\", sub_queries=['What is web voyager', 'What are reflection agents', 'Do web voyager and reflection agents use langgraph?'], publish_year=None)"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -377,7 +462,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_high_cardinality.ipynb

@@ -33,12 +33,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
-    "# %pip install -qU langchain langchain-community langchain-openai faker langchain-chroma"
+    "%pip install -qU langchain langchain-community langchain-openai faker langchain-chroma"
    ]
   },
   {
@@ -53,15 +61,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "40e2979e-a818-4b96-ac25-039336f94319",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:54.036110Z",
+     "iopub.status.busy": "2024-09-11T02:34:54.035829Z",
+     "iopub.status.idle": "2024-09-11T02:34:54.038746Z",
+     "shell.execute_reply": "2024-09-11T02:34:54.038430Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
     "# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
@@ -80,9 +96,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "e5ba65c2",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:54.040738Z",
+     "iopub.status.busy": "2024-09-11T02:34:54.040515Z",
+     "iopub.status.idle": "2024-09-11T02:34:54.622643Z",
+     "shell.execute_reply": "2024-09-11T02:34:54.622382Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from faker import Faker\n",
@@ -102,17 +125,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "c901ea97",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:54.624195Z",
+     "iopub.status.busy": "2024-09-11T02:34:54.624106Z",
+     "iopub.status.idle": "2024-09-11T02:34:54.627231Z",
+     "shell.execute_reply": "2024-09-11T02:34:54.626971Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Hayley Gonzalez'"
+       "'Jacob Adams'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123,17 +153,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "b0d42ae2",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:54.628545Z",
+     "iopub.status.busy": "2024-09-11T02:34:54.628460Z",
+     "iopub.status.idle": "2024-09-11T02:34:54.630474Z",
+     "shell.execute_reply": "2024-09-11T02:34:54.630282Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Jesse Knight'"
+       "'Eric Acevedo'"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -154,19 +191,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "0ae69afc",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:54.631758Z",
+     "iopub.status.busy": "2024-09-11T02:34:54.631678Z",
+     "iopub.status.idle": "2024-09-11T02:34:54.666448Z",
+     "shell.execute_reply": "2024-09-11T02:34:54.666216Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field"
+    "from pydantic import BaseModel, Field, model_validator"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "6c9485ce",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:54.667852Z",
+     "iopub.status.busy": "2024-09-11T02:34:54.667733Z",
+     "iopub.status.idle": "2024-09-11T02:34:54.700224Z",
+     "shell.execute_reply": "2024-09-11T02:34:54.700004Z"
+    }
+   },
    "outputs": [],
    "source": [
     "class Search(BaseModel):\n",
@@ -176,19 +227,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "aebd704a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/workplace/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n",
-      "  warn_beta(\n"
-     ]
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:54.701556Z",
+     "iopub.status.busy": "2024-09-11T02:34:54.701465Z",
+     "iopub.status.idle": "2024-09-11T02:34:55.179986Z",
+     "shell.execute_reply": "2024-09-11T02:34:55.179640Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
@@ -201,7 +250,7 @@
     "        (\"human\", \"{question}\"),\n",
     "    ]\n",
     ")\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm = llm.with_structured_output(Search)\n",
     "query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
    ]
@@ -216,17 +265,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 9,
    "id": "cc0d344b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:55.181603Z",
+     "iopub.status.busy": "2024-09-11T02:34:55.181500Z",
+     "iopub.status.idle": "2024-09-11T02:34:55.778884Z",
+     "shell.execute_reply": "2024-09-11T02:34:55.778324Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='books about aliens', author='Jesse Knight')"
+       "Search(query='aliens', author='Jesse Knight')"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -245,17 +301,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 10,
    "id": "82b6b2ad",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:55.784266Z",
+     "iopub.status.busy": "2024-09-11T02:34:55.782603Z",
+     "iopub.status.idle": "2024-09-11T02:34:56.206779Z",
+     "shell.execute_reply": "2024-09-11T02:34:56.206068Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='books about aliens', author='Jess Knight')"
+       "Search(query='aliens', author='Jess Knight')"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -276,9 +339,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 11,
    "id": "98788a94",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:56.210043Z",
+     "iopub.status.busy": "2024-09-11T02:34:56.209657Z",
+     "iopub.status.idle": "2024-09-11T02:34:56.213962Z",
+     "shell.execute_reply": "2024-09-11T02:34:56.213413Z"
+    }
+   },
    "outputs": [],
    "source": [
     "system = \"\"\"Generate a relevant search query for a library system.\n",
@@ -299,9 +369,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 12,
    "id": "e65412f5",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:56.216144Z",
+     "iopub.status.busy": "2024-09-11T02:34:56.216005Z",
+     "iopub.status.idle": "2024-09-11T02:34:56.218754Z",
+     "shell.execute_reply": "2024-09-11T02:34:56.218416Z"
+    }
+   },
    "outputs": [],
    "source": [
     "query_analyzer_all = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
@@ -317,18 +394,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 13,
    "id": "696b000f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Error code: 400 - {'error': {'message': \"This model's maximum context length is 16385 tokens. However, your messages resulted in 33885 tokens (33855 in the messages, 30 in the functions). Please reduce the length of the messages or functions.\", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}\n"
-     ]
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:56.220827Z",
+     "iopub.status.busy": "2024-09-11T02:34:56.220680Z",
+     "iopub.status.idle": "2024-09-11T02:34:58.846872Z",
+     "shell.execute_reply": "2024-09-11T02:34:58.846273Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "try:\n",
     "    res = query_analyzer_all.invoke(\"what are books about aliens by jess knight\")\n",
@@ -346,9 +422,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 14,
    "id": "0f0d0757",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:58.850318Z",
+     "iopub.status.busy": "2024-09-11T02:34:58.850100Z",
+     "iopub.status.idle": "2024-09-11T02:34:58.873883Z",
+     "shell.execute_reply": "2024-09-11T02:34:58.873525Z"
+    }
+   },
    "outputs": [],
    "source": [
     "llm_long = ChatOpenAI(model=\"gpt-4-turbo-preview\", temperature=0)\n",
@@ -358,17 +441,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 15,
    "id": "03e5b7b2",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:34:58.875940Z",
+     "iopub.status.busy": "2024-09-11T02:34:58.875811Z",
+     "iopub.status.idle": "2024-09-11T02:35:02.947273Z",
+     "shell.execute_reply": "2024-09-11T02:35:02.946220Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='aliens', author='Kevin Knight')"
+       "Search(query='aliens', author='jess knight')"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -389,9 +479,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 16,
    "id": "32b19e07",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:35:02.951939Z",
+     "iopub.status.busy": "2024-09-11T02:35:02.951583Z",
+     "iopub.status.idle": "2024-09-11T02:35:41.777839Z",
+     "shell.execute_reply": "2024-09-11T02:35:41.777392Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_chroma import Chroma\n",
@@ -403,9 +500,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 17,
    "id": "774cb7b0",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:35:41.780883Z",
+     "iopub.status.busy": "2024-09-11T02:35:41.780774Z",
+     "iopub.status.idle": "2024-09-11T02:35:41.782739Z",
+     "shell.execute_reply": "2024-09-11T02:35:41.782498Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def select_names(question):\n",
@@ -416,9 +520,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 18,
    "id": "1173159c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:35:41.783992Z",
+     "iopub.status.busy": "2024-09-11T02:35:41.783913Z",
+     "iopub.status.idle": "2024-09-11T02:35:41.785911Z",
+     "shell.execute_reply": "2024-09-11T02:35:41.785632Z"
+    }
+   },
    "outputs": [],
    "source": [
     "create_prompt = {\n",
@@ -429,9 +540,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 19,
    "id": "0a892607",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:35:41.787082Z",
+     "iopub.status.busy": "2024-09-11T02:35:41.787008Z",
+     "iopub.status.idle": "2024-09-11T02:35:41.788543Z",
+     "shell.execute_reply": "2024-09-11T02:35:41.788362Z"
+    }
+   },
    "outputs": [],
    "source": [
     "query_analyzer_select = create_prompt | structured_llm"
@@ -439,17 +557,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 20,
    "id": "8195d7cd",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:35:41.789624Z",
+     "iopub.status.busy": "2024-09-11T02:35:41.789551Z",
+     "iopub.status.idle": "2024-09-11T02:35:42.099839Z",
+     "shell.execute_reply": "2024-09-11T02:35:42.099042Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ChatPromptValue(messages=[SystemMessage(content='Generate a relevant search query for a library system.\\n\\n`author` attribute MUST be one of:\\n\\nJesse Knight, Kelly Knight, Scott Knight, Richard Knight, Andrew Knight, Katherine Knight, Erica Knight, Ashley Knight, Becky Knight, Kevin Knight\\n\\nDo NOT hallucinate author name!'), HumanMessage(content='what are books by jess knight')])"
+       "ChatPromptValue(messages=[SystemMessage(content='Generate a relevant search query for a library system.\\n\\n`author` attribute MUST be one of:\\n\\nJennifer Knight, Jill Knight, John Knight, Dr. Jeffrey Knight, Christopher Knight, Andrea Knight, Brandy Knight, Jennifer Keller, Becky Chambers, Sarah Knapp\\n\\nDo NOT hallucinate author name!'), HumanMessage(content='what are books by jess knight')])"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -460,17 +585,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 21,
    "id": "d3228b4e",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:35:42.106571Z",
+     "iopub.status.busy": "2024-09-11T02:35:42.105861Z",
+     "iopub.status.idle": "2024-09-11T02:35:42.909738Z",
+     "shell.execute_reply": "2024-09-11T02:35:42.908875Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='books about aliens', author='Jesse Knight')"
+       "Search(query='books about aliens', author='Jennifer Knight')"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -492,28 +624,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 22,
    "id": "a2e8b434",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:35:42.915376Z",
+     "iopub.status.busy": "2024-09-11T02:35:42.914923Z",
+     "iopub.status.idle": "2024-09-11T02:35:42.923958Z",
+     "shell.execute_reply": "2024-09-11T02:35:42.922391Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import validator\n",
-    "\n",
-    "\n",
     "class Search(BaseModel):\n",
     "    query: str\n",
     "    author: str\n",
     "\n",
-    "    @validator(\"author\")\n",
-    "    def double(cls, v: str) -> str:\n",
-    "        return vectorstore.similarity_search(v, k=1)[0].page_content"
+    "    @model_validator(mode=\"before\")\n",
+    "    @classmethod\n",
+    "    def double(cls, values: dict) -> dict:\n",
+    "        author = values[\"author\"]\n",
+    "        closest_valid_author = vectorstore.similarity_search(author, k=1)[\n",
+    "            0\n",
+    "        ].page_content\n",
+    "        values[\"author\"] = closest_valid_author\n",
+    "        return values"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 23,
    "id": "919c0601",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:35:42.927718Z",
+     "iopub.status.busy": "2024-09-11T02:35:42.927428Z",
+     "iopub.status.idle": "2024-09-11T02:35:42.933784Z",
+     "shell.execute_reply": "2024-09-11T02:35:42.933344Z"
+    }
+   },
    "outputs": [],
    "source": [
     "system = \"\"\"Generate a relevant search query for a library system\"\"\"\n",
@@ -531,17 +680,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 24,
    "id": "6c4f3e9a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:35:42.936506Z",
+     "iopub.status.busy": "2024-09-11T02:35:42.936186Z",
+     "iopub.status.idle": "2024-09-11T02:35:43.711754Z",
+     "shell.execute_reply": "2024-09-11T02:35:43.710695Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='books about aliens', author='Jesse Knight')"
+       "Search(query='aliens', author='John Knight')"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -552,9 +708,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "a309cb11",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:35:43.717567Z",
+     "iopub.status.busy": "2024-09-11T02:35:43.717189Z",
+     "iopub.status.idle": "2024-09-11T02:35:43.722339Z",
+     "shell.execute_reply": "2024-09-11T02:35:43.720537Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# TODO: show trigram similarity"
@@ -563,9 +726,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-311",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-311"
   },
   "language_info": {
    "codemirror_mode": {
@@ -577,7 +740,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_multiple_queries.ipynb

@@ -33,10 +33,25 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:41:53.160868Z",
+     "iopub.status.busy": "2024-09-11T02:41:53.160512Z",
+     "iopub.status.idle": "2024-09-11T02:41:57.605370Z",
+     "shell.execute_reply": "2024-09-11T02:41:57.604888Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
-    "# %pip install -qU langchain langchain-community langchain-openai langchain-chroma"
+    "%pip install -qU langchain langchain-community langchain-openai langchain-chroma"
    ]
   },
   {
@@ -51,15 +66,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "40e2979e-a818-4b96-ac25-039336f94319",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:41:57.607874Z",
+     "iopub.status.busy": "2024-09-11T02:41:57.607697Z",
+     "iopub.status.idle": "2024-09-11T02:41:57.610422Z",
+     "shell.execute_reply": "2024-09-11T02:41:57.610012Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
     "# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
@@ -78,9 +101,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "1f621694",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:41:57.612276Z",
+     "iopub.status.busy": "2024-09-11T02:41:57.612146Z",
+     "iopub.status.idle": "2024-09-11T02:41:59.074590Z",
+     "shell.execute_reply": "2024-09-11T02:41:59.074052Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_chroma import Chroma\n",
@@ -108,14 +138,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:41:59.077712Z",
+     "iopub.status.busy": "2024-09-11T02:41:59.077514Z",
+     "iopub.status.idle": "2024-09-11T02:41:59.081509Z",
+     "shell.execute_reply": "2024-09-11T02:41:59.081112Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Search(BaseModel):\n",
@@ -129,19 +166,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/workplace/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n",
-      "  warn_beta(\n"
-     ]
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:41:59.083613Z",
+     "iopub.status.busy": "2024-09-11T02:41:59.083492Z",
+     "iopub.status.idle": "2024-09-11T02:41:59.204636Z",
+     "shell.execute_reply": "2024-09-11T02:41:59.204377Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
@@ -159,7 +194,7 @@
     "        (\"human\", \"{question}\"),\n",
     "    ]\n",
     ")\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm = llm.with_structured_output(Search)\n",
     "query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
    ]
@@ -174,17 +209,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "bc1d3863",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:41:59.206178Z",
+     "iopub.status.busy": "2024-09-11T02:41:59.206101Z",
+     "iopub.status.idle": "2024-09-11T02:41:59.817758Z",
+     "shell.execute_reply": "2024-09-11T02:41:59.817310Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(queries=['Harrison work location'])"
+       "Search(queries=['Harrison Work', 'Harrison employment history'])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -195,17 +237,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:41:59.820168Z",
+     "iopub.status.busy": "2024-09-11T02:41:59.819990Z",
+     "iopub.status.idle": "2024-09-11T02:42:00.309034Z",
+     "shell.execute_reply": "2024-09-11T02:42:00.308578Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(queries=['Harrison work place', 'Ankush work place'])"
+       "Search(queries=['Harrison work history', 'Ankush work history'])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -226,9 +275,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "1e047d87",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:00.311131Z",
+     "iopub.status.busy": "2024-09-11T02:42:00.310972Z",
+     "iopub.status.idle": "2024-09-11T02:42:00.313365Z",
+     "shell.execute_reply": "2024-09-11T02:42:00.313025Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.runnables import chain"
@@ -236,9 +292,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 9,
    "id": "8dac7866",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:00.315138Z",
+     "iopub.status.busy": "2024-09-11T02:42:00.315016Z",
+     "iopub.status.idle": "2024-09-11T02:42:00.317427Z",
+     "shell.execute_reply": "2024-09-11T02:42:00.317088Z"
+    }
+   },
    "outputs": [],
    "source": [
     "@chain\n",
@@ -255,17 +318,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 10,
    "id": "232ad8a7-7990-4066-9228-d35a555f7293",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:00.318951Z",
+     "iopub.status.busy": "2024-09-11T02:42:00.318829Z",
+     "iopub.status.idle": "2024-09-11T02:42:01.512855Z",
+     "shell.execute_reply": "2024-09-11T02:42:01.512321Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='Harrison worked at Kensho')]"
+       "[Document(page_content='Harrison worked at Kensho'),\n",
+       " Document(page_content='Harrison worked at Kensho')]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -276,9 +347,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 11,
    "id": "28e14ba5",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:01.515743Z",
+     "iopub.status.busy": "2024-09-11T02:42:01.515400Z",
+     "iopub.status.idle": "2024-09-11T02:42:02.349930Z",
+     "shell.execute_reply": "2024-09-11T02:42:02.349382Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -287,7 +365,7 @@
        " Document(page_content='Ankush worked at Facebook')]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -321,7 +399,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_multiple_retrievers.ipynb

@@ -33,10 +33,25 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:13.105266Z",
+     "iopub.status.busy": "2024-09-11T02:42:13.104556Z",
+     "iopub.status.idle": "2024-09-11T02:42:17.936922Z",
+     "shell.execute_reply": "2024-09-11T02:42:17.936478Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
-    "# %pip install -qU langchain langchain-community langchain-openai langchain-chroma"
+    "%pip install -qU langchain langchain-community langchain-openai langchain-chroma"
    ]
   },
   {
@@ -51,15 +66,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "40e2979e-a818-4b96-ac25-039336f94319",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:17.939072Z",
+     "iopub.status.busy": "2024-09-11T02:42:17.938929Z",
+     "iopub.status.idle": "2024-09-11T02:42:17.941266Z",
+     "shell.execute_reply": "2024-09-11T02:42:17.940968Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
     "# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
@@ -78,9 +101,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "id": "1f621694",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:17.942794Z",
+     "iopub.status.busy": "2024-09-11T02:42:17.942674Z",
+     "iopub.status.idle": "2024-09-11T02:42:19.939459Z",
+     "shell.execute_reply": "2024-09-11T02:42:19.938842Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_chroma import Chroma\n",
@@ -110,14 +140,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:19.942780Z",
+     "iopub.status.busy": "2024-09-11T02:42:19.942567Z",
+     "iopub.status.idle": "2024-09-11T02:42:19.947709Z",
+     "shell.execute_reply": "2024-09-11T02:42:19.947252Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import List, Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Search(BaseModel):\n",
@@ -135,9 +172,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 5,
    "id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:19.949936Z",
+     "iopub.status.busy": "2024-09-11T02:42:19.949778Z",
+     "iopub.status.idle": "2024-09-11T02:42:20.073883Z",
+     "shell.execute_reply": "2024-09-11T02:42:20.073556Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
@@ -154,7 +198,7 @@
     "        (\"human\", \"{question}\"),\n",
     "    ]\n",
     ")\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm = llm.with_structured_output(Search)\n",
     "query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
    ]
@@ -169,17 +213,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 6,
    "id": "bc1d3863",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:20.075511Z",
+     "iopub.status.busy": "2024-09-11T02:42:20.075428Z",
+     "iopub.status.idle": "2024-09-11T02:42:20.902011Z",
+     "shell.execute_reply": "2024-09-11T02:42:20.901558Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='workplace', person='HARRISON')"
+       "Search(query='work history', person='HARRISON')"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -190,17 +241,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 7,
    "id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:20.904384Z",
+     "iopub.status.busy": "2024-09-11T02:42:20.904195Z",
+     "iopub.status.idle": "2024-09-11T02:42:21.468172Z",
+     "shell.execute_reply": "2024-09-11T02:42:21.467639Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Search(query='workplace', person='ANKUSH')"
+       "Search(query='work history', person='ANKUSH')"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -221,9 +279,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 8,
    "id": "1e047d87",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:21.470953Z",
+     "iopub.status.busy": "2024-09-11T02:42:21.470736Z",
+     "iopub.status.idle": "2024-09-11T02:42:21.473544Z",
+     "shell.execute_reply": "2024-09-11T02:42:21.473064Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.runnables import chain"
@@ -231,9 +296,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 9,
    "id": "4ec0c7fe",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:21.476024Z",
+     "iopub.status.busy": "2024-09-11T02:42:21.475835Z",
+     "iopub.status.idle": "2024-09-11T02:42:21.478359Z",
+     "shell.execute_reply": "2024-09-11T02:42:21.477932Z"
+    }
+   },
    "outputs": [],
    "source": [
     "retrievers = {\n",
@@ -244,9 +316,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 10,
    "id": "8dac7866",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:21.480247Z",
+     "iopub.status.busy": "2024-09-11T02:42:21.480084Z",
+     "iopub.status.idle": "2024-09-11T02:42:21.482732Z",
+     "shell.execute_reply": "2024-09-11T02:42:21.482382Z"
+    }
+   },
    "outputs": [],
    "source": [
     "@chain\n",
@@ -258,9 +337,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 11,
    "id": "232ad8a7-7990-4066-9228-d35a555f7293",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:21.484480Z",
+     "iopub.status.busy": "2024-09-11T02:42:21.484361Z",
+     "iopub.status.idle": "2024-09-11T02:42:22.136704Z",
+     "shell.execute_reply": "2024-09-11T02:42:22.136244Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -268,7 +354,7 @@
        "[Document(page_content='Harrison worked at Kensho')]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -279,9 +365,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 12,
    "id": "28e14ba5",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:22.139305Z",
+     "iopub.status.busy": "2024-09-11T02:42:22.139106Z",
+     "iopub.status.idle": "2024-09-11T02:42:23.479739Z",
+     "shell.execute_reply": "2024-09-11T02:42:23.479170Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -289,7 +382,7 @@
        "[Document(page_content='Ankush worked at Facebook')]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -323,7 +416,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/query_no_queries.ipynb

@@ -35,10 +35,25 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:33.121714Z",
+     "iopub.status.busy": "2024-09-11T02:42:33.121392Z",
+     "iopub.status.idle": "2024-09-11T02:42:36.998607Z",
+     "shell.execute_reply": "2024-09-11T02:42:36.998126Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
-    "# %pip install -qU langchain langchain-community langchain-openai langchain-chroma"
+    "%pip install -qU langchain langchain-community langchain-openai langchain-chroma"
    ]
   },
   {
@@ -53,15 +68,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "40e2979e-a818-4b96-ac25-039336f94319",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:37.001017Z",
+     "iopub.status.busy": "2024-09-11T02:42:37.000859Z",
+     "iopub.status.idle": "2024-09-11T02:42:37.003704Z",
+     "shell.execute_reply": "2024-09-11T02:42:37.003335Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
     "# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
@@ -80,9 +103,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "1f621694",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:37.005644Z",
+     "iopub.status.busy": "2024-09-11T02:42:37.005493Z",
+     "iopub.status.idle": "2024-09-11T02:42:38.288481Z",
+     "shell.execute_reply": "2024-09-11T02:42:38.287904Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_chroma import Chroma\n",
@@ -110,14 +140,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:38.291700Z",
+     "iopub.status.busy": "2024-09-11T02:42:38.291468Z",
+     "iopub.status.idle": "2024-09-11T02:42:38.295796Z",
+     "shell.execute_reply": "2024-09-11T02:42:38.295205Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Search(BaseModel):\n",
@@ -131,9 +168,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:38.297840Z",
+     "iopub.status.busy": "2024-09-11T02:42:38.297712Z",
+     "iopub.status.idle": "2024-09-11T02:42:38.420456Z",
+     "shell.execute_reply": "2024-09-11T02:42:38.420140Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
@@ -149,7 +193,7 @@
     "        (\"human\", \"{question}\"),\n",
     "    ]\n",
     ")\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm = llm.bind_tools([Search])\n",
     "query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
    ]
@@ -164,17 +208,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "bc1d3863",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:38.421934Z",
+     "iopub.status.busy": "2024-09-11T02:42:38.421831Z",
+     "iopub.status.idle": "2024-09-11T02:42:39.048915Z",
+     "shell.execute_reply": "2024-09-11T02:42:39.048519Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_ZnoVX4j9Mn8wgChaORyd1cvq', 'function': {'arguments': '{\"query\":\"Harrison\"}', 'name': 'Search'}, 'type': 'function'}]})"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_korLZrh08PTRL94f4L7rFqdj', 'function': {'arguments': '{\"query\":\"Harrison\"}', 'name': 'Search'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 95, 'total_tokens': 109}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-ea94d376-37bf-4f80-abe6-e3b42b767ea0-0', tool_calls=[{'name': 'Search', 'args': {'query': 'Harrison'}, 'id': 'call_korLZrh08PTRL94f4L7rFqdj', 'type': 'tool_call'}], usage_metadata={'input_tokens': 95, 'output_tokens': 14, 'total_tokens': 109})"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -185,17 +236,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:39.050923Z",
+     "iopub.status.busy": "2024-09-11T02:42:39.050785Z",
+     "iopub.status.idle": "2024-09-11T02:42:40.090421Z",
+     "shell.execute_reply": "2024-09-11T02:42:40.089454Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Hello! How can I assist you today?')"
+       "AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 93, 'total_tokens': 103}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'stop', 'logprobs': None}, id='run-ebdfc44a-455a-4ca6-be85-84559886b1e1-0', usage_metadata={'input_tokens': 93, 'output_tokens': 10, 'total_tokens': 103})"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -216,9 +274,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "1e047d87",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:40.093716Z",
+     "iopub.status.busy": "2024-09-11T02:42:40.093472Z",
+     "iopub.status.idle": "2024-09-11T02:42:40.097732Z",
+     "shell.execute_reply": "2024-09-11T02:42:40.097274Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
@@ -229,9 +294,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "8dac7866",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:40.100028Z",
+     "iopub.status.busy": "2024-09-11T02:42:40.099882Z",
+     "iopub.status.idle": "2024-09-11T02:42:40.103105Z",
+     "shell.execute_reply": "2024-09-11T02:42:40.102734Z"
+    }
+   },
    "outputs": [],
    "source": [
     "@chain\n",
@@ -248,9 +320,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "232ad8a7-7990-4066-9228-d35a555f7293",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:40.105092Z",
+     "iopub.status.busy": "2024-09-11T02:42:40.104917Z",
+     "iopub.status.idle": "2024-09-11T02:42:41.341967Z",
+     "shell.execute_reply": "2024-09-11T02:42:41.341455Z"
+    }
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -265,7 +344,7 @@
        "[Document(page_content='Harrison worked at Kensho')]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -276,17 +355,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "28e14ba5",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:42:41.344639Z",
+     "iopub.status.busy": "2024-09-11T02:42:41.344411Z",
+     "iopub.status.idle": "2024-09-11T02:42:41.798332Z",
+     "shell.execute_reply": "2024-09-11T02:42:41.798054Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Hello! How can I assist you today?')"
+       "AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 93, 'total_tokens': 103}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'stop', 'logprobs': None}, id='run-e87f058d-30c0-4075-8a89-a01b982d557e-0', usage_metadata={'input_tokens': 93, 'output_tokens': 10, 'total_tokens': 103})"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -320,7 +406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/sequence.ipynb

@@ -62,7 +62,8 @@
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass()\n",
+    "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass()\n",
     "\n",
     "model = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
    ]

docs/docs/how_to/serialization.ipynb

@@ -44,7 +44,7 @@
     "    ],\n",
     ")\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", api_key=\"llm-api-key\")\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", api_key=\"llm-api-key\")\n",
     "\n",
     "chain = prompt | llm"
    ]

docs/docs/how_to/sql_large_db.ipynb

@@ -136,7 +136,7 @@
    "source": [
     "from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Table(BaseModel):\n",

docs/docs/how_to/structured_output.ipynb

@@ -101,7 +101,7 @@
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "# Pydantic\n",
@@ -257,17 +257,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 19,
    "id": "9194bcf2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Response(output=Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=8))"
+       "FinalResponse(final_output=Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=7))"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,28 +293,28 @@
     "    response: str = Field(description=\"A conversational response to the user's query\")\n",
     "\n",
     "\n",
-    "class Response(BaseModel):\n",
-    "    output: Union[Joke, ConversationalResponse]\n",
+    "class FinalResponse(BaseModel):\n",
+    "    final_output: Union[Joke, ConversationalResponse]\n",
     "\n",
     "\n",
-    "structured_llm = llm.with_structured_output(Response)\n",
+    "structured_llm = llm.with_structured_output(FinalResponse)\n",
     "\n",
     "structured_llm.invoke(\"Tell me a joke about cats\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "id": "84d86132",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Response(output=ConversationalResponse(response=\"I'm just a digital assistant, so I don't have feelings, but I'm here and ready to help you. How can I assist you today?\"))"
+       "FinalResponse(final_output=ConversationalResponse(response=\"I'm just a bunch of code, so I don't have feelings, but I'm here and ready to help you! How can I assist you today?\"))"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -667,7 +667,7 @@
     "\n",
     "from langchain_core.output_parsers import PydanticOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -794,7 +794,7 @@
     "\n",
     "from langchain_core.messages import AIMessage\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",
@@ -915,9 +915,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-2",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-2"
   },
   "language_info": {
    "codemirror_mode": {

docs/docs/how_to/tool_calling.ipynb

@@ -64,7 +64,14 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:43:40.609832Z",
+     "iopub.status.busy": "2024-09-11T02:43:40.609565Z",
+     "iopub.status.idle": "2024-09-11T02:43:40.617860Z",
+     "shell.execute_reply": "2024-09-11T02:43:40.617391Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# The function name, type hints, and docstring are all part of the tool\n",
@@ -109,10 +116,17 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:43:40.620257Z",
+     "iopub.status.busy": "2024-09-11T02:43:40.620084Z",
+     "iopub.status.idle": "2024-09-11T02:43:40.689214Z",
+     "shell.execute_reply": "2024-09-11T02:43:40.688938Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class add(BaseModel):\n",
@@ -144,7 +158,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:43:40.690850Z",
+     "iopub.status.busy": "2024-09-11T02:43:40.690739Z",
+     "iopub.status.idle": "2024-09-11T02:43:40.693436Z",
+     "shell.execute_reply": "2024-09-11T02:43:40.693199Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing_extensions import Annotated, TypedDict\n",
@@ -201,7 +222,8 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]
@@ -209,12 +231,19 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:43:42.447839Z",
+     "iopub.status.busy": "2024-09-11T02:43:42.447760Z",
+     "iopub.status.idle": "2024-09-11T02:43:43.181171Z",
+     "shell.execute_reply": "2024-09-11T02:43:43.180680Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_BwYJ4UgU5pRVCBOUmiu7NhF9', 'function': {'arguments': '{\"a\":3,\"b\":12}', 'name': 'multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 80, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_ba606877f9', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-7f05e19e-4561-40e2-a2d0-8f4e28e9a00f-0', tool_calls=[{'name': 'multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_BwYJ4UgU5pRVCBOUmiu7NhF9', 'type': 'tool_call'}], usage_metadata={'input_tokens': 80, 'output_tokens': 17, 'total_tokens': 97})"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_iXj4DiW1p7WLjTAQMRO0jxMs', 'function': {'arguments': '{\"a\":3,\"b\":12}', 'name': 'multiply'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 80, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-0b620986-3f62-4df7-9ba3-4595089f9ad4-0', tool_calls=[{'name': 'multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_iXj4DiW1p7WLjTAQMRO0jxMs', 'type': 'tool_call'}], usage_metadata={'input_tokens': 80, 'output_tokens': 17, 'total_tokens': 97})"
       ]
      },
      "execution_count": 5,
@@ -259,18 +288,25 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:43:43.184004Z",
+     "iopub.status.busy": "2024-09-11T02:43:43.183777Z",
+     "iopub.status.idle": "2024-09-11T02:43:43.743024Z",
+     "shell.execute_reply": "2024-09-11T02:43:43.742171Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[{'name': 'multiply',\n",
        "  'args': {'a': 3, 'b': 12},\n",
-       "  'id': 'call_rcdMie7E89Xx06lEKKxJyB5N',\n",
+       "  'id': 'call_1fyhJAbJHuKQe6n0PacubGsL',\n",
        "  'type': 'tool_call'},\n",
        " {'name': 'add',\n",
        "  'args': {'a': 11, 'b': 49},\n",
-       "  'id': 'call_nheGN8yfvSJsnIuGZaXihou3',\n",
+       "  'id': 'call_fc2jVkKzwuPWyU7kS9qn1hyG',\n",
        "  'type': 'tool_call'}]"
       ]
      },
@@ -306,7 +342,14 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:43:43.746273Z",
+     "iopub.status.busy": "2024-09-11T02:43:43.746020Z",
+     "iopub.status.idle": "2024-09-11T02:43:44.586236Z",
+     "shell.execute_reply": "2024-09-11T02:43:44.585619Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -321,7 +364,7 @@
    ],
    "source": [
     "from langchain_core.output_parsers import PydanticToolsParser\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class add(BaseModel):\n",

docs/docs/how_to/tool_calling_parallel.ipynb

@@ -57,9 +57,10 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]
   },
   {

docs/docs/how_to/tool_choice.ipynb

@@ -14,7 +14,7 @@
     "- [How to use a model to call tools](/docs/how_to/tool_calling)\n",
     ":::\n",
     "\n",
-    "In order to force our LLM to spelect a specific tool, we can use the `tool_choice` parameter to ensure certain behavior. First, let's define our model and tools:"
+    "In order to force our LLM to select a specific tool, we can use the `tool_choice` parameter to ensure certain behavior. First, let's define our model and tools:"
    ]
   },
   {
@@ -57,9 +57,10 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]
   },
   {
@@ -77,7 +78,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_9cViskmLvPnHjXk9tbVla5HA', 'function': {'arguments': '{\"a\":2,\"b\":4}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 103, 'total_tokens': 112}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-095b827e-2bdd-43bb-8897-c843f4504883-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 2, 'b': 4}, 'id': 'call_9cViskmLvPnHjXk9tbVla5HA'}], usage_metadata={'input_tokens': 103, 'output_tokens': 9, 'total_tokens': 112})"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_9cViskmLvPnHjXk9tbVla5HA', 'function': {'arguments': '{\"a\":2,\"b\":4}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 103, 'total_tokens': 112}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-095b827e-2bdd-43bb-8897-c843f4504883-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 2, 'b': 4}, 'id': 'call_9cViskmLvPnHjXk9tbVla5HA'}], usage_metadata={'input_tokens': 103, 'output_tokens': 9, 'total_tokens': 112})"
       ]
      },
      "metadata": {},
@@ -111,7 +112,7 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W', 'function': {'arguments': '{\"a\":1,\"b\":2}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 94, 'total_tokens': 109}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-28f75260-9900-4bed-8cd3-f1579abb65e5-0', tool_calls=[{'name': 'Add', 'args': {'a': 1, 'b': 2}, 'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W'}], usage_metadata={'input_tokens': 94, 'output_tokens': 15, 'total_tokens': 109})"
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W', 'function': {'arguments': '{\"a\":1,\"b\":2}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 94, 'total_tokens': 109}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-28f75260-9900-4bed-8cd3-f1579abb65e5-0', tool_calls=[{'name': 'Add', 'args': {'a': 1, 'b': 2}, 'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W'}], usage_metadata={'input_tokens': 94, 'output_tokens': 15, 'total_tokens': 109})"
       ]
      },
      "metadata": {},

docs/docs/how_to/tool_results_pass_to_model.ipynb

@@ -53,7 +53,8 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]

docs/docs/how_to/tool_runtime.ipynb

@@ -55,13 +55,20 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:51.802901Z",
+     "iopub.status.busy": "2024-09-11T02:52:51.802682Z",
+     "iopub.status.idle": "2024-09-11T02:52:52.398167Z",
+     "shell.execute_reply": "2024-09-11T02:52:52.397911Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# | output: false\n",
     "# | echo: false\n",
     "\n",
-    "# %pip install -qU langchain langchain_openai\n",
+    "%pip install -qU langchain langchain_openai\n",
     "\n",
     "import os\n",
     "from getpass import getpass\n",
@@ -71,7 +78,7 @@
     "if \"OPENAI_API_KEY\" not in os.environ:\n",
     "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]
   },
   {
@@ -86,7 +93,14 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:52.399922Z",
+     "iopub.status.busy": "2024-09-11T02:52:52.399796Z",
+     "iopub.status.idle": "2024-09-11T02:52:52.406349Z",
+     "shell.execute_reply": "2024-09-11T02:52:52.406077Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import List\n",
@@ -141,22 +155,29 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:52.407763Z",
+     "iopub.status.busy": "2024-09-11T02:52:52.407691Z",
+     "iopub.status.idle": "2024-09-11T02:52:52.411761Z",
+     "shell.execute_reply": "2024-09-11T02:52:52.411512Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'title': 'update_favorite_petsSchema',\n",
-       " 'description': 'Add the list of favorite pets.',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'pets': {'title': 'Pets',\n",
-       "   'description': 'List of favorite pets to set.',\n",
-       "   'type': 'array',\n",
-       "   'items': {'type': 'string'}},\n",
-       "  'user_id': {'title': 'User Id',\n",
-       "   'description': \"User's ID.\",\n",
+       "{'description': 'Add the list of favorite pets.',\n",
+       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
+       "   'items': {'type': 'string'},\n",
+       "   'title': 'Pets',\n",
+       "   'type': 'array'},\n",
+       "  'user_id': {'description': \"User's ID.\",\n",
+       "   'title': 'User Id',\n",
        "   'type': 'string'}},\n",
-       " 'required': ['pets', 'user_id']}"
+       " 'required': ['pets', 'user_id'],\n",
+       " 'title': 'update_favorite_petsSchema',\n",
+       " 'type': 'object'}"
       ]
      },
      "execution_count": 3,
@@ -178,19 +199,26 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:52.427826Z",
+     "iopub.status.busy": "2024-09-11T02:52:52.427691Z",
+     "iopub.status.idle": "2024-09-11T02:52:52.431791Z",
+     "shell.execute_reply": "2024-09-11T02:52:52.431574Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'title': 'update_favorite_pets',\n",
-       " 'description': 'Add the list of favorite pets.',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'pets': {'title': 'Pets',\n",
-       "   'description': 'List of favorite pets to set.',\n",
-       "   'type': 'array',\n",
-       "   'items': {'type': 'string'}}},\n",
-       " 'required': ['pets']}"
+       "{'description': 'Add the list of favorite pets.',\n",
+       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
+       "   'items': {'type': 'string'},\n",
+       "   'title': 'Pets',\n",
+       "   'type': 'array'}},\n",
+       " 'required': ['pets'],\n",
+       " 'title': 'update_favorite_pets',\n",
+       " 'type': 'object'}"
       ]
      },
      "execution_count": 4,
@@ -212,7 +240,14 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:52.433096Z",
+     "iopub.status.busy": "2024-09-11T02:52:52.433014Z",
+     "iopub.status.idle": "2024-09-11T02:52:52.437499Z",
+     "shell.execute_reply": "2024-09-11T02:52:52.437239Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -240,14 +275,21 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:52.439148Z",
+     "iopub.status.busy": "2024-09-11T02:52:52.438742Z",
+     "iopub.status.idle": "2024-09-11T02:52:53.394524Z",
+     "shell.execute_reply": "2024-09-11T02:52:53.394005Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[{'name': 'update_favorite_pets',\n",
        "  'args': {'pets': ['cats', 'parrots']},\n",
-       "  'id': 'call_W3cn4lZmJlyk8PCrKN4PRwqB',\n",
+       "  'id': 'call_pZ6XVREGh1L0BBSsiGIf1xVm',\n",
        "  'type': 'tool_call'}]"
       ]
      },
@@ -284,14 +326,21 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:53.397134Z",
+     "iopub.status.busy": "2024-09-11T02:52:53.396972Z",
+     "iopub.status.idle": "2024-09-11T02:52:53.403332Z",
+     "shell.execute_reply": "2024-09-11T02:52:53.402787Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[{'name': 'update_favorite_pets',\n",
        "  'args': {'pets': ['cats', 'parrots'], 'user_id': '123'},\n",
-       "  'id': 'call_W3cn4lZmJlyk8PCrKN4PRwqB',\n",
+       "  'id': 'call_pZ6XVREGh1L0BBSsiGIf1xVm',\n",
        "  'type': 'tool_call'}]"
       ]
      },
@@ -329,12 +378,19 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:53.405183Z",
+     "iopub.status.busy": "2024-09-11T02:52:53.405048Z",
+     "iopub.status.idle": "2024-09-11T02:52:54.248576Z",
+     "shell.execute_reply": "2024-09-11T02:52:54.248107Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[ToolMessage(content='null', name='update_favorite_pets', tool_call_id='call_HUyF6AihqANzEYxQnTUKxkXj')]"
+       "[ToolMessage(content='null', name='update_favorite_pets', tool_call_id='call_oYCD0THSedHTbwNAY3NW6uUj')]"
       ]
      },
      "execution_count": 8,
@@ -365,7 +421,14 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:54.251169Z",
+     "iopub.status.busy": "2024-09-11T02:52:54.250948Z",
+     "iopub.status.idle": "2024-09-11T02:52:54.254279Z",
+     "shell.execute_reply": "2024-09-11T02:52:54.253889Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -394,22 +457,29 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:54.256425Z",
+     "iopub.status.busy": "2024-09-11T02:52:54.256279Z",
+     "iopub.status.idle": "2024-09-11T02:52:54.262533Z",
+     "shell.execute_reply": "2024-09-11T02:52:54.262228Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'title': 'UpdateFavoritePetsSchema',\n",
-       " 'description': 'Update list of favorite pets',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'pets': {'title': 'Pets',\n",
-       "   'description': 'List of favorite pets to set.',\n",
-       "   'type': 'array',\n",
-       "   'items': {'type': 'string'}},\n",
-       "  'user_id': {'title': 'User Id',\n",
-       "   'description': \"User's ID.\",\n",
+       "{'description': 'Update list of favorite pets',\n",
+       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
+       "   'items': {'type': 'string'},\n",
+       "   'title': 'Pets',\n",
+       "   'type': 'array'},\n",
+       "  'user_id': {'description': \"User's ID.\",\n",
+       "   'title': 'User Id',\n",
        "   'type': 'string'}},\n",
-       " 'required': ['pets', 'user_id']}"
+       " 'required': ['pets', 'user_id'],\n",
+       " 'title': 'UpdateFavoritePetsSchema',\n",
+       " 'type': 'object'}"
       ]
      },
      "execution_count": 10,
@@ -418,8 +488,8 @@
     }
    ],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "from langchain_core.tools import BaseTool\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class UpdateFavoritePetsSchema(BaseModel):\n",
@@ -440,19 +510,26 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:54.264192Z",
+     "iopub.status.busy": "2024-09-11T02:52:54.264074Z",
+     "iopub.status.idle": "2024-09-11T02:52:54.267400Z",
+     "shell.execute_reply": "2024-09-11T02:52:54.267113Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'title': 'update_favorite_pets',\n",
-       " 'description': 'Update list of favorite pets',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'pets': {'title': 'Pets',\n",
-       "   'description': 'List of favorite pets to set.',\n",
-       "   'type': 'array',\n",
-       "   'items': {'type': 'string'}}},\n",
-       " 'required': ['pets']}"
+       "{'description': 'Update list of favorite pets',\n",
+       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
+       "   'items': {'type': 'string'},\n",
+       "   'title': 'Pets',\n",
+       "   'type': 'array'}},\n",
+       " 'required': ['pets'],\n",
+       " 'title': 'update_favorite_pets',\n",
+       " 'type': 'object'}"
       ]
      },
      "execution_count": 11,
@@ -466,26 +543,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:54.269027Z",
+     "iopub.status.busy": "2024-09-11T02:52:54.268905Z",
+     "iopub.status.idle": "2024-09-11T02:52:54.276123Z",
+     "shell.execute_reply": "2024-09-11T02:52:54.275876Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'title': 'UpdateFavoritePetsSchema',\n",
-       " 'description': 'Update list of favorite pets',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'pets': {'title': 'Pets',\n",
-       "   'description': 'List of favorite pets to set.',\n",
-       "   'type': 'array',\n",
-       "   'items': {'type': 'string'}},\n",
-       "  'user_id': {'title': 'User Id',\n",
-       "   'description': \"User's ID.\",\n",
+       "{'description': 'Update list of favorite pets',\n",
+       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
+       "   'items': {'type': 'string'},\n",
+       "   'title': 'Pets',\n",
+       "   'type': 'array'},\n",
+       "  'user_id': {'description': \"User's ID.\",\n",
+       "   'title': 'User Id',\n",
        "   'type': 'string'}},\n",
-       " 'required': ['pets', 'user_id']}"
+       " 'required': ['pets', 'user_id'],\n",
+       " 'title': 'UpdateFavoritePetsSchema',\n",
+       " 'type': 'object'}"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -508,23 +592,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:54.277497Z",
+     "iopub.status.busy": "2024-09-11T02:52:54.277400Z",
+     "iopub.status.idle": "2024-09-11T02:52:54.280323Z",
+     "shell.execute_reply": "2024-09-11T02:52:54.280072Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'title': 'update_favorite_pets',\n",
-       " 'description': 'Update list of favorite pets',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'pets': {'title': 'Pets',\n",
-       "   'description': 'List of favorite pets to set.',\n",
-       "   'type': 'array',\n",
-       "   'items': {'type': 'string'}}},\n",
-       " 'required': ['pets']}"
+       "{'description': 'Update list of favorite pets',\n",
+       " 'properties': {'pets': {'description': 'List of favorite pets to set.',\n",
+       "   'items': {'type': 'string'},\n",
+       "   'title': 'Pets',\n",
+       "   'type': 'array'}},\n",
+       " 'required': ['pets'],\n",
+       " 'title': 'update_favorite_pets',\n",
+       " 'type': 'object'}"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -535,23 +626,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:54.281741Z",
+     "iopub.status.busy": "2024-09-11T02:52:54.281642Z",
+     "iopub.status.idle": "2024-09-11T02:52:54.288857Z",
+     "shell.execute_reply": "2024-09-11T02:52:54.288632Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'title': 'update_favorite_petsSchema',\n",
-       " 'description': 'Use the tool.\\n\\nAdd run_manager: Optional[CallbackManagerForToolRun] = None\\nto child implementations to enable tracing.',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'pets': {'title': 'Pets',\n",
-       "   'type': 'array',\n",
-       "   'items': {'type': 'string'}},\n",
+       "{'description': 'Use the tool.\\n\\nAdd run_manager: Optional[CallbackManagerForToolRun] = None\\nto child implementations to enable tracing.',\n",
+       " 'properties': {'pets': {'items': {'type': 'string'},\n",
+       "   'title': 'Pets',\n",
+       "   'type': 'array'},\n",
        "  'user_id': {'title': 'User Id', 'type': 'string'}},\n",
-       " 'required': ['pets', 'user_id']}"
+       " 'required': ['pets', 'user_id'],\n",
+       " 'title': 'update_favorite_petsSchema',\n",
+       " 'type': 'object'}"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -570,22 +668,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T02:52:54.290237Z",
+     "iopub.status.busy": "2024-09-11T02:52:54.290145Z",
+     "iopub.status.idle": "2024-09-11T02:52:54.294273Z",
+     "shell.execute_reply": "2024-09-11T02:52:54.294053Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'title': 'update_favorite_pets',\n",
-       " 'description': 'Update list of favorite pets',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'pets': {'title': 'Pets',\n",
-       "   'type': 'array',\n",
-       "   'items': {'type': 'string'}}},\n",
-       " 'required': ['pets']}"
+       "{'description': 'Update list of favorite pets',\n",
+       " 'properties': {'pets': {'items': {'type': 'string'},\n",
+       "   'title': 'Pets',\n",
+       "   'type': 'array'}},\n",
+       " 'required': ['pets'],\n",
+       " 'title': 'update_favorite_pets',\n",
+       " 'type': 'object'}"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -597,7 +702,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -611,7 +716,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tool_streaming.ipynb

@@ -58,9 +58,10 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "llm_with_tools = llm.bind_tools(tools)"
    ]
   },

docs/docs/how_to/tools_builtin.ipynb

@@ -46,19 +46,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "84f70856-b865-4658-9930-7577fb4712ce",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:01:11.847104Z",
+     "iopub.status.busy": "2024-09-11T03:01:11.846727Z",
+     "iopub.status.idle": "2024-09-11T03:01:13.200038Z",
+     "shell.execute_reply": "2024-09-11T03:01:13.199355Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "!pip install -qU wikipedia"
+    "!pip install -qU langchain-community wikipedia"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 2,
    "id": "b4eaed85-c5a6-4ba9-b401-40258b0131c2",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:01:13.203356Z",
+     "iopub.status.busy": "2024-09-11T03:01:13.202996Z",
+     "iopub.status.idle": "2024-09-11T03:01:14.740686Z",
+     "shell.execute_reply": "2024-09-11T03:01:14.739748Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -89,18 +103,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 3,
    "id": "7f094f01-2e98-4947-acc4-0846963a96e0",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:01:14.745018Z",
+     "iopub.status.busy": "2024-09-11T03:01:14.744347Z",
+     "iopub.status.idle": "2024-09-11T03:01:14.752527Z",
+     "shell.execute_reply": "2024-09-11T03:01:14.752112Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Name: wiki-tool\n",
-      "Description: look up things in wikipedia\n",
-      "args schema: {'query': {'title': 'Query', 'description': 'query to look up in Wikipedia, should be 3 or less words', 'type': 'string'}}\n",
-      "returns directly?: True\n"
+      "Name: wikipedia\n",
+      "Description: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.\n",
+      "args schema: {'query': {'description': 'query to look up on wikipedia', 'title': 'Query', 'type': 'string'}}\n",
+      "returns directly?: False\n"
      ]
     }
    ],
@@ -124,9 +145,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 4,
    "id": "1365784c-e666-41c8-a1bb-e50f822b5936",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:01:14.755274Z",
+     "iopub.status.busy": "2024-09-11T03:01:14.755068Z",
+     "iopub.status.idle": "2024-09-11T03:01:15.375704Z",
+     "shell.execute_reply": "2024-09-11T03:01:15.374841Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -140,7 +168,7 @@
    "source": [
     "from langchain_community.tools import WikipediaQueryRun\n",
     "from langchain_community.utilities import WikipediaAPIWrapper\n",
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class WikiInputs(BaseModel):\n",
@@ -164,9 +192,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 5,
    "id": "6e8850d6-6840-443e-a2be-adf64b30975c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:01:15.378598Z",
+     "iopub.status.busy": "2024-09-11T03:01:15.378414Z",
+     "iopub.status.idle": "2024-09-11T03:01:15.382248Z",
+     "shell.execute_reply": "2024-09-11T03:01:15.381801Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -174,7 +209,7 @@
      "text": [
       "Name: wiki-tool\n",
       "Description: look up things in wikipedia\n",
-      "args schema: {'query': {'title': 'Query', 'description': 'query to look up in Wikipedia, should be 3 or less words', 'type': 'string'}}\n",
+      "args schema: {'query': {'description': 'query to look up in Wikipedia, should be 3 or less words', 'title': 'Query', 'type': 'string'}}\n",
       "returns directly?: True\n"
      ]
     }
@@ -212,9 +247,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv-311",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv-311"
   },
   "language_info": {
    "codemirror_mode": {

docs/docs/how_to/tools_chain.ipynb

@@ -166,7 +166,7 @@
     "\n",
     "from langchain_openai.chat_models import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]
   },
   {

docs/docs/how_to/tools_error.ipynb

@@ -53,7 +53,14 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "08785b6d-722d-4620-b6ec-36deb3842c69",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:10:25.005243Z",
+     "iopub.status.busy": "2024-09-11T03:10:25.005074Z",
+     "iopub.status.idle": "2024-09-11T03:10:25.007679Z",
+     "shell.execute_reply": "2024-09-11T03:10:25.007361Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import getpass\n",
@@ -81,9 +88,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "86258950-5e61-4340-81b9-84a5d26e8773",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:10:25.009496Z",
+     "iopub.status.busy": "2024-09-11T03:10:25.009371Z",
+     "iopub.status.idle": "2024-09-11T03:10:25.552917Z",
+     "shell.execute_reply": "2024-09-11T03:10:25.552592Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# | echo: false\n",
@@ -91,16 +105,24 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "1d20604e-c4d1-4d21-841b-23e4f61aec36",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:10:25.554543Z",
+     "iopub.status.busy": "2024-09-11T03:10:25.554439Z",
+     "iopub.status.idle": "2024-09-11T03:10:25.631610Z",
+     "shell.execute_reply": "2024-09-11T03:10:25.631346Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Define tool\n",
@@ -131,26 +153,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "d354664c-ac44-4967-a35f-8912b3ad9477",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:10:25.633050Z",
+     "iopub.status.busy": "2024-09-11T03:10:25.632978Z",
+     "iopub.status.idle": "2024-09-11T03:10:26.556508Z",
+     "shell.execute_reply": "2024-09-11T03:10:26.556233Z"
+    }
+   },
    "outputs": [
     {
      "ename": "ValidationError",
-     "evalue": "1 validation error for complex_toolSchema\ndict_arg\n  field required (type=value_error.missing)",
+     "evalue": "1 validation error for complex_toolSchema\ndict_arg\n  Field required [type=missing, input_value={'int_arg': 5, 'float_arg': 2.1}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.8/v/missing",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muse complex tool. the args are 5, 2.1, empty dictionary. don\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt forget dict_arg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/runnables/base.py:2572\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2570\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m step\u001b[38;5;241m.\u001b[39minvoke(\u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   2571\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2572\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2573\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   2574\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:380\u001b[0m, in \u001b[0;36mBaseTool.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    373\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    375\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, Dict],\n\u001b[1;32m    376\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    377\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    378\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    379\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m--> 380\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    381\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    382\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    383\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    384\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    385\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    386\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    387\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    388\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    389\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:537\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, **kwargs)\u001b[0m\n\u001b[1;32m    535\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    536\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error:\n\u001b[0;32m--> 537\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    538\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error, \u001b[38;5;28mbool\u001b[39m):\n\u001b[1;32m    539\u001b[0m         observation \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTool input validation error\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:526\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, **kwargs)\u001b[0m\n\u001b[1;32m    524\u001b[0m context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m    525\u001b[0m context\u001b[38;5;241m.\u001b[39mrun(_set_config_context, child_config)\n\u001b[0;32m--> 526\u001b[0m parsed_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    527\u001b[0m tool_args, tool_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_to_args_and_kwargs(parsed_input)\n\u001b[1;32m    528\u001b[0m observation \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    529\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(\n\u001b[1;32m    530\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run, \u001b[38;5;241m*\u001b[39mtool_args, run_manager\u001b[38;5;241m=\u001b[39mrun_manager, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m context\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run, \u001b[38;5;241m*\u001b[39mtool_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs)\n\u001b[1;32m    534\u001b[0m )\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/langchain_core/tools.py:424\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m    422\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    423\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m input_args \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 424\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43minput_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    425\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m    426\u001b[0m             k: \u001b[38;5;28mgetattr\u001b[39m(result, k)\n\u001b[1;32m    427\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdict()\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m    428\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m tool_input\n\u001b[1;32m    429\u001b[0m         }\n\u001b[1;32m    430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tool_input\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/pydantic/main.py:526\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.parse_obj\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.5/lib/python3.10/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for complex_toolSchema\ndict_arg\n  field required (type=value_error.missing)"
+      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muse complex tool. the args are 5, 2.1, empty dictionary. don\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt forget dict_arg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/runnables/base.py:2998\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2996\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   2997\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2998\u001b[0m             \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2999\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   3000\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/tools/base.py:456\u001b[0m, in \u001b[0;36mBaseTool.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    449\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    450\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    451\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, Dict, ToolCall],\n\u001b[1;32m    452\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    453\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    454\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    455\u001b[0m     tool_input, kwargs \u001b[38;5;241m=\u001b[39m _prep_run_args(\u001b[38;5;28minput\u001b[39m, config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 456\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/tools/base.py:659\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, tool_call_id, **kwargs)\u001b[0m\n\u001b[1;32m    657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error_to_raise:\n\u001b[1;32m    658\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_tool_error(error_to_raise)\n\u001b[0;32m--> 659\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m error_to_raise\n\u001b[1;32m    660\u001b[0m output \u001b[38;5;241m=\u001b[39m _format_output(content, artifact, tool_call_id, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, status)\n\u001b[1;32m    661\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_tool_end(output, color\u001b[38;5;241m=\u001b[39mcolor, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/tools/base.py:622\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, tool_call_id, **kwargs)\u001b[0m\n\u001b[1;32m    620\u001b[0m context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m    621\u001b[0m context\u001b[38;5;241m.\u001b[39mrun(_set_config_context, child_config)\n\u001b[0;32m--> 622\u001b[0m tool_args, tool_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_to_args_and_kwargs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    623\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m signature(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    624\u001b[0m     tool_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n",
+      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/tools/base.py:545\u001b[0m, in \u001b[0;36mBaseTool._to_args_and_kwargs\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m    544\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_to_args_and_kwargs\u001b[39m(\u001b[38;5;28mself\u001b[39m, tool_input: Union[\u001b[38;5;28mstr\u001b[39m, Dict]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[Tuple, Dict]:\n\u001b[0;32m--> 545\u001b[0m     tool_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    546\u001b[0m     \u001b[38;5;66;03m# For backwards compatibility, if run_input is a string,\u001b[39;00m\n\u001b[1;32m    547\u001b[0m     \u001b[38;5;66;03m# pass as a positional argument.\u001b[39;00m\n\u001b[1;32m    548\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tool_input, \u001b[38;5;28mstr\u001b[39m):\n",
+      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/langchain_core/tools/base.py:487\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m    485\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m input_args \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    486\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(input_args, BaseModel):\n\u001b[0;32m--> 487\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43minput_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_validate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    488\u001b[0m         result_dict \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39mmodel_dump()\n\u001b[1;32m    489\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(input_args, BaseModelV1):\n",
+      "File \u001b[0;32m~/langchain/.venv/lib/python3.11/site-packages/pydantic/main.py:568\u001b[0m, in \u001b[0;36mBaseModel.model_validate\u001b[0;34m(cls, obj, strict, from_attributes, context)\u001b[0m\n\u001b[1;32m    566\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m    567\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 568\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    569\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_attributes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_attributes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontext\u001b[49m\n\u001b[1;32m    570\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for complex_toolSchema\ndict_arg\n  Field required [type=missing, input_value={'int_arg': 5, 'float_arg': 2.1}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.8/v/missing"
      ]
     }
    ],
@@ -172,9 +201,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "8fedb550-683d-45ae-8876-ae7acb332019",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:10:26.558131Z",
+     "iopub.status.busy": "2024-09-11T03:10:26.558031Z",
+     "iopub.status.idle": "2024-09-11T03:10:27.399844Z",
+     "shell.execute_reply": "2024-09-11T03:10:27.399201Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -186,9 +222,10 @@
       "\n",
       "raised the following error:\n",
       "\n",
-      "<class 'pydantic.error_wrappers.ValidationError'>: 1 validation error for complex_toolSchema\n",
+      "<class 'pydantic_core._pydantic_core.ValidationError'>: 1 validation error for complex_toolSchema\n",
       "dict_arg\n",
-      "  field required (type=value_error.missing)\n"
+      "  Field required [type=missing, input_value={'int_arg': 5, 'float_arg': 2.1}, input_type=dict]\n",
+      "    For further information visit https://errors.pydantic.dev/2.8/v/missing\n"
      ]
     }
    ],
@@ -226,9 +263,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "02cc4223-35fa-4240-976a-012299ca703c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:10:27.404122Z",
+     "iopub.status.busy": "2024-09-11T03:10:27.403539Z",
+     "iopub.status.idle": "2024-09-11T03:10:38.080547Z",
+     "shell.execute_reply": "2024-09-11T03:10:38.079955Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -236,7 +280,7 @@
        "10.5"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -277,9 +321,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "b5659956-9454-468a-9753-a3ff9052b8f5",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:10:38.083810Z",
+     "iopub.status.busy": "2024-09-11T03:10:38.083623Z",
+     "iopub.status.idle": "2024-09-11T03:10:38.090089Z",
+     "shell.execute_reply": "2024-09-11T03:10:38.089682Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from langchain_core.messages import AIMessage, HumanMessage, ToolCall, ToolMessage\n",
@@ -335,9 +386,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "4c45f5bd-cbb4-47d5-b4b6-aec50673c750",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-11T03:10:38.092152Z",
+     "iopub.status.busy": "2024-09-11T03:10:38.092021Z",
+     "iopub.status.idle": "2024-09-11T03:10:39.592443Z",
+     "shell.execute_reply": "2024-09-11T03:10:39.591990Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -345,7 +403,7 @@
        "10.5"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -401,7 +459,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tools_few_shot.ipynb

@@ -46,9 +46,10 @@
     "\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "llm_with_tools = llm.bind_tools(tools)"
    ]
   },

docs/docs/how_to/tools_human.ipynb

@@ -129,13 +129,13 @@
     "\n",
     "@tool\n",
     "def count_emails(last_n_days: int) -> int:\n",
-    "    \"\"\"Multiply two integers together.\"\"\"\n",
+    "    \"\"\"Dummy function to count number of e-mails. Returns 2 * last_n_days.\"\"\"\n",
     "    return last_n_days * 2\n",
     "\n",
     "\n",
     "@tool\n",
     "def send_email(message: str, recipient: str) -> str:\n",
-    "    \"Add two integers.\"\n",
+    "    \"\"\"Dummy function for sending an e-mail.\"\"\"\n",
     "    return f\"Successfully sent email to {recipient}.\"\n",
     "\n",
     "\n",

docs/docs/integrations/chat/ai21.ipynb

@@ -60,7 +60,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"AI21_API_KEY\"] = getpass()"
+    "if \"AI21_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"AI21_API_KEY\"] = getpass()"
    ]
   },
   {
@@ -115,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "c40756fb-cbf8-4d44-a293-3989d707237e",
    "metadata": {},
    "outputs": [],
@@ -135,21 +136,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "46b982dc-5d8a-46da-a711-81c03ccd6adc",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore programmer.\", id='run-2e8d16d6-a06e-45cb-8d0c-1c8208645033-0')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "messages = [\n",
     "    (\n",
@@ -174,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "39353473fce5dd2e",
    "metadata": {
     "collapsed": false,
@@ -182,18 +172,7 @@
      "outputs_hidden": false
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', id='run-e1bd82dc-1a7e-4b2e-bde9-ac995929ac0f-0')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
@@ -217,6 +196,93 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "39c0ccd229927eab",
+   "metadata": {},
+   "source": "# Tool Calls / Function Calling"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bf6b40be07fe2d4",
+   "metadata": {},
+   "source": "This example shows how to use tool calling with AI21 models:"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a181a28df77120fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "from langchain_ai21.chat_models import ChatAI21\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
+    "\n",
+    "if \"AI21_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"AI21_API_KEY\"] = getpass()\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(location: str, date: str) -> str:\n",
+    "    \"\"\"“Provide the weather for the specified location on the given date.”\"\"\"\n",
+    "    if location == \"New York\" and date == \"2024-12-05\":\n",
+    "        return \"25 celsius\"\n",
+    "    elif location == \"New York\" and date == \"2024-12-06\":\n",
+    "        return \"27 celsius\"\n",
+    "    elif location == \"London\" and date == \"2024-12-05\":\n",
+    "        return \"22 celsius\"\n",
+    "    return \"32 celsius\"\n",
+    "\n",
+    "\n",
+    "llm = ChatAI21(model=\"jamba-1.5-mini\")\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools([convert_to_openai_tool(get_weather)])\n",
+    "\n",
+    "chat_messages = [\n",
+    "    SystemMessage(\n",
+    "        content=\"You are a helpful assistant. You can use the provided tools \"\n",
+    "        \"to assist with various tasks and provide accurate information\"\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "human_messages = [\n",
+    "    HumanMessage(\n",
+    "        content=\"What is the forecast for the weather in New York on December 5, 2024?\"\n",
+    "    ),\n",
+    "    HumanMessage(content=\"And what about the 2024-12-06?\"),\n",
+    "    HumanMessage(content=\"OK, thank you.\"),\n",
+    "    HumanMessage(content=\"What is the expected weather in London on December 5, 2024?\"),\n",
+    "]\n",
+    "\n",
+    "\n",
+    "for human_message in human_messages:\n",
+    "    print(f\"User: {human_message.content}\")\n",
+    "    chat_messages.append(human_message)\n",
+    "    response = llm_with_tools.invoke(chat_messages)\n",
+    "    chat_messages.append(response)\n",
+    "    if response.tool_calls:\n",
+    "        tool_call = response.tool_calls[0]\n",
+    "        if tool_call[\"name\"] == \"get_weather\":\n",
+    "            weather = get_weather.invoke(\n",
+    "                {\n",
+    "                    \"location\": tool_call[\"args\"][\"location\"],\n",
+    "                    \"date\": tool_call[\"args\"][\"date\"],\n",
+    "                }\n",
+    "            )\n",
+    "            chat_messages.append(\n",
+    "                ToolMessage(content=weather, tool_call_id=tool_call[\"id\"])\n",
+    "            )\n",
+    "            llm_answer = llm_with_tools.invoke(chat_messages)\n",
+    "            print(f\"Assistant: {llm_answer.content}\")\n",
+    "    else:\n",
+    "        print(f\"Assistant: {response.content}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e79de691-9dd6-4697-b57e-59a4a3cc073a",

docs/docs/integrations/chat/anthropic.ipynb

@@ -59,7 +59,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass(\"Enter your Anthropic API key: \")"
+    "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass(\"Enter your Anthropic API key: \")"
    ]
   },
   {
@@ -274,7 +275,7 @@
     }
    ],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
     "class GetWeather(BaseModel):\n",

docs/docs/integrations/chat/anthropic_functions.ipynb

@@ -69,7 +69,7 @@
     }
    ],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel\n",
+    "from pydantic import BaseModel\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",

docs/docs/integrations/chat/anyscale.ipynb

@@ -54,7 +54,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"ANYSCALE_API_KEY\"] = getpass()"
+    "if \"ANYSCALE_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"ANYSCALE_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/chat/azure_chat_openai.ipynb

@@ -58,7 +58,10 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\"Enter your AzureOpenAI API key: \")\n",
+    "if \"AZURE_OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your AzureOpenAI API key: \"\n",
+    "    )\n",
     "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://YOUR-ENDPOINT.openai.azure.com/\""
    ]
   },

docs/docs/integrations/chat/cerebras.ipynb

@@ -76,7 +76,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"CEREBRAS_API_KEY\"] = getpass.getpass(\"Enter your Cerebras API key: \")"
+    "if \"CEREBRAS_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"CEREBRAS_API_KEY\"] = getpass.getpass(\"Enter your Cerebras API key: \")"
    ]
   },
   {

docs/docs/integrations/chat/databricks.ipynb

@@ -90,7 +90,10 @@
     "import os\n",
     "\n",
     "os.environ[\"DATABRICKS_HOST\"] = \"https://your-workspace.cloud.databricks.com\"\n",
-    "os.environ[\"DATABRICKS_TOKEN\"] = getpass.getpass(\"Enter your Databricks access token: \")"
+    "if \"DATABRICKS_TOKEN\" not in os.environ:\n",
+    "    os.environ[\"DATABRICKS_TOKEN\"] = getpass.getpass(\n",
+    "        \"Enter your Databricks access token: \"\n",
+    "    )"
    ]
   },
   {

docs/docs/integrations/chat/deepinfra.ipynb

@@ -123,8 +123,8 @@
     "from dotenv import find_dotenv, load_dotenv\n",
     "from langchain_community.chat_models import ChatDeepInfra\n",
     "from langchain_core.messages import HumanMessage\n",
-    "from langchain_core.pydantic_v1 import BaseModel\n",
     "from langchain_core.tools import tool\n",
+    "from pydantic import BaseModel\n",
     "\n",
     "model_name = \"meta-llama/Meta-Llama-3-70B-Instruct\"\n",
     "\n",

docs/docs/integrations/chat/edenai.ipynb

@@ -264,7 +264,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from pydantic import BaseModel, Field\n",
     "\n",
     "llm = ChatEdenAI(provider=\"openai\", temperature=0.2, max_tokens=500)\n",
     "\n",

docs/docs/integrations/chat/everlyai.ipynb

@@ -47,7 +47,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"EVERLYAI_API_KEY\"] = getpass()"
+    "if \"EVERLYAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"EVERLYAI_API_KEY\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/chat/fireworks.ipynb

@@ -52,7 +52,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
+    "if \"FIREWORKS_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
    ]
   },
   {

docs/docs/integrations/chat/friendli.ipynb

@@ -44,7 +44,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"
+    "if \"FRIENDLI_TOKEN\" not in os.environ:\n",
+    "    os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"
    ]
   },
   {

docs/docs/integrations/chat/gigachat.ipynb

@@ -47,7 +47,8 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
+    "if \"GIGACHAT_CREDENTIALS\" not in os.environ:\n",
+    "    os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
    ]
   },
   {

docs/docs/integrations/chat/google_generative_ai.ipynb

@@ -60,7 +60,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Enter your Google AI API key: \")"
+    "if \"GOOGLE_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Enter your Google AI API key: \")"
    ]
   },
   {