standard-tests[patch]: fix oai usage metadata test (#27122)

Thank you for contributing to LangChain!

**Description:** Box AI can return responses, but it can also be
configured to return citations. This change allows the developer to
decide if they want the answer, the citations, or both. Regardless of
the combination, this is returned as a single List[Document] object.

**Dependencies:** Updated to the latest Box Python SDK, v1.5.1
**Twitter handle:** BoxPlatform


- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

Co-authored-by: Erick Friis <erick@langchain.dev>

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,7 +1,7 @@
 Thank you for contributing to LangChain!
 
 - [ ] **PR title**: "package: description"
-  - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes.
+  - Where "package" is whichever of langchain, community, core, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes.
   - Example: "community: add foobar LLM"
 
 

.github/scripts/check_diff.py

@@ -15,7 +15,6 @@ LANGCHAIN_DIRS = [
     "libs/text-splitters",
     "libs/langchain",
     "libs/community",
-    "libs/experimental",
 ]
 
 # when set to True, we are ignoring core dependents
@@ -153,14 +152,19 @@ def _get_pydantic_test_configs(
     core_min_pydantic_version = get_min_version_from_toml(
         "./libs/core/pyproject.toml", "release", python_version, include=["pydantic"]
     )["pydantic"]
-    core_min_pydantic_minor = core_min_pydantic_version.split(".")[1] if "." in core_min_pydantic_version else "0"
-    dir_min_pydantic_version = (
-        get_min_version_from_toml(
-            f"./{dir_}/pyproject.toml", "release", python_version, include=["pydantic"]
-        )
-        .get("pydantic", "0.0.0")
+    core_min_pydantic_minor = (
+        core_min_pydantic_version.split(".")[1]
+        if "." in core_min_pydantic_version
+        else "0"
+    )
+    dir_min_pydantic_version = get_min_version_from_toml(
+        f"./{dir_}/pyproject.toml", "release", python_version, include=["pydantic"]
+    ).get("pydantic", "0.0.0")
+    dir_min_pydantic_minor = (
+        dir_min_pydantic_version.split(".")[1]
+        if "." in dir_min_pydantic_version
+        else "0"
     )
-    dir_min_pydantic_minor = dir_min_pydantic_version.split(".")[1] if "." in dir_min_pydantic_version else "0"
 
     custom_mins = {
         # depends on pydantic-settings 2.4 which requires pydantic 2.7

.github/workflows/_release.yml

@@ -269,6 +269,7 @@ jobs:
           AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
           AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
           AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_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 }}
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
@@ -293,7 +294,6 @@ jobs:
           VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
           UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
-          UNSTRUCTURED_API_KEY: ${{ secrets.UNSTRUCTURED_API_KEY }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 

.github/workflows/_test_doc_imports.yml

@@ -31,7 +31,7 @@ jobs:
 
       - name: Install langchain editable
         run: |
-          poetry run pip install -e libs/core libs/langchain libs/community libs/experimental
+          poetry run pip install langchain-experimental -e libs/core libs/langchain libs/community
 
       - name: Check doc imports
         shell: bash

.github/workflows/api_doc_build.yml

@@ -0,0 +1,153 @@
+name: API docs build
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron:  '0 13 * * *'
+env:
+  POETRY_VERSION: "1.8.1"
+  PYTHON_VERSION: "3.11"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    permissions: write-all
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          path: langchain
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-api-docs-html
+          path: langchain-api-docs-html
+          token: ${{ secrets.TOKEN_GITHUB_API_DOCS_HTML }}
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-google
+          path: langchain-google
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-datastax
+          path: langchain-datastax
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-nvidia
+          path: langchain-nvidia
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-cohere
+          path: langchain-cohere
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-elastic
+          path: langchain-elastic
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-postgres
+          path: langchain-postgres
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-aws
+          path: langchain-aws
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-weaviate
+          path: langchain-weaviate
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-ai21
+          path: langchain-ai21
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-together
+          path: langchain-together
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-experimental
+          path: langchain-experimental
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-milvus
+          path: langchain-milvus
+      - uses: actions/checkout@v4
+        with:
+          repository: langchain-ai/langchain-unstructured
+          path: langchain-unstructured
+
+
+      - name: Set Git config
+        working-directory: langchain
+        run: |
+          git config --local user.email "actions@github.com"
+          git config --local user.name "Github Actions"
+          
+      - name: Move libs
+        run: |
+          rm -rf \
+            langchain/libs/partners/google-genai \
+            langchain/libs/partners/google-vertexai \
+            langchain/libs/partners/astradb \
+            langchain/libs/partners/nvidia-trt \
+            langchain/libs/partners/nvidia-ai-endpoints \
+            langchain/libs/partners/cohere \
+            langchain/libs/partners/elasticsearch \
+            langchain/libs/partners/upstage \
+            langchain/libs/partners/ai21 \
+            langchain/libs/partners/together \
+            langchain/libs/standard-tests \
+            langchain/libs/experimental \
+            langchain/libs/partners/milvus \
+            langchain/libs/partners/unstructured
+          mv langchain-google/libs/genai langchain/libs/partners/google-genai
+          mv langchain-google/libs/vertexai langchain/libs/partners/google-vertexai
+          mv langchain-google/libs/community langchain/libs/partners/google-community
+          # mv langchain-datastax/libs/astradb langchain/libs/partners/astradb
+          # mv langchain-nvidia/libs/ai-endpoints langchain/libs/partners/nvidia-ai-endpoints
+          mv langchain-cohere/libs/cohere langchain/libs/partners/cohere
+          mv langchain-elastic/libs/elasticsearch langchain/libs/partners/elasticsearch
+          mv langchain-postgres langchain/libs/partners/postgres
+          mv langchain-aws/libs/aws langchain/libs/partners/aws
+          mv langchain-weaviate/libs/weaviate langchain/libs/partners/weaviate
+          mv langchain-ai21/libs/ai21 langchain/libs/partners/ai21
+          mv langchain-together/libs/together langchain/libs/partners/together
+          mv langchain-experimental/libs/experimental langchain/libs/experimental
+          mv langchain-milvus/libs/milvus langchain/libs/partners/milvus
+          mv langchain-unstructured/libs/unstructured langchain/libs/partners/unstructured
+
+      - name: Rm old html
+        run:
+          rm -rf langchain-api-docs-html/api_reference_build/html
+
+      - name: Set up Python ${{ env.PYTHON_VERSION }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./langchain/.github/actions/poetry_setup"
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: api-docs
+          working-directory: langchain
+
+      - name: Install dependencies
+        working-directory: langchain
+        run: |
+          python -m pip install -U uv
+          python -m uv pip install --upgrade --no-cache-dir pip setuptools
+          # skip airbyte and ibm due to pandas dependency issue
+          python -m uv pip install $(ls ./libs/partners | grep -vE "airbyte|ibm" | xargs -I {} echo "./libs/partners/{}")
+          python -m uv pip install libs/core libs/langchain libs/text-splitters libs/community libs/experimental
+          python -m uv pip install -r docs/api_reference/requirements.txt
+
+      - name: Build docs
+        working-directory: langchain
+        run: |
+          python docs/api_reference/create_api_rst.py
+          python -m sphinx -T -E -b html -d ../langchain-api-docs-html/_build/doctrees -c docs/api_reference docs/api_reference ../langchain-api-docs-html/api_reference_build/html -j auto
+          python docs/api_reference/scripts/custom_formatter.py ../langchain-api-docs-html/api_reference_build/html
+          # Default index page is blank so we copy in the actual home page.
+          cp ../langchain-api-docs-html/api_reference_build/html/{reference,index}.html
+          rm -rf ../langchain-api-docs-html/_build/
+
+      # https://github.com/marketplace/actions/add-commit
+      - uses: EndBug/add-and-commit@v9
+        with:
+          cwd: langchain-api-docs-html
+          message: 'Update API docs build'

.github/workflows/run_notebooks.yml

@@ -0,0 +1,63 @@
+name: Run notebooks
+
+on:
+  workflow_dispatch:
+    inputs:
+      python_version:
+        description: 'Python version'
+        required: false
+        default: '3.11'
+      working-directory:
+        description: 'Working directory or subset (e.g., docs/docs/tutorials/llm_chain.ipynb)'
+        required: false
+        default: 'all'
+  schedule:
+    - cron: '0 13 * * *'
+
+env:
+  POETRY_VERSION: "1.7.1"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    name: "Test docs"
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ github.event.inputs.python_version || '3.11' }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: run-notebooks
+
+      - name: Install dependencies
+        run: |
+          pip install -e libs/core
+          pip install -e libs/langchain
+          pip install -e libs/community
+          pip install --upgrade langchain-experimental
+          pip install -e libs//partners/anthropic
+          pip install -e libs//partners/chroma
+          pip install -e libs//partners/openai
+          pip install -e libs//partners/mistralai
+          pip install jupyter langgraph click pypdf vcrpy
+
+      - name: Pre-download tiktoken files
+        run: |
+          python docs/scripts/download_tiktoken.py
+
+      - name: Prepare notebooks
+        run: |
+          python docs/scripts/prepare_notebooks_for_ci.py --comment-install-cells
+
+      - name: Run notebooks
+        env:
+          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+          MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
+        run: |
+          ./docs/scripts/execute_notebooks.sh ${{ github.event.inputs.working-directory || 'all' }}

README.md

@@ -38,7 +38,7 @@ conda install langchain -c conda-forge
 
 For these applications, LangChain simplifies the entire application lifecycle:
 
-- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/v0.2/docs/concepts#langchain-expression-language-lcel), [components](https://python.langchain.com/v0.2/docs/concepts), and [third-party integrations](https://python.langchain.com/v0.2/docs/integrations/platforms/).
+- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel), [components](https://python.langchain.com/docs/concepts/), and [third-party integrations](https://python.langchain.com/docs/integrations/platforms/).
   Use [LangGraph](https://langchain-ai.github.io/langgraph/) to build stateful agents with first-class streaming and human-in-the-loop support.
 - **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
 - **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
@@ -65,20 +65,20 @@ For these applications, LangChain simplifies the entire application lifecycle:
 
 **❓ Question answering with RAG**
 
-- [Documentation](https://python.langchain.com/v0.2/docs/tutorials/rag/)
+- [Documentation](https://python.langchain.com/docs/tutorials/rag/)
 - End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
 
 **🧱 Extracting structured output**
 
-- [Documentation](https://python.langchain.com/v0.2/docs/tutorials/extraction/)
+- [Documentation](https://python.langchain.com/docs/tutorials/extraction/)
 - End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain-extract/)
 
 **🤖 Chatbots**
 
-- [Documentation](https://python.langchain.com/v0.2/docs/tutorials/chatbot/)
+- [Documentation](https://python.langchain.com/docs/tutorials/chatbot/)
 - End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
 
-And much more! Head to the [Tutorials](https://python.langchain.com/v0.2/docs/tutorials/) section of the docs for more.
+And much more! Head to the [Tutorials](https://python.langchain.com/docs/tutorials/) section of the docs for more.
 
 ## 🚀 How does LangChain help?
 
@@ -93,10 +93,10 @@ Off-the-shelf chains make it easy to get started. Components make it easy to cus
 
 LCEL is a key part of LangChain, allowing you to build and organize chains of processes in a straightforward, declarative manner. It was designed to support taking prototypes directly into production without needing to alter any code. This means you can use LCEL to set up everything from basic "prompt + LLM" setups to intricate, multi-step workflows.
 
-- **[Overview](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel)**: LCEL and its benefits
-- **[Interface](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface)**: The standard Runnable interface for LCEL objects
-- **[Primitives](https://python.langchain.com/v0.2/docs/how_to/#langchain-expression-language-lcel)**: More on the primitives LCEL includes
-- **[Cheatsheet](https://python.langchain.com/v0.2/docs/how_to/lcel_cheatsheet/)**: Quick overview of the most common usage patterns
+- **[Overview](https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel)**: LCEL and its benefits
+- **[Interface](https://python.langchain.com/docs/concepts/#runnable-interface)**: The standard Runnable interface for LCEL objects
+- **[Primitives](https://python.langchain.com/docs/how_to/#langchain-expression-language-lcel)**: More on the primitives LCEL includes
+- **[Cheatsheet](https://python.langchain.com/docs/how_to/lcel_cheatsheet/)**: Quick overview of the most common usage patterns
 
 ## Components
 
@@ -104,24 +104,24 @@ Components fall into the following **modules**:
 
 **📃 Model I/O**
 
-This includes [prompt management](https://python.langchain.com/v0.2/docs/concepts/#prompt-templates), [prompt optimization](https://python.langchain.com/v0.2/docs/concepts/#example-selectors), a generic interface for [chat models](https://python.langchain.com/v0.2/docs/concepts/#chat-models) and [LLMs](https://python.langchain.com/v0.2/docs/concepts/#llms), and common utilities for working with [model outputs](https://python.langchain.com/v0.2/docs/concepts/#output-parsers).
+This includes [prompt management](https://python.langchain.com/docs/concepts/#prompt-templates), [prompt optimization](https://python.langchain.com/docs/concepts/#example-selectors), a generic interface for [chat models](https://python.langchain.com/docs/concepts/#chat-models) and [LLMs](https://python.langchain.com/docs/concepts/#llms), and common utilities for working with [model outputs](https://python.langchain.com/docs/concepts/#output-parsers).
 
 **📚 Retrieval**
 
-Retrieval Augmented Generation involves [loading data](https://python.langchain.com/v0.2/docs/concepts/#document-loaders) from a variety of sources, [preparing it](https://python.langchain.com/v0.2/docs/concepts/#text-splitters), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/v0.2/docs/concepts/#retrievers) it for use in the generation step.
+Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/concepts/#document-loaders) from a variety of sources, [preparing it](https://python.langchain.com/docs/concepts/#text-splitters), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/docs/concepts/#retrievers) it for use in the generation step.
 
 **🤖 Agents**
 
-Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/v0.2/docs/concepts/#agents), along with [LangGraph](https://github.com/langchain-ai/langgraph) for building custom agents.
+Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/docs/concepts/#agents), along with [LangGraph](https://github.com/langchain-ai/langgraph) for building custom agents.
 
 ## 📖 Documentation
 
 Please see [here](https://python.langchain.com) for full documentation, which includes:
 
-- [Introduction](https://python.langchain.com/v0.2/docs/introduction/): Overview of the framework and the structure of the docs.
+- [Introduction](https://python.langchain.com/docs/introduction/): Overview of the framework and the structure of the docs.
 - [Tutorials](https://python.langchain.com/docs/use_cases/): If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
-- [How-to guides](https://python.langchain.com/v0.2/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
-- [Conceptual guide](https://python.langchain.com/v0.2/docs/concepts/): Conceptual explanations of the key parts of the framework.
+- [How-to guides](https://python.langchain.com/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
+- [Conceptual guide](https://python.langchain.com/docs/concepts/): Conceptual explanations of the key parts of the framework.
 - [API Reference](https://api.python.langchain.com): Thorough documentation of every class and method.
 
 ## 🌐 Ecosystem
@@ -134,7 +134,7 @@ Please see [here](https://python.langchain.com) for full documentation, which in
 
 As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
 
-For detailed information on how to contribute, see [here](https://python.langchain.com/v0.2/docs/contributing/).
+For detailed information on how to contribute, see [here](https://python.langchain.com/docs/contributing/).
 
 ## 🌟 Contributors
 

docs/Makefile

@@ -65,7 +65,7 @@ render:
 	$(PYTHON) scripts/notebook_convert.py $(INTERMEDIATE_DIR) $(OUTPUT_NEW_DOCS_DIR)
 
 md-sync:
-	rsync -avm --include="*/" --include="*.mdx" --include="*.md" --include="*.png" --include="*/_category_.yml" --exclude="*" $(INTERMEDIATE_DIR)/ $(OUTPUT_NEW_DOCS_DIR)
+	rsync -avmq --include="*/" --include="*.mdx" --include="*.md" --include="*.png" --include="*/_category_.yml" --exclude="*" $(INTERMEDIATE_DIR)/ $(OUTPUT_NEW_DOCS_DIR)
 
 append-related:
 	$(PYTHON) scripts/append_related_links.py $(OUTPUT_NEW_DOCS_DIR)
@@ -82,9 +82,9 @@ vercel-build: install-vercel-deps build generate-references
 	mv $(OUTPUT_NEW_DOCS_DIR) docs
 	rm -rf build
 	mkdir static/api_reference
-	git clone --depth=1 https://github.com/baskaryan/langchain-api-docs-build.git
-	mv langchain-api-docs-build/api_reference_build/html/* static/api_reference/
-	rm -rf langchain-api-docs-build
+	git clone --depth=1 https://github.com/langchain-ai/langchain-api-docs-html.git
+	mv langchain-api-docs-html/api_reference_build/html/* static/api_reference/
+	rm -rf langchain-api-docs-html
 	NODE_OPTIONS="--max-old-space-size=5000" yarn run docusaurus build
 
 start:

docs/api_reference/conf.py

@@ -26,7 +26,6 @@ from sphinx.util.docutils import SphinxDirective
 _DIR = Path(__file__).parent.absolute()
 sys.path.insert(0, os.path.abspath("."))
 sys.path.insert(0, os.path.abspath("../../libs/langchain"))
-sys.path.insert(0, os.path.abspath("../../libs/experimental"))
 
 with (_DIR.parents[1] / "libs" / "langchain" / "pyproject.toml").open("r") as f:
     data = toml.load(f)

docs/cassettes/agents_114ba50d.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/agents_24460239.msgpack.zlib

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docs/cassettes/agents_482ce13d.msgpack.zlib

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docs/cassettes/agents_532d6557.msgpack.zlib

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docs/cassettes/agents_56d8028b-5dbc-40b2-86f5-ed60631d86a3.msgpack.zlib

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docs/cassettes/agents_688b465d.msgpack.zlib

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docs/cassettes/agents_77c2f769.msgpack.zlib

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docs/cassettes/agents_a13462d0-2d02-4474-921e-15a1ba1fa274.msgpack.zlib

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docs/cassettes/agents_a3fb262c.msgpack.zlib

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docs/cassettes/agents_b6a7e925.msgpack.zlib

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docs/cassettes/agents_c96c960b.msgpack.zlib

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docs/cassettes/chatbot_16.msgpack.zlib

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docs/cassettes/chatbot_17.msgpack.zlib

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docs/cassettes/chatbot_19.msgpack.zlib

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docs/cassettes/chatbot_21.msgpack.zlib

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docs/cassettes/chatbot_28.msgpack.zlib

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docs/cassettes/chatbot_29.msgpack.zlib

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docs/cassettes/chatbot_34.msgpack.zlib

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docs/cassettes/chatbot_36.msgpack.zlib

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docs/cassettes/chatbot_43.msgpack.zlib

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docs/cassettes/chatbot_45.msgpack.zlib

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docs/cassettes/chatbot_48.msgpack.zlib

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docs/cassettes/chatbot_5.msgpack.zlib

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docs/cassettes/chatbot_7.msgpack.zlib

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docs/cassettes/chatbot_9.msgpack.zlib

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docs/cassettes/classification_0bdfcb05.msgpack.zlib

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docs/cassettes/classification_1c12fa00.msgpack.zlib

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docs/cassettes/classification_5509b6a6.msgpack.zlib

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docs/cassettes/classification_9154474c.msgpack.zlib

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docs/cassettes/data_generation_ff823394.msgpack.zlib

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docs/cassettes/extraction_cf7062cc-1d1d-4a37-9122-509d1b87f0a6.msgpack.zlib

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docs/cassettes/rag_ff01d415-7b0f-469d-bfda-b9cb672da611.msgpack.zlib

@@ -0,0 +1 @@
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

docs/docs/concepts.mdx

@@ -116,14 +116,14 @@ These also have corresponding async methods that should be used with [asyncio](h
 
 The **input type** and **output type** varies by component:
 
-| Component | Input Type | Output Type |
-| --- | --- | --- |
-| Prompt | Dictionary | PromptValue |
-| ChatModel | Single string, list of chat messages or a PromptValue | ChatMessage |
-| LLM | Single string, list of chat messages or a PromptValue | String |
-| OutputParser | The output of an LLM or ChatModel | Depends on the parser |
-| Retriever | Single string | List of Documents |
-| Tool | Single string or dictionary, depending on the tool | Depends on the tool |
+| Component    | Input Type                                            | Output Type           |
+|--------------|-------------------------------------------------------|-----------------------|
+| Prompt       | Dictionary                                            | PromptValue           |
+| ChatModel    | Single string, list of chat messages or a PromptValue | ChatMessage           |
+| LLM          | Single string, list of chat messages or a PromptValue | String                |
+| OutputParser | The output of an LLM or ChatModel                     | Depends on the parser |
+| Retriever    | Single string                                         | List of Documents     |
+| Tool         | Single string or dictionary, depending on the tool    | Depends on the tool   |
 
 
 All runnables expose input and output **schemas** to inspect the inputs and outputs:
@@ -236,7 +236,7 @@ This is where information like log-probs and token usage may be stored.
 
 **`tool_calls`**
 
-These represent a decision from an language model to call a tool. They are included as part of an `AIMessage` output.
+These represent a decision from a language model to call a tool. They are included as part of an `AIMessage` output.
 They can be accessed from there with the `.tool_calls` property.
 
 This property returns a list of `ToolCall`s. A `ToolCall` is a dictionary with the following arguments:
@@ -256,6 +256,8 @@ This represents a message with role "tool", which contains the result of calling
 - a `tool_call_id` field which conveys the id of the call to the tool that was called to produce this result.
 - an `artifact` field which can be used to pass along arbitrary artifacts of the tool execution which are useful to track but which should not be sent to the model.
 
+With most chat models, a `ToolMessage` can only appear in the chat history after an `AIMessage` that has a populated `tool_calls` field.
+
 #### (Legacy) FunctionMessage
 
 This is a legacy message type, corresponding to OpenAI's legacy function-calling API. `ToolMessage` should be used instead to correspond to the updated tool-calling API.

docs/docs/contributing/code/setup.mdx

@@ -73,9 +73,9 @@ make docker_tests
 
 There are also [integration tests and code-coverage](/docs/contributing/testing/) available.
 
-### Only develop langchain_core or langchain_experimental
+### Only develop langchain_core or langchain_community
 
-If you are only developing `langchain_core` or `langchain_experimental`, you can simply install the dependencies for the respective projects and run tests:
+If you are only developing `langchain_core` or `langchain_community`, you can simply install the dependencies for the respective projects and run tests:
 
 ```bash
 cd libs/core
@@ -86,7 +86,7 @@ make test
 Or:
 
 ```bash
-cd libs/experimental
+cd libs/community
 poetry install --with test
 make test
 ```

docs/docs/contributing/index.mdx

@@ -3,8 +3,8 @@ sidebar_position: 0
 ---
 # Welcome Contributors
 
-Hi there! Thank you for even being interested in contributing to LangChain.
-As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
+Hi there! Thank you for your interest in contributing to LangChain.
+As an open-source project in a fast developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
 
 ## 🗺️ Guidelines
 

docs/docs/how_to/chatbots_memory.ipynb

@@ -23,6 +23,17 @@
     "\n",
     "We'll go into more detail on a few techniques below!\n",
     "\n",
+    ":::note\n",
+    "\n",
+    "This how-to guide previously built a chatbot using [RunnableWithMessageHistory](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html). You can access this version of the guide in the [v0.2 docs](https://python.langchain.com/v0.2/docs/how_to/chatbots_memory/).\n",
+    "\n",
+    "As of the v0.3 release of LangChain, we recommend that LangChain users take advantage of [LangGraph persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) to incorporate `memory` into new LangChain applications.\n",
+    "\n",
+    "If your code is already relying on `RunnableWithMessageHistory` or `BaseChatMessageHistory`, you do **not** need to make any changes. We do not plan on deprecating this functionality in the near future as it works for simple chat applications and any code that uses `RunnableWithMessageHistory` will continue to work as expected.\n",
+    "\n",
+    "Please see [How to migrate to LangGraph Memory](/docs/versions/migrating_memory/) for more details.\n",
+    ":::\n",
+    "\n",
     "## Setup\n",
     "\n",
     "You'll need to install a few packages, and have your OpenAI API key set as an environment variable named `OPENAI_API_KEY`:"
@@ -34,32 +45,21 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stdin",
      "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 23.3.2 is available.\n",
-      "You should consider upgrading via the '/Users/jacoblee/.pyenv/versions/3.10.5/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
-      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
+      "OpenAI API Key: ········\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "%pip install --upgrade --quiet langchain langchain-openai\n",
+    "%pip install --upgrade --quiet langchain langchain-openai langgraph\n",
     "\n",
-    "# Set env var OPENAI_API_KEY or load from a .env file:\n",
-    "import dotenv\n",
+    "import getpass\n",
+    "import os\n",
     "\n",
-    "dotenv.load_dotenv()"
+    "if not os.environ.get(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
    ]
   },
   {
@@ -71,13 +71,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "chat = ChatOpenAI(model=\"gpt-4o-mini\")"
+    "model = ChatOpenAI(model=\"gpt-4o-mini\")"
    ]
   },
   {
@@ -98,34 +98,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "I said \"J'adore la programmation,\" which means \"I love programming\" in French.\n"
+      "I said, \"I love programming\" in French: \"J'adore la programmation.\"\n"
      ]
     }
    ],
    "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant. Answer all questions to the best of your ability.\",\n",
+    "        SystemMessage(\n",
+    "            content=\"You are a helpful assistant. Answer all questions to the best of your ability.\"\n",
     "        ),\n",
-    "        (\"placeholder\", \"{messages}\"),\n",
+    "        MessagesPlaceholder(variable_name=\"messages\"),\n",
     "    ]\n",
     ")\n",
     "\n",
-    "chain = prompt | chat\n",
+    "chain = prompt | model\n",
     "\n",
     "ai_msg = chain.invoke(\n",
     "    {\n",
     "        \"messages\": [\n",
-    "            (\n",
-    "                \"human\",\n",
-    "                \"Translate this sentence from English to French: I love programming.\",\n",
+    "            HumanMessage(\n",
+    "                content=\"Translate from English to French: I love programming.\"\n",
     "            ),\n",
-    "            (\"ai\", \"J'adore la programmation.\"),\n",
-    "            (\"human\", \"What did you just say?\"),\n",
+    "            AIMessage(content=\"J'adore la programmation.\"),\n",
+    "            HumanMessage(content=\"What did you just say?\"),\n",
     "        ],\n",
     "    }\n",
     ")\n",
@@ -136,51 +135,57 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can see that by passing the previous conversation into a chain, it can use it as context to answer questions. This is the basic concept underpinning chatbot memory - the rest of the guide will demonstrate convenient techniques for passing or reformatting messages.\n",
-    "\n",
-    "## Chat history\n",
-    "\n",
-    "It's perfectly fine to store and pass messages directly as an array, but we can use LangChain's built-in [message history class](https://python.langchain.com/api_reference/langchain/index.html#module-langchain.memory) to store and load messages as well. Instances of this class are responsible for storing and loading chat messages from persistent storage. LangChain integrates with many providers - you can see a [list of integrations here](/docs/integrations/memory) - but for this demo we will use an ephemeral demo class.\n",
-    "\n",
-    "Here's an example of the API:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[HumanMessage(content='Translate this sentence from English to French: I love programming.'),\n",
-       " AIMessage(content=\"J'adore la programmation.\")]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
-    "\n",
-    "demo_ephemeral_chat_history = ChatMessageHistory()\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_user_message(\n",
-    "    \"Translate this sentence from English to French: I love programming.\"\n",
-    ")\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_ai_message(\"J'adore la programmation.\")\n",
-    "\n",
-    "demo_ephemeral_chat_history.messages"
+    "We can see that by passing the previous conversation into a chain, it can use it as context to answer questions. This is the basic concept underpinning chatbot memory - the rest of the guide will demonstrate convenient techniques for passing or reformatting messages."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can use it directly to store conversation turns for our chain:"
+    "## Automatic history management\n",
+    "\n",
+    "The previous examples pass messages to the chain (and model) explicitly. This is a completely acceptable approach, but it does require external management of new messages. LangChain also provides a way to build applications that have memory using LangGraph's [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/). You can [enable persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence/) in LangGraph applications by providing a `checkpointer` when compiling the graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import START, MessagesState, StateGraph\n",
+    "\n",
+    "workflow = StateGraph(state_schema=MessagesState)\n",
+    "\n",
+    "\n",
+    "# Define the function that calls the model\n",
+    "def call_model(state: MessagesState):\n",
+    "    system_prompt = (\n",
+    "        \"You are a helpful assistant. \"\n",
+    "        \"Answer all questions to the best of your ability.\"\n",
+    "    )\n",
+    "    messages = [SystemMessage(content=system_prompt)] + state[\"messages\"]\n",
+    "    response = model.invoke(messages)\n",
+    "    return {\"messages\": response}\n",
+    "\n",
+    "\n",
+    "# Define the node and edge\n",
+    "workflow.add_node(\"model\", call_model)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "\n",
+    "# Add simple in-memory checkpointer\n",
+    "# highlight-start\n",
+    "memory = MemorySaver()\n",
+    "app = workflow.compile(checkpointer=memory)\n",
+    "# highlight-end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We'll pass the latest input to the conversation here and let the LangGraph keep track of the conversation history using the checkpointer:"
    ]
   },
   {
@@ -191,7 +196,8 @@
     {
      "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-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})"
+       "{'messages': [HumanMessage(content='Translate to French: I love programming.', additional_kwargs={}, response_metadata={}, id='be5e7099-3149-4293-af49-6b36c8ccd71b'),\n",
+       "  AIMessage(content=\"J'aime programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 4, 'prompt_tokens': 35, 'total_tokens': 39, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_e9627b5346', 'finish_reason': 'stop', 'logprobs': None}, id='run-8a753d7a-b97b-4d01-a661-626be6f41b38-0', usage_metadata={'input_tokens': 35, 'output_tokens': 4, 'total_tokens': 39})]}"
       ]
      },
      "execution_count": 5,
@@ -200,159 +206,35 @@
     }
    ],
    "source": [
-    "demo_ephemeral_chat_history = ChatMessageHistory()\n",
-    "\n",
-    "input1 = \"Translate this sentence from English to French: I love programming.\"\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_user_message(input1)\n",
-    "\n",
-    "response = chain.invoke(\n",
-    "    {\n",
-    "        \"messages\": demo_ephemeral_chat_history.messages,\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_ai_message(response)\n",
-    "\n",
-    "input2 = \"What did I just ask you?\"\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_user_message(input2)\n",
-    "\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"messages\": demo_ephemeral_chat_history.messages,\n",
-    "    }\n",
+    "app.invoke(\n",
+    "    {\"messages\": [HumanMessage(content=\"Translate to French: I love programming.\")]},\n",
+    "    config={\"configurable\": {\"thread_id\": \"1\"}},\n",
     ")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Automatic history management\n",
-    "\n",
-    "The previous examples pass messages to the chain explicitly. This is a completely acceptable approach, but it does require external management of new messages. LangChain also includes an wrapper for LCEL chains that can handle this process automatically called `RunnableWithMessageHistory`.\n",
-    "\n",
-    "To show how it works, let's slightly modify the above prompt to take a final `input` variable that populates a `HumanMessage` template after the chat history. This means that we will expect a `chat_history` parameter that contains all messages BEFORE the current messages instead of all messages:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant. Answer all questions to the best of your ability.\",\n",
-    "        ),\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | chat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " We'll pass the latest input to the conversation here and let the `RunnableWithMessageHistory` class wrap our chain and do the work of appending that `input` variable to the chat history.\n",
-    " \n",
-    " Next, let's declare our wrapped chain:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
-    "\n",
-    "demo_ephemeral_chat_history_for_chain = ChatMessageHistory()\n",
-    "\n",
-    "chain_with_message_history = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    lambda session_id: demo_ephemeral_chat_history_for_chain,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"chat_history\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This class takes a few parameters in addition to the chain that we want to wrap:\n",
-    "\n",
-    "- A factory function that returns a message history for a given session id. This allows your chain to handle multiple users at once by loading different messages for different conversations.\n",
-    "- An `input_messages_key` that specifies which part of the input should be tracked and stored in the chat history. In this example, we want to track the string passed in as `input`.\n",
-    "- A `history_messages_key` that specifies what the previous messages should be injected into the prompt as. Our prompt has a `MessagesPlaceholder` named `chat_history`, so we specify this property to match.\n",
-    "- (For chains with multiple outputs) an `output_messages_key` which specifies which output to store as history. This is the inverse of `input_messages_key`.\n",
-    "\n",
-    "We can invoke this new chain as normal, with an additional `configurable` field that specifies the particular `session_id` to pass to the factory function. This is unused for the demo, but in real-world chains, you'll want to return a chat history corresponding to the passed session:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Parent run dc4e2f79-4bcd-4a36-9506-55ace9040588 not found for run 34b5773e-3ced-46a6-8daf-4d464c15c940. Treating as a root run.\n"
-     ]
-    },
     {
      "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-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})"
+       "{'messages': [HumanMessage(content='Translate to French: I love programming.', additional_kwargs={}, response_metadata={}, id='be5e7099-3149-4293-af49-6b36c8ccd71b'),\n",
+       "  AIMessage(content=\"J'aime programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 4, 'prompt_tokens': 35, 'total_tokens': 39, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_e9627b5346', 'finish_reason': 'stop', 'logprobs': None}, id='run-8a753d7a-b97b-4d01-a661-626be6f41b38-0', usage_metadata={'input_tokens': 35, 'output_tokens': 4, 'total_tokens': 39}),\n",
+       "  HumanMessage(content='What did I just ask you?', additional_kwargs={}, response_metadata={}, id='c667529b-7c41-4cc0-9326-0af47328b816'),\n",
+       "  AIMessage(content='You asked me to translate \"I love programming\" into French.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 54, 'total_tokens': 67, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-134a7ea0-d3a4-4923-bd58-25e5a43f6a1f-0', usage_metadata={'input_tokens': 54, 'output_tokens': 13, 'total_tokens': 67})]}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain_with_message_history.invoke(\n",
-    "    {\"input\": \"Translate this sentence from English to French: I love programming.\"},\n",
-    "    {\"configurable\": {\"session_id\": \"unused\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Parent run cc14b9d8-c59e-40db-a523-d6ab3fc2fa4f not found for run 5b75e25c-131e-46ee-9982-68569db04330. Treating as a root run.\n"
-     ]
-    },
-    {
-     "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-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,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain_with_message_history.invoke(\n",
-    "    {\"input\": \"What did I just ask you?\"}, {\"configurable\": {\"session_id\": \"unused\"}}\n",
+    "app.invoke(\n",
+    "    {\"messages\": [HumanMessage(content=\"What did I just ask you?\")]},\n",
+    "    config={\"configurable\": {\"thread_id\": \"1\"}},\n",
     ")"
    ]
   },
@@ -366,80 +248,44 @@
     "\n",
     "### Trimming messages\n",
     "\n",
-    "LLMs and chat models have limited context windows, and even if you're not directly hitting limits, you may want to limit the amount of distraction the model has to deal with. One solution is trim the historic messages before passing them to the model. Let's use an example history with some preloaded messages:"
+    "LLMs and chat models have limited context windows, and even if you're not directly hitting limits, you may want to limit the amount of distraction the model has to deal with. One solution is trim the history messages before passing them to the model. Let's use an example history with the `app` we declared above:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[HumanMessage(content=\"Hey there! I'm Nemo.\"),\n",
-       " AIMessage(content='Hello!'),\n",
-       " HumanMessage(content='How are you today?'),\n",
-       " AIMessage(content='Fine thanks!')]"
+       "{'messages': [HumanMessage(content=\"Hey there! I'm Nemo.\", additional_kwargs={}, response_metadata={}, id='6b4cab70-ce18-49b0-bb06-267bde44e037'),\n",
+       "  AIMessage(content='Hello!', additional_kwargs={}, response_metadata={}, id='ba3714f4-8876-440b-a651-efdcab2fcb4c'),\n",
+       "  HumanMessage(content='How are you today?', additional_kwargs={}, response_metadata={}, id='08d032c0-1577-4862-a3f2-5c1b90687e21'),\n",
+       "  AIMessage(content='Fine thanks!', additional_kwargs={}, response_metadata={}, id='21790e16-db05-4537-9a6b-ecad0fcec436'),\n",
+       "  HumanMessage(content=\"What's my name?\", additional_kwargs={}, response_metadata={}, id='c933eca3-5fd8-4651-af16-20fe2d49c216'),\n",
+       "  AIMessage(content='Your name is Nemo.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 63, 'total_tokens': 68, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-a0b21acc-9dbb-4fb6-a953-392020f37d88-0', usage_metadata={'input_tokens': 63, 'output_tokens': 5, 'total_tokens': 68})]}"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "demo_ephemeral_chat_history = ChatMessageHistory()\n",
+    "demo_ephemeral_chat_history = [\n",
+    "    HumanMessage(content=\"Hey there! I'm Nemo.\"),\n",
+    "    AIMessage(content=\"Hello!\"),\n",
+    "    HumanMessage(content=\"How are you today?\"),\n",
+    "    AIMessage(content=\"Fine thanks!\"),\n",
+    "]\n",
     "\n",
-    "demo_ephemeral_chat_history.add_user_message(\"Hey there! I'm Nemo.\")\n",
-    "demo_ephemeral_chat_history.add_ai_message(\"Hello!\")\n",
-    "demo_ephemeral_chat_history.add_user_message(\"How are you today?\")\n",
-    "demo_ephemeral_chat_history.add_ai_message(\"Fine thanks!\")\n",
-    "\n",
-    "demo_ephemeral_chat_history.messages"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's use this message history with the `RunnableWithMessageHistory` chain we declared above:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Parent run 7ff2d8ec-65e2-4f67-8961-e498e2c4a591 not found for run 3881e990-6596-4326-84f6-2b76949e0657. Treating as a root run.\n"
-     ]
-    },
-    {
-     "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-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,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain_with_message_history = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    lambda session_id: demo_ephemeral_chat_history,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"chat_history\",\n",
-    ")\n",
-    "\n",
-    "chain_with_message_history.invoke(\n",
-    "    {\"input\": \"What's my name?\"},\n",
-    "    {\"configurable\": {\"session_id\": \"unused\"}},\n",
+    "app.invoke(\n",
+    "    {\n",
+    "        \"messages\": demo_ephemeral_chat_history\n",
+    "        + [HumanMessage(content=\"What's my name?\")]\n",
+    "    },\n",
+    "    config={\"configurable\": {\"thread_id\": \"2\"}},\n",
     ")"
    ]
   },
@@ -447,35 +293,88 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can see the chain remembers the preloaded name.\n",
+    "We can see the app remembers the preloaded name.\n",
     "\n",
-    "But let's say we have a very small context window, and we want to trim the number of messages passed to the chain to only the 2 most recent ones. We can use the built in [trim_messages](/docs/how_to/trim_messages/) util to trim messages based on their token count before they reach our prompt. In this case we'll count each message as 1 \"token\" and keep only the last two messages:"
+    "But let's say we have a very small context window, and we want to trim the number of messages passed to the model to only the 2 most recent ones. We can use the built in [trim_messages](/docs/how_to/trim_messages/) util to trim messages based on their token count before they reach our prompt. In this case we'll count each message as 1 \"token\" and keep only the last two messages:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from operator import itemgetter\n",
-    "\n",
     "from langchain_core.messages import trim_messages\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import START, MessagesState, StateGraph\n",
     "\n",
+    "# Define trimmer\n",
+    "# highlight-start\n",
+    "# count each message as 1 \"token\" (token_counter=len) and keep only the last two messages\n",
     "trimmer = trim_messages(strategy=\"last\", max_tokens=2, token_counter=len)\n",
+    "# highlight-end\n",
     "\n",
-    "chain_with_trimming = (\n",
-    "    RunnablePassthrough.assign(chat_history=itemgetter(\"chat_history\") | trimmer)\n",
-    "    | prompt\n",
-    "    | chat\n",
-    ")\n",
+    "workflow = StateGraph(state_schema=MessagesState)\n",
     "\n",
-    "chain_with_trimmed_history = RunnableWithMessageHistory(\n",
-    "    chain_with_trimming,\n",
-    "    lambda session_id: demo_ephemeral_chat_history,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"chat_history\",\n",
+    "\n",
+    "# Define the function that calls the model\n",
+    "def call_model(state: MessagesState):\n",
+    "    # highlight-start\n",
+    "    trimmed_messages = trimmer.invoke(state[\"messages\"])\n",
+    "    system_prompt = (\n",
+    "        \"You are a helpful assistant. \"\n",
+    "        \"Answer all questions to the best of your ability.\"\n",
+    "    )\n",
+    "    messages = [SystemMessage(content=system_prompt)] + trimmed_messages\n",
+    "    # highlight-end\n",
+    "    response = model.invoke(messages)\n",
+    "    return {\"messages\": response}\n",
+    "\n",
+    "\n",
+    "# Define the node and edge\n",
+    "workflow.add_node(\"model\", call_model)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "\n",
+    "# Add simple in-memory checkpointer\n",
+    "memory = MemorySaver()\n",
+    "app = workflow.compile(checkpointer=memory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's call this new app and check the response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'messages': [HumanMessage(content=\"Hey there! I'm Nemo.\", additional_kwargs={}, response_metadata={}, id='6b4cab70-ce18-49b0-bb06-267bde44e037'),\n",
+       "  AIMessage(content='Hello!', additional_kwargs={}, response_metadata={}, id='ba3714f4-8876-440b-a651-efdcab2fcb4c'),\n",
+       "  HumanMessage(content='How are you today?', additional_kwargs={}, response_metadata={}, id='08d032c0-1577-4862-a3f2-5c1b90687e21'),\n",
+       "  AIMessage(content='Fine thanks!', additional_kwargs={}, response_metadata={}, id='21790e16-db05-4537-9a6b-ecad0fcec436'),\n",
+       "  HumanMessage(content='What is my name?', additional_kwargs={}, response_metadata={}, id='a22ab7c5-8617-4821-b3e9-a9e7dca1ff78'),\n",
+       "  AIMessage(content=\"I'm sorry, but I don't have access to personal information about you unless you share it with me. How can I assist you today?\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 39, 'total_tokens': 66, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-f7b32d72-9f57-4705-be7e-43bf1c3d293b-0', usage_metadata={'input_tokens': 39, 'output_tokens': 27, 'total_tokens': 66})]}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "app.invoke(\n",
+    "    {\n",
+    "        \"messages\": demo_ephemeral_chat_history\n",
+    "        + [HumanMessage(content=\"What is my name?\")]\n",
+    "    },\n",
+    "    config={\"configurable\": {\"thread_id\": \"3\"}},\n",
     ")"
    ]
   },
@@ -483,101 +382,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's call this new chain and check the messages afterwards:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Parent run 775cde65-8d22-4c44-80bb-f0b9811c32ca not found for run 5cf71d0e-4663-41cd-8dbe-e9752689cfac. Treating as a root run.\n"
-     ]
-    },
-    {
-     "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-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,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain_with_trimmed_history.invoke(\n",
-    "    {\"input\": \"Where does P. Sherman live?\"},\n",
-    "    {\"configurable\": {\"session_id\": \"unused\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[HumanMessage(content=\"Hey there! I'm Nemo.\"),\n",
-       " AIMessage(content='Hello!'),\n",
-       " 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-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-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,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "demo_ephemeral_chat_history.messages"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can see that our history has removed the two oldest messages while still adding the most recent conversation at the end. The next time the chain is called, `trim_messages` will be called again, and only the two most recent messages will be passed to the model. In this case, this means that the model will forget the name we gave it the next time we invoke it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Parent run fde7123f-6fd3-421a-a3fc-2fb37dead119 not found for run 061a4563-2394-470d-a3ed-9bf1388ca431. Treating as a root run.\n"
-     ]
-    },
-    {
-     "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-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,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain_with_trimmed_history.invoke(\n",
-    "    {\"input\": \"What is my name?\"},\n",
-    "    {\"configurable\": {\"session_id\": \"unused\"}},\n",
-    ")"
+    "We can see that `trim_messages` was called and only the two most recent messages will be passed to the model. In this case, this means that the model forgot the name we gave it."
    ]
   },
   {
@@ -593,114 +398,84 @@
    "source": [
     "### Summary memory\n",
     "\n",
-    "We can use this same pattern in other ways too. For example, we could use an additional LLM call to generate a summary of the conversation before calling our chain. Let's recreate our chat history and chatbot chain:"
+    "We can use this same pattern in other ways too. For example, we could use an additional LLM call to generate a summary of the conversation before calling our app. Let's recreate our chat history:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[HumanMessage(content=\"Hey there! I'm Nemo.\"),\n",
-       " AIMessage(content='Hello!'),\n",
-       " HumanMessage(content='How are you today?'),\n",
-       " AIMessage(content='Fine thanks!')]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "demo_ephemeral_chat_history = ChatMessageHistory()\n",
-    "\n",
-    "demo_ephemeral_chat_history.add_user_message(\"Hey there! I'm Nemo.\")\n",
-    "demo_ephemeral_chat_history.add_ai_message(\"Hello!\")\n",
-    "demo_ephemeral_chat_history.add_user_message(\"How are you today?\")\n",
-    "demo_ephemeral_chat_history.add_ai_message(\"Fine thanks!\")\n",
-    "\n",
-    "demo_ephemeral_chat_history.messages"
+    "demo_ephemeral_chat_history = [\n",
+    "    HumanMessage(content=\"Hey there! I'm Nemo.\"),\n",
+    "    AIMessage(content=\"Hello!\"),\n",
+    "    HumanMessage(content=\"How are you today?\"),\n",
+    "    AIMessage(content=\"Fine thanks!\"),\n",
+    "]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll slightly modify the prompt to make the LLM aware that will receive a condensed summary instead of a chat history:"
+    "And now, let's update the model-calling function to distill previous interactions into a summary:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant. Answer all questions to the best of your ability. The provided chat history includes facts about the user you are speaking with.\",\n",
-    "        ),\n",
-    "        (\"placeholder\", \"{chat_history}\"),\n",
-    "        (\"user\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
+    "from langchain_core.messages import HumanMessage, RemoveMessage\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import START, MessagesState, StateGraph\n",
     "\n",
-    "chain = prompt | chat\n",
+    "workflow = StateGraph(state_schema=MessagesState)\n",
     "\n",
-    "chain_with_message_history = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    lambda session_id: demo_ephemeral_chat_history,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"chat_history\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now, let's create a function that will distill previous interactions into a summary. We can add this one to the front of the chain too:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def summarize_messages(chain_input):\n",
-    "    stored_messages = demo_ephemeral_chat_history.messages\n",
-    "    if len(stored_messages) == 0:\n",
-    "        return False\n",
-    "    summarization_prompt = ChatPromptTemplate.from_messages(\n",
-    "        [\n",
-    "            (\"placeholder\", \"{chat_history}\"),\n",
-    "            (\n",
-    "                \"user\",\n",
-    "                \"Distill the above chat messages into a single summary message. Include as many specific details as you can.\",\n",
-    "            ),\n",
-    "        ]\n",
+    "\n",
+    "# Define the function that calls the model\n",
+    "def call_model(state: MessagesState):\n",
+    "    system_prompt = (\n",
+    "        \"You are a helpful assistant. \"\n",
+    "        \"Answer all questions to the best of your ability. \"\n",
+    "        \"The provided chat history includes a summary of the earlier conversation.\"\n",
     "    )\n",
-    "    summarization_chain = summarization_prompt | chat\n",
+    "    system_message = SystemMessage(content=system_prompt)\n",
+    "    message_history = state[\"messages\"][:-1]  # exclude the most recent user input\n",
+    "    # Summarize the messages if the chat history reaches a certain size\n",
+    "    if len(message_history) >= 4:\n",
+    "        last_human_message = state[\"messages\"][-1]\n",
+    "        # Invoke the model to generate conversation summary\n",
+    "        summary_prompt = (\n",
+    "            \"Distill the above chat messages into a single summary message. \"\n",
+    "            \"Include as many specific details as you can.\"\n",
+    "        )\n",
+    "        summary_message = model.invoke(\n",
+    "            message_history + [HumanMessage(content=summary_prompt)]\n",
+    "        )\n",
     "\n",
-    "    summary_message = summarization_chain.invoke({\"chat_history\": stored_messages})\n",
+    "        # Delete messages that we no longer want to show up\n",
+    "        delete_messages = [RemoveMessage(id=m.id) for m in state[\"messages\"]]\n",
+    "        # Re-add user message\n",
+    "        human_message = HumanMessage(content=last_human_message.content)\n",
+    "        # Call the model with summary & response\n",
+    "        response = model.invoke([system_message, summary_message, human_message])\n",
+    "        message_updates = [summary_message, human_message, response] + delete_messages\n",
+    "    else:\n",
+    "        message_updates = model.invoke([system_message] + state[\"messages\"])\n",
     "\n",
-    "    demo_ephemeral_chat_history.clear()\n",
-    "\n",
-    "    demo_ephemeral_chat_history.add_message(summary_message)\n",
-    "\n",
-    "    return True\n",
+    "    return {\"messages\": message_updates}\n",
     "\n",
     "\n",
-    "chain_with_summarization = (\n",
-    "    RunnablePassthrough.assign(messages_summarized=summarize_messages)\n",
-    "    | chain_with_message_history\n",
-    ")"
+    "# Define the node and edge\n",
+    "workflow.add_node(\"model\", call_model)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "\n",
+    "# Add simple in-memory checkpointer\n",
+    "memory = MemorySaver()\n",
+    "app = workflow.compile(checkpointer=memory)"
    ]
   },
   {
@@ -712,54 +487,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='You introduced yourself as Nemo. How can I assist you today, Nemo?')"
+       "{'messages': [AIMessage(content=\"Nemo greeted me, and I responded positively, indicating that I'm doing well.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 16, 'prompt_tokens': 60, 'total_tokens': 76, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-ee42f98d-907d-4bad-8f16-af2db789701d-0', usage_metadata={'input_tokens': 60, 'output_tokens': 16, 'total_tokens': 76}),\n",
+       "  HumanMessage(content='What did I say my name was?', additional_kwargs={}, response_metadata={}, id='788555ea-5b1f-4c29-a2f2-a92f15d147be'),\n",
+       "  AIMessage(content='You mentioned that your name is Nemo.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 67, 'total_tokens': 75, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_1bb46167f9', 'finish_reason': 'stop', 'logprobs': None}, id='run-099a43bd-a284-4969-bb6f-0be486614cd8-0', usage_metadata={'input_tokens': 67, 'output_tokens': 8, 'total_tokens': 75})]}"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain_with_summarization.invoke(\n",
-    "    {\"input\": \"What did I say my name was?\"},\n",
-    "    {\"configurable\": {\"session_id\": \"unused\"}},\n",
+    "app.invoke(\n",
+    "    {\n",
+    "        \"messages\": demo_ephemeral_chat_history\n",
+    "        + [HumanMessage(\"What did I say my name was?\")]\n",
+    "    },\n",
+    "    config={\"configurable\": {\"thread_id\": \"4\"}},\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[AIMessage(content='The conversation is between Nemo and an AI. Nemo introduces himself and the AI responds with a greeting. Nemo then asks the AI how it is doing, and the AI responds that it is fine.'),\n",
-       " HumanMessage(content='What did I say my name was?'),\n",
-       " AIMessage(content='You introduced yourself as Nemo. How can I assist you today, Nemo?')]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "demo_ephemeral_chat_history.messages"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that invoking the chain again will generate another summary generated from the initial summary plus new messages and so on. You could also design a hybrid approach where a certain number of messages are retained in chat history while others are summarized."
+    "Note that invoking the app again will keep accumulating the history until it reaches the specified number of messages (four in our case). At that point we will generate another summary generated from the initial summary plus new messages and so on."
    ]
   }
  ],
@@ -779,7 +537,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/document_loader_web.ipynb

@@ -0,0 +1,486 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7414502a-4532-4da3-aef0-71aac4d0d4dd",
+   "metadata": {},
+   "source": [
+    "# How to load web pages\n",
+    "\n",
+    "This guide covers how to load web pages into the LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) format that we use downstream. Web pages contain text, images, and other multimedia elements, and are typically represented with HTML. They may include links to other pages or resources.\n",
+    "\n",
+    "LangChain integrates with a host of parsers that are appropriate for web pages. The right parser will depend on your needs. Below we demonstrate two possibilities:\n",
+    "\n",
+    "- [Simple and fast](/docs/how_to/document_loader_web#simple-and-fast-text-extraction) parsing, in which we recover one `Document` per web page with its content represented as a \"flattened\" string;\n",
+    "- [Advanced](/docs/how_to/document_loader_web#advanced-parsing) parsing, in which we recover multiple `Document` objects per page, allowing one to identify and traverse sections, links, tables, and other structures.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "For the \"simple and fast\" parsing, we will need `langchain-community` and the `beautifulsoup4` library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89bc7be9-ab50-4c5a-860a-deee7b469f67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-community beautifulsoup4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a07f5ca3-e2b7-4d9c-b1f2-7547856cbdf7",
+   "metadata": {},
+   "source": [
+    "For advanced parsing, we will use `langchain-unstructured`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a3ef1fc-dfde-4814-b7f6-b6c0c649f044",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-unstructured"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ef11005-1bd0-43a3-8d52-ea823c830c34",
+   "metadata": {},
+   "source": [
+    "## Simple and fast text extraction\n",
+    "\n",
+    "If you are looking for a simple string representation of text that is embedded in a web page, the method below is appropriate. It will return a list of `Document` objects -- one per page -- containing a single string of the page's text. Under the hood it uses the `beautifulsoup4` Python library.\n",
+    "\n",
+    "LangChain document loaders implement `lazy_load` and its async variant, `alazy_load`, which return iterators of `Document objects`. We will use these below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7faeccbc-4e56-4b88-99db-2274ed0680c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import bs4\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "\n",
+    "page_url = \"https://python.langchain.com/docs/how_to/chatbots_memory/\"\n",
+    "\n",
+    "loader = WebBaseLoader(web_paths=[page_url])\n",
+    "docs = []\n",
+    "async for doc in loader.alazy_load():\n",
+    "    docs.append(doc)\n",
+    "\n",
+    "assert len(docs) == 1\n",
+    "doc = docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "21199a0d-3bd2-4410-a060-763649b14691",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': 'https://python.langchain.com/docs/how_to/chatbots_memory/', 'title': 'How to add memory to chatbots | \\uf8ffü¶úÔ∏è\\uf8ffüîó LangChain', 'description': 'A key feature of chatbots is their ability to use content of previous conversation turns as context. This state management can take several forms, including:', 'language': 'en'}\n",
+      "\n",
+      "How to add memory to chatbots | 🦜️🔗 LangChain\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "Skip to main contentShare your thoughts on AI agents. Take the 3-min survey.IntegrationsAPI ReferenceMoreContributingPeopleLangSmithLangGraphLangChain HubLangChain JS/TSv0.3v0.3v0.2v0.1💬SearchIntroductionTutorialsBuild a Question Answering application over a Graph DatabaseTutorialsBuild a Simple LLM Application with LCELBuild a Query Analysis SystemBuild a ChatbotConversational RAGBuild an Extraction ChainBuild an AgentTaggingd\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"{doc.metadata}\\n\")\n",
+    "print(doc.page_content[:500].strip())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23189e91-5237-4a9e-a4bb-cb79e130c364",
+   "metadata": {},
+   "source": [
+    "This is essentially a dump of the text from the page's HTML. It may contain extraneous information like headings and navigation bars. If you are familiar with the expected HTML, you can specify desired `<div>` classes and other parameters via BeautifulSoup. Below we parse only the body text of the article:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4211b1a6-e636-415b-a556-ae01969399a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = WebBaseLoader(\n",
+    "    web_paths=[page_url],\n",
+    "    bs_kwargs={\n",
+    "        \"parse_only\": bs4.SoupStrainer(class_=\"theme-doc-markdown markdown\"),\n",
+    "    },\n",
+    "    bs_get_text_kwargs={\"separator\": \" | \", \"strip\": True},\n",
+    ")\n",
+    "\n",
+    "docs = []\n",
+    "async for doc in loader.alazy_load():\n",
+    "    docs.append(doc)\n",
+    "\n",
+    "assert len(docs) == 1\n",
+    "doc = docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7edf6ed0-e22f-4c64-b986-8ba019c14757",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'source': 'https://python.langchain.com/docs/how_to/chatbots_memory/'}\n",
+      "\n",
+      "How to add memory to chatbots | A key feature of chatbots is their ability to use content of previous conversation turns as context. This state management can take several forms, including: | Simply stuffing previous messages into a chat model prompt. | The above, but trimming old messages to reduce the amount of distracting information the model has to deal with. | More complex modifications like synthesizing summaries for long running conversations. | We'll go into more detail on a few techniq\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"{doc.metadata}\\n\")\n",
+    "print(doc.page_content[:500])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6ab1ba2b-3b22-4c5d-8ad3-f6809d075d26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a greeting. Nemo then asks the AI how it is doing, and the AI responds that it is fine.'), | HumanMessage(content='What did I say my name was?'), | AIMessage(content='You introduced yourself as Nemo. How can I assist you today, Nemo?')] | Note that invoking the chain again will generate another summary generated from the initial summary plus new messages and so on. You could also design a hybrid approach where a certain number of messages are retained in chat history while others are summarized.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(doc.page_content[-500:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a411144-a234-4505-956c-930d399ffefb",
+   "metadata": {},
+   "source": [
+    "Note that this required advance technical knowledge of how the body text is represented in the underlying HTML.\n",
+    "\n",
+    "We can parameterize `WebBaseLoader` with a variety of settings, allowing for specification of request headers, rate limits, and parsers and other kwargs for BeautifulSoup. See its [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) for detail."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cdfc8f68-2b08-4a6c-9705-c17e6deaf411",
+   "metadata": {},
+   "source": [
+    "## Advanced parsing\n",
+    "\n",
+    "This method is appropriate if we want more granular control or processing of the page content. Below, instead of generating one `Document` per page and controlling its content via BeautifulSoup, we generate multiple `Document` objects representing distinct structures on a page. These structures can include section titles and their corresponding body texts, lists or enumerations, tables, and more.\n",
+    "\n",
+    "Under the hood it uses the `langchain-unstructured` library. See the [integration docs](/docs/integrations/document_loaders/unstructured_file/) for more information about using [Unstructured](https://docs.unstructured.io/welcome) with LangChain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7a6bbfef-ebd5-4357-a7f5-9c989dda092d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+      "INFO: NumExpr defaulting to 8 threads.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_unstructured import UnstructuredLoader\n",
+    "\n",
+    "page_url = \"https://python.langchain.com/docs/how_to/chatbots_memory/\"\n",
+    "loader = UnstructuredLoader(web_url=page_url)\n",
+    "\n",
+    "docs = []\n",
+    "async for doc in loader.alazy_load():\n",
+    "    docs.append(doc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53a600b0-fcd2-4074-80b6-a1dd2c0d9235",
+   "metadata": {},
+   "source": [
+    "Note that with no advance knowledge of the page HTML structure, we recover a natural organization of the body text:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "198b7469-587f-4a80-a49f-440e6157b241",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "How to add memory to chatbots\n",
+      "A key feature of chatbots is their ability to use content of previous conversation turns as context. This state management can take several forms, including:\n",
+      "Simply stuffing previous messages into a chat model prompt.\n",
+      "The above, but trimming old messages to reduce the amount of distracting information the model has to deal with.\n",
+      "More complex modifications like synthesizing summaries for long running conversations.\n",
+      "ERROR! Session/line number was not unique in database. History logging moved to new session 2747\n"
+     ]
+    }
+   ],
+   "source": [
+    "for doc in docs[:5]:\n",
+    "    print(doc.page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f4254b5-5e3b-45c4-9cd0-7ed753687783",
+   "metadata": {},
+   "source": [
+    "### Extracting content from specific sections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "627d9b7a-31fe-4923-bc9a-4b0caae1d760",
+   "metadata": {},
+   "source": [
+    "Each `Document` object represents an element of the page. Its metadata contains useful information, such as its category:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2fa19a61-a53d-42e0-a01a-7ea99fc40810",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Title: How to add memory to chatbots\n",
+      "NarrativeText: A key feature of chatbots is their ability to use content of previous conversation turns as context. This state management can take several forms, including:\n",
+      "ListItem: Simply stuffing previous messages into a chat model prompt.\n",
+      "ListItem: The above, but trimming old messages to reduce the amount of distracting information the model has to deal with.\n",
+      "ListItem: More complex modifications like synthesizing summaries for long running conversations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for doc in docs[:5]:\n",
+    "    print(f'{doc.metadata[\"category\"]}: {doc.page_content}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca0f8025-58b8-4d2a-95aa-124d7a8ee812",
+   "metadata": {},
+   "source": [
+    "Elements may also have parent-child relationships -- for example, a paragraph might belong to a section with a title. If a section is of particular interest (e.g., for indexing) we can isolate the corresponding `Document` objects.\n",
+    "\n",
+    "As an example, below we load the content of the \"Setup\" sections for two web pages:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "793018fd-1365-4a8a-8690-6d51dad2e1cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "\n",
+    "async def _get_setup_docs_from_url(url: str) -> List[Document]:\n",
+    "    loader = UnstructuredLoader(web_url=url)\n",
+    "\n",
+    "    setup_docs = []\n",
+    "    parent_id = -1\n",
+    "    async for doc in loader.alazy_load():\n",
+    "        if doc.metadata[\"category\"] == \"Title\" and doc.page_content.startswith(\"Setup\"):\n",
+    "            parent_id = doc.metadata[\"element_id\"]\n",
+    "        if doc.metadata.get(\"parent_id\") == parent_id:\n",
+    "            setup_docs.append(doc)\n",
+    "\n",
+    "    return setup_docs\n",
+    "\n",
+    "\n",
+    "page_urls = [\n",
+    "    \"https://python.langchain.com/docs/how_to/chatbots_memory/\",\n",
+    "    \"https://python.langchain.com/docs/how_to/chatbots_tools/\",\n",
+    "]\n",
+    "setup_docs = []\n",
+    "for url in page_urls:\n",
+    "    page_setup_docs = await _get_setup_docs_from_url(url)\n",
+    "    setup_docs.extend(page_setup_docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a67e0745-abfc-4baa-94b3-2e8815bfa52a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'https://python.langchain.com/docs/how_to/chatbots_memory/': \"You'll need to install a few packages, and have your OpenAI API key set as an environment variable named OPENAI_API_KEY:\\n%pip install --upgrade --quiet langchain langchain-openai\\n\\n# Set env var OPENAI_API_KEY or load from a .env file:\\nimport dotenv\\n\\ndotenv.load_dotenv()\\n[33mWARNING: You are using pip version 22.0.4; however, version 23.3.2 is available.\\nYou should consider upgrading via the '/Users/jacoblee/.pyenv/versions/3.10.5/bin/python -m pip install --upgrade pip' command.[0m[33m\\n[0mNote: you may need to restart the kernel to use updated packages.\\n\",\n",
+       " 'https://python.langchain.com/docs/how_to/chatbots_tools/': \"For this guide, we'll be using a tool calling agent with a single tool for searching the web. The default will be powered by Tavily, but you can switch it out for any similar tool. The rest of this section will assume you're using Tavily.\\nYou'll need to sign up for an account on the Tavily website, and install the following packages:\\n%pip install --upgrade --quiet langchain-community langchain-openai tavily-python\\n\\n# Set env var OPENAI_API_KEY or load from a .env file:\\nimport dotenv\\n\\ndotenv.load_dotenv()\\nYou will also need your OpenAI key set as OPENAI_API_KEY and your Tavily API key set as TAVILY_API_KEY.\\n\"}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "\n",
+    "setup_text = defaultdict(str)\n",
+    "\n",
+    "for doc in setup_docs:\n",
+    "    url = doc.metadata[\"url\"]\n",
+    "    setup_text[url] += f\"{doc.page_content}\\n\"\n",
+    "\n",
+    "dict(setup_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cd42892-24a6-4969-92c8-c928680be9b5",
+   "metadata": {},
+   "source": [
+    "### Vector search over page content\n",
+    "\n",
+    "Once we have loaded the page contents into LangChain `Document` objects, we can index them (e.g., for a RAG application) in the usual way. Below we use OpenAI [embeddings](/docs/concepts/#embedding-models), although any LangChain embeddings model will suffice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a6cbb01-6e0d-418f-9f76-2031622bebb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "598e612c-180d-494d-8caa-761c89f84eae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if \"OPENAI_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5eeaeb54-ea03-4634-8a79-b60c22ab2b66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+      "INFO: HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Page https://python.langchain.com/docs/how_to/chatbots_tools/: You'll need to sign up for an account on the Tavily website, and install the following packages:\n",
+      "\n",
+      "Page https://python.langchain.com/docs/how_to/chatbots_tools/: For this guide, we'll be using a tool calling agent with a single tool for searching the web. The default will be powered by Tavily, but you can switch it out for any similar tool. The rest of this section will assume you're using Tavily.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "\n",
+    "vector_store = InMemoryVectorStore.from_documents(setup_docs, OpenAIEmbeddings())\n",
+    "retrieved_docs = vector_store.similarity_search(\"Install Tavily\", k=2)\n",
+    "for doc in retrieved_docs:\n",
+    "    print(f'Page {doc.metadata[\"url\"]}: {doc.page_content[:300]}\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67be9c94-dbde-4fdd-87d0-e83ed6066d2b",
+   "metadata": {},
+   "source": [
+    "## Other web page loaders\n",
+    "\n",
+    "For a list of available LangChain web page loaders, please see [this table](/docs/integrations/document_loaders/#webpages)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how_to/extraction_examples.ipynb

@@ -436,7 +436,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "people=[]\n"
+      "people=[Person(name='earth', hair_color='null', height_in_meters='null')]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "people=[Person(name='earth', hair_color='null', height_in_meters='null')]\n"
      ]
     },
     {
@@ -450,14 +457,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "people=[]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "people=[]\n"
+      "people=[Person(name='earth', hair_color='null', height_in_meters='null')]\n"
      ]
     },
     {

docs/docs/how_to/index.mdx

@@ -126,13 +126,14 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
 
 [Document Loaders](/docs/concepts/#document-loaders) are responsible for loading documents from a variety of sources.
 
+- [How to: load PDF files](/docs/how_to/document_loader_pdf)
+- [How to: load web pages](/docs/how_to/document_loader_web)
 - [How to: load CSV data](/docs/how_to/document_loader_csv)
 - [How to: load data from a directory](/docs/how_to/document_loader_directory)
 - [How to: load HTML data](/docs/how_to/document_loader_html)
 - [How to: load JSON data](/docs/how_to/document_loader_json)
 - [How to: load Markdown data](/docs/how_to/document_loader_markdown)
 - [How to: load Microsoft Office data](/docs/how_to/document_loader_office_file)
-- [How to: load PDF files](/docs/how_to/document_loader_pdf)
 - [How to: write a custom document loader](/docs/how_to/document_loader_custom)
 
 ### Text splitters

docs/docs/how_to/migrate_agent.ipynb

@@ -34,6 +34,12 @@
     "LangChain agents (the [AgentExecutor](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor) in particular) have multiple configuration parameters.\n",
     "In this notebook we will show how those parameters map to the LangGraph react agent executor using the [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) prebuilt helper method.\n",
     "\n",
+    "\n",
+    ":::note\n",
+    "In LangGraph, the graph replaces LangChain's agent executor. It manages the agent's cycles and tracks the scratchpad as messages within its state. The LangChain \"agent\" corresponds to the state_modifier and LLM you've provided.\n",
+    ":::\n",
+    "\n",
+    "\n",
     "#### Prerequisites\n",
     "\n",
     "This how-to guide uses OpenAI as the LLM. Install the dependencies to run."
@@ -183,10 +189,10 @@
    "source": [
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "app = create_react_agent(model, tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools)\n",
     "\n",
     "\n",
-    "messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
+    "messages = langgraph_agent_executor.invoke({\"messages\": [(\"human\", query)]})\n",
     "{\n",
     "    \"input\": query,\n",
     "    \"output\": messages[\"messages\"][-1].content,\n",
@@ -216,7 +222,9 @@
     "\n",
     "new_query = \"Pardon?\"\n",
     "\n",
-    "messages = app.invoke({\"messages\": message_history + [(\"human\", new_query)]})\n",
+    "messages = langgraph_agent_executor.invoke(\n",
+    "    {\"messages\": message_history + [(\"human\", new_query)]}\n",
+    ")\n",
     "{\n",
     "    \"input\": new_query,\n",
     "    \"output\": messages[\"messages\"][-1].content,\n",
@@ -309,10 +317,12 @@
     "# This could also be a SystemMessage object\n",
     "# system_message = SystemMessage(content=\"You are a helpful assistant. Respond only in Spanish.\")\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=system_message)\n",
+    "langgraph_agent_executor = create_react_agent(\n",
+    "    model, tools, state_modifier=system_message\n",
+    ")\n",
     "\n",
     "\n",
-    "messages = app.invoke({\"messages\": [(\"user\", query)]})"
+    "messages = langgraph_agent_executor.invoke({\"messages\": [(\"user\", query)]})"
    ]
   },
   {
@@ -356,10 +366,12 @@
     "    ]\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
+    "langgraph_agent_executor = create_react_agent(\n",
+    "    model, tools, state_modifier=_modify_state_messages\n",
+    ")\n",
     "\n",
     "\n",
-    "messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
+    "messages = langgraph_agent_executor.invoke({\"messages\": [(\"human\", query)]})\n",
     "print(\n",
     "    {\n",
     "        \"input\": query,\n",
@@ -503,13 +515,13 @@
     "# system_message = SystemMessage(content=\"You are a helpful assistant. Respond only in Spanish.\")\n",
     "\n",
     "memory = MemorySaver()\n",
-    "app = create_react_agent(\n",
+    "langgraph_agent_executor = create_react_agent(\n",
     "    model, tools, state_modifier=system_message, checkpointer=memory\n",
     ")\n",
     "\n",
     "config = {\"configurable\": {\"thread_id\": \"test-thread\"}}\n",
     "print(\n",
-    "    app.invoke(\n",
+    "    langgraph_agent_executor.invoke(\n",
     "        {\n",
     "            \"messages\": [\n",
     "                (\"user\", \"Hi, I'm polly! What's the output of magic_function of 3?\")\n",
@@ -520,15 +532,15 @@
     ")\n",
     "print(\"---\")\n",
     "print(\n",
-    "    app.invoke({\"messages\": [(\"user\", \"Remember my name?\")]}, config)[\"messages\"][\n",
-    "        -1\n",
-    "    ].content\n",
+    "    langgraph_agent_executor.invoke(\n",
+    "        {\"messages\": [(\"user\", \"Remember my name?\")]}, config\n",
+    "    )[\"messages\"][-1].content\n",
     ")\n",
     "print(\"---\")\n",
     "print(\n",
-    "    app.invoke({\"messages\": [(\"user\", \"what was that output again?\")]}, config)[\n",
-    "        \"messages\"\n",
-    "    ][-1].content\n",
+    "    langgraph_agent_executor.invoke(\n",
+    "        {\"messages\": [(\"user\", \"what was that output again?\")]}, config\n",
+    "    )[\"messages\"][-1].content\n",
     ")"
    ]
   },
@@ -636,9 +648,13 @@
     "    return prompt.invoke({\"messages\": state[\"messages\"]}).to_messages()\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
+    "langgraph_agent_executor = create_react_agent(\n",
+    "    model, tools, state_modifier=_modify_state_messages\n",
+    ")\n",
     "\n",
-    "for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
+    "for step in langgraph_agent_executor.stream(\n",
+    "    {\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"\n",
+    "):\n",
     "    print(step)"
    ]
   },
@@ -707,9 +723,9 @@
    "source": [
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "app = create_react_agent(model, tools=tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools=tools)\n",
     "\n",
-    "messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
+    "messages = langgraph_agent_executor.invoke({\"messages\": [(\"human\", query)]})\n",
     "\n",
     "messages"
    ]
@@ -839,10 +855,10 @@
     "\n",
     "RECURSION_LIMIT = 2 * 3 + 1\n",
     "\n",
-    "app = create_react_agent(model, tools=tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools=tools)\n",
     "\n",
     "try:\n",
-    "    for chunk in app.stream(\n",
+    "    for chunk in langgraph_agent_executor.stream(\n",
     "        {\"messages\": [(\"human\", query)]},\n",
     "        {\"recursion_limit\": RECURSION_LIMIT},\n",
     "        stream_mode=\"values\",\n",
@@ -953,12 +969,12 @@
    "source": [
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "app = create_react_agent(model, tools=tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools=tools)\n",
     "# Set the max timeout for each step here\n",
-    "app.step_timeout = 2\n",
+    "langgraph_agent_executor.step_timeout = 2\n",
     "\n",
     "try:\n",
-    "    for chunk in app.stream({\"messages\": [(\"human\", query)]}):\n",
+    "    for chunk in langgraph_agent_executor.stream({\"messages\": [(\"human\", query)]}):\n",
     "        print(chunk)\n",
     "        print(\"------\")\n",
     "except TimeoutError:\n",
@@ -994,17 +1010,21 @@
     "\n",
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "app = create_react_agent(model, tools=tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools=tools)\n",
     "\n",
     "\n",
-    "async def stream(app, inputs):\n",
-    "    async for chunk in app.astream({\"messages\": [(\"human\", query)]}):\n",
+    "async def stream(langgraph_agent_executor, inputs):\n",
+    "    async for chunk in langgraph_agent_executor.astream(\n",
+    "        {\"messages\": [(\"human\", query)]}\n",
+    "    ):\n",
     "        print(chunk)\n",
     "        print(\"------\")\n",
     "\n",
     "\n",
     "try:\n",
-    "    task = asyncio.create_task(stream(app, {\"messages\": [(\"human\", query)]}))\n",
+    "    task = asyncio.create_task(\n",
+    "        stream(langgraph_agent_executor, {\"messages\": [(\"human\", query)]})\n",
+    "    )\n",
     "    await asyncio.wait_for(task, timeout=3)\n",
     "except TimeoutError:\n",
     "    print(\"Task Cancelled.\")"
@@ -1108,10 +1128,10 @@
     "\n",
     "RECURSION_LIMIT = 2 * 1 + 1\n",
     "\n",
-    "app = create_react_agent(model, tools=tools)\n",
+    "langgraph_agent_executor = create_react_agent(model, tools=tools)\n",
     "\n",
     "try:\n",
-    "    for chunk in app.stream(\n",
+    "    for chunk in langgraph_agent_executor.stream(\n",
     "        {\"messages\": [(\"human\", query)]},\n",
     "        {\"recursion_limit\": RECURSION_LIMIT},\n",
     "        stream_mode=\"values\",\n",
@@ -1289,10 +1309,14 @@
     "    return [(\"system\", \"You are a helpful assistant\"), state[\"messages\"][0]]\n",
     "\n",
     "\n",
-    "app = create_react_agent(model, tools, state_modifier=_modify_state_messages)\n",
+    "langgraph_agent_executor = create_react_agent(\n",
+    "    model, tools, state_modifier=_modify_state_messages\n",
+    ")\n",
     "\n",
     "try:\n",
-    "    for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
+    "    for step in langgraph_agent_executor.stream(\n",
+    "        {\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"\n",
+    "    ):\n",
     "        pass\n",
     "except GraphRecursionError as e:\n",
     "    print(\"Stopping agent prematurely due to triggering stop condition\")"

docs/docs/how_to/qa_chat_history_how_to.ipynb

@@ -7,6 +7,18 @@
    "source": [
     "# How to add chat history\n",
     "\n",
+    ":::note\n",
+    "\n",
+    "This guide previously used the [RunnableWithMessageHistory](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html) abstraction. You can access this version of the documentation in the [v0.2 docs](https://python.langchain.com/v0.2/docs/how_to/qa_chat_history_how_to/).\n",
+    "\n",
+    "As of the v0.3 release of LangChain, we recommend that LangChain users take advantage of [LangGraph persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) to incorporate `memory` into new LangChain applications.\n",
+    "\n",
+    "If your code is already relying on `RunnableWithMessageHistory` or `BaseChatMessageHistory`, you do **not** need to make any changes. We do not plan on deprecating this functionality in the near future as it works for simple chat applications and any code that uses `RunnableWithMessageHistory` will continue to work as expected.\n",
+    "\n",
+    "Please see [How to migrate to LangGraph Memory](/docs/versions/migrating_memory/) for more details.\n",
+    ":::\n",
+    "\n",
+    "\n",
     "In many Q&A applications we want to allow the user to have a back-and-forth conversation, meaning the application needs some sort of \"memory\" of past questions and answers, and some logic for incorporating those into its current thinking.\n",
     "\n",
     "In this guide we focus on **adding logic for incorporating historical messages.**\n",
@@ -29,7 +41,7 @@
     "\n",
     "### Dependencies\n",
     "\n",
-    "We'll use OpenAI embeddings and a Chroma vector store in this walkthrough, but everything shown here works with any [Embeddings](/docs/concepts#embedding-models), and [VectorStore](/docs/concepts#vectorstores) or [Retriever](/docs/concepts#retrievers). \n",
+    "We'll use OpenAI embeddings and an InMemory vector store in this walkthrough, but everything shown here works with any [Embeddings](/docs/concepts#embedding-models), and [VectorStore](/docs/concepts#vectorstores) or [Retriever](/docs/concepts#retrievers). \n",
     "\n",
     "We'll use the following packages:"
    ]
@@ -42,7 +54,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install --upgrade --quiet  langchain langchain-community langchain-chroma beautifulsoup4"
+    "%pip install --upgrade --quiet  langchain langchain-community beautifulsoup4"
    ]
   },
   {
@@ -56,7 +68,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "143787ca-d8e6-4dc9-8281-4374f4d71720",
+   "id": "3b156b76-22a1-43af-a509-137acdccc5d0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,11 +76,7 @@
     "import os\n",
     "\n",
     "if not os.environ.get(\"OPENAI_API_KEY\"):\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
-    "\n",
-    "# import dotenv\n",
-    "\n",
-    "# dotenv.load_dotenv()"
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()"
    ]
   },
   {
@@ -153,7 +161,7 @@
    "id": "15f8ad59-19de-42e3-85a8-3ba95ee0bd43",
    "metadata": {},
    "source": [
-    "For the retriever, we will use [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load the content of a web page. Here we instantiate a `Chroma` vectorstore and then use its [.as_retriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method to build a retriever that can be incorporated into [LCEL](/docs/concepts/#langchain-expression-language) chains."
+    "For the retriever, we will use [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load the content of a web page. Here we instantiate a `InMemoryVectorStore` vectorstore and then use its [.as_retriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method to build a retriever that can be incorporated into [LCEL](/docs/concepts/#langchain-expression-language) chains."
    ]
   },
   {
@@ -161,16 +169,24 @@
    "execution_count": 5,
    "id": "820244ae-74b4-4593-b392-822979dd91b8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
+     ]
+    }
+   ],
    "source": [
     "import bs4\n",
     "from langchain.chains import create_retrieval_chain\n",
     "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.document_loaders import WebBaseLoader\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "\n",
@@ -186,7 +202,8 @@
     "\n",
     "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
     "splits = text_splitter.split_documents(docs)\n",
-    "vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
+    "vectorstore = InMemoryVectorStore(embedding=OpenAIEmbeddings())\n",
+    "vectorstore.add_documents(splits)\n",
     "retriever = vectorstore.as_retriever()"
    ]
   },
@@ -286,8 +303,8 @@
     "        (\"human\", \"{input}\"),\n",
     "    ]\n",
     ")\n",
-    "question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)\n",
     "\n",
+    "question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)\n",
     "rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)"
    ]
   },
@@ -296,20 +313,17 @@
    "id": "53a662c2-f38b-45f9-95c4-66de15637614",
    "metadata": {},
    "source": [
-    "### Adding chat history\n",
+    "### Stateful Management of chat history\n",
     "\n",
-    "To manage the chat history, we will need:\n",
+    "We have added application logic for incorporating chat history, but we are still manually plumbing it through our application. In production, the Q&A application we usually persist the chat history into a database, and be able to read and update it appropriately.\n",
     "\n",
-    "1. An object for storing the chat history;\n",
-    "2. An object that wraps our chain and manages updates to the chat history.\n",
+    "[LangGraph](https://langchain-ai.github.io/langgraph/) implements a built-in [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), making it ideal for chat applications that support multiple conversational turns.\n",
     "\n",
-    "For these we will use [BaseChatMessageHistory](https://python.langchain.com/api_reference/core/chat_history/langchain_core.chat_history.BaseChatMessageHistory.html) and [RunnableWithMessageHistory](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html). The latter is a wrapper for an LCEL chain and a `BaseChatMessageHistory` that handles injecting chat history into inputs and updating it after each invocation.\n",
+    "Wrapping our chat model in a minimal LangGraph application allows us to automatically persist the message history, simplifying the development of multi-turn applications.\n",
     "\n",
-    "For a detailed walkthrough of how to use these classes together to create a stateful conversational chain, head to the [How to add message history (memory)](/docs/how_to/message_history/) LCEL how-to guide.\n",
+    "LangGraph comes with a simple [in-memory checkpointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/#memorysaver), which we use below. See its documentation for more detail, including how to use different persistence backends (e.g., SQLite or Postgres).\n",
     "\n",
-    "Below, we implement a simple example of the second option, in which chat histories are stored in a simple dict. LangChain manages memory integrations with [Redis](/docs/integrations/memory/redis_chat_message_history/) and other technologies to provide for more robust persistence.\n",
-    "\n",
-    "Instances of `RunnableWithMessageHistory` manage the chat history for you. They accept a config with a key (`\"session_id\"` by default) that specifies what conversation history to fetch and prepend to the input, and append the output to the same conversation history. Below is an example:"
+    "For a detailed walkthrough of how to manage message history, head to the How to add message history (memory) guide."
    ]
   },
   {
@@ -319,26 +333,48 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
-    "from langchain_core.chat_history import BaseChatMessageHistory\n",
-    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
+    "from typing import Sequence\n",
     "\n",
-    "store = {}\n",
+    "from langchain_core.messages import AIMessage, BaseMessage, HumanMessage\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import START, StateGraph\n",
+    "from langgraph.graph.message import add_messages\n",
+    "from typing_extensions import Annotated, TypedDict\n",
     "\n",
     "\n",
-    "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
-    "    if session_id not in store:\n",
-    "        store[session_id] = ChatMessageHistory()\n",
-    "    return store[session_id]\n",
+    "# We define a dict representing the state of the application.\n",
+    "# This state has the same input and output keys as `rag_chain`.\n",
+    "class State(TypedDict):\n",
+    "    input: str\n",
+    "    chat_history: Annotated[Sequence[BaseMessage], add_messages]\n",
+    "    context: str\n",
+    "    answer: str\n",
     "\n",
     "\n",
-    "conversational_rag_chain = RunnableWithMessageHistory(\n",
-    "    rag_chain,\n",
-    "    get_session_history,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"chat_history\",\n",
-    "    output_messages_key=\"answer\",\n",
-    ")"
+    "# We then define a simple node that runs the `rag_chain`.\n",
+    "# The `return` values of the node update the graph state, so here we just\n",
+    "# update the chat history with the input message and response.\n",
+    "def call_model(state: State):\n",
+    "    response = rag_chain.invoke(state)\n",
+    "    return {\n",
+    "        \"chat_history\": [\n",
+    "            HumanMessage(state[\"input\"]),\n",
+    "            AIMessage(response[\"answer\"]),\n",
+    "        ],\n",
+    "        \"context\": response[\"context\"],\n",
+    "        \"answer\": response[\"answer\"],\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "# Our graph consists only of one node:\n",
+    "workflow = StateGraph(state_schema=State)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "workflow.add_node(\"model\", call_model)\n",
+    "\n",
+    "# Finally, we compile the graph with a checkpointer object.\n",
+    "# This persists the state, in this case in memory.\n",
+    "memory = MemorySaver()\n",
+    "app = workflow.compile(checkpointer=memory)"
    ]
   },
   {
@@ -348,23 +384,21 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "'Task decomposition involves breaking down a complex task into smaller and simpler steps to make it more manageable and easier to accomplish. This process can be done using techniques like Chain of Thought (CoT) or Tree of Thoughts to guide the model in breaking down tasks effectively. Task decomposition can be facilitated by providing simple prompts to a language model, task-specific instructions, or human inputs.'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. This process helps agents or models tackle difficult tasks by dividing them into more manageable subtasks. Task decomposition can be achieved through methods like Chain of Thought (CoT) or Tree of Thoughts, which guide the agent in thinking step by step or exploring multiple reasoning possibilities at each step.\n"
+     ]
     }
    ],
    "source": [
-    "conversational_rag_chain.invoke(\n",
+    "config = {\"configurable\": {\"thread_id\": \"abc123\"}}\n",
+    "\n",
+    "result = app.invoke(\n",
     "    {\"input\": \"What is Task Decomposition?\"},\n",
-    "    config={\n",
-    "        \"configurable\": {\"session_id\": \"abc123\"}\n",
-    "    },  # constructs a key \"abc123\" in `store`.\n",
-    ")[\"answer\"]"
+    "    config=config,\n",
+    ")\n",
+    "print(result[\"answer\"])"
    ]
   },
   {
@@ -374,21 +408,19 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "'Task decomposition can be achieved through various methods, including using techniques like Chain of Thought (CoT) or Tree of Thoughts to guide the model in breaking down tasks effectively. Common ways of task decomposition include providing simple prompts to a language model, task-specific instructions, or human inputs to break down complex tasks into smaller and more manageable steps. Additionally, task decomposition can involve utilizing resources like internet access for information gathering, long-term memory management, and GPT-3.5 powered agents for delegation of simple tasks.'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "One way of task decomposition is by using Large Language Models (LLMs) with simple prompting, such as providing instructions like \"Steps for XYZ\" or asking about subgoals for achieving a specific task. This method leverages the power of LLMs to break down tasks into smaller components for easier handling. Additionally, task decomposition can also be done using task-specific instructions tailored to the nature of the task, like requesting a story outline for writing a novel.\n"
+     ]
     }
    ],
    "source": [
-    "conversational_rag_chain.invoke(\n",
-    "    {\"input\": \"What are common ways of doing it?\"},\n",
-    "    config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
-    ")[\"answer\"]"
+    "result = app.invoke(\n",
+    "    {\"input\": \"What is one way of doing it?\"},\n",
+    "    config=config,\n",
+    ")\n",
+    "print(result[\"answer\"])"
    ]
   },
   {
@@ -396,7 +428,7 @@
    "id": "3ab59258-84bc-4904-880e-2ebfebbca563",
    "metadata": {},
    "source": [
-    "The conversation history can be inspected in the `store` dict:"
+    "The conversation history can be inspected via the state of the application:"
    ]
   },
   {
@@ -409,27 +441,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "User: What is Task Decomposition?\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
-      "AI: Task decomposition involves breaking down a complex task into smaller and simpler steps to make it more manageable and easier to accomplish. This process can be done using techniques like Chain of Thought (CoT) or Tree of Thoughts to guide the model in breaking down tasks effectively. Task decomposition can be facilitated by providing simple prompts to a language model, task-specific instructions, or human inputs.\n",
+      "What is Task Decomposition?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "User: What are common ways of doing it?\n",
+      "Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. This process helps agents or models tackle difficult tasks by dividing them into more manageable subtasks. Task decomposition can be achieved through methods like Chain of Thought (CoT) or Tree of Thoughts, which guide the agent in thinking step by step or exploring multiple reasoning possibilities at each step.\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
-      "AI: Task decomposition can be achieved through various methods, including using techniques like Chain of Thought (CoT) or Tree of Thoughts to guide the model in breaking down tasks effectively. Common ways of task decomposition include providing simple prompts to a language model, task-specific instructions, or human inputs to break down complex tasks into smaller and more manageable steps. Additionally, task decomposition can involve utilizing resources like internet access for information gathering, long-term memory management, and GPT-3.5 powered agents for delegation of simple tasks.\n",
-      "\n"
+      "What is one way of doing it?\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "One way of task decomposition is by using Large Language Models (LLMs) with simple prompting, such as providing instructions like \"Steps for XYZ\" or asking about subgoals for achieving a specific task. This method leverages the power of LLMs to break down tasks into smaller components for easier handling. Additionally, task decomposition can also be done using task-specific instructions tailored to the nature of the task, like requesting a story outline for writing a novel.\n"
      ]
     }
    ],
    "source": [
-    "from langchain_core.messages import AIMessage\n",
-    "\n",
-    "for message in store[\"abc123\"].messages:\n",
-    "    if isinstance(message, AIMessage):\n",
-    "        prefix = \"AI\"\n",
-    "    else:\n",
-    "        prefix = \"User\"\n",
-    "\n",
-    "    print(f\"{prefix}: {message.content}\\n\")"
+    "chat_history = app.get_state(config).values[\"chat_history\"]\n",
+    "for message in chat_history:\n",
+    "    message.pretty_print()"
    ]
   },
   {
@@ -457,17 +487,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from typing import Sequence\n",
+    "\n",
     "import bs4\n",
     "from langchain.chains import create_history_aware_retriever, create_retrieval_chain\n",
     "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
-    "from langchain_chroma import Chroma\n",
-    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
     "from langchain_community.document_loaders import WebBaseLoader\n",
-    "from langchain_core.chat_history import BaseChatMessageHistory\n",
+    "from langchain_core.messages import AIMessage, BaseMessage, HumanMessage\n",
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
     "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import START, StateGraph\n",
+    "from langgraph.graph.message import add_messages\n",
+    "from typing_extensions import Annotated, TypedDict\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",
@@ -485,7 +520,9 @@
     "\n",
     "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
     "splits = text_splitter.split_documents(docs)\n",
-    "vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore(embedding=OpenAIEmbeddings())\n",
+    "vectorstore.add_documents(documents=splits)\n",
     "retriever = vectorstore.as_retriever()\n",
     "\n",
     "\n",
@@ -532,22 +569,41 @@
     "\n",
     "\n",
     "### Statefully manage chat history ###\n",
-    "store = {}\n",
     "\n",
     "\n",
-    "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
-    "    if session_id not in store:\n",
-    "        store[session_id] = ChatMessageHistory()\n",
-    "    return store[session_id]\n",
+    "# We define a dict representing the state of the application.\n",
+    "# This state has the same input and output keys as `rag_chain`.\n",
+    "class State(TypedDict):\n",
+    "    input: str\n",
+    "    chat_history: Annotated[Sequence[BaseMessage], add_messages]\n",
+    "    context: str\n",
+    "    answer: str\n",
     "\n",
     "\n",
-    "conversational_rag_chain = RunnableWithMessageHistory(\n",
-    "    rag_chain,\n",
-    "    get_session_history,\n",
-    "    input_messages_key=\"input\",\n",
-    "    history_messages_key=\"chat_history\",\n",
-    "    output_messages_key=\"answer\",\n",
-    ")"
+    "# We then define a simple node that runs the `rag_chain`.\n",
+    "# The `return` values of the node update the graph state, so here we just\n",
+    "# update the chat history with the input message and response.\n",
+    "def call_model(state: State):\n",
+    "    response = rag_chain.invoke(state)\n",
+    "    return {\n",
+    "        \"chat_history\": [\n",
+    "            HumanMessage(state[\"input\"]),\n",
+    "            AIMessage(response[\"answer\"]),\n",
+    "        ],\n",
+    "        \"context\": response[\"context\"],\n",
+    "        \"answer\": response[\"answer\"],\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "# Our graph consists only of one node:\n",
+    "workflow = StateGraph(state_schema=State)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "workflow.add_node(\"model\", call_model)\n",
+    "\n",
+    "# Finally, we compile the graph with a checkpointer object.\n",
+    "# This persists the state, in this case in memory.\n",
+    "memory = MemorySaver()\n",
+    "app = workflow.compile(checkpointer=memory)"
    ]
   },
   {
@@ -557,23 +613,21 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "'Task decomposition involves breaking down a complex task into smaller and simpler steps to make it more manageable. Techniques like Chain of Thought (CoT) and Tree of Thoughts help in decomposing hard tasks into multiple manageable tasks by instructing models to think step by step and explore multiple reasoning possibilities at each step. Task decomposition can be achieved through various methods such as using prompting techniques, task-specific instructions, or human inputs.'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. This process helps agents or models handle difficult tasks by dividing them into more manageable subtasks. Different methods like Chain of Thought and Tree of Thoughts are used to decompose tasks into multiple steps, enhancing performance and aiding in the interpretation of the thinking process.\n"
+     ]
     }
    ],
    "source": [
-    "conversational_rag_chain.invoke(\n",
+    "config = {\"configurable\": {\"thread_id\": \"abc123\"}}\n",
+    "\n",
+    "result = app.invoke(\n",
     "    {\"input\": \"What is Task Decomposition?\"},\n",
-    "    config={\n",
-    "        \"configurable\": {\"session_id\": \"abc123\"}\n",
-    "    },  # constructs a key \"abc123\" in `store`.\n",
-    ")[\"answer\"]"
+    "    config=config,\n",
+    ")\n",
+    "print(result[\"answer\"])"
    ]
   },
   {
@@ -583,21 +637,19 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "'Task decomposition can be done in common ways such as using prompting techniques like Chain of Thought (CoT) or Tree of Thoughts, which instruct models to think step by step and explore multiple reasoning possibilities at each step. Another way is to provide task-specific instructions, such as asking to \"Write a story outline\" for writing a novel, to guide the decomposition process. Additionally, task decomposition can also involve human inputs to break down complex tasks into smaller and simpler steps.'"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "One way of task decomposition is by using Large Language Models (LLMs) with simple prompting, such as providing instructions like \"Steps for XYZ\" or asking about subgoals for achieving a specific task. This method leverages the power of LLMs to break down tasks into smaller components for easier handling and processing.\n"
+     ]
     }
    ],
    "source": [
-    "conversational_rag_chain.invoke(\n",
-    "    {\"input\": \"What are common ways of doing it?\"},\n",
-    "    config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
-    ")[\"answer\"]"
+    "result = app.invoke(\n",
+    "    {\"input\": \"What is one way of doing it?\"},\n",
+    "    config=config,\n",
+    ")\n",
+    "print(result[\"answer\"])"
    ]
   },
   {
@@ -670,22 +722,11 @@
    "id": "52ae46d9-43f7-481b-96d5-df750be3ad65",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Error in LangChainTracer.on_tool_end callback: TracerException(\"Found chain run at ID 5cd28d13-88dd-4eac-a465-3770ac27eff6, but expected {'tool'} run.\")\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_TbhPPPN05GKi36HLeaN4QM90', 'function': {'arguments': '{\"query\":\"Task Decomposition\"}', 'name': 'blog_post_retriever'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 68, 'total_tokens': 87}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-2e60d910-879a-4a2a-b1e9-6a6c5c7d7ebc-0', tool_calls=[{'name': 'blog_post_retriever', 'args': {'query': 'Task Decomposition'}, 'id': 'call_TbhPPPN05GKi36HLeaN4QM90'}])]}}\n",
-      "----\n",
-      "{'tools': {'messages': [ToolMessage(content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', name='blog_post_retriever', tool_call_id='call_TbhPPPN05GKi36HLeaN4QM90')]}}\n",
-      "----\n",
-      "{'agent': {'messages': [AIMessage(content='Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. This approach helps in transforming big tasks into multiple manageable tasks, making it easier for autonomous agents to handle and interpret the thinking process. One common method for task decomposition is the Chain of Thought (CoT) technique, where models are instructed to \"think step by step\" to decompose hard tasks. Another extension of CoT is the Tree of Thoughts, which explores multiple reasoning possibilities at each step by creating a tree structure of multiple thoughts per step. Task decomposition can be facilitated through various methods such as using simple prompts, task-specific instructions, or human inputs.', response_metadata={'token_usage': {'completion_tokens': 130, 'prompt_tokens': 636, 'total_tokens': 766}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-3ef17638-65df-4030-a7fe-795e6da91c69-0')]}}\n",
+      "{'agent': {'messages': [AIMessage(content='Task decomposition is a problem-solving strategy that involves breaking down a complex task or problem into smaller, more manageable subtasks. By decomposing a task into smaller components, it becomes easier to understand, analyze, and solve the overall problem. This approach allows individuals to focus on one specific aspect of the task at a time, leading to a more systematic and organized problem-solving process. Task decomposition is commonly used in various fields such as project management, software development, and engineering to simplify complex tasks and improve efficiency.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 102, 'prompt_tokens': 68, 'total_tokens': 170, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-a0925ffd-f500-4677-a108-c7015987e9ae-0', usage_metadata={'input_tokens': 68, 'output_tokens': 102, 'total_tokens': 170})]}}\n",
       "----\n"
      ]
     }
@@ -746,7 +787,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='Hello Bob! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 67, 'total_tokens': 78}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-1cd17562-18aa-4839-b41b-403b17a0fc20-0')]}}\n",
+      "{'agent': {'messages': [AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 67, 'total_tokens': 78, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d9011a17-9dbb-4348-9a58-ff89419a4bca-0', usage_metadata={'input_tokens': 67, 'output_tokens': 11, 'total_tokens': 78})]}}\n",
       "----\n"
      ]
     }
@@ -775,22 +816,15 @@
    "id": "e2c570ae-dd91-402c-8693-ae746de63b16",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Error in LangChainTracer.on_tool_end callback: TracerException(\"Found chain run at ID c54381c0-c5d9-495a-91a0-aca4ae755663, but expected {'tool'} run.\")\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_rg7zKTE5e0ICxVSslJ1u9LMg', 'function': {'arguments': '{\"query\":\"Task Decomposition\"}', 'name': 'blog_post_retriever'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 91, 'total_tokens': 110}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-122bf097-7ff1-49aa-b430-e362b51354ad-0', tool_calls=[{'name': 'blog_post_retriever', 'args': {'query': 'Task Decomposition'}, 'id': 'call_rg7zKTE5e0ICxVSslJ1u9LMg'}])]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_qVHvDTfYmWqcbgVhTwsH03aJ', 'function': {'arguments': '{\"query\":\"Task Decomposition\"}', 'name': 'blog_post_retriever'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 91, 'total_tokens': 110, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-bf9df2a6-ad56-43af-8d57-16f850accfd1-0', tool_calls=[{'name': 'blog_post_retriever', 'args': {'query': 'Task Decomposition'}, 'id': 'call_qVHvDTfYmWqcbgVhTwsH03aJ', 'type': 'tool_call'}], usage_metadata={'input_tokens': 91, 'output_tokens': 19, 'total_tokens': 110})]}}\n",
       "----\n",
-      "{'tools': {'messages': [ToolMessage(content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', name='blog_post_retriever', tool_call_id='call_rg7zKTE5e0ICxVSslJ1u9LMg')]}}\n",
+      "{'tools': {'messages': [ToolMessage(content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\n\\n(3) Task execution: Expert models execute on the specific tasks and log results.\\nInstruction:\\n\\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user\\'s request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.\\n\\nFig. 11. Illustration of how HuggingGPT works. (Image source: Shen et al. 2023)\\nThe system comprises of 4 stages:\\n(1) Task planning: LLM works as the brain and parses the user requests into multiple tasks. There are four attributes associated with each task: task type, ID, dependencies, and arguments. They use few-shot examples to guide LLM to do task parsing and planning.\\nInstruction:', name='blog_post_retriever', id='742ab53d-6f34-4607-bde7-13f2d75e0055', tool_call_id='call_qVHvDTfYmWqcbgVhTwsH03aJ')]}}\n",
       "----\n",
-      "{'agent': {'messages': [AIMessage(content='Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. This approach helps in managing and solving intricate problems by dividing them into more manageable components. By decomposing tasks, agents or models can better understand the steps involved and plan their actions accordingly. Techniques like Chain of Thought (CoT) and Tree of Thoughts are examples of methods that enhance model performance on complex tasks by breaking them down into smaller steps.', response_metadata={'token_usage': {'completion_tokens': 87, 'prompt_tokens': 659, 'total_tokens': 746}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-b9166386-83e5-4b82-9a4b-590e5fa76671-0')]}}\n",
+      "{'agent': {'messages': [AIMessage(content='Task decomposition is a technique used in autonomous agent systems to break down complex tasks into smaller and simpler steps. This approach helps the agent to manage and execute tasks more effectively by dividing them into manageable subtasks. One common method for task decomposition is the Chain of Thought (CoT) technique, which prompts the model to think step by step and decompose hard tasks into smaller steps. Another extension of CoT is the Tree of Thoughts, which explores multiple reasoning possibilities at each step by creating a tree structure of thought steps.\\n\\nTask decomposition can be achieved through various methods, such as using language models with simple prompting, task-specific instructions, or human inputs. By breaking down tasks into smaller components, autonomous agents can plan and execute tasks more efficiently.\\n\\nIf you would like more detailed information or examples related to task decomposition, feel free to ask!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 168, 'prompt_tokens': 611, 'total_tokens': 779, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-0f51a1cf-ff0a-474a-93f5-acf54e0d8cd6-0', usage_metadata={'input_tokens': 611, 'output_tokens': 168, 'total_tokens': 779})]}}\n",
       "----\n"
      ]
     }
@@ -825,24 +859,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_6kbxTU5CDWLmF9mrvR7bWSkI', 'function': {'arguments': '{\"query\":\"Common ways of task decomposition\"}', 'name': 'blog_post_retriever'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 769, 'total_tokens': 790}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-2d2c8327-35cd-484a-b8fd-52436657c2d8-0', tool_calls=[{'name': 'blog_post_retriever', 'args': {'query': 'Common ways of task decomposition'}, 'id': 'call_6kbxTU5CDWLmF9mrvR7bWSkI'}])]}}\n",
-      "----\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Error in LangChainTracer.on_tool_end callback: TracerException(\"Found chain run at ID 29553415-e0f4-41a9-8921-ba489e377f68, but expected {'tool'} run.\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'tools': {'messages': [ToolMessage(content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', name='blog_post_retriever', tool_call_id='call_6kbxTU5CDWLmF9mrvR7bWSkI')]}}\n",
+      "{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_n7vUrFacrvl5wUGmz5EGpmCS', 'function': {'arguments': '{\"query\":\"Common ways of task decomposition\"}', 'name': 'blog_post_retriever'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 802, 'total_tokens': 823, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4d949be3-00e5-49e5-af26-6a217efc8858-0', tool_calls=[{'name': 'blog_post_retriever', 'args': {'query': 'Common ways of task decomposition'}, 'id': 'call_n7vUrFacrvl5wUGmz5EGpmCS', 'type': 'tool_call'}], usage_metadata={'input_tokens': 802, 'output_tokens': 21, 'total_tokens': 823})]}}\n",
       "----\n",
-      "{'agent': {'messages': [AIMessage(content='Common ways of task decomposition include:\\n1. Using LLM with simple prompting like \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\"\\n2. Using task-specific instructions, for example, \"Write a story outline\" for writing a novel.\\n3. Involving human inputs in the task decomposition process.', response_metadata={'token_usage': {'completion_tokens': 67, 'prompt_tokens': 1339, 'total_tokens': 1406}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-9ad14cde-ca75-4238-a868-f865e0fc50dd-0')]}}\n",
+      "{'tools': {'messages': [ToolMessage(content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\n\\nResources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\\n\\n(3) Task execution: Expert models execute on the specific tasks and log results.\\nInstruction:\\n\\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user\\'s request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.', name='blog_post_retriever', id='90fcbc1e-0736-47bc-9a96-347ad837e0e3', tool_call_id='call_n7vUrFacrvl5wUGmz5EGpmCS')]}}\n",
+      "----\n",
+      "{'agent': {'messages': [AIMessage(content='According to the blog post, common ways of task decomposition include:\\n\\n1. Using Language Models (LLM) with Simple Prompting: Language models can be utilized with simple prompts like \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\" to break down tasks into smaller steps.\\n\\n2. Task-Specific Instructions: Providing task-specific instructions to guide the decomposition process. For example, using instructions like \"Write a story outline\" for writing a novel can help in breaking down the task effectively.\\n\\n3. Human Inputs: Involving human inputs in the task decomposition process. Human insights and expertise can contribute to breaking down complex tasks into manageable subtasks.\\n\\nThese methods of task decomposition help autonomous agents in planning and executing tasks more efficiently by breaking them down into smaller and simpler components.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 160, 'prompt_tokens': 1347, 'total_tokens': 1507, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-087ce1b5-f897-40d0-8ef4-eb1c6852a835-0', usage_metadata={'input_tokens': 1347, 'output_tokens': 160, 'total_tokens': 1507})]}}\n",
       "----\n"
      ]
     }
@@ -877,18 +898,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 1,
    "id": "b1d2b4d4-e604-497d-873d-d345b808578e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
+     ]
+    }
+   ],
    "source": [
     "import bs4\n",
     "from langchain.tools.retriever import create_retriever_tool\n",
-    "from langchain_chroma import Chroma\n",
     "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
     "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.prebuilt import create_react_agent\n",
     "\n",
     "memory = MemorySaver()\n",
     "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
@@ -907,7 +937,8 @@
     "\n",
     "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
     "splits = text_splitter.split_documents(docs)\n",
-    "vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
+    "vectorstore = InMemoryVectorStore(embedding=OpenAIEmbeddings())\n",
+    "vectorstore.add_documents(documents=splits)\n",
     "retriever = vectorstore.as_retriever()\n",
     "\n",
     "\n",
@@ -959,7 +990,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/trim_messages.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b5ee5b75-6876-4d62-9ade-5a7a808ae5a2",
+   "id": "eaad9a82-0592-4315-9931-0621054bdd0e",
    "metadata": {},
    "source": [
     "# How to trim messages\n",
@@ -22,37 +22,83 @@
     "\n",
     "All models have finite context windows, meaning there's a limit to how many tokens they can take as input. If you have very long messages or a chain/agent that accumulates a long message is history, you'll need to manage the length of the messages you're passing in to the model.\n",
     "\n",
-    "The `trim_messages` util provides some basic strategies for trimming a list of messages to be of a certain token length.\n",
+    "[trim_messages](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.trim_messages.html) can be used to reduce the size of a chat history to a specified token count or specified message count.\n",
     "\n",
-    "## Getting the last `max_tokens` tokens\n",
     "\n",
-    "To get the last `max_tokens` in the list of Messages we can set `strategy=\"last\"`. Notice that for our `token_counter` we can pass in a function (more on that below) or a language model (since language models have a message token counting method). It makes sense to pass in a model when you're trimming your messages to fit into the context window of that specific model:"
+    "If passing the trimmed chat history back into a chat model directly, the trimmed chat history should satisfy the following properties:\n",
+    "\n",
+    "1. The resulting chat history should be **valid**. Usually this means that the following properties should be satisfied:\n",
+    "   - The chat history **starts** with either (1) a `HumanMessage` or (2) a [SystemMessage](/docs/concepts/#systemmessage) followed by a `HumanMessage`.\n",
+    "   - The chat history **ends** with either a `HumanMessage` or a `ToolMessage`.\n",
+    "   - A `ToolMessage` can only appear after an `AIMessage` that involved a tool call. \n",
+    "   This can be achieved by setting `start_on=\"human\"` and `ends_on=(\"human\", \"tool\")`.\n",
+    "3. It includes recent messages and drops old messages in the chat history.\n",
+    "   This can be achieved by setting `strategy=\"last\"`.\n",
+    "4. Usually, the new chat history should include the `SystemMessage` if it\n",
+    "   was present in the original chat history since the `SystemMessage` includes\n",
+    "   special instructions to the chat model. The `SystemMessage` is almost always\n",
+    "   the first message in the history if present. This can be achieved by setting\n",
+    "   `include_system=True`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4bffc37-78c0-46c3-ad0c-b44de0ed3e90",
+   "metadata": {},
+   "source": [
+    "## Trimming based on token count\n",
+    "\n",
+    "Here, we'll trim the chat history based on token count. The trimmed chat history will produce a **valid** chat history that includes the `SystemMessage`.\n",
+    "\n",
+    "To keep the most recent messages, we set `strategy=\"last\"`.  We'll also set `include_system=True` to include the `SystemMessage`, and `start_on=\"human\"` to make sure the resulting chat history is valid. \n",
+    "\n",
+    "This is a good default configuration when using `trim_messages` based on token count. Remember to adjust `token_counter` and `max_tokens` for your use case.\n",
+    "\n",
+    "Notice that for our `token_counter` we can pass in a function (more on that below) or a language model (since language models have a message token counting method). It makes sense to pass in a model when you're trimming your messages to fit into the context window of that specific model:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c974633b-3bd0-4844-8a8f-85e3e25f13fe",
+   "id": "c91edeb2-9978-4665-9fdb-fc96cdb51caa",
+   "metadata": {},
+   "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-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "40ea972c-d424-4bc4-9f2e-82f01c3d7598",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[AIMessage(content=\"Hmmm let me think.\\n\\nWhy, he's probably chasing after the last cup of coffee in the office!\"),\n",
-       " HumanMessage(content='what do you call a speechless parrot')]"
+       "[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
+       " HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# pip install -U langchain-openai\n",
     "from langchain_core.messages import (\n",
     "    AIMessage,\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
+    "    ToolMessage,\n",
     "    trim_messages,\n",
     ")\n",
     "from langchain_openai import ChatOpenAI\n",
@@ -70,36 +116,69 @@
     "    HumanMessage(\"what do you call a speechless parrot\"),\n",
     "]\n",
     "\n",
+    "\n",
     "trim_messages(\n",
     "    messages,\n",
-    "    max_tokens=45,\n",
+    "    # Keep the last <= n_count tokens of the messages.\n",
     "    strategy=\"last\",\n",
+    "    # highlight-start\n",
+    "    # Remember to adjust based on your model\n",
+    "    # or else pass a custom token_encoder\n",
     "    token_counter=ChatOpenAI(model=\"gpt-4o\"),\n",
+    "    # highlight-end\n",
+    "    # Most chat models expect that chat history starts with either:\n",
+    "    # (1) a HumanMessage or\n",
+    "    # (2) a SystemMessage followed by a HumanMessage\n",
+    "    # highlight-start\n",
+    "    # Remember to adjust based on the desired conversation\n",
+    "    # length\n",
+    "    max_tokens=45,\n",
+    "    # highlight-end\n",
+    "    # Most chat models expect that chat history starts with either:\n",
+    "    # (1) a HumanMessage or\n",
+    "    # (2) a SystemMessage followed by a HumanMessage\n",
+    "    start_on=\"human\",\n",
+    "    # Most chat models expect that chat history ends with either:\n",
+    "    # (1) a HumanMessage or\n",
+    "    # (2) a ToolMessage\n",
+    "    end_on=(\"human\", \"tool\"),\n",
+    "    # Usually, we want to keep the SystemMessage\n",
+    "    # if it's present in the original history.\n",
+    "    # The SystemMessage has special instructions for the model.\n",
+    "    include_system=True,\n",
+    "    allow_partial=False,\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d3f46654-c4b2-4136-b995-91c3febe5bf9",
+   "id": "28fcfc94-0d4a-415c-9506-8ae7634253a2",
    "metadata": {},
    "source": [
-    "If we want to always keep the initial system message we can specify `include_system=True`:"
+    "## Trimming based on message count\n",
+    "\n",
+    "Alternatively, we can trim the chat history based on **message count**, by setting `token_counter=len`. In this case, each message will count as a single token, and `max_tokens` will control\n",
+    "the maximum number of messages.\n",
+    "\n",
+    "This is a good default configuration when using `trim_messages` based on message count. Remember to adjust `max_tokens` for your use case."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "589b0223-3a73-44ec-8315-2dba3ee6117d",
+   "execution_count": 3,
+   "id": "c8fdedae-0e6b-4901-a222-81fc95e265c2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
-       " HumanMessage(content='what do you call a speechless parrot')]"
+       "[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
+       " HumanMessage(content='and who is harrison chasing anyways', additional_kwargs={}, response_metadata={}),\n",
+       " AIMessage(content=\"Hmmm let me think.\\n\\nWhy, he's probably chasing after the last cup of coffee in the office!\", additional_kwargs={}, response_metadata={}),\n",
+       " HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -107,36 +186,59 @@
    "source": [
     "trim_messages(\n",
     "    messages,\n",
-    "    max_tokens=45,\n",
+    "    # Keep the last <= n_count tokens of the messages.\n",
     "    strategy=\"last\",\n",
-    "    token_counter=ChatOpenAI(model=\"gpt-4o\"),\n",
+    "    # highlight-next-line\n",
+    "    token_counter=len,\n",
+    "    # When token_counter=len, each message\n",
+    "    # will be counted as a single token.\n",
+    "    # highlight-start\n",
+    "    # Remember to adjust for your use case\n",
+    "    max_tokens=5,\n",
+    "    # highlight-end\n",
+    "    # Most chat models expect that chat history starts with either:\n",
+    "    # (1) a HumanMessage or\n",
+    "    # (2) a SystemMessage followed by a HumanMessage\n",
+    "    start_on=\"human\",\n",
+    "    # Most chat models expect that chat history ends with either:\n",
+    "    # (1) a HumanMessage or\n",
+    "    # (2) a ToolMessage\n",
+    "    end_on=(\"human\", \"tool\"),\n",
+    "    # Usually, we want to keep the SystemMessage\n",
+    "    # if it's present in the original history.\n",
+    "    # The SystemMessage has special instructions for the model.\n",
     "    include_system=True,\n",
     ")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "8a8b542c-04d1-4515-8d82-b999ea4fac4f",
+   "id": "9367857f-7f9a-4d17-9f9c-6ffc5aae909c",
    "metadata": {},
    "source": [
+    "## Advanced Usage\n",
+    "\n",
+    "You can use `trim_message` as a building-block to create more complex processing logic.\n",
+    "\n",
     "If we want to allow splitting up the contents of a message we can specify `allow_partial=True`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "8c46a209-dddd-4d01-81f6-f6ae55d3225c",
+   "execution_count": 4,
+   "id": "8bcca1fe-674c-4713-bacc-8e8e6d6f56c3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
-       " AIMessage(content=\"\\nWhy, he's probably chasing after the last cup of coffee in the office!\"),\n",
-       " HumanMessage(content='what do you call a speechless parrot')]"
+       "[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
+       " AIMessage(content=\"\\nWhy, he's probably chasing after the last cup of coffee in the office!\", additional_kwargs={}, response_metadata={}),\n",
+       " HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -154,26 +256,26 @@
   },
   {
    "cell_type": "markdown",
-   "id": "306adf9c-41cd-495c-b4dc-e4f43dd7f8f8",
+   "id": "245bee9b-e515-4e89-8f2a-84bda9a25de8",
    "metadata": {},
    "source": [
-    "If we need to make sure that our first message (excluding the system message) is always of a specific type, we can specify `start_on`:"
+    "By default, the `SystemMessage` will not be included, so you can drop it by either setting `include_system=False` or by dropping the `include_system` argument."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "878a730b-fe44-4e9d-ab65-7b8f7b069de8",
+   "execution_count": 5,
+   "id": "94351736-28a1-44a3-aac7-82356c81d171",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
-       " HumanMessage(content='what do you call a speechless parrot')]"
+       "[AIMessage(content=\"Hmmm let me think.\\n\\nWhy, he's probably chasing after the last cup of coffee in the office!\", additional_kwargs={}, response_metadata={}),\n",
+       " HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -181,11 +283,9 @@
    "source": [
     "trim_messages(\n",
     "    messages,\n",
-    "    max_tokens=60,\n",
+    "    max_tokens=45,\n",
     "    strategy=\"last\",\n",
     "    token_counter=ChatOpenAI(model=\"gpt-4o\"),\n",
-    "    include_system=True,\n",
-    "    start_on=\"human\",\n",
     ")"
    ]
   },
@@ -194,25 +294,23 @@
    "id": "7f5d391d-235b-4091-b2de-c22866b478f3",
    "metadata": {},
    "source": [
-    "## Getting the first `max_tokens` tokens\n",
-    "\n",
     "We can perform the flipped operation of getting the *first* `max_tokens` by specifying `strategy=\"first\"`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "5f56ae54-1a39-4019-9351-3b494c003d5b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
-       " HumanMessage(content=\"i wonder why it's called langchain\")]"
+       "[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
+       " HumanMessage(content=\"i wonder why it's called langchain\", additional_kwargs={}, response_metadata={})]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -238,18 +336,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
+   "id": "d930c089-e8e6-4980-9d39-11d41e794772",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install -qU tiktoken"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "1c1c3b1e-2ece-49e7-a3b6-e69877c1633b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[AIMessage(content=\"Hmmm let me think.\\n\\nWhy, he's probably chasing after the last cup of coffee in the office!\"),\n",
-       " HumanMessage(content='what do you call a speechless parrot')]"
+       "[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
+       " HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -257,7 +373,6 @@
    "source": [
     "from typing import List\n",
     "\n",
-    "# pip install tiktoken\n",
     "import tiktoken\n",
     "from langchain_core.messages import BaseMessage, ToolMessage\n",
     "\n",
@@ -298,9 +413,28 @@
     "\n",
     "trim_messages(\n",
     "    messages,\n",
-    "    max_tokens=45,\n",
-    "    strategy=\"last\",\n",
+    "    # highlight-next-line\n",
     "    token_counter=tiktoken_counter,\n",
+    "    # Keep the last <= n_count tokens of the messages.\n",
+    "    strategy=\"last\",\n",
+    "    # When token_counter=len, each message\n",
+    "    # will be counted as a single token.\n",
+    "    # highlight-start\n",
+    "    # Remember to adjust for your use case\n",
+    "    max_tokens=45,\n",
+    "    # highlight-end\n",
+    "    # Most chat models expect that chat history starts with either:\n",
+    "    # (1) a HumanMessage or\n",
+    "    # (2) a SystemMessage followed by a HumanMessage\n",
+    "    start_on=\"human\",\n",
+    "    # Most chat models expect that chat history ends with either:\n",
+    "    # (1) a HumanMessage or\n",
+    "    # (2) a ToolMessage\n",
+    "    end_on=(\"human\", \"tool\"),\n",
+    "    # Usually, we want to keep the SystemMessage\n",
+    "    # if it's present in the original history.\n",
+    "    # The SystemMessage has special instructions for the model.\n",
+    "    include_system=True,\n",
     ")"
    ]
   },
@@ -311,22 +445,22 @@
    "source": [
     "## Chaining\n",
     "\n",
-    "`trim_messages` can be used in an imperatively (like above) or declaratively, making it easy to compose with other components in a chain"
+    "`trim_messages` can be used imperatively (like above) or declaratively, making it easy to compose with other components in a chain"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "96aa29b2-01e0-437c-a1ab-02fb0141cb57",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='A: A \"Polly-gone\"!', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_66b29dffce', 'finish_reason': 'stop', 'logprobs': None}, id='run-83e96ddf-bcaa-4f63-824c-98b0f8a0d474-0', usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41})"
+       "AIMessage(content='A polygon! Because it\\'s a \"poly-gone\" quiet!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 32, 'total_tokens': 45, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_057232b607', 'finish_reason': 'stop', 'logprobs': None}, id='run-4fa026e7-9137-4fef-b596-54243615e3b3-0', usage_metadata={'input_tokens': 32, 'output_tokens': 13, 'total_tokens': 45})"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -337,9 +471,24 @@
     "# Notice we don't pass in messages. This creates\n",
     "# a RunnableLambda that takes messages as input\n",
     "trimmer = trim_messages(\n",
-    "    max_tokens=45,\n",
-    "    strategy=\"last\",\n",
     "    token_counter=llm,\n",
+    "    # Keep the last <= n_count tokens of the messages.\n",
+    "    strategy=\"last\",\n",
+    "    # When token_counter=len, each message\n",
+    "    # will be counted as a single token.\n",
+    "    # Remember to adjust for your use case\n",
+    "    max_tokens=45,\n",
+    "    # Most chat models expect that chat history starts with either:\n",
+    "    # (1) a HumanMessage or\n",
+    "    # (2) a SystemMessage followed by a HumanMessage\n",
+    "    start_on=\"human\",\n",
+    "    # Most chat models expect that chat history ends with either:\n",
+    "    # (1) a HumanMessage or\n",
+    "    # (2) a ToolMessage\n",
+    "    end_on=(\"human\", \"tool\"),\n",
+    "    # Usually, we want to keep the SystemMessage\n",
+    "    # if it's present in the original history.\n",
+    "    # The SystemMessage has special instructions for the model.\n",
     "    include_system=True,\n",
     ")\n",
     "\n",
@@ -359,18 +508,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "1ff02d0a-353d-4fac-a77c-7c2c5262abd9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
-       " HumanMessage(content='what do you call a speechless parrot')]"
+       "[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\", additional_kwargs={}, response_metadata={}),\n",
+       " HumanMessage(content='what do you call a speechless parrot', additional_kwargs={}, response_metadata={})]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -391,17 +540,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "a9517858-fc2f-4dc3-898d-bf98a0e905a0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='A \"polly-no-wanna-cracker\"!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 32, 'total_tokens': 42}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_5bf7397cd3', 'finish_reason': 'stop', 'logprobs': None}, id='run-054dd309-3497-4e7b-b22a-c1859f11d32e-0', usage_metadata={'input_tokens': 32, 'output_tokens': 10, 'total_tokens': 42})"
+       "AIMessage(content='A \"polygon\"!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 4, 'prompt_tokens': 32, 'total_tokens': 36, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_c17d3befe7', 'finish_reason': 'stop', 'logprobs': None}, id='run-71d9fce6-bb0c-4bb3-acc8-d5eaee6ae7bc-0', usage_metadata={'input_tokens': 32, 'output_tokens': 4, 'total_tokens': 36})"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -425,7 +574,15 @@
     "    max_tokens=45,\n",
     "    strategy=\"last\",\n",
     "    token_counter=llm,\n",
+    "    # Usually, we want to keep the SystemMessage\n",
+    "    # if it's present in the original history.\n",
+    "    # The SystemMessage has special instructions for the model.\n",
     "    include_system=True,\n",
+    "    # Most chat models expect that chat history starts with either:\n",
+    "    # (1) a HumanMessage or\n",
+    "    # (2) a SystemMessage followed by a HumanMessage\n",
+    "    # start_on=\"human\" makes sure we produce a valid chat history\n",
+    "    start_on=\"human\",\n",
     ")\n",
     "\n",
     "chain = trimmer | llm\n",
@@ -471,7 +628,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/airbyte.ipynb

@@ -29,7 +29,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "% pip install -qU langchain-airbyte"
+    "%pip install -qU langchain-airbyte"
    ]
   },
   {

docs/docs/integrations/document_loaders/browserbase.ipynb

@@ -26,7 +26,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "% pip install browserbase"
+    "%pip install browserbase"
    ]
   },
   {

docs/docs/integrations/document_loaders/index.mdx

@@ -25,6 +25,8 @@ data = loader.load()
 
 The below document loaders allow you to load webpages.
 
+See this guide for a starting point: [How to: load web pages](/docs/how_to/document_loader_web).
+
 <CategoryTable category="webpage_loaders" />
 
 ## PDFs

docs/docs/integrations/document_loaders/pebblo.ipynb

@@ -124,6 +124,39 @@
   {
    "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "### Anonymize the snippets to redact all PII details\n",
+    "\n",
+    "Set `anonymize_snippets` to `True` to anonymize all personally identifiable information (PII) from the snippets going into VectorDB and the generated reports.\n",
+    "\n",
+    "> Note: The _Pebblo Entity Classifier_ effectively identifies personally identifiable information (PII) and is continuously evolving. While its recall is not yet 100%, it is steadily improving.\n",
+    "> For more details, please refer to the [_Pebblo Entity Classifier docs_](https://daxa-ai.github.io/pebblo/entityclassifier/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import CSVLoader, PebbloSafeLoader\n",
+    "\n",
+    "loader = PebbloSafeLoader(\n",
+    "    CSVLoader(\"data/corp_sens_data.csv\"),\n",
+    "    name=\"acme-corp-rag-1\",  # App name (Mandatory)\n",
+    "    owner=\"Joe Smith\",  # Owner (Optional)\n",
+    "    description=\"Support productivity RAG application\",  # Description (Optional)\n",
+    "    anonymize_snippets=True,  # Whether to anonymize entities in the PDF Report (Optional, default=False)\n",
+    ")\n",
+    "documents = loader.load()\n",
+    "print(documents[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],

docs/docs/integrations/document_loaders/upstage.ipynb

@@ -25,9 +25,9 @@
     }
    },
    "source": [
-    "# UpstageLayoutAnalysisLoader\n",
+    "# UpstageDocumentParseLoader\n",
     "\n",
-    "This notebook covers how to get started with `UpstageLayoutAnalysisLoader`.\n",
+    "This notebook covers how to get started with `UpstageDocumentParseLoader`.\n",
     "\n",
     "## Installation\n",
     "\n",
@@ -89,10 +89,10 @@
     }
    ],
    "source": [
-    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
+    "from langchain_upstage import UpstageDocumentParseLoader\n",
     "\n",
     "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
-    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
+    "layzer = UpstageDocumentParseLoader(file_path, split=\"page\")\n",
     "\n",
     "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
     "docs = layzer.load()  # or layzer.lazy_load()\n",

docs/docs/integrations/document_transformers/voyageai-reranker.ipynb

@@ -87,7 +87,9 @@
     "## Set up the base vector store retriever\n",
     "Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs. You can use any of the following Embeddings models: ([source](https://docs.voyageai.com/docs/embeddings)):\n",
     "\n",
-    "- `voyage-large-2` (default)\n",
+    "- `voyage-3`\n",
+    "- `voyage-3-lite` \n",
+    "- `voyage-large-2`\n",
     "- `voyage-code-2`\n",
     "- `voyage-2`\n",
     "- `voyage-law-2`\n",
@@ -341,6 +343,8 @@
     "## Doing reranking with VoyageAIRerank\n",
     "Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll use the Voyage AI reranker to rerank the returned results. You can use any of the following Reranking models: ([source](https://docs.voyageai.com/docs/reranker)):\n",
     "\n",
+    "- `rerank-2`\n",
+    "- `rerank-2-lite`\n",
     "- `rerank-1`\n",
     "- `rerank-lite-1`"
    ]

docs/docs/integrations/graphs/azure_cosmosdb_gremlin.ipynb

@@ -80,7 +80,7 @@
    "outputs": [],
    "source": [
     "graph = GremlinGraph(\n",
-    "    url=f\"=wss://{cosmosdb_name}.gremlin.cosmos.azure.com:443/\",\n",
+    "    url=f\"wss://{cosmosdb_name}.gremlin.cosmos.azure.com:443/\",\n",
     "    username=f\"/dbs/{cosmosdb_db_id}/colls/{cosmosdb_db_graph_id}\",\n",
     "    password=cosmosdb_access_Key,\n",
     ")"