deepseek: temporarily bypass tests (#30423)

Deepseek infra is not stable enough to get through integration tests.

Previous two attempts had two tests time out, they both pass locally.

.github/CODEOWNERS

@@ -1,2 +1,2 @@
-/.github/   @efriis @baskaryan @ccurme
-/libs/packages.yml   @efriis
+/.github/   @baskaryan @ccurme
+/libs/packages.yml   @ccurme

.github/PULL_REQUEST_TEMPLATE.md

@@ -26,4 +26,4 @@ Additional guidelines:
 - 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.
+If no one reviews your PR within a few days, please @-mention one of baskaryan, eyurtsev, ccurme, vbarda, hwchase17.

.github/scripts/check_diff.py

@@ -39,7 +39,6 @@ IGNORED_PARTNERS = [
 
 PY_312_MAX_PACKAGES = [
     "libs/partners/huggingface",  # https://github.com/pytorch/pytorch/issues/130249
-    "libs/partners/pinecone",
     "libs/partners/voyageai",
 ]
 

.github/workflows/_integration_test.yml

@@ -6,6 +6,7 @@ on:
       working-directory:
         required: true
         type: string
+        description: "From which folder this pipeline executes"
       python-version:
         required: true
         type: string
@@ -64,8 +65,6 @@ jobs:
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
           WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
-          PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
-          PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }}
           ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
           ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
           ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}

.github/workflows/_release.yml

@@ -12,6 +12,7 @@ on:
       working-directory:
         required: true
         type: string
+        description: "From which folder this pipeline executes"
         default: 'libs/langchain'
       dangerous-nonmaster-release:
         required: false
@@ -100,15 +101,32 @@ jobs:
           PKG_NAME: ${{ needs.build.outputs.pkg-name }}
           VERSION: ${{ needs.build.outputs.version }}
         run: |
-          PREV_TAG="$PKG_NAME==${VERSION%.*}.$(( ${VERSION##*.} - 1 ))"; [[ "${VERSION##*.}" -eq 0 ]] && PREV_TAG=""
+          # Handle regular versions and pre-release versions differently
+          if [[ "$VERSION" == *"-"* ]]; then
+            # This is a pre-release version (contains a hyphen)
+            # Extract the base version without the pre-release suffix
+            BASE_VERSION=${VERSION%%-*}
+            # Look for the latest release of the same base version
+            REGEX="^$PKG_NAME==$BASE_VERSION\$"
+            PREV_TAG=$(git tag --sort=-creatordate | (grep -P "$REGEX" || true) | head -1)
+            
+            # If no exact base version match, look for the latest release of any kind
+            if [ -z "$PREV_TAG" ]; then
+              REGEX="^$PKG_NAME==\\d+\\.\\d+\\.\\d+\$"
+              PREV_TAG=$(git tag --sort=-creatordate | (grep -P "$REGEX" || true) | head -1)
+            fi
+          else
+            # Regular version handling
+            PREV_TAG="$PKG_NAME==${VERSION%.*}.$(( ${VERSION##*.} - 1 ))"; [[ "${VERSION##*.}" -eq 0 ]] && PREV_TAG=""
 
-          # backup case if releasing e.g. 0.3.0, looks up last release
-          # note if last release (chronologically) was e.g. 0.1.47 it will get 
-          # that instead of the last 0.2 release
-          if [ -z "$PREV_TAG" ]; then
-            REGEX="^$PKG_NAME==\\d+\\.\\d+\\.\\d+\$"
-            echo $REGEX
-            PREV_TAG=$(git tag --sort=-creatordate | (grep -P $REGEX || true) | head -1)
+            # backup case if releasing e.g. 0.3.0, looks up last release
+            # note if last release (chronologically) was e.g. 0.1.47 it will get 
+            # that instead of the last 0.2 release
+            if [ -z "$PREV_TAG" ]; then
+              REGEX="^$PKG_NAME==\\d+\\.\\d+\\.\\d+\$"
+              echo $REGEX
+              PREV_TAG=$(git tag --sort=-creatordate | (grep -P $REGEX || true) | head -1)
+            fi
           fi
 
           # if PREV_TAG is empty, let it be empty
@@ -297,8 +315,6 @@ jobs:
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
           WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
           WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
-          PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
-          PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }}
           ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
           ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
           ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
@@ -314,12 +330,89 @@ jobs:
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 
+  # Test select published packages against new core
+  test-prior-published-packages-against-new-core:
+    needs:
+      - build
+      - release-notes
+      - test-pypi-publish
+      - pre-release-checks
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        partner: [openai, anthropic]
+      fail-fast: false  # Continue testing other partners if one fails
+    env:
+      ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
+      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: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
+    steps:
+      - uses: actions/checkout@v4
+
+      # We implement this conditional as Github Actions does not have good support
+      # for conditionally needing steps. https://github.com/actions/runner/issues/491
+      - name: Check if libs/core
+        run: |
+          if [ "${{ startsWith(inputs.working-directory, 'libs/core') }}" != "true" ]; then
+            echo "Not in libs/core. Exiting successfully."
+            exit 0
+          fi
+
+      - name: Set up Python + uv
+        if: startsWith(inputs.working-directory, 'libs/core')
+        uses: "./.github/actions/uv_setup"
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+
+      - uses: actions/download-artifact@v4
+        if: startsWith(inputs.working-directory, 'libs/core')
+        with:
+          name: dist
+          path: ${{ inputs.working-directory }}/dist/
+
+      - name: Test against ${{ matrix.partner }}
+        if: startsWith(inputs.working-directory, 'libs/core')
+        run: |
+          # Identify latest tag
+          LATEST_PACKAGE_TAG="$(
+            git ls-remote --tags origin "langchain-${{ matrix.partner }}*" \
+            | awk '{print $2}' \
+            | sed 's|refs/tags/||' \
+            | sort -Vr \
+            | head -n 1
+          )"
+          echo "Latest package tag: $LATEST_PACKAGE_TAG"
+
+          # Shallow-fetch just that single tag
+          git fetch --depth=1 origin tag "$LATEST_PACKAGE_TAG"
+
+          # Checkout the latest package files
+          rm -rf $GITHUB_WORKSPACE/libs/partners/${{ matrix.partner }}/*
+          cd $GITHUB_WORKSPACE/libs/partners/${{ matrix.partner }}
+          git checkout "$LATEST_PACKAGE_TAG" -- .
+
+          # Print as a sanity check
+          echo "Version number from pyproject.toml: "
+          cat pyproject.toml | grep "version = "
+
+          # Run tests
+          uv sync --group test --group test_integration
+          uv pip install ../../core/dist/*.whl
+          make integration_tests
+
   publish:
     needs:
       - build
       - release-notes
       - test-pypi-publish
       - pre-release-checks
+      - test-prior-published-packages-against-new-core
     runs-on: ubuntu-latest
     permissions:
       # This permission is used for trusted publishing:

.github/workflows/check_diffs.yml

@@ -1,4 +1,3 @@
----
 name: CI
 
 on:

.github/workflows/check_new_docs.yml

@@ -1,4 +1,3 @@
----
 name: Integration docs lint
 
 on:

.github/workflows/codespell.yml

@@ -1,4 +1,3 @@
----
 name: CI / cd . / make spell_check
 
 on:

.github/workflows/scheduled_test.yml

@@ -15,7 +15,7 @@ on:
 env:
   POETRY_VERSION: "1.8.4"
   UV_FROZEN: "true"
-  DEFAULT_LIBS: '["libs/partners/openai", "libs/partners/anthropic", "libs/partners/fireworks", "libs/partners/groq", "libs/partners/mistralai", "libs/partners/google-vertexai", "libs/partners/google-genai", "libs/partners/aws"]'
+  DEFAULT_LIBS: '["libs/partners/openai", "libs/partners/anthropic", "libs/partners/fireworks", "libs/partners/groq", "libs/partners/mistralai", "libs/partners/xai", "libs/partners/google-vertexai", "libs/partners/google-genai", "libs/partners/aws"]'
   POETRY_LIBS: ("libs/partners/google-vertexai" "libs/partners/google-genai" "libs/partners/aws")
 
 jobs:
@@ -139,6 +139,7 @@ jobs:
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
           HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
+          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
           COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}

.pre-commit-config.yaml

@@ -97,12 +97,6 @@ repos:
       entry: make -C libs/partners/openai format
       files: ^libs/partners/openai/
       pass_filenames: false
-    - id: pinecone
-      name: format partners/pinecone
-      language: system
-      entry: make -C libs/partners/pinecone format
-      files: ^libs/partners/pinecone/
-      pass_filenames: false
     - id: prompty
       name: format partners/prompty
       language: system

.readthedocs.yaml

@@ -1,12 +1,7 @@
 # Read the Docs configuration file
 # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
-
-# Required
 version: 2
 
-formats:
-  - pdf
-
 # Set the version of Python and other tools you might need
 build:
   os: ubuntu-22.04
@@ -15,15 +10,16 @@ build:
   commands:
     - mkdir -p $READTHEDOCS_OUTPUT
     - cp -r api_reference_build/* $READTHEDOCS_OUTPUT
+
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
    configuration: docs/api_reference/conf.py
 
 # If using Sphinx, optionally build your docs in additional formats such as PDF
-# formats:
-#    - pdf
+formats:
+  - pdf
 
 # Optionally declare the Python requirements required to build your docs
 python:
    install:
-   - requirements: docs/api_reference/requirements.txt
+     - requirements: docs/api_reference/requirements.txt

Makefile

@@ -2,7 +2,6 @@
 
 .EXPORT_ALL_VARIABLES:
 UV_FROZEN = true
-UV_NO_SYNC = true
 
 ## help: Show this help info.
 help: Makefile
@@ -83,3 +82,6 @@ lint lint_package lint_tests:
 format format_diff:
 	uv run --group lint ruff format docs cookbook
 	uv run --group lint ruff check --select I --fix docs cookbook
+
+update-package-downloads:
+	uv run python docs/scripts/packages_yml_get_downloads.py

README.md

@@ -1,6 +1,12 @@
-# 🦜️🔗 LangChain
+<picture>
+  <source media="(prefers-color-scheme: light)" srcset="docs/static/img/logo-dark.svg">
+  <source media="(prefers-color-scheme: dark)" srcset="docs/static/img/logo-light.svg">
+  <img alt="LangChain Logo" src="docs/static/img/logo-dark.svg" width="80%">
+</picture>
 
-⚡ Build context-aware reasoning applications ⚡
+<div>
+<br>
+</div>
 
 [![Release Notes](https://img.shields.io/github/release/langchain-ai/langchain?style=flat-square)](https://github.com/langchain-ai/langchain/releases)
 [![CI](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml)
@@ -12,131 +18,65 @@
 [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/langchain)
 [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
 
-Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
+> [!NOTE]
+> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
 
-To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
-[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.
-Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team.
-
-## Quick Install
-
-With pip:
+LangChain is a framework for building LLM-powered applications. It helps you chain
+together interoperable components and third-party integrations to simplify AI
+application development —  all while future-proofing decisions as the underlying
+technology evolves.
 
 ```bash
-pip install langchain
+pip install -U langchain
 ```
 
-With conda:
+To learn more about LangChain, check out
+[the docs](https://python.langchain.com/docs/introduction/). If you’re looking for more
+advanced customization or agent orchestration, check out
+[LangGraph](https://langchain-ai.github.io/langgraph/), our framework for building
+controllable agent workflows.
 
-```bash
-conda install langchain -c conda-forge
-```
+## Why use LangChain?
 
-## 🤔 What is LangChain?
+LangChain helps developers build applications powered by LLMs through a standard
+interface for models, embeddings, vector stores, and more. 
 
-**LangChain** is a framework for developing applications powered by large language models (LLMs).
+Use LangChain for:
+- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and
+external / internal systems, drawing from LangChain’s vast library of integrations with
+model providers, tools, vector stores, retrievers, and more.
+- **Model interoperability**. Swap models in and out as your engineering team
+experiments to find the best choice for your application’s needs. As the industry
+frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without
+losing momentum.
 
-For these applications, LangChain simplifies the entire application lifecycle:
+## LangChain’s ecosystem
+While the LangChain framework can be used standalone, it also integrates seamlessly
+with any LangChain product, giving developers a full suite of tools when building LLM
+applications. 
 
+To improve your LLM application development, pair LangChain with:
 
-- **Open-source libraries**: Build your applications using LangChain's open-source
-[components](https://python.langchain.com/docs/concepts/) and
-[third-party integrations](https://python.langchain.com/docs/integrations/providers/).
-  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 Platform](https://langchain-ai.github.io/langgraph/cloud/).
+- [LangSmith](http://www.langchain.com/langsmith) - Helpful for agent evals and
+observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain
+visibility in production, and improve performance over time.
+- [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can
+reliably handle complex tasks with LangGraph, our low-level agent orchestration
+framework. LangGraph offers customizable architecture, long-term memory, and
+human-in-the-loop workflows — and is trusted in production by companies like LinkedIn,
+Uber, Klarna, and GitLab.
+- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) - Deploy
+and scale agents effortlessly with a purpose-built deployment platform for long
+running, stateful workflows. Discover, reuse, configure, and share agents across
+teams — and iterate quickly with visual prototyping in
+[LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
 
-### Open-source libraries
-
-- **`langchain-core`**: Base abstractions.
-- **Integration packages** (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
-- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
-- **`langchain-community`**: Third-party integrations that are community maintained.
-- **[LangGraph](https://langchain-ai.github.io/langgraph)**: LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more. Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
-
-### Productionization:
-
-- **[LangSmith](https://docs.smith.langchain.com/)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
-
-### Deployment:
-
-- **[LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/)**: Turn your LangGraph applications into production-ready APIs and Assistants.
-
-![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/svg/langchain_stack_112024.svg#gh-light-mode-only "LangChain Architecture Overview")
-![Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers.](docs/static/svg/langchain_stack_112024_dark.svg#gh-dark-mode-only "LangChain Architecture Overview")
-
-## 🧱 What can you build with LangChain?
-
-**❓ Question answering with 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/docs/tutorials/extraction/)
-- End-to-end Example: [LangChain Extract](https://github.com/langchain-ai/langchain-extract/)
-
-**🤖 Chatbots**
-
-- [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/docs/tutorials/) section of the docs for more.
-
-## 🚀 How does LangChain help?
-
-The main value props of the LangChain libraries are:
-
-1. **Components**: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not.
-2. **Easy orchestration with LangGraph**: [LangGraph](https://langchain-ai.github.io/langgraph/),
-built on top of `langchain-core`, has built-in support for [messages](https://python.langchain.com/docs/concepts/messages/), [tools](https://python.langchain.com/docs/concepts/tools/),
-and other LangChain abstractions. This makes it easy to combine components into
-production-ready applications with persistence, streaming, and other key features.
-Check out the LangChain [tutorials page](https://python.langchain.com/docs/tutorials/#orchestration) for examples.
-
-## Components
-
-Components fall into the following **modules**:
-
-**📃 Model I/O**
-
-This includes [prompt management](https://python.langchain.com/docs/concepts/prompt_templates/)
-and a generic interface for [chat models](https://python.langchain.com/docs/concepts/chat_models/), including a consistent interface for [tool-calling](https://python.langchain.com/docs/concepts/tool_calling/) and [structured output](https://python.langchain.com/docs/concepts/structured_outputs/) across model providers.
-
-**📚 Retrieval**
-
-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. [LangGraph](https://langchain-ai.github.io/langgraph/) makes it easy to use
-LangChain components to build both [custom](https://langchain-ai.github.io/langgraph/tutorials/)
-and [built-in](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/)
-LLM agents.
-
-## 📖 Documentation
-
-Please see [here](https://python.langchain.com) for full documentation, which includes:
-
-- [Introduction](https://python.langchain.com/docs/introduction/): Overview of the framework and the structure of the docs.
-- [Tutorials](https://python.langchain.com/docs/tutorials/): 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/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://python.langchain.com/api_reference/): Thorough documentation of every class and method.
-
-## 🌐 Ecosystem
-
-- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
-- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
-- [🦜🕸️ LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): Deploy LLM applications built with LangGraph into production.
-
-## 💁 Contributing
-
-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/docs/contributing/).
-
-## 🌟 Contributors
-
-[![langchain contributors](https://contrib.rocks/image?repo=langchain-ai/langchain&max=2000)](https://github.com/langchain-ai/langchain/graphs/contributors)
+## Additional resources
+- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with
+guided examples on getting started with LangChain.
+- [How-to Guides](https://python.langchain.com/docs/how_to/): Quick, actionable code
+snippets for topics such as tool calling, RAG use cases, and more.
+- [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key
+concepts behind the LangChain framework.
+- [API Reference](https://python.langchain.com/api_reference/): Detailed reference on
+navigating base packages and integrations for LangChain.

cookbook/README.md

@@ -21,7 +21,6 @@ Notebook | Description
 [code-analysis-deeplake.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/code-analysis-deeplake.ipynb) | Analyze its own code base with the help of gpt and activeloop's deep lake.
 [custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval.ipynb) | Build a custom agent that can interact with ai plugins by retrieving tools and creating natural language wrappers around openapi endpoints.
 [custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb) | Build a custom agent with plugin retrieval functionality, utilizing ai plugins from the `plugnplai` directory.
-[databricks_sql_db.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/databricks_sql_db.ipynb) | Connect to databricks runtimes and databricks sql.
 [deeplake_semantic_search_over_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/deeplake_semantic_search_over_chat.ipynb) | Perform semantic search and question-answering over a group chat using activeloop's deep lake with gpt4.
 [elasticsearch_db_qa.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/elasticsearch_db_qa.ipynb) | Interact with elasticsearch analytics databases in natural language and build search queries via the elasticsearch dsl API.
 [extraction_openai_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/extraction_openai_tools.ipynb) | Structured Data Extraction with OpenAI Tools

cookbook/code-analysis-deeplake.ipynb

@@ -66,7 +66,7 @@
    },
    "outputs": [],
    "source": [
-    "#!python3 -m pip install --upgrade langchain deeplake openai"
+    "#!python3 -m pip install --upgrade langchain langchain-deeplake openai"
    ]
   },
   {
@@ -666,89 +666,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Your Deep Lake dataset has been successfully created!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='hub://adilkhan/langchain-code', tensors=['embedding', 'id', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor      htype       shape       dtype  compression\n",
-      "  -------    -------     -------     -------  ------- \n",
-      " embedding  embedding  (8244, 1536)  float32   None   \n",
-      "    id        text      (8244, 1)      str     None   \n",
-      " metadata     json      (8244, 1)      str     None   \n",
-      "   text       text      (8244, 1)      str     None   \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<langchain_community.vectorstores.deeplake.DeepLake at 0x7fe1b67d7a30>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from langchain_community.vectorstores import DeepLake\n",
+    "from langchain_deeplake.vectorstores import DeeplakeVectorStore\n",
     "\n",
     "username = \"<USERNAME_OR_ORG>\"\n",
     "\n",
     "\n",
-    "db = DeepLake.from_documents(\n",
-    "    texts, embeddings, dataset_path=f\"hub://{username}/langchain-code\", overwrite=True\n",
+    "db = DeeplakeVectorStore.from_documents(\n",
+    "    documents=texts,\n",
+    "    embedding=embeddings,\n",
+    "    dataset_path=f\"hub://{username}/langchain-code\",\n",
+    "    overwrite=True,\n",
     ")\n",
     "db"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from langchain_community.vectorstores import DeepLake\n",
-    "\n",
-    "# db = DeepLake.from_documents(\n",
-    "#     texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\", runtime={\"tensor_db\": True}\n",
-    "# )\n",
-    "# db"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -760,24 +697,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Deep Lake Dataset in hub://adilkhan/langchain-code already exists, loading from the storage\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "db = DeepLake(\n",
+    "db = DeeplakeVectorStore(\n",
     "    dataset_path=f\"hub://{username}/langchain-code\",\n",
     "    read_only=True,\n",
-    "    embedding=embeddings,\n",
+    "    embedding_function=embeddings,\n",
     ")"
    ]
   },
@@ -796,36 +725,6 @@
     "retriever.search_kwargs[\"k\"] = 20"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also specify user defined functions using [Deep Lake filters](https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def filter(x):\n",
-    "    # filter based on source code\n",
-    "    if \"something\" in x[\"text\"].data()[\"value\"]:\n",
-    "        return False\n",
-    "\n",
-    "    # filter based on path e.g. extension\n",
-    "    metadata = x[\"metadata\"].data()[\"value\"]\n",
-    "    return \"only_this\" in metadata[\"source\"] or \"also_that\" in metadata[\"source\"]\n",
-    "\n",
-    "\n",
-    "### turn on below for custom filtering\n",
-    "# retriever.search_kwargs['filter'] = filter"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -837,10 +736,8 @@
     "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(\n",
-    "    model_name=\"gpt-3.5-turbo-0613\"\n",
-    ")  # 'ada' 'gpt-3.5-turbo-0613' 'gpt-4',\n",
-    "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo-0613\")  # 'ada' 'gpt-3.5-turbo-0613' 'gpt-4',\n",
+    "qa = RetrievalQA.from_llm(model, retriever=retriever)"
    ]
   },
   {

cookbook/databricks_sql_db.ipynb

@@ -1,273 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "707d13a7",
-   "metadata": {},
-   "source": [
-    "# Databricks\n",
-    "\n",
-    "This notebook covers how to connect to the [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain.\n",
-    "It is broken into 3 parts: installation and setup, connecting to Databricks, and examples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0076d072",
-   "metadata": {},
-   "source": [
-    "## Installation and Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "739b489b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install databricks-sql-connector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "73113163",
-   "metadata": {},
-   "source": [
-    "## Connecting to Databricks\n",
-    "\n",
-    "You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the `SQLDatabase.from_databricks()` method.\n",
-    "\n",
-    "### Syntax\n",
-    "```python\n",
-    "SQLDatabase.from_databricks(\n",
-    "    catalog: str,\n",
-    "    schema: str,\n",
-    "    host: Optional[str] = None,\n",
-    "    api_token: Optional[str] = None,\n",
-    "    warehouse_id: Optional[str] = None,\n",
-    "    cluster_id: Optional[str] = None,\n",
-    "    engine_args: Optional[dict] = None,\n",
-    "    **kwargs: Any)\n",
-    "```\n",
-    "### Required Parameters\n",
-    "* `catalog`: The catalog name in the Databricks database.\n",
-    "* `schema`: The schema name in the catalog.\n",
-    "\n",
-    "### Optional Parameters\n",
-    "There following parameters are optional. When executing the method in a Databricks notebook, you don't need to provide them in most of the cases.\n",
-    "* `host`: The Databricks workspace hostname, excluding 'https://' part. Defaults to 'DATABRICKS_HOST' environment variable or current workspace if in a Databricks notebook.\n",
-    "* `api_token`: The Databricks personal access token for accessing the Databricks SQL warehouse or the cluster. Defaults to 'DATABRICKS_TOKEN' environment variable or a temporary one is generated if in a Databricks notebook.\n",
-    "* `warehouse_id`: The warehouse ID in the Databricks SQL.\n",
-    "* `cluster_id`: The cluster ID in the Databricks Runtime. If running in a Databricks notebook and both 'warehouse_id' and 'cluster_id' are None, it uses the ID of the cluster the notebook is attached to.\n",
-    "* `engine_args`: The arguments to be used when connecting Databricks.\n",
-    "* `**kwargs`: Additional keyword arguments for the `SQLDatabase.from_uri` method."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b11c7e48",
-   "metadata": {},
-   "source": [
-    "## Examples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8102bca0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Connecting to Databricks with SQLDatabase wrapper\n",
-    "from langchain_community.utilities import SQLDatabase\n",
-    "\n",
-    "db = SQLDatabase.from_databricks(catalog=\"samples\", schema=\"nyctaxi\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9dd36f58",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Creating a OpenAI Chat LLM wrapper\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b5c5f1a",
-   "metadata": {},
-   "source": [
-    "### SQL Chain example\n",
-    "\n",
-    "This example demonstrates the use of the [SQL Chain](https://python.langchain.com/en/latest/modules/chains/examples/sqlite.html) for answering a question over a Databricks database."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "36f2270b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.utilities import SQLDatabaseChain\n",
-    "\n",
-    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "4e2b5f25",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
-      "What is the average duration of taxi rides that start between midnight and 6am?\n",
-      "SQLQuery:\u001b[32;1m\u001b[1;3mSELECT AVG(UNIX_TIMESTAMP(tpep_dropoff_datetime) - UNIX_TIMESTAMP(tpep_pickup_datetime)) as avg_duration\n",
-      "FROM trips\n",
-      "WHERE HOUR(tpep_pickup_datetime) >= 0 AND HOUR(tpep_pickup_datetime) < 6\u001b[0m\n",
-      "SQLResult: \u001b[33;1m\u001b[1;3m[(987.8122786304605,)]\u001b[0m\n",
-      "Answer:\u001b[32;1m\u001b[1;3mThe average duration of taxi rides that start between midnight and 6am is 987.81 seconds.\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The average duration of taxi rides that start between midnight and 6am is 987.81 seconds.'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db_chain.run(\n",
-    "    \"What is the average duration of taxi rides that start between midnight and 6am?\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e496d5e5",
-   "metadata": {},
-   "source": [
-    "### SQL Database Agent example\n",
-    "\n",
-    "This example demonstrates the use of the [SQL Database Agent](/docs/integrations/tools/sql_database) for answering questions over a Databricks database."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9918e86a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import create_sql_agent\n",
-    "from langchain_community.agent_toolkits import SQLDatabaseToolkit\n",
-    "\n",
-    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
-    "agent = create_sql_agent(llm=llm, toolkit=toolkit, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c484a76e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
-      "Action Input: \u001b[0m\n",
-      "Observation: \u001b[38;5;200m\u001b[1;3mtrips\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI should check the schema of the trips table to see if it has the necessary columns for trip distance and duration.\n",
-      "Action: schema_sql_db\n",
-      "Action Input: trips\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3m\n",
-      "CREATE TABLE trips (\n",
-      "\ttpep_pickup_datetime TIMESTAMP, \n",
-      "\ttpep_dropoff_datetime TIMESTAMP, \n",
-      "\ttrip_distance FLOAT, \n",
-      "\tfare_amount FLOAT, \n",
-      "\tpickup_zip INT, \n",
-      "\tdropoff_zip INT\n",
-      ") USING DELTA\n",
-      "\n",
-      "/*\n",
-      "3 rows from trips table:\n",
-      "tpep_pickup_datetime\ttpep_dropoff_datetime\ttrip_distance\tfare_amount\tpickup_zip\tdropoff_zip\n",
-      "2016-02-14 16:52:13+00:00\t2016-02-14 17:16:04+00:00\t4.94\t19.0\t10282\t10171\n",
-      "2016-02-04 18:44:19+00:00\t2016-02-04 18:46:00+00:00\t0.28\t3.5\t10110\t10110\n",
-      "2016-02-17 17:13:57+00:00\t2016-02-17 17:17:55+00:00\t0.7\t5.0\t10103\t10023\n",
-      "*/\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe trips table has the necessary columns for trip distance and duration. I will write a query to find the longest trip distance and its duration.\n",
-      "Action: query_checker_sql_db\n",
-      "Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
-      "Observation: \u001b[31;1m\u001b[1;3mSELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe query is correct. I will now execute it to find the longest trip distance and its duration.\n",
-      "Action: query_sql_db\n",
-      "Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m[(30.6, '0 00:43:31.000000000')]\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
-      "Final Answer: The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"What is the longest trip distance and how long did it take?\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/elasticsearch_db_qa.ipynb

@@ -115,7 +115,7 @@
     "\n",
     "PROMPT_TEMPLATE = \"\"\"Given an input question, create a syntactically correct Elasticsearch query to run. Unless the user specifies in their question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n",
     "\n",
-    "Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question.\n",
+    "Unless told to do not query for all the columns from a specific index, only ask for a few relevant columns given the question.\n",
     "\n",
     "Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json.\n",
     "\n",

cookbook/multi_modal_RAG_vdms.ipynb

@@ -21,40 +21,6 @@
     "* Passing raw images and text chunks to a multimodal LLM for answer synthesis "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "6a6b6e73",
-   "metadata": {},
-   "source": [
-    "## Start VDMS Server\n",
-    "\n",
-    "Let's start a VDMS docker using port 55559 instead of default 55555. \n",
-    "Keep note of the port and hostname as this is needed for the vector store as it uses the VDMS Python client to connect to the server."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5f483872",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a1b9206b08ef626e15b356bf9e031171f7c7eb8f956a2733f196f0109246fe2b\n"
-     ]
-    }
-   ],
-   "source": [
-    "! docker run --rm -d -p 55559:55555 --name vdms_rag_nb intellabs/vdms:latest\n",
-    "\n",
-    "# Connect to VDMS Vector Store\n",
-    "from langchain_community.vectorstores.vdms import VDMS_Client\n",
-    "\n",
-    "vdms_client = VDMS_Client(port=55559)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "2498a0a1",
@@ -67,20 +33,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "febbc459-ebba-4c1a-a52b-fed7731593f8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install --quiet -U vdms langchain-experimental\n",
+    "! pip install --quiet -U langchain-vdms langchain-experimental langchain-ollama\n",
     "\n",
     "# lock to 0.10.19 due to a persistent bug in more recent versions\n",
-    "! pip install --quiet pdf2image \"unstructured[all-docs]==0.10.19\" pillow pydantic lxml open_clip_torch"
+    "! pip install --quiet pdf2image \"unstructured[all-docs]==0.10.19\" \"onnxruntime==1.17.0\" pillow pydantic lxml open_clip_torch"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "78ac6543",
    "metadata": {},
    "outputs": [],
@@ -89,6 +55,40 @@
     "# load_dotenv(find_dotenv(), override=True);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e5c8916e",
+   "metadata": {},
+   "source": [
+    "## Start VDMS Server\n",
+    "\n",
+    "Let's start a VDMS docker using port 55559 instead of default 55555. \n",
+    "Keep note of the port and hostname as this is needed for the vector store as it uses the VDMS Python client to connect to the server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e6e2c15",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a701e5ac3523006e9540b5355e2d872d5d78383eab61562a675d5b9ac21fde65\n"
+     ]
+    }
+   ],
+   "source": [
+    "! docker run --rm -d -p 55559:55555 --name vdms_rag_nb intellabs/vdms:latest\n",
+    "\n",
+    "# Connect to VDMS Vector Store\n",
+    "from langchain_vdms.vectorstores import VDMS_Client\n",
+    "\n",
+    "vdms_client = VDMS_Client(port=55559)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1e94b3fb-8e3e-4736-be0a-ad881626c7bd",
@@ -115,11 +115,12 @@
     "import requests\n",
     "\n",
     "# Folder to store pdf and extracted images\n",
-    "datapath = Path(\"./data/multimodal_files\").resolve()\n",
+    "base_datapath = Path(\"./data/multimodal_files\").resolve()\n",
+    "datapath = base_datapath / \"images\"\n",
     "datapath.mkdir(parents=True, exist_ok=True)\n",
     "\n",
     "pdf_url = \"https://www.loc.gov/lcm/pdf/LCM_2020_1112.pdf\"\n",
-    "pdf_path = str(datapath / pdf_url.split(\"/\")[-1])\n",
+    "pdf_path = str(base_datapath / pdf_url.split(\"/\")[-1])\n",
     "with open(pdf_path, \"wb\") as f:\n",
     "    f.write(requests.get(pdf_url).content)"
    ]
@@ -185,8 +186,8 @@
    "source": [
     "import os\n",
     "\n",
-    "from langchain_community.vectorstores import VDMS\n",
     "from langchain_experimental.open_clip import OpenCLIPEmbeddings\n",
+    "from langchain_vdms import VDMS\n",
     "\n",
     "# Create VDMS\n",
     "vectorstore = VDMS(\n",
@@ -312,10 +313,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.llms.ollama import Ollama\n",
-    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_ollama.llms import OllamaLLM\n",
     "\n",
     "\n",
     "def prompt_func(data_dict):\n",
@@ -340,8 +341,8 @@
     "            \"As an expert art critic and historian, your task is to analyze and interpret images, \"\n",
     "            \"considering their historical and cultural significance. Alongside the images, you will be \"\n",
     "            \"provided with related text to offer context. Both will be retrieved from a vectorstore based \"\n",
-    "            \"on user-input keywords. Please convert answers to english and use your extensive knowledge \"\n",
-    "            \"and analytical skills to provide a comprehensive summary that includes:\\n\"\n",
+    "            \"on user-input keywords. Please use your extensive knowledge and analytical skills to provide a \"\n",
+    "            \"comprehensive summary that includes:\\n\"\n",
     "            \"- A detailed description of the visual elements in the image.\\n\"\n",
     "            \"- The historical and cultural context of the image.\\n\"\n",
     "            \"- An interpretation of the image's symbolism and meaning.\\n\"\n",
@@ -359,7 +360,7 @@
     "    \"\"\"Multi-modal RAG chain\"\"\"\n",
     "\n",
     "    # Multi-modal LLM\n",
-    "    llm_model = Ollama(\n",
+    "    llm_model = OllamaLLM(\n",
     "        verbose=True, temperature=0.5, model=\"llava\", base_url=\"http://localhost:11434\"\n",
     "    )\n",
     "\n",
@@ -419,6 +420,121 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "© 2017 LARRY D. MOORE\n",
+      "\n",
+      "contemporary criticism of the less-than- thoughtful circumstances under which Lange photographed Thomson, the picture’s power to engage has not diminished. Artists in other countries have appropriated the image, changing the mother’s features into those of other ethnicities, but keeping her expression and the positions of her clinging children. Long after anyone could help the Thompson family, this picture has resonance in another time of national crisis, unemployment and food shortages.\n",
+      "\n",
+      "A striking, but very different picture is a 1900 portrait of the legendary Hin-mah-too-yah- lat-kekt (Chief Joseph) of the Nez Percé people. The Bureau of American Ethnology in Washington, D.C., regularly arranged for its photographer, De Lancey Gill, to photograph Native American delegations that came to the capital to confer with officials about tribal needs and concerns. Although Gill described Chief Joseph as having “an air of gentleness and quiet reserve,” the delegate skeptically appraises the photographer, which is not surprising given that the United States broke five treaties with Chief Joseph and his father between 1855 and 1885.\n",
+      "\n",
+      "More than a glance, second looks may reveal new knowledge into complex histories.\n",
+      "\n",
+      "Anne Wilkes Tucker is the photography curator emeritus of the Museum of Fine Arts, Houston and curator of the “Not an Ostrich” exhibition.\n",
+      "\n",
+      "28\n",
+      "\n",
+      "28 LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "THEYRE WILLING TO HAVE MEENTERTAIN THEM DURING THE DAY,BUT AS SOON AS IT STARTSGETTING DARK, THEY ALLGO OFF, AND LEAVE ME!   \n",
+      "ROSA PARKS: IN HER OWN WORDS\n",
+      "\n",
+      "COMIC ART: 120 YEARS OF PANELS AND PAGES\n",
+      "\n",
+      "SHALL NOT BE DENIED: WOMEN FIGHT FOR THE VOTE\n",
+      "\n",
+      "More information loc.gov/exhibits\n",
+      "Nuestra Sefiora de las Iguanas\n",
+      "\n",
+      "Graciela Iturbide’s 1979 portrait of Zobeida Díaz in the town of Juchitán in southeastern Mexico conveys the strength of women and reflects their important contributions to the economy. Díaz, a merchant, was selling iguanas to cook and eat, carrying them on her head, as is customary.\n",
+      "\n",
+      "GRACIELA ITURBIDE. “NUESTRA SEÑORA DE LAS IGUANAS.” 1979. GELATIN SILVER PRINT. © GRACIELA ITURBIDE, USED BY PERMISSION. PRINTS AND PHOTOGRAPHS DIVISION.\n",
+      "\n",
+      "Iturbide requested permission to take a photograph, but this proved challenging because the iguanas were constantly moving, causing Díaz to laugh. The result, however, was a brilliant portrait that the inhabitants of Juchitán claimed with pride. They have reproduced it on posters and erected a statue honoring Díaz and her iguanas. The photo now appears throughout the world, inspiring supporters of feminism, women’s rights and gender equality.\n",
+      "\n",
+      "—Adam Silvia is a curator in the Prints and Photographs Division.\n",
+      "\n",
+      "6\n",
+      "\n",
+      "6 LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "‘Migrant Mother’ is Florence Owens Thompson\n",
+      "\n",
+      "The iconic portrait that became the face of the Great Depression is also the most famous photograph in the collections of the Library of Congress.\n",
+      "\n",
+      "The Library holds the original source of the photo — a nitrate negative measuring 4 by 5 inches. Do you see a faint thumb in the bottom right? The photographer, Dorothea Lange, found the thumb distracting and after a few years had the negative altered to make the thumb almost invisible. Lange’s boss at the Farm Security Administration, Roy Stryker, criticized her action because altering a negative undermines the credibility of a documentary photo.\n",
+      "Shrimp Picker\n",
+      "\n",
+      "The photos and evocative captions of Lewis Hine served as source material for National Child Labor Committee reports and exhibits exposing abusive child labor practices in the United States in the first decades of the 20th century.\n",
+      "\n",
+      "LEWIS WICKES HINE. “MANUEL, THE YOUNG SHRIMP-PICKER, FIVE YEARS OLD, AND A MOUNTAIN OF CHILD-LABOR OYSTER SHELLS BEHIND HIM. HE WORKED LAST YEAR. UNDERSTANDS NOT A WORD OF ENGLISH. DUNBAR, LOPEZ, DUKATE COMPANY. LOCATION: BILOXI, MISSISSIPPI.” FEBRUARY 1911. NATIONAL CHILD LABOR COMMITTEE COLLECTION. PRINTS AND PHOTOGRAPHS DIVISION.\n",
+      "\n",
+      "For 15 years, Hine\n",
+      "\n",
+      "crisscrossed the country, documenting the practices of the worst offenders. His effective use of photography made him one of the committee's greatest publicists in the campaign for legislation to ban child labor.\n",
+      "\n",
+      "Hine was a master at taking photos that catch attention and convey a message and, in this photo, he framed Manuel in a setting that drove home the boy’s small size and unsafe environment.\n",
+      "\n",
+      "Captions on photos of other shrimp pickers emphasized their long working hours as well as one hazard of the job: The acid from the shrimp made pickers’ hands sore and “eats the shoes off your feet.”\n",
+      "\n",
+      "Such images alerted viewers to all that workers, their families and the nation sacrificed when children were part of the labor force. The Library holds paper records of the National Child Labor Committee as well as over 5,000 photographs.\n",
+      "\n",
+      "—Barbara Natanson is head of the Reference Section in the Prints and Photographs Division.\n",
+      "\n",
+      "8\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "Intergenerational Portrait\n",
+      "\n",
+      "Raised on the Apsáalooke (Crow) reservation in Montana, photographer Wendy Red Star created her “Apsáalooke Feminist” self-portrait series with her daughter Beatrice. With a dash of wry humor, mother and daughter are their own first-person narrators.\n",
+      "\n",
+      "Red Star explains the significance of their appearance: “The dress has power: You feel strong and regal wearing it. In my art, the elk tooth dress specifically symbolizes Crow womanhood and the matrilineal line connecting me to my ancestors. As a mother, I spend hours searching for the perfect elk tooth dress materials to make a prized dress for my daughter.”\n",
+      "\n",
+      "In a world that struggles with cultural identities, this photograph shows us the power and beauty of blending traditional and contemporary styles.\n",
+      "‘American Gothic’ Product #216040262 Price: $24\n",
+      "\n",
+      "U.S. Capitol at Night Product #216040052 Price: $24\n",
+      "\n",
+      "Good Reading Ahead Product #21606142 Price: $24\n",
+      "\n",
+      "Gordon Parks created an iconic image with this 1942 photograph of cleaning woman Ella Watson.\n",
+      "\n",
+      "Snow blankets the U.S. Capitol in this classic image by Ernest L. Crandall.\n",
+      "\n",
+      "Start your new year out right with a poster promising good reading for months to come.\n",
+      "\n",
+      "▪ Order online: loc.gov/shop ▪ Order by phone: 888.682.3557\n",
+      "\n",
+      "26\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "LIBRARY OF CONGRESS MAGAZINE\n",
+      "\n",
+      "SUPPORT\n",
+      "\n",
+      "A PICTURE OF PHILANTHROPY Annenberg Foundation Gives $1 Million and a Photographic Collection to the Library.\n",
+      "\n",
+      "A major gift by Wallis Annenberg and the Annenberg Foundation in Los Angeles will support the effort to reimagine the visitor experience at the Library of Congress. The foundation also is donating 1,000 photographic prints from its Annenberg Space for Photography exhibitions to the Library.\n",
+      "\n",
+      "The Library is pursuing a multiyear plan to transform the experience of its nearly 2 million annual visitors, share more of its treasures with the public and show how Library collections connect with visitors’ own creativity and research. The project is part of a strategic plan established by Librarian of Congress Carla Hayden to make the Library more user-centered for Congress, creators and learners of all ages.\n",
+      "\n",
+      "A 2018 exhibition at the Annenberg Space for Photography in Los Angeles featured over 400 photographs from the Library. The Library is planning a future photography exhibition, based on the Annenberg-curated show, along with a documentary film on the Library and its history, produced by the Annenberg Space for Photography.\n",
+      "\n",
+      "“The nation’s library is honored to have the strong support of Wallis Annenberg and the Annenberg Foundation as we enhance the experience for our visitors,” Hayden said. “We know that visitors will find new connections to the Library through the incredible photography collections and countless other treasures held here to document our nation’s history and creativity.”\n",
+      "\n",
+      "To enhance the Library’s holdings, the foundation is giving the Library photographic prints for long-term preservation from 10 other exhibitions hosted at the Annenberg Space for Photography. The Library holds one of the world’s largest photography collections, with about 14 million photos and over 1 million images digitized and available online.\n",
+      "18  LIBRARY OF CONGRESS MAGAZINE\n"
+     ]
     }
    ],
    "source": [
@@ -461,10 +577,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " The image depicts a woman with several children. The woman appears to be of Cherokee heritage, as suggested by the text provided. The image is described as having been initially regretted by the subject, Florence Owens Thompson, due to her feeling that it did not accurately represent her leadership qualities.\n",
-      "The historical and cultural context of the image is tied to the Great Depression and the Dust Bowl, both of which affected the Cherokee people in Oklahoma. The photograph was taken during this period, and its subject, Florence Owens Thompson, was a leader within her community who worked tirelessly to help those affected by these crises.\n",
-      "The image's symbolism and meaning can be interpreted as a representation of resilience and strength in the face of adversity. The woman is depicted with multiple children, which could signify her role as a caregiver and protector during difficult times.\n",
-      "Connections between the image and the related text include Florence Owens Thompson's leadership qualities and her regretted feelings about the photograph. Additionally, the mention of Dorothea Lange, the photographer who took this photo, ties the image to its historical context and the broader narrative of the Great Depression and Dust Bowl in Oklahoma. \n"
+      " The image is a black and white photograph by Dorothea Lange titled \"Destitute Pea Pickers in California. Mother of Seven Children. Age Thirty-Two. Nipomo, California.\" It was taken in March 1936 as part of the Farm Security Administration-Office of War Information Collection.\n",
+      "\n",
+      "The photograph features a woman with seven children, who appear to be in a state of poverty and hardship. The woman is seated, looking directly at the camera, while three of her children are standing behind her. They all seem to be dressed in ragged clothing, indicative of their impoverished condition.\n",
+      "\n",
+      "The historical context of this image is related to the Great Depression, which was a period of economic hardship in the United States that lasted from 1929 to 1939. During this time, many people struggled to make ends meet, and poverty was widespread. This photograph captures the plight of one such family during this difficult period.\n",
+      "\n",
+      "The symbolism of the image is multifaceted. The woman's direct gaze at the camera can be seen as a plea for help or an expression of desperation. The ragged clothing of the children serves as a stark reminder of the poverty and hardship experienced by many during this time.\n",
+      "\n",
+      "In terms of connections to the related text, it is mentioned that Florence Owens Thompson, the woman in the photograph, initially regretted having her picture taken. However, she later came to appreciate the importance of the image as a representation of the struggles faced by many during the Great Depression. The mention of Helena Zinkham suggests that she may have played a role in the creation or distribution of this photograph.\n",
+      "\n",
+      "Overall, this image is a powerful depiction of poverty and hardship during the Great Depression, capturing the resilience and struggles of one family amidst difficult times. \n"
      ]
     }
    ],
@@ -491,11 +614,17 @@
    "source": [
     "! docker kill vdms_rag_nb"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe4a98ee",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".langchain-venv",
+   "display_name": ".test-venv",
    "language": "python",
    "name": "python3"
   },
@@ -509,7 +638,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,

cookbook/sql_db_qa.mdx

@@ -233,7 +233,7 @@ Question: {input}"""
 
 _DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
 
-Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
+Never query for all the columns from a specific table, only ask for a few relevant columns given the question.
 
 Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
 

cookbook/visual_RAG_vdms.ipynb

@@ -26,7 +26,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2e44b44201c8778b462342ac97f5ccf05a4e02aa8a04505ecde97bf20dcc4cbb\n"
+      "76e78b89cee4d6d31154823f93592315df79c28410dfbfc87c9f70cbfdfa648b\n"
      ]
     }
    ],
@@ -49,7 +49,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! pip install --quiet -U vdms langchain-experimental sentence-transformers opencv-python open_clip_torch torch accelerate"
+    "! pip install --quiet -U langchain-vdms langchain-experimental sentence-transformers opencv-python open_clip_torch torch accelerate"
    ]
   },
   {
@@ -63,7 +63,16 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/data1/cwlacewe/apps/cwlacewe_langchain/.langchain-venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import json\n",
     "import os\n",
@@ -80,10 +89,10 @@
     "from langchain_community.embeddings.sentence_transformer import (\n",
     "    SentenceTransformerEmbeddings,\n",
     ")\n",
-    "from langchain_community.vectorstores.vdms import VDMS, VDMS_Client\n",
     "from langchain_core.callbacks.manager import CallbackManagerForLLMRun\n",
     "from langchain_core.runnables import ConfigurableField\n",
     "from langchain_experimental.open_clip import OpenCLIPEmbeddings\n",
+    "from langchain_vdms.vectorstores import VDMS, VDMS_Client\n",
     "from transformers import (\n",
     "    AutoModelForCausalLM,\n",
     "    AutoTokenizer,\n",
@@ -363,7 +372,7 @@
       "\t\tThere are 2 shoppers in this video. Shopper 1 is wearing a plaid shirt and a spectacle. Shopper 2 who is not completely captured in the frame seems to wear a black shirt and is moving away with his back turned towards the camera. There is a shelf towards the right of the camera frame. Shopper 2 is hanging an item back to a hanger and then quickly walks away in a similar fashion as shopper 2. Contents of the nearer side of the shelf with respect to camera seems to be camping lanterns and cleansing agents, arranged at the top. In the middle part of the shelf, various tools including grommets, a pocket saw, candles, and other helpful camping items can be observed. Midway through the shelf contains items which appear to be steel containers and items made up of plastic with red, green, orange, and yellow colors, while those at the bottom are packed in cardboard boxes. Contents at the farther part of the shelf are well stocked and organized but are not glaringly visible.\n",
       "\n",
       "\tMetadata:\n",
-      "\t\t{'fps': 24.0, 'id': 'c6e5f894-b905-46f5-ac9e-4487a9235561', 'total_frames': 120.0, 'video': 'clip16.mp4'}\n",
+      "\t\t{'fps': 24.0, 'total_frames': 120.0, 'video': 'clip16.mp4'}\n",
       "Retrieved Top matching video!\n",
       "\n",
       "\n"
@@ -392,18 +401,12 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3edf8783e114487ca490d8dec5c46884",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading checkpoint shards: 100%|██████████| 2/2 [00:18<00:00,  9.01s/it]\n",
+      "WARNING:accelerate.big_modeling:Some parameters are on the meta device because they were offloaded to the cpu.\n"
+     ]
     }
    ],
    "source": [
@@ -555,7 +558,7 @@
       "\t\tA single shopper is seen in this video standing facing the shelf and in the bottom part of the frame. He's wearing a light-colored shirt and a spectacle. The shopper is carrying a red colored basket in his left hand. The entire basket is not clearly visible, but it does seem to contain something in a blue colored package which the shopper has just placed in the basket given his right hand was seen inside the basket. Then the shopper leans towards the shelf and checks out an item in orange package. He picks this single item with his right hand and proceeds to place the item in the basket. The entire shelf looks well stocked except for the top part of the shelf which is empty. The shopper has not picked any item from this part of the shelf. The rest of the shelf looks well stocked and does not need any restocking. The contents on the farther part of the shelf consists of items, majority of which are packed in black, yellow, and green packages. No other details are visible of these items.\n",
       "\n",
       "\tMetadata:\n",
-      "\t\t{'fps': 24.0, 'id': '37ddc212-994e-4db0-877f-5ed09965ab90', 'total_frames': 162.0, 'video': 'clip10.mp4'}\n",
+      "\t\t{'fps': 24.0, 'total_frames': 162.0, 'video': 'clip10.mp4'}\n",
       "Retrieved Top matching video!\n",
       "\n",
       "\n"
@@ -585,7 +588,7 @@
       "User :  Find a man holding a red shopping basket\n",
       "Assistant :  Most relevant retrieved video is **clip9.mp4** \n",
       "\n",
-      "I see a person standing in front of a well-stocked shelf, they are wearing a light-colored shirt and glasses, and they have a red shopping basket in their left hand. They are leaning forward and picking up an item from the shelf with their right hand. The item is packaged in a blue-green box. Based on the scene description, I can confirm that the person is indeed holding a red shopping basket.</s>\n"
+      "I see a person standing in front of a well-stocked shelf, they are wearing a light-colored shirt and glasses, and they have a red shopping basket in their left hand. They are leaning forward and picking up an item from the shelf with their right hand. The item is packaged in a blue-green box. Based on the available information, I cannot confirm whether the basket is empty or contains items. However, the rest of the\n"
      ]
     }
    ],
@@ -655,7 +658,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": ".langchain-venv",
    "language": "python",
    "name": "python3"
   },
@@ -669,7 +672,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,

docs/api_reference/_static/css/custom.css

@@ -328,7 +328,7 @@ html[data-theme=dark] .MathJax_SVG * {
 }
 
 .bd-sidebar-primary {
-    width: 22%;  /* Adjust this value to your preference */
+    width: max-content;  /* Adjust this value to your preference */
     line-height: 1.4;
 }
 

docs/cassettes/configure_093d1c7d-1a64-4e6a-849f-075526b9b3ca.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/configure_277e3232-9b77-4828-8082-b62f4d97127f.msgpack.zlib

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docs/cassettes/configure_42647fb7.msgpack.zlib

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docs/cassettes/configure_48b45337.msgpack.zlib

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docs/cassettes/configure_4f83245c.msgpack.zlib

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docs/cassettes/configure_5cf53202.msgpack.zlib

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docs/cassettes/configure_61a67769-4a15-49e2-a945-1f4e7ef19d8c.msgpack.zlib

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docs/cassettes/configure_71248a9f.msgpack.zlib

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docs/cassettes/configure_7ba735f4.msgpack.zlib

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docs/cassettes/configure_927297a1.msgpack.zlib

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docs/cassettes/configure_97538c23.msgpack.zlib

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docs/cassettes/configure_9a9ea077.msgpack.zlib

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docs/cassettes/configure_9f6a7c6c.msgpack.zlib

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docs/cassettes/configure_c09fac15.msgpack.zlib

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docs/cassettes/configure_e4ee9fbc.msgpack.zlib

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docs/cassettes/configure_e75ae678.msgpack.zlib

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docs/cassettes/configure_f33f3cf2.msgpack.zlib

@@ -1 +1 @@
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

docs/docs/concepts/few_shot_prompting.mdx

@@ -30,7 +30,7 @@ At a high-level, the basic ways to generate examples are:
 - User feedback: users (or labelers) leave feedback on interactions with the application and examples are generated based on that feedback (for example, all interactions with positive feedback could be turned into examples).
 - LLM feedback: same as user feedback but the process is automated by having models evaluate themselves.
 
-Which approach is best depends on your task. For tasks where a small number core principles need to be understood really well, it can be valuable hand-craft a few really good examples.
+Which approach is best depends on your task. For tasks where a small number of core principles need to be understood really well, it can be valuable hand-craft a few really good examples.
 For tasks where the space of correct behaviors is broader and more nuanced, it can be useful to generate many examples in a more automated fashion so that there's a higher likelihood of there being some highly relevant examples for any runtime input.
 
 **Single-turn v.s. multi-turn examples**
@@ -39,8 +39,8 @@ Another dimension to think about when generating examples is what the example is
 
 The simplest types of examples just have a user input and an expected model output. These are single-turn examples.
 
-One more complex type if example is where the example is an entire conversation, usually in which a model initially responds incorrectly and a user then tells the model how to correct its answer.
-This is called a multi-turn example. Multi-turn examples can be useful for more nuanced tasks where its useful to show common errors and spell out exactly why they're wrong and what should be done instead.
+One more complex type of example is where the example is an entire conversation, usually in which a model initially responds incorrectly and a user then tells the model how to correct its answer.
+This is called a multi-turn example. Multi-turn examples can be useful for more nuanced tasks where it's useful to show common errors and spell out exactly why they're wrong and what should be done instead.
 
 ## 2. Number of examples
 
@@ -77,7 +77,7 @@ If we insert our examples as messages, where each example is represented as a se
 One area where formatting examples as messages can be tricky is when our example outputs have tool calls. This is because different models have different constraints on what types of message sequences are allowed when any tool calls are generated.
 - Some models require that any AIMessage with tool calls be immediately followed by ToolMessages for every tool call,
 - Some models additionally require that any ToolMessages be immediately followed by an AIMessage before the next HumanMessage,
-- Some models require that tools are passed in to the model if there are any tool calls / ToolMessages in the chat history.
+- Some models require that tools are passed into the model if there are any tool calls / ToolMessages in the chat history.
 
 These requirements are model-specific and should be checked for the model you are using. If your model requires ToolMessages after tool calls and/or AIMessages after ToolMessages and your examples only include expected tool calls and not the actual tool outputs, you can try adding dummy ToolMessages / AIMessages to the end of each example with generic contents to satisfy the API constraints.
 In these cases it's especially worth experimenting with inserting your examples as strings versus messages, as having dummy messages can adversely affect certain models.

docs/docs/concepts/retrieval.mdx

@@ -74,6 +74,8 @@ As an example, query decomposition can simply be accomplished using prompting an
 These can then be run sequentially or in parallel on a downstream retrieval system.
 
 ```python
+from typing import List
+
 from pydantic import BaseModel, Field
 from langchain_openai import ChatOpenAI
 from langchain_core.messages import SystemMessage, HumanMessage

docs/docs/concepts/streaming.mdx

@@ -91,7 +91,7 @@ For more information, please see:
 
 #### Usage with LCEL
 
-If you compose multiple Runnables using [LangChain’s Expression Language (LCEL)](/docs/concepts/lcel), the `stream()` and `astream()` methods will, by convention, stream the output of the last step in the chain. This allows the final processed result to be streamed incrementally. **LCEL** tries to optimize streaming latency in pipelines such that the streaming results from the last step are available as soon as possible.
+If you compose multiple Runnables using [LangChain’s Expression Language (LCEL)](/docs/concepts/lcel), the `stream()` and `astream()` methods will, by convention, stream the output of the last step in the chain. This allows the final processed result to be streamed incrementally. **LCEL** tries to optimize streaming latency in pipelines so that the streaming results from the last step are available as soon as possible.
 
 
 
@@ -104,7 +104,7 @@ Use the `astream_events` API to access custom data and intermediate outputs from
 While this API is available for use with [LangGraph](/docs/concepts/architecture#langgraph) as well, it is usually not necessary when working with LangGraph, as the `stream` and `astream` methods provide comprehensive streaming capabilities for LangGraph graphs.
 :::
 
-For chains constructed using **LCEL**, the `.stream()` method only streams the output of the final step from te chain. This might be sufficient for some applications, but as you build more complex chains of several LLM calls together, you may want to use the intermediate values of the chain alongside the final output. For example, you may want to return sources alongside the final generation when building a chat-over-documents app.
+For chains constructed using **LCEL**, the `.stream()` method only streams the output of the final step from the chain. This might be sufficient for some applications, but as you build more complex chains of several LLM calls together, you may want to use the intermediate values of the chain alongside the final output. For example, you may want to return sources alongside the final generation when building a chat-over-documents app.
 
 There are ways to do this [using callbacks](/docs/concepts/callbacks), or by constructing your chain in such a way that it passes intermediate
 values to the end with something like chained [`.assign()`](/docs/how_to/passthrough/) calls, but LangChain also includes an
@@ -125,7 +125,7 @@ prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
 parser = StrOutputParser()
 chain = prompt | model | parser
 
-async for event in chain.astream_events({"topic": "parrot"}, version="v2"):
+async for event in chain.astream_events({"topic": "parrot"}):
     kind = event["event"]
     if kind == "on_chat_model_stream":
         print(event, end="|", flush=True)

docs/docs/concepts/structured_outputs.mdx

@@ -16,7 +16,7 @@ This need motivates the concept of structured output, where models can be instru
 
 ## Recommended usage
 
-This pseudo-code illustrates the recommended workflow when using structured output.
+This pseudocode illustrates the recommended workflow when using structured output.
 LangChain provides a method, [`with_structured_output()`](/docs/how_to/structured_output/#the-with_structured_output-method), that automates the process of binding the schema to the [model](/docs/concepts/chat_models/) and parsing the output.
 This helper function is available for all model providers that support structured output. 
 
@@ -99,20 +99,10 @@ Here is an example of how to use JSON mode with OpenAI:
 
 ```python
 from langchain_openai import ChatOpenAI
-model = ChatOpenAI(model="gpt-4o", model_kwargs={ "response_format": { "type": "json_object" } })
+model = ChatOpenAI(model="gpt-4o").with_structured_output(method="json_mode")
 ai_msg = model.invoke("Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]")
-ai_msg.content
-'\n{\n  "random_ints": [23, 47, 89, 15, 34, 76, 58, 3, 62, 91]\n}'
-```
-
-One important point to flag: the model *still* returns a string, which needs to be parsed into a JSON object.
-This can, of course, simply use the `json` library or a JSON output parser if you need more advanced functionality.
-See this [how-to guide on the JSON output parser](/docs/how_to/output_parser_json) for more details.
-
-```python
-import json
-json_object = json.loads(ai_msg.content)
-{'random_ints': [23, 47, 89, 15, 34, 76, 58, 3, 62, 91]}
+ai_msg
+{'random_ints': [45, 67, 12, 34, 89, 23, 78, 56, 90, 11]}
 ```
 
 ## Structured output method 

docs/docs/concepts/tool_calling.mdx

@@ -30,7 +30,7 @@ You will sometimes hear the term `function calling`. We use this term interchang
 
 ## Recommended usage
 
-This pseudo-code illustrates the recommended workflow for using tool calling. 
+This pseudocode illustrates the recommended workflow for using tool calling.
 Created tools are passed to `.bind_tools()` method as a list.
 This model can be called, as usual. If a tool call is made, model's response will contain the tool call arguments.
 The tool call arguments can be passed directly to the tool.

docs/docs/contributing/how_to/documentation/setup.mdx

@@ -50,11 +50,6 @@ locally to ensure that it looks good and is free of errors.
 If you're unable to build it locally that's okay as well, as you will be able to
 see a preview of the documentation on the pull request page.
 
-From the **monorepo root**, run the following command to install the dependencies:
-
-```bash
-poetry install --with lint,docs --no-root
-````
 
 ### Building
 
@@ -158,14 +153,6 @@ the working directory to the `langchain-community` directory:
 cd [root]/libs/langchain-community
 ```
 
-Set up a virtual environment for the package if you haven't done so already.
-
-Install the dependencies for the package.
-
-```bash
-poetry install --with lint
-```
-
 Then you can run the following commands to lint and format the in-code documentation:
 
 ```bash

docs/docs/contributing/how_to/integrations/index.mdx

@@ -73,17 +73,18 @@ In order to contribute an integration, you should follow these steps:
 
 ## Co-Marketing
 
-With over 20 million monthly downloads, LangChain has a large audience of developers building LLM applications.
-Besides just adding integrations, we also like to show them examples of cool tools or APIs they can use.
+With over 20 million monthly downloads, LangChain has a large audience of developers
+building LLM applications. Beyond just listing integrations, we aim to highlight
+high-quality, educational examples that inspire developers and advance the ecosystem.
 
-While traditionally called "co-marketing", we like to think of this more as "co-education".
-For that reason, while we are happy to highlight your integration through our social media channels, we prefer to highlight examples that also serve some educational purpose.
-Our main social media channels are Twitter and LinkedIn.
+While we occasionally share integrations, we prioritize content that provides
+meaningful insights and best practices. Our main social channels are Twitter and
+LinkedIn, where we highlight the best examples.
 
 Here are some heuristics for types of content we are excited to promote:
 
-- **Integration announcement:** If you announce the integration with a link to the LangChain documentation page, we are happy to re-tweet/re-share on Twitter/LinkedIn.
-- **Educational content:** We highlight good educational content on the weekends - if you write a good blog or make a good YouTube video, we are happy to share there! Note that we prefer content that is NOT framed as "here's how to use integration XYZ", but rather "here's how to do ABC", as we find that is more educational and helpful for developers.
+- **Integration announcement:** Announcements of new integrations with a link to the LangChain documentation page.
+- **Educational content:** Blogs, YouTube videos and other media showcasing educational content. Note that we prefer content that is NOT framed as "here's how to use integration XYZ", but rather "here's how to do ABC", as we find that is more educational and helpful for developers.
 - **End-to-end applications:** End-to-end applications are great resources for developers looking to build. We prefer to highlight applications that are more complex/agentic in nature, and that use [LangGraph](https://github.com/langchain-ai/langgraph) as the orchestration framework. We get particularly excited about anything involving long-term memory, human-in-the-loop interaction patterns, or multi-agent architectures.
 - **Research:** We love highlighting novel research! Whether it is research built on top of LangChain or that integrates with it.
 

docs/docs/contributing/how_to/integrations/package.mdx

@@ -35,7 +35,7 @@ from a template, which can be edited to implement your LangChain components.
 - [GitHub](https://github.com) account
 - [PyPi](https://pypi.org/) account
 
-### Boostrapping a new Python package with langchain-cli
+### Bootstrapping a new Python package with langchain-cli
 
 First, install `langchain-cli` and `poetry`:
 
@@ -104,7 +104,7 @@ dependency management and packaging, and you're welcome to use any other tools y
 - [GitHub](https://github.com) account
 - [PyPi](https://pypi.org/) account
 
-### Boostrapping a new Python package with Poetry
+### Bootstrapping a new Python package with Poetry
 
 First, install Poetry:
 

docs/docs/contributing/reference/faq.mdx

@@ -35,5 +35,5 @@ Please reference our [Review Process](review_process.mdx).
 
 ### I think my PR was closed in a way that didn't follow the review process. What should I do?
 
-Tag `@efriis` in the PR comments referencing the portion of the review
+Tag `@ccurme` in the PR comments referencing the portion of the review
 process that you believe was not followed. We'll take a look!

docs/docs/how_to/chat_streaming.ipynb

@@ -127,20 +127,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "27bd1dfd-8ae2-49d6-b526-97180c81b5f4",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 3,
+   "id": "5a03086e-2813-4cb1-b12b-d00e7eeba122",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chat_model_start', 'run_id': '08da631a-12a0-4f07-baee-fc9a175ad4ba', 'name': 'ChatAnthropic', 'tags': [], 'metadata': {}, 'data': {'input': 'Write me a 1 verse song about goldfish on the moon'}}\n",
-      "{'event': 'on_chat_model_stream', 'run_id': '08da631a-12a0-4f07-baee-fc9a175ad4ba', 'tags': [], 'metadata': {}, 'name': 'ChatAnthropic', 'data': {'chunk': AIMessageChunk(content='Here', id='run-08da631a-12a0-4f07-baee-fc9a175ad4ba')}}\n",
-      "{'event': 'on_chat_model_stream', 'run_id': '08da631a-12a0-4f07-baee-fc9a175ad4ba', 'tags': [], 'metadata': {}, 'name': 'ChatAnthropic', 'data': {'chunk': AIMessageChunk(content=\"'s\", id='run-08da631a-12a0-4f07-baee-fc9a175ad4ba')}}\n",
-      "{'event': 'on_chat_model_stream', 'run_id': '08da631a-12a0-4f07-baee-fc9a175ad4ba', 'tags': [], 'metadata': {}, 'name': 'ChatAnthropic', 'data': {'chunk': AIMessageChunk(content=' a', id='run-08da631a-12a0-4f07-baee-fc9a175ad4ba')}}\n",
+      "{'event': 'on_chat_model_start', 'data': {'input': 'Write me a 1 verse song about goldfish on the moon'}, 'name': 'ChatAnthropic', 'tags': [], 'run_id': '1d430164-52b1-4d00-8c00-b16460f7737e', 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-haiku-20240307', 'ls_model_type': 'chat', 'ls_temperature': None, 'ls_max_tokens': 1024}, 'parent_ids': []}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '1d430164-52b1-4d00-8c00-b16460f7737e', 'name': 'ChatAnthropic', 'tags': [], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-haiku-20240307', 'ls_model_type': 'chat', 'ls_temperature': None, 'ls_max_tokens': 1024}, 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-1d430164-52b1-4d00-8c00-b16460f7737e', usage_metadata={'input_tokens': 21, 'output_tokens': 2, 'total_tokens': 23, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'parent_ids': []}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '1d430164-52b1-4d00-8c00-b16460f7737e', 'name': 'ChatAnthropic', 'tags': [], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-haiku-20240307', 'ls_model_type': 'chat', 'ls_temperature': None, 'ls_max_tokens': 1024}, 'data': {'chunk': AIMessageChunk(content=\"Here's\", additional_kwargs={}, response_metadata={}, id='run-1d430164-52b1-4d00-8c00-b16460f7737e')}, 'parent_ids': []}\n",
+      "{'event': 'on_chat_model_stream', 'run_id': '1d430164-52b1-4d00-8c00-b16460f7737e', 'name': 'ChatAnthropic', 'tags': [], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-haiku-20240307', 'ls_model_type': 'chat', 'ls_temperature': None, 'ls_max_tokens': 1024}, 'data': {'chunk': AIMessageChunk(content=' a short one-verse song', additional_kwargs={}, response_metadata={}, id='run-1d430164-52b1-4d00-8c00-b16460f7737e')}, 'parent_ids': []}\n",
       "...Truncated\n"
      ]
     }
@@ -152,7 +150,7 @@
     "idx = 0\n",
     "\n",
     "async for event in chat.astream_events(\n",
-    "    \"Write me a 1 verse song about goldfish on the moon\", version=\"v1\"\n",
+    "    \"Write me a 1 verse song about goldfish on the moon\"\n",
     "):\n",
     "    idx += 1\n",
     "    if idx >= 5:  # Truncate the output\n",
@@ -178,7 +176,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/configure.ipynb

@@ -21,7 +21,7 @@
     ":::info Prerequisites\n",
     "\n",
     "This guide assumes familiarity with the following concepts:\n",
-    "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
+    "- [The Runnable interface](/docs/concepts/runnables/)\n",
     "- [Chaining runnables](/docs/how_to/sequence/)\n",
     "- [Binding runtime arguments](/docs/how_to/binding/)\n",
     "\n",
@@ -62,6 +62,163 @@
     "    os.environ[\"OPENAI_API_KEY\"] = getpass()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9d25f63f-a048-42f3-ac2f-e20ba99cff16",
+   "metadata": {},
+   "source": [
+    "### Configuring fields on a chat model\n",
+    "\n",
+    "If using [init_chat_model](/docs/how_to/chat_models_universal_init/) to create a chat model, you can specify configurable fields in the constructor:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "92ba5e49-b2b4-432b-b8bc-b03de46dc2bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "llm = init_chat_model(\n",
+    "    \"openai:gpt-4o-mini\",\n",
+    "    # highlight-next-line\n",
+    "    configurable_fields=(\"temperature\",),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61ef4976-9943-492b-9554-0d10e3d3ba76",
+   "metadata": {},
+   "source": [
+    "You can then set the parameter at runtime using `.with_config`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "277e3232-9b77-4828-8082-b62f4d97127f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello! How can I assist you today?\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = llm.with_config({\"temperature\": 0}).invoke(\"Hello\")\n",
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44c5fe89-f0a6-4ff0-b419-b927e51cc9fa",
+   "metadata": {},
+   "source": [
+    ":::tip\n",
+    "\n",
+    "In addition to invocation parameters like temperature, configuring fields this way extends to clients and other attributes.\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fed7e600-4d5e-4875-8d37-082ec926e66f",
+   "metadata": {},
+   "source": [
+    "#### Use with tools\n",
+    "\n",
+    "This method is applicable when [binding tools](/docs/concepts/tool_calling/) as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "61a67769-4a15-49e2-a945-1f4e7ef19d8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'get_weather',\n",
+       "  'args': {'location': 'San Francisco'},\n",
+       "  'id': 'call_B93EttzlGyYUhzbIIiMcl3bE',\n",
+       "  'type': 'tool_call'}]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(location: str):\n",
+    "    \"\"\"Get the weather.\"\"\"\n",
+    "    return \"It's sunny.\"\n",
+    "\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools([get_weather])\n",
+    "response = llm_with_tools.with_config({\"temperature\": 0}).invoke(\n",
+    "    \"What's the weather in SF?\"\n",
+    ")\n",
+    "response.tool_calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b71c7bf5-f351-4b90-ae86-1100d2dcdfaa",
+   "metadata": {},
+   "source": [
+    "In addition to `.with_config`, we can now include the parameter when passing a configuration directly. See example below, where we allow the underlying model temperature to be configurable inside of a [langgraph agent](/docs/tutorials/agents/):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bb36a46-7b67-4f11-b043-771f3005f493",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install --upgrade langgraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "093d1c7d-1a64-4e6a-849f-075526b9b3ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "agent = create_react_agent(llm, [get_weather])\n",
+    "\n",
+    "response = agent.invoke(\n",
+    "    {\"messages\": \"What's the weather in Boston?\"},\n",
+    "    {\"configurable\": {\"temperature\": 0}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dc5be03-528f-4532-8cb0-1f149dddedc9",
+   "metadata": {},
+   "source": [
+    "### Configuring fields on arbitrary Runnables\n",
+    "\n",
+    "You can also use the `.configurable_fields` method on arbitrary [Runnables](/docs/concepts/runnables/), as shown below:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -604,7 +761,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/custom_tools.ipynb

@@ -22,7 +22,7 @@
     "2. LangChain [Runnables](/docs/concepts/runnables);\n",
     "3. By sub-classing from [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html) -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.\n",
     "\n",
-    "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.structured.StructuredTool.html#langchain_core.tools.structured.StructuredTool.from_function) class method.\n",
+    "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.structured.StructuredTool.html#langchain_core.tools.structured.StructuredTool.from_function) class method.\n",
     "\n",
     "In this guide we provide an overview of these methods.\n",
     "\n",
@@ -492,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "1dad8f8e",
    "metadata": {
     "execution": {
@@ -504,13 +504,14 @@
    },
    "outputs": [],
    "source": [
-    "from typing import Optional, Type\n",
+    "from typing import Optional\n",
     "\n",
     "from langchain_core.callbacks import (\n",
     "    AsyncCallbackManagerForToolRun,\n",
     "    CallbackManagerForToolRun,\n",
     ")\n",
     "from langchain_core.tools import BaseTool\n",
+    "from langchain_core.tools.base import ArgsSchema\n",
     "from pydantic import BaseModel, Field\n",
     "\n",
     "\n",
@@ -524,7 +525,7 @@
     "class CustomCalculatorTool(BaseTool):\n",
     "    name: str = \"Calculator\"\n",
     "    description: str = \"useful for when you need to answer questions about math\"\n",
-    "    args_schema: Type[BaseModel] = CalculatorInput\n",
+    "    args_schema: Optional[ArgsSchema] = CalculatorInput\n",
     "    return_direct: bool = True\n",
     "\n",
     "    def _run(\n",

docs/docs/how_to/document_loader_custom.ipynb

@@ -551,7 +551,7 @@
     "\n",
     "While a parser encapsulates the logic needed to parse binary data into documents, *blob loaders* encapsulate the logic that's necessary to load blobs from a given storage location.\n",
     "\n",
-    "A the moment, `LangChain` only supports `FileSystemBlobLoader`.\n",
+    "At the moment, `LangChain` only supports `FileSystemBlobLoader`.\n",
     "\n",
     "You can use the `FileSystemBlobLoader` to load blobs and then use the parser to parse them."
    ]

docs/docs/how_to/document_loader_html.ipynb

@@ -11,7 +11,7 @@
     "\n",
     "This covers how to load `HTML` documents into a LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects that we can use downstream.\n",
     "\n",
-    "Parsing HTML files often requires specialized tools. Here we demonstrate parsing via [Unstructured](https://unstructured-io.github.io/unstructured/) and [BeautifulSoup4](https://beautiful-soup-4.readthedocs.io/en/latest/), which can be installed via pip. Head over to the integrations page to find integrations with additional services, such as [Azure AI Document Intelligence](/docs/integrations/document_loaders/azure_document_intelligence) or [FireCrawl](/docs/integrations/document_loaders/firecrawl).\n",
+    "Parsing HTML files often requires specialized tools. Here we demonstrate parsing via [Unstructured](https://docs.unstructured.io) and [BeautifulSoup4](https://beautiful-soup-4.readthedocs.io/en/latest/), which can be installed via pip. Head over to the integrations page to find integrations with additional services, such as [Azure AI Document Intelligence](/docs/integrations/document_loaders/azure_document_intelligence) or [FireCrawl](/docs/integrations/document_loaders/firecrawl).\n",
     "\n",
     "## Loading HTML with Unstructured"
    ]

docs/docs/how_to/local_llms.ipynb

@@ -68,7 +68,7 @@
     "\n",
     "### Formatting prompts\n",
     "\n",
-    "Some providers have [chat model](/docs/concepts/chat_models) wrappers that takes care of formatting your input prompt for the specific local model you're using. However, if you are prompting local models with a [text-in/text-out LLM](/docs/concepts/text_llms) wrapper, you may need to use a prompt tailed for your specific model.\n",
+    "Some providers have [chat model](/docs/concepts/chat_models) wrappers that takes care of formatting your input prompt for the specific local model you're using. However, if you are prompting local models with a [text-in/text-out LLM](/docs/concepts/text_llms) wrapper, you may need to use a prompt tailored for your specific model.\n",
     "\n",
     "This can [require the inclusion of special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). [Here's an example for LLaMA 2](https://smith.langchain.com/hub/rlm/rag-prompt-llama).\n",
     "\n",

docs/docs/how_to/migrate_agent.ipynb

@@ -329,7 +329,7 @@
    "id": "fc6059fd-0df7-4b6f-a84c-b5874e983638",
    "metadata": {},
    "source": [
-    "We can also pass in an arbitrary function or a runnable. This function/runnable should take in a the graph state and output a list of messages.\n",
+    "We can also pass in an arbitrary function or a runnable. This function/runnable should take in a graph state and output a list of messages.\n",
     "We can do all types of arbitrary formatting of messages here. In this case, let's add a SystemMessage to the start of the list of messages and append another user message at the end."
    ]
   },

docs/docs/how_to/split_html.ipynb

@@ -599,7 +599,7 @@
     "\n",
     "The `HTMLSemanticPreservingSplitter` is designed to split HTML content into manageable chunks while preserving the semantic structure of important elements like tables, lists, and other HTML components. This ensures that such elements are not split across chunks, causing loss of contextual relevancy such as table headers, list headers etc.\n",
     "\n",
-    "This splitter is designed at its heart, to create contextually relevant chunks. General Recursive splitting with `HTMLHeaderTextSplitter` can cause tables, lists and other structered elements to be split in the middle, losing signifcant context and creating bad chunks.\n",
+    "This splitter is designed at its heart, to create contextually relevant chunks. General Recursive splitting with `HTMLHeaderTextSplitter` can cause tables, lists and other structured elements to be split in the middle, losing significant context and creating bad chunks.\n",
     "\n",
     "The `HTMLSemanticPreservingSplitter` is essential for splitting HTML content that includes structured elements like tables and lists, especially when it's critical to preserve these elements intact. Additionally, its ability to define custom handlers for specific HTML tags makes it a versatile tool for processing complex HTML documents.\n",
     "\n",
@@ -832,7 +832,7 @@
     "#### Explanation\n",
     "In this example, we defined a custom handler for `iframe` tags that converts them into Markdown-like links. When the splitter processes the HTML content, it uses this custom handler to transform the `iframe` tags while preserving other elements like tables and lists. The resulting `Document` objects show how the iframe is handled according to the custom logic you provided.\n",
     "\n",
-    "**Important**: When presvering items such as links, you should be mindful not to include `.` in your seperators, or leave seperators blank. `RecursiveCharacterTextSplitter` splits on full stop, which will cut links in half. Ensure you provide a seperator list with `. `  instead."
+    "**Important**: When presvering items such as links, you should be mindful not to include `.` in your separators, or leave separators blank. `RecursiveCharacterTextSplitter` splits on full stop, which will cut links in half. Ensure you provide a separator list with `. `  instead."
    ]
   },
   {

docs/docs/how_to/sql_large_db.ipynb

@@ -512,44 +512,6 @@
     "db.run(query)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "fcdd8432-07a4-4609-8214-b1591dd94950",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SELECT DISTINCT Genre.Name\n",
-      "FROM Genre\n",
-      "JOIN Track ON Genre.GenreId = Track.GenreId\n",
-      "JOIN Album ON Track.AlbumId = Album.AlbumId\n",
-      "JOIN Artist ON Album.ArtistId = Artist.ArtistId\n",
-      "WHERE Artist.Name = 'Elenis Moriset'\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "''"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Without retrieval\n",
-    "query = query_chain.invoke(\n",
-    "    {\"question\": \"What are all the genres of elenis moriset songs\", \"proper_nouns\": \"\"}\n",
-    ")\n",
-    "print(query)\n",
-    "db.run(query)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 14,

docs/docs/how_to/streaming.ipynb

@@ -720,22 +720,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "c00df46e-7f6b-4e06-8abf-801898c8d57f",
+   "execution_count": 13,
+   "id": "bab5f910-fee0-4a94-9f05-b469006333b8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/eugene/src/langchain/libs/core/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: This API is in beta and may change in the future.\n",
-      "  warn_beta(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "events = []\n",
-    "async for event in model.astream_events(\"hello\", version=\"v2\"):\n",
+    "async for event in model.astream_events(\"hello\"):\n",
     "    events.append(event)"
    ]
   },
@@ -746,15 +737,7 @@
    "source": [
     ":::note\n",
     "\n",
-    "Hey what's that funny version=\"v2\" parameter in the API?! 😾\n",
-    "\n",
-    "This is a **beta API**, and we're almost certainly going to make some changes to it (in fact, we already have!)\n",
-    "\n",
-    "This version parameter will allow us to minimize such breaking changes to your code. \n",
-    "\n",
-    "In short, we are annoying you now, so we don't have to annoy you later.\n",
-    "\n",
-    "`v2` is only available for langchain-core>=0.2.0.\n",
+    "For `langchain-core<0.3.37`, set the `version` kwarg explicitly (e.g., `model.astream_events(\"hello\", version=\"v2\")`).\n",
     "\n",
     ":::"
    ]
@@ -769,8 +752,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "ce31b525-f47d-4828-85a7-912ce9f2e79b",
+   "execution_count": 14,
+   "id": "c4a2f5dc-2c75-4be4-a8ca-b5b84a3cdbef",
    "metadata": {},
    "outputs": [
     {
@@ -780,23 +763,38 @@
        "  'data': {'input': 'hello'},\n",
        "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
-       "  'metadata': {}},\n",
+       "  'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n",
+       "  'metadata': {'ls_provider': 'anthropic',\n",
+       "   'ls_model_name': 'claude-3-sonnet-20240229',\n",
+       "   'ls_model_type': 'chat',\n",
+       "   'ls_temperature': 0.0,\n",
+       "   'ls_max_tokens': 1024},\n",
+       "  'parent_ids': []},\n",
        " {'event': 'on_chat_model_stream',\n",
-       "  'data': {'chunk': AIMessageChunk(content='Hello', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
+       "  'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n",
        "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
-       "  'metadata': {}},\n",
+       "  'metadata': {'ls_provider': 'anthropic',\n",
+       "   'ls_model_name': 'claude-3-sonnet-20240229',\n",
+       "   'ls_model_type': 'chat',\n",
+       "   'ls_temperature': 0.0,\n",
+       "   'ls_max_tokens': 1024},\n",
+       "  'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66', usage_metadata={'input_tokens': 8, 'output_tokens': 4, 'total_tokens': 12, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})},\n",
+       "  'parent_ids': []},\n",
        " {'event': 'on_chat_model_stream',\n",
-       "  'data': {'chunk': AIMessageChunk(content='!', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
+       "  'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n",
        "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
-       "  'metadata': {}}]"
+       "  'metadata': {'ls_provider': 'anthropic',\n",
+       "   'ls_model_name': 'claude-3-sonnet-20240229',\n",
+       "   'ls_model_type': 'chat',\n",
+       "   'ls_temperature': 0.0,\n",
+       "   'ls_max_tokens': 1024},\n",
+       "  'data': {'chunk': AIMessageChunk(content='Hello! How can', additional_kwargs={}, response_metadata={}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66')},\n",
+       "  'parent_ids': []}]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -807,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "76cfe826-ee63-4310-ad48-55a95eb3b9d6",
    "metadata": {},
    "outputs": [
@@ -815,20 +813,30 @@
      "data": {
       "text/plain": [
        "[{'event': 'on_chat_model_stream',\n",
-       "  'data': {'chunk': AIMessageChunk(content='?', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
+       "  'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n",
        "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
-       "  'metadata': {}},\n",
+       "  'metadata': {'ls_provider': 'anthropic',\n",
+       "   'ls_model_name': 'claude-3-sonnet-20240229',\n",
+       "   'ls_model_type': 'chat',\n",
+       "   'ls_temperature': 0.0,\n",
+       "   'ls_max_tokens': 1024},\n",
+       "  'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66', usage_metadata={'input_tokens': 0, 'output_tokens': 12, 'total_tokens': 12, 'input_token_details': {}})},\n",
+       "  'parent_ids': []},\n",
        " {'event': 'on_chat_model_end',\n",
-       "  'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
-       "  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
+       "  'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-b18d016d-8b9b-49e7-a555-44db498fcf66', usage_metadata={'input_tokens': 8, 'output_tokens': 16, 'total_tokens': 24, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})},\n",
+       "  'run_id': 'b18d016d-8b9b-49e7-a555-44db498fcf66',\n",
        "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
-       "  'metadata': {}}]"
+       "  'metadata': {'ls_provider': 'anthropic',\n",
+       "   'ls_model_name': 'claude-3-sonnet-20240229',\n",
+       "   'ls_model_type': 'chat',\n",
+       "   'ls_temperature': 0.0,\n",
+       "   'ls_max_tokens': 1024},\n",
+       "  'parent_ids': []}]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -849,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "4328c56c-a303-427b-b1f2-f354e9af555c",
    "metadata": {},
    "outputs": [],
@@ -864,7 +872,6 @@
     "        \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
     "        'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
     "        \"Each country should have the key `name` and `population`\",\n",
-    "        version=\"v2\",\n",
     "    )\n",
     "]"
    ]
@@ -947,29 +954,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Chat model chunk: ''\n",
       "Chat model chunk: '{'\n",
       "Parser chunk: {}\n",
-      "Chat model chunk: '\\n  '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'countries'\n",
-      "Chat model chunk: '\":'\n",
-      "Chat model chunk: ' ['\n",
+      "Chat model chunk: '\\n  \"countries'\n",
+      "Chat model chunk: '\": [\\n    '\n",
       "Parser chunk: {'countries': []}\n",
-      "Chat model chunk: '\\n    '\n",
-      "Chat model chunk: '{'\n",
+      "Chat model chunk: '{\\n      \"'\n",
       "Parser chunk: {'countries': [{}]}\n",
-      "Chat model chunk: '\\n      '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'name'\n",
-      "Chat model chunk: '\":'\n",
-      "Chat model chunk: ' \"'\n",
-      "Parser chunk: {'countries': [{'name': ''}]}\n",
-      "Chat model chunk: 'France'\n",
+      "Chat model chunk: 'name\": \"France'\n",
       "Parser chunk: {'countries': [{'name': 'France'}]}\n",
-      "Chat model chunk: '\",'\n",
-      "Chat model chunk: '\\n      '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'population'\n",
+      "Chat model chunk: '\",\\n      \"'\n",
+      "Chat model chunk: 'population\": 67'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67}]}\n",
+      "Chat model chunk: '413'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413}]}\n",
+      "Chat model chunk: '000\\n    },'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}]}\n",
+      "Chat model chunk: '\\n    {'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {}]}\n",
+      "Chat model chunk: '\\n      \"name\":'\n",
       "...\n"
      ]
     }
@@ -981,7 +985,6 @@
     "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
     "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
     "    \"Each country should have the key `name` and `population`\",\n",
-    "    version=\"v2\",\n",
     "):\n",
     "    kind = event[\"event\"]\n",
     "    if kind == \"on_chat_model_stream\":\n",
@@ -1023,24 +1026,24 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "4f0b581b-be63-4663-baba-c6d2b625cdf9",
+   "id": "42145735-25e8-4e67-b081-b0c15ea45dd1",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_parser_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'my_parser', 'tags': ['seq:step:2'], 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': []}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': ''}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France'}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': ''}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
+      "{'event': 'on_parser_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'my_parser', 'tags': ['seq:step:2'], 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'metadata': {}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain'}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '37ee9e85-481c-415e-863b-c9e132d24948', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47}]}}, 'parent_ids': ['5a0bc625-09fd-4bdf-9932-54909a9a8c29']}\n",
       "...\n"
      ]
     }
@@ -1055,7 +1058,6 @@
     "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
     "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
     "    \"Each country should have the key `name` and `population`\",\n",
-    "    version=\"v2\",\n",
     "    include_names=[\"my_parser\"],\n",
     "):\n",
     "    print(event)\n",
@@ -1077,24 +1079,24 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "096cd904-72f0-4ebe-a8b7-d0e730faea7f",
+   "id": "2a7d8fe0-47ca-4ab4-9c10-b34e3f6106ee",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chat_model_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'model', 'tags': ['seq:step:1'], 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n  ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='countries', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\":', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' [', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n    ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n      ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
+      "{'event': 'on_chat_model_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'model', 'tags': ['seq:step:1'], 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c', usage_metadata={'input_tokens': 56, 'output_tokens': 1, 'total_tokens': 57, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n  \"countries', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\": [\\n    ', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{\\n      \"', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='name\": \"France', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\",\\n      \"', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='population\": 67', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='413', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='000\\n    },', additional_kwargs={}, response_metadata={}, id='run-156c3e40-82fb-49ff-8e41-9e998061be8c')}, 'run_id': '156c3e40-82fb-49ff-8e41-9e998061be8c', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['7b927055-bc1b-4b50-a34c-10d3cfcb3899']}\n",
       "...\n"
      ]
     }
@@ -1107,7 +1109,6 @@
     "max_events = 0\n",
     "async for event in chain.astream_events(\n",
     "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
-    "    version=\"v2\",\n",
     "    include_types=[\"chat_model\"],\n",
     "):\n",
     "    print(event)\n",
@@ -1136,24 +1137,24 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "26bac0d2-76d9-446e-b346-82790236b88d",
+   "id": "c237c218-5fd6-4146-ac68-020a038cf582",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chain_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'RunnableSequence', 'tags': ['my_chain'], 'run_id': 'fd68dd64-7a4d-4bdb-a0c2-ee592db0d024', 'metadata': {}}\n",
-      "{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}, 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_parser_start', 'data': {}, 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'run_id': 'afde30b9-beac-4b36-b4c7-dbbe423ddcdb', 'metadata': {}}\n",
-      "{'event': 'on_parser_stream', 'data': {'chunk': {}}, 'run_id': 'afde30b9-beac-4b36-b4c7-dbbe423ddcdb', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chain_stream', 'data': {'chunk': {}}, 'run_id': 'fd68dd64-7a4d-4bdb-a0c2-ee592db0d024', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n  ', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='countries', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\":', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' [', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
+      "{'event': 'on_chain_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'RunnableSequence', 'tags': ['my_chain'], 'run_id': '58d1302e-36ce-4df7-a3cb-47cb73d57e44', 'metadata': {}, 'parent_ids': []}\n",
+      "{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`', additional_kwargs={}, response_metadata={})]]}}, 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b', usage_metadata={'input_tokens': 56, 'output_tokens': 1, 'total_tokens': 57, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_parser_start', 'data': {}, 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'run_id': '75604c84-e1e6-494a-8b2a-950f45d932e8', 'metadata': {}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b')}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '75604c84-e1e6-494a-8b2a-950f45d932e8', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {}}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_chain_stream', 'run_id': '58d1302e-36ce-4df7-a3cb-47cb73d57e44', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'chunk': {}}, 'parent_ids': []}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n  \"countries', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b')}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\": [\\n    ', additional_kwargs={}, response_metadata={}, id='run-8222e8a1-d978-4f30-87fc-b2dba838774b')}, 'run_id': '8222e8a1-d978-4f30-87fc-b2dba838774b', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-sonnet-20240229', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_parser_stream', 'run_id': '75604c84-e1e6-494a-8b2a-950f45d932e8', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': ['58d1302e-36ce-4df7-a3cb-47cb73d57e44']}\n",
+      "{'event': 'on_chain_stream', 'run_id': '58d1302e-36ce-4df7-a3cb-47cb73d57e44', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': []}}, 'parent_ids': []}\n",
       "...\n"
      ]
     }
@@ -1164,7 +1165,6 @@
     "max_events = 0\n",
     "async for event in chain.astream_events(\n",
     "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
-    "    version=\"v2\",\n",
     "    include_tags=[\"my_chain\"],\n",
     "):\n",
     "    print(event)\n",
@@ -1263,40 +1263,40 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "b08215cd-bffa-4e76-aaf3-c52ee34f152c",
+   "id": "2c83701e-b801-429f-b2ac-47ed44d2d11a",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Chat model chunk: ''\n",
       "Chat model chunk: '{'\n",
       "Parser chunk: {}\n",
-      "Chat model chunk: '\\n  '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'countries'\n",
-      "Chat model chunk: '\":'\n",
-      "Chat model chunk: ' ['\n",
+      "Chat model chunk: '\\n  \"countries'\n",
+      "Chat model chunk: '\": [\\n    '\n",
       "Parser chunk: {'countries': []}\n",
-      "Chat model chunk: '\\n    '\n",
-      "Chat model chunk: '{'\n",
+      "Chat model chunk: '{\\n      \"'\n",
       "Parser chunk: {'countries': [{}]}\n",
-      "Chat model chunk: '\\n      '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'name'\n",
-      "Chat model chunk: '\":'\n",
-      "Chat model chunk: ' \"'\n",
-      "Parser chunk: {'countries': [{'name': ''}]}\n",
-      "Chat model chunk: 'France'\n",
+      "Chat model chunk: 'name\": \"France'\n",
       "Parser chunk: {'countries': [{'name': 'France'}]}\n",
-      "Chat model chunk: '\",'\n",
-      "Chat model chunk: '\\n      '\n",
-      "Chat model chunk: '\"'\n",
-      "Chat model chunk: 'population'\n",
-      "Chat model chunk: '\":'\n",
-      "Chat model chunk: ' '\n",
-      "Chat model chunk: '67'\n",
+      "Chat model chunk: '\",\\n      \"'\n",
+      "Chat model chunk: 'population\": 67'\n",
       "Parser chunk: {'countries': [{'name': 'France', 'population': 67}]}\n",
+      "Chat model chunk: '413'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413}]}\n",
+      "Chat model chunk: '000\\n    },'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}]}\n",
+      "Chat model chunk: '\\n    {'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {}]}\n",
+      "Chat model chunk: '\\n      \"name\":'\n",
+      "Chat model chunk: ' \"Spain\",'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain'}]}\n",
+      "Chat model chunk: '\\n      \"population\":'\n",
+      "Chat model chunk: ' 47'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47}]}\n",
+      "Chat model chunk: '351'\n",
+      "Parser chunk: {'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351}]}\n",
       "...\n"
      ]
     }
@@ -1308,7 +1308,6 @@
     "    \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
     "    'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
     "    \"Each country should have the key `name` and `population`\",\n",
-    "    version=\"v2\",\n",
     "):\n",
     "    kind = event[\"event\"]\n",
     "    if kind == \"on_chat_model_stream\":\n",
@@ -1376,7 +1375,7 @@
     "    return reverse_word.invoke(word)\n",
     "\n",
     "\n",
-    "async for event in bad_tool.astream_events(\"hello\", version=\"v2\"):\n",
+    "async for event in bad_tool.astream_events(\"hello\"):\n",
     "    print(event)"
    ]
   },
@@ -1412,7 +1411,7 @@
     "    return reverse_word.invoke(word, {\"callbacks\": callbacks})\n",
     "\n",
     "\n",
-    "async for event in correct_tool.astream_events(\"hello\", version=\"v2\"):\n",
+    "async for event in correct_tool.astream_events(\"hello\"):\n",
     "    print(event)"
    ]
   },
@@ -1454,7 +1453,7 @@
     "\n",
     "await reverse_and_double.ainvoke(\"1234\")\n",
     "\n",
-    "async for event in reverse_and_double.astream_events(\"1234\", version=\"v2\"):\n",
+    "async for event in reverse_and_double.astream_events(\"1234\"):\n",
     "    print(event)"
    ]
   },
@@ -1495,7 +1494,7 @@
     "\n",
     "await reverse_and_double.ainvoke(\"1234\")\n",
     "\n",
-    "async for event in reverse_and_double.astream_events(\"1234\", version=\"v2\"):\n",
+    "async for event in reverse_and_double.astream_events(\"1234\"):\n",
     "    print(event)"
    ]
   },
@@ -1528,7 +1527,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tool_artifacts.ipynb

@@ -87,13 +87,6 @@
      "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Failed to batch ingest runs: LangSmithRateLimitError('Rate limit exceeded for https://api.smith.langchain.com/runs/batch. HTTPError(\\'429 Client Error: Too Many Requests for url: https://api.smith.langchain.com/runs/batch\\', \\'{\"detail\":\"Monthly unique traces usage limit exceeded\"}\\')')\n"
-     ]
     }
    ],
    "source": [

docs/docs/how_to/tool_stream_events.ipynb

@@ -131,13 +131,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "stream = special_summarization_tool.astream_events(\n",
-    "    {\"long_text\": LONG_TEXT}, version=\"v2\"\n",
-    ")\n",
+    "stream = special_summarization_tool.astream_events({\"long_text\": LONG_TEXT})\n",
     "\n",
     "async for event in stream:\n",
     "    if event[\"event\"] == \"on_chat_model_end\":\n",
@@ -156,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,21 +188,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chat_model_end', 'data': {'output': AIMessage(content='Bee defies physics; Barry chooses outfit for graduation day.', response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-d23abc80-0dce-4f74-9d7b-fb98ca4f2a9e', usage_metadata={'input_tokens': 182, 'output_tokens': 16, 'total_tokens': 198}), 'input': {'messages': [[HumanMessage(content=\"You are an expert writer. Summarize the following text in 10 words or less:\\n\\n\\nNARRATOR:\\n(Black screen with text; The sound of buzzing bees can be heard)\\nAccording to all known laws of aviation, there is no way a bee should be able to fly. Its wings are too small to get its fat little body off the ground. The bee, of course, flies anyway because bees don't care what humans think is impossible.\\nBARRY BENSON:\\n(Barry is picking out a shirt)\\nYellow, black. Yellow, black. Yellow, black. Yellow, black. Ooh, black and yellow! Let's shake it up a little.\\nJANET BENSON:\\nBarry! Breakfast is ready!\\nBARRY:\\nComing! Hang on a second.\\n\")]]}}, 'run_id': 'd23abc80-0dce-4f74-9d7b-fb98ca4f2a9e', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['f25c41fe-8972-4893-bc40-cecf3922c1fa']}\n"
+      "{'event': 'on_chat_model_end', 'data': {'output': AIMessage(content='Bee defies physics; Barry chooses outfit for graduation day.', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-337ac14e-8da8-4c6d-a69f-1573f93b651e', usage_metadata={'input_tokens': 182, 'output_tokens': 19, 'total_tokens': 201, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}}), 'input': {'messages': [[HumanMessage(content=\"You are an expert writer. Summarize the following text in 10 words or less:\\n\\n\\nNARRATOR:\\n(Black screen with text; The sound of buzzing bees can be heard)\\nAccording to all known laws of aviation, there is no way a bee should be able to fly. Its wings are too small to get its fat little body off the ground. The bee, of course, flies anyway because bees don't care what humans think is impossible.\\nBARRY BENSON:\\n(Barry is picking out a shirt)\\nYellow, black. Yellow, black. Yellow, black. Yellow, black. Ooh, black and yellow! Let's shake it up a little.\\nJANET BENSON:\\nBarry! Breakfast is ready!\\nBARRY:\\nComing! Hang on a second.\\n\", additional_kwargs={}, response_metadata={})]]}}, 'run_id': '337ac14e-8da8-4c6d-a69f-1573f93b651e', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['225beaa6-af73-4c91-b2d3-1afbbb88d53e']}\n"
      ]
     }
    ],
    "source": [
-    "stream = special_summarization_tool_with_config.astream_events(\n",
-    "    {\"long_text\": LONG_TEXT}, version=\"v2\"\n",
-    ")\n",
+    "stream = special_summarization_tool_with_config.astream_events({\"long_text\": LONG_TEXT})\n",
     "\n",
     "async for event in stream:\n",
     "    if event[\"event\"] == \"on_chat_model_end\":\n",
@@ -222,33 +218,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42', usage_metadata={'input_tokens': 182, 'output_tokens': 0, 'total_tokens': 182})}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='Bee', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' def', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='ies physics', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=';', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' Barry', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' cho', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='oses outfit', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' for', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' graduation', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' day', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='.', id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42')}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n",
-      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-f24ab147-0b82-4e63-810a-b12bd8d1fb42', usage_metadata={'input_tokens': 0, 'output_tokens': 16, 'total_tokens': 16})}, 'run_id': 'f24ab147-0b82-4e63-810a-b12bd8d1fb42', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['385f3612-417c-4a70-aae0-cce3a5ba6fb6']}\n"
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', usage_metadata={'input_tokens': 182, 'output_tokens': 2, 'total_tokens': 184, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='Bee', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' defies physics;', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' Barry chooses outfit for', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' graduation day.', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n",
+      "{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', usage_metadata={'input_tokens': 0, 'output_tokens': 17, 'total_tokens': 17, 'input_token_details': {}})}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}\n"
      ]
     }
    ],
    "source": [
-    "stream = special_summarization_tool_with_config.astream_events(\n",
-    "    {\"long_text\": LONG_TEXT}, version=\"v2\"\n",
-    ")\n",
+    "stream = special_summarization_tool_with_config.astream_events({\"long_text\": LONG_TEXT})\n",
     "\n",
     "async for event in stream:\n",
     "    if event[\"event\"] == \"on_chat_model_stream\":\n",
@@ -290,7 +277,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tools_human.ipynb

@@ -161,7 +161,7 @@
    "source": [
     "## Adding human approval\n",
     "\n",
-    "Let's add a step in the chain that will ask a person to approve or reject the tall call request.\n",
+    "Let's add a step in the chain that will ask a person to approve or reject the tool call request.\n",
     "\n",
     "On rejection, the step will raise an exception which will stop execution of the rest of the chain."
    ]

docs/docs/how_to/trim_messages.ipynb

@@ -30,7 +30,8 @@
     "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/messages/#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",
+    "   - A `ToolMessage` can only appear after an `AIMessage` that involved a tool call.\n",
+    "\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",

docs/docs/integrations/chat/abso.ipynb

@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Abso\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatAbso\n",
+    "\n",
+    "This will help you getting started with ChatAbso [chat models](https://python.langchain.com/docs/concepts/chat_models/). For detailed documentation of all ChatAbso features and configurations head to the [API reference](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html).\n",
+    "\n",
+    "- You can find the full documentation for the Abso router [here] (https://abso.ai)\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/abso) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatAbso](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html) | [langchain-abso](https://python.langchain.com/api_reference/en/latest/abso_api_reference.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-abso?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-abso?style=flat-square&label=%20) |\n",
+    "\n",
+    "## Setup\n",
+    "To access ChatAbso models you'll need to create an OpenAI account, get an API key, and install the `langchain-abso` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "- TODO: Update with relevant info.\n",
+    "\n",
+    "Head to (TODO: link) to sign up to ChatAbso and generate an API key. Once you've done this set the ABSO_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain ChatAbso integration lives in the `langchain-abso` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-abso"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_abso import ChatAbso\n",
+    "\n",
+    "llm = ChatAbso(fast_model=\"gpt-4o\", slow_model=\"o3-mini\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatAbso features and configurations head to the API reference: https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/chat/anthropic.ipynb

@@ -102,6 +102,16 @@
     "%pip install -qU langchain-anthropic"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "fe4993ad-4a9b-4021-8ebd-f0fbbc739f49",
+   "metadata": {},
+   "source": [
+    ":::info This guide requires ``langchain-anthropic>=0.3.10``\n",
+    "\n",
+    ":::"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a38cde65-254d-4219-a441-068766c0d4b5",
@@ -245,7 +255,7 @@
    "source": [
     "## Content blocks\n",
     "\n",
-    "One key difference to note between Anthropic models and most others is that the contents of a single Anthropic AI message can either be a single string or a **list of content blocks**. For example when an Anthropic model invokes a tool, the tool invocation is part of the message content (as well as being exposed in the standardized `AIMessage.tool_calls`):"
+    "Content from a single Anthropic AI message can either be a single string or a **list of content blocks**. For example when an Anthropic model invokes a tool, the tool invocation is part of the message content (as well as being exposed in the standardized `AIMessage.tool_calls`):"
    ]
   },
   {
@@ -315,6 +325,430 @@
     "ai_msg.tool_calls"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6e36d25c-f358-49e5-aefa-b99fbd3fec6b",
+   "metadata": {},
+   "source": [
+    "## Extended thinking\n",
+    "\n",
+    "Claude 3.7 Sonnet supports an [extended thinking](https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking) feature, which will output the step-by-step reasoning process that led to its final answer.\n",
+    "\n",
+    "To use it, specify the `thinking` parameter when initializing `ChatAnthropic`. It can also be passed in as a kwarg during invocation.\n",
+    "\n",
+    "You will need to specify a token budget to use this feature. See usage example below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a34cf93b-8522-43a6-a3f3-8a189ddf54a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "  {\n",
+      "    \"signature\": \"ErUBCkYIARgCIkCx7bIPj35jGPHpoVOB2y5hvPF8MN4lVK75CYGftmVNlI4axz2+bBbSexofWsN1O/prwNv8yPXnIXQmwT6zrJsKEgwJzvks0yVRZtaGBScaDOm9xcpOxbuhku1zViIw9WDgil/KZL8DsqWrhVpC6TzM0RQNCcsHcmgmyxbgG9g8PR0eJGLxCcGoEw8zMQu1Kh1hQ1/03hZ2JCOgigpByR9aNPTwwpl64fQUe6WwIw==\",\n",
+      "    \"thinking\": \"To find the cube root of 50.653, I need to find the value of $x$ such that $x^3 = 50.653$.\\n\\nI can try to estimate this first. \\n$3^3 = 27$\\n$4^3 = 64$\\n\\nSo the cube root of 50.653 will be somewhere between 3 and 4, but closer to 4.\\n\\nLet me try to compute this more precisely. I can use the cube root function:\\n\\ncube root of 50.653 = 50.653^(1/3)\\n\\nLet me calculate this:\\n50.653^(1/3) \\u2248 3.6998\\n\\nLet me verify:\\n3.6998^3 \\u2248 50.6533\\n\\nThat's very close to 50.653, so I'm confident that the cube root of 50.653 is approximately 3.6998.\\n\\nActually, let me compute this more precisely:\\n50.653^(1/3) \\u2248 3.69981\\n\\nLet me verify once more:\\n3.69981^3 \\u2248 50.652998\\n\\nThat's extremely close to 50.653, so I'll say that the cube root of 50.653 is approximately 3.69981.\",\n",
+      "    \"type\": \"thinking\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"text\": \"The cube root of 50.653 is approximately 3.6998.\\n\\nTo verify: 3.6998\\u00b3 = 50.6530, which is very close to our original number.\",\n",
+      "    \"type\": \"text\"\n",
+      "  }\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(\n",
+    "    model=\"claude-3-7-sonnet-latest\",\n",
+    "    max_tokens=5000,\n",
+    "    thinking={\"type\": \"enabled\", \"budget_tokens\": 2000},\n",
+    ")\n",
+    "\n",
+    "response = llm.invoke(\"What is the cube root of 50.653?\")\n",
+    "print(json.dumps(response.content, indent=2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34349dfe-5d81-4887-a4f4-cd01e9587cdc",
+   "metadata": {},
+   "source": [
+    "## Prompt caching\n",
+    "\n",
+    "Anthropic supports [caching](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching) of [elements of your prompts](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#what-can-be-cached), including messages, tool definitions, tool results, images and documents. This allows you to re-use large documents, instructions, [few-shot documents](/docs/concepts/few_shot_prompting/), and other data to reduce latency and costs.\n",
+    "\n",
+    "To enable caching on an element of a prompt, mark its associated content block using the `cache_control` key. See examples below:\n",
+    "\n",
+    "### Messages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "babb44a5-33f7-4200-9dfc-be867cf2c217",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First invocation:\n",
+      "{'cache_read': 0, 'cache_creation': 1458}\n",
+      "\n",
+      "Second:\n",
+      "{'cache_read': 1458, 'cache_creation': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\")\n",
+    "\n",
+    "# Pull LangChain readme\n",
+    "get_response = requests.get(\n",
+    "    \"https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md\"\n",
+    ")\n",
+    "readme = get_response.text\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"system\",\n",
+    "        \"content\": [\n",
+    "            {\n",
+    "                \"type\": \"text\",\n",
+    "                \"text\": \"You are a technology expert.\",\n",
+    "            },\n",
+    "            {\n",
+    "                \"type\": \"text\",\n",
+    "                \"text\": f\"{readme}\",\n",
+    "                # highlight-next-line\n",
+    "                \"cache_control\": {\"type\": \"ephemeral\"},\n",
+    "            },\n",
+    "        ],\n",
+    "    },\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": \"What's LangChain, according to its README?\",\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "response_1 = llm.invoke(messages)\n",
+    "response_2 = llm.invoke(messages)\n",
+    "\n",
+    "usage_1 = response_1.usage_metadata[\"input_token_details\"]\n",
+    "usage_2 = response_2.usage_metadata[\"input_token_details\"]\n",
+    "\n",
+    "print(f\"First invocation:\\n{usage_1}\")\n",
+    "print(f\"\\nSecond:\\n{usage_2}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "141ce9c5-012d-4502-9d61-4a413b5d959a",
+   "metadata": {},
+   "source": [
+    "### Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1de82015-810f-4ed4-a08b-9866ea8746ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First invocation:\n",
+      "{'cache_read': 0, 'cache_creation': 1809}\n",
+      "\n",
+      "Second:\n",
+      "{'cache_read': 1809, 'cache_creation': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_anthropic import convert_to_anthropic_tool\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "# For demonstration purposes, we artificially expand the\n",
+    "# tool description.\n",
+    "description = (\n",
+    "    f\"Get the weather at a location. By the way, check out this readme: {readme}\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "@tool(description=description)\n",
+    "def get_weather(location: str) -> str:\n",
+    "    return \"It's sunny.\"\n",
+    "\n",
+    "\n",
+    "# Enable caching on the tool\n",
+    "# highlight-start\n",
+    "weather_tool = convert_to_anthropic_tool(get_weather)\n",
+    "weather_tool[\"cache_control\"] = {\"type\": \"ephemeral\"}\n",
+    "# highlight-end\n",
+    "\n",
+    "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\")\n",
+    "llm_with_tools = llm.bind_tools([weather_tool])\n",
+    "query = \"What's the weather in San Francisco?\"\n",
+    "\n",
+    "response_1 = llm_with_tools.invoke(query)\n",
+    "response_2 = llm_with_tools.invoke(query)\n",
+    "\n",
+    "usage_1 = response_1.usage_metadata[\"input_token_details\"]\n",
+    "usage_2 = response_2.usage_metadata[\"input_token_details\"]\n",
+    "\n",
+    "print(f\"First invocation:\\n{usage_1}\")\n",
+    "print(f\"\\nSecond:\\n{usage_2}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a763830d-82cb-448a-ab30-f561522791b9",
+   "metadata": {},
+   "source": [
+    "### Incremental caching in conversational applications\n",
+    "\n",
+    "Prompt caching can be used in [multi-turn conversations](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#continuing-a-multi-turn-conversation) to maintain context from earlier messages without redundant processing.\n",
+    "\n",
+    "We can enable incremental caching by marking the final message with `cache_control`. Claude will automatically use the longest previously-cached prefix for follow-up messages.\n",
+    "\n",
+    "Below, we implement a simple chatbot that incorporates this feature. We follow the LangChain [chatbot tutorial](/docs/tutorials/chatbot/), but add a custom [reducer](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that automatically marks the last content block in each user message with `cache_control`. See below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "07fde4db-344c-49bc-a5b4-99e2d20fb394",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import START, StateGraph, add_messages\n",
+    "from typing_extensions import Annotated, TypedDict\n",
+    "\n",
+    "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\")\n",
+    "\n",
+    "# Pull LangChain readme\n",
+    "get_response = requests.get(\n",
+    "    \"https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md\"\n",
+    ")\n",
+    "readme = get_response.text\n",
+    "\n",
+    "\n",
+    "def messages_reducer(left: list, right: list) -> list:\n",
+    "    # Update last user message\n",
+    "    for i in range(len(right) - 1, -1, -1):\n",
+    "        if right[i].type == \"human\":\n",
+    "            right[i].content[-1][\"cache_control\"] = {\"type\": \"ephemeral\"}\n",
+    "            break\n",
+    "\n",
+    "    return add_messages(left, right)\n",
+    "\n",
+    "\n",
+    "class State(TypedDict):\n",
+    "    messages: Annotated[list, messages_reducer]\n",
+    "\n",
+    "\n",
+    "workflow = StateGraph(state_schema=State)\n",
+    "\n",
+    "\n",
+    "# Define the function that calls the model\n",
+    "def call_model(state: State):\n",
+    "    response = llm.invoke(state[\"messages\"])\n",
+    "    return {\"messages\": [response]}\n",
+    "\n",
+    "\n",
+    "# Define the (single) node in the graph\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "workflow.add_node(\"model\", call_model)\n",
+    "\n",
+    "# Add memory\n",
+    "memory = MemorySaver()\n",
+    "app = workflow.compile(checkpointer=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "40013035-eb22-4327-8aaf-1ee974d9ff46",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Hello, Bob! It's nice to meet you. How are you doing today? Is there something I can help you with?\n",
+      "\n",
+      "{'cache_read': 0, 'cache_creation': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "config = {\"configurable\": {\"thread_id\": \"abc123\"}}\n",
+    "\n",
+    "query = \"Hi! I'm Bob.\"\n",
+    "\n",
+    "input_message = HumanMessage([{\"type\": \"text\", \"text\": query}])\n",
+    "output = app.invoke({\"messages\": [input_message]}, config)\n",
+    "output[\"messages\"][-1].pretty_print()\n",
+    "print(f'\\n{output[\"messages\"][-1].usage_metadata[\"input_token_details\"]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "22371f68-7913-4c4f-ab4a-2b4265095469",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "I can see you've shared the README from the LangChain GitHub repository. This is the documentation for LangChain, which is a popular framework for building applications powered by Large Language Models (LLMs). Here's a summary of what the README contains:\n",
+      "\n",
+      "LangChain is:\n",
+      "- A framework for developing LLM-powered applications\n",
+      "- Helps chain together components and integrations to simplify AI application development\n",
+      "- Provides a standard interface for models, embeddings, vector stores, etc.\n",
+      "\n",
+      "Key features/benefits:\n",
+      "- Real-time data augmentation (connect LLMs to diverse data sources)\n",
+      "- Model interoperability (swap models easily as needed)\n",
+      "- Large ecosystem of integrations\n",
+      "\n",
+      "The LangChain ecosystem includes:\n",
+      "- LangSmith - For evaluations and observability\n",
+      "- LangGraph - For building complex agents with customizable architecture\n",
+      "- LangGraph Platform - For deployment and scaling of agents\n",
+      "\n",
+      "The README also mentions installation instructions (`pip install -U langchain`) and links to various resources including tutorials, how-to guides, conceptual guides, and API references.\n",
+      "\n",
+      "Is there anything specific about LangChain you'd like to know more about, Bob?\n",
+      "\n",
+      "{'cache_read': 0, 'cache_creation': 1498}\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = f\"Check out this readme: {readme}\"\n",
+    "\n",
+    "input_message = HumanMessage([{\"type\": \"text\", \"text\": query}])\n",
+    "output = app.invoke({\"messages\": [input_message]}, config)\n",
+    "output[\"messages\"][-1].pretty_print()\n",
+    "print(f'\\n{output[\"messages\"][-1].usage_metadata[\"input_token_details\"]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0e6798fc-8a80-4324-b4e3-f18706256c61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Your name is Bob. You introduced yourself at the beginning of our conversation.\n",
+      "\n",
+      "{'cache_read': 1498, 'cache_creation': 269}\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"What was my name again?\"\n",
+    "\n",
+    "input_message = HumanMessage([{\"type\": \"text\", \"text\": query}])\n",
+    "output = app.invoke({\"messages\": [input_message]}, config)\n",
+    "output[\"messages\"][-1].pretty_print()\n",
+    "print(f'\\n{output[\"messages\"][-1].usage_metadata[\"input_token_details\"]}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa4b3647-c672-4782-a88c-a55fd3bf969f",
+   "metadata": {},
+   "source": [
+    "In the [LangSmith trace](https://smith.langchain.com/public/4d0584d8-5f9e-4b91-8704-93ba2ccf416a/r), toggling \"raw output\" will show exactly what messages are sent to the chat model, including `cache_control` keys."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "029009f2-2795-418b-b5fc-fb996c6fe99e",
+   "metadata": {},
+   "source": [
+    "## Token-efficient tool use\n",
+    "\n",
+    "Anthropic supports a (beta) [token-efficient tool use](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/token-efficient-tool-use) feature. To use it, specify the relevant beta-headers when instantiating the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "206cff65-33b8-4a88-9b1a-050b4d57772a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'name': 'get_weather', 'args': {'location': 'San Francisco'}, 'id': 'toolu_01EoeE1qYaePcmNbUvMsWtmA', 'type': 'tool_call'}]\n",
+      "\n",
+      "Total tokens: 408\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_anthropic import ChatAnthropic\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "llm = ChatAnthropic(\n",
+    "    model=\"claude-3-7-sonnet-20250219\",\n",
+    "    temperature=0,\n",
+    "    # highlight-start\n",
+    "    model_kwargs={\n",
+    "        \"extra_headers\": {\"anthropic-beta\": \"token-efficient-tools-2025-02-19\"}\n",
+    "    },\n",
+    "    # highlight-end\n",
+    ")\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(location: str) -> str:\n",
+    "    \"\"\"Get the weather at a location.\"\"\"\n",
+    "    return \"It's sunny.\"\n",
+    "\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools([get_weather])\n",
+    "response = llm_with_tools.invoke(\"What's the weather in San Francisco?\")\n",
+    "print(response.tool_calls)\n",
+    "print(f'\\nTotal tokens: {response.usage_metadata[\"total_tokens\"]}')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "301d372f-4dec-43e6-b58c-eee25633e1a6",
@@ -472,6 +906,58 @@
     "response.content"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cbfec7a9-d9df-4d12-844e-d922456dd9bf",
+   "metadata": {},
+   "source": [
+    "## Built-in tools\n",
+    "\n",
+    "Anthropic supports a variety of [built-in tools](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/text-editor-tool), which can be bound to the model in the [usual way](/docs/how_to/tool_calling/). Claude will generate tool calls adhering to its internal schema for the tool:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "30a0af36-2327-4b1d-9ba5-e47cb72db0be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I'd be happy to help you fix the syntax error in your primes.py file. First, let's look at the current content of the file to identify the error.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'str_replace_editor',\n",
+       "  'args': {'command': 'view', 'path': '/repo/primes.py'},\n",
+       "  'id': 'toolu_01VdNgt1YV7kGfj9LFLm6HyQ',\n",
+       "  'type': 'tool_call'}]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(model=\"claude-3-7-sonnet-20250219\")\n",
+    "\n",
+    "tool = {\"type\": \"text_editor_20250124\", \"name\": \"str_replace_editor\"}\n",
+    "llm_with_tools = llm.bind_tools([tool])\n",
+    "\n",
+    "response = llm_with_tools.invoke(\n",
+    "    \"There's a syntax error in my primes.py file. Can you help me fix it?\"\n",
+    ")\n",
+    "print(response.text())\n",
+    "response.tool_calls"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",

docs/docs/integrations/chat/azure_ai.ipynb

@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: AzureAIChatCompletionsModel\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# AzureAIChatCompletionsModel\n",
+    "\n",
+    "This will help you getting started with AzureAIChatCompletionsModel [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureAIChatCompletionsModel features and configurations head to the [API reference](https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html)\n",
+    "\n",
+    "The AzureAIChatCompletionsModel class uses the Azure AI Foundry SDK. AI Foundry has several chat models including AzureOpenAI, Cohere, Llama, Phi-3/4, and DeepSeek-R1 to name a few. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/azure/ai-studio/how-to/model-catalog-overview).\n",
+    "\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://v03.api.js.langchain.com/classes/_langchain_openai.AzureChatOpenAI.html) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [AzureAIChatCompletionsModel](https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html) | [langchain-azure-ai](https://python.langchain.com/api_reference/langchain_azure_ai/index.html) | ❌ | ✅ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-azure-ai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-azure-ai?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅| \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access AzureAIChatCompletionsModel models you'll need to create an [Azure account](https://azure.microsoft.com/pricing/purchase-options/azure-account), get an API key, and install the `langchain-azure-ai` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "\n",
+    "Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/develop/sdk-overview?tabs=sync&pivots=programming-language-python) to see how to create your deployment and generate an API key. Once your model is deployed you click the 'get endpoint' button in AI Foundry. This will show you your endpoint and api key. Once you've done this set the AZURE_INFERENCE_CREDENTIAL and AZURE_INFERENCE_ENDPOINT environment variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"AZURE_INFERENCE_CREDENTIAL\"):\n",
+    "    os.environ[\"AZURE_INFERENCE_CREDENTIAL\"] = getpass.getpass(\n",
+    "        \"Enter your AzureAIChatCompletionsModel API key: \"\n",
+    "    )\n",
+    "\n",
+    "if not os.getenv(\"AZURE_INFERENCE_ENDPOINT\"):\n",
+    "    os.environ[\"AZURE_INFERENCE_ENDPOINT\"] = getpass.getpass(\n",
+    "        \"Enter your model endpoint: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain AzureAIChatCompletionsModel integration lives in the `langchain-azure-ai` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-azure-ai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel\n",
+    "\n",
+    "llm = AzureAIChatCompletionsModel(\n",
+    "    model_name=\"gpt-4\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore programmer.\", additional_kwargs={}, response_metadata={'model': 'gpt-4o-2024-05-13', 'token_usage': {'input_tokens': 31, 'output_tokens': 4, 'total_tokens': 35}, 'finish_reason': 'stop'}, id='run-c082dffd-b1de-4b3f-943f-863836663ddb-0', usage_metadata={'input_tokens': 31, 'output_tokens': 4, 'total_tokens': 35})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore programmer.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'model': 'gpt-4o-2024-05-13', 'token_usage': {'input_tokens': 26, 'output_tokens': 5, 'total_tokens': 31}, 'finish_reason': 'stop'}, id='run-01ba6587-6ff4-4554-8039-13204a7d95db-0', usage_metadata={'input_tokens': 26, 'output_tokens': 5, 'total_tokens': 31})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all AzureAIChatCompletionsModel features and configurations head to the API reference: https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain-3-9",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/chat/contextual.ipynb

@@ -0,0 +1,253 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: ContextualAI\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatContextual\n",
+    "\n",
+    "This will help you getting started with Contextual AI's Grounded Language Model [chat models](/docs/concepts/chat_models/).\n",
+    "\n",
+    "To learn more about Contextual AI, please visit our [documentation](https://docs.contextual.ai/).\n",
+    "\n",
+    "This integration requires the `contextual-client` Python SDK. Learn more about it [here](https://github.com/ContextualAI/contextual-client-python).\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This integration invokes Contextual AI's Grounded Language Model.\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatContextual](https://github.com/ContextualAI//langchain-contextual) | [langchain-contextual](https://pypi.org/project/langchain-contextual/) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-contextual?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-contextual?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Contextual models you'll need to create a Contextual AI account, get an API key, and install the `langchain-contextual` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [app.contextual.ai](https://app.contextual.ai) to sign up to Contextual and generate an API key. Once you've done this set the CONTEXTUAL_AI_API_KEY environment variable:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"CONTEXTUAL_AI_API_KEY\"):\n",
+    "    os.environ[\"CONTEXTUAL_AI_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your Contextual API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Contextual integration lives in the `langchain-contextual` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-contextual"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions.\n",
+    "\n",
+    "The chat client can be instantiated with these following additional settings:\n",
+    "\n",
+    "| Parameter | Type | Description | Default |\n",
+    "|-----------|------|-------------|---------|\n",
+    "| temperature | Optional[float] | The sampling temperature, which affects the randomness in the response. Note that higher temperature values can reduce groundedness. | 0 |\n",
+    "| top_p | Optional[float] | A parameter for nucleus sampling, an alternative to temperature which also affects the randomness of the response. Note that higher top_p values can reduce groundedness. | 0.9 |\n",
+    "| max_new_tokens | Optional[int] | The maximum number of tokens that the model can generate in the response. Minimum is 1 and maximum is 2048. | 1024 |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_contextual import ChatContextual\n",
+    "\n",
+    "llm = ChatContextual(\n",
+    "    model=\"v1\",  # defaults to `v1`\n",
+    "    api_key=\"\",\n",
+    "    temperature=0,  # defaults to 0\n",
+    "    top_p=0.9,  # defaults to 0.9\n",
+    "    max_new_tokens=1024,  # defaults to 1024\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "The Contextual Grounded Language Model accepts additional `kwargs` when calling the `ChatContextual.invoke` method.\n",
+    "\n",
+    "These additional inputs are:\n",
+    "\n",
+    "| Parameter | Type | Description |\n",
+    "|-----------|------|-------------|\n",
+    "| knowledge | list[str] | Required: A list of strings of knowledge sources the grounded language model can use when generating a response. |\n",
+    "| system_prompt | Optional[str] | Optional: Instructions the model should follow when generating responses. Note that we do not guarantee that the model follows these instructions exactly. |\n",
+    "| avoid_commentary | Optional[bool] | Optional (Defaults to `False`): Flag to indicate whether the model should avoid providing additional commentary in responses. Commentary is conversational in nature and does not contain verifiable claims; therefore, commentary is not strictly grounded in available context. However, commentary may provide useful context which improves the helpfulness of responses. |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# include a system prompt (optional)\n",
+    "system_prompt = \"You are a helpful assistant that uses all of the provided knowledge to answer the user's query to the best of your ability.\"\n",
+    "\n",
+    "# provide your own knowledge from your knowledge-base here in an array of string\n",
+    "knowledge = [\n",
+    "    \"There are 2 types of dogs in the world: good dogs and best dogs.\",\n",
+    "    \"There are 2 types of cats in the world: good cats and best cats.\",\n",
+    "]\n",
+    "\n",
+    "# create your message\n",
+    "messages = [\n",
+    "    (\"human\", \"What type of cats are there in the world and what are the types?\"),\n",
+    "]\n",
+    "\n",
+    "# invoke the GLM by providing the knowledge strings, optional system prompt\n",
+    "# if you want to turn off the GLM's commentary, pass True to the `avoid_commentary` argument\n",
+    "ai_msg = llm.invoke(\n",
+    "    messages, knowledge=knowledge, system_prompt=system_prompt, avoid_commentary=True\n",
+    ")\n",
+    "\n",
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c35a9e0",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can chain the Contextual Model with output parsers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "545e1e16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "chain = llm | StrOutputParser\n",
+    "\n",
+    "chain.invoke(\n",
+    "    messages, knowledge=knowledge, systemp_prompt=system_prompt, avoid_commentary=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatContextual features and configurations head to the Github page: https://github.com/ContextualAI//langchain-contextual"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/chat/deepseek.ipynb

@@ -38,6 +38,12 @@
     "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
     "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
     "\n",
+    ":::note\n",
+    "\n",
+    "DeepSeek-R1, specified via `model=\"deepseek-reasoner\"`, does not support tool calling or structured output. Those features [are supported](https://api-docs.deepseek.com/guides/function_calling) by DeepSeek-V3 (specified via `model=\"deepseek-chat\"`).\n",
+    "\n",
+    ":::\n",
+    "\n",
     "## Setup\n",
     "\n",
     "To access DeepSeek models you'll need to create a/an DeepSeek account, get an API key, and install the `langchain-deepseek` integration package.\n",

docs/docs/integrations/chat/groq.ipynb

@@ -85,21 +85,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "execution_count": null,
+   "id": "3f3f510e-2afe-4e76-be41-c5a9665aea63",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install -qU langchain-groq"
    ]
@@ -116,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
    "metadata": {},
    "outputs": [],
@@ -124,7 +113,7 @@
     "from langchain_groq import ChatGroq\n",
     "\n",
     "llm = ChatGroq(\n",
-    "    model=\"mixtral-8x7b-32768\",\n",
+    "    model=\"llama-3.1-8b-instant\",\n",
     "    temperature=0,\n",
     "    max_tokens=None,\n",
     "    timeout=None,\n",
@@ -143,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "62e0dbc3",
    "metadata": {
     "tags": []
@@ -152,10 +141,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='I enjoy programming. (The French translation is: \"J\\'aime programmer.\")\\n\\nNote: I chose to translate \"I love programming\" as \"J\\'aime programmer\" instead of \"Je suis amoureux de programmer\" because the latter has a romantic connotation that is not present in the original English sentence.', response_metadata={'token_usage': {'completion_tokens': 73, 'prompt_tokens': 31, 'total_tokens': 104, 'completion_time': 0.1140625, 'prompt_time': 0.003352463, 'queue_time': None, 'total_time': 0.117414963}, 'model_name': 'mixtral-8x7b-32768', 'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop', 'logprobs': None}, id='run-64433c19-eadf-42fc-801e-3071e3c40160-0', usage_metadata={'input_tokens': 31, 'output_tokens': 73, 'total_tokens': 104})"
+       "AIMessage(content='The translation of \"I love programming\" to French is:\\n\\n\"J\\'adore le programmation.\"', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 55, 'total_tokens': 77, 'completion_time': 0.029333333, 'prompt_time': 0.003502892, 'queue_time': 0.553054073, 'total_time': 0.032836225}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-2b2da04a-993c-40ab-becc-201eab8b1a1b-0', usage_metadata={'input_tokens': 55, 'output_tokens': 22, 'total_tokens': 77})"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -174,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
    "metadata": {},
    "outputs": [
@@ -182,9 +171,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "I enjoy programming. (The French translation is: \"J'aime programmer.\")\n",
+      "The translation of \"I love programming\" to French is:\n",
       "\n",
-      "Note: I chose to translate \"I love programming\" as \"J'aime programmer\" instead of \"Je suis amoureux de programmer\" because the latter has a romantic connotation that is not present in the original English sentence.\n"
+      "\"J'adore le programmation.\"\n"
      ]
     }
    ],
@@ -204,17 +193,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='That\\'s great! I can help you translate English phrases related to programming into German.\\n\\n\"I love programming\" can be translated as \"Ich liebe Programmieren\" in German.\\n\\nHere are some more programming-related phrases translated into German:\\n\\n* \"Programming language\" = \"Programmiersprache\"\\n* \"Code\" = \"Code\"\\n* \"Variable\" = \"Variable\"\\n* \"Function\" = \"Funktion\"\\n* \"Array\" = \"Array\"\\n* \"Object-oriented programming\" = \"Objektorientierte Programmierung\"\\n* \"Algorithm\" = \"Algorithmus\"\\n* \"Data structure\" = \"Datenstruktur\"\\n* \"Debugging\" = \"Fehlersuche\"\\n* \"Compile\" = \"Kompilieren\"\\n* \"Link\" = \"Verknüpfen\"\\n* \"Run\" = \"Ausführen\"\\n* \"Test\" = \"Testen\"\\n* \"Deploy\" = \"Bereitstellen\"\\n* \"Version control\" = \"Versionskontrolle\"\\n* \"Open source\" = \"Open Source\"\\n* \"Software development\" = \"Softwareentwicklung\"\\n* \"Agile methodology\" = \"Agile Methodik\"\\n* \"DevOps\" = \"DevOps\"\\n* \"Cloud computing\" = \"Cloud Computing\"\\n\\nI hope this helps! Let me know if you have any other questions or if you need further translations.', response_metadata={'token_usage': {'completion_tokens': 331, 'prompt_tokens': 25, 'total_tokens': 356, 'completion_time': 0.520006542, 'prompt_time': 0.00250165, 'queue_time': None, 'total_time': 0.522508192}, 'model_name': 'mixtral-8x7b-32768', 'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop', 'logprobs': None}, id='run-74207fb7-85d3-417d-b2b9-621116b75d41-0', usage_metadata={'input_tokens': 25, 'output_tokens': 331, 'total_tokens': 356})"
+       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 50, 'total_tokens': 56, 'completion_time': 0.008, 'prompt_time': 0.003337935, 'queue_time': 0.20949214500000002, 'total_time': 0.011337935}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-e33b48dc-5e55-466e-9ebd-7b48c81c3cbd-0', usage_metadata={'input_tokens': 50, 'output_tokens': 6, 'total_tokens': 56})"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -269,7 +258,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/ibm_watsonx.ipynb

@@ -210,7 +210,7 @@
    "id": "96ed13d4",
    "metadata": {},
    "source": [
-    "Instead of `model_id`, you can also pass the `deployment_id` of the previously tuned model. The entire model tuning workflow is described in [Working with TuneExperiment and PromptTuner](https://ibm.github.io/watsonx-ai-python-sdk/pt_working_with_class_and_prompt_tuner.html)."
+    "Instead of `model_id`, you can also pass the `deployment_id` of the previously [deployed model with reference to a Prompt Template](https://cloud.ibm.com/apidocs/watsonx-ai#deployments-text-chat)."
    ]
   },
   {
@@ -228,6 +228,31 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3d29767c",
+   "metadata": {},
+   "source": [
+    "For certain requirements, there is an option to pass the IBM's [`APIClient`](https://ibm.github.io/watsonx-ai-python-sdk/base.html#apiclient) object into the `ChatWatsonx` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ae9531e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ibm_watsonx_ai import APIClient\n",
+    "\n",
+    "api_client = APIClient(...)\n",
+    "\n",
+    "chat = ChatWatsonx(\n",
+    "    model_id=\"ibm/granite-34b-code-instruct\",\n",
+    "    watsonx_client=api_client,\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f571001d",
@@ -448,9 +473,7 @@
    "source": [
     "## Tool calling\n",
     "\n",
-    "### ChatWatsonx.bind_tools()\n",
-    "\n",
-    "Please note that `ChatWatsonx.bind_tools` is on beta state, so we recommend using `mistralai/mistral-large` model."
+    "### ChatWatsonx.bind_tools()"
    ]
   },
   {
@@ -563,7 +586,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "langchain_ibm",
    "language": "python",
    "name": "python3"
   },

docs/docs/integrations/chat/openai.ipynb

@@ -322,7 +322,7 @@
    "source": [
     "### ``strict=True``\n",
     "\n",
-    ":::info Requires ``langchain-openai>=0.1.21rc1``\n",
+    ":::info Requires ``langchain-openai>=0.1.21``\n",
     "\n",
     ":::\n",
     "\n",
@@ -397,6 +397,405 @@
     "For more on binding tools and tool call outputs, head to the [tool calling](/docs/how_to/function_calling) docs."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "84833dd0-17e9-4269-82ed-550639d65751",
+   "metadata": {},
+   "source": [
+    "## Responses API\n",
+    "\n",
+    ":::info Requires ``langchain-openai>=0.3.9``\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "OpenAI supports a [Responses](https://platform.openai.com/docs/guides/responses-vs-chat-completions) API that is oriented toward building [agentic](/docs/concepts/agents/) applications. It includes a suite of [built-in tools](https://platform.openai.com/docs/guides/tools?api-mode=responses), including web and file search. It also supports management of [conversation state](https://platform.openai.com/docs/guides/conversation-state?api-mode=responses), allowing you to continue a conversational thread without explicitly passing in previous messages.\n",
+    "\n",
+    "`ChatOpenAI` will route to the Responses API if one of these features is used. You can also specify `use_responses_api=True` when instantiating `ChatOpenAI`.\n",
+    "\n",
+    "### Built-in tools\n",
+    "\n",
+    "Equipping `ChatOpenAI` with built-in tools will ground its responses with outside information, such as via context in files or the web. The [AIMessage](/docs/concepts/messages/#aimessage) generated from the model will include information about the built-in tool invocation.\n",
+    "\n",
+    "#### Web search\n",
+    "\n",
+    "To trigger a web search, pass `{\"type\": \"web_search_preview\"}` to the model as you would another tool.\n",
+    "\n",
+    ":::tip\n",
+    "\n",
+    "You can also pass built-in tools as invocation params:\n",
+    "```python\n",
+    "llm.invoke(\"...\", tools=[{\"type\": \"web_search_preview\"}])\n",
+    "```\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0d8bfe89-948b-42d4-beac-85ef2a72491d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "\n",
+    "tool = {\"type\": \"web_search_preview\"}\n",
+    "llm_with_tools = llm.bind_tools([tool])\n",
+    "\n",
+    "response = llm_with_tools.invoke(\"What was a positive news story from today?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9fe67c6-38ff-40a5-93b3-a4b7fca76372",
+   "metadata": {},
+   "source": [
+    "Note that the response includes structured [content blocks](/docs/concepts/messages/#content-1) that include both the text of the response and OpenAI [annotations](https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses#output-and-citations) citing its sources:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3ea5a4b1-f57a-4c8a-97f4-60ab8330a804",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'type': 'text',\n",
+       "  'text': 'Today, a heartwarming story emerged from Minnesota, where a group of high school robotics students built a custom motorized wheelchair for a 2-year-old boy named Cillian Jackson. Born with a genetic condition that limited his mobility, Cillian\\'s family couldn\\'t afford the $20,000 wheelchair he needed. The students at Farmington High School\\'s Rogue Robotics team took it upon themselves to modify a Power Wheels toy car into a functional motorized wheelchair for Cillian, complete with a joystick, safety bumpers, and a harness. One team member remarked, \"I think we won here more than we do in our competitions. Instead of completing a task, we\\'re helping change someone\\'s life.\" ([boredpanda.com](https://www.boredpanda.com/wholesome-global-positive-news/?utm_source=openai))\\n\\nThis act of kindness highlights the profound impact that community support and innovation can have on individuals facing challenges. ',\n",
+       "  'annotations': [{'end_index': 778,\n",
+       "    'start_index': 682,\n",
+       "    'title': '“Global Positive News”: 40 Posts To Remind Us There’s Good In The World',\n",
+       "    'type': 'url_citation',\n",
+       "    'url': 'https://www.boredpanda.com/wholesome-global-positive-news/?utm_source=openai'}]}]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response.content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95fbc34c-2f12-4d51-92c5-bf62a2f8900c",
+   "metadata": {},
+   "source": [
+    ":::tip\n",
+    "\n",
+    "You can recover just the text content of the response as a string by using `response.text()`. For example, to stream response text:\n",
+    "\n",
+    "```python\n",
+    "for token in llm_with_tools.stream(\"...\"):\n",
+    "    print(token.text(), end=\"|\")\n",
+    "```\n",
+    "\n",
+    "See the [streaming guide](/docs/how_to/chat_streaming/) for more detail.\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a332940-d409-41ee-ac36-2e9bee900e83",
+   "metadata": {},
+   "source": [
+    "The output message will also contain information from any tool invocations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a8011049-6c90-4fcb-82d4-850c72b46941",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'tool_outputs': [{'id': 'ws_67d192aeb6cc81918e736ad4a57937570d6f8507990d9d71',\n",
+       "   'status': 'completed',\n",
+       "   'type': 'web_search_call'}]}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response.additional_kwargs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "288d47bb-3ccb-412f-a3d3-9f6cee0e6214",
+   "metadata": {},
+   "source": [
+    "#### File search\n",
+    "\n",
+    "To trigger a file search, pass a [file search tool](https://platform.openai.com/docs/guides/tools-file-search) to the model as you would another tool. You will need to populate an OpenAI-managed vector store and include the vector store ID in the tool definition. See [OpenAI documentation](https://platform.openai.com/docs/guides/tools-file-search) for more detail."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "1f758726-33ef-4c04-8a54-49adb783bbb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Deep Research by OpenAI is a new capability integrated into ChatGPT that allows for the execution of multi-step research tasks independently. It can synthesize extensive amounts of online information and produce comprehensive reports similar to what a research analyst would do, significantly speeding up processes that would typically take hours for a human.\n",
+      "\n",
+      "### Key Features:\n",
+      "- **Independent Research**: Users simply provide a prompt, and the model can find, analyze, and synthesize information from hundreds of online sources.\n",
+      "- **Multi-Modal Capabilities**: The model is also able to browse user-uploaded files, plot graphs using Python, and embed visualizations in its outputs.\n",
+      "- **Training**: Deep Research has been trained using reinforcement learning on real-world tasks that require extensive browsing and reasoning.\n",
+      "\n",
+      "### Applications:\n",
+      "- Useful for professionals in sectors like finance, science, policy, and engineering, enabling them to obtain accurate and thorough research quickly.\n",
+      "- It can also be beneficial for consumers seeking personalized recommendations on complex purchases.\n",
+      "\n",
+      "### Limitations:\n",
+      "Although Deep Research presents significant advancements, it has some limitations, such as the potential to hallucinate facts or struggle with authoritative information. \n",
+      "\n",
+      "Deep Research aims to facilitate access to thorough and documented information, marking a significant step toward the broader goal of developing artificial general intelligence (AGI).\n"
+     ]
+    }
+   ],
+   "source": [
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "\n",
+    "openai_vector_store_ids = [\n",
+    "    \"vs_...\",  # your IDs here\n",
+    "]\n",
+    "\n",
+    "tool = {\n",
+    "    \"type\": \"file_search\",\n",
+    "    \"vector_store_ids\": openai_vector_store_ids,\n",
+    "}\n",
+    "llm_with_tools = llm.bind_tools([tool])\n",
+    "\n",
+    "response = llm_with_tools.invoke(\"What is deep research by OpenAI?\")\n",
+    "print(response.text())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f88bbd71-83b0-45a6-9141-46ec9da93df6",
+   "metadata": {},
+   "source": [
+    "As with [web search](#web-search), the response will include content blocks with citations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "865bc14e-1599-438e-be44-857891004979",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'file_id': 'file-3UzgX7jcC8Dt9ZAFzywg5k',\n",
+       "  'index': 346,\n",
+       "  'type': 'file_citation',\n",
+       "  'filename': 'deep_research_blog.pdf'},\n",
+       " {'file_id': 'file-3UzgX7jcC8Dt9ZAFzywg5k',\n",
+       "  'index': 575,\n",
+       "  'type': 'file_citation',\n",
+       "  'filename': 'deep_research_blog.pdf'}]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response.content[0][\"annotations\"][:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd00f6be-2862-4634-a0c3-14ee39915c90",
+   "metadata": {},
+   "source": [
+    "It will also include information from the built-in tool invocations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e16a7110-d2d8-45fa-b372-5109f330540b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'tool_outputs': [{'id': 'fs_67d196fbb83c8191ba20586175331687089228ce932eceb1',\n",
+       "   'queries': ['What is deep research by OpenAI?'],\n",
+       "   'status': 'completed',\n",
+       "   'type': 'file_search_call'}]}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response.additional_kwargs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fda05f0-4b81-4709-9407-f316d760ad50",
+   "metadata": {},
+   "source": [
+    "### Managing conversation state\n",
+    "\n",
+    "The Responses API supports management of [conversation state](https://platform.openai.com/docs/guides/conversation-state?api-mode=responses).\n",
+    "\n",
+    "#### Manually manage state\n",
+    "\n",
+    "You can manage the state manually or using [LangGraph](/docs/tutorials/chatbot/), as with other chat models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "51d3e4d3-ea78-426c-9205-aecb0937fca7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "As of March 12, 2025, here are some positive news stories that highlight recent uplifting events:\n",
+      "\n",
+      "*...  exemplify positive developments in health, environmental sustainability, and community well-being. \n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
+    "\n",
+    "tool = {\"type\": \"web_search_preview\"}\n",
+    "llm_with_tools = llm.bind_tools([tool])\n",
+    "\n",
+    "first_query = \"What was a positive news story from today?\"\n",
+    "messages = [{\"role\": \"user\", \"content\": first_query}]\n",
+    "\n",
+    "response = llm_with_tools.invoke(messages)\n",
+    "response_text = response.text()\n",
+    "print(f\"{response_text[:100]}... {response_text[-100:]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5da9d20f-9712-46f4-a395-5be5a7c1bc62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your question was: \"What was a positive news story from today?\"\n",
+      "\n",
+      "The last sentence of my answer was: \"These stories exemplify positive developments in health, environmental sustainability, and community well-being.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "second_query = (\n",
+    "    \"Repeat my question back to me, as well as the last sentence of your answer.\"\n",
+    ")\n",
+    "\n",
+    "messages.extend(\n",
+    "    [\n",
+    "        response,\n",
+    "        {\"role\": \"user\", \"content\": second_query},\n",
+    "    ]\n",
+    ")\n",
+    "second_response = llm_with_tools.invoke(messages)\n",
+    "print(second_response.text())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fd8ca21-8a5e-4294-af32-11f26a040171",
+   "metadata": {},
+   "source": [
+    ":::tip\n",
+    "\n",
+    "You can use [LangGraph](https://langchain-ai.github.io/langgraph/) to manage conversational threads for you in a variety of backends, including in-memory and Postgres. See [this tutorial](/docs/tutorials/chatbot/) to get started.\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "\n",
+    "#### Passing `previous_response_id`\n",
+    "\n",
+    "When using the Responses API, LangChain messages will include an `\"id\"` field in its metadata. Passing this ID to subsequent invocations will continue the conversation. Note that this is [equivalent](https://platform.openai.com/docs/guides/conversation-state?api-mode=responses#openai-apis-for-conversation-state) to manually passing in messages from a billing perspective."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "009e541a-b372-410e-b9dd-608a8052ce09",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hi Bob! How can I assist you today?\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(\n",
+    "    model=\"gpt-4o-mini\",\n",
+    "    use_responses_api=True,\n",
+    ")\n",
+    "response = llm.invoke(\"Hi, I'm Bob.\")\n",
+    "print(response.text())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "393a443a-4c5f-4a07-bc0e-c76e529b35e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your name is Bob. How can I help you today, Bob?\n"
+     ]
+    }
+   ],
+   "source": [
+    "second_response = llm.invoke(\n",
+    "    \"What is my name?\",\n",
+    "    previous_response_id=response.response_metadata[\"id\"],\n",
+    ")\n",
+    "print(second_response.text())"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "57e27714",

docs/docs/integrations/chat/pipeshift.ipynb

@@ -69,8 +69,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
    ]
   },
   {

docs/docs/integrations/chat/sambanova.ipynb

@@ -19,7 +19,7 @@
    "source": [
     "# ChatSambaNovaCloud\n",
     "\n",
-    "This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.ChatSambaNovaCloud.html).\n",
+    "This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\n",
     "\n",
     "**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models\n",
     "\n",
@@ -28,7 +28,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatSambaNovaCloud](https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.ChatSambaNovaCloud.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_sambanova?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_sambanova?style=flat-square&label=%20) |\n",
+    "| [ChatSambaNovaCloud](https://docs.sambanova.ai/cloud/docs/get-started/overview) | [langchain-sambanova](https://python.langchain.com/docs/integrations/providers/sambanova/) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_sambanova?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_sambanova?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "\n",
@@ -81,8 +81,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
    ]
   },
   {
@@ -545,7 +545,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatSambaNovaCloud features and configurations head to the API reference: https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.ChatSambaNovaCloud.html"
+    "For detailed documentation of all SambaNovaCloud features and configurations head to the API reference: https://docs.sambanova.ai/cloud/docs/get-started/overview"
    ]
   }
  ],

docs/docs/integrations/chat/sambastudio.ipynb

@@ -19,7 +19,7 @@
    "source": [
     "# ChatSambaStudio\n",
     "\n",
-    "This will help you getting started with SambaStudio [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.chat_models.sambanova.ChatSambaStudio.html).\n",
+    "This will help you getting started with SambaStudio [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://docs.sambanova.ai/sambastudio/latest/index.html).\n",
     "\n",
     "**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) SambaStudio is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova [DataScale](https://sambanova.ai/products/datascale) systems.\n",
     "\n",
@@ -28,7 +28,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatSambaStudio](https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.chat_models.sambanova.ChatSambaStudio.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_sambanova?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_sambanova?style=flat-square&label=%20) |\n",
+    "| [ChatSambaStudio](https://docs.sambanova.ai/sambastudio/latest/index.html) | [langchain-sambanova](https://python.langchain.com/docs/integrations/providers/sambanova/) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_sambanova?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_sambanova?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "\n",
@@ -84,8 +84,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
    ]
   },
   {
@@ -483,7 +483,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatSambaStudio features and configurations head to the API reference: https://python.langchain.com/api_reference/sambanova/chat_models/langchain_sambanova.sambanova.chat_models.ChatSambaStudio.html"
+    "For detailed documentation of all SambaStudio features and configurations head to the API reference: https://docs.sambanova.ai/sambastudio/latest/api-ref-landing.html"
    ]
   }
  ],

docs/docs/integrations/chat/tongyi.ipynb

@@ -26,22 +26,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Install the package\n",
     "%pip install --upgrade --quiet  dashscope"
@@ -49,8 +36,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-05T01:11:20.457141Z",
+     "start_time": "2025-03-05T01:11:18.810160Z"
+    },
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
@@ -66,8 +57,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-05T01:11:24.270318Z",
+     "start_time": "2025-03-05T01:11:24.268064Z"
+    },
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
@@ -266,6 +261,52 @@
     "ai_message"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Partial Mode\n",
+    "Enable the large model to continue generating content from the initial text you provide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-05T01:31:29.155824Z",
+     "start_time": "2025-03-05T01:31:27.239667Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=' has cast off its heavy cloak of snow, donning instead a vibrant garment of fresh greens and floral hues; it is as if the world has woken from a long slumber, stretching and reveling in the warm caress of the sun. Everywhere I look, there is a symphony of life: birdsong fills the air, bees dance from flower to flower, and a gentle breeze carries the sweet fragrance of blossoms. It is in this season that my heart finds particular joy, for it whispers promises of renewal and growth, reminding me that even after the coldest winters, there will always be a spring to follow.', additional_kwargs={}, response_metadata={'model_name': 'qwen-turbo', 'finish_reason': 'stop', 'request_id': '447283e9-ee31-9d82-8734-af572921cb05', 'token_usage': {'input_tokens': 40, 'output_tokens': 127, 'prompt_tokens_details': {'cached_tokens': 0}, 'total_tokens': 167}}, id='run-6a35a91c-cc12-4afe-b56f-fd26d9035357-0')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_community.chat_models.tongyi import ChatTongyi\n",
+    "from langchain_core.messages import AIMessage, HumanMessage\n",
+    "\n",
+    "messages = [\n",
+    "    HumanMessage(\n",
+    "        content=\"\"\"Please continue the sentence \"Spring has arrived, and the earth\" to express the beauty of spring and the author's joy.\"\"\"\n",
+    "    ),\n",
+    "    AIMessage(\n",
+    "        content=\"Spring has arrived, and the earth\", additional_kwargs={\"partial\": True}\n",
+    "    ),\n",
+    "]\n",
+    "chatLLM = ChatTongyi()\n",
+    "ai_message = chatLLM.invoke(messages)\n",
+    "ai_message"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

docs/docs/integrations/chat/writer.ipynb

@@ -1,362 +1,231 @@
 {
  "cells": [
-  {
-   "cell_type": "raw",
-   "id": "85e07aae70a15572",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Writer\n",
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "id": "cb4dd00a-8893-4a45-96f7-9a9fc341cd61",
+   "id": "e815de6298bf07ca",
    "metadata": {},
    "source": [
-    "# ChatWriter\n",
+    "# Chat Writer\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Writer [chat models](/docs/concepts/chat_models).\n",
+    "This notebook provides a quick overview for getting started with Writer [chat](/docs/concepts/chat_models/).\n",
+    "\n",
+    "Writer has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Writer docs](https://dev.writer.com/home).\n",
     "\n",
-    "Writer has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Writer docs](https://dev.writer.com/home/models).\n",
     "\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "617a6e98205ab7c8",
-   "metadata": {},
-   "source": [
     "## Overview\n",
     "\n",
     "### Integration details\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |:----------:| :---: | :---: |\n",
-    "| ChatWriter | langchain-community | ❌ | ❌ |     ❌      | ❌ | ❌ |\n",
-    "\n",
+    "| Class                                                                                                                    | Package          | Local | Serializable | JS support |                                        Package downloads                                         |                                        Package latest                                         |\n",
+    "|:-------------------------------------------------------------------------------------------------------------------------|:-----------------| :---: | :---: |:----------:|:------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|\n",
+    "| [ChatWriter](https://github.com/writer/langchain-writer/blob/main/langchain_writer/chat_models.py#L308) | [langchain-writer](https://pypi.org/project/langchain-writer/) |      ❌       |                                       ❌                                       | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-writer?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-writer?style=flat-square&label=%20) |\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | Structured output | JSON mode | Image input | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async |         [Token usage](/docs/how_to/chat_token_usage_tracking/)          | Logprobs |\n",
     "| :---: |:-----------------:| :---: | :---: |  :---: | :---: | :---: | :---: |:--------------------------------:|:--------:|\n",
-    "| ✅ |         ❌         | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ |                ✅                 |    ❌     |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access Writer models you'll need to create a Writer account, get an API key, and install the `writer-sdk` and `langchain-community` packages.\n",
-    "\n",
+    "| ✅ |         ❌         | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ |                ✅                 |    ❌     |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fd9903e685808d9",
+   "metadata": {},
+   "source": [
     "### Credentials\n",
     "\n",
-    "Head to [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) to sign up to OpenAI and generate an API key. Once you've done this set the WRITER_API_KEY environment variable:"
+    "Sign up for [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) and follow this [Quickstart](https://dev.writer.com/api-guides/quickstart) to obtain an API key. Then, set the WRITER_API_KEY environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "e817fe2e-4f1d-4533-b19e-2400b1cf6ce8",
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:26.800627Z",
-     "start_time": "2024-11-14T09:27:59.652281Z"
+    "jupyter": {
+     "is_executing": true
     }
    },
-   "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
     "\n",
-    "if not os.environ.get(\"WRITER_API_KEY\"):\n",
-    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key:\")"
-   ]
+    "if not os.getenv(\"WRITER_API_KEY\"):\n",
+    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "c59722a9-6dbb-45f7-ae59-5be50ca5733d",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "source": [
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
    "metadata": {},
    "source": [
     "### Installation\n",
     "\n",
-    "The LangChain Writer integration lives in the `langchain-community` package:"
+    "`ChatWriter` is available from the `langchain-writer` package. Install it with:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "2113471c-75d7-45df-b784-d78da4ef7aba",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:32.415354Z",
-     "start_time": "2024-11-14T09:46:26.826112Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\r\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
    "source": [
-    "%pip install -qU langchain-community writer-sdk"
-   ]
+    "%pip install -qU langchain-writer"
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "1098bc9d-ce83-462b-8c19-f85bf3a159dc",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
    "metadata": {},
    "source": [
-    "## Instantiation\n",
+    "### Instantiation\n",
     "\n",
-    "Now we can instantiate our model object and generate chat completions:"
+    "Now we can instantiate our model object in order to generate chat completions:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "522686de",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:33.504711Z",
-     "start_time": "2024-11-14T09:46:32.574505Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
    "source": [
-    "from langchain_community.chat_models.writer import ChatWriter\n",
+    "from langchain_writer import ChatWriter\n",
     "\n",
     "llm = ChatWriter(\n",
     "    model=\"palmyra-x-004\",\n",
-    "    temperature=0.7,\n",
-    "    max_tokens=1000,\n",
-    "    # other params...\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "6511982a-734a-4193-a47d-254f8dcaff5e",
+   "id": "2b4f3e15",
    "metadata": {},
    "source": [
-    "## Invocation"
+    "## Usage\n",
+    "\n",
+    "To use the model, you pass in a list of messages and call the `invoke` method:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
+   "id": "62e0dbc3",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:38.856174Z",
-     "start_time": "2024-11-14T09:46:33.520062Z"
-    },
     "tags": []
    },
-   "outputs": [],
    "source": [
     "messages = [\n",
     "    (\n",
     "        \"system\",\n",
-    "        \"You are a helpful assistant that writes poems about the Python programming language.\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
     "    ),\n",
-    "    (\"human\", \"Write a poem about Python.\"),\n",
+    "    (\"human\", \"I love programming.\"),\n",
     "]\n",
-    "ai_msg = llm.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2cd224b8-4499-41fb-a604-d53a7ff17b2e",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:38.866651Z",
-     "start_time": "2024-11-14T09:46:38.863817Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In realms of code, where logic weaves and flows,\n",
-      "A language rises, Python by its name,\n",
-      "With syntax clear, where elegance it shows,\n",
-      "A serpent, wise, that time and space can tame.\n",
-      "\n",
-      "Born from the mind of Guido, pure and bright,\n",
-      "Its beauty lies in simplicity and grace,\n",
-      "A tool of power, yet gentle in its might,\n",
-      "In every programmer's heart, a cherished place.\n",
-      "\n",
-      "It dances through the data, vast and deep,\n",
-      "With libraries that span the digital realm,\n",
-      "From machine learning's secrets to keep,\n",
-      "To web development, it wields the helm.\n",
-      "\n",
-      "In the hands of the novice and the sage,\n",
-      "Python spins the threads of digital dreams,\n",
-      "A language that can turn the age,\n",
-      "With a gentle learning curve, its appeal gleams.\n",
-      "\n",
-      "It's more than code, a community it builds,\n",
-      "Where knowledge freely flows, and all are heard,\n",
-      "In Python's world, the future unfolds,\n",
-      "A language of the people, for the world.\n",
-      "\n",
-      "So here's to Python, in its gentle might,\n",
-      "A master of the modern coding art,\n",
-      "May it continue to light our path each night,\n",
-      "In the vast, evolving world of code, its heart.\n"
-     ]
-    }
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
    ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "35b3a5b3dabef65",
+   "id": "5cf7293d",
    "metadata": {},
    "source": [
-    "## Streaming"
+    "Then, you can access the content of the message:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "2725770182bf96dc",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:38.914883Z",
-     "start_time": "2024-11-14T09:46:38.912564Z"
-    }
-   },
-   "outputs": [],
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
    "source": [
-    "ai_stream = llm.stream(messages)"
+    "print(ai_msg.content)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4391289ce0a80e19",
+   "metadata": {},
+   "source": [
+    "## Streaming\n",
+    "\n",
+    "You can also stream the response. First, create a stream:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "a48410d9488162e3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:43.226449Z",
-     "start_time": "2024-11-14T09:46:38.955512Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In realms of code where logic weaves,\n",
-      "A language rises, Python, it breezes,\n",
-      "With syntax clear and simple to read,\n",
-      "Through its elegance, our spirits are fed.\n",
-      "\n",
-      "Like rivers flowing, smooth and serene,\n",
-      "Its structure harmonious, a coder's dream,\n",
-      "Indentations guide the flow of control,\n",
-      "In Python's world, confusion takes no toll.\n",
-      "\n",
-      "A vast library, a treasure trove so bright,\n",
-      "For web and data, it offers its might,\n",
-      "With modules and packages, a rich array,\n",
-      "Python empowers us to code in play.\n",
-      "\n",
-      "From AI to scripts, in flexibility it thrives,\n",
-      "A language of the future, as many now derive,\n",
-      "Its community, a beacon of support and cheer,\n",
-      "With Python, the possibilities are vast, far and near.\n",
-      "\n",
-      "So here's to Python, in its gentle grace,\n",
-      "A tool that enhances, a language that embraces,\n",
-      "The art of coding, with a fluent, flowing pen,\n",
-      "In the Python world, we code, and we begin."
-     ]
-    }
+   "id": "4a0f2112b3a4c79e",
+   "metadata": {},
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming. Sing a song about it\"),\n",
+    "]\n",
+    "ai_stream = llm.stream(messages)\n",
+    "ai_stream"
    ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23cc74b6",
+   "metadata": {},
+   "source": [
+    "Then, iterate over the stream to get the chunks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "8c4b7b9b9308c757",
+   "metadata": {},
    "source": [
     "for chunk in ai_stream:\n",
     "    print(chunk.content, end=\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "778f912a-66ea-4a5d-b3de-6c7db4baba26",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "fbb043e6",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:50.721645Z",
-     "start_time": "2024-11-14T09:46:43.234590Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessageChunk(content='In the realm of code, where logic weaves and flows,  \\nA language rises, like a phoenix from the code\\'s throes.  \\nJava, the name, a cup of coffee\\'s steam,  \\nBrewed in the minds, where digital dreams gleam.\\n\\nWith syntax clear, like morning\\'s misty hue,  \\nIn classes and objects, it spins a tale so true.  \\nA platform agnostic, with a byte to spare,  \\nAcross the devices, it journeys everywhere.\\n\\nInheritance and polymorphism, its power\\'s core,  \\nLike ancient runes, in every line they bore.  \\nEncapsulation, a shield, with data it does hide,  \\nIn the vast jungle of code, it stands as a guide.\\n\\nFrom applets small, to vast, server-side apps,  \\nIts threads run swift, through the computing traps.  \\nA language of the people, by the people, for the people’s use,  \\nBuilt on the principle, \"write once, run anywhere, with no excuse.\"\\n\\nIn the heart of Android, it beats, a steady drum,  \\nCrafting experiences, in every smartphone\\'s hum.  \\nIn the cloud, in the enterprise, its presence is vast,  \\nA cornerstone of computing, built to last.\\n\\nOh Java, thy elegance, thy robust design,  \\nA language that stands, in any computing line.  \\nWith every update, with every new release,  \\nThy community grows, with a vibrant, diverse peace.\\n\\nSo here\\'s to Java, the versatile, the grand,  \\nA language that shapes the digital land.  \\nMay it continue to evolve, to grow, to inspire,  \\nIn the endless quest of turning thoughts into digital fire.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 345, 'prompt_tokens': 33, 'total_tokens': 378, 'completion_tokens_details': None, 'prompt_token_details': None}, 'model_name': 'palmyra-x-004', 'system_fingerprint': 'v1', 'finish_reason': 'stop'}, id='run-a5b4be59-0eb0-41bd-80f7-72477861b0bd-0')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
    ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that writes poems about the {input_language} programming language.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"Java\",\n",
-    "        \"input\": \"Write a poem about Java.\",\n",
-    "    }\n",
-    ")"
-   ]
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "0b1b52a5-b58d-40c9-bcdd-88eb8fb351e2",
+   "id": "e632bf7d0873f933",
    "metadata": {},
    "source": [
     "## Tool calling\n",
     "\n",
-    "Writer supports [tool calling](https://dev.writer.com/api-guides/tool-calling), which lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool.\n",
+    "Writer models like Palmyra X 004 support [tool calling](https://dev.writer.com/api-guides/tool-calling), which lets you describe tools and their arguments. The model will return a JSON object with a tool to invoke and the inputs to that tool.\n",
     "\n",
-    "### ChatWriter.bind_tools()\n",
+    "### Binding tools\n",
     "\n",
-    "With `ChatWriter.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to tool schemas, which looks like:\n",
+    "With `ChatWriter.bind_tools`, you can easily pass in Pydantic classes, dictionary schemas, LangChain tools, or even functions as tools to the model. Under the hood, these are converted to tool schemas, which look like this:\n",
     "```\n",
     "{\n",
     "    \"name\": \"...\",\n",
@@ -364,20 +233,15 @@
     "    \"parameters\": {...}  # JSONSchema\n",
     "}\n",
     "```\n",
-    "and passed in every model invocation."
+    "These are passed in every model invocation.\n",
+    "\n",
+    "For example, to use a tool that gets the weather in a given location, you can define a Pydantic class and pass it to `ChatWriter.bind_tools`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "b7ea7690-ec7a-4337-b392-e87d1f39a6ec",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:50.891937Z",
-     "start_time": "2024-11-14T09:46:50.733463Z"
-    }
-   },
-   "outputs": [],
+   "id": "47e2f0faceca533",
+   "metadata": {},
    "source": [
     "from pydantic import BaseModel, Field\n",
     "\n",
@@ -388,86 +252,175 @@
     "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
     "\n",
     "\n",
-    "llm_with_tools = llm.bind_tools([GetWeather])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "1d1ab955-6a68-42f8-bb5d-86eb1111478a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:51.725422Z",
-     "start_time": "2024-11-14T09:46:50.904699Z"
-    }
-   },
+    "llm.bind_tools([GetWeather])"
+   ],
    "outputs": [],
-   "source": [
-    "ai_msg = llm_with_tools.invoke(\n",
-    "    \"what is the weather like in New York City\",\n",
-    ")"
-   ]
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "768d1ae4-4b1a-48eb-a329-c8d5051067a3",
+   "id": "68e22d3b",
    "metadata": {},
    "source": [
-    "### AIMessage.tool_calls\n",
-    "Notice that the AIMessage has a `tool_calls` attribute. This contains in a standardized ToolCall format that is model-provider agnostic."
+    "Then, you can invoke the model with the tool:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "166cb7ce-831d-4a7c-9721-abc107f11084",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-14T09:46:51.744202Z",
-     "start_time": "2024-11-14T09:46:51.738431Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'GetWeather',\n",
-       "  'args': {'location': 'New York City, NY'},\n",
-       "  'id': 'chatcmpl-tool-fe70912c800d40fc8700d604d4823001',\n",
-       "  'type': 'tool_call'}]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
+   "id": "765527dd533ec967",
+   "metadata": {},
+   "source": [
+    "ai_msg = llm.invoke(\n",
+    "    \"what is the weather like in New York City\",\n",
+    ")\n",
+    "ai_msg"
    ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57544bdf",
+   "metadata": {},
+   "source": [
+    "Finally, you can access the tool calls and proceed to execute your functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f361c4769e772fe",
+   "metadata": {},
    "source": [
     "print(ai_msg.tool_calls)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
-   "id": "e082c9ac-c7c7-4aff-a8ec-8e220262a59c",
+   "id": "3baf53021834d2ff",
    "metadata": {},
    "source": [
-    "For more on binding tools and tool call outputs, head to the [tool calling](/docs/how_to/function_calling) docs."
+    "### A note on tool binding\n",
+    "\n",
+    "The `ChatWriter.bind_tools()` method does not create new instance with bound tools, but stores the received `tools` and `tool_choice` in the initial class instance attributes to pass them as parameters during the Palmyra LLM call while using `ChatWriter` invocation. This approach allows the support of different tool types, e.g. `function` and `graph`. `Graph` is one of the remotely called Writer Palmyra tools. For further information visit our [docs](https://dev.writer.com/api-guides/knowledge-graph#knowledge-graph). \n",
+    "\n",
+    "For more information about tool usage in LangChain, visit the [LangChain tool calling documentation](https://python.langchain.com/docs/concepts/tool_calling/)."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a796d728-971b-408b-88d5-440015bbb941",
+   "id": "a4674b1b82ce9d1f",
+   "metadata": {},
+   "source": [
+    "## Batching\n",
+    "\n",
+    "You can also batch requests and set the `max_concurrency`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "c8a217f6190747fe",
+   "metadata": {},
+   "source": [
+    "ai_batch = llm.batch(\n",
+    "    [\n",
+    "        \"How to cook pancakes?\",\n",
+    "        \"How to compose poem?\",\n",
+    "        \"How to run faster?\",\n",
+    "    ],\n",
+    "    config={\"max_concurrency\": 3},\n",
+    ")\n",
+    "ai_batch"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2eb81e1d",
+   "metadata": {},
+   "source": [
+    "Then, iterate over the batch to get the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b6a228d448f3df23",
+   "metadata": {},
+   "source": [
+    "for batch in ai_batch:\n",
+    "    print(batch.content)\n",
+    "    print(\"-\" * 100)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58a9ab241fe09a71",
+   "metadata": {},
+   "source": [
+    "## Asynchronous usage\n",
+    "\n",
+    "All features above (invocation, streaming, batching, tools calling) also support asynchronous usage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Prompt templates\n",
+    "\n",
+    "[Prompt templates](https://python.langchain.com/docs/concepts/prompt_templates/) help to translate user input and parameters into instructions for a language model. You can use `ChatWriter` with a prompt templates like so:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
    "metadata": {},
    "source": [
     "## API reference\n",
+    "For detailed documentation of all ChatWriter features and configurations head to the [API reference](https://python.langchain.com/api_reference/writer/chat_models/langchain_writer.chat_models.ChatWriter.html#langchain_writer.chat_models.ChatWriter).\n",
     "\n",
-    "For detailed documentation of all Writer features, head to our [API reference](https://dev.writer.com/api-guides/api-reference/completion-api/chat-completion)."
+    "## Additional resources\n",
+    "You can find information about Writer's models (including costs, context windows, and supported input types) and tools in the [Writer docs](https://dev.writer.com/home)."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -481,7 +434,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/xinference.ipynb

@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Xinference\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01ca90da",
+   "metadata": {},
+   "source": [
+    "# ChatXinference\n",
+    "\n",
+    "[Xinference](https://github.com/xorbitsai/inference) is a powerful and versatile library designed to serve LLMs, \n",
+    "speech recognition models, and multimodal models, even on your laptop. It supports a variety of models compatible with GGML, such as chatglm, baichuan, whisper, vicuna, orca, and many others.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support] | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| ChatXinference| langchain-xinference | ✅ | ❌ | ✅ | ✅ | ✅ |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: |:----------------------------------------------------:| :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ |                          ✅                           | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Install `Xinference` through PyPI:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  \"xinference[all]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fb27b941602401d91542211134fc71a",
+   "metadata": {},
+   "source": [
+    "### Deploy Xinference Locally or in a Distributed Cluster.\n",
+    "\n",
+    "For local deployment, run `xinference`. \n",
+    "\n",
+    "To deploy Xinference in a cluster, first start an Xinference supervisor using the `xinference-supervisor`. You can also use the option -p to specify the port and -H to specify the host. The default port is 8080 and the default host is 0.0.0.0.\n",
+    "\n",
+    "Then, start the Xinference workers using `xinference-worker` on each server you want to run them on. \n",
+    "\n",
+    "You can consult the README file from [Xinference](https://github.com/xorbitsai/inference) for more information.\n",
+    "### Wrapper\n",
+    "\n",
+    "To use Xinference with LangChain, you need to first launch a model. You can use command line interface (CLI) to do so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "acae54e37e7d407bbb7b55eff062a284",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model uid: 7167b2b0-2a04-11ee-83f0-d29396a3f064\n"
+     ]
+    }
+   ],
+   "source": [
+    "%xinference launch -n vicuna-v1.3 -f ggmlv3 -q q4_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a63283cbaf04dbcab1f6479b197f3a8",
+   "metadata": {},
+   "source": [
+    "A model UID is returned for you to use. Now you can use Xinference with LangChain:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dd0d8092fe74a7c96281538738b07e2",
+   "metadata": {},
+   "source": [
+    "## Installation\n",
+    "\n",
+    "The LangChain Xinference integration lives in the `langchain-xinference` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72eea5119410473aa328ad9291626812",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-xinference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8edb47106e1a46a883d545849b8ab81b",
+   "metadata": {},
+   "source": [
+    "Make sure you're using the latest Xinference version for structured outputs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55935996",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "10185d26023b46108eb7d9f57d49d2b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_xinference.chat_models import ChatXinference\n",
+    "\n",
+    "llm = ChatXinference(\n",
+    "    server_url=\"your_server_url\", model_uid=\"7167b2b0-2a04-11ee-83f0-d29396a3f064\"\n",
+    ")\n",
+    "\n",
+    "llm.invoke(\n",
+    "    \"Q: where can we visit in the capital of France?\",\n",
+    "    config={\"max_tokens\": 1024},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8763a12b2bbd4a93a75aff182afb95dc",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "50f6c0e4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain_xinference.chat_models import ChatXinference\n",
+    "\n",
+    "llm = ChatXinference(\n",
+    "    server_url=\"your_server_url\", model_uid=\"7167b2b0-2a04-11ee-83f0-d29396a3f064\"\n",
+    ")\n",
+    "\n",
+    "system_message = \"You are a helpful assistant that translates English to French. Translate the user sentence.\"\n",
+    "human_message = \"I love programming.\"\n",
+    "\n",
+    "llm.invoke([HumanMessage(content=human_message), SystemMessage(content=system_message)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7623eae2785240b9bd12b16a66d81610",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "317d05c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_xinference.chat_models import ChatXinference\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "    input=[\"country\"], template=\"Q: where can we visit in the capital of {country}? A:\"\n",
+    ")\n",
+    "\n",
+    "llm = ChatXinference(\n",
+    "    server_url=\"your_server_url\", model_uid=\"7167b2b0-2a04-11ee-83f0-d29396a3f064\"\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(input={\"country\": \"France\"})\n",
+    "chain.stream(input={\"country\": \"France\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66f7fb26",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatXinference features and configurations head to the API reference: https://github.com/TheSongg/langchain-xinference"
+   ]
+  }
+ ],
+ "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/document_loaders/agentql.ipynb

@@ -0,0 +1,265 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wkUAAcGZNSJ3"
+      },
+      "source": [
+        "# AgentQLLoader\n",
+        "\n",
+        "[AgentQL](https://www.agentql.com/)'s document loader provides structured data extraction from any web page using an [AgentQL query](https://docs.agentql.com/agentql-query). AgentQL can be used across multiple languages and web pages without breaking over time and change.\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "`AgentQLLoader` requires the following two parameters:\n",
+        "- `url`: The URL of the web page you want to extract data from.\n",
+        "- `query`: The AgentQL query to execute. Learn more about [how to write an AgentQL query in the docs](https://docs.agentql.com/agentql-query) or test one out in the [AgentQL Playground](https://dev.agentql.com/playground).\n",
+        "\n",
+        "Setting the following parameters are optional:\n",
+        "- `api_key`: Your AgentQL API key from [dev.agentql.com](https://dev.agentql.com). **`Optional`.**\n",
+        "- `timeout`: The number of seconds to wait for a request before timing out. **Defaults to `900`.**\n",
+        "- `is_stealth_mode_enabled`: Whether to enable experimental anti-bot evasion strategies. This feature may not work for all websites at all times. Data extraction may take longer to complete with this mode enabled. **Defaults to `False`.**\n",
+        "- `wait_for`: The number of seconds to wait for the page to load before extracting data. **Defaults to `0`.**\n",
+        "- `is_scroll_to_bottom_enabled`: Whether to scroll to bottom of the page before extracting data. **Defaults to `False`.**\n",
+        "- `mode`: `\"standard\"` uses deep data analysis, while `\"fast\"` trades some depth of analysis for speed and is adequate for most usecases. [Learn more about the modes in this guide.](https://docs.agentql.com/accuracy/standard-mode) **Defaults to `\"fast\"`.**\n",
+        "- `is_screenshot_enabled`: Whether to take a screenshot before extracting data. Returned in 'metadata' as a Base64 string. **Defaults to `False`.**\n",
+        "\n",
+        "AgentQLLoader is implemented with AgentQL's [REST API](https://docs.agentql.com/rest-api/api-reference)\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support |\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| AgentQLLoader| langchain-agentql | ✅ | ❌ | ❌ |\n",
+        "\n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: |\n",
+        "| AgentQLLoader | ✅ | ❌ |"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CaKa2QrnwPXq"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "To use the AgentQL Document Loader, you will need to configure the `AGENTQL_API_KEY` environment variable, or use the `api_key` parameter. You can acquire an API key from our [Dev Portal](https://dev.agentql.com)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mZNJvUQBNSJ5"
+      },
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain-agentql**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IblRoJJDNSJ5"
+      },
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_agentql"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SNsUT60YvfCm"
+      },
+      "source": [
+        "### Set Credentials"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "2D1EN7Egvk1c"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "os.environ[\"AGENTQL_API_KEY\"] = \"YOUR_AGENTQL_API_KEY\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D4hnJV_6NSJ5"
+      },
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Next instantiate your model object:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "oMJdxL_KNSJ5"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_agentql.document_loaders import AgentQLLoader\n",
+        "\n",
+        "loader = AgentQLLoader(\n",
+        "    url=\"https://www.agentql.com/blog\",\n",
+        "    query=\"\"\"\n",
+        "    {\n",
+        "        posts[] {\n",
+        "            title\n",
+        "            url\n",
+        "            date\n",
+        "            author\n",
+        "        }\n",
+        "    }\n",
+        "    \"\"\",\n",
+        "    is_scroll_to_bottom_enabled=True,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SRxIOx90NSJ5"
+      },
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bNnnCZ1oNSJ5",
+        "outputId": "d0eb8cb4-9742-4f0c-80f1-0509a3af1808"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'request_id': 'bdb9dbe7-8a7f-427f-bc16-839ccc02cae6', 'generated_query': None, 'screenshot': None}, page_content=\"{'posts': [{'title': 'Launch Week Recap—make the web AI-ready', 'url': 'https://www.agentql.com/blog/2024-launch-week-recap', 'date': 'Nov 18, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Accurate data extraction from PDFs and images with AgentQL', 'url': 'https://www.agentql.com/blog/accurate-data-extraction-pdfs-images', 'date': 'Feb 1, 2025', 'author': 'Rachel-Lee Nabors'}, {'title': 'Introducing Scheduled Scraping Workflows', 'url': 'https://www.agentql.com/blog/scheduling', 'date': 'Dec 2, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Updates to Our Pricing Model', 'url': 'https://www.agentql.com/blog/2024-pricing-update', 'date': 'Nov 19, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Get data from any page: AgentQL’s REST API Endpoint—Launch week day 5', 'url': 'https://www.agentql.com/blog/data-rest-api', 'date': 'Nov 15, 2024', 'author': 'Rachel-Lee Nabors'}]}\")"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wtPMNh72NSJ5",
+        "outputId": "59d529a4-3c22-445c-f5cf-dc7b24168906"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'request_id': 'bdb9dbe7-8a7f-427f-bc16-839ccc02cae6', 'generated_query': None, 'screenshot': None}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7RMuEwl4NSJ5"
+      },
+      "source": [
+        "## Lazy Load\n",
+        "\n",
+        "`AgentQLLoader` currently only loads one `Document` at a time. Therefore, `load()` and `lazy_load()` behave the same:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FIYddZBONSJ5",
+        "outputId": "c39a7a6d-bc52-4ef9-b36f-e1d138590b79"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[Document(metadata={'request_id': '06273abd-b2ef-4e15-b0ec-901cba7b4825', 'generated_query': None, 'screenshot': None}, page_content=\"{'posts': [{'title': 'Launch Week Recap—make the web AI-ready', 'url': 'https://www.agentql.com/blog/2024-launch-week-recap', 'date': 'Nov 18, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Accurate data extraction from PDFs and images with AgentQL', 'url': 'https://www.agentql.com/blog/accurate-data-extraction-pdfs-images', 'date': 'Feb 1, 2025', 'author': 'Rachel-Lee Nabors'}, {'title': 'Introducing Scheduled Scraping Workflows', 'url': 'https://www.agentql.com/blog/scheduling', 'date': 'Dec 2, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Updates to Our Pricing Model', 'url': 'https://www.agentql.com/blog/2024-pricing-update', 'date': 'Nov 19, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Get data from any page: AgentQL’s REST API Endpoint—Launch week day 5', 'url': 'https://www.agentql.com/blog/data-rest-api', 'date': 'Nov 15, 2024', 'author': 'Rachel-Lee Nabors'}]}\")]"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pages = [doc for doc in loader.lazy_load()]\n",
+        "pages"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For more information on how to use this integration, please refer to the [git repo](https://github.com/tinyfish-io/agentql-integrations/tree/main/langchain) or the [langchain integration documentation](https://docs.agentql.com/integrations/langchain)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

docs/docs/integrations/document_loaders/apify_dataset.ipynb

@@ -2,7 +2,9 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "xwiDq5fOuoRn"
+   },
    "source": [
     "# Apify Dataset\n",
     "\n",
@@ -20,33 +22,63 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "id": "qRW2-mokuoRp",
     "tags": []
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  apify-client"
+    "%pip install --upgrade --quiet langchain langchain-apify langchain-openai"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "8jRVq16LuoRq"
+   },
    "source": [
     "First, import `ApifyDatasetLoader` into your source code:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "id": "umXQHqIJuoRq"
+   },
    "outputs": [],
    "source": [
-    "from langchain_community.document_loaders import ApifyDatasetLoader\n",
+    "from langchain_apify import ApifyDatasetLoader\n",
     "from langchain_core.documents import Document"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "NjGwKy59vz1X"
+   },
+   "source": [
+    "Find your [Apify API token](https://console.apify.com/account/integrations) and [OpenAI API key](https://platform.openai.com/account/api-keys) and initialize these into environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "AvzNtyCxwDdr"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"APIFY_API_TOKEN\"] = \"your-apify-api-token\"\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d1O-KL48uoRr"
+   },
    "source": [
     "Then provide a function that maps Apify dataset record fields to LangChain `Document` format.\n",
     "\n",
@@ -64,8 +96,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "id": "m1SpA7XZuoRr"
+   },
    "outputs": [],
    "source": [
     "loader = ApifyDatasetLoader(\n",
@@ -78,8 +112,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "id": "0hWX7ABsuoRs"
+   },
    "outputs": [],
    "source": [
     "data = loader.load()"
@@ -87,7 +123,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "EJCVFVKNuoRs"
+   },
    "source": [
     "## An example with question answering\n",
     "\n",
@@ -96,21 +134,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "id": "sNisJKzZuoRt"
+   },
    "outputs": [],
    "source": [
     "from langchain.indexes import VectorstoreIndexCreator\n",
-    "from langchain_community.utilities import ApifyWrapper\n",
+    "from langchain_apify import ApifyWrapper\n",
     "from langchain_core.documents import Document\n",
-    "from langchain_openai import OpenAI\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "from langchain_openai import ChatOpenAI\n",
     "from langchain_openai.embeddings import OpenAIEmbeddings"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "id": "qcfmnbdDuoRu"
+   },
    "outputs": [],
    "source": [
     "loader = ApifyDatasetLoader(\n",
@@ -123,27 +166,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "id": "8b0xzKJxuoRv"
+   },
    "outputs": [],
    "source": [
-    "index = VectorstoreIndexCreator(embedding=OpenAIEmbeddings()).from_loaders([loader])"
+    "index = VectorstoreIndexCreator(\n",
+    "    vectorstore_cls=InMemoryVectorStore, embedding=OpenAIEmbeddings()\n",
+    ").from_loaders([loader])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "id": "7zPXGsVFwUGA"
+   },
+   "outputs": [],
+   "source": [
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "ecWrdM4guoRv"
+   },
    "outputs": [],
    "source": [
     "query = \"What is Apify?\"\n",
-    "result = index.query_with_sources(query, llm=OpenAI())"
+    "result = index.query_with_sources(query, llm=llm)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "id": "QH8r44e9uoRv",
+    "outputId": "361fe050-f75d-4d5a-c327-5e7bd190fba5"
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -162,6 +225,9 @@
   }
  ],
  "metadata": {
+  "colab": {
+   "provenance": []
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -181,5 +247,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
-}
+ "nbformat_minor": 0
+}

docs/docs/integrations/document_loaders/docling.ipynb

@@ -521,9 +521,9 @@
    "source": [
     "## API reference\n",
     "\n",
-    "- [LangChain Docling integration GitHub](https://github.com/DS4SD/docling-langchain)\n",
-    "- [Docling GitHub](https://github.com/DS4SD/docling)\n",
-    "- [Docling docs](https://ds4sd.github.io/docling/)"
+    "- [LangChain Docling integration GitHub](https://github.com/docling-project/docling-langchain)\n",
+    "- [Docling GitHub](https://github.com/docling-project/docling)\n",
+    "- [Docling docs](https://docling-project.github.io/docling//)"
    ]
   },
   {

docs/docs/integrations/document_loaders/parsers/writer_pdf_parser.ipynb

@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "db23d51760310705",
+   "metadata": {},
+   "source": [
+    "# Writer PDF Parser\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with the Writer `PDFParser` [document loader](/docs/concepts/document_loaders/).\n",
+    "\n",
+    "Writer's [PDF Parser](https://dev.writer.com/api-guides/api-reference/tool-api/pdf-parser#parse-pdf) converts PDF documents into other formats like text or Markdown. This is particularly useful when you need to extract and process text content from PDF files for further analysis or integration into your workflow. In `langchain-writer`, we provide usage of Writer's PDF Parser as a LangChain document parser.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "| Class                                                                                                                              | Package          | Local | Serializable | JS support |                                        Package downloads                                         |                                        Package latest                                         |\n",
+    "|:-----------------------------------------------------------------------------------------------------------------------------------|:-----------------| :---: | :---: |:----------:|:------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|\n",
+    "| [PDFParser](https://github.com/writer/langchain-writer/blob/main/langchain_writer/pdf_parser.py#L55) | [langchain-writer](https://pypi.org/project/langchain-writer/) |      ❌       |                                       ❌                                       | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-writer?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-writer?style=flat-square&label=%20) |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5f08d23df5dc127",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The `PDFParser` is available in the `langchain-writer` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a8d653f15b7ee32d",
+   "metadata": {},
+   "source": [
+    "%pip install --quiet -U langchain-writer"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b9709c26797edf",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "Sign up for [Writer AI Studio](https://app.writer.com/aistudio/signup?utm_campaign=devrel) to generate an API key (you can follow this [Quickstart](https://dev.writer.com/api-guides/quickstart)). Then, set the WRITER_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "2983e19c9d555e58",
+   "metadata": {},
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"WRITER_API_KEY\"):\n",
+    "    os.environ[\"WRITER_API_KEY\"] = getpass.getpass(\"Enter your Writer API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92a22c77f03d43dc",
+   "metadata": {},
+   "source": [
+    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability. If you wish to do so, you can set the `LANGSMITH_TRACING` and `LANGSMITH_API_KEY` environment variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "98d8422ecee77403",
+   "metadata": {},
+   "source": [
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67ab78950a3da8ba",
+   "metadata": {},
+   "source": [
+    "### Instantiation\n",
+    "\n",
+    "Next, instantiate an instance of the Writer PDF Parser with the desired output format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "787b3ba8af32533f",
+   "metadata": {},
+   "source": [
+    "from langchain_writer.pdf_parser import PDFParser\n",
+    "\n",
+    "parser = PDFParser(format=\"markdown\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d91c6f752fd31cee",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "There are two ways to use the PDF Parser, either synchronously or asynchronously. In either case, the PDF Parser will return a list of `Document` objects, each containing the parsed content of a page from the PDF file.\n",
+    "\n",
+    "### Synchronous usage\n",
+    "\n",
+    "To invoke the PDF Parser synchronously, pass a `Blob` object to the `parse` method referencing the PDF file you want to parse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d1a24b81a8a96f09",
+   "metadata": {},
+   "source": [
+    "from langchain_core.documents.base import Blob\n",
+    "\n",
+    "file = Blob.from_path(\"../example_data/layout-parser-paper.pdf\")\n",
+    "\n",
+    "parsed_pages = parser.parse(blob=file)\n",
+    "parsed_pages"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f89c048c7d23807a",
+   "metadata": {},
+   "source": [
+    "### Asynchronous usage\n",
+    "\n",
+    "To invoke the PDF Parser asynchronously, pass a `Blob` object to the `aparse` method referencing the PDF file you want to parse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e2f7fd52b7188c6c",
+   "metadata": {},
+   "source": [
+    "parsed_pages_async = await parser.aparse(blob=file)\n",
+    "parsed_pages_async"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab25a3bed8437a05",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `PDFParser` features and configurations, head to the [API reference](https://python.langchain.com/api_reference/writer/pdf_parser/langchain_writer.pdf_parser.PDFParser.html#langchain_writer.pdf_parser.PDFParser).\n",
+    "\n",
+    "## Additional resources\n",
+    "You can find information about Writer's models (including costs, context windows, and supported input types) and tools in the [Writer docs](https://dev.writer.com/home).\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/document_loaders/powerscale.ipynb

@@ -0,0 +1,244 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dell PowerScale Document Loader\n",
+    "\n",
+    "[Dell PowerScale](https://www.dell.com/en-us/shop/powerscale-family/sf/powerscale) is an enterprise scale out storage system that hosts industry leading OneFS filesystem that can be hosted on-prem or deployed in the cloud.\n",
+    "\n",
+    "This document loader utilizes unique capabilities from PowerScale that can determine what files that have been modified since an application's last run and only returns modified files for processing. This will eliminate the need to re-process (chunk and embed) files that have not been changed, improving the overall data ingestion workflow.\n",
+    "\n",
+    "This loader requires PowerScale's MetadataIQ feature enabled. Additional information can be found on our GitHub Repo: [https://github.com/dell/powerscale-rag-connector](https://github.com/dell/powerscale-rag-connector)\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/__module_name___loader)|\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [PowerScaleDocumentLoader](https://github.com/dell/powerscale-rag-connector/blob/main/src/powerscale_rag_connector/PowerScaleDocumentLoader.py) | [powerscale-rag-connector](https://github.com/dell/powerscale-rag-connector) | ✅ | ❌ | ❌ | \n",
+    "| [PowerScaleUnstructuredLoader](https://github.com/dell/powerscale-rag-connector/blob/main/src/powerscale_rag_connector/PowerScaleUnstructuredLoader.py) | [powerscale-rag-connector](https://github.com/dell/powerscale-rag-connector) | ✅ | ❌ | ❌ | \n",
+    "### Loader features\n",
+    "| Source | Document Lazy Loading | Native Async Support\n",
+    "| :---: | :---: | :---: | \n",
+    "| PowerScaleDocumentLoader | ✅ | ✅ | \n",
+    "| PowerScaleUnstructuredLoader | ✅ | ✅ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "This document loader requires the use of a Dell PowerScale system with MetadataIQ enabled. Additional information can be found on our github page: [https://github.com/dell/powerscale-rag-connector](https://github.com/dell/powerscale-rag-connector)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The document loader lives in an external pip package and can be installed using standard tooling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  powerscale-rag-connector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate document loader:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generic Document Loader\n",
+    "\n",
+    "Our generic document loader can be used to incrementally load all files from PowerScale in the following manner:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from powerscale_rag_connector import PowerScaleDocumentLoader\n",
+    "\n",
+    "loader = PowerScaleDocumentLoader(\n",
+    "    es_host_url=\"http://elasticsearch:9200\",\n",
+    "    es_index_name=\"metadataiq\",\n",
+    "    es_api_key=\"your-api-key\",\n",
+    "    folder_path=\"/ifs/data\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### UnstructuredLoader Loader\n",
+    "\n",
+    "Optionally, the `PowerScaleUnstructuredLoader` can be used to locate the changed files _and_ automatically process the files producing elements of the source file. This is done using LangChain's `UnstructuredLoader` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from powerscale_rag_connector import PowerScaleUnstructuredLoader\n",
+    "\n",
+    "# Or load files with the Unstructured Loader\n",
+    "loader = PowerScaleUnstructuredLoader(\n",
+    "    es_host_url=\"http://elasticsearch:9200\",\n",
+    "    es_index_name=\"metadataiq\",\n",
+    "    es_api_key=\"your-api-key\",\n",
+    "    folder_path=\"/ifs/data\",\n",
+    "    # 'elements' mode splits the document into more granular chunks\n",
+    "    # Use 'single' mode if you want the entire document as a single chunk\n",
+    "    mode=\"elements\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fields:\n",
+    " - `es_host_url` is the endpoint to to MetadataIQ Elasticsearch database\n",
+    " - `es_index_index` is the name of the index where PowerScale writes it file system metadata\n",
+    " - `es_api_key` is the **encoded** version of your elasticsearch API key\n",
+    " - `folder_path` is the path on PowerScale to be queried for changes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load\n",
+    "\n",
+    "Internally, all code is asynchronous with PowerScale and MetadataIQ and the load and lazy load methods will return a python generator. We recommend using the lazy load function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='' metadata={'source': '/ifs/pdfs/1994-Graph.Theoretic.Obstacles.to.Perfect.Hashing.TR0257.pdf', 'snapshot': 20834, 'change_types': ['ENTRY_ADDED']}),\n",
+       "Document(page_content='' metadata={'source': '/ifs/pdfs/New.sendfile-FreeBSD.20.Feb.2015.pdf', 'snapshot': 20920, 'change_types': ['ENTRY_MODIFIED']}),\n",
+       "Document(page_content='' metadata={'source': '/ifs/pdfs/FAST-Fast.Architecture.Sensitive.Tree.Search.on.Modern.CPUs.and.GPUs-Slides.pdf', 'snapshot': 20924, 'change_types': ['ENTRY_ADDED']})]\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for doc in loader.load():\n",
+    "    print(doc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Returned Object\n",
+    "\n",
+    "Both document loaders will keep track of what files were previously returned to your application. When called again, the document loader will only return new or modified files since your previous run.\n",
+    "\n",
+    " - The `metadata` fields in the returned `Document` will return the path on PowerScale that contains the modified file. You will use this path to read the data via NFS (or S3) and process the data in your application (e.g.: create chunks and embedding). \n",
+    " - The `source` field is the path on PowerScale and not necessarily on your local system (depending on your mount strategy); OneFS expresses the entire storage system as a single tree rooted at `/ifs`.\n",
+    " - The `change_types` property will inform you on what change occurred since the last one - e.g.: new, modified or delete.\n",
+    "\n",
+    "Your RAG application can use the information from `change_types` to add, update or delete entries your chunk and vector store.\n",
+    "\n",
+    "When using `PowerScaleUnstructuredLoader` the `page_content` field will be filled with data from the Unstructured Loader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load\n",
+    "\n",
+    "Internally, all code is asynchronous with PowerScale and MetadataIQ and the load and lazy load methods will return a python generator. We recommend using the lazy load function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for doc in loader.lazy_load():\n",
+    "    print(doc)  # do something specific with the document"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same `Document` is returned as the load function with all the same properties mentioned above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional Examples\n",
+    "\n",
+    "Additional examples and code can be found on our public github webpage: [https://github.com/dell/powerscale-rag-connector/tree/main/examples](https://github.com/dell/powerscale-rag-connector/tree/main/examples) that provide full working examples. \n",
+    "\n",
+    " - [PowerScale LangChain Document Loader](https://github.com/dell/powerscale-rag-connector/blob/main/examples/powerscale_langchain_doc_loader.py) - Working example of our standard document loader\n",
+    " - [PowerScale LangChain Unstructured Loader](https://github.com/dell/powerscale-rag-connector/blob/main/examples/powerscale_langchain_unstructured_loader.py) - Working example of our standard document loader using unstructured loader for chunking and embedding\n",
+    " - [PowerScale NVIDIA Retriever Microservice Loader](https://github.com/dell/powerscale-rag-connector/blob/main/examples/powerscale_nvingest_example.py) - Working example of our document loader with NVIDIA NeMo Retriever microservices for chunking and embedding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all PowerScale Document Loader features and configurations head to the github page: [https://github.com/dell/powerscale-rag-connector/](https://github.com/dell/powerscale-rag-connector/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/integrations/document_loaders/pymupdf4llm.ipynb

@@ -0,0 +1,721 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: PyMuPDF4LLM\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyMuPDF4LLMLoader\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with PyMuPDF4LLM [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all PyMuPDF4LLMLoader features and configurations head to the [GitHub repository](https://github.com/lakinduboteju/langchain-pymupdf4llm).\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support |\n",
+    "| :--- | :--- | :---: | :---: |  :---: |\n",
+    "| [PyMuPDF4LLMLoader](https://github.com/lakinduboteju/langchain-pymupdf4llm) | [langchain_pymupdf4llm](https://pypi.org/project/langchain-pymupdf4llm) | ✅ | ❌ | ❌ |\n",
+    "\n",
+    "### Loader features\n",
+    "\n",
+    "| Source | Document Lazy Loading | Native Async Support | Extract Images | Extract Tables |\n",
+    "| :---: | :---: | :---: | :---: | :---: |\n",
+    "| PyMuPDF4LLMLoader | ✅ | ❌ | ✅ | ✅ |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access PyMuPDF4LLM document loader you'll need to install the `langchain-pymupdf4llm` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "No credentials are required to use PyMuPDF4LLMLoader."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_community** and **langchain-pymupdf4llm**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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_community langchain-pymupdf4llm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "Now we can instantiate our model object and load documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_pymupdf4llm import PyMuPDF4LLMLoader\n",
+    "\n",
+    "file_path = \"./example_data/layout-parser-paper.pdf\"\n",
+    "loader = PyMuPDF4LLMLoader(file_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Document(metadata={'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'trapped': '', 'modDate': 'D:20210622012710Z', 'creationDate': 'D:20210622012710Z', 'page': 0}, page_content='```\\nLayoutParser: A Unified Toolkit for Deep\\n\\n## Learning Based Document Image Analysis\\n\\n```\\n\\nZejiang Shen[1] (<28>), Ruochen Zhang[2], Melissa Dell[3], Benjamin Charles Germain\\nLee[4], Jacob Carlson[3], and Weining Li[5]\\n\\n1 Allen Institute for AI\\n```\\n              shannons@allenai.org\\n\\n```\\n2 Brown University\\n```\\n             ruochen zhang@brown.edu\\n\\n```\\n3 Harvard University\\n_{melissadell,jacob carlson}@fas.harvard.edu_\\n4 University of Washington\\n```\\n              bcgl@cs.washington.edu\\n\\n```\\n5 University of Waterloo\\n```\\n              w422li@uwaterloo.ca\\n\\n```\\n\\n**Abstract. Recent advances in document image analysis (DIA) have been**\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of important innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applications. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout detection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digitization pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\n[The library is publicly available at https://layout-parser.github.io.](https://layout-parser.github.io)\\n\\n**Keywords: Document Image Analysis · Deep Learning · Layout Analysis**\\n\\n    - Character Recognition · Open Source library · Toolkit.\\n\\n### 1 Introduction\\n\\n\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [11,\\n\\n')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs = loader.load()\n",
+    "docs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z',\n",
+      " 'page': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pprint\n",
+    "\n",
+    "pprint.pp(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lazy Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pages = []\n",
+    "for doc in loader.lazy_load():\n",
+    "    pages.append(doc)\n",
+    "    if len(pages) >= 10:\n",
+    "        # do some paged operation, e.g.\n",
+    "        # index.upsert(page)\n",
+    "\n",
+    "        pages = []\n",
+    "len(pages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Markdown, display\n",
+    "\n",
+    "part = pages[0].page_content[778:1189]\n",
+    "print(part)\n",
+    "# Markdown rendering\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z',\n",
+      " 'page': 10}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint.pp(pages[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The metadata attribute contains at least the following keys:\n",
+    "- source\n",
+    "- page (if in mode *page*)\n",
+    "- total_page\n",
+    "- creationdate\n",
+    "- creator\n",
+    "- producer\n",
+    "\n",
+    "Additional metadata are specific to each parser.\n",
+    "These pieces of information can be helpful (to categorize your PDFs for example)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Splitting mode & custom pages delimiter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When loading the PDF file you can split it in two different ways:\n",
+    "- By page\n",
+    "- As a single text flow\n",
+    "\n",
+    "By default PyMuPDF4LLMLoader will split the PDF by page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract the PDF by page. Each page is extracted as a langchain Document object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16\n",
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z',\n",
+      " 'page': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(len(docs))\n",
+    "pprint.pp(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this mode the pdf is split by pages and the resulting Documents metadata contains the `page` (page number). But in some cases we could want to process the pdf as a single text flow (so we don't cut some paragraphs in half). In this case you can use the *single* mode :"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract the whole PDF as a single langchain Document object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "{'producer': 'pdfTeX-1.40.21',\n",
+      " 'creator': 'LaTeX with hyperref',\n",
+      " 'creationdate': '2021-06-22T01:27:10+00:00',\n",
+      " 'source': './example_data/layout-parser-paper.pdf',\n",
+      " 'file_path': './example_data/layout-parser-paper.pdf',\n",
+      " 'total_pages': 16,\n",
+      " 'format': 'PDF 1.5',\n",
+      " 'title': '',\n",
+      " 'author': '',\n",
+      " 'subject': '',\n",
+      " 'keywords': '',\n",
+      " 'moddate': '2021-06-22T01:27:10+00:00',\n",
+      " 'trapped': '',\n",
+      " 'modDate': 'D:20210622012710Z',\n",
+      " 'creationDate': 'D:20210622012710Z'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"single\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(len(docs))\n",
+    "pprint.pp(docs[0].metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Logically, in this mode, the `page` (page_number) metadata disappears. Here's how to clearly identify where pages end in the text flow :"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add a custom *pages_delimiter* to identify where are ends of pages in *single* mode:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"single\",\n",
+    "    pages_delimiter=\"\\n-------THIS IS A CUSTOM END OF PAGE-------\\n\\n\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[0].page_content[10663:11317]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The default `pages_delimiter` is \\n-----\\n\\n.\n",
+    "But this could simply be \\n, or \\f to clearly indicate a page change, or \\<!-- PAGE BREAK --> for seamless injection in a Markdown viewer without a visual effect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract images from the PDF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can extract images from your PDFs (in text form) with a choice of three different solutions:\n",
+    "- rapidOCR (lightweight Optical Character Recognition tool)\n",
+    "- Tesseract (OCR tool with high precision)\n",
+    "- Multimodal language model\n",
+    "\n",
+    "The result is inserted at the end of text of the page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract images from the PDF with rapidOCR:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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 rapidocr-onnxruntime pillow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders.parsers import RapidOCRBlobParser\n",
+    "\n",
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    extract_images=True,\n",
+    "    images_parser=RapidOCRBlobParser(),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[5].page_content[1863:]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Be careful, RapidOCR is designed to work with Chinese and English, not other languages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract images from the PDF with Tesseract:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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 pytesseract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders.parsers import TesseractBlobParser\n",
+    "\n",
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    extract_images=True,\n",
+    "    images_parser=TesseractBlobParser(),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(docs[5].page_content[1863:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract images from the PDF with multimodal model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from getpass import getpass\n",
+    "\n",
+    "if not os.environ.get(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"OpenAI API key =\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders.parsers import LLMImageBlobParser\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    extract_images=True,\n",
+    "    images_parser=LLMImageBlobParser(\n",
+    "        model=ChatOpenAI(model=\"gpt-4o-mini\", max_tokens=1024)\n",
+    "    ),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "print(docs[5].page_content[1863:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract tables from the PDF\n",
+    "\n",
+    "With PyMUPDF4LLM you can extract tables from your PDFs in *markdown* format :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = PyMuPDF4LLMLoader(\n",
+    "    \"./example_data/layout-parser-paper.pdf\",\n",
+    "    mode=\"page\",\n",
+    "    # \"lines_strict\" is the default strategy and\n",
+    "    # is the most accurate for tables with column and row lines,\n",
+    "    # but may not work well with all documents.\n",
+    "    # \"lines\" is a less strict strategy that may work better with\n",
+    "    # some documents.\n",
+    "    # \"text\" is the least strict strategy and may work better\n",
+    "    # with documents that do not have tables with lines.\n",
+    "    table_strategy=\"lines\",\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[4].page_content[3210:]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with Files\n",
+    "\n",
+    "Many document loaders involve parsing files. The difference between such loaders usually stems from how the file is parsed, rather than how the file is loaded. For example, you can use `open` to read the binary content of either a PDF or a markdown file, but you need different parsing logic to convert that binary data into text.\n",
+    "\n",
+    "As a result, it can be helpful to decouple the parsing logic from the loading logic, which makes it easier to re-use a given parser regardless of how the data was loaded.\n",
+    "You can use this strategy to analyze different files, with the same parsing parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import FileSystemBlobLoader\n",
+    "from langchain_community.document_loaders.generic import GenericLoader\n",
+    "from langchain_pymupdf4llm import PyMuPDF4LLMParser\n",
+    "\n",
+    "loader = GenericLoader(\n",
+    "    blob_loader=FileSystemBlobLoader(\n",
+    "        path=\"./example_data/\",\n",
+    "        glob=\"*.pdf\",\n",
+    "    ),\n",
+    "    blob_parser=PyMuPDF4LLMParser(),\n",
+    ")\n",
+    "docs = loader.load()\n",
+    "\n",
+    "part = docs[0].page_content[:562]\n",
+    "print(part)\n",
+    "display(Markdown(part))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all PyMuPDF4LLMLoader features and configurations head to the GitHub repository: https://github.com/lakinduboteju/langchain-pymupdf4llm"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.21"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/integrations/document_transformers/google_cloud_vertexai_rerank.ipynb

@@ -546,7 +546,7 @@
     "id": "ud_cnGszb1i9"
    },
    "source": [
-    "Let's inspect a couple of reranked documents. We observe that the retriever still returns the relevant Langchain type [documents](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) but as part of the metadata field, we also recieve the `relevance_score` from the Ranking API."
+    "Let's inspect a couple of reranked documents. We observe that the retriever still returns the relevant Langchain type [documents](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) but as part of the metadata field, we also receive the `relevance_score` from the Ranking API."
    ]
   },
   {

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

@@ -88,13 +88,13 @@
     "from langchain_openai import OpenAIEmbeddings\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "\n",
-    "documents = TextLoader(\"../../modules/state_of_the_union.txt\").load()\n",
+    "documents = TextLoader(\"../document_loaders/example_data/state_of_the_union.txt\").load()\n",
     "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
     "texts = text_splitter.split_documents(documents)\n",
     "for idx, text in enumerate(texts):\n",
     "    text.metadata[\"id\"] = idx\n",
     "\n",
-    "embedding = OpenAIEmbeddings(model=\"text-embedding-ada-002\")\n",
+    "embedding = OpenAIEmbeddings(model=\"text-embedding-3-large\")\n",
     "retriever = FAISS.from_documents(texts, embedding).as_retriever(search_kwargs={\"k\": 20})"
    ]
   },
@@ -114,25 +114,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-02-17 04:37:08,458 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Document 1:\n",
       "\n",
-      "And with an unwavering resolve that freedom will always triumph over tyranny. \n",
-      "\n",
-      "Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n",
-      "\n",
-      "He thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n",
-      "\n",
-      "He met the Ukrainian people.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 2:\n",
-      "\n",
       "Together with our allies –we are right now enforcing powerful economic sanctions. \n",
       "\n",
       "We are cutting off Russia’s largest banks from the international financial system.  \n",
@@ -141,12 +138,22 @@
       "\n",
       "We are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come.\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "Document 3:\n",
+      "Document 2:\n",
       "\n",
       "And tonight I am announcing that we will join our allies in closing off American air space to all Russian flights – further isolating Russia – and adding an additional squeeze –on their economy. The Ruble has lost 30% of its value. \n",
       "\n",
       "The Russian stock market has lost 40% of its value and trading remains suspended. Russia’s economy is reeling and Putin alone is to blame.\n",
       "----------------------------------------------------------------------------------------------------\n",
+      "Document 3:\n",
+      "\n",
+      "And now that he has acted the free world is holding him accountable. \n",
+      "\n",
+      "Along with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland. \n",
+      "\n",
+      "We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. \n",
+      "\n",
+      "Together with our allies –we are right now enforcing powerful economic sanctions.\n",
+      "----------------------------------------------------------------------------------------------------\n",
       "Document 4:\n",
       "\n",
       "I spent countless hours unifying our European allies. We shared with the world in advance what we knew Putin was planning and precisely how he would try to falsely justify his aggression.  \n",
@@ -167,50 +174,24 @@
       "----------------------------------------------------------------------------------------------------\n",
       "Document 6:\n",
       "\n",
-      "And now that he has acted the free world is holding him accountable. \n",
-      "\n",
-      "Along with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland. \n",
-      "\n",
-      "We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. \n",
-      "\n",
-      "Together with our allies –we are right now enforcing powerful economic sanctions.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 7:\n",
-      "\n",
       "To all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world. \n",
       "\n",
       "And I’m taking robust action to make sure the pain of our sanctions  is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. \n",
       "\n",
       "Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world.\n",
       "----------------------------------------------------------------------------------------------------\n",
+      "Document 7:\n",
+      "\n",
+      "And with an unwavering resolve that freedom will always triumph over tyranny. \n",
+      "\n",
+      "Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n",
+      "\n",
+      "He thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n",
+      "\n",
+      "He met the Ukrainian people.\n",
+      "----------------------------------------------------------------------------------------------------\n",
       "Document 8:\n",
       "\n",
-      "And we remain clear-eyed. The Ukrainians are fighting back with pure courage. But the next few days weeks, months, will be hard on them.  \n",
-      "\n",
-      "Putin has unleashed violence and chaos.  But while he may make gains on the battlefield – he will pay a continuing high price over the long run. \n",
-      "\n",
-      "And a proud Ukrainian people, who have known 30 years  of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 9:\n",
-      "\n",
-      "Tonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. \n",
-      "\n",
-      "The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.  \n",
-      "\n",
-      "We are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 10:\n",
-      "\n",
-      "America will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies.  \n",
-      "\n",
-      "These steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. \n",
-      "\n",
-      "But I want you to know that we are going to be okay. \n",
-      "\n",
-      "When the history of this era is written Putin’s war on Ukraine will have left Russia weaker and the rest of the world stronger.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 11:\n",
-      "\n",
       "They keep moving.   \n",
       "\n",
       "And the costs and the threats to America and the world keep rising.   \n",
@@ -225,51 +206,39 @@
       "\n",
       "He rejected repeated efforts at diplomacy.\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "Document 12:\n",
-      "\n",
-      "Our forces are not going to Europe to fight in Ukraine, but to defend our NATO Allies – in the event that Putin decides to keep moving west.  \n",
-      "\n",
-      "For that purpose we’ve mobilized American ground forces, air squadrons, and ship deployments to protect NATO countries including Poland, Romania, Latvia, Lithuania, and Estonia. \n",
-      "\n",
-      "As I have made crystal clear the United States and our Allies will defend every inch of territory of NATO countries with the full force of our collective power.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 13:\n",
+      "Document 9:\n",
       "\n",
       "While it shouldn’t have taken something so terrible for people around the world to see what’s at stake now everyone sees it clearly. \n",
       "\n",
       "We see the unity among leaders of nations and a more unified Europe a more unified West. And we see unity among the people who are gathering in cities in large crowds around the world even in Russia to demonstrate their support for Ukraine.\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "Document 14:\n",
+      "Document 10:\n",
       "\n",
-      "He met the Ukrainian people. \n",
+      "America will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies.  \n",
       "\n",
-      "From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n",
+      "These steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. \n",
       "\n",
-      "Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n",
+      "But I want you to know that we are going to be okay. \n",
       "\n",
-      "In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight.\n",
+      "When the history of this era is written Putin’s war on Ukraine will have left Russia weaker and the rest of the world stronger.\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "Document 15:\n",
+      "Document 11:\n",
       "\n",
-      "In the battle between democracy and autocracy, democracies are rising to the moment, and the world is clearly choosing the side of peace and security. \n",
+      "Tonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. \n",
       "\n",
-      "This is a real test. It’s going to take time. So let us continue to draw inspiration from the iron will of the Ukrainian people. \n",
+      "The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.  \n",
       "\n",
-      "To our fellow Ukrainian Americans who forge a deep bond that connects our two nations we stand with you. \n",
-      "\n",
-      "Putin may circle Kyiv with tanks, but he will never gain the hearts and souls of the Ukrainian people.\n",
+      "We are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains.\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "Document 16:\n",
+      "Document 12:\n",
       "\n",
-      "Together with our allies we are providing support to the Ukrainians in their fight for freedom. Military assistance. Economic assistance. Humanitarian assistance. \n",
+      "And we remain clear-eyed. The Ukrainians are fighting back with pure courage. But the next few days weeks, months, will be hard on them.  \n",
       "\n",
-      "We are giving more than $1 Billion in direct assistance to Ukraine. \n",
+      "Putin has unleashed violence and chaos.  But while he may make gains on the battlefield – he will pay a continuing high price over the long run. \n",
       "\n",
-      "And we will continue to aid the Ukrainian people as they defend their country and to help ease their suffering.  \n",
-      "\n",
-      "Let me be clear, our forces are not engaged and will not engage in conflict with Russian forces in Ukraine.\n",
+      "And a proud Ukrainian people, who have known 30 years  of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards.\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "Document 17:\n",
+      "Document 13:\n",
       "\n",
       "Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. \n",
       "\n",
@@ -281,7 +250,17 @@
       "\n",
       "And the costs and the threats to America and the world keep rising.\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "Document 18:\n",
+      "Document 14:\n",
+      "\n",
+      "Together with our allies we are providing support to the Ukrainians in their fight for freedom. Military assistance. Economic assistance. Humanitarian assistance. \n",
+      "\n",
+      "We are giving more than $1 Billion in direct assistance to Ukraine. \n",
+      "\n",
+      "And we will continue to aid the Ukrainian people as they defend their country and to help ease their suffering.  \n",
+      "\n",
+      "Let me be clear, our forces are not engaged and will not engage in conflict with Russian forces in Ukraine.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 15:\n",
       "\n",
       "It fueled our efforts to vaccinate the nation and combat COVID-19. It delivered immediate economic relief for tens of millions of Americans.  \n",
       "\n",
@@ -289,25 +268,56 @@
       "\n",
       "And as my Dad used to say, it gave people a little breathing room.\n",
       "----------------------------------------------------------------------------------------------------\n",
+      "Document 16:\n",
+      "\n",
+      "In the battle between democracy and autocracy, democracies are rising to the moment, and the world is clearly choosing the side of peace and security. \n",
+      "\n",
+      "This is a real test. It’s going to take time. So let us continue to draw inspiration from the iron will of the Ukrainian people. \n",
+      "\n",
+      "To our fellow Ukrainian Americans who forge a deep bond that connects our two nations we stand with you. \n",
+      "\n",
+      "Putin may circle Kyiv with tanks, but he will never gain the hearts and souls of the Ukrainian people.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 17:\n",
+      "\n",
+      "He met the Ukrainian people. \n",
+      "\n",
+      "From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n",
+      "\n",
+      "Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n",
+      "\n",
+      "In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 18:\n",
+      "\n",
+      "Our forces are not going to Europe to fight in Ukraine, but to defend our NATO Allies – in the event that Putin decides to keep moving west.  \n",
+      "\n",
+      "For that purpose we’ve mobilized American ground forces, air squadrons, and ship deployments to protect NATO countries including Poland, Romania, Latvia, Lithuania, and Estonia. \n",
+      "\n",
+      "As I have made crystal clear the United States and our Allies will defend every inch of territory of NATO countries with the full force of our collective power.\n",
+      "----------------------------------------------------------------------------------------------------\n",
       "Document 19:\n",
       "\n",
-      "My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.  \n",
+      "I understand. \n",
       "\n",
-      "Our troops in Iraq and Afghanistan faced many dangers. \n",
+      "I remember when my Dad had to leave our home in Scranton, Pennsylvania to find work. I grew up in a family where if the price of food went up, you felt it. \n",
       "\n",
-      "One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. \n",
+      "That’s why one of the first things I did as President was fight to pass the American Rescue Plan.  \n",
       "\n",
-      "When they came home, many of the world’s fittest and best trained warriors were never the same. \n",
+      "Because people were hurting. We needed to act, and we did. \n",
       "\n",
-      "Headaches. Numbness. Dizziness.\n",
+      "Few pieces of legislation have done more in a critical moment in our history to lift us out of crisis.\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Document 20:\n",
       "\n",
-      "Every Administration says they’ll do it, but we are actually doing it. \n",
+      "And as my Dad used to say, it gave people a little breathing room. \n",
       "\n",
-      "We will buy American to make sure everything from the deck of an aircraft carrier to the steel on highway guardrails are made in America. \n",
+      "And unlike the $2 Trillion tax cut passed in the previous administration that benefitted the top 1% of Americans, the American Rescue Plan helped working people—and left no one behind. \n",
       "\n",
-      "But to compete for the best jobs of the future, we also need to level the playing field with China and other competitors.\n"
+      "And it worked. It created jobs. Lots of jobs. \n",
+      "\n",
+      "In fact—our economy created over 6.5 Million new jobs just last year, more jobs created in one year  \n",
+      "than ever before in the history of America.\n"
      ]
     }
    ],
@@ -456,34 +466,35 @@
       "----------------------------------------------------------------------------------------------------\n",
       "Document 2:\n",
       "\n",
-      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
-      "\n",
-      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 3:\n",
-      "\n",
       "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
       "\n",
       "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.\n",
       "----------------------------------------------------------------------------------------------------\n",
+      "Document 3:\n",
+      "\n",
+      "We cannot let this happen. \n",
+      "\n",
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\n",
+      "----------------------------------------------------------------------------------------------------\n",
       "Document 4:\n",
       "\n",
-      "He met the Ukrainian people. \n",
+      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
       "\n",
-      "From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n",
-      "\n",
-      "Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n",
-      "\n",
-      "In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight.\n",
+      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Document 5:\n",
       "\n",
-      "But that trickle-down theory led to weaker economic growth, lower wages, bigger deficits, and the widest gap between those at the top and everyone else in nearly a century. \n",
+      "Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \n",
       "\n",
-      "Vice President Harris and I ran for office with a new economic vision for America. \n",
+      "Last year COVID-19 kept us apart. This year we are finally together again. \n",
       "\n",
-      "Invest in America. Educate Americans. Grow the workforce. Build the economy from the bottom up  \n",
-      "and the middle out, not from the top down.\n",
+      "Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n",
+      "\n",
+      "With a duty to one another to the American people to the Constitution. \n",
+      "\n",
+      "And with an unwavering resolve that freedom will always triumph over tyranny.\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Document 6:\n",
       "\n",
@@ -501,6 +512,14 @@
       "----------------------------------------------------------------------------------------------------\n",
       "Document 7:\n",
       "\n",
+      "So that’s my plan. It will grow the economy and lower costs for families. \n",
+      "\n",
+      "So what are we waiting for? Let’s get this done. And while you’re at it, confirm my nominees to the Federal Reserve, which plays a critical role in fighting inflation.  \n",
+      "\n",
+      "My plan will not only lower costs to give families a fair shot, it will lower the deficit.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 8:\n",
+      "\n",
       "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n",
       "\n",
       "I’ve worked on these issues a long time. \n",
@@ -509,7 +528,15 @@
       "\n",
       "So let’s not abandon our streets. Or choose between safety and equal justice.\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "Document 8:\n",
+      "Document 9:\n",
+      "\n",
+      "So let’s not abandon our streets. Or choose between safety and equal justice. \n",
+      "\n",
+      "Let’s come together to protect our communities, restore trust, and hold law enforcement accountable. \n",
+      "\n",
+      "That’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers.\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Document 10:\n",
       "\n",
       "As I’ve told Xi Jinping, it is never a good bet to bet against the American people. \n",
       "\n",
@@ -517,60 +544,18 @@
       "\n",
       "And we’ll do it all to withstand the devastating effects of the climate crisis and promote environmental justice.\n",
       "----------------------------------------------------------------------------------------------------\n",
-      "Document 9:\n",
-      "\n",
-      "Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \n",
-      "\n",
-      "Last year COVID-19 kept us apart. This year we are finally together again. \n",
-      "\n",
-      "Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n",
-      "\n",
-      "With a duty to one another to the American people to the Constitution. \n",
-      "\n",
-      "And with an unwavering resolve that freedom will always triumph over tyranny.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 10:\n",
-      "\n",
-      "As Ohio Senator Sherrod Brown says, “It’s time to bury the label “Rust Belt.” \n",
-      "\n",
-      "It’s time. \n",
-      "\n",
-      "But with all the bright spots in our economy, record job growth and higher wages, too many families are struggling to keep up with the bills.  \n",
-      "\n",
-      "Inflation is robbing them of the gains they might otherwise feel. \n",
-      "\n",
-      "I get it. That’s why my top priority is getting prices under control.\n",
-      "----------------------------------------------------------------------------------------------------\n",
       "Document 11:\n",
       "\n",
-      "I’m also calling on Congress: pass a law to make sure veterans devastated by toxic exposures in Iraq and Afghanistan finally get the benefits and comprehensive health care they deserve. \n",
+      "Let’s pass the Paycheck Fairness Act and paid leave.  \n",
       "\n",
-      "And fourth, let’s end cancer as we know it. \n",
+      "Raise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n",
       "\n",
-      "This is personal to me and Jill, to Kamala, and to so many of you. \n",
+      "Let’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges. \n",
       "\n",
-      "Cancer is the #2 cause of death in America–second only to heart disease.\n",
+      "And let’s pass the PRO Act when a majority of workers want to form a union—they shouldn’t be stopped.\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Document 12:\n",
       "\n",
-      "Headaches. Numbness. Dizziness. \n",
-      "\n",
-      "A cancer that would put them in a flag-draped coffin. \n",
-      "\n",
-      "I know. \n",
-      "\n",
-      "One of those soldiers was my son Major Beau Biden. \n",
-      "\n",
-      "We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \n",
-      "\n",
-      "But I’m committed to finding out everything we can. \n",
-      "\n",
-      "Committed to military families like Danielle Robinson from Ohio. \n",
-      "\n",
-      "The widow of Sergeant First Class Heath Robinson.\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Document 13:\n",
-      "\n",
       "He will never extinguish their love of freedom. He will never weaken the resolve of the free world. \n",
       "\n",
       "We meet tonight in an America that has lived through two of the hardest years this nation has ever faced. \n",
@@ -581,73 +566,105 @@
       "\n",
       "I understand.\n",
       "----------------------------------------------------------------------------------------------------\n",
+      "Document 13:\n",
+      "\n",
+      "Well I know this nation.  \n",
+      "\n",
+      "We will meet the test. \n",
+      "\n",
+      "To protect freedom and liberty, to expand fairness and opportunity. \n",
+      "\n",
+      "We will save democracy. \n",
+      "\n",
+      "As hard as these times have been, I am more optimistic about America today than I have been my whole life. \n",
+      "\n",
+      "Because I see the future that is within our grasp. \n",
+      "\n",
+      "Because I know there is simply nothing beyond our capacity. \n",
+      "\n",
+      "We are the only nation on Earth that has always turned every crisis we have faced into an opportunity.\n",
+      "----------------------------------------------------------------------------------------------------\n",
       "Document 14:\n",
       "\n",
-      "When we invest in our workers, when we build the economy from the bottom up and the middle out together, we can do something we haven’t done in a long time: build a better America. \n",
+      "If we want to go forward—not backward—we must protect access to health care. Preserve a woman’s right to choose. And let’s continue to advance maternal health care in America. \n",
       "\n",
-      "For more than two years, COVID-19 has impacted every decision in our lives and the life of the nation. \n",
-      "\n",
-      "And I know you’re tired, frustrated, and exhausted. \n",
-      "\n",
-      "But I also know this.\n",
+      "And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong.\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Document 15:\n",
       "\n",
-      "My plan to fight inflation will lower your costs and lower the deficit. \n",
+      "He met the Ukrainian people. \n",
       "\n",
-      "17 Nobel laureates in economics say my plan will ease long-term inflationary pressures. Top business leaders and most Americans support my plan. And here’s the plan: \n",
+      "From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n",
       "\n",
-      "First – cut the cost of prescription drugs. Just look at insulin. One in ten Americans has diabetes. In Virginia, I met a 13-year-old boy named Joshua Davis.\n",
+      "Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n",
+      "\n",
+      "In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight.\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Document 16:\n",
       "\n",
-      "And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n",
+      "Tonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. \n",
       "\n",
-      "So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \n",
+      "The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.  \n",
       "\n",
-      "First, beat the opioid epidemic. \n",
-      "\n",
-      "There is so much we can do. Increase funding for prevention, treatment, harm reduction, and recovery.\n",
+      "We are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains.\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Document 17:\n",
       "\n",
-      "My plan will not only lower costs to give families a fair shot, it will lower the deficit. \n",
+      "It’s not only the right thing to do—it’s the economically smart thing to do. \n",
       "\n",
-      "The previous Administration not only ballooned the deficit with tax cuts for the very wealthy and corporations, it undermined the watchdogs whose job was to keep pandemic relief funds from being wasted. \n",
+      "That’s why immigration reform is supported by everyone from labor unions to religious leaders to the U.S. Chamber of Commerce. \n",
       "\n",
-      "But in my administration, the watchdogs have been welcomed back. \n",
+      "Let’s get it done once and for all. \n",
       "\n",
-      "We’re going after the criminals who stole billions in relief money meant for small businesses and millions of Americans.\n",
+      "Advancing liberty and justice also requires protecting the rights of women. \n",
+      "\n",
+      "The constitutional right affirmed in Roe v. Wade—standing precedent for half a century—is under attack as never before.\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Document 18:\n",
       "\n",
-      "So let’s not abandon our streets. Or choose between safety and equal justice. \n",
+      "Smartphones. The Internet. Technology we have yet to invent. \n",
       "\n",
-      "Let’s come together to protect our communities, restore trust, and hold law enforcement accountable. \n",
+      "But that’s just the beginning. \n",
       "\n",
-      "That’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers.\n",
+      "Intel’s CEO, Pat Gelsinger, who is here tonight, told me they are ready to increase their investment from  \n",
+      "$20 billion to $100 billion. \n",
+      "\n",
+      "That would be one of the biggest investments in manufacturing in American history. \n",
+      "\n",
+      "And all they’re waiting for is for you to pass this bill. \n",
+      "\n",
+      "So let’s not wait any longer. Send it to my desk. I’ll sign it.  \n",
+      "\n",
+      "And we will really take off.\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Document 19:\n",
       "\n",
-      "I understand. \n",
+      "And as my Dad used to say, it gave people a little breathing room. \n",
       "\n",
-      "I remember when my Dad had to leave our home in Scranton, Pennsylvania to find work. I grew up in a family where if the price of food went up, you felt it. \n",
+      "And unlike the $2 Trillion tax cut passed in the previous administration that benefitted the top 1% of Americans, the American Rescue Plan helped working people—and left no one behind. \n",
       "\n",
-      "That’s why one of the first things I did as President was fight to pass the American Rescue Plan.  \n",
+      "And it worked. It created jobs. Lots of jobs. \n",
       "\n",
-      "Because people were hurting. We needed to act, and we did. \n",
-      "\n",
-      "Few pieces of legislation have done more in a critical moment in our history to lift us out of crisis.\n",
+      "In fact—our economy created over 6.5 Million new jobs just last year, more jobs created in one year  \n",
+      "than ever before in the history of America.\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Document 20:\n",
       "\n",
+      "The only nation that can be defined by a single word: possibilities. \n",
+      "\n",
+      "So on this night, in our 245th year as a nation, I have come to report on the State of the Union. \n",
+      "\n",
+      "And my report is this: the State of the Union is strong—because you, the American people, are strong. \n",
+      "\n",
+      "We are stronger today than we were a year ago. \n",
+      "\n",
+      "And we will be stronger a year from now than we are today. \n",
+      "\n",
+      "Now is our moment to meet and overcome the challenges of our time. \n",
+      "\n",
       "And we will, as one people. \n",
       "\n",
-      "One America. \n",
-      "\n",
-      "The United States of America. \n",
-      "\n",
-      "May God bless you all. May God protect our troops.\n"
+      "One America.\n"
      ]
     }
    ],
@@ -666,14 +683,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.retrievers.contextual_compression import ContextualCompressionRetriever\n",
     "from langchain_community.document_compressors.rankllm_rerank import RankLLMRerank\n",
     "\n",
-    "compressor = RankLLMRerank(top_n=3, model=\"gpt\", gpt_model=\"gpt-3.5-turbo\")\n",
+    "compressor = RankLLMRerank(top_n=3, model=\"gpt\", gpt_model=\"gpt-4o-mini\")\n",
     "compression_retriever = ContextualCompressionRetriever(\n",
     "    base_compressor=compressor, base_retriever=retriever\n",
     ")"
@@ -702,9 +719,11 @@
       "----------------------------------------------------------------------------------------------------\n",
       "Document 3:\n",
       "\n",
-      "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n",
+      "We cannot let this happen. \n",
       "\n",
-      "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.\n"
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\n"
      ]
     },
     {
@@ -729,17 +748,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2153001/1437145854.py:10: LangChainDeprecationWarning: The method `Chain.__call__` was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
+      "  chain({\"query\": query})\n",
+      "2025-02-17 04:30:00,016 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+      "  0%|          | 0/1 [00:00<?, ?it/s]2025-02-17 04:30:01,649 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+      "100%|██████████| 1/1 [00:01<00:00,  1.63s/it]\n",
+      "2025-02-17 04:30:02,415 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "{'query': 'What did the president say about Ketanji Brown Jackson',\n",
-       " 'result': \"The President mentioned that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence. He highlighted her background as a former top litigator in private practice and a former federal public defender, as well as coming from a family of public school educators and police officers. He also mentioned that since her nomination, she has received broad support from various groups, including the Fraternal Order of Police and former judges appointed by Democrats and Republicans.\"}"
+       " 'result': \"The President mentioned that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and comes from a family of public school educators and police officers. He also highlighted her as a consensus builder and noted the broad range of support she has received since being nominated.\"}"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/docs/integrations/llms/ibm_watsonx.ipynb

@@ -195,7 +195,7 @@
    "id": "96ed13d4",
    "metadata": {},
    "source": [
-    "Instead of `model_id`, you can also pass the `deployment_id` of the previously tuned model. The entire model tuning workflow is described [here](https://ibm.github.io/watsonx-ai-python-sdk/pt_working_with_class_and_prompt_tuner.html)."
+    "Instead of `model_id`, you can also pass the `deployment_id` of the previously tuned model. The entire model tuning workflow is described in [Working with TuneExperiment and PromptTuner](https://ibm.github.io/watsonx-ai-python-sdk/pt_tune_experiment_run.html)."
    ]
   },
   {
@@ -420,7 +420,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "langchain_ibm",
    "language": "python",
    "name": "python3"
   },

docs/docs/integrations/llms/lmformatenforcer_experimental.ipynb

@@ -221,7 +221,7 @@
    "source": [
     "## JSONFormer LLM Wrapper\n",
     "\n",
-    "Let's try that again, now providing a the Action input's JSON Schema to the model."
+    "Let's try that again, now providing the Action input's JSON Schema to the model."
    ]
   },
   {

docs/docs/integrations/llms/pipeshift.ipynb

@@ -64,8 +64,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
    ]
   },
   {

docs/docs/integrations/llms/sagemaker.ipynb

@@ -104,8 +104,8 @@
     "\n",
     "import boto3\n",
     "from langchain.chains.question_answering import load_qa_chain\n",
-    "from langchain_community.llms import SagemakerEndpoint\n",
-    "from langchain_community.llms.sagemaker_endpoint import LLMContentHandler\n",
+    "from langchain_aws.llms import SagemakerEndpoint\n",
+    "from langchain_aws.llms.sagemaker_endpoint import LLMContentHandler\n",
     "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "query = \"\"\"How long was Elizabeth hospitalized?\n",
@@ -174,8 +174,8 @@
     "from typing import Dict\n",
     "\n",
     "from langchain.chains.question_answering import load_qa_chain\n",
-    "from langchain_community.llms import SagemakerEndpoint\n",
-    "from langchain_community.llms.sagemaker_endpoint import LLMContentHandler\n",
+    "from langchain_aws.llms import SagemakerEndpoint\n",
+    "from langchain_aws.llms.sagemaker_endpoint import LLMContentHandler\n",
     "from langchain_core.prompts import PromptTemplate\n",
     "\n",
     "query = \"\"\"How long was Elizabeth hospitalized?\n",

docs/docs/integrations/providers/abso.md

@@ -0,0 +1,14 @@
+# Abso
+
+[Abso](https://abso.ai/#router) is an open-source LLM proxy that automatically routes requests between fast and slow models based on prompt complexity. It uses various heuristics to chose the proper model. It's very fast and has low latency.
+
+
+## Installation and setup
+
+```bash
+pip install langchain-abso
+```
+
+## Chat Model
+
+See usage details [here](/docs/integrations/chat/abso)

docs/docs/integrations/providers/ads4gpts.mdx

@@ -0,0 +1,82 @@
+# ADS4GPTs
+
+> [ADS4GPTs](https://www.ads4gpts.com/) is building the open monetization backbone of the AI-Native internet. It helps AI applications monetize through advertising with a UX and Privacy first approach. 
+
+## Installation and Setup
+
+### Using pip
+You can install the package directly from PyPI:
+
+```bash
+pip install ads4gpts-langchain
+```
+
+### From Source
+Alternatively, install from source:
+
+```bash
+git clone https://github.com/ADS4GPTs/ads4gpts.git
+cd ads4gpts/libs/python-sdk/ads4gpts-langchain
+pip install .
+```
+
+## Prerequisites
+
+- Python 3.11+
+- ADS4GPTs API Key ([Obtain API Key](https://www.ads4gpts.com))
+
+## Environment Variables
+Set the following environment variables for API authentication:
+
+```bash
+export ADS4GPTS_API_KEY='your-ads4gpts-api-key'
+```
+
+Alternatively, API keys can be passed directly when initializing classes or stored in a `.env` file.
+
+## Tools
+
+ADS4GPTs provides two main tools for monetization:
+
+### Ads4gptsInlineSponsoredResponseTool
+This tool fetches native, sponsored responses that can be seamlessly integrated within your AI application's outputs.
+
+```python
+from ads4gpts_langchain import Ads4gptsInlineSponsoredResponseTool
+```
+
+### Ads4gptsSuggestedPromptTool
+Generates sponsored prompt suggestions to enhance user engagement and provide monetization opportunities.
+
+```python
+from ads4gpts_langchain import Ads4gptsSuggestedPromptTool
+```
+### Ads4gptsInlineConversationalTool
+Delivers conversational sponsored content that naturally fits within chat interfaces and dialogs.
+
+```python
+from ads4gpts_langchain import Ads4gptsInlineConversationalTool
+```
+
+### Ads4gptsInlineBannerTool
+Provides inline banner advertisements that can be displayed within your AI application's response.
+
+```python
+from ads4gpts_langchain import Ads4gptsInlineBannerTool
+```
+
+### Ads4gptsSuggestedBannerTool
+Generates banner advertisement suggestions that can be presented to users as recommended content.
+
+```python
+from ads4gpts_langchain import Ads4gptsSuggestedBannerTool
+```
+
+## Toolkit
+
+The `Ads4gptsToolkit` combines these tools for convenient access in LangChain applications.
+
+```python
+from ads4gpts_langchain import Ads4gptsToolkit
+```
+

docs/docs/integrations/providers/agentql.mdx

@@ -0,0 +1,35 @@
+# AgentQL
+
+[AgentQL](https://www.agentql.com/) provides web interaction and structured data extraction from any web page using an [AgentQL query](https://docs.agentql.com/agentql-query) or a Natural Language prompt. AgentQL can be used across multiple languages and web pages without breaking over time and change.
+
+## Installation and Setup
+
+Install the integration package:
+
+```bash
+pip install langchain-agentql
+```
+
+## API Key
+
+Get an API Key from our [Dev Portal](https://dev.agentql.com/) and add it to your environment variables:
+```
+export AGENTQL_API_KEY="your-api-key-here"
+```
+
+## DocumentLoader
+AgentQL's document loader provides structured data extraction from any web page using an AgentQL query.
+
+```python
+from langchain_agentql.document_loaders import AgentQLLoader
+```
+See our [document loader documentation and usage example](/docs/integrations/document_loaders/agentql).
+
+## Tools and Toolkits
+AgentQL tools provides web interaction and structured data extraction from any web page using an AgentQL query or a Natural Language prompt.
+
+```python
+from langchain_agentql.tools import ExtractWebDataTool, ExtractWebDataBrowserTool, GetWebElementBrowserTool
+from langchain_agentql import AgentQLBrowserToolkit
+```
+See our [tools documentation and usage example](/docs/integrations/tools/agentql).

docs/docs/integrations/providers/apify.mdx

@@ -14,20 +14,34 @@ blogs, or knowledge bases.
 
 ## Installation and Setup
 
-- Install the Apify API client for Python with `pip install apify-client`
+- Install the LangChain Apify package for Python with:
+```bash
+pip install langchain-apify
+```
 - Get your [Apify API token](https://console.apify.com/account/integrations) and either set it as
-  an environment variable (`APIFY_API_TOKEN`) or pass it to the `ApifyWrapper` as `apify_api_token` in the constructor.
+  an environment variable (`APIFY_API_TOKEN`) or pass it as `apify_api_token` in the constructor.
 
+## Tool
 
-## Utility
+You can use the `ApifyActorsTool` to use Apify Actors with agents.
+
+```python
+from langchain_apify import ApifyActorsTool
+```
+
+See [this notebook](/docs/integrations/tools/apify_actors) for example usage and a full example of a tool-calling agent with LangGraph in the [Apify LangGraph agent Actor template](https://apify.com/templates/python-langgraph).
+
+For more information on how to use this tool, visit [the Apify integration documentation](https://docs.apify.com/platform/integrations/langgraph).
+
+## Wrapper
 
 You can use the `ApifyWrapper` to run Actors on the Apify platform.
 
 ```python
-from langchain_community.utilities import ApifyWrapper
+from langchain_apify import ApifyWrapper
 ```
 
-For more information on this wrapper, see [the API reference](https://python.langchain.com/api_reference/community/utilities/langchain_community.utilities.apify.ApifyWrapper.html).
+For more information on how to use this wrapper, see [the Apify integration documentation](https://docs.apify.com/platform/integrations/langchain).
 
 
 ## Document loader
@@ -35,7 +49,10 @@ For more information on this wrapper, see [the API reference](https://python.lan
 You can also use our `ApifyDatasetLoader` to get data from Apify dataset.
 
 ```python
-from langchain_community.document_loaders import ApifyDatasetLoader
+from langchain_apify import ApifyDatasetLoader
 ```
 
 For a more detailed walkthrough of this loader, see [this notebook](/docs/integrations/document_loaders/apify_dataset).
+
+
+Source code for this integration can be found in the [LangChain Apify repository](https://github.com/apify/langchain-apify).

docs/docs/integrations/providers/azure_ai.mdx

@@ -0,0 +1,59 @@
+# Azure AI
+
+All functionality related to [Azure AI Foundry](https://learn.microsoft.com/en-us/azure/developer/python/get-started) and its related projects.
+
+Integration packages for Azure AI, Dynamic Sessions, SQL Server are maintained in 
+the [langchain-azure](https://github.com/langchain-ai/langchain-azure) repository.
+
+## Chat models
+
+We recommend developers start with the (`langchain-azure-ai`) to access all the models available in [Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/model-catalog-overview). 
+
+### Azure AI Chat Completions Model 
+
+Access models like Azure OpenAI, DeepSeek R1, Cohere, Phi and Mistral using the `AzureAIChatCompletionsModel` class.
+
+```bash
+pip install -U langchain-azure-ai
+```
+
+Configure your API key and Endpoint.
+
+```bash
+export AZURE_INFERENCE_CREDENTIAL=your-api-key
+export AZURE_INFERENCE_ENDPOINT=your-endpoint
+```
+
+```python
+from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
+
+llm = AzureAIChatCompletionsModel(
+    model_name="gpt-4o",
+    api_version="2024-05-01-preview",
+)
+
+llm.invoke('Tell me a joke and include some emojis')
+```
+
+## Embedding models
+
+### Azure AI model inference for embeddings
+
+```bash
+pip install -U langchain-azure-ai
+```
+
+Configure your API key and Endpoint.
+
+```bash
+export AZURE_INFERENCE_CREDENTIAL=your-api-key
+export AZURE_INFERENCE_ENDPOINT=your-endpoint
+```
+
+```python
+from langchain_azure_ai.embeddings import AzureAIEmbeddingsModel
+
+embed_model = AzureAIEmbeddingsModel(
+    model_name="text-embedding-ada-002"
+)
+```

docs/docs/integrations/providers/cognee.mdx

@@ -0,0 +1,27 @@
+# Cognee
+
+Cognee implements scalable, modular ECL (Extract, Cognify, Load) pipelines that allow
+you to interconnect and retrieve past conversations, documents, and audio
+transcriptions while reducing hallucinations, developer effort, and cost.
+
+Cognee merges graph and vector databases to uncover hidden relationships and new
+patterns in your data. You can automatically model, load and retrieve entities and
+objects representing your business domain and analyze their relationships, uncovering
+insights that neither vector stores nor graph stores alone can provide.
+
+Try it in a Google Colab  <a href="https://colab.research.google.com/drive/1g-Qnx6l_ecHZi0IOw23rg0qC4TYvEvWZ?usp=sharing">notebook</a>  or have a look at the <a href="https://docs.cognee.ai">documentation</a>.
+
+If you have questions, join cognee <a href="https://discord.gg/NQPKmU5CCg">Discord</a> community.
+
+Have you seen cognee's <a href="https://github.com/topoteretes/cognee-starter">starter repo</a>? Check it out!
+
+
+## Installation and Setup
+
+```bash
+pip install langchain-cognee
+```
+
+## Retrievers
+
+See detail on available retrievers [here](/docs/integrations/retrievers/cognee).

docs/docs/integrations/providers/contextual.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Contextual AI\n",
+    "\n",
+    "Contextual AI is a platform that offers state-of-the-art Retrieval-Augmented Generation (RAG) technology for enterprise applications. Our platformant models helps innovative teams build production-ready AI applications that can process millions of pages of documents with exceptional accuracy.\n",
+    "\n",
+    "## Grounded Language Model (GLM)\n",
+    "\n",
+    "The Grounded Language Model (GLM) is specifically engineered to minimize hallucinations in RAG and agentic applications. The GLM achieves:\n",
+    "\n",
+    "- State-of-the-art performance on the FACTS benchmark\n",
+    "- Responses strictly grounded in provided knowledge sources\n",
+    "\n",
+    "## Using Contextual AI with LangChain\n",
+    "\n",
+    "See details [here](/docs/integrations/chat/contextual).\n",
+    "\n",
+    "This integration allows you to easily incorporate Contextual AI's GLM into your LangChain workflows. Whether you're building applications for regulated industries or security-conscious environments, Contextual AI provides the grounded and reliable responses your use cases demand.\n",
+    "\n",
+    "Get started with a free trial today and experience the most grounded language model for enterprise AI applications."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "y8ku6X96sebl"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "According to the information available, there are two types of cats in the world:\n",
+      "\n",
+      "1. Good cats\n",
+      "2. Best cats\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "from langchain_contextual import ChatContextual\n",
+    "\n",
+    "# Set credentials\n",
+    "if not os.getenv(\"CONTEXTUAL_AI_API_KEY\"):\n",
+    "    os.environ[\"CONTEXTUAL_AI_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your Contextual API key: \"\n",
+    "    )\n",
+    "\n",
+    "# initialize Contextual llm\n",
+    "llm = ChatContextual(\n",
+    "    model=\"v1\",\n",
+    "    api_key=\"\",\n",
+    ")\n",
+    "# include a system prompt (optional)\n",
+    "system_prompt = \"You are a helpful assistant that uses all of the provided knowledge to answer the user's query to the best of your ability.\"\n",
+    "\n",
+    "# provide your own knowledge from your knowledge-base here in an array of string\n",
+    "knowledge = [\n",
+    "    \"There are 2 types of dogs in the world: good dogs and best dogs.\",\n",
+    "    \"There are 2 types of cats in the world: good cats and best cats.\",\n",
+    "]\n",
+    "\n",
+    "# create your message\n",
+    "messages = [\n",
+    "    (\"human\", \"What type of cats are there in the world and what are the types?\"),\n",
+    "]\n",
+    "\n",
+    "# invoke the GLM by providing the knowledge strings, optional system prompt\n",
+    "# if you want to turn off the GLM's commentary, pass True to the `avoid_commentary` argument\n",
+    "ai_msg = llm.invoke(\n",
+    "    messages, knowledge=knowledge, system_prompt=system_prompt, avoid_commentary=True\n",
+    ")\n",
+    "\n",
+    "print(ai_msg.content)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/docs/integrations/providers/databricks.md

@@ -103,14 +103,7 @@ See [MLflow LangChain Integration](/docs/integrations/providers/mlflow_tracking)
 
 SQLDatabase
 -----------
-You can connect to Databricks SQL using the SQLDatabase wrapper of LangChain.
-```
-from langchain.sql_database import SQLDatabase
-
-db = SQLDatabase.from_databricks(catalog="samples", schema="nyctaxi")
-```
-
-See [Databricks SQL Agent](https://docs.databricks.com/en/large-language-models/langchain.html#databricks-sql-agent) for how to connect Databricks SQL with your LangChain Agent as a powerful querying tool.
+To connect to Databricks SQL or query structured data, see the [Databricks structured retriever tool documentation](https://docs.databricks.com/en/generative-ai/agent-framework/structured-retrieval-tools.html#table-query-tool) and to create an agent using the above created SQL UDF see [Databricks UC Integration](https://docs.unitycatalog.io/ai/integrations/langchain/).
 
 Open Models
 -----------

docs/docs/integrations/providers/deeplake.mdx

@@ -0,0 +1,16 @@
+# Deeplake
+
+[Deeplake](https://www.deeplake.ai/) is a database optimized for AI and deep learning
+applications.
+
+
+## Installation and Setup
+
+```bash
+pip install langchain-deeplake
+```
+
+## Vector stores
+
+See detail on available vector stores
+[here](/docs/integrations/vectorstores/activeloop_deeplake).

docs/docs/integrations/providers/dell.mdx

@@ -0,0 +1,22 @@
+# Dell
+
+Dell is a global technology company that provides a range of hardware, software, and
+services, including AI solutions. Their AI portfolio includes purpose-built
+infrastructure for AI workloads, including Dell PowerScale storage systems optimized
+for AI data management.
+
+## PowerScale
+
+Dell [PowerScale](https://www.dell.com/en-us/shop/powerscale-family/sf/powerscale) is
+an enterprise scale out storage system that hosts industry leading OneFS filesystem
+that can be hosted on-prem or deployed in the cloud.
+
+### Installation and Setup
+
+```bash
+pip install powerscale-rag-connector
+```
+
+### Document loaders
+
+See detail on available loaders [here](/docs/integrations/document_loaders/powerscale).

docs/docs/integrations/providers/discord-shikenso.mdx

@@ -0,0 +1,65 @@
+# Discord
+
+> [Discord](https://discord.com/) is an instant messaging, voice, and video communication platform widely used by communities of all types.
+
+## Installation and Setup
+
+Install the `langchain-discord-shikenso` package:
+
+```bash
+pip install langchain-discord-shikenso
+```
+
+You must provide a bot token via environment variable so the tools can authenticate with the Discord API:
+
+```bash
+export DISCORD_BOT_TOKEN="your-discord-bot-token"
+```
+
+If `DISCORD_BOT_TOKEN` is not set, the tools will raise a `ValueError` when instantiated.
+
+---
+
+## Tools
+
+Below is a snippet showing how you can read and send messages in Discord. For more details, see the [documentation for Discord tools](/docs/integrations/tools/discord).
+
+```python
+from langchain_discord.tools.discord_read_messages import DiscordReadMessages
+from langchain_discord.tools.discord_send_messages import DiscordSendMessage
+
+# Create tool instances
+read_tool = DiscordReadMessages()
+send_tool = DiscordSendMessage()
+
+# Example: Read the last 3 messages from channel 1234567890
+read_result = read_tool({"channel_id": "1234567890", "limit": 3})
+print(read_result)
+
+# Example: Send a message to channel 1234567890
+send_result = send_tool({"channel_id": "1234567890", "message": "Hello from Markdown example!"})
+print(send_result)
+```
+
+---
+
+## Toolkit
+
+`DiscordToolkit` groups multiple Discord-related tools into a single interface. For a usage example, see [the Discord toolkit docs](/docs/integrations/tools/discord).
+
+```python
+from langchain_discord.toolkits import DiscordToolkit
+
+toolkit = DiscordToolkit()
+tools = toolkit.get_tools()
+
+read_tool = tools[0]  # DiscordReadMessages
+send_tool = tools[1]  # DiscordSendMessage
+```
+
+---
+
+## Future Integrations
+
+Additional integrations (e.g., document loaders, chat loaders) could be added for Discord.
+Check the [Discord Developer Docs](https://discord.com/developers/docs/intro) for more information, and watch for updates or advanced usage examples in the [langchain_discord GitHub repo](https://github.com/Shikenso-Analytics/langchain-discord).

docs/docs/integrations/providers/discord.mdx

@@ -1,4 +1,4 @@
-# Discord
+# Discord (community loader)
 
 >[Discord](https://discord.com/) is a VoIP and instant messaging social platform. Users have the ability to communicate 
 > with voice calls, video calls, text messaging, media and files in private chats or as part of communities called 

docs/docs/integrations/providers/gitlab.mdx

@@ -27,5 +27,5 @@ from langchain_community.agent_toolkits.gitlab.toolkit import GitLabToolkit
 Tool for interacting with the GitLab API.
 
 ```python
-from langchain_community.tools.github.tool import GitHubAction
+from langchain_community.tools.gitlab.tool import GitLabAction
 ```