openai: release 0.3.14 (#30908)

Instead of stuffing provider-specific fields in `metadata`, they can go
directly on the content block.

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -29,14 +29,14 @@ body:
       options:
         - label: I added a very descriptive title to this issue.
           required: true
-        - label: I searched the LangChain documentation with the integrated search.
-          required: true
         - label: I used the GitHub search to find a similar question and didn't find it.
           required: true
         - label: I am sure that this is a bug in LangChain rather than my code.
           required: true
         - label: The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).
           required: true
+        - label: I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.
+          required: true
   - type: textarea
     id: reproduction
     validations:

.github/workflows/_integration_test.yml

@@ -76,6 +76,7 @@ jobs:
           COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
           UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
           XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
+          PPLX_API_KEY: ${{ secrets.PPLX_API_KEY }}
         run: |
           make integration_tests
 

.github/workflows/_release.yml

@@ -327,6 +327,7 @@ jobs:
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
           XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
           DEEPSEEK_API_KEY: ${{ secrets.DEEPSEEK_API_KEY }}
+          PPLX_API_KEY: ${{ secrets.PPLX_API_KEY }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 
@@ -394,8 +395,11 @@ jobs:
 
           # 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" -- .
+          rm -rf $GITHUB_WORKSPACE/libs/standard-tests/*
+          cd $GITHUB_WORKSPACE/libs/
+          git checkout "$LATEST_PACKAGE_TAG" -- standard-tests/
+          git checkout "$LATEST_PACKAGE_TAG" -- partners/${{ matrix.partner }}/
+          cd partners/${{ matrix.partner }}
 
           # Print as a sanity check
           echo "Version number from pyproject.toml: "

.github/workflows/check_core_versions.yml

@@ -0,0 +1,29 @@
+name: Check `langchain-core` version equality
+
+on:
+  pull_request:
+    paths:
+      - 'libs/core/pyproject.toml'
+      - 'libs/core/langchain_core/version.py'
+
+jobs:
+  check_version_equality:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Check version equality
+        run: |
+          PYPROJECT_VERSION=$(grep -Po '(?<=^version = ")[^"]*' libs/core/pyproject.toml)
+          VERSION_PY_VERSION=$(grep -Po '(?<=^VERSION = ")[^"]*' libs/core/langchain_core/version.py)
+
+          # Compare the two versions
+          if [ "$PYPROJECT_VERSION" != "$VERSION_PY_VERSION" ]; then
+            echo "langchain-core versions in pyproject.toml and version.py do not match!"
+            echo "pyproject.toml version: $PYPROJECT_VERSION"
+            echo "version.py version: $VERSION_PY_VERSION"
+            exit 1
+          else
+            echo "Versions match: $PYPROJECT_VERSION"
+          fi

.github/workflows/codspeed.yml

@@ -0,0 +1,44 @@
+name: CodSpeed
+
+on:
+  push:
+    branches:
+        - master
+  pull_request:
+    paths:
+      - 'libs/core/**'
+  # `workflow_dispatch` allows CodSpeed to trigger backtest
+  # performance analysis in order to generate initial data.
+  workflow_dispatch:
+
+jobs:
+  codspeed:
+    name: Run benchmarks
+    if: (github.event_name == 'pull_request' && contains(github.event.pull_request.labels.*.name, 'run-codspeed-benchmarks')) || github.event_name == 'workflow_dispatch' || github.event_name == 'push'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      # We have to use 3.12, 3.13 is not yet supported
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          python-version: "3.12"
+
+      # Using this action is still necessary for CodSpeed to work
+      - uses: actions/setup-python@v3
+        with:
+          python-version: "3.12"
+
+      - name: install deps
+        run: uv sync --group test
+        working-directory: ./libs/core
+
+      - name: Run benchmarks
+        uses: CodSpeedHQ/action@v3
+        with:
+          token: ${{ secrets.CODSPEED_TOKEN }}
+          run: |
+            cd libs/core
+            uv run --no-sync pytest ./tests/benchmarks --codspeed
+          mode: walltime

.github/workflows/people.yml

@@ -6,11 +6,6 @@ on:
   push:
     branches: [jacob/people]
   workflow_dispatch:
-    inputs:
-      debug_enabled:
-        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
-        required: false
-        default: 'false'
 
 jobs:
   langchain-people:
@@ -26,12 +21,6 @@ jobs:
       # Ref: https://github.com/actions/runner/issues/2033
       - name: Fix git safe.directory in container
         run: mkdir -p /home/runner/work/_temp/_github_home && printf "[safe]\n\tdirectory = /github/workspace" > /home/runner/work/_temp/_github_home/.gitconfig
-      # Allow debugging with tmate
-      - name: Setup tmate session
-        uses: mxschmitt/action-tmate@v3
-        if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled == 'true' }}
-        with:
-          limit-access-to-actor: true
       - uses: ./.github/actions/people
         with:
           token: ${{ secrets.LANGCHAIN_PEOPLE_GITHUB_TOKEN }}

.github/workflows/scheduled_test.yml

@@ -145,6 +145,7 @@ jobs:
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
+          PPLX_API_KEY: ${{ secrets.PPLX_API_KEY }}
         run: |
           cd langchain/${{ matrix.working-directory }}
           make integration_tests

.gitignore

@@ -59,6 +59,7 @@ coverage.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
+.codspeed/
 
 # Translations
 *.mo

README.md

@@ -15,8 +15,9 @@
 [![GitHub star chart](https://img.shields.io/github/stars/langchain-ai/langchain?style=flat-square)](https://star-history.com/#langchain-ai/langchain)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langchain?style=flat-square)](https://github.com/langchain-ai/langchain/issues)
 [![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode&style=flat-square)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
-[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/langchain)
+[<img src="https://github.com/codespaces/badge.svg" title="Open in Github Codespace" width="150" height="20">](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)
+[![CodSpeed Badge](https://img.shields.io/endpoint?url=https://codspeed.io/badge.json)](https://codspeed.io/langchain-ai/langchain)
 
 > [!NOTE]
 > Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).

cookbook/advanced_rag_eval.ipynb

@@ -30,7 +30,7 @@
    "outputs": [],
    "source": [
     "# lock to 0.10.19 due to a persistent bug in more recent versions\n",
-    "! pip install \"unstructured[all-docs]==0.10.19\" pillow pydantic lxml pillow matplotlib tiktoken open_clip_torch torch"
+    "! pip install \"unstructured[all-docs]==0.10.19\" pillow pydantic lxml matplotlib tiktoken open_clip_torch torch"
    ]
   },
   {
@@ -409,7 +409,7 @@
     "    table_summaries,\n",
     "    tables,\n",
     "    image_summaries,\n",
-    "    image_summaries,\n",
+    "    img_base64_list,\n",
     ")"
    ]
   },

docs/api_reference/conf.py

@@ -275,3 +275,7 @@ if os.environ.get("READTHEDOCS", "") == "True":
     html_context["READTHEDOCS"] = True
 
 master_doc = "index"
+
+# If a signature’s length in characters exceeds 60,
+# each parameter within the signature will be displayed on an individual logical line
+maximum_signature_line_length = 60

docs/cassettes/sql_qa_11.msgpack.zlib

@@ -1 +1 @@
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

docs/cassettes/sql_qa_39.msgpack.zlib

@@ -1 +1 @@
-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

docs/docs/changes/changelog/core.mdx

@@ -6,5 +6,5 @@
 
 - `BaseChatModel` methods `__call__`, `call_as_llm`, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.invoke` instead.
 - `BaseChatModel` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.ainvoke` instead.
-- `BaseLLM` methods `__call__, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseLLM.invoke` instead.
+- `BaseLLM` methods `__call__`, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseLLM.invoke` instead.
 - `BaseLLM` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseLLM.ainvoke` instead.

docs/docs/concepts/retrieval.mdx

@@ -92,7 +92,7 @@ structured_model = model.with_structured_output(Questions)
 
 # Define the system prompt
 system = """You are a helpful assistant that generates multiple sub-questions related to an input question. \n
-The goal is to break down the input into a set of sub-problems / sub-questions that can be answers in isolation. \n"""
+The goal is to break down the input into a set of sub-problems / sub-questions that can be answered independently. \n"""
 
 # Pass the question to the model
 question = """What are the main components of an LLM-powered autonomous agent system?"""

docs/docs/concepts/runnables.mdx

@@ -126,7 +126,7 @@ Please see the [Configurable Runnables](#configurable-runnables) section for mor
 LangChain will automatically try to infer the input and output types of a Runnable based on available information.
 
 Currently, this inference does not work well for more complex Runnables that are built using [LCEL](/docs/concepts/lcel) composition, and the inferred input and / or output types may be incorrect. In these cases, we recommend that users override the inferred input and output types using the `with_types` method ([API Reference](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_types
-).
+)).
 
 ## RunnableConfig
 
@@ -194,7 +194,7 @@ In Python 3.11 and above, this works out of the box, and you do not need to do a
 In Python 3.9 and 3.10, if you are using **async code**, you need to manually pass the `RunnableConfig` through to the `Runnable` when invoking it. 
 
 This is due to a limitation in [asyncio's tasks](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)  in Python 3.9 and 3.10 which did
-not accept a `context` argument).
+not accept a `context` argument.
 
 Propagating the `RunnableConfig` manually is done like so:
 

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

@@ -83,7 +83,6 @@ LinkedIn, where we highlight the best examples.
 
 Here are some heuristics for types of content we are excited to promote:
 
-- **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/how_to/code_splitter.ipynb

@@ -40,7 +40,7 @@
     "\n",
     "To view the list of separators for a given language, pass a value from this enum into\n",
     "```python\n",
-    "RecursiveCharacterTextSplitter.get_separators_for_language`\n",
+    "RecursiveCharacterTextSplitter.get_separators_for_language\n",
     "```\n",
     "\n",
     "To instantiate a splitter that is tailored for a specific language, pass a value from the enum into\n",

docs/docs/how_to/index.mdx

@@ -351,7 +351,7 @@ LangSmith allows you to closely trace, monitor and evaluate your LLM application
 It seamlessly integrates with LangChain and LangGraph, and you can use it to inspect and debug individual steps of your chains and agents as you build.
 
 LangSmith documentation is hosted on a separate site.
-You can peruse [LangSmith how-to guides here](https://docs.smith.langchain.com/how_to_guides/), but we'll highlight a few sections that are particularly
+You can peruse [LangSmith how-to guides here](https://docs.smith.langchain.com/), but we'll highlight a few sections that are particularly
 relevant to LangChain below:
 
 ### Evaluation

docs/docs/how_to/tools_human.ipynb

@@ -15,7 +15,7 @@
     "\n",
     "To build a production application, you will need to do more work to keep track of application state appropriately.\n",
     "\n",
-    "We recommend using `langgraph` for powering such a capability. For more details, please see this [guide](https://langchain-ai.github.io/langgraph/how-tos/human-in-the-loop/).\n",
+    "We recommend using `langgraph` for powering such a capability. For more details, please see this [guide](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/).\n",
     ":::\n"
    ]
   },
@@ -209,7 +209,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "Do you approve of the following tool invocations\n",
@@ -252,7 +252,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "Do you approve of the following tool invocations\n",

docs/docs/integrations/chat/bedrock.ipynb

@@ -1,378 +1,401 @@
 {
-  "cells": [
-    {
-      "cell_type": "raw",
-      "id": "afaf8039",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "sidebar_label: AWS Bedrock\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "e49f1e0d",
-      "metadata": {},
-      "source": [
-        "# ChatBedrock\n",
-        "\n",
-        "This doc will help you get started with AWS Bedrock [chat models](/docs/concepts/chat_models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
-        "\n",
-        "For more information on which models are accessible via Bedrock, head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/models-features.html).\n",
-        "\n",
-        "For detailed documentation of all ChatBedrock features and configurations head to the [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html).\n",
-        "\n",
-        "## Overview\n",
-        "### Integration details\n",
-        "\n",
-        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/bedrock) | Package downloads | Package latest |\n",
-        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-        "| [ChatBedrock](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?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 Bedrock models you'll need to create an AWS account, set up the Bedrock API service, get an access key ID and secret key, and install the `langchain-aws` integration package.\n",
-        "\n",
-        "### Credentials\n",
-        "\n",
-        "Head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/setting-up.html) to sign up to AWS and setup your credentials. You'll also need to turn on model access for your account, which you can do by following [these instructions](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-      "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-      "metadata": {},
-      "source": [
-        "### Installation\n",
-        "\n",
-        "The LangChain Bedrock integration lives in the `langchain-aws` package:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install -qU langchain-aws"
-      ]
-    },
-    {
-      "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": 1,
-      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain_aws import ChatBedrock\n",
-        "\n",
-        "llm = ChatBedrock(\n",
-        "    model_id=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
-        "    model_kwargs=dict(temperature=0),\n",
-        "    # other params...\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "2b4f3e15",
-      "metadata": {},
-      "source": [
-        "## Invocation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "62e0dbc3",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", additional_kwargs={'usage': {'prompt_tokens': 29, 'completion_tokens': 21, 'total_tokens': 50}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 29, 'completion_tokens': 21, 'total_tokens': 50}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, id='run-fdb07dc3-ff72-430d-b22b-e7824b15c766-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})"
-            ]
-          },
-          "execution_count": 5,
-          "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": 6,
-      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Voici la traduction en français :\n",
-            "\n",
-            "J'aime la programmation.\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": 7,
-      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={'usage': {'prompt_tokens': 23, 'completion_tokens': 11, 'total_tokens': 34}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 23, 'completion_tokens': 11, 'total_tokens': 34}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, id='run-5ad005ce-9f31-4670-baa0-9373d418698a-0', usage_metadata={'input_tokens': 23, 'output_tokens': 11, 'total_tokens': 34})"
-            ]
-          },
-          "execution_count": 7,
-          "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 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": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-      "metadata": {},
-      "source": [
-        "## Bedrock Converse API\n",
-        "\n",
-        "AWS has recently released the Bedrock Converse API which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). To improve reliability the ChatBedrock integration will switch to using the Bedrock Converse API as soon as it has feature parity with the existing Bedrock API. Until then a separate [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) integration has been released.\n",
-        "\n",
-        "We recommend using `ChatBedrockConverse` for users who do not need to use custom models.\n",
-        "\n",
-        "You can use it like so:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "ae728e59-94d4-40cf-9d24-25ad8723fc59",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", response_metadata={'ResponseMetadata': {'RequestId': '4fcbfbe9-f916-4df2-b0bd-ea1147b550aa', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 21 Aug 2024 17:23:49 GMT', 'content-type': 'application/json', 'content-length': '243', 'connection': 'keep-alive', 'x-amzn-requestid': '4fcbfbe9-f916-4df2-b0bd-ea1147b550aa'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': 672}}, id='run-77ee9810-e32b-45dc-9ccb-6692253b1f45-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from langchain_aws import ChatBedrockConverse\n",
-        "\n",
-        "llm = ChatBedrockConverse(\n",
-        "    model=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
-        "    temperature=0,\n",
-        "    max_tokens=None,\n",
-        "    # other params...\n",
-        ")\n",
-        "\n",
-        "llm.invoke(messages)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "4da16f3e-e80b-48c0-8036-c1cc5f7c8c05",
-      "metadata": {},
-      "source": [
-        "### Streaming\n",
-        "\n",
-        "Note that `ChatBedrockConverse` emits content blocks while streaming:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "7794b32e-d8de-4973-bf0f-39807dc745f0",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "content=[] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'Vo', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'ici', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' la', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' tra', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'duction', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' en', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' français', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' :', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': '\\n\\nJ', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': \"'\", 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'a', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'ime', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' la', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' programm', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'ation', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': '.', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[] response_metadata={'stopReason': 'end_turn'} id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[] response_metadata={'metrics': {'latencyMs': 713}} id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8' usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50}\n"
-          ]
-        }
-      ],
-      "source": [
-        "for chunk in llm.stream(messages):\n",
-        "    print(chunk)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0ef05abb-9c04-4dc3-995e-f857779644d5",
-      "metadata": {},
-      "source": [
-        "An output parser can be used to filter to text, if desired:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "2a4e743f-ea7d-4e5a-9b12-f9992362de8b",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "|Vo|ici| la| tra|duction| en| français| :|\n",
-            "\n",
-            "J|'|a|ime| la| programm|ation|.||||"
-          ]
-        }
-      ],
-      "source": [
-        "from langchain_core.output_parsers import StrOutputParser\n",
-        "\n",
-        "chain = llm | StrOutputParser()\n",
-        "\n",
-        "for chunk in chain.stream(messages):\n",
-        "    print(chunk, end=\"|\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-      "metadata": {},
-      "source": [
-        "## API reference\n",
-        "\n",
-        "For detailed documentation of all ChatBedrock features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html\n",
-        "\n",
-        "For detailed documentation of all ChatBedrockConverse features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.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": "3.10.4"
-    }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: AWS Bedrock\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatBedrock\n",
+    "\n",
+    "This doc will help you get started with AWS Bedrock [chat models](/docs/concepts/chat_models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
+    "\n",
+    "AWS Bedrock maintains a [Converse API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html) which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html).\n",
+    "\n",
+    ":::info\n",
+    "\n",
+    "We recommend the Converse API for users who do not need to use custom models. It can be accessed using [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html).\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "For detailed documentation of all Bedrock features and configurations head to the [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/bedrock) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatBedrock](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?style=flat-square&label=%20) |\n",
+    "| [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "\n",
+    "The below apply to both `ChatBedrock` and `ChatBedrockConverse`.\n",
+    "\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 Bedrock models you'll need to create an AWS account, set up the Bedrock API service, get an access key ID and secret key, and install the `langchain-aws` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/setting-up.html) to sign up to AWS and setup your credentials. You'll also need to turn on model access for your account, which you can do by following [these instructions](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Bedrock integration lives in the `langchain-aws` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-aws"
+   ]
+  },
+  {
+   "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": 1,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_aws import ChatBedrockConverse\n",
+    "\n",
+    "llm = ChatBedrockConverse(\n",
+    "    model_id=\"anthropic.claude-3-5-sonnet-20240620-v1:0\",\n",
+    "    # temperature=...,\n",
+    "    # max_tokens=...,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fcd8de52-4a1b-4875-b463-d41b031e06a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={'ResponseMetadata': {'RequestId': 'b07d1630-06f2-44b1-82bf-e82538dd2215', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 16 Apr 2025 19:35:34 GMT', 'content-type': 'application/json', 'content-length': '206', 'connection': 'keep-alive', 'x-amzn-requestid': 'b07d1630-06f2-44b1-82bf-e82538dd2215'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': [488]}, 'model_name': 'anthropic.claude-3-5-sonnet-20240620-v1:0'}, id='run-d09ed928-146a-4336-b1fd-b63c9e623494-0', usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})"
+      ]
+     },
+     "execution_count": 2,
+     "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": 3,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore la programmation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4da16f3e-e80b-48c0-8036-c1cc5f7c8c05",
+   "metadata": {},
+   "source": [
+    "### Streaming\n",
+    "\n",
+    "Note that `ChatBedrockConverse` emits content blocks while streaming:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "605e04fa-1a76-47ac-8c92-fe128659663e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content=[] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[{'type': 'text', 'text': 'J', 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[{'type': 'text', 'text': \"'adore la\", 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[{'type': 'text', 'text': ' programmation.', 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[{'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[] additional_kwargs={} response_metadata={'stopReason': 'end_turn'} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[] additional_kwargs={} response_metadata={'metrics': {'latencyMs': 600}, 'model_name': 'anthropic.claude-3-5-sonnet-20240620-v1:0'} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd' usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in llm.stream(messages):\n",
+    "    print(chunk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ef05abb-9c04-4dc3-995e-f857779644d5",
+   "metadata": {},
+   "source": [
+    "You can filter to text using the [.text()](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.text) method on the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2a4e743f-ea7d-4e5a-9b12-f9992362de8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|J|'adore la| programmation.||||"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in llm.stream(messages):\n",
+    "    print(chunk.text(), end=\"|\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a77519e5-897d-41a0-a9bb-55300fa79efc",
+   "metadata": {},
+   "source": [
+    "## Prompt caching\n",
+    "\n",
+    "Bedrock supports [caching](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html) of elements of your prompts, including messages and tools. 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",
+    ":::note\n",
+    "\n",
+    "Not all models support prompt caching. See supported models [here](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html#prompt-caching-models).\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "To enable caching on an element of a prompt, mark its associated content block using the `cachePoint` key. See example below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d5f63d01-85e8-4797-a2be-0fea747a6049",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First invocation:\n",
+      "{'cache_creation': 1528, 'cache_read': 0}\n",
+      "\n",
+      "Second:\n",
+      "{'cache_creation': 0, 'cache_read': 1528}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "from langchain_aws import ChatBedrockConverse\n",
+    "\n",
+    "llm = ChatBedrockConverse(model=\"us.anthropic.claude-3-7-sonnet-20250219-v1:0\")\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\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\n",
+    "                \"type\": \"text\",\n",
+    "                \"text\": \"What's LangChain, according to its README?\",\n",
+    "            },\n",
+    "            {\n",
+    "                \"type\": \"text\",\n",
+    "                \"text\": f\"{readme}\",\n",
+    "            },\n",
+    "            {\n",
+    "                \"cachePoint\": {\"type\": \"default\"},\n",
+    "            },\n",
+    "        ],\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": "1b550667-af5b-4557-b84f-c8f865dad6cb",
+   "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": 7,
+   "id": "6033f3fa-0e96-46e3-abb3-1530928fea88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Here's the German translation:\\n\\nIch liebe das Programmieren.\", additional_kwargs={}, response_metadata={'ResponseMetadata': {'RequestId': '1de3d7c0-8062-4f7e-bb8a-8f725b97a8b0', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 16 Apr 2025 19:32:51 GMT', 'content-type': 'application/json', 'content-length': '243', 'connection': 'keep-alive', 'x-amzn-requestid': '1de3d7c0-8062-4f7e-bb8a-8f725b97a8b0'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': [719]}, 'model_name': 'anthropic.claude-3-5-sonnet-20240620-v1:0'}, id='run-7021fcd7-704e-496b-a92e-210139614402-0', usage_metadata={'input_tokens': 23, 'output_tokens': 19, 'total_tokens': 42, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})"
+      ]
+     },
+     "execution_count": 7,
+     "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 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 ChatBedrock features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html\n",
+    "\n",
+    "For detailed documentation of all ChatBedrockConverse features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }

docs/docs/integrations/chat/cloudflare_workersai.ipynb

@@ -1,262 +1,393 @@
 {
-  "cells": [
-    {
-      "cell_type": "raw",
-      "id": "30373ae2-f326-4e96-a1f7-062f57396886",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "sidebar_label: Cloudflare Workers AI\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "f679592d",
-      "metadata": {},
-      "source": [
-        "# ChatCloudflareWorkersAI\n",
-        "\n",
-        "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all available Cloudflare WorkersAI models head to the [API reference](https://developers.cloudflare.com/workers-ai/).\n",
-        "\n",
-        "\n",
-        "## Overview\n",
-        "### Integration details\n",
-        "\n",
-        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cloudflare_workersai) | Package downloads | Package latest |\n",
-        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-        "| ChatCloudflareWorkersAI | langchain-community| ❌ | ❌ | ✅ | ❌  | ❌ |\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 Cloudflare Workers AI models you'll need to create a Cloudflare account, get an account number and API key, and install the `langchain-community` package.\n",
-        "\n",
-        "\n",
-        "### Credentials\n",
-        "\n",
-        "\n",
-        "Head to [this document](https://developers.cloudflare.com/workers-ai/get-started/rest-api/) to sign up to Cloudflare Workers AI and generate an API key."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "4a524cff",
-      "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "71b53c25",
-      "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": "777a8526",
-      "metadata": {},
-      "source": [
-        "### Installation\n",
-        "\n",
-        "The LangChain ChatCloudflareWorkersAI integration lives in the `langchain-community` package:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "54990998",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install -qU langchain-community"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "629ba46f",
-      "metadata": {},
-      "source": [
-        "## Instantiation\n",
-        "\n",
-        "Now we can instantiate our model object and generate chat completions:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "ec13c2d9",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain_community.chat_models.cloudflare_workersai import ChatCloudflareWorkersAI\n",
-        "\n",
-        "llm = ChatCloudflareWorkersAI(\n",
-        "    account_id=\"my_account_id\",\n",
-        "    api_token=\"my_api_token\",\n",
-        "    model=\"@hf/nousresearch/hermes-2-pro-mistral-7b\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "119b6732",
-      "metadata": {},
-      "source": [
-        "## Invocation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "2438a906",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "2024-11-07 15:55:14 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to French. Translate the user sentence.\\nrole: user, content: I love programming.', 'tools': None}\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content='{\\'result\\': {\\'response\\': \\'Je suis un assistant virtuel qui peut traduire l\\\\\\'anglais vers le français. La phrase que vous avez dite est : \"J\\\\\\'aime programmer.\" En français, cela se traduit par : \"J\\\\\\'adore programmer.\"\\'}, \\'success\\': True, \\'errors\\': [], \\'messages\\': []}', additional_kwargs={}, response_metadata={}, id='run-838fd398-8594-4ca5-9055-03c72993caf6-0')"
-            ]
-          },
-          "execution_count": 8,
-          "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": 9,
-      "id": "1b4911bd",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "{'result': {'response': 'Je suis un assistant virtuel qui peut traduire l\\'anglais vers le français. La phrase que vous avez dite est : \"J\\'aime programmer.\" En français, cela se traduit par : \"J\\'adore programmer.\"'}, 'success': True, 'errors': [], 'messages': []}\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(ai_msg.content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "111aa5d4",
-      "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": 10,
-      "id": "b2a14282",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "2024-11-07 15:55:24 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to German.\\nrole: user, content: I love programming.', 'tools': None}\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content=\"{'result': {'response': 'role: system, content: Das ist sehr nett zu hören! Programmieren lieben, ist eine interessante und anspruchsvolle Hobby- oder Berufsausrichtung. Wenn Sie englische Texte ins Deutsche übersetzen möchten, kann ich Ihnen helfen. Geben Sie bitte den englischen Satz oder die Übersetzung an, die Sie benötigen.'}, 'success': True, 'errors': [], 'messages': []}\", additional_kwargs={}, response_metadata={}, id='run-0d3be9a6-3d74-4dde-b49a-4479d6af00ef-0')"
-            ]
-          },
-          "execution_count": 10,
-          "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 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": "e1f311bd",
-      "metadata": {},
-      "source": [
-        "## API reference\n",
-        "\n",
-        "For detailed documentation on `ChatCloudflareWorkersAI` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.cloudflare_workersai.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": "3.10.4"
-    }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: CloudflareWorkersAI\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatCloudflareWorkersAI\n",
+    "\n",
+    "\n",
+    "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCloudflareWorkersAI features and configurations head to the [API reference](https://python.langchain.com/docs/integrations/chat/cloudflare_workersai/).\n",
+    "\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cloudflare) | Package downloads | Package latest |\n",
+    "| :--- | :--- |:-----:|:------------:|:------------------------------------------------------------------------:| :---: | :---: |\n",
+    "| [ChatCloudflareWorkersAI](https://python.langchain.com/docs/integrations/chat/cloudflare_workersai/) | [langchain-cloudflare](https://pypi.org/project/langchain-cloudflare/) |   ✅   |      ❌       |                                     ❌                                     | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-cloudflare?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-cloudflare?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 CloudflareWorkersAI models you'll need to create a/an CloudflareWorkersAI account, get an API key, and install the `langchain-cloudflare` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "\n",
+    "Head to https://www.cloudflare.com/developer-platform/products/workers-ai/ to sign up to CloudflareWorkersAI and generate an API key. Once you've done this set the CF_API_KEY environment variable and the CF_ACCOUNT_ID environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"CF_API_KEY\"):\n",
+    "    os.environ[\"CF_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your CloudflareWorkersAI API key: \"\n",
+    "    )\n",
+    "\n",
+    "if not os.getenv(\"CF_ACCOUNT_ID\"):\n",
+    "    os.environ[\"CF_ACCOUNT_ID\"] = getpass.getpass(\n",
+    "        \"Enter your CloudflareWorkersAI account ID: \"\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 CloudflareWorkersAI integration lives in the `langchain-cloudflare` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-cloudflare"
+   ]
+  },
+  {
+   "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",
+    "- Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-07T17:48:31.193773Z",
+     "start_time": "2025-04-07T17:48:31.179196Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_cloudflare.chat_models import ChatCloudflareWorkersAI\n",
+    "\n",
+    "llm = ChatCloudflareWorkersAI(\n",
+    "    model=\"@cf/meta/llama-3.3-70b-instruct-fp8-fast\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=1024,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 37, 'completion_tokens': 9, 'total_tokens': 46}, 'model_name': '@cf/meta/llama-3.3-70b-instruct-fp8-fast'}, id='run-995d1970-b6be-49f3-99ae-af4cdba02304-0', usage_metadata={'input_tokens': 37, 'output_tokens': 9, 'total_tokens': 46})"
+      ]
+     },
+     "execution_count": 19,
+     "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": 20,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore la programmation.\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:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 32, 'completion_tokens': 7, 'total_tokens': 39}, 'model_name': '@cf/meta/llama-3.3-70b-instruct-fp8-fast'}, id='run-d1b677bc-194e-4473-90f1-aa65e8e46d50-0', usage_metadata={'input_tokens': 32, 'output_tokens': 7, 'total_tokens': 39})"
+      ]
+     },
+     "execution_count": 21,
+     "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": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "metadata": {},
+   "source": [
+    "## Structured Outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "91cae406-14d7-46c9-b942-2d1476588423",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'setup': 'Why did the cat join a band?',\n",
+       " 'punchline': 'Because it wanted to be the purr-cussionist',\n",
+       " 'rating': '8'}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "json_schema = {\n",
+    "    \"title\": \"joke\",\n",
+    "    \"description\": \"Joke to tell user.\",\n",
+    "    \"type\": \"object\",\n",
+    "    \"properties\": {\n",
+    "        \"setup\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"description\": \"The setup of the joke\",\n",
+    "        },\n",
+    "        \"punchline\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"description\": \"The punchline to the joke\",\n",
+    "        },\n",
+    "        \"rating\": {\n",
+    "            \"type\": \"integer\",\n",
+    "            \"description\": \"How funny the joke is, from 1 to 10\",\n",
+    "            \"default\": None,\n",
+    "        },\n",
+    "    },\n",
+    "    \"required\": [\"setup\", \"punchline\"],\n",
+    "}\n",
+    "structured_llm = llm.with_structured_output(json_schema)\n",
+    "\n",
+    "structured_llm.invoke(\"Tell me a joke about cats\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbfc0c43-e76b-446e-bbb1-d351640bb7be",
+   "metadata": {},
+   "source": [
+    "## Bind tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0765265e-4d00-4030-bf48-7e8d8c9af2ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'validate_user',\n",
+       "  'args': {'user_id': '123',\n",
+       "   'addresses': '[\"123 Fake St in Boston MA\", \"234 Pretend Boulevard in Houston TX\"]'},\n",
+       "  'id': '31ec7d6a-9ce5-471b-be64-8ea0492d1387',\n",
+       "  'type': 'tool_call'}]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def validate_user(user_id: int, addresses: List[str]) -> bool:\n",
+    "    \"\"\"Validate user using historical addresses.\n",
+    "\n",
+    "    Args:\n",
+    "        user_id (int): the user ID.\n",
+    "        addresses (List[str]): Previous addresses as a list of strings.\n",
+    "    \"\"\"\n",
+    "    return True\n",
+    "\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools([validate_user])\n",
+    "\n",
+    "result = llm_with_tools.invoke(\n",
+    "    \"Could you validate user 123? They previously lived at \"\n",
+    "    \"123 Fake St in Boston MA and 234 Pretend Boulevard in \"\n",
+    "    \"Houston TX.\"\n",
+    ")\n",
+    "result.tool_calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "https://developers.cloudflare.com/workers-ai/\n",
+    "https://developers.cloudflare.com/agents/"
+   ]
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }

docs/docs/integrations/chat/litellm.ipynb

@@ -3,7 +3,9 @@
   {
    "cell_type": "raw",
    "id": "59148044",
-   "metadata": {},
+   "metadata": {
+    "id": "59148044"
+   },
    "source": [
     "---\n",
     "sidebar_label: LiteLLM\n",
@@ -11,120 +13,139 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
-   "id": "bf733a38-db84-4363-89e2-de6735c37230",
-   "metadata": {},
+   "id": "5bcea387",
+   "metadata": {
+    "id": "5bcea387"
+   },
    "source": [
     "# ChatLiteLLM\n",
     "\n",
-    "[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc. \n",
+    "[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc.\n",
     "\n",
-    "This notebook covers how to get started with using Langchain + the LiteLLM I/O library. "
+    "This notebook covers how to get started with using Langchain + the LiteLLM I/O library.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support| Package downloads | Package latest |\n",
+    "| :---  | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatLiteLLM](https://python.langchain.com/docs/integrations/chat/litellm/) | [langchain-litellm](https://pypi.org/project/langchain-litellm/)| ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-litellm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-litellm?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](https://python.langchain.com/docs/how_to/tool_calling/) | [Structured output](https://python.langchain.com/docs/how_to/structured_output/) | JSON mode | Image input | Audio input | Video input | [Token-level streaming](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellm-also-supports-async-and-streaming-functionality) | [Native async](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellm-also-supports-async-and-streaming-functionality) | [Token usage](https://python.langchain.com/docs/how_to/chat_token_usage_tracking/) | [Logprobs](https://python.langchain.com/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ |\n",
+    "\n",
+    "### Setup\n",
+    "To access ChatLiteLLM models you'll need to install the `langchain-litellm` package and create an OpenAI, Anthropic, Azure, Replicate, OpenRouter, Hugging Face, Together AI or Cohere account. Then you have to get an API key, and export it as an environment variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a2f8164",
+   "metadata": {
+    "id": "0a2f8164"
+   },
+   "source": [
+    "## Credentials\n",
+    "\n",
+    "You have to choose the LLM provider you want and sign up with them to get their API key.\n",
+    "\n",
+    "### Example - Anthropic\n",
+    "Head to https://console.anthropic.com/ to sign up for Anthropic and generate an API key. Once you've done this set the ANTHROPIC_API_KEY environment variable.\n",
+    "\n",
+    "\n",
+    "### Example - OpenAI\n",
+    "Head to https://platform.openai.com/api-keys to sign up for OpenAI and generate an API key. Once you've done this set the OPENAI_API_KEY environment variable."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
+   "id": "7595eddf",
    "metadata": {
-    "tags": []
+    "id": "7595eddf"
    },
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatLiteLLM\n",
-    "from langchain_core.messages import HumanMessage"
+    "## set ENV variables\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-key\"\n",
+    "os.environ[\"ANTHROPIC_API_KEY\"] = \"your-anthropic-key\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74c3ad30",
+   "metadata": {
+    "id": "74c3ad30"
+   },
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain LiteLLM integration lives in the `langchain-litellm` package:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
+   "id": "ca3f8a25",
    "metadata": {
-    "tags": []
+    "id": "ca3f8a25"
    },
    "outputs": [],
    "source": [
-    "chat = ChatLiteLLM(model=\"gpt-3.5-turbo\")"
+    "%pip install -qU langchain-litellm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc1182b4",
+   "metadata": {
+    "id": "bc1182b4"
+   },
+   "source": [
+    "## Instantiation\n",
+    "Now we can instantiate our model object and generate chat completions:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
+   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
    "metadata": {
+    "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
-    "    )\n",
-    "]\n",
-    "chat(messages)"
+    "from langchain_litellm import ChatLiteLLM\n",
+    "\n",
+    "llm = ChatLiteLLM(model=\"gpt-3.5-turbo\")"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
-   "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
-   "metadata": {},
+   "id": "63d98454",
+   "metadata": {
+    "id": "63d98454"
+   },
    "source": [
-    "## `ChatLiteLLM` also supports async and streaming functionality:"
+    "## Invocation"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LLMResult(generations=[[ChatGeneration(text=\" J'aime programmer.\", generation_info=None, message=AIMessage(content=\" J'aime programmer.\", additional_kwargs={}, example=False))]], llm_output={}, run=[RunInfo(run_id=UUID('8cc8fb68-1c35-439c-96a0-695036a93652'))])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await chat.agenerate([messages])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
+   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
+    "outputId": "a4c0e5f5-a859-43fa-dd78-74fc0922ecb2",
     "tags": []
    },
    "outputs": [
@@ -132,41 +153,75 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " J'aime la programmation."
+      "content='Neutral' additional_kwargs={} response_metadata={'token_usage': Usage(completion_tokens=2, prompt_tokens=30, total_tokens=32, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0, text_tokens=None), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=0, cached_tokens=0, text_tokens=None, image_tokens=None)), 'model': 'gpt-3.5-turbo', 'finish_reason': 'stop', 'model_name': 'gpt-3.5-turbo'} id='run-ab6a3b21-eae8-4c27-acb2-add65a38221a-0' usage_metadata={'input_tokens': 30, 'output_tokens': 2, 'total_tokens': 32}\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "chat = ChatLiteLLM(\n",
-    "    streaming=True,\n",
-    "    verbose=True,\n",
-    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
+    "response = await llm.ainvoke(\n",
+    "    \"Classify the text into neutral, negative or positive. Text: I think the food was okay. Sentiment:\"\n",
     ")\n",
-    "chat(messages)"
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
+   "metadata": {
+    "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c"
+   },
+   "source": [
+    "## `ChatLiteLLM` also supports async and streaming functionality:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "c253883f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "execution_count": 5,
+   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
+    "outputId": "ee8cdda1-d992-4696-9ad0-aa146360a3ee",
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Antibiotics are medications that fight bacterial infections in the body. They work by targeting specific bacteria and either killing them or preventing their growth and reproduction.\n",
+      "\n",
+      "There are several different mechanisms by which antibiotics work. Some antibiotics work by disrupting the cell walls of bacteria, causing them to burst and die. Others interfere with the protein synthesis of bacteria, preventing them from growing and reproducing. Some antibiotics target the DNA or RNA of bacteria, disrupting their ability to replicate.\n",
+      "\n",
+      "It is important to note that antibiotics only work against bacterial infections and not viral infections. It is also crucial to take antibiotics as prescribed by a healthcare professional and to complete the full course of treatment, even if symptoms improve before the medication is finished. This helps to prevent antibiotic resistance, where bacteria become resistant to the effects of antibiotics."
+     ]
+    }
+   ],
+   "source": [
+    "async for token in llm.astream(\"Hello, please explain how antibiotics work\"):\n",
+    "    print(token.text(), end=\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88af2a9b",
+   "metadata": {
+    "id": "88af2a9b"
+   },
+   "source": [
+    "## API reference\n",
+    "For detailed documentation of all `ChatLiteLLM` features and configurations head to the API reference: https://github.com/Akshay-Dongare/langchain-litellm"
+   ]
   }
  ],
  "metadata": {
+  "colab": {
+   "provenance": []
+  },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "g6_alda",
    "language": "python",
    "name": "python3"
   },
@@ -180,7 +235,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/ollama.ipynb

@@ -115,9 +115,7 @@
       "source": [
         "## Instantiation\n",
         "\n",
-        "Now we can instantiate our model object and generate chat completions:\n",
-        "\n",
-        "- TODO: Update model instantiation with relevant params."
+        "Now we can instantiate our model object and generate chat completions:\n"
       ]
     },
     {

docs/docs/integrations/chat/openai.ipynb

@@ -408,7 +408,7 @@
     "\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",
+    "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, as well as the output from [reasoning processes](https://platform.openai.com/docs/guides/reasoning?api-mode=responses).\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",
@@ -1056,6 +1056,77 @@
     "print(second_response.text())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "67bf5bd2-0935-40a0-b1cd-c6662b681d4b",
+   "metadata": {},
+   "source": [
+    "### Reasoning output\n",
+    "\n",
+    "Some OpenAI models will generate separate text content illustrating their reasoning process. See OpenAI's [reasoning documentation](https://platform.openai.com/docs/guides/reasoning?api-mode=responses) for details.\n",
+    "\n",
+    "OpenAI can return a summary of the model's reasoning (although it doesn't expose the raw reasoning tokens). To configure `ChatOpenAI` to return this summary, specify the `reasoning` parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8d322f3a-0732-45ab-ac95-dfd4596e0d85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3^3 = 3 × 3 × 3 = 27.'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "reasoning = {\n",
+    "    \"effort\": \"medium\",  # 'low', 'medium', or 'high'\n",
+    "    \"summary\": \"auto\",  # 'detailed', 'auto', or None\n",
+    "}\n",
+    "\n",
+    "llm = ChatOpenAI(\n",
+    "    model=\"o4-mini\",\n",
+    "    use_responses_api=True,\n",
+    "    model_kwargs={\"reasoning\": reasoning},\n",
+    ")\n",
+    "response = llm.invoke(\"What is 3^3?\")\n",
+    "\n",
+    "# Output\n",
+    "response.text()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d7dcc082-b7c8-41b7-a5e2-441b9679e41b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "**Calculating power of three**\n",
+      "\n",
+      "The user is asking for the result of 3 to the power of 3, which I know is 27. It's a straightforward question, so I’ll keep my answer concise: 27. I could explain that this is the same as multiplying 3 by itself twice: 3 × 3 × 3 equals 27. However, since the user likely just needs the answer, I’ll simply respond with 27.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Reasoning\n",
+    "reasoning = response.additional_kwargs[\"reasoning\"]\n",
+    "for block in reasoning[\"summary\"]:\n",
+    "    print(block[\"text\"])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "57e27714",

docs/docs/integrations/chat/perplexity.ipynb

@@ -17,12 +17,66 @@
    "source": [
     "# ChatPerplexity\n",
     "\n",
-    "This notebook covers how to get started with `Perplexity` chat models."
+    "\n",
+    "This page will help you get started with Perplexity [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatPerplexity` features and configurations head to the [API reference](https://python.langchain.com/api_reference/perplexity/chat_models/langchain_perplexity.chat_models.ChatPerplexity.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/xai) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatPerplexity](https://python.langchain.com/api_reference/perplexity/chat_models/langchain_perplexity.chat_models.ChatPerplexity.html) | [langchain-perplexity](https://python.langchain.com/api_reference/perplexity/perplexity.html) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-perplexity?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-perplexity?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Perplexity models you'll need to create a Perplexity account, get an API key, and install the `langchain-perplexity` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [this page](https://www.perplexity.ai/) to sign up for Perplexity and generate an API key. Once you've done this set the `PPLX_API_KEY` environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "2243f329",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if \"PPLX_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"PPLX_API_KEY\"] = getpass.getpass(\"Enter your Perplexity API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dfe47c4",
+   "metadata": {},
+   "source": [
+    "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10a791fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
    "metadata": {
     "ExecuteTime": {
@@ -33,8 +87,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatPerplexity\n",
-    "from langchain_core.prompts import ChatPromptTemplate"
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_perplexity import ChatPerplexity"
    ]
   },
   {
@@ -62,29 +116,9 @@
    "id": "97a8ce3a",
    "metadata": {},
    "source": [
-    "The code provided assumes that your PPLX_API_KEY is set in your environment variables. If you would like to manually specify your API key and also choose a different model, you can use the following code:\n",
-    "\n",
-    "```python\n",
-    "chat = ChatPerplexity(temperature=0, pplx_api_key=\"YOUR_API_KEY\", model=\"llama-3.1-sonar-small-128k-online\")\n",
-    "```\n",
-    "\n",
     "You can check a list of available models [here](https://docs.perplexity.ai/docs/model-cards). For reproducibility, we can set the API key dynamically by taking it as an input in this notebook."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d3e49d78",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "PPLX_API_KEY = getpass()\n",
-    "os.environ[\"PPLX_API_KEY\"] = PPLX_API_KEY"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -305,7 +339,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -319,7 +353,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/qwq.ipynb

@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: Qwen QwQ\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatQwQ\n",
+    "\n",
+    "This will help you getting started with QwQ [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatQwQ features and configurations head to the [API reference](https://pypi.org/project/langchain-qwq/).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | Serializable | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatQwQ](https://pypi.org/project/langchain-qwq/) | [langchain-qwq](https://pypi.org/project/langchain-qwq/) | ❌ | beta | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-qwq?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-qwq?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ✅ | ✅ |❌  | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access QwQ models you'll need to create an Alibaba Cloud account, get an API key, and install the `langchain-qwq` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [Alibaba's API Key page](https://account.alibabacloud.com/login/login.htm?oauth_callback=https%3A%2F%2Fbailian.console.alibabacloud.com%2F%3FapiKey%3D1&lang=en#/api-key) to sign up to Alibaba Cloud and generate an API key. Once you've done this set the `DASHSCOPE_API_KEY` environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"DASHSCOPE_API_KEY\"):\n",
+    "    os.environ[\"DASHSCOPE_API_KEY\"] = getpass.getpass(\"Enter your Dashscope API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain QwQ integration lives in the `langchain-qwq` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-qwq"
+   ]
+  },
+  {
+   "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": 3,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_qwq import ChatQwQ\n",
+    "\n",
+    "llm = ChatQwQ(\n",
+    "    model=\"qwq-plus\",\n",
+    "    max_tokens=3_000,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'aime la programmation.\", additional_kwargs={'reasoning_content': 'Okay, the user wants me to translate \"I love programming.\" into French. Let\\'s start by breaking down the sentence. The subject is \"I\", which in French is \"Je\". The verb is \"love\", which in this context is present tense, so \"aime\". The object is \"programming\". Now, \"programming\" in French can be \"la programmation\". \\n\\nWait, should it be \"programmation\" or \"programmation\"? Let me confirm the spelling. Yes, \"programmation\" is correct. Now, putting it all together: \"Je aime la programmation.\" Hmm, but in French, there\\'s a tendency to contract \"je\" and \"aime\". Wait, actually, \"je\" followed by a vowel sound usually takes \"j\\'\". So it should be \"J\\'aime la programmation.\" \\n\\nLet me double-check. \"J\\'aime\" is the correct contraction for \"I love\". The definite article \"la\" is needed because \"programmation\" is a feminine noun. Yes, \"programmation\" is a feminine noun, so \"la\" is correct. \\n\\nIs there any other way to say it? Maybe \"J\\'adore la programmation\" for \"I love\" in a stronger sense, but the user didn\\'t specify the intensity. Since the original is straightforward, \"J\\'aime la programmation.\" is the direct translation. \\n\\nI think that\\'s it. No mistakes there. So the final translation should be \"J\\'aime la programmation.\"'}, response_metadata={'model_name': 'qwq-plus'}, id='run-5045cd6a-edbd-4b2f-bf24-b7bdf3777fb9-0', usage_metadata={'input_tokens': 32, 'output_tokens': 326, 'total_tokens': 358, 'input_token_details': {}, 'output_token_details': {}})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French.\"\n",
+    "        \"Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](../../how_to/sequence.ipynb) 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 das Programmieren.', additional_kwargs={'reasoning_content': 'Okay, the user wants me to translate \"I love programming.\" into German. Let me think. The verb \"love\" is \"lieben\" or \"mögen\" in German, but \"lieben\" is more like love, while \"mögen\" is prefer. Since it\\'s about programming, which is a strong affection, \"lieben\" is better. The subject is \"I\", which is \"ich\". Then \"programming\" is \"Programmierung\" or \"Coding\". But \"Programmierung\" is more formal. Alternatively, sometimes people say \"ich liebe es zu programmieren\" which is \"I love to program\". Hmm, maybe the direct translation would be \"Ich liebe die Programmierung.\" But maybe the more natural way is \"Ich liebe es zu programmieren.\" Let me check. Both are correct, but the second one might sound more natural in everyday speech. The user might prefer the concise version. Alternatively, maybe \"Ich liebe die Programmierung.\" is better. Wait, the original is \"programming\" as a noun. So using the noun form would be appropriate. So \"Ich liebe die Programmierung.\" But sometimes people also use \"Coding\" in German, like \"Ich liebe das Coding.\" But that\\'s more anglicism. Probably better to stick with \"Programmierung\". Alternatively, \"Programmieren\" as a noun. Oh right! \"Programmieren\" can be a noun when used in the accusative case. So \"Ich liebe das Programmieren.\" That\\'s correct and natural. Yes, that\\'s the best translation. So the answer is \"Ich liebe das Programmieren.\"'}, response_metadata={'model_name': 'qwq-plus'}, id='run-2c418451-51d8-4319-8269-2ce129363a1a-0', usage_metadata={'input_tokens': 28, 'output_tokens': 341, 'total_tokens': 369, 'input_token_details': {}, 'output_token_details': {}})"
+      ]
+     },
+     "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\"\n",
+    "            \"{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": "8d1b3ef3",
+   "metadata": {},
+   "source": [
+    "## Tool Calling\n",
+    "ChatQwQ supports tool calling API that 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."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6db1a355",
+   "metadata": {},
+   "source": [
+    "### Use with `bind_tools`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "15fb6a6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content='' additional_kwargs={'reasoning_content': 'Okay, the user is asking \"What\\'s 5 times forty two\". Let me break this down. First, I need to identify the numbers involved. The first number is 5, which is straightforward. The second number is forty two, which is 42 in digits. The operation they want is multiplication.\\n\\nLooking at the tools provided, there\\'s a function called multiply that takes two integers. So I should use that. The parameters are first_int and second_int. \\n\\nI need to convert \"forty two\" to 42. Since the function requires integers, both numbers should be in integer form. So 5 and 42. \\n\\nNow, I\\'ll structure the tool call. The function name is multiply, and the arguments should be first_int: 5 and second_int: 42. I\\'ll make sure the JSON is correctly formatted without any syntax errors. Let me double-check the parameters to ensure they\\'re required and of the right type. Yep, both are required and integers. \\n\\nNo examples were provided, but the function\\'s purpose is clear. So the correct tool call should be to multiply those two numbers. I think that\\'s all. No other functions are needed here.'} response_metadata={'model_name': 'qwq-plus'} id='run-638895aa-fdde-4567-bcfa-7d8e5d4f24af-0' tool_calls=[{'name': 'multiply', 'args': {'first_int': 5, 'second_int': 42}, 'id': 'call_d088275851c140529ed2ad', 'type': 'tool_call'}] usage_metadata={'input_tokens': 176, 'output_tokens': 277, 'total_tokens': 453, 'input_token_details': {}, 'output_token_details': {}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.tools import tool\n",
+    "from langchain_qwq import ChatQwQ\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def multiply(first_int: int, second_int: int) -> int:\n",
+    "    \"\"\"Multiply two integers together.\"\"\"\n",
+    "    return first_int * second_int\n",
+    "\n",
+    "\n",
+    "llm = ChatQwQ()\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools([multiply])\n",
+    "\n",
+    "msg = llm_with_tools.invoke(\"What's 5 times forty two\")\n",
+    "\n",
+    "print(msg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatQwQ features and configurations head to the [API reference](https://pypi.org/project/langchain-qwq/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/chat/runpod.ipynb

@@ -0,0 +1,276 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RunPod Chat Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get started with RunPod chat models.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This guide covers how to use the LangChain `ChatRunPod` class to interact with chat models hosted on [RunPod Serverless](https://www.runpod.io/serverless-gpu)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "1.  **Install the package:**\n",
+    "    ```bash\n",
+    "    pip install -qU langchain-runpod\n",
+    "    ```\n",
+    "2.  **Deploy a Chat Model Endpoint:** Follow the setup steps in the [RunPod Provider Guide](/docs/integrations/providers/runpod#setup) to deploy a compatible chat model endpoint on RunPod Serverless and get its Endpoint ID.\n",
+    "3.  **Set Environment Variables:** Make sure `RUNPOD_API_KEY` and `RUNPOD_ENDPOINT_ID` (or a specific `RUNPOD_CHAT_ENDPOINT_ID`) are set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "# Make sure environment variables are set (or pass them directly to ChatRunPod)\n",
+    "if \"RUNPOD_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"RUNPOD_API_KEY\"] = getpass.getpass(\"Enter your RunPod API Key: \")\n",
+    "\n",
+    "if \"RUNPOD_ENDPOINT_ID\" not in os.environ:\n",
+    "    os.environ[\"RUNPOD_ENDPOINT_ID\"] = input(\n",
+    "        \"Enter your RunPod Endpoint ID (used if RUNPOD_CHAT_ENDPOINT_ID is not set): \"\n",
+    "    )\n",
+    "\n",
+    "# Optionally use a different endpoint ID specifically for chat models\n",
+    "# if \"RUNPOD_CHAT_ENDPOINT_ID\" not in os.environ:\n",
+    "#     os.environ[\"RUNPOD_CHAT_ENDPOINT_ID\"] = input(\"Enter your RunPod Chat Endpoint ID (Optional): \")\n",
+    "\n",
+    "chat_endpoint_id = os.environ.get(\n",
+    "    \"RUNPOD_CHAT_ENDPOINT_ID\", os.environ.get(\"RUNPOD_ENDPOINT_ID\")\n",
+    ")\n",
+    "if not chat_endpoint_id:\n",
+    "    raise ValueError(\n",
+    "        \"No RunPod Endpoint ID found. Please set RUNPOD_ENDPOINT_ID or RUNPOD_CHAT_ENDPOINT_ID.\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Initialize the `ChatRunPod` class. You can pass model-specific parameters via `model_kwargs` and configure polling behavior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_runpod import ChatRunPod\n",
+    "\n",
+    "chat = ChatRunPod(\n",
+    "    runpod_endpoint_id=chat_endpoint_id,  # Specify the correct endpoint ID\n",
+    "    model_kwargs={\n",
+    "        \"max_new_tokens\": 512,\n",
+    "        \"temperature\": 0.7,\n",
+    "        \"top_p\": 0.9,\n",
+    "        # Add other parameters supported by your endpoint handler\n",
+    "    },\n",
+    "    # Optional: Adjust polling\n",
+    "    # poll_interval=0.2,\n",
+    "    # max_polling_attempts=150\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "Use the standard LangChain `.invoke()` and `.ainvoke()` methods to call the model. Streaming is also supported via `.stream()` and `.astream()` (simulated by polling the RunPod `/stream` endpoint)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "\n",
+    "messages = [\n",
+    "    SystemMessage(content=\"You are a helpful AI assistant.\"),\n",
+    "    HumanMessage(content=\"What is the RunPod Serverless API flow?\"),\n",
+    "]\n",
+    "\n",
+    "# Invoke (Sync)\n",
+    "try:\n",
+    "    response = chat.invoke(messages)\n",
+    "    print(\"--- Sync Invoke Response ---\")\n",
+    "    print(response.content)\n",
+    "except Exception as e:\n",
+    "    print(\n",
+    "        f\"Error invoking Chat Model: {e}. Ensure endpoint ID/API key are correct and endpoint is active/compatible.\"\n",
+    "    )\n",
+    "\n",
+    "# Stream (Sync, simulated via polling /stream)\n",
+    "print(\"\\n--- Sync Stream Response ---\")\n",
+    "try:\n",
+    "    for chunk in chat.stream(messages):\n",
+    "        print(chunk.content, end=\"\", flush=True)\n",
+    "    print()  # Newline\n",
+    "except Exception as e:\n",
+    "    print(\n",
+    "        f\"\\nError streaming Chat Model: {e}. Ensure endpoint handler supports streaming output format.\"\n",
+    "    )\n",
+    "\n",
+    "### Async Usage\n",
+    "\n",
+    "# AInvoke (Async)\n",
+    "try:\n",
+    "    async_response = await chat.ainvoke(messages)\n",
+    "    print(\"--- Async Invoke Response ---\")\n",
+    "    print(async_response.content)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error invoking Chat Model asynchronously: {e}.\")\n",
+    "\n",
+    "# AStream (Async)\n",
+    "print(\"\\n--- Async Stream Response ---\")\n",
+    "try:\n",
+    "    async for chunk in chat.astream(messages):\n",
+    "        print(chunk.content, end=\"\", flush=True)\n",
+    "    print()  # Newline\n",
+    "except Exception as e:\n",
+    "    print(\n",
+    "        f\"\\nError streaming Chat Model asynchronously: {e}. Ensure endpoint handler supports streaming output format.\\n\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "The chat model integrates seamlessly with LangChain Expression Language (LCEL) chains."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You are a helpful assistant.\"),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "parser = StrOutputParser()\n",
+    "\n",
+    "chain = prompt | chat | parser\n",
+    "\n",
+    "try:\n",
+    "    chain_response = chain.invoke(\n",
+    "        {\"input\": \"Explain the concept of serverless computing in simple terms.\"}\n",
+    "    )\n",
+    "    print(\"--- Chain Response ---\")\n",
+    "    print(chain_response)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error running chain: {e}\")\n",
+    "\n",
+    "\n",
+    "# Async chain\n",
+    "try:\n",
+    "    async_chain_response = await chain.ainvoke(\n",
+    "        {\"input\": \"What are the benefits of using RunPod for AI/ML workloads?\"}\n",
+    "    )\n",
+    "    print(\"--- Async Chain Response ---\")\n",
+    "    print(async_chain_response)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error running async chain: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Features (Endpoint Dependent)\n",
+    "\n",
+    "The availability of advanced features depends **heavily** on the specific implementation of your RunPod endpoint handler. The `ChatRunPod` integration provides the basic framework, but the handler must support the underlying functionality.\n",
+    "\n",
+    "| Feature                                                    | Integration Support | Endpoint Dependent? | Notes                                                                                                                                                                      |\n",
+    "| :--------------------------------------------------------- | :-----------------: | :-----------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n",
+    "| [Tool calling](/docs/how_to/tool_calling)                | ❌                  | ✅                  | Requires handler to process tool definitions and return tool calls (e.g., OpenAI format). Integration needs parsing logic.                                                 |\n",
+    "| [Structured output](/docs/how_to/structured_output)        | ❌                  | ✅                  | Requires handler support for forcing structured output (JSON mode, function calling). Integration needs parsing logic.                                                   |\n",
+    "| JSON mode                                                  | ❌                  | ✅                  | Requires handler to accept a `json_mode` parameter (or similar) and guarantee JSON output.                                                                               |\n",
+    "| [Image input](/docs/how_to/multimodal_inputs)            | ❌                  | ✅                  | Requires multimodal handler accepting image data (e.g., base64). Integration does not support multimodal messages.                                                       |\n",
+    "| Audio input                                                | ❌                  | ✅                  | Requires handler accepting audio data. Integration does not support audio messages.                                                                                        |\n",
+    "| Video input                                                | ❌                  | ✅                  | Requires handler accepting video data. Integration does not support video messages.                                                                                        |\n",
+    "| [Token-level streaming](/docs/how_to/chat_streaming)       | ✅ (Simulated)      | ✅                  | Polls `/stream`. Requires handler to populate `stream` list in status response with token chunks (e.g., `[{\"output\": \"token\"}]`). True low-latency streaming not built-in. |\n",
+    "| Native async                                               | ✅                  | ✅                  | Core `ainvoke`/`astream` implemented. Relies on endpoint handler performance.                                                                                              |\n",
+    "| [Token usage](/docs/how_to/chat_token_usage_tracking)    | ❌                  | ✅                  | Requires handler to return `prompt_tokens`, `completion_tokens` in the final response. Integration currently does not parse this.                                           |\n",
+    "| [Logprobs](/docs/how_to/logprobs)                          | ❌                  | ✅                  | Requires handler to return log probabilities. Integration currently does not parse this.                                                                                  |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Key Takeaway:** Standard chat invocation and simulated streaming work if the endpoint follows basic RunPod API conventions. Advanced features require specific handler implementations and potentially extending or customizing this integration package."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of the `ChatRunPod` class, parameters, and methods, refer to the source code or the generated API reference (if available).\n",
+    "\n",
+    "Link to source code: [https://github.com/runpod/langchain-runpod/blob/main/langchain_runpod/chat_models.py](https://github.com/runpod/langchain-runpod/blob/main/langchain_runpod/chat_models.py)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/chat/seekrflow.ipynb

@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "62d5a1ea",
+   "metadata": {},
+   "source": [
+    "# ChatSeekrFlow\n",
+    "\n",
+    "> [Seekr](https://www.seekr.com/) provides AI-powered solutions for structured, explainable, and transparent AI interactions.\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with Seekr [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatSeekrFlow` features and configurations, head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.seekrflow.ChatSeekrFlow.html).\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "`ChatSeekrFlow` class wraps a chat model endpoint hosted on SeekrFlow, enabling seamless integration with LangChain applications.\n",
+    "\n",
+    "### Integration Details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
+    "| [ChatSeekrFlow](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.seekrflow.ChatSeekrFlow.html) | [seekrai](https://python.langchain.com/docs/integrations/providers/seekr/) | ❌ | beta | ![PyPI - Downloads](https://img.shields.io/pypi/dm/seekrai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/seekrai?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model Features\n",
+    "\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",
+    "### Supported Methods\n",
+    "`ChatSeekrFlow` supports all methods of `ChatModel`, **except async APIs**.\n",
+    "\n",
+    "### Endpoint Requirements\n",
+    "\n",
+    "The serving endpoint `ChatSeekrFlow` wraps **must** have OpenAI-compatible chat input/output format. It can be used for:\n",
+    "1. **Fine-tuned Seekr models**\n",
+    "2. **Custom SeekrFlow models**\n",
+    "3. **RAG-enabled models using Seekr's retrieval system**\n",
+    "\n",
+    "For async usage, please refer to `AsyncChatSeekrFlow` (coming soon).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93fea471",
+   "metadata": {},
+   "source": [
+    "# Getting Started with ChatSeekrFlow in LangChain\n",
+    "\n",
+    "This notebook covers how to use SeekrFlow as a chat model in LangChain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f320c17",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Ensure you have the necessary dependencies installed:\n",
+    "\n",
+    "```bash\n",
+    "pip install seekrai langchain langchain-community\n",
+    "```\n",
+    "\n",
+    "You must also have an API key from Seekr to authenticate requests.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "911ca53c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard library\n",
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "# Third-party\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema import HumanMessage\n",
+    "from langchain_core.runnables import RunnableSequence\n",
+    "\n",
+    "# OSS SeekrFlow integration\n",
+    "from langchain_seekrflow import ChatSeekrFlow\n",
+    "from seekrai import SeekrFlow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "150461cb",
+   "metadata": {},
+   "source": [
+    "## API Key Setup\n",
+    "\n",
+    "You'll need to set your API key as an environment variable to authenticate requests.\n",
+    "\n",
+    "Run the below cell.\n",
+    "\n",
+    "Or manually assign it before running queries:\n",
+    "\n",
+    "```python\n",
+    "SEEKR_API_KEY = \"your-api-key-here\"\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38afcd6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"SEEKR_API_KEY\"] = getpass.getpass(\"Enter your Seekr API key:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82d83c0e",
+   "metadata": {},
+   "source": [
+    "## Instantiation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71b14751",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"SEEKR_API_KEY\"]\n",
+    "seekr_client = SeekrFlow(api_key=SEEKR_API_KEY)\n",
+    "\n",
+    "llm = ChatSeekrFlow(\n",
+    "    client=seekr_client, model_name=\"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1046e86c",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f61a60f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello there! I'm Seekr, nice to meet you! What brings you here today? Do you have a question, or are you looking for some help with something? I'm all ears (or rather, all text)!\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = llm.invoke([HumanMessage(content=\"Hello, Seekr!\")])\n",
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "853b0349",
+   "metadata": {},
+   "source": [
+    "## Chaining"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "35fca3ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content='The translation of \"Good morning\" in French is:\\n\\n\"Bonne journée\"' additional_kwargs={} response_metadata={}\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = ChatPromptTemplate.from_template(\"Translate to French: {text}\")\n",
+    "\n",
+    "chain: RunnableSequence = prompt | llm\n",
+    "result = chain.invoke({\"text\": \"Good morning\"})\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a7b28b8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🔹 Testing Sync `stream()` (Streaming)...\n",
+      "Here is a haiku:\n",
+      "\n",
+      "Golden sunset fades\n",
+      "Ripples on the quiet lake\n",
+      "Peaceful evening sky"
+     ]
+    }
+   ],
+   "source": [
+    "def test_stream():\n",
+    "    \"\"\"Test synchronous invocation in streaming mode.\"\"\"\n",
+    "    print(\"\\n🔹 Testing Sync `stream()` (Streaming)...\")\n",
+    "\n",
+    "    for chunk in llm.stream([HumanMessage(content=\"Write me a haiku.\")]):\n",
+    "        print(chunk.content, end=\"\", flush=True)\n",
+    "\n",
+    "\n",
+    "# ✅ Ensure streaming is enabled\n",
+    "llm = ChatSeekrFlow(\n",
+    "    client=seekr_client,\n",
+    "    model_name=\"meta-llama/Meta-Llama-3-8B-Instruct\",\n",
+    "    streaming=True,  # ✅ Enable streaming\n",
+    ")\n",
+    "\n",
+    "# ✅ Run sync streaming test\n",
+    "test_stream()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3847b34",
+   "metadata": {},
+   "source": [
+    "## Error Handling & Debugging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6bc38b48",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running test: Missing Client\n",
+      "✅ Expected Error: SeekrFlow client cannot be None.\n",
+      "Running test: Missing Model Name\n",
+      "✅ Expected Error: A valid model name must be provided.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define a minimal mock SeekrFlow client\n",
+    "class MockSeekrClient:\n",
+    "    \"\"\"Mock SeekrFlow API client that mimics the real API structure.\"\"\"\n",
+    "\n",
+    "    class MockChat:\n",
+    "        \"\"\"Mock Chat object with a completions method.\"\"\"\n",
+    "\n",
+    "        class MockCompletions:\n",
+    "            \"\"\"Mock Completions object with a create method.\"\"\"\n",
+    "\n",
+    "            def create(self, *args, **kwargs):\n",
+    "                return {\n",
+    "                    \"choices\": [{\"message\": {\"content\": \"Mock response\"}}]\n",
+    "                }  # Mimic API response\n",
+    "\n",
+    "        completions = MockCompletions()\n",
+    "\n",
+    "    chat = MockChat()\n",
+    "\n",
+    "\n",
+    "def test_initialization_errors():\n",
+    "    \"\"\"Test that invalid ChatSeekrFlow initializations raise expected errors.\"\"\"\n",
+    "\n",
+    "    test_cases = [\n",
+    "        {\n",
+    "            \"name\": \"Missing Client\",\n",
+    "            \"args\": {\"client\": None, \"model_name\": \"seekrflow-model\"},\n",
+    "            \"expected_error\": \"SeekrFlow client cannot be None.\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"name\": \"Missing Model Name\",\n",
+    "            \"args\": {\"client\": MockSeekrClient(), \"model_name\": \"\"},\n",
+    "            \"expected_error\": \"A valid model name must be provided.\",\n",
+    "        },\n",
+    "    ]\n",
+    "\n",
+    "    for test in test_cases:\n",
+    "        try:\n",
+    "            print(f\"Running test: {test['name']}\")\n",
+    "            faulty_llm = ChatSeekrFlow(**test[\"args\"])\n",
+    "\n",
+    "            # If no error is raised, fail the test\n",
+    "            print(f\"❌ Test '{test['name']}' failed: No error was raised!\")\n",
+    "        except Exception as e:\n",
+    "            error_msg = str(e)\n",
+    "            assert test[\"expected_error\"] in error_msg, f\"Unexpected error: {error_msg}\"\n",
+    "            print(f\"✅ Expected Error: {error_msg}\")\n",
+    "\n",
+    "\n",
+    "# Run test\n",
+    "test_initialization_errors()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1c9ddf3",
+   "metadata": {},
+   "source": [
+    "## API reference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "411a8bea",
+   "metadata": {},
+   "source": [
+    "- `ChatSeekrFlow` class: [`langchain_seekrflow.ChatSeekrFlow`](https://github.com/benfaircloth/langchain-seekrflow/blob/main/langchain_seekrflow/seekrflow.py)\n",
+    "- PyPI package: [`langchain-seekrflow`](https://pypi.org/project/langchain-seekrflow/)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ef00a51",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/llm_caching.ipynb

@@ -2669,7 +2669,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU langchain_couchbase couchbase"
+    "%pip install -qU langchain_couchbase"
    ]
   },
   {

docs/docs/integrations/llms/runpod.ipynb

@@ -0,0 +1,266 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RunPod LLM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get started with RunPod LLMs.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This guide covers how to use the LangChain `RunPod` LLM class to interact with text generation models hosted on [RunPod Serverless](https://www.runpod.io/serverless-gpu)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "1. **Install the package:**\n",
+    "   ```bash\n",
+    "   pip install -qU langchain-runpod\n",
+    "   ```\n",
+    "2. **Deploy an LLM Endpoint:** Follow the setup steps in the [RunPod Provider Guide](/docs/integrations/providers/runpod#setup) to deploy a compatible text generation endpoint on RunPod Serverless and get its Endpoint ID.\n",
+    "3. **Set Environment Variables:** Make sure `RUNPOD_API_KEY` and `RUNPOD_ENDPOINT_ID` are set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "# Make sure environment variables are set (or pass them directly to RunPod)\n",
+    "if \"RUNPOD_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"RUNPOD_API_KEY\"] = getpass.getpass(\"Enter your RunPod API Key: \")\n",
+    "if \"RUNPOD_ENDPOINT_ID\" not in os.environ:\n",
+    "    os.environ[\"RUNPOD_ENDPOINT_ID\"] = input(\"Enter your RunPod Endpoint ID: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Initialize the `RunPod` class. You can pass model-specific parameters via `model_kwargs` and configure polling behavior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_runpod import RunPod\n",
+    "\n",
+    "llm = RunPod(\n",
+    "    # runpod_endpoint_id can be passed here if not set in env\n",
+    "    model_kwargs={\n",
+    "        \"max_new_tokens\": 256,\n",
+    "        \"temperature\": 0.6,\n",
+    "        \"top_k\": 50,\n",
+    "        # Add other parameters supported by your endpoint handler\n",
+    "    },\n",
+    "    # Optional: Adjust polling\n",
+    "    # poll_interval=0.3,\n",
+    "    # max_polling_attempts=100\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "Use the standard LangChain `.invoke()` and `.ainvoke()` methods to call the model. Streaming is also supported via `.stream()` and `.astream()` (simulated by polling the RunPod `/stream` endpoint)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "prompt = \"Write a tagline for an ice cream shop on the moon.\"\n",
+    "\n",
+    "# Invoke (Sync)\n",
+    "try:\n",
+    "    response = llm.invoke(prompt)\n",
+    "    print(\"--- Sync Invoke Response ---\")\n",
+    "    print(response)\n",
+    "except Exception as e:\n",
+    "    print(\n",
+    "        f\"Error invoking LLM: {e}. Ensure endpoint ID/API key are correct and endpoint is active/compatible.\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Stream (Sync, simulated via polling /stream)\n",
+    "print(\"\\n--- Sync Stream Response ---\")\n",
+    "try:\n",
+    "    for chunk in llm.stream(prompt):\n",
+    "        print(chunk, end=\"\", flush=True)\n",
+    "    print()  # Newline\n",
+    "except Exception as e:\n",
+    "    print(\n",
+    "        f\"\\nError streaming LLM: {e}. Ensure endpoint handler supports streaming output format.\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Async Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# AInvoke (Async)\n",
+    "try:\n",
+    "    async_response = await llm.ainvoke(prompt)\n",
+    "    print(\"--- Async Invoke Response ---\")\n",
+    "    print(async_response)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error invoking LLM asynchronously: {e}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# AStream (Async)\n",
+    "print(\"\\n--- Async Stream Response ---\")\n",
+    "try:\n",
+    "    async for chunk in llm.astream(prompt):\n",
+    "        print(chunk, end=\"\", flush=True)\n",
+    "    print()  # Newline\n",
+    "except Exception as e:\n",
+    "    print(\n",
+    "        f\"\\nError streaming LLM asynchronously: {e}. Ensure endpoint handler supports streaming output format.\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "The LLM integrates seamlessly with LangChain Expression Language (LCEL) chains."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import PromptTemplate\n",
+    "\n",
+    "# Assumes 'llm' variable is instantiated from the 'Instantiation' cell\n",
+    "prompt_template = PromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
+    "parser = StrOutputParser()\n",
+    "\n",
+    "chain = prompt_template | llm | parser\n",
+    "\n",
+    "try:\n",
+    "    chain_response = chain.invoke({\"topic\": \"bears\"})\n",
+    "    print(\"--- Chain Response ---\")\n",
+    "    print(chain_response)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error running chain: {e}\")\n",
+    "\n",
+    "# Async chain\n",
+    "try:\n",
+    "    async_chain_response = await chain.ainvoke({\"topic\": \"robots\"})\n",
+    "    print(\"--- Async Chain Response ---\")\n",
+    "    print(async_chain_response)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error running async chain: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Endpoint Considerations\n",
+    "\n",
+    "- **Input:** The endpoint handler should expect the prompt string within `{\"input\": {\"prompt\": \"...\", ...}}`.\n",
+    "- **Output:** The handler should return the generated text within the `\"output\"` key of the final status response (e.g., `{\"output\": \"Generated text...\"}` or `{\"output\": {\"text\": \"...\"}}`).\n",
+    "- **Streaming:** For simulated streaming via the `/stream` endpoint, the handler must populate the `\"stream\"` key in the status response with a list of chunk dictionaries, like `[{\"output\": \"token1\"}, {\"output\": \"token2\"}]`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of the `RunPod` LLM class, parameters, and methods, refer to the source code or the generated API reference (if available).\n",
+    "\n",
+    "Link to source code: [https://github.com/runpod/langchain-runpod/blob/main/langchain_runpod/llms.py](https://github.com/runpod/langchain-runpod/blob/main/langchain_runpod/llms.py)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/providers/alibaba_cloud.mdx

@@ -46,6 +46,14 @@ See [installation instructions and a usage example](/docs/integrations/chat/tong
 from langchain_community.chat_models.tongyi import ChatTongyi
 ```
 
+### Qwen QwQ Chat
+
+See [installation instructions and a usage example](/docs/integrations/chat/qwq)
+
+```python
+from langchain_qwq import ChatQwQ
+```
+
 ## Document Loaders
 
 ### Alibaba Cloud MaxCompute

docs/docs/integrations/providers/aws.mdx

@@ -35,9 +35,18 @@ from langchain_aws import ChatBedrock
 ```
 
 ### Bedrock Converse
-AWS has recently released the Bedrock Converse API which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). To improve reliability the ChatBedrock integration will switch to using the Bedrock Converse API as soon as it has feature parity with the existing Bedrock API. Until then a separate [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) integration has been released.
+AWS Bedrock maintains a [Converse API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html)
+that provides a unified conversational interface for Bedrock models. This API does not
+yet support custom models. You can see a list of all
+[models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html).
 
-We recommend using `ChatBedrockConverse` for users who do not need to use custom models. See the [docs](/docs/integrations/chat/bedrock/#bedrock-converse-api) and [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) for more detail.
+:::info
+
+We recommend the Converse API for users who do not need to use custom models. It can be accessed using [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html).
+
+:::
+
+See a [usage example](/docs/integrations/chat/bedrock).
 
 ```python
 from langchain_aws import ChatBedrockConverse

docs/docs/integrations/providers/cloudflare.mdx

@@ -8,18 +8,34 @@
 > learning models, on the `Cloudflare` network, from your code via REST API.
 
 
+## ChatModels
+
+See [installation instructions and usage example](/docs/integrations/chat/cloudflare_workersai).
+
+```python
+from langchain_cloudflare import ChatCloudflareWorkersAI
+```
+
+## VectorStore
+
+See [installation instructions and usage example](/docs/integrations/vectorstores/cloudflare_vectorize).
+
+```python
+from langchain_cloudflare import CloudflareVectorize
+```
+
+## Embeddings
+
+See [installation instructions and usage example](/docs/integrations/text_embedding/cloudflare_workersai).
+
+```python
+from langchain_cloudflare import CloudflareWorkersAIEmbeddings
+```
+
 ## LLMs
 
 See [installation instructions and usage example](/docs/integrations/llms/cloudflare_workersai).
 
 ```python
 from langchain_community.llms.cloudflare_workersai import CloudflareWorkersAI
-```
-
-## Embedding models
-
-See [installation instructions and usage example](/docs/integrations/text_embedding/cloudflare_workersai).
-
-```python
-from langchain_community.embeddings.cloudflare_workersai import CloudflareWorkersAIEmbeddings
-```
+```

docs/docs/integrations/providers/couchbase.mdx

@@ -17,7 +17,7 @@ pip install langchain-couchbase
 See a [usage example](/docs/integrations/vectorstores/couchbase).
 
 ```python
-from langchain_couchbase import CouchbaseVectorStore
+from langchain_couchbase import CouchbaseSearchVectorStore
 ```
 
 ## Document loader

docs/docs/integrations/providers/galaxia.mdx

@@ -0,0 +1,32 @@
+# Smabbler
+> Smabbler’s graph-powered platform boosts AI development by transforming data into a structured knowledge foundation.
+
+# Galaxia
+
+> Galaxia Knowledge Base is an integrated knowledge base and retrieval mechanism for RAG. In contrast to standard solution, it is based on Knowledge Graphs built using symbolic NLP and Knowledge Representation solutions. Provided texts are analysed and transformed into Graphs containing text, language and semantic information. This rich structure allows for retrieval that is based on semantic information, not on vector similarity/distance.
+
+Implementing RAG using Galaxia involves first uploading your files to [Galaxia](https://beta.cloud.smabbler.com/home), analyzing them there and then building a model (knowledge graph). When the model is built, you can use `GalaxiaRetriever` to connect to the API and start retrieving.
+
+More information: [docs](https://smabbler.gitbook.io/smabbler)
+
+## Installation
+```
+pip install langchain-galaxia-retriever
+```
+
+## Usage
+
+```
+from langchain_galaxia_retriever.retriever import GalaxiaRetriever
+
+gr = GalaxiaRetriever(
+    api_url="beta.api.smabbler.com",
+    api_key="<key>",
+    knowledge_base_id="<knowledge_base_id>",
+    n_retries=10,
+    wait_time=5,
+)
+
+result = gr.invoke('<test question>')
+print(result)
+

docs/docs/integrations/providers/hyperbrowser.mdx

@@ -25,6 +25,114 @@ And you should configure credentials by setting the following environment variab
 
 Make sure to get your API Key from https://app.hyperbrowser.ai/
 
+## Available Tools
+
+Hyperbrowser provides two main categories of tools that are particularly useful for:
+- Web scraping and data extraction from complex websites
+- Automating repetitive web tasks
+- Interacting with web applications that require authentication
+- Performing research across multiple websites
+- Testing web applications
+
+### Browser Agent Tools
+
+Hyperbrowser provides a number of Browser Agents tools. Currently we supported
+ - Claude Computer Use
+ - OpenAI CUA
+ - Browser Use
+
+You can see more details [here](/docs/integrations/tools/hyperbrowser_browser_agent_tools) 
+
+#### Browser Use Tool
+A general-purpose browser automation tool that can handle various web tasks through natural language instructions.
+
+```python
+from langchain_hyperbrowser import HyperbrowserBrowserUseTool
+
+tool = HyperbrowserBrowserUseTool()
+result = tool.run({
+    "task": "Go to npmjs.com, find the React package, and tell me when it was last updated"
+})
+print(result)
+```
+
+#### OpenAI CUA Tool
+Leverages OpenAI's Computer Use Agent capabilities for advanced web interactions and information gathering.
+
+```python
+from langchain_hyperbrowser import HyperbrowserOpenAICUATool
+
+tool = HyperbrowserOpenAICUATool()
+result = tool.run({
+    "task": "Go to Hacker News and summarize the top 5 posts right now"
+})
+print(result)
+```
+
+#### Claude Computer Use Tool
+Utilizes Anthropic's Claude for sophisticated web browsing and information processing tasks.
+
+```python
+from langchain_hyperbrowser import HyperbrowserClaudeComputerUseTool
+
+tool = HyperbrowserClaudeComputerUseTool()
+result = tool.run({
+    "task": "Go to GitHub's trending repositories page, and list the top 3 posts there right now"
+})
+print(result)
+```
+
+### Web Scraping Tools
+
+Here is a brief description of the Web Scraping Tools available with Hyperbrowser. You can see more details [here](/docs/integrations/tools/hyperbrowser_web_scraping_tools)
+
+#### Scrape Tool
+The Scrape Tool allows you to extract content from a single webpage in markdown, HTML, or link format.
+
+```python
+from langchain_hyperbrowser import HyperbrowserScrapeTool
+
+tool = HyperbrowserScrapeTool()
+result = tool.run({
+    "url": "https://example.com",
+    "scrape_options": {"formats": ["markdown"]}
+})
+print(result)
+```
+
+#### Crawl Tool
+The Crawl Tool enables you to traverse entire websites, starting from a given URL, with configurable page limits.
+
+```python
+from langchain_hyperbrowser import HyperbrowserCrawlTool
+
+tool = HyperbrowserCrawlTool()
+result = tool.run({
+    "url": "https://example.com",
+    "max_pages": 2,
+    "scrape_options": {"formats": ["markdown"]}
+})
+print(result)
+```
+
+#### Extract Tool
+The Extract Tool uses AI to pull structured data from web pages based on predefined schemas, making it perfect for data extraction tasks.
+
+```python
+from langchain_hyperbrowser import HyperbrowserExtractTool
+from pydantic import BaseModel
+
+class SimpleExtractionModel(BaseModel):
+    title: str
+
+tool = HyperbrowserExtractTool()
+result = tool.run({
+    "url": "https://example.com",
+    "schema": SimpleExtractionModel
+})
+print(result)
+```
+
 ## Document Loader
 
 The `HyperbrowserLoader` class in `langchain-hyperbrowser` can easily be used to load content from any single page or multiple pages as well as crawl an entire site.
@@ -39,7 +147,7 @@ docs = loader.load()
 print(docs[0])
 ```
 
-## Advanced Usage
+### Advanced Usage
 
 You can specify the operation to be performed by the loader. The default operation is `scrape`. For `scrape`, you can provide a single URL or a list of URLs to be scraped. For `crawl`, you can only provide a single URL. The `crawl` operation will crawl the provided page and subpages and return a document for each page.
 

docs/docs/integrations/providers/langfuse.mdx

@@ -0,0 +1,165 @@
+# Langfuse 🪢
+
+> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open source LLM engineering platform that helps teams trace API calls, monitor performance, and debug issues in their AI applications.
+
+## Tracing LangChain
+
+[Langfuse Tracing](https://langfuse.com/docs/tracing) integrates with Langchain using Langchain Callbacks ([Python](https://python.langchain.com/docs/how_to/#callbacks), [JS](https://js.langchain.com/docs/how_to/#callbacks)). Thereby, the Langfuse SDK automatically creates a nested trace for every run of your Langchain applications. This allows you to log, analyze and debug your LangChain application.
+
+You can configure the integration via (1) constructor arguments or (2) environment variables. Get your Langfuse credentials by signing up at [cloud.langfuse.com](https://cloud.langfuse.com) or [self-hosting Langfuse](https://langfuse.com/self-hosting).
+
+### Constructor arguments 
+
+```python
+pip install langfuse
+```
+
+```python
+# Initialize Langfuse handler
+from langfuse.callback import CallbackHandler
+langfuse_handler = CallbackHandler(
+    secret_key="sk-lf-...",
+    public_key="pk-lf-...",
+    host="https://cloud.langfuse.com", # 🇪🇺 EU region
+  # host="https://us.cloud.langfuse.com", # 🇺🇸 US region
+)
+ 
+# Your Langchain code
+ 
+# Add Langfuse handler as callback (classic and LCEL)
+chain.invoke({"input": "<user_input>"}, config={"callbacks": [langfuse_handler]})
+```
+
+### Environment variables
+
+```bash filename=".env"
+LANGFUSE_SECRET_KEY="sk-lf-..."
+LANGFUSE_PUBLIC_KEY="pk-lf-..."
+# 🇪🇺 EU region
+LANGFUSE_HOST="https://cloud.langfuse.com"
+# 🇺🇸 US region
+# LANGFUSE_HOST="https://us.cloud.langfuse.com"
+```
+
+```python
+# Initialize Langfuse handler
+from langfuse.callback import CallbackHandler
+langfuse_handler = CallbackHandler()
+ 
+# Your Langchain code
+ 
+# Add Langfuse handler as callback (classic and LCEL)
+chain.invoke({"input": "<user_input>"}, config={"callbacks": [langfuse_handler]})
+```
+
+To see how to use this integration together with other Langfuse features, check out [this end-to-end example](https://langfuse.com/docs/integrations/langchain/example-python).
+
+## Tracing LangGraph
+
+This part demonstrates how [Langfuse](https://langfuse.com/docs) helps to debug, analyze, and iterate on your LangGraph application using the [LangChain integration](https://langfuse.com/docs/integrations/langchain/tracing).
+
+### Initialize Langfuse
+
+**Note:** You need to run at least Python 3.11 ([GitHub Issue](https://github.com/langfuse/langfuse/issues/1926)).
+
+Initialize the Langfuse client with your [API keys](https://langfuse.com/faq/all/where-are-langfuse-api-keys) from the project settings in the Langfuse UI and add them to your environment.
+
+
+```python
+%pip install langfuse
+%pip install langchain langgraph langchain_openai langchain_community
+```
+
+
+```python
+import os
+
+# get keys for your project from https://cloud.langfuse.com
+os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-***"
+os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-***"
+os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # for EU data region
+# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # for US data region
+
+# your openai key
+os.environ["OPENAI_API_KEY"] = "***"
+```
+
+### Simple chat app with LangGraph
+
+**What we will do in this section:**
+
+*   Build a support chatbot in LangGraph that can answer common questions
+*   Tracing the chatbot's input and output using Langfuse
+
+We will start with a basic chatbot and build a more advanced multi agent setup in the next section, introducing key LangGraph concepts along the way.
+
+#### Create Agent
+
+Start by creating a `StateGraph`. A `StateGraph` object defines our chatbot's structure as a state machine. We will add nodes to represent the LLM and functions the chatbot can call, and edges to specify how the bot transitions between these functions.
+
+
+```python
+from typing import Annotated
+
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from typing_extensions import TypedDict
+
+from langgraph.graph import StateGraph
+from langgraph.graph.message import add_messages
+
+class State(TypedDict):
+    # Messages have the type "list". The `add_messages` function in the annotation defines how this state key should be updated
+    # (in this case, it appends messages to the list, rather than overwriting them)
+    messages: Annotated[list, add_messages]
+
+graph_builder = StateGraph(State)
+
+llm = ChatOpenAI(model = "gpt-4o", temperature = 0.2)
+
+# The chatbot node function takes the current State as input and returns an updated messages list. This is the basic pattern for all LangGraph node functions.
+def chatbot(state: State):
+    return {"messages": [llm.invoke(state["messages"])]}
+
+# Add a "chatbot" node. Nodes represent units of work. They are typically regular python functions.
+graph_builder.add_node("chatbot", chatbot)
+
+# Add an entry point. This tells our graph where to start its work each time we run it.
+graph_builder.set_entry_point("chatbot")
+
+# Set a finish point. This instructs the graph "any time this node is run, you can exit."
+graph_builder.set_finish_point("chatbot")
+
+# To be able to run our graph, call "compile()" on the graph builder. This creates a "CompiledGraph" we can use invoke on our state.
+graph = graph_builder.compile()
+```
+
+#### Add Langfuse as callback to the invocation
+
+Now, we will add then [Langfuse callback handler for LangChain](https://langfuse.com/docs/integrations/langchain/tracing) to trace the steps of our application: `config={"callbacks": [langfuse_handler]}`
+
+
+```python
+from langfuse.callback import CallbackHandler
+
+# Initialize Langfuse CallbackHandler for Langchain (tracing)
+langfuse_handler = CallbackHandler()
+
+for s in graph.stream({"messages": [HumanMessage(content = "What is Langfuse?")]},
+                      config={"callbacks": [langfuse_handler]}):
+    print(s)
+```
+
+```
+{'chatbot': {'messages': [AIMessage(content='Langfuse is a tool designed to help developers monitor and observe the performance of their Large Language Model (LLM) applications. It provides detailed insights into how these applications are functioning, allowing for better debugging, optimization, and overall management. Langfuse offers features such as tracking key metrics, visualizing data, and identifying potential issues in real-time, making it easier for developers to maintain and improve their LLM-based solutions.', response_metadata={'token_usage': {'completion_tokens': 86, 'prompt_tokens': 13, 'total_tokens': 99}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_400f27fa1f', 'finish_reason': 'stop', 'logprobs': None}, id='run-9a0c97cb-ccfe-463e-902c-5a5900b796b4-0', usage_metadata={'input_tokens': 13, 'output_tokens': 86, 'total_tokens': 99})]}}
+```
+
+
+#### View traces in Langfuse
+
+Example trace in Langfuse: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/d109e148-d188-4d6e-823f-aac0864afbab
+
+![Trace view of chat app in Langfuse](https://langfuse.com/images/cookbook/integration-langgraph/integration_langgraph_chatapp_trace.png)
+
+- Check out the [full notebook](https://langfuse.com/docs/integrations/langchain/example-python-langgraph) to see more examples.
+- To learn how to evaluate the performance of your LangGraph application, check out the [LangGraph evaluation guide](https://langfuse.com/docs/integrations/langchain/example-langgraph-agents).

docs/docs/integrations/providers/litellm.mdx

@@ -0,0 +1,18 @@
+# LiteLLM
+
+>[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc.
+
+## Installation and setup
+
+```bash
+pip install langchain-litellm
+```
+
+## Chat Models
+```python
+from langchain_litellm import ChatLiteLLM
+```
+See more detail in the guide [here](/docs/integrations/chat/litellm).
+
+## API reference
+For detailed documentation of all `ChatLiteLLM` features and configurations head to the API reference: https://github.com/Akshay-Dongare/langchain-litellm

docs/docs/integrations/providers/littlellm.md

@@ -1,37 +0,0 @@
-# LiteLLM
-
->[LiteLLM](https://docs.litellm.ai/docs/) is a library that simplifies calling Anthropic, 
-> Azure, Huggingface, Replicate, etc. LLMs in a unified way.
-> 
->You can use `LiteLLM` through either:
->
->* [LiteLLM Proxy Server](https://docs.litellm.ai/docs/#openai-proxy) - Server to call 100+ LLMs, load balance, cost tracking across projects
->* [LiteLLM python SDK](https://docs.litellm.ai/docs/#basic-usage) - Python Client to call 100+ LLMs, load balance, cost tracking
-
-## Installation and setup
-
-Install the `litellm` python package.
-
-```bash
-pip install litellm
-```
-
-## Chat models
-
-### ChatLiteLLM
-
-See a [usage example](/docs/integrations/chat/litellm).
-
-```python
-from langchain_community.chat_models import ChatLiteLLM
-```
-
-### ChatLiteLLMRouter
-
-You also can use the `ChatLiteLLMRouter` to route requests to different LLMs or LLM providers.
-
-See a [usage example](/docs/integrations/chat/litellm_router).
-
-```python
-from langchain_community.chat_models import ChatLiteLLMRouter
-```

docs/docs/integrations/providers/mariadb.mdx

@@ -0,0 +1,41 @@
+# MariaDB
+
+This page covers how to use the [MariaDB](https://github.com/mariadb/) ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific PGVector wrappers.
+
+## Installation
+- Install c/c connector
+
+on Debian, Ubuntu
+```bash
+sudo apt install libmariadb3 libmariadb-dev
+```
+
+on CentOS, RHEL, Rocky Linux
+```bash
+sudo yum install MariaDB-shared MariaDB-devel
+```
+
+- Install the Python connector package with `pip install mariadb`
+
+
+## Setup
+1. The first step is to have a MariaDB 11.7.1 or later installed.
+
+    The docker image is the easiest way to get started.
+
+## Wrappers
+
+### VectorStore
+
+There exists a wrapper around MariaDB vector databases, allowing you to use it as a vectorstore,
+whether for semantic search or example selection.
+
+To import this vectorstore:
+```python
+from langchain_mariadb import MariaDBStore
+```
+
+### Usage
+
+For a more detailed walkthrough of the MariaDB wrapper, see [this notebook](/docs/integrations/vectorstores/mariadb)

docs/docs/integrations/providers/oxylabs.mdx

@@ -0,0 +1,18 @@
+# Oxylabs
+
+[Oxylabs](https://oxylabs.io/) is a market-leading web intelligence collection platform, driven by the highest business,
+ethics, and compliance standards, enabling companies worldwide to unlock data-driven insights.
+
+[langchain-oxylabs](https://pypi.org/project/langchain-oxylabs/) implements
+tools enabling LLMs to interact with Oxylabs Web Scraper API.
+
+
+## Installation and Setup
+
+```bash
+pip install langchain-oxylabs
+```
+
+## Tools
+
+See details on available tools [here](/docs/integrations/tools/oxylabs/).

docs/docs/integrations/providers/perplexity.mdx

@@ -8,18 +8,18 @@
 
 ## Installation and Setup
 
-Install a Python package:
+Install the Perplexity x LangChain integration package:
 
 ```bash
-pip install openai
+pip install langchain-perplexity
 ````
 
 Get your API key from [here](https://docs.perplexity.ai/docs/getting-started).
 
 ## Chat models
 
-See a [usage example](/docs/integrations/chat/perplexity).
+See a variety of usage examples [here](/docs/integrations/chat/perplexity).
 
 ```python
-from langchain_community.chat_models import ChatPerplexity
+from langchain_perplexity import ChatPerplexity
 ```

docs/docs/integrations/providers/redis.mdx

@@ -59,7 +59,7 @@ with the default values of "service-name = mymaster" and "db-number = 0" if not
 The service-name is the redis server monitoring group name as configured within the Sentinel. 
 
 The current url format limits the connection string to one sentinel host only (no list can be given) and
-booth Redis server and sentinel must have the same password set (if used).
+both Redis server and sentinel must have the same password set (if used).
 
 #### Redis Cluster connection url
 

docs/docs/integrations/providers/runpod.ipynb

@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Runpod\n",
+    "\n",
+    "[RunPod](https://www.runpod.io/) provides GPU cloud infrastructure, including Serverless endpoints optimized for deploying and scaling AI models.\n",
+    "\n",
+    "This guide covers how to use the `langchain-runpod` integration package to connect LangChain applications to models hosted on [RunPod Serverless](https://www.runpod.io/serverless-gpu).\n",
+    "\n",
+    "The integration offers interfaces for both standard Language Models (LLMs) and Chat Models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Intstallation\n",
+    "\n",
+    "Install the dedicated partner package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-runpod"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "### 1. Deploy an Endpoint on RunPod\n",
+    "- Navigate to your [RunPod Serverless Console](https://www.runpod.io/console/serverless/user/endpoints).\n",
+    "- Create a \\\"New Endpoint\\\", selecting an appropriate GPU and template (e.g., vLLM, TGI, text-generation-webui) compatible with your model and the expected input/output format (see component guides or the package [README](https://github.com/runpod/langchain-runpod)).\n",
+    "- Configure settings and deploy.\n",
+    "- **Crucially, copy the Endpoint ID** after deployment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Set API Credentials\n",
+    "The integration needs your RunPod API Key and the Endpoint ID. Set them as environment variables for secure access:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"RUNPOD_API_KEY\"] = getpass.getpass(\"Enter your RunPod API Key: \")\n",
+    "os.environ[\"RUNPOD_ENDPOINT_ID\"] = input(\"Enter your RunPod Endpoint ID: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*(Optional)* If using different endpoints for LLM and Chat models, you might need to set `RUNPOD_CHAT_ENDPOINT_ID` or pass the ID directly during initialization."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Components\n",
+    "This package provides two main components:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. LLM\n",
+    "\n",
+    "For interacting with standard text completion models.\n",
+    "\n",
+    "See the [RunPod LLM Integration Guide](/docs/integrations/llms/runpod) for detailed usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_runpod import RunPod\n",
+    "\n",
+    "# Example initialization (uses environment variables)\n",
+    "llm = RunPod(model_kwargs={\"max_new_tokens\": 100})  # Add generation params here\n",
+    "\n",
+    "# Example Invocation\n",
+    "try:\n",
+    "    response = llm.invoke(\"Write a short poem about the cloud.\")\n",
+    "    print(response)\n",
+    "except Exception as e:\n",
+    "    print(\n",
+    "        f\"Error invoking LLM: {e}. Ensure endpoint ID and API key are correct and endpoint is active.\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Chat Model\n",
+    "\n",
+    "For interacting with conversational models.\n",
+    "\n",
+    "See the [RunPod Chat Model Integration Guide](/docs/integrations/chat/runpod) for detailed usage and feature support."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_runpod import ChatRunPod\n",
+    "\n",
+    "# Example initialization (uses environment variables)\n",
+    "chat = ChatRunPod(model_kwargs={\"temperature\": 0.8})  # Add generation params here\n",
+    "\n",
+    "# Example Invocation\n",
+    "try:\n",
+    "    response = chat.invoke(\n",
+    "        [HumanMessage(content=\"Explain RunPod Serverless in one sentence.\")]\n",
+    "    )\n",
+    "    print(response.content)\n",
+    "except Exception as e:\n",
+    "    print(\n",
+    "        f\"Error invoking Chat Model: {e}. Ensure endpoint ID and API key are correct and endpoint is active.\"\n",
+    "    )"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/providers/vectara.ipynb

@@ -14,7 +14,7 @@
     "3. The [Boomerang](https://vectara.com/how-boomerang-takes-retrieval-augmented-generation-to-the-next-level-via-grounded-generation/) embeddings model.\n",
     "4. Its own internal vector database where text chunks and embedding vectors are stored.\n",
     "5. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments, including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) as well as multiple reranking options such as the [multi-lingual relevance reranker](https://www.vectara.com/blog/deep-dive-into-vectara-multilingual-reranker-v1-state-of-the-art-reranker-across-100-languages), [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/), [UDF reranker](https://www.vectara.com/blog/rag-with-user-defined-functions-based-reranking). \n",
-    "6. An LLM to for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.\n",
+    "6. An LLM for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.\n",
     "\n",
     "For more information:\n",
     "- [Documentation](https://docs.vectara.com/docs/)\n",

docs/docs/integrations/providers/ydb.mdx

@@ -0,0 +1,23 @@
+# YDB
+
+All functionality related to YDB.
+
+> [YDB](https://ydb.tech/) is a versatile open source Distributed SQL Database that combines
+> high availability and scalability with strong consistency and ACID transactions.
+> It accommodates transactional (OLTP), analytical (OLAP), and streaming workloads simultaneously.
+
+## Installation and Setup
+
+```bash
+pip install langchain-ydb
+```
+
+## Vector Store
+
+To import YDB vector store:
+
+```python
+from langchain_ydb.vectorstores import YDB
+```
+
+For a more detailed walkthrough of the YDB vector store, see [this notebook](/docs/integrations/vectorstores/ydb).

docs/docs/integrations/retrievers/azure_ai_search.ipynb

@@ -40,11 +40,21 @@
     "\n",
     "+ An existing index with vector fields. There are several ways to create one, including using the [vector store module](../vectorstores/azuresearch.ipynb). Or, [try the Azure AI Search REST APIs](https://learn.microsoft.com/azure/search/search-get-started-vector).\n",
     "\n",
-    "+ An API key. API keys are generated when you create the search service. If you're just querying an index, you can use the query API key, otherwise use an admin API key. See [Find your API keys](https://learn.microsoft.com/azure/search/search-security-api-keys?tabs=rest-use%2Cportal-find%2Cportal-query#find-existing-keys) for details.\n",
+    "+ An API key or Azure AD Token.\n",
+    "  + API keys are generated when you create the search service. If you're just querying an index, you can use the query API key, otherwise use an admin API key. See [Find your API keys](https://learn.microsoft.com/azure/search/search-security-api-keys?tabs=rest-use%2Cportal-find%2Cportal-query#find-existing-keys) for details.\n",
+    "  + Azure AD Token can be used with Azure Managed Identity. See [Connect your app to Azure AI Search using identities](https://learn.microsoft.com/en-us/azure/search/keyless-connections?tabs=python%2Cazure-cli) for details.\n",
     "\n",
     "We can then set the search service name, index name, and API key as environment variables (alternatively, you can pass them as arguments to `AzureAISearchRetriever`). The search index provides the searchable content."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2af9655d",
+   "metadata": {},
+   "source": [
+    "With an API Key"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -59,6 +69,28 @@
     "os.environ[\"AZURE_AI_SEARCH_API_KEY\"] = \"<YOUR_API_KEY>\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "35ee4780",
+   "metadata": {},
+   "source": [
+    "With an Azure AD Token"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f33263c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"AZURE_AI_SEARCH_SERVICE_NAME\"] = \"<YOUR_SEARCH_SERVICE_NAME>\"\n",
+    "os.environ[\"AZURE_AI_SEARCH_INDEX_NAME\"] = \"<YOUR_SEARCH_INDEX_NAME>\"\n",
+    "os.environ[\"AZURE_AI_SEARCH_AD_TOKEN\"] = \"<YOUR_AZURE_AD_TOKEN>\""
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3e635218-8634-4f39-abc5-39e319eeb136",

docs/docs/integrations/retrievers/galaxia-retriever.ipynb

@@ -0,0 +1,213 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "2af1fec5-4ca6-4167-8ee1-13314aac3258",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: Galaxia\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d7d6cbc-4373-4fb5-94dd-acd610165452",
+   "metadata": {},
+   "source": [
+    "# Galaxia Retriever\n",
+    "\n",
+    "Galaxia is GraphRAG solution, which automates document processing, knowledge base (Graph Language Model) creation and retrieval:\n",
+    "[galaxia-rag](https://smabbler.gitbook.io/smabbler/api-rag/smabblers-api-rag)\n",
+    "\n",
+    "To use Galaxia first upload your texts and create a Graph Language Model here: [smabbler-cloud](https://beta.cloud.smabbler.com)\n",
+    "\n",
+    "After the model is built and activated, you will be able to use this integration to retrieve what you need.\n",
+    "\n",
+    "The module repository is located here: [github](https://github.com/rrozanski-smabbler/galaxia-langchain)\n",
+    "\n",
+    "### Integration details\n",
+    "| Retriever | Self-host | Cloud offering | Package |\n",
+    "| :--- | :--- | :---: | :---: |\n",
+    "[Galaxia Retriever](https://github.com/rrozanski-smabbler/galaxia-langchain) | ❌ | ✅ | __langchain-galaxia-retriever__ |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82fa1c05-c205-4429-a74c-e6c81c4e8611",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "Before you can retrieve anything you need to create your Graph Language Model here: [smabbler-cloud](https://beta.cloud.smabbler.com)\n",
+    "\n",
+    "following these 3 simple steps: [rag-instruction](https://smabbler.gitbook.io/smabbler/api-rag/build-rag-model-in-3-steps)\n",
+    "\n",
+    "Don't forget to activate the model after building it!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91897867-eb39-4c3b-8df8-5427043ecdcd",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "The retriever is implemented in the following package: [pypi](https://pypi.org/project/langchain-galaxia-retriever/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ceca36f2-013c-4b28-81fe-8808d0cf6419",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-galaxia-retriever"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "019e0e50-5e66-440b-9cf1-d21b4009bf13",
+   "metadata": {},
+   "source": [
+    "## Instantiation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7188217-4b26-4201-b15a-b7a5f263f815",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_galaxia_retriever.retriever import GalaxiaRetriever\n",
+    "\n",
+    "gr = GalaxiaRetriever(\n",
+    "    api_url=\"beta.api.smabbler.com\",\n",
+    "    api_key=\"<key>\",  # you can find it here: https://beta.cloud.smabbler.com/user/account\n",
+    "    knowledge_base_id=\"<knowledge_base_id>\",  # you can find it in https://beta.cloud.smabbler.com , in the model table\n",
+    "    n_retries=10,\n",
+    "    wait_time=5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02d288a5-4f76-472e-9a60-eea8e6b8dc7a",
+   "metadata": {},
+   "source": [
+    "## Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f79e03f-77a6-4eb6-b41d-f3da2f897654",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = gr.invoke(\"<test question>\")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffb2a595-a901-477a-a374-efd091bc1c9a",
+   "metadata": {},
+   "source": [
+    "## Use within a chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c2e2394-ca33-47be-a851-551b4216daea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | output: false\n",
+    "# | echo: false\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed8699d6-d65d-40ea-8c58-8d809cc512cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\n",
+    "    \"\"\"Answer the question based only on the context provided.\n",
+    "\n",
+    "Context: {context}\n",
+    "\n",
+    "Question: {question}\"\"\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "\n",
+    "chain = (\n",
+    "    {\"context\": gr | format_docs, \"question\": RunnablePassthrough()}\n",
+    "    | prompt\n",
+    "    | llm\n",
+    "    | StrOutputParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9b944d7-8800-4926-b1ce-fcdc52ecda1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain.invoke(\"<test question>\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11b5c9a5-0a66-415f-98f8-f12080cad30a",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For more information about Galaxia Retriever check its implementation on github [github](https://github.com/rrozanski-smabbler/galaxia-langchain)"
+   ]
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/text_embedding/cloudflare_workersai.ipynb

@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.embeddings.cloudflare_workersai import (\n",
+    "from langchain_cloudflare.embeddings import (\n",
     "    CloudflareWorkersAIEmbeddings,\n",
     ")"
    ]

docs/docs/integrations/tools/brave_search.ipynb

@@ -43,12 +43,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "5b14008a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "tool = BraveSearch.from_api_key(api_key=api_key, search_kwargs={\"count\": 3})"
+    "tool = BraveSearch.from_api_key(api_key=api_key, search_kwargs={\"count\": 3})\n",
+    "\n",
+    "# or if you want to get the api key from environment variable BRAVE_SEARCH_API_KEY, and leave search_kwargs empty\n",
+    "# tool = BraveSearch()\n",
+    "\n",
+    "# or if you want to provide just the api key, and leave search_kwargs empty\n",
+    "# tool = BraveSearch.from_api_key(api_key=api_key)\n",
+    "\n",
+    "# or if you want to provide just the search_kwargs and read the api key from the BRAVE_SEARCH_API_KEY environment variable\n",
+    "# tool = BraveSearch.from_search_kwargs(search_kwargs={\"count\": 3})"
    ]
   },
   {

docs/docs/integrations/tools/dalle_image_generator.ipynb

@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "id": "q-k8wmp0zquh"
    },
@@ -34,7 +34,7 @@
     "\n",
     "from langchain_openai import OpenAI\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"<your-key-here>\""
+    "os.environ[\"OPENAI_API_KEY\"] = \"insertapikey\""
    ]
   },
   {
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,20 +75,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://oaidalleapiprodscus.blob.core.windows.net/private/org-i0zjYONU3PemzJ222esBaAzZ/user-f6uEIOFxoiUZivy567cDSWni/img-i7Z2ZxvJ4IbbdAiO6OXJgS3v.png?st=2023-08-11T14%3A03%3A14Z&se=2023-08-11T16%3A03%3A14Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-08-10T20%3A58%3A32Z&ske=2023-08-11T20%3A58%3A32Z&sks=b&skv=2021-08-06&sig=/sECe7C0EAq37ssgBm7g7JkVIM/Q1W3xOstd0Go6slA%3D'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "image_url"
    ]
@@ -135,34 +124,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m What is the best way to turn this description into an image?\n",
-      "Action: Dall-E Image Generator\n",
-      "Action Input: A spooky Halloween night at a haunted museum\u001b[0mhttps://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D\n",
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mhttps://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m With the image generated, I can now make my final answer.\n",
-      "Final Answer: An image of a Halloween night at a haunted museum can be seen here: https://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from langchain.agents import initialize_agent, load_tools\n",
+    "from langchain_community.tools.openai_dalle_image_generation import (\n",
+    "    OpenAIDALLEImageGenerationTool,\n",
+    ")\n",
+    "from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "tools = load_tools([\"dalle-image-generator\"])\n",
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)\n",
-    "output = agent.run(\"Create an image of a halloween night at a haunted museum\")"
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
+    "api_wrapper = DallEAPIWrapper()\n",
+    "dalle_tool = OpenAIDALLEImageGenerationTool(api_wrapper=api_wrapper)\n",
+    "\n",
+    "tools = [dalle_tool]\n",
+    "\n",
+    "agent = create_react_agent(llm, tools, debug=True)\n",
+    "\n",
+    "# User prompt\n",
+    "prompt = \"Create an image of a halloween night at a haunted museum\"\n",
+    "\n",
+    "messages = [\n",
+    "    # \"role\": \"user\" Indicates message is coming from user\n",
+    "    # \"content\": prompt is where the user's input is placed\n",
+    "    {\"role\": \"user\", \"content\": prompt}\n",
+    "]\n",
+    "\n",
+    "# Sending the message to be processed and adjusted by ChatGPT, after which is sent through DALL-E\n",
+    "response = agent.invoke({\"messages\": messages})\n",
+    "\n",
+    "print(response)"
    ]
   }
  ],
@@ -171,9 +164,9 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python (langchain-env)",
    "language": "python",
-   "name": "python3"
+   "name": "langchain-env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -185,12 +178,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "3570c8892273ffbeee7ead61dc7c022b73551d9f55fb2584ac0e8e8920b18a89"
-   }
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,

docs/docs/integrations/tools/google_calendar.ipynb

@@ -0,0 +1,335 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "# Google Calendar Toolkit\n",
+    "\n",
+    "> [Google Calendar](https://workspace.google.com/intl/en-419/products/calendar/) is a product of Google Workspace that allows users to organize their schedules and events. It is a cloud-based calendar that allows users to create, edit, and delete events. It also allows users to share their calendars with others.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This notebook will help you get started with the Google Calendar Toolkit. This toolkit interacts with the Google Calendar API to perform various operations on the calendar. It allows you to:\n",
+    "\n",
+    "- Create events.\n",
+    "- Search events.\n",
+    "- Update events.\n",
+    "- Move events between different calendars.\n",
+    "- Delete events.\n",
+    "- List events.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To use this toolkit, you will need to:\n",
+    "\n",
+    "1. Have a Google account with access to Google Calendar.\n",
+    "2. Set up your credentials as explained in the [Google Calendar API docs](https://developers.google.com/calendar/api/quickstart/python#authorize_credentials_for_a_desktop_application). Once you've downloaded the `credentials.json` file, you can start using the Google Calendar API.\n",
+    "\n",
+    "To enable automated tracing of individual tools, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "This toolkit lives in the `langchain-google-community` package of the [langchain-google](https://github.com/langchain-ai/langchain-google) repository. We'll need the `calendar` extra:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-google-community\\[calendar\\]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "By default the toolkit reads the local `credentials.json` file. You can also manually provide a `Credentials` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_community import CalendarToolkit\n",
+    "\n",
+    "toolkit = CalendarToolkit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Customizing Authentication\n",
+    "\n",
+    "Behind the scenes, a `googleapi` resource is created using the following methods. you can manually build a `googleapi` resource for more auth control."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_community import CalendarToolkit\n",
+    "from langchain_google_community.calendar.utils import (\n",
+    "    build_resource_service,\n",
+    "    get_google_credentials,\n",
+    ")\n",
+    "\n",
+    "# Can review scopes here: https://developers.google.com/calendar/api/auth\n",
+    "# For instance, readonly scope is https://www.googleapis.com/auth/calendar.readonly\n",
+    "credentials = get_google_credentials(\n",
+    "    token_file=\"token.json\",\n",
+    "    scopes=[\"https://www.googleapis.com/auth/calendar\"],\n",
+    "    client_secrets_file=\"credentials.json\",\n",
+    ")\n",
+    "\n",
+    "api_resource = build_resource_service(credentials=credentials)\n",
+    "toolkit = CalendarToolkit(api_resource=api_resource)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tools\n",
+    "View available tools:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[CalendarCreateEvent(api_resource=<googleapiclient.discovery.Resource object at 0x10ad13fb0>),\n",
+       " CalendarSearchEvents(api_resource=<googleapiclient.discovery.Resource object at 0x10ad13fb0>),\n",
+       " CalendarUpdateEvent(api_resource=<googleapiclient.discovery.Resource object at 0x10ad13fb0>),\n",
+       " GetCalendarsInfo(api_resource=<googleapiclient.discovery.Resource object at 0x10ad13fb0>),\n",
+       " CalendarMoveEvent(api_resource=<googleapiclient.discovery.Resource object at 0x10ad13fb0>),\n",
+       " CalendarDeleteEvent(api_resource=<googleapiclient.discovery.Resource object at 0x10ad13fb0>),\n",
+       " GetCurrentDatetime(api_resource=<googleapiclient.discovery.Resource object at 0x10ad13fb0>)]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tools = toolkit.get_tools()\n",
+    "tools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- [CalendarCreateEvent](https://python.langchain.com/api_reference/google_community/calendar/langchain_google_community.calendar.create_event.CalendarCreateEvent.html)\n",
+    "- [CalendarSearchEvents](https://python.langchain.com/api_reference/google_community/calendar/langchain_google_community.calendar.search_events.CalendarSearchEvents.html)\n",
+    "- [CalendarUpdateEvent](https://python.langchain.com/api_reference/google_community/calendar/langchain_google_community.calendar.update_event.CalendarUpdateEvent.html)\n",
+    "- [GetCalendarsInfo](https://python.langchain.com/api_reference/google_community/calendar/langchain_google_community.calendar.get_calendars_info.GetCalendarsInfo.html)\n",
+    "- [CalendarMoveEvent](https://python.langchain.com/api_reference/google_community/calendar/langchain_google_community.calendar.move_event.CalendarMoveEvent.html)\n",
+    "- [CalendarDeleteEvent](https://python.langchain.com/api_reference/google_community/calendar/langchain_google_community.calendar.delete_event.CalendarDeleteEvent.html)\n",
+    "- [GetCurrentDatetime](https://python.langchain.com/api_reference/google_community/calendar/langchain_google_community.calendar.current_datetime.GetCurrentDatetime.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "### [Invoke directly with args](/docs/concepts/tools/#use-the-tool-directly)\n",
+    "\n",
+    "You can invoke the tool directly by passing the required arguments in a dictionary format. Here is an example of creating a new event using the `CalendarCreateEvent` tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Event created: https://www.google.com/calendar/event?eid=amoxdjVsM2UzMW51Yjk2czc4ajhvaGdkcGcgam9yZ2VhbmczM0Bt'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_google_community.calendar.create_event import CalendarCreateEvent\n",
+    "\n",
+    "tool = CalendarCreateEvent()\n",
+    "tool.invoke(\n",
+    "    {\n",
+    "        \"summary\": \"Calculus exam\",\n",
+    "        \"start_datetime\": \"2025-07-11 11:00:00\",\n",
+    "        \"end_datetime\": \"2025-07-11 13:00:00\",\n",
+    "        \"timezone\": \"America/Mexico_City\",\n",
+    "        \"location\": \"UAM Cuajimalpa\",\n",
+    "        \"description\": \"Event created from the LangChain toolkit\",\n",
+    "        \"reminders\": [{\"method\": \"popup\", \"minutes\": 60}],\n",
+    "        \"conference_data\": True,\n",
+    "        \"color_id\": \"5\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use within an agent\n",
+    "\n",
+    "Below we show how to incorporate the toolkit into an [agent](/docs/tutorials/agents).\n",
+    "\n",
+    "We will need a LLM or chat model:\n",
+    "\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\" />"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | output: false\n",
+    "# | echo: false\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "agent_executor = create_react_agent(llm, tools)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "Create a green event for this afternoon to go for a 30-minute run.\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  get_current_datetime (call_drHRRhm6pdvcAuqagONUEKs5)\n",
+      " Call ID: call_drHRRhm6pdvcAuqagONUEKs5\n",
+      "  Args:\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: get_current_datetime\n",
+      "\n",
+      "Time zone: America/Mexico_City, Date and time: 2025-04-02 19:07:30\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  create_calendar_event (call_p60zSVMmmjTy5Ctezzmlb9zD)\n",
+      " Call ID: call_p60zSVMmmjTy5Ctezzmlb9zD\n",
+      "  Args:\n",
+      "    summary: Run\n",
+      "    start_datetime: 2025-04-02 19:30:00\n",
+      "    end_datetime: 2025-04-02 20:00:00\n",
+      "    timezone: America/Mexico_City\n",
+      "    color_id: 2\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: create_calendar_event\n",
+      "\n",
+      "Event created: https://www.google.com/calendar/event?eid=czZyZHVpcG43ajNiY241dmJmNWwycjE0NWsgam9yZ2VhbmczM0Bt\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "I have created a green event for your run this afternoon. You can view it [here](https://www.google.com/calendar/event?eid=czZyZHVpcG43ajNiY241dmJmNWwycjE0NWsgam9yZ2VhbmczM0Bt). Enjoy your run!\n"
+     ]
+    }
+   ],
+   "source": [
+    "example_query = \"Create a green event for this afternoon to go for a 30-minute run.\"\n",
+    "\n",
+    "events = agent_executor.stream(\n",
+    "    {\"messages\": [(\"user\", example_query)]},\n",
+    "    stream_mode=\"values\",\n",
+    ")\n",
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "- Refer to the [Google Calendar API overview](https://developers.google.com/calendar/api/guides/overview) for more details from Google Calendar API.\n",
+    "- For detailed documentation of all Google Calendar Toolkit features and configurations head to the [calendar documentation](https://python.langchain.com/api_reference/google_community/calendar.html)."
+   ]
+  }
+ ],
+ "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/tools/hyperbrowser_browser_agent_tools.ipynb

@@ -0,0 +1,392 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Hyperbrowser Browser Agent Tools\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hyperbrowser Browser Agent Tools\n",
+    "\n",
+    "[Hyperbrowser](https://hyperbrowser.ai) is a platform for running, running browser agents, and scaling headless browsers. It lets you launch and manage browser sessions at scale and provides easy to use solutions for any webscraping needs, such as scraping a single page or crawling an entire site.\n",
+    "\n",
+    "Key Features:\n",
+    "- Instant Scalability - Spin up hundreds of browser sessions in seconds without infrastructure headaches\n",
+    "- Simple Integration - Works seamlessly with popular tools like Puppeteer and Playwright\n",
+    "- Powerful APIs - Easy to use APIs for scraping/crawling any site, and much more\n",
+    "- Bypass Anti-Bot Measures - Built-in stealth mode, ad blocking, automatic CAPTCHA solving, and rotating proxies\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with Hyperbrowser tools.\n",
+    "\n",
+    "For more information about Hyperbrowser, please visit the [Hyperbrowser website](https://hyperbrowser.ai) or if you want to check out the docs, you can visit the [Hyperbrowser docs](https://docs.hyperbrowser.ai).\n",
+    "\n",
+    "\n",
+    "## Browser Agents\n",
+    "\n",
+    "Hyperbrowser provides powerful browser agent tools that enable AI models to interact with web browsers programmatically. These browser agents can navigate websites, fill forms, click buttons, extract data, and perform complex web automation tasks.\n",
+    "\n",
+    "Browser agents are particularly useful for:\n",
+    "- Web scraping and data extraction from complex websites\n",
+    "- Automating repetitive web tasks\n",
+    "- Interacting with web applications that require authentication\n",
+    "- Performing research across multiple websites\n",
+    "- Testing web applications\n",
+    "\n",
+    "Hyperbrowser offers three types of browser agent tools:\n",
+    "- **Browser Use Tool**: A general-purpose browser automation tool\n",
+    "- **OpenAI CUA Tool**: Integration with OpenAI's Computer Use Agent\n",
+    "- **Claude Computer Use Tool**: Integration with Anthropic's Claude for computer use\n",
+    "\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Tool                      | Package                | Local | Serializable | JS support |\n",
+    "| :-----------------------  | :--------------------- | :---: | :----------: | :--------: |\n",
+    "| Browser Use Tool          | langchain-hyperbrowser |  ❌   |      ❌      |      ❌    |\n",
+    "| OpenAI CUA Tool           | langchain-hyperbrowser |  ❌   |      ❌      |      ❌    |\n",
+    "| Claude Computer Use Tool  | langchain-hyperbrowser |  ❌   |      ❌      |     ❌     |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "To access the Hyperbrowser tools you'll need to install the `langchain-hyperbrowser` integration package, and create a Hyperbrowser account and get an API key.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [Hyperbrowser](https://app.hyperbrowser.ai/) to sign up and generate an API key. Once you've done this set the HYPERBROWSER_API_KEY environment variable:\n",
+    "\n",
+    "```bash\n",
+    "export HYPERBROWSER_API_KEY=<your-api-key>\n",
+    "```\n",
+    "\n",
+    "### Installation\n",
+    "\n",
+    "Install **langchain-hyperbrowser**.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-hyperbrowser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "### Browser Use Tool\n",
+    "\n",
+    "The `HyperbrowserBrowserUseTool` is a tool to perform web automation tasks using a browser agent, specifically the Browser-Use agent.\n",
+    "\n",
+    "```python\n",
+    "from langchain_hyperbrowser import HyperbrowserBrowserUseTool\n",
+    "tool = HyperbrowserBrowserUseTool()\n",
+    "```\n",
+    "\n",
+    "### OpenAI CUA Tool\n",
+    "\n",
+    "The `HyperbrowserOpenAICUATool` is a specialized tool that leverages OpenAI's Computer Use Agent (CUA) capabilities through Hyperbrowser.\n",
+    "\n",
+    "```python\n",
+    "from langchain_hyperbrowser import HyperbrowserOpenAICUATool\n",
+    "tool = HyperbrowserOpenAICUATool()\n",
+    "```\n",
+    "\n",
+    "### Claude Computer Use Tool\n",
+    "\n",
+    "The `HyperbrowserClaudeComputerUseTool` is a specialized tool that leverages Claude's computer use capabilities through Hyperbrowser.\n",
+    "\n",
+    "```python\n",
+    "from langchain_hyperbrowser import HyperbrowserClaudeComputerUseTool\n",
+    "tool = HyperbrowserClaudeComputerUseTool()\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "### Basic Usage\n",
+    "\n",
+    "#### Browser Use Tool\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': 'The top 5 posts on Hacker News right now are:\\n1. Stop Syncing Everything - https://sqlsync.dev/posts/stop-syncing-everything/\\n2. Move fast, break things: A review of Abundance by Ezra Klein and Derek Thompson - https://networked.substack.com/p/move-fast-and-break-things\\n3. DEDA – Tracking Dots Extraction, Decoding and Anonymisation Toolkit - https://github.com/dfd-tud/deda\\n4. Electron band structure in germanium, my ass (2001) - https://pages.cs.wisc.edu/~kovar/hall.html\\n5. Show HN: I vibecoded a 35k LoC recipe app - https://www.recipeninja.ai', 'error': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_hyperbrowser import HyperbrowserBrowserUseTool\n",
+    "\n",
+    "tool = HyperbrowserBrowserUseTool()\n",
+    "result = tool.run({\"task\": \"Go to Hacker News and summarize the top 5 posts right now\"})\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### OpenAI CUA Tool\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': 'Here are the titles of the top 5 posts on Hacker News right now:\\n\\n1. \"DEDA – Tracking Dots Extraction, Decoding and Anonymisation Toolkit\"\\n2. \"A man powers home for eight years using a thousand old laptop batteries\"\\n3. \"Electron band structure in Germanium, my ass\"\\n4. \"Bletchley code breaker Betty Webb dies aged 101\"\\n5. \"Show HN: Zig Topological Sort Library for Parallel Processing\"', 'error': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_hyperbrowser import HyperbrowserOpenAICUATool\n",
+    "\n",
+    "tool = HyperbrowserOpenAICUATool()\n",
+    "result = tool.run(\n",
+    "    {\"task\": \"Go to Hacker News and get me the title of the top 5 posts right now\"}\n",
+    ")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Claude Computer Use Tool\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': \"Now I'll summarize the top 5 posts on Hacker News as of April 1, 2025:\\n\\n### Top 5 Hacker News Posts Summary\\n\\n1. **A man powers home for eight years using a thousand old laptop batteries** (techoreon.com)\\n   - 267 points, posted 5 hours ago\\n   - An innovative DIY project where someone managed to power their home using recycled laptop batteries for an extended period.\\n\\n2. **Electron band structure in germanium, my ass** (wisc.edu)\\n   - 611 points, posted 8 hours ago\\n   - Academic or technical discussion about electron band structure in germanium, possibly with a controversial or humorous take given the title.\\n\\n3. **Bletchley code breaker Betty Webb dies aged 101** (bbc.com)\\n   - 575 points, posted 8 hours ago\\n   - Obituary for Betty Webb, who worked as a code breaker at Bletchley Park during WWII, passing away at the age of 101.\\n\\n4. **Show HN: Zig Topological Sort Library for Parallel Processing** (github.com/williamw520)\\n   - 55 points, posted 3 hours ago\\n   - A developer sharing a library written in Zig programming language for topological sorting that supports parallel processing.\\n\\n5. **The Myst Graph: A New Perspective on Myst** (githr.com)\\n   - 107 points, posted 5 hours ago\\n   - An article presenting a new analysis or visualization of the classic video game Myst, likely using graph theory.\\n\\nThese are the top 5 posts currently trending on Hacker News as of April 1, 2025.\", 'error': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_hyperbrowser import HyperbrowserClaudeComputerUseTool\n",
+    "\n",
+    "tool = HyperbrowserClaudeComputerUseTool()\n",
+    "result = tool.run({\"task\": \"Go to Hacker News and summarize the top 5 posts right now\"})\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### With Custom Session Options\n",
+    "\n",
+    "All tools support custom session options:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': 'I have found that the react package was last published 11 hours ago. This is the most recently updated package I could find.', 'error': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = tool.run(\n",
+    "    {\n",
+    "        \"task\": \"Go to npmjs.com, and tell me when react package was last updated.\",\n",
+    "        \"session_options\": {\n",
+    "            \"session_options\": {\"use_proxy\": True, \"accept_cookies\": True}\n",
+    "        },\n",
+    "    }\n",
+    ")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Async Usage\n",
+    "\n",
+    "All tools support async usage:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': 'The page displays information about the \"Example Domain,\" stating that it is used for illustrative purposes and can be utilized without permission. There\\'s a link to \"More information...\" but no specific contact details are provided.', 'error': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "async def browse_website():\n",
+    "    tool = HyperbrowserBrowserUseTool()\n",
+    "    result = await tool.arun(\n",
+    "        {\n",
+    "            \"task\": \"Go to npmjs.com, click the first visible package, and tell me when it was updated\"\n",
+    "        }\n",
+    "    )\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "result = await browse_website()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use within an agent\n",
+    "\n",
+    "Here's how to use any of the Hyperbrowser tools within an agent:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "Go to npmjs.com, and tell me when react package was last updated.\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  hyperbrowser_browser_use (call_pkAaDjn6kKH9yT3rHDb4hmET)\n",
+      " Call ID: call_pkAaDjn6kKH9yT3rHDb4hmET\n",
+      "  Args:\n",
+      "    task: Go to npmjs.com and find the last updated date of the React package.\n",
+      "    session_options: None\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: hyperbrowser_browser_use\n",
+      "\n",
+      "{\"data\": \"The last updated date of the React package is a day ago.\", \"error\": null}\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The React package was last updated a day ago.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_hyperbrowser import browser_use_tool\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "llm = ChatOpenAI(temperature=0)\n",
+    "\n",
+    "# You can use any of the three tools here\n",
+    "browser_use_tool = HyperbrowserBrowserUseTool()\n",
+    "agent = create_react_agent(llm, [browser_use_tool])\n",
+    "\n",
+    "user_input = \"Go to npmjs.com, and tell me when react package was last updated.\"\n",
+    "for step in agent.stream(\n",
+    "    {\"messages\": user_input},\n",
+    "    stream_mode=\"values\",\n",
+    "):\n",
+    "    step[\"messages\"][-1].pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuration Options\n",
+    "\n",
+    "Claude Computer Use, OpenAI CUA, and Browser Use have the following params available:\n",
+    "\n",
+    "- `task`: The task to execute using the agent\n",
+    "- `max_steps`: The maximum number of interaction steps the agent can take to complete the task\n",
+    "- `session_options`: Browser session configuration\n",
+    "\n",
+    "For more details, see the respective API references:\n",
+    "- [Browser Use API Reference](https://docs.hyperbrowser.ai/reference/api-reference/agents/browser-use)\n",
+    "- [OpenAI CUA API Reference](https://docs.hyperbrowser.ai/reference/api-reference/agents/openai-cua)\n",
+    "- [Claude Computer Use API Reference](https://docs.hyperbrowser.ai/reference/api-reference/agents/claude-computer-use)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "- [GitHub](https://github.com/hyperbrowserai/langchain-hyperbrowser/)\n",
+    "- [PyPi](https://pypi.org/project/langchain-hyperbrowser/)\n",
+    "- [Hyperbrowser Docs](https://docs.hyperbrowser.ai/)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain-hyperbrowser-AlekOQAq-py3.12",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/integrations/tools/hyperbrowser_web_scraping_tools.ipynb

@@ -0,0 +1,559 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Hyperbrowser Web Scraping Tools\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hyperbrowser Web Scraping Tools\n",
+    "\n",
+    "[Hyperbrowser](https://hyperbrowser.ai) is a platform for running and scaling headless browsers. It lets you launch and manage browser sessions at scale and provides easy to use solutions for any webscraping needs, such as scraping a single page or crawling an entire site.\n",
+    "\n",
+    "Key Features:\n",
+    "\n",
+    "- Instant Scalability - Spin up hundreds of browser sessions in seconds without infrastructure headaches\n",
+    "- Simple Integration - Works seamlessly with popular tools like Puppeteer and Playwright\n",
+    "- Powerful APIs - Easy to use APIs for scraping/crawling any site, and much more\n",
+    "- Bypass Anti-Bot Measures - Built-in stealth mode, ad blocking, automatic CAPTCHA solving, and rotating proxies\n",
+    "\n",
+    "This notebook provides a quick overview for getting started with Hyperbrowser web tools.\n",
+    "\n",
+    "For more information about Hyperbrowser, please visit the [Hyperbrowser website](https://hyperbrowser.ai) or if you want to check out the docs, you can visit the [Hyperbrowser docs](https://docs.hyperbrowser.ai).\n",
+    "\n",
+    "## Key Capabilities\n",
+    "\n",
+    "### Scrape\n",
+    "\n",
+    "Hyperbrowser provides powerful scraping capabilities that allow you to extract data from any webpage. The scraping tool can convert web content into structured formats like markdown or HTML, making it easy to process and analyze the data.\n",
+    "\n",
+    "### Crawl\n",
+    "\n",
+    "The crawling functionality enables you to navigate through multiple pages of a website automatically. You can set parameters like page limits to control how extensively the crawler explores the site, collecting data from each page it visits.\n",
+    "\n",
+    "### Extract\n",
+    "\n",
+    "Hyperbrowser's extraction capabilities use AI to pull specific information from webpages according to your defined schema. This allows you to transform unstructured web content into structured data that matches your exact requirements.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Tool         | Package                | Local | Serializable | JS support |\n",
+    "| :----------- | :--------------------- | :---: | :----------: | :--------: |\n",
+    "| Crawl Tool   | langchain-hyperbrowser |  ❌   |      ❌      |     ❌     |\n",
+    "| Scrape Tool  | langchain-hyperbrowser |  ❌   |      ❌      |     ❌     |\n",
+    "| Extract Tool | langchain-hyperbrowser |  ❌   |      ❌      |     ❌     |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "To access the Hyperbrowser web tools you'll need to install the `langchain-hyperbrowser` integration package, and create a Hyperbrowser account and get an API key.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [Hyperbrowser](https://app.hyperbrowser.ai/) to sign up and generate an API key. Once you've done this set the HYPERBROWSER_API_KEY environment variable:\n",
+    "\n",
+    "```bash\n",
+    "export HYPERBROWSER_API_KEY=<your-api-key>\n",
+    "```\n",
+    "\n",
+    "### Installation\n",
+    "\n",
+    "Install **langchain-hyperbrowser**.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-hyperbrowser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "### Crawl Tool\n",
+    "\n",
+    "The `HyperbrowserCrawlTool` is a powerful tool that can crawl entire websites, starting from a given URL. It supports configurable page limits and scraping options.\n",
+    "\n",
+    "```python\n",
+    "from langchain_hyperbrowser import HyperbrowserCrawlTool\n",
+    "tool = HyperbrowserCrawlTool()\n",
+    "```\n",
+    "\n",
+    "### Scrape Tool\n",
+    "\n",
+    "The `HyperbrowserScrapeTool` is a tool that can scrape content from web pages. It supports both markdown and HTML output formats, along with metadata extraction.\n",
+    "\n",
+    "```python\n",
+    "from langchain_hyperbrowser import HyperbrowserScrapeTool\n",
+    "tool = HyperbrowserScrapeTool()\n",
+    "```\n",
+    "\n",
+    "### Extract Tool\n",
+    "\n",
+    "The `HyperbrowserExtractTool` is a powerful tool that uses AI to extract structured data from web pages. It can extract information based predefined schemas.\n",
+    "\n",
+    "```python\n",
+    "from langchain_hyperbrowser import HyperbrowserExtractTool\n",
+    "tool = HyperbrowserExtractTool()\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "### Basic Usage\n",
+    "\n",
+    "#### Crawl Tool\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': [CrawledPage(metadata={'url': 'https://www.example.com/', 'title': 'Example Domain', 'viewport': 'width=device-width, initial-scale=1', 'sourceURL': 'https://example.com'}, html=None, markdown='Example Domain\\n\\n# Example Domain\\n\\nThis domain is for use in illustrative examples in documents. You may use this\\ndomain in literature without prior coordination or asking for permission.\\n\\n[More information...](https://www.iana.org/domains/example)', links=None, screenshot=None, url='https://example.com', status='completed', error=None)], 'error': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_hyperbrowser import HyperbrowserCrawlTool\n",
+    "\n",
+    "result = HyperbrowserCrawlTool().invoke(\n",
+    "    {\n",
+    "        \"url\": \"https://example.com\",\n",
+    "        \"max_pages\": 2,\n",
+    "        \"scrape_options\": {\"formats\": [\"markdown\"]},\n",
+    "    }\n",
+    ")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Scrape Tool\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': ScrapeJobData(metadata={'url': 'https://www.example.com/', 'title': 'Example Domain', 'viewport': 'width=device-width, initial-scale=1', 'sourceURL': 'https://example.com'}, html=None, markdown='Example Domain\\n\\n# Example Domain\\n\\nThis domain is for use in illustrative examples in documents. You may use this\\ndomain in literature without prior coordination or asking for permission.\\n\\n[More information...](https://www.iana.org/domains/example)', links=None, screenshot=None), 'error': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_hyperbrowser import HyperbrowserScrapeTool\n",
+    "\n",
+    "result = HyperbrowserScrapeTool().invoke(\n",
+    "    {\"url\": \"https://example.com\", \"scrape_options\": {\"formats\": [\"markdown\"]}}\n",
+    ")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Extract Tool\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': {'title': 'Example Domain'}, 'error': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_hyperbrowser import HyperbrowserExtractTool\n",
+    "from pydantic import BaseModel\n",
+    "\n",
+    "\n",
+    "class SimpleExtractionModel(BaseModel):\n",
+    "    title: str\n",
+    "\n",
+    "\n",
+    "result = HyperbrowserExtractTool().invoke(\n",
+    "    {\n",
+    "        \"url\": \"https://example.com\",\n",
+    "        \"schema\": SimpleExtractionModel,\n",
+    "    }\n",
+    ")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### With Custom Options\n",
+    "\n",
+    "#### Crawl Tool with Custom Options\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': [CrawledPage(metadata={'url': 'https://www.example.com/', 'title': 'Example Domain', 'viewport': 'width=device-width, initial-scale=1', 'sourceURL': 'https://example.com'}, html=None, markdown='Example Domain\\n\\n# Example Domain\\n\\nThis domain is for use in illustrative examples in documents. You may use this\\ndomain in literature without prior coordination or asking for permission.\\n\\n[More information...](https://www.iana.org/domains/example)', links=None, screenshot=None, url='https://example.com', status='completed', error=None)], 'error': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = HyperbrowserCrawlTool().run(\n",
+    "    {\n",
+    "        \"url\": \"https://example.com\",\n",
+    "        \"max_pages\": 2,\n",
+    "        \"scrape_options\": {\n",
+    "            \"formats\": [\"markdown\", \"html\"],\n",
+    "        },\n",
+    "        \"session_options\": {\"use_proxy\": True, \"solve_captchas\": True},\n",
+    "    }\n",
+    ")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Scrape Tool with Custom Options\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': ScrapeJobData(metadata={'url': 'https://www.example.com/', 'title': 'Example Domain', 'viewport': 'width=device-width, initial-scale=1', 'sourceURL': 'https://example.com'}, html='<html><head>\\n    <title>Example Domain</title>\\n\\n    <meta charset=\"utf-8\">\\n    <meta http-equiv=\"Content-type\" content=\"text/html; charset=utf-8\">\\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\\n        \\n</head>\\n\\n<body>\\n<div>\\n    <h1>Example Domain</h1>\\n    <p>This domain is for use in illustrative examples in documents. You may use this\\n    domain in literature without prior coordination or asking for permission.</p>\\n    <p><a href=\"https://www.iana.org/domains/example\">More information...</a></p>\\n</div>\\n\\n\\n</body></html>', markdown='Example Domain\\n\\n# Example Domain\\n\\nThis domain is for use in illustrative examples in documents. You may use this\\ndomain in literature without prior coordination or asking for permission.\\n\\n[More information...](https://www.iana.org/domains/example)', links=None, screenshot=None), 'error': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = HyperbrowserScrapeTool().run(\n",
+    "    {\n",
+    "        \"url\": \"https://example.com\",\n",
+    "        \"scrape_options\": {\n",
+    "            \"formats\": [\"markdown\", \"html\"],\n",
+    "        },\n",
+    "        \"session_options\": {\"use_proxy\": True, \"solve_captchas\": True},\n",
+    "    }\n",
+    ")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Extract Tool with Custom Schema\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': {'products': [{'price': 9.99, 'title': 'Essence Mascara Lash Princess'}, {'price': 19.99, 'title': 'Eyeshadow Palette with Mirror'}, {'price': 14.99, 'title': 'Powder Canister'}, {'price': 12.99, 'title': 'Red Lipstick'}, {'price': 8.99, 'title': 'Red Nail Polish'}, {'price': 49.99, 'title': 'Calvin Klein CK One'}, {'price': 129.99, 'title': 'Chanel Coco Noir Eau De'}, {'price': 89.99, 'title': \"Dior J'adore\"}, {'price': 69.99, 'title': 'Dolce Shine Eau de'}, {'price': 79.99, 'title': 'Gucci Bloom Eau de'}]}, 'error': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from pydantic import BaseModel\n",
+    "\n",
+    "\n",
+    "class ProductSchema(BaseModel):\n",
+    "    title: str\n",
+    "    price: float\n",
+    "\n",
+    "\n",
+    "class ProductsSchema(BaseModel):\n",
+    "    products: List[ProductSchema]\n",
+    "\n",
+    "\n",
+    "result = HyperbrowserExtractTool().run(\n",
+    "    {\n",
+    "        \"url\": \"https://dummyjson.com/products?limit=10\",\n",
+    "        \"schema\": ProductsSchema,\n",
+    "        \"session_options\": {\"session_options\": {\"use_proxy\": True}},\n",
+    "    }\n",
+    ")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Async Usage\n",
+    "\n",
+    "All tools support async usage:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'List' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlangchain_hyperbrowser\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m      2\u001b[39m     HyperbrowserCrawlTool,\n\u001b[32m      3\u001b[39m     HyperbrowserExtractTool,\n\u001b[32m      4\u001b[39m     HyperbrowserScrapeTool,\n\u001b[32m      5\u001b[39m )\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpydantic\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BaseModel\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[38;5;28;43;01mclass\u001b[39;49;00m\u001b[38;5;250;43m \u001b[39;49m\u001b[34;43;01mExtractionSchema\u001b[39;49;00m\u001b[43m(\u001b[49m\u001b[43mBaseModel\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m     11\u001b[39m \u001b[43m    \u001b[49m\u001b[43mpopular_library_name\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mList\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[32m     14\u001b[39m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mweb_operations\u001b[39m():\n\u001b[32m     15\u001b[39m     \u001b[38;5;66;03m# Crawl\u001b[39;00m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 11\u001b[39m, in \u001b[36mExtractionSchema\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m     10\u001b[39m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mExtractionSchema\u001b[39;00m(BaseModel):\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m     popular_library_name: \u001b[43mList\u001b[49m[\u001b[38;5;28mstr\u001b[39m]\n",
+      "\u001b[31mNameError\u001b[39m: name 'List' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from langchain_hyperbrowser import (\n",
+    "    HyperbrowserCrawlTool,\n",
+    "    HyperbrowserExtractTool,\n",
+    "    HyperbrowserScrapeTool,\n",
+    ")\n",
+    "from pydantic import BaseModel\n",
+    "\n",
+    "\n",
+    "class ExtractionSchema(BaseModel):\n",
+    "    popular_library_name: List[str]\n",
+    "\n",
+    "\n",
+    "async def web_operations():\n",
+    "    # Crawl\n",
+    "    crawl_tool = HyperbrowserCrawlTool()\n",
+    "    crawl_result = await crawl_tool.arun(\n",
+    "        {\n",
+    "            \"url\": \"https://example.com\",\n",
+    "            \"max_pages\": 5,\n",
+    "            \"scrape_options\": {\"formats\": [\"markdown\"]},\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "    # Scrape\n",
+    "    scrape_tool = HyperbrowserScrapeTool()\n",
+    "    scrape_result = await scrape_tool.arun(\n",
+    "        {\"url\": \"https://example.com\", \"scrape_options\": {\"formats\": [\"markdown\"]}}\n",
+    "    )\n",
+    "\n",
+    "    # Extract\n",
+    "    extract_tool = HyperbrowserExtractTool()\n",
+    "    extract_result = await extract_tool.arun(\n",
+    "        {\n",
+    "            \"url\": \"https://npmjs.com\",\n",
+    "            \"schema\": ExtractionSchema,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "    return crawl_result, scrape_result, extract_result\n",
+    "\n",
+    "\n",
+    "results = await web_operations()\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use within an agent\n",
+    "\n",
+    "Here's how to use any of the web tools within an agent:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "Crawl https://example.com and get content from up to 5 pages\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  hyperbrowser_crawl_data (call_G2ofdHOqjdnJUZu4hhbuga58)\n",
+      " Call ID: call_G2ofdHOqjdnJUZu4hhbuga58\n",
+      "  Args:\n",
+      "    url: https://example.com\n",
+      "    max_pages: 5\n",
+      "    scrape_options: {'formats': ['markdown']}\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: hyperbrowser_crawl_data\n",
+      "\n",
+      "{'data': [CrawledPage(metadata={'url': 'https://www.example.com/', 'title': 'Example Domain', 'viewport': 'width=device-width, initial-scale=1', 'sourceURL': 'https://example.com'}, html=None, markdown='Example Domain\\n\\n# Example Domain\\n\\nThis domain is for use in illustrative examples in documents. You may use this\\ndomain in literature without prior coordination or asking for permission.\\n\\n[More information...](https://www.iana.org/domains/example)', links=None, screenshot=None, url='https://example.com', status='completed', error=None)], 'error': None}\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "I have crawled the website [https://example.com](https://example.com) and retrieved content from the first page. Here is the content in markdown format:\n",
+      "\n",
+      "```\n",
+      "Example Domain\n",
+      "\n",
+      "# Example Domain\n",
+      "\n",
+      "This domain is for use in illustrative examples in documents. You may use this\n",
+      "domain in literature without prior coordination or asking for permission.\n",
+      "\n",
+      "[More information...](https://www.iana.org/domains/example)\n",
+      "```\n",
+      "\n",
+      "If you would like to crawl more pages or need additional information, please let me know!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_hyperbrowser import HyperbrowserCrawlTool\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "# Initialize the crawl tool\n",
+    "crawl_tool = HyperbrowserCrawlTool()\n",
+    "\n",
+    "# Create the agent with the crawl tool\n",
+    "llm = ChatOpenAI(temperature=0)\n",
+    "\n",
+    "agent = create_react_agent(llm, [crawl_tool])\n",
+    "user_input = \"Crawl https://example.com and get content from up to 5 pages\"\n",
+    "for step in agent.stream(\n",
+    "    {\"messages\": user_input},\n",
+    "    stream_mode=\"values\",\n",
+    "):\n",
+    "    step[\"messages\"][-1].pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuration Options\n",
+    "\n",
+    "### Common Options\n",
+    "\n",
+    "All tools support these basic configuration options:\n",
+    "\n",
+    "- `url`: The URL to process\n",
+    "- `session_options`: Browser session configuration\n",
+    "  - `use_proxy`: Whether to use a proxy\n",
+    "  - `solve_captchas`: Whether to automatically solve CAPTCHAs\n",
+    "  - `accept_cookies`: Whether to accept cookies\n",
+    "\n",
+    "### Tool-Specific Options\n",
+    "\n",
+    "#### Crawl Tool\n",
+    "\n",
+    "- `max_pages`: Maximum number of pages to crawl\n",
+    "- `scrape_options`: Options for scraping each page\n",
+    "  - `formats`: List of output formats (markdown, html)\n",
+    "\n",
+    "#### Scrape Tool\n",
+    "\n",
+    "- `scrape_options`: Options for scraping the page\n",
+    "  - `formats`: List of output formats (markdown, html)\n",
+    "\n",
+    "#### Extract Tool\n",
+    "\n",
+    "- `schema`: Pydantic model defining the structure to extract\n",
+    "- `extraction_prompt`: Natural language prompt for extraction\n",
+    "\n",
+    "For more details, see the respective API references:\n",
+    "\n",
+    "- [Crawl API Reference](https://docs.hyperbrowser.ai/reference/api-reference/crawl)\n",
+    "- [Scrape API Reference](https://docs.hyperbrowser.ai/reference/api-reference/scrape)\n",
+    "- [Extract API Reference](https://docs.hyperbrowser.ai/reference/api-reference/extract)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "- [GitHub](https://github.com/hyperbrowserai/langchain-hyperbrowser/)\n",
+    "- [PyPi](https://pypi.org/project/langchain-hyperbrowser/)\n",
+    "- [Hyperbrowser Docs](https://docs.hyperbrowser.ai/)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain-hyperbrowser-AlekOQAq-py3.12",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/integrations/tools/ibm_watsonx.ipynb

@@ -166,23 +166,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[WatsonxTool(name='GoogleSearch', description='Search for online trends, news, current events, real-time information, or research topics.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Search for online trends, news, current events, real-time information, or research topics.', tool_config_schema={'title': 'config schema for GoogleSearch tool', 'type': 'object', 'properties': {'maxResults': {'title': 'Max number of results to return', 'type': 'integer', 'minimum': 1, 'maximum': 20}}}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
-       " WatsonxTool(name='WebCrawler', description='Useful for when you need to summarize a webpage. Do not use for Web search.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Useful for when you need to summarize a webpage. Do not use for Web search.', tool_input_schema={'type': 'object', 'properties': {'url': {'title': 'url', 'description': 'URL for the webpage to be scraped', 'type': 'string', 'pattern': '^(https?:\\\\/\\\\/)?([\\\\da-z\\\\.-]+)\\\\.([a-z\\\\.]{2,6})([\\\\/\\\\w \\\\.-]*)*\\\\/?$'}}, 'required': ['url']}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
-       " WatsonxTool(name='PythonInterpreter', description='Run Python code generated by the agent model.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Run Python code and return the console output. Use for isolated calculations, computations or data manipulation. In Python, the following modules are available: Use numpy, pandas, scipy and sympy for working with data. Use matplotlib to plot charts. Other Python libraries are also available -- however, prefer using the ones above. Prefer using qualified imports -- `import library; library.thing()` instead of `import thing from library`. Do not attempt to install libraries manually -- it will not work. Do not use this tool multiple times in a row, always write the full code you want to run in a single invocation. If you get an error running Python code, try to generate a better one that will pass. If the tool returns result that starts with IMAGE(, follow instructions for rendering images.', watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
-       " WatsonxTool(name='SDXLTurbo', description='Generate an image from text using Stability.ai', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Generate an image from text. Not for image refining. Use very precise language about the desired image, including setting, lighting, style, filters and lenses used. Do not ask the tool to refine an image.', watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
-       " WatsonxTool(name='Weather', description='Find the weather for a city.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Find the weather for a city.', tool_input_schema={'type': 'object', 'properties': {'location': {'title': 'location', 'description': 'Name of the location', 'type': 'string'}, 'country': {'title': 'country', 'description': 'Name of the state or country', 'type': 'string'}}, 'required': ['location']}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
-       " WatsonxTool(name='RAGQuery', description='Search the documents in a vector index.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Search information in documents to provide context to a user query. Useful when asked to ground the answer in specific knowledge about {indexName}', tool_config_schema={'title': 'config schema for RAGQuery tool', 'type': 'object', 'properties': {'vectorIndexId': {'title': 'Vector index identifier', 'type': 'string'}, 'projectId': {'title': 'Project identifier', 'type': 'string'}, 'spaceId': {'title': 'Space identifier', 'type': 'string'}}, 'required': ['vectorIndexId'], 'oneOf': [{'required': ['projectId']}, {'required': ['spaceId']}]}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>)]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[WatsonxTool(name='GoogleSearch', description='Search for online trends, news, current events, real-time information, or research topics.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Search for online trends, news, current events, real-time information, or research topics.', tool_config_schema={'title': 'config schema for GoogleSearch tool', 'type': 'object', 'properties': {'maxResults': {'title': 'Max number of results to return', 'type': 'integer', 'minimum': 1, 'maximum': 20}}}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
+      " WatsonxTool(name='WebCrawler', description='Useful for when you need to summarize a webpage. Do not use for Web search.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Useful for when you need to summarize a webpage. Do not use for Web search.', tool_input_schema={'type': 'object', 'properties': {'url': {'title': 'url', 'description': 'URL for the webpage to be scraped', 'type': 'string', 'pattern': '^(https?:\\/\\/)?([\\da-z\\.-]+)\\.([a-z\\.]{2,6})([\\/\\w \\.-]*)*\\/?$'}}, 'required': ['url']}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
+      " WatsonxTool(name='SDXLTurbo', description='Generate an image from text using Stability.ai', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Generate an image from text. Not for image refining. Use very precise language about the desired image, including setting, lighting, style, filters and lenses used. Do not ask the tool to refine an image.', watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
+      " WatsonxTool(name='Weather', description='Find the weather for a city.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Find the weather for a city.', tool_input_schema={'type': 'object', 'properties': {'location': {'title': 'location', 'description': 'Name of the location', 'type': 'string'}, 'country': {'title': 'country', 'description': 'Name of the state or country', 'type': 'string'}}, 'required': ['location']}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>),\n",
+      " WatsonxTool(name='RAGQuery', description='Search the documents in a vector index.', args_schema=<class 'langchain_ibm.toolkit.ToolArgsSchema'>, agent_description='Search information in documents to provide context to a user query. Useful when asked to ground the answer in specific knowledge about {indexName}', tool_config_schema={'title': 'config schema for RAGQuery tool', 'type': 'object', 'properties': {'vectorIndexId': {'title': 'Vector index identifier', 'type': 'string'}, 'projectId': {'title': 'Project identifier', 'type': 'string'}, 'spaceId': {'title': 'Space identifier', 'type': 'string'}}, 'required': ['vectorIndexId'], 'oneOf': [{'required': ['projectId']}, {'required': ['spaceId']}]}, watsonx_client=<ibm_watsonx_ai.client.APIClient object at 0x127e0f490>)]\n",
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -228,7 +226,7 @@
     {
      "data": {
       "text/plain": [
-       "{'output': '[{\"title\":\"IBM - United States\",\"description\":\"Technology & Consulting. From next-generation AI to cutting edge hybrid cloud solutions to the deep expertise of IBM Consulting, IBM has what it takes to help\\xa0...\",\"url\":\"https://www.ibm.com/us-en\"},{\"title\":\"IBM - Wikipedia\",\"description\":\"International Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in\\xa0...\",\"url\":\"https://en.wikipedia.org/wiki/IBM\"},{\"title\":\"IBM Envizi ESG Suite\",\"description\":\"Envizi systemizes the capture, transformation and consolidation of disparate sustainability data into a single source of truth and delivers actionable insights.\",\"url\":\"https://www.ibm.com/products/envizi\"},{\"title\":\"IBM Research\",\"description\":\"Tools + Code · BeeAI Framework. Open-source framework for building, deploying, and serving powerful agentic workflows at scale. · Docling. An open-source tool\\xa0...\",\"url\":\"https://research.ibm.com/\"},{\"title\":\"IBM SkillsBuild: Free Skills-Based Learning From Technology Experts\",\"description\":\"IBM SkillsBuildPower your future in tech with job skills, courses, and credentials—for free. Power your future in tech with job skills, courses, and credentials\\xa0...\",\"url\":\"https://skillsbuild.org/\"},{\"title\":\"IBM | LinkedIn\",\"description\":\"Locations · Primary. International Business Machines Corp. · 590 Madison Ave · 90 Grayston Dr · Plaza Independencia 721 · 388 Phahon Yothin Road · Jalan Prof.\",\"url\":\"https://www.linkedin.com/company/ibm\"},{\"title\":\"International Business Machines Corporation (IBM) Stock Price ...\",\"description\":\"Mar 18, 2025 ... International Business Machines Corporation (IBM) · 1.19% · -2.77% · 11.49% · 12.00% · 29.04% · 138.39% · 3,238.84%. Key Events. Mountain.\",\"url\":\"https://finance.yahoo.com/quote/IBM/\"},{\"title\":\"Zurich - IBM Research\",\"description\":\"The location in Zurich is one of IBM\\'s 12 global research labs. IBM has maintained a research laboratory in Switzerland since 1956.\",\"url\":\"https://research.ibm.com/labs/zurich\"},{\"title\":\"IBM Newsroom\",\"description\":\"News and press releases from around the IBM world. Media contacts. Sources by topic and by region. IBM Media center. Explore IBM\\'s latest and most popular\\xa0...\",\"url\":\"https://newsroom.ibm.com/\"},{\"title\":\"IBM (@ibm) • Instagram photos and videos\",\"description\":\"Science, Technology & Engineering. We partner with developers, data scientists, CTOs and other creators to make the world work better.\",\"url\":\"https://www.instagram.com/ibm/?hl=en\"}]'}"
+       "{'output': '[{\"title\":\"IBM - United States\",\"description\":\"Technology & Consulting. From next-generation AI to cutting edge hybrid cloud solutions to the deep expertise of IBM Consulting, IBM has what it takes to help\\xa0...\",\"url\":\"https://www.ibm.com/us-en\"},{\"title\":\"IBM - Wikipedia\",\"description\":\"International Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in\\xa0...\",\"url\":\"https://en.wikipedia.org/wiki/IBM\"},{\"title\":\"IBM Envizi ESG Suite\",\"description\":\"Envizi systemizes the capture, transformation and consolidation of disparate sustainability data into a single source of truth and delivers actionable insights.\",\"url\":\"https://www.ibm.com/products/envizi\"},{\"title\":\"IBM Research\",\"description\":\"Tools + Code · BeeAI Framework. Open-source framework for building, deploying, and serving powerful agentic workflows at scale. · Docling. An open-source tool\\xa0...\",\"url\":\"https://research.ibm.com/\"},{\"title\":\"IBM SkillsBuild: Free Skills-Based Learning From Technology Experts\",\"description\":\"IBM SkillsBuildPower your future in tech with job skills, courses, and credentials—for free. Power your future in tech with job skills, courses, and credentials\\xa0...\",\"url\":\"https://skillsbuild.org/\"},{\"title\":\"IBM | LinkedIn\",\"description\":\"Locations · Primary. International Business Machines Corp. · 590 Madison Ave · 90 Grayston Dr · Plaza Independencia 721 · 388 Phahon Yothin Road · Jalan Prof.\",\"url\":\"https://www.linkedin.com/company/ibm\"},{\"title\":\"International Business Machines Corporation (IBM)\",\"description\":\"PROFITABILITY_AND_INCOME_STATEMENT · 9.60% · (TTM). 3.06% · (TTM). 24.06% · (TTM). 62.75B · (TTM). 6.02B · (TTM). 6.41. BALANCE_SHEET_AND_CASH_FLOW. (MRQ).\",\"url\":\"https://finance.yahoo.com/quote/IBM/\"},{\"title\":\"Zurich - IBM Research\",\"description\":\"The location in Zurich is one of IBM\\'s 12 global research labs. IBM has maintained a research laboratory in Switzerland since 1956.\",\"url\":\"https://research.ibm.com/labs/zurich\"},{\"title\":\"IBM (@ibm) • Instagram photos and videos\",\"description\":\"Science, Technology & Engineering. We partner with developers, data scientists, CTOs and other creators to make the world work better.\",\"url\":\"https://www.instagram.com/ibm/?hl=en\"},{\"title\":\"IBM Newsroom\",\"description\":\"News and press releases from around the IBM world. Media contacts. Sources by topic and by region. IBM Media center. Explore IBM\\'s latest and most popular\\xa0...\",\"url\":\"https://newsroom.ibm.com/\"}]'}"
       ]
      },
      "execution_count": 5,
@@ -416,7 +414,7 @@
     {
      "data": {
       "text/plain": [
-       "{'output': 'Current weather in New York:\\nTemperature: 12.1°C\\nRain: 0mm\\nRelative humidity: 36%\\nWind: 8.1km/h\\n'}"
+       "{'output': 'Current weather in New York:\\nTemperature: 0°C\\nRain: 0mm\\nRelative humidity: 63%\\nWind: 7.6km/h\\n'}"
       ]
      },
      "execution_count": 12,
@@ -450,10 +448,10 @@
      "output_type": "stream",
      "text": [
       "Current weather in New York:\n",
-      "Temperature: 12.1°C\n",
+      "Temperature: 0°C\n",
       "Rain: 0mm\n",
-      "Relative humidity: 36%\n",
-      "Wind: 8.1km/h\n",
+      "Relative humidity: 63%\n",
+      "Wind: 7.6km/h\n",
       "\n"
      ]
     }
@@ -480,7 +478,7 @@
     {
      "data": {
       "text/plain": [
-       "ToolMessage(content='{\"output\": \"Current weather in Los Angeles:\\\\nTemperature: 13.3°C\\\\nRain: 0mm\\\\nRelative humidity: 89%\\\\nWind: 3.3km/h\\\\n\"}', name='Weather', tool_call_id='1')"
+       "ToolMessage(content='{\"output\": \"Current weather in Los Angeles:\\\\nTemperature: 8.6°C\\\\nRain: 0mm\\\\nRelative humidity: 61%\\\\nWind: 8.4km/h\\\\n\"}', name='Weather', tool_call_id='1')"
       ]
      },
      "execution_count": 14,
@@ -510,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -537,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -549,21 +547,17 @@
       "What is the weather in Boston?\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "Tool Calls:\n",
-      "  Weather (chatcmpl-tool-fe07ca2f5b5a4fd584eaedc889bea5b7)\n",
-      " Call ID: chatcmpl-tool-fe07ca2f5b5a4fd584eaedc889bea5b7\n",
+      "  Weather (chatcmpl-tool-6a6c21402c824e43bdd2e8ba390af4a8)\n",
+      " Call ID: chatcmpl-tool-6a6c21402c824e43bdd2e8ba390af4a8\n",
       "  Args:\n",
       "    location: Boston\n",
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
       "Name: Weather\n",
       "\n",
-      "{\"output\": \"Current weather in Boston:\\nTemperature: 9.1°C\\nRain: 0mm\\nRelative humidity: 41%\\nWind: 24.5km/h\\n\"}\n",
+      "{\"output\": \"Current weather in Boston:\\nTemperature: -1°C\\nRain: 0mm\\nRelative humidity: 53%\\nWind: 8.3km/h\\n\"}\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "The current weather in Boston is:\n",
-      "Temperature: 9.1°C\n",
-      "Rain: 0mm\n",
-      "Relative humidity: 41%\n",
-      "Wind: 24.5km/h\n"
+      "The current weather in Boston is -1°C with 0mm of rain, a relative humidity of 53%, and a wind speed of 8.3km/h.\n"
      ]
     }
    ],
@@ -590,7 +584,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "langchain-ibm",
+   "display_name": "langchain_env",
    "language": "python",
    "name": "python3"
   },

docs/docs/integrations/tools/jira.ipynb

@@ -14,10 +14,12 @@
     "## Installation and setup\n",
     "\n",
     "To use this tool, you must first set as environment variables:\n",
-    "    JIRA_API_TOKEN\n",
-    "    JIRA_USERNAME\n",
-    "    JIRA_INSTANCE_URL\n",
-    "    JIRA_CLOUD"
+    "    JIRA_INSTANCE_URL,\n",
+    "    JIRA_CLOUD\n",
+    "\n",
+    "You have the choice between two authentication methods:\n",
+    "- API token authentication: set the JIRA_API_TOKEN (and JIRA_USERNAME if needed) environment variables\n",
+    "- OAuth2.0 authentication: set the JIRA_OAUTH2 environment variable as a dict having as fields \"client_id\" and \"token\" which is a dict containing at least \"access_token\" and \"token_type\""
    ]
   },
   {
@@ -79,6 +81,12 @@
     "from langchain_openai import OpenAI"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3c925f1468696e4c",
+   "metadata": {},
+   "source": "For authentication with API token"
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -109,6 +117,27 @@
     "os.environ[\"JIRA_CLOUD\"] = \"True\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "325ea81fb416aac6",
+   "metadata": {},
+   "source": "For authentication with a Oauth2.0"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "917e83e3a764d91a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"JIRA_OAUTH2\"] = (\n",
+    "    '{\"client_id\": \"123\", \"token\": {\"access_token\": \"abc\", \"token_type\": \"bearer\"}}'\n",
+    ")\n",
+    "os.environ[\"JIRA_INSTANCE_URL\"] = \"https://jira.atlassian.com\"\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"xyz\"\n",
+    "os.environ[\"JIRA_CLOUD\"] = \"True\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -215,15 +244,15 @@
      "text": [
       "\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to create an issue in project PW\n",
+      "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+      "\u001B[32;1m\u001B[1;3m I need to create an issue in project PW\n",
       "Action: Create Issue\n",
-      "Action Input: {\"summary\": \"Make more fried rice\", \"description\": \"Reminder to make more fried rice\", \"issuetype\": {\"name\": \"Task\"}, \"priority\": {\"name\": \"Low\"}, \"project\": {\"key\": \"PW\"}}\u001b[0m\n",
-      "Observation: \u001b[38;5;200m\u001b[1;3mNone\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: A new issue has been created in project PW with the summary \"Make more fried rice\" and description \"Reminder to make more fried rice\".\u001b[0m\n",
+      "Action Input: {\"summary\": \"Make more fried rice\", \"description\": \"Reminder to make more fried rice\", \"issuetype\": {\"name\": \"Task\"}, \"priority\": {\"name\": \"Low\"}, \"project\": {\"key\": \"PW\"}}\u001B[0m\n",
+      "Observation: \u001B[38;5;200m\u001B[1;3mNone\u001B[0m\n",
+      "Thought:\u001B[32;1m\u001B[1;3m I now know the final answer\n",
+      "Final Answer: A new issue has been created in project PW with the summary \"Make more fried rice\" and description \"Reminder to make more fried rice\".\u001B[0m\n",
       "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "\u001B[1m> Finished chain.\u001B[0m\n"
      ]
     },
     {

docs/docs/integrations/tools/oxylabs.ipynb

@@ -0,0 +1,298 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "65142ccb18a6add5",
+   "metadata": {},
+   "source": "# Oxylabs"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19976df355f9c461",
+   "metadata": {},
+   "source": [
+    ">[Oxylabs](https://oxylabs.io/) is a market-leading web intelligence collection platform, driven by the highest business, ethics, and compliance standards, enabling companies worldwide to unlock data-driven insights.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This package contains the LangChain integration with Oxylabs, providing tools to scrape Google search results with Oxylabs Web Scraper API using LangChain's framework.\n",
+    "\n",
+    "The following classes are provided by this package:\n",
+    "- `OxylabsSearchRun` - A tool that returns scraped Google search results in a formatted text\n",
+    "- `OxylabsSearchResults` - A tool that returns scraped Google search results in a JSON format\n",
+    "- `OxylabsSearchAPIWrapper` - An API wrapper for initializing Oxylabs API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76e6d8030199710d",
+   "metadata": {},
+   "source": [
+    "|             Pricing             |\n",
+    "|:-------------------------------:|\n",
+    "| ✅ Free 5,000 results for 1 week |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb59c5f3051be9d4",
+   "metadata": {},
+   "source": "## Setup"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45951d881c460419",
+   "metadata": {},
+   "source": "Install the required dependencies."
+  },
+  {
+   "cell_type": "code",
+   "id": "576e08c41f16ceda",
+   "metadata": {},
+   "source": [
+    "%pip install -qU langchain-oxylabs"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce19bb3a52a3ab60",
+   "metadata": {},
+   "source": "### Credentials"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8330dfd5861482e",
+   "metadata": {},
+   "source": "Set up the proper API keys and environment variables. Create your API user credentials: Sign up for a free trial or purchase the product in the [Oxylabs dashboard](https://dashboard.oxylabs.io/en/registration) to create your API user credentials (OXYLABS_USERNAME and OXYLABS_PASSWORD)."
+  },
+  {
+   "cell_type": "code",
+   "id": "474b6eaf6e35efda",
+   "metadata": {},
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"OXYLABS_USERNAME\"] = getpass.getpass(\"Enter your Oxylabs username: \")\n",
+    "os.environ[\"OXYLABS_PASSWORD\"] = getpass.getpass(\"Enter your Oxylabs password: \")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae310f86b113bd78",
+   "metadata": {},
+   "source": "## Instantiation"
+  },
+  {
+   "cell_type": "code",
+   "id": "69ea8139f6152b48",
+   "metadata": {},
+   "source": [
+    "from langchain_oxylabs import OxylabsSearchAPIWrapper, OxylabsSearchRun\n",
+    "\n",
+    "oxylabs_wrapper = OxylabsSearchAPIWrapper()\n",
+    "tool_ = OxylabsSearchRun(wrapper=oxylabs_wrapper)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "636efff6267b9bc1",
+   "metadata": {},
+   "source": "## Invocation"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "272f5fdaed5fa000",
+   "metadata": {},
+   "source": "### Invoke directly with args"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c26724911f6cfdf",
+   "metadata": {},
+   "source": "The `OxylabsSearchRun` tool takes a single \"query\" argument, which should be a natural language query and returns combined string format result:"
+  },
+  {
+   "cell_type": "code",
+   "id": "b5f4d423cb3abf",
+   "metadata": {},
+   "source": [
+    "tool_.invoke({\"query\": \"Restaurants in Paris.\"})"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "784aad18bba31062",
+   "metadata": {},
+   "source": "### Invoke with ToolCall"
+  },
+  {
+   "cell_type": "code",
+   "id": "8afd1652e2c4e7c9",
+   "metadata": {},
+   "source": [
+    "tool_ = OxylabsSearchRun(\n",
+    "    wrapper=oxylabs_wrapper,\n",
+    "    kwargs={\n",
+    "        \"result_categories\": [\n",
+    "            \"local_information\",\n",
+    "            \"combined_search_result\",\n",
+    "        ]\n",
+    "    },\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "1b903fb2f8272df2",
+   "metadata": {},
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "model_generated_tool_call = {\n",
+    "    \"args\": {\n",
+    "        \"query\": \"Visit restaurants in Vilnius.\",\n",
+    "        \"geo_location\": \"Vilnius,Lithuania\",\n",
+    "    },\n",
+    "    \"id\": \"1\",\n",
+    "    \"name\": \"oxylabs_search\",\n",
+    "    \"type\": \"tool_call\",\n",
+    "}\n",
+    "tool_call_result = tool_.invoke(model_generated_tool_call)\n",
+    "\n",
+    "# The content is a JSON string of results\n",
+    "pprint(tool_call_result.content)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a436fc39ce17b6da",
+   "metadata": {},
+   "source": [
+    "## Use within an agent\n",
+    "Install the required dependencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a6f23e65092e8d27",
+   "metadata": {},
+   "source": "%pip install -qU \"langchain[openai]\" langgraph",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "21078a0c265759ff",
+   "metadata": {},
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter API key for OpenAI: \")\n",
+    "llm = init_chat_model(\"gpt-4o-mini\", model_provider=\"openai\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "396548ee2f08fb30",
+   "metadata": {},
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "# Initialize OxylabsSearchRun tool\n",
+    "tool_ = OxylabsSearchRun(wrapper=oxylabs_wrapper)\n",
+    "\n",
+    "agent = create_react_agent(llm, [tool_])\n",
+    "\n",
+    "user_input = \"What happened in the latest Burning Man floods?\"\n",
+    "\n",
+    "for step in agent.stream(\n",
+    "    {\"messages\": user_input},\n",
+    "    stream_mode=\"values\",\n",
+    "):\n",
+    "    step[\"messages\"][-1].pretty_print()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b10bbcf5ce299fd5",
+   "metadata": {},
+   "source": [
+    "## JSON results\n",
+    "`OxylabsSearchResults` tool can be used as an alternative to `OxylabsSearchRun` to retrieve results in a JSON format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "ef0d856f571c4938",
+   "metadata": {},
+   "source": [
+    "import json\n",
+    "\n",
+    "from langchain_oxylabs import OxylabsSearchResults\n",
+    "\n",
+    "tool_ = OxylabsSearchResults(wrapper=oxylabs_wrapper)\n",
+    "\n",
+    "response_results = tool_.invoke({\"query\": \"What are the most famous artists?\"})\n",
+    "response_results = json.loads(response_results)\n",
+    "\n",
+    "for result in response_results:\n",
+    "    for key, value in result.items():\n",
+    "        print(f\"{key}: {value}\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f16702d224dabb2",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "More information about this integration package can be found here: https://github.com/oxylabs/langchain-oxylabs\n",
+    "\n",
+    "Oxylabs Web Scraper API documentation: https://developers.oxylabs.io/scraper-apis/web-scraper-api\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/tools/serpapi.ipynb

@@ -106,9 +106,9 @@
     "from langchain_core.tools import Tool\n",
     "\n",
     "# You can create the tool to pass to an agent\n",
-    "repl_tool = Tool(\n",
-    "    name=\"python_repl\",\n",
-    "    description=\"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n",
+    "custom_tool = Tool(\n",
+    "    name=\"web search\",\n",
+    "    description=\"Search the web for information\",\n",
     "    func=search.run,\n",
     ")"
    ]

docs/docs/integrations/tools/tableau.ipynb

@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "310d21b3",
    "metadata": {},
    "outputs": [],
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "ccfb4159-34ac-4816-a8f0-795c5442c0b2",
    "metadata": {},
    "outputs": [],
@@ -148,16 +148,16 @@
     "    \"TABLEAU_JWT_SECRET\"\n",
     ")  # a JWT secret ID (obtained through Tableau's admin UI)\n",
     "tableau_api_version = \"3.21\"  # the current Tableau REST API Version\n",
-    "tableau_user = \"joe.constantino@salesforce.com\"  # replace with the username querying the target Tableau Data Source\n",
+    "tableau_user = \"joe.constantino@salesforce.com\"  # enter the username querying the target Tableau Data Source\n",
     "\n",
     "# For this cookbook we are connecting to the Superstore dataset that comes by default with every Tableau server\n",
     "datasource_luid = (\n",
     "    \"0965e61b-a072-43cf-994c-8c6cf526940d\"  # the target data source for this Tool\n",
     ")\n",
-    "\n",
+    "model_provider = \"openai\"  # the name of the model provider you are using for your Agent\n",
     "# Add variables to control LLM models for the Agent and Tools\n",
     "os.environ[\"OPENAI_API_KEY\"]  # set an your model API key as an environment variable\n",
-    "tooling_llm_model = \"gpt-4o\""
+    "tooling_llm_model = \"gpt-4o-mini\""
    ]
   },
   {
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "72ee3eca",
    "metadata": {},
    "outputs": [],
@@ -194,6 +194,7 @@
     "    tableau_user=tableau_user,\n",
     "    datasource_luid=datasource_luid,\n",
     "    tooling_llm_model=tooling_llm_model,\n",
+    "    model_provider=model_provider,\n",
     ")\n",
     "\n",
     "# load the List of Tools to be used by the Agent. In this case we will just load our data source Q&A tool.\n",
@@ -211,47 +212,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "06a1d3f7-79a8-452e-b37e-9070d15445b0",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "Here are the results for the states with the highest sales and profits based on the data queried:\n",
-       "\n",
-       "### States with the Most Sales\n",
-       "1. **California**: $457,687.63\n",
-       "2. **New York**: $310,876.27\n",
-       "3. **Texas**: $170,188.05\n",
-       "4. **Washington**: $138,641.27\n",
-       "5. **Pennsylvania**: $116,511.91\n",
-       "\n",
-       "### States with the Most Profit\n",
-       "1. **California**: $76,381.39\n",
-       "2. **New York**: $74,038.55\n",
-       "3. **Washington**: $33,402.65\n",
-       "4. **Michigan**: $24,463.19\n",
-       "5. **Virginia**: $18,597.95\n",
-       "\n",
-       "### Comparison\n",
-       "- **California** and **New York** are the only states that appear in both lists, indicating they are the top sellers and also generate the most profit.\n",
-       "- **Texas**, while having the third highest sales, does not rank in the top five for profit, showing a potential issue with profitability despite high sales.\n",
-       "\n",
-       "This analysis suggests that high sales do not always correlate with high profits, as seen with Texas."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from IPython.display import Markdown, display\n",
     "\n",
-    "model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
     "\n",
     "tableauAgent = create_react_agent(model, tools)\n",
     "\n",
@@ -261,13 +229,13 @@
     "        \"messages\": [\n",
     "            (\n",
     "                \"human\",\n",
-    "                \"which states sell the most? Are those the same states with the most profits?\",\n",
+    "                \"what's going on with table sales?\",\n",
     "            )\n",
     "        ]\n",
     "    }\n",
     ")\n",
     "messages\n",
-    "# display(Markdown(messages['messages'][4].content)) #display a nicely formatted answer for successful generations"
+    "# display(Markdown(messages['messages'][3].content)) #display a nicely formatted answer for successful generations"
    ]
   },
   {
@@ -293,9 +261,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (package_test_env)",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "package_test_env"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

docs/docs/integrations/vectorstores/azure_cosmos_db_no_sql.ipynb

@@ -643,9 +643,7 @@
     "}\n",
     "\n",
     "results = vector_search.similarity_search_with_score(\n",
-    "    query=query,\n",
-    "    k=5,\n",
-    "    query_type=CosmosDBQueryType.HYBRID,\n",
+    "    query=query, k=5, query_type=CosmosDBQueryType.HYBRID, pre_filter=pre_filter\n",
     ")\n",
     "\n",
     "# Display results\n",

docs/docs/integrations/vectorstores/clickhouse.ipynb

@@ -23,7 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! docker run -d -p 8123:8123 -p9000:9000 --name langchain-clickhouse-server --ulimit nofile=262144:262144 clickhouse/clickhouse-server:23.4.2.11"
+    "! docker run -d -p 8123:8123 -p9000:9000 --name langchain-clickhouse-server --ulimit nofile=262144:262144 clickhouse/clickhouse-server:24.7.6.8"
    ]
   },
   {

docs/docs/integrations/vectorstores/couchbase.ipynb

@@ -20,15 +20,26 @@
    "source": [
     "## Setup\n",
     "\n",
-    "To access the `CouchbaseVectorStore` you first need to install the `langchain-couchbase` partner package:"
+    "To access the `CouchbaseSearchVectorStore` you first need to install the `langchain-couchbase` partner package:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "bec8d532-fec7-4dc7-9be3-020aa7bdb01f",
    "metadata": {},
-   "outputs": [],
+   "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;49m24.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "pip install -qU langchain-couchbase"
    ]
@@ -45,10 +56,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "d98e3baa",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter the connection string for the Couchbase cluster:  ········\n",
+      "Enter the username for the Couchbase cluster:  ········\n",
+      "Enter the password for the Couchbase cluster:  ········\n"
+     ]
+    }
+   ],
    "source": [
     "import getpass\n",
     "\n",
@@ -104,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "9986c6b9",
    "metadata": {},
    "outputs": [],
@@ -135,14 +156,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "1b1d0a26-e9d4-4823-9800-9549d24d3d16",
    "metadata": {},
    "outputs": [],
    "source": [
     "BUCKET_NAME = \"langchain_bucket\"\n",
     "SCOPE_NAME = \"_default\"\n",
-    "COLLECTION_NAME = \"default\"\n",
+    "COLLECTION_NAME = \"_default\"\n",
     "SEARCH_INDEX_NAME = \"langchain-test-index\""
    ]
   },
@@ -174,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "6706efdd",
    "metadata": {},
    "outputs": [],
@@ -188,14 +209,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "33db4670-76c5-49ba-94d6-a8fa35583058",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_couchbase.vectorstores import CouchbaseVectorStore\n",
+    "from langchain_couchbase.vectorstores import CouchbaseSearchVectorStore\n",
     "\n",
-    "vector_store = CouchbaseVectorStore(\n",
+    "vector_store = CouchbaseSearchVectorStore(\n",
     "    cluster=cluster,\n",
     "    bucket_name=BUCKET_NAME,\n",
     "    scope_name=SCOPE_NAME,\n",
@@ -217,12 +238,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "49c38634",
    "metadata": {},
    "outputs": [],
    "source": [
-    "vector_store_specific = CouchbaseVectorStore(\n",
+    "vector_store_specific = CouchbaseSearchVectorStore(\n",
     "    cluster=cluster,\n",
     "    bucket_name=BUCKET_NAME,\n",
     "    scope_name=SCOPE_NAME,\n",
@@ -250,10 +271,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "65a35f00",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['4a6b5252-24ca-4e48-97a9-c33211fc7736',\n",
+       " '594a413d-761a-44f1-8f0c-6418700b198d',\n",
+       " 'fdd8461c-f4e3-4c85-af8e-7782ce4d2311',\n",
+       " '3f6a82b2-7464-4eee-b209-cbca5a236a8a',\n",
+       " 'df8b87ad-464e-4f83-a007-ccf5a8fa4ff5',\n",
+       " 'aa18502e-6fb4-4578-9c63-b9a299259b01',\n",
+       " '8c55a17d-5fa7-4c30-a55d-7ded0d39bf46',\n",
+       " '41b68c5a-ebf5-4d7a-a079-5e32926ca484',\n",
+       " '146ac3e0-474a-422a-b0ac-c9fee718396b',\n",
+       " 'e44941e9-fb3a-4090-88a0-9ffecee3e80e']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from uuid import uuid4\n",
     "\n",
@@ -336,10 +377,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "3a05f294",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "vector_store.delete(ids=[uuids[-1]])"
    ]
@@ -362,10 +414,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "8e00bb23",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]\n",
+      "* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]\n"
+     ]
+    }
+   ],
    "source": [
     "results = vector_store.similarity_search(\n",
     "    \"LangChain provides abstractions to make working with LLMs easy\",\n",
@@ -387,10 +448,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "24b146b2-55a2-4fe8-8659-3649032f5dc7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* [SIM=0.553145] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]\n"
+     ]
+    }
+   ],
    "source": [
     "results = vector_store.similarity_search_with_score(\"Will it be hot tomorrow?\", k=1)\n",
     "for res, score in results:\n",
@@ -415,10 +484,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "ffa743dc-4e89-405b-ad71-7390338889e6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='I had chocalate chip pancakes and scrambled eggs for breakfast this morning.' metadata={'source': 'tweet'}\n"
+     ]
+    }
+   ],
    "source": [
     "query = \"What did I eat for breakfast today?\"\n",
     "results = vector_store.similarity_search(query, fields=[\"metadata.source\"])\n",
@@ -452,10 +529,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "7d2e607d-6bbc-4cef-83e3-b6a28bb269ea",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'author': 'John Doe', 'date': '2016-01-01', 'rating': 2, 'source': '../../how_to/state_of_the_union.txt'}\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain_community.document_loaders import TextLoader\n",
     "from langchain_text_splitters import CharacterTextSplitter\n",
@@ -489,10 +574,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "dc06ba4a-8a6b-4c55-bb69-95cd92db273f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.' metadata={'author': 'John Doe'}\n"
+     ]
+    }
+   ],
    "source": [
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "results = vector_store.similarity_search(\n",
@@ -516,10 +611,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "fd4749e6-ef4f-4cb5-95ff-37c4fa8283d8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "\n",
+      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.' metadata={'author': 'Jane Doe'}\n"
+     ]
+    }
+   ],
    "source": [
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "results = vector_store.similarity_search(\n",
@@ -543,10 +648,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "b7b47e7d-c32f-4999-bce9-3c3c3cebffd0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='We are cutting off Russia’s largest banks from the international financial system.  \n",
+      "\n",
+      "Preventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless.   \n",
+      "\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",
+      "Tonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more.' metadata={'author': 'Jane Doe', 'date': '2017-01-01', 'rating': 3, 'source': '../../how_to/state_of_the_union.txt'}\n"
+     ]
+    }
+   ],
    "source": [
     "query = \"Any mention about independence?\"\n",
     "results = vector_store.similarity_search(\n",
@@ -575,10 +694,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "7e8bf7c5-07d1-4c3f-86d7-1fa3a454dc7f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(Document(id='8616f24425b94a52af3d32d20e6ffb4b', metadata={'author': 'John Doe', 'date': '2014-01-01', 'rating': 5, 'source': '../../how_to/state_of_the_union.txt'}, page_content='In this Capitol, generation after generation, Americans have debated great questions amid great strife, and have done great things. \\n\\nWe have fought for freedom, expanded liberty, defeated totalitarianism and terror. \\n\\nAnd built the strongest, freest, and most prosperous nation the world has ever known. \\n\\nNow is the hour. \\n\\nOur moment of responsibility. \\n\\nOur test of resolve and conscience, of history itself.'), 0.361933544533826)\n"
+     ]
+    }
+   ],
    "source": [
     "query = \"Any mention about independence?\"\n",
     "results = vector_store.similarity_search_with_score(\n",
@@ -609,10 +736,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "dd0fe7f1-aa40-4c6f-889b-99ad5efcd88b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(Document(id='d9b36ef70b8942dda4db63563f51cf0f', metadata={'author': 'Jane Doe', 'date': '2017-01-01', 'rating': 3, 'source': '../../how_to/state_of_the_union.txt'}, page_content='We are cutting off Russia’s largest banks from the international financial system.  \\n\\nPreventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless.   \\n\\nWe are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come.  \\n\\nTonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more.'), 0.7107075545629284)\n"
+     ]
+    }
+   ],
    "source": [
     "query = \"Any mention about independence?\"\n",
     "results = vector_store.similarity_search_with_score(\n",
@@ -655,13 +790,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "3666265a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='3f6a82b2-7464-4eee-b209-cbca5a236a8a', metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "retriever = vector_store.as_retriever(\n",
-    "    search_type=\"similarity_score_threshold\",\n",
+    "    search_type=\"similarity\",\n",
     "    search_kwargs={\"k\": 1, \"score_threshold\": 0.5},\n",
     ")\n",
     "retriever.invoke(\"Stealing from the bank is a crime\", filter={\"source\": \"news\"})"
@@ -740,7 +886,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `CouchbaseVectorStore` features and configurations head to the API reference: https://python.langchain.com/api_reference/couchbase/vectorstores/langchain_couchbase.vectorstores.CouchbaseVectorStore.html"
+    "For detailed documentation of all `CouchbaseSearchVectorStore` features and configurations head to the API reference: https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html "
    ]
   }
  ],
@@ -760,7 +906,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

docs/docs/integrations/vectorstores/mariadb.ipynb

@@ -0,0 +1,298 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "7679dd7b-7ed4-4755-a499-824deadba708",
+      "metadata": {},
+      "source": [
+        "# MariaDB\n",
+        "\n",
+        "LangChain's MariaDB integration (langchain-mariadb) provides vector capabilities for working with MariaDB version 11.7.1 and above, distributed under the MIT license. Users can use the provided implementations as-is or customize them for specific needs.\n",
+        " Key features include:\n",
+        "\n",
+        " * Built-in vector similarity search\n",
+        " * Support for cosine and euclidean distance metrics\n",
+        " * Robust metadata filtering options\n",
+        " * Performance optimization through connection pooling\n",
+        " * Configurable table and column settings\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "Launch a MariaDB Docker container with:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "92df32f0",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!docker run --name mariadb-container -e MARIADB_ROOT_PASSWORD=langchain -e MARIADB_DATABASE=langchain -p 3306:3306 -d mariadb:11.7"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installing the Package\n",
+        "\n",
+        "The package uses SQLAlchemy but works best with the MariaDB connector, which requires C/C++ components:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2acbaf9b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Debian, Ubuntu\n",
+        "!sudo apt install libmariadb3 libmariadb-dev\n",
+        "\n",
+        "# CentOS, RHEL, Rocky Linux\n",
+        "!sudo yum install MariaDB-shared MariaDB-devel\n",
+        "\n",
+        "# Install Python connector\n",
+        "!pip install -U mariadb"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0dd87fcc",
+      "metadata": {},
+      "source": [
+        "Then install `langchain-mariadb` package\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2acbaf9b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pip install -U langchain-mariadb\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0dd87fca",
+      "metadata": {},
+      "source": [
+        "VectorStore works along with an LLM model, here using `langchain-openai` as example.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2acbaf9b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pip install langchain-openai\n",
+        "export OPENAI_API_KEY=...\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0dd87fcc",
+      "metadata": {},
+      "source": [
+        "## Initialization"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "94f5c129",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.documents import Document\n",
+        "from langchain_mariadb import MariaDBStore\n",
+        "from langchain_openai import OpenAIEmbeddings\n",
+        "\n",
+        "# connection string\n",
+        "url = f\"mariadb+mariadbconnector://myuser:mypassword@localhost/langchain\"\n",
+        "\n",
+        "# Initialize vector store\n",
+        "vectorstore = MariaDBStore(\n",
+        "    embeddings=OpenAIEmbeddings(),\n",
+        "    embedding_length=1536,\n",
+        "    datasource=url,\n",
+        "    collection_name=\"my_docs\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "61a224a1-d70b-4daf-86ba-ab6e43c08b50",
+      "metadata": {},
+      "source": [
+        "## Manage vector store\n",
+        "\n",
+        "### Adding Data\n",
+        "You can add data as documents with metadata:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "94f5d129",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "docs = [\n",
+        "    Document(\n",
+        "        page_content=\"there are cats in the pond\",\n",
+        "        metadata={\"id\": 1, \"location\": \"pond\", \"topic\": \"animals\"},\n",
+        "    ),\n",
+        "    Document(\n",
+        "        page_content=\"ducks are also found in the pond\",\n",
+        "        metadata={\"id\": 2, \"location\": \"pond\", \"topic\": \"animals\"},\n",
+        "    ),\n",
+        "    # More documents...\n",
+        "]\n",
+        "vectorstore.add_documents(docs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0c712fa3",
+      "metadata": {},
+      "source": [
+        "\n",
+        "Or as plain text with optional metadata:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "a5b2b71f-49eb-407d-b03a-dea4c0a517d6",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "texts = [\n",
+        "    \"a sculpture exhibit is also at the museum\",\n",
+        "    \"a new coffee shop opened on Main Street\",\n",
+        "]\n",
+        "metadatas = [\n",
+        "    {\"id\": 6, \"location\": \"museum\", \"topic\": \"art\"},\n",
+        "    {\"id\": 7, \"location\": \"Main Street\", \"topic\": \"food\"},\n",
+        "]\n",
+        "\n",
+        "vectorstore.add_texts(texts=texts, metadatas=metadatas)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "59f82250-7903-4279-8300-062542c83416",
+      "metadata": {},
+      "source": [
+        "## Query vector store"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "f15a2359-6dc3-4099-8214-785f167a9ca4",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Basic similarity search\n",
+        "results = vectorstore.similarity_search(\"Hello\", k=2)\n",
+        "\n",
+        "# Search with metadata filtering\n",
+        "results = vectorstore.similarity_search(\"Hello\", filter={\"category\": \"greeting\"})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7ecd77a0",
+      "metadata": {},
+      "source": [
+        "\n",
+        "### Filter Options\n",
+        "\n",
+        "The system supports various filtering operations on metadata:\n",
+        "\n",
+        "* Equality: $eq\n",
+        "* Inequality: $ne\n",
+        "* Comparisons: $lt, $lte, $gt, $gte\n",
+        "* List operations: $in, $nin\n",
+        "* Text matching: $like, $nlike\n",
+        "* Logical operations: $and, $or, $not\n",
+        "\n",
+        "Example:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "f15a2359-6dc3-4099-8214-785f167a9cb4",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Search with simple filter\n",
+        "results = vectorstore.similarity_search(\n",
+        "    \"kitty\", k=10, filter={\"id\": {\"$in\": [1, 5, 2, 9]}}\n",
+        ")\n",
+        "\n",
+        "# Search with multiple conditions (AND)\n",
+        "results = vectorstore.similarity_search(\n",
+        "    \"ducks\",\n",
+        "    k=10,\n",
+        "    filter={\"id\": {\"$in\": [1, 5, 2, 9]}, \"location\": {\"$in\": [\"pond\", \"market\"]}},\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "90a65b31",
+      "metadata": {},
+      "source": [
+        "## Usage for retrieval-augmented generation\n",
+        "\n",
+        "TODO: document example"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f08d3a3c",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "See the repo [here](https://github.com/mariadb-corporation/langchain-mariadb) for more detail."
+      ]
+    }
+  ],
+  "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/vectorstores/opengauss.ipynb

@@ -0,0 +1,373 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "1957f5cb",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: openGauss\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef1f0986",
+   "metadata": {},
+   "source": [
+    "# openGauss VectorStore\n",
+    "\n",
+    "This notebook covers how to get started with the openGauss VectorStore. [openGauss](https://opengauss.org/en/) is a high-performance relational database with native vector storage and retrieval capabilities. This integration enables ACID-compliant vector operations within LangChain applications, combining traditional SQL functionality with modern AI-driven similarity search.\n",
+    " vector store."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36fdc060",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "### Launch openGauss Container"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "```bash\n",
+    "docker run --name opengauss \\\n",
+    "  -d \\\n",
+    "  -e GS_PASSWORD='MyStrongPass@123' \\\n",
+    "  -p 8888:5432 \\\n",
+    "  opengauss/opengauss-server:latest\n",
+    "```"
+   ],
+   "id": "e006fdc593107ef5"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a51b3f07b83b8a1d",
+   "metadata": {},
+   "source": "### Install langchain-opengauss"
+  },
+  {
+   "cell_type": "raw",
+   "id": "ad030f666e228cc8",
+   "metadata": {},
+   "source": [
+    "```bash\n",
+    "pip install langchain-opengauss\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d14f2f5f8ab0df7",
+   "metadata": {},
+   "source": [
+    "**System Requirements**:\n",
+    "- openGauss ≥ 7.0.0\n",
+    "- Python ≥ 3.8\n",
+    "- psycopg2-binary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9695dee7",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "Using your openGauss Credentials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93df377e",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
+    "\n",
+    "<EmbeddingTabs/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc37144c-208d-4ab3-9f3a-0407a69fe052",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_opengauss import OpenGauss, OpenGaussSettings\n",
+    "\n",
+    "# Configure with schema validation\n",
+    "config = OpenGaussSettings(\n",
+    "    table_name=\"test_langchain\",\n",
+    "    embedding_dimension=384,\n",
+    "    index_type=\"HNSW\",\n",
+    "    distance_strategy=\"COSINE\",\n",
+    ")\n",
+    "vector_store = OpenGauss(embedding=embeddings, config=config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac6071d4",
+   "metadata": {},
+   "source": [
+    "## Manage vector store\n",
+    "\n",
+    "### Add items to vector store\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17f5efc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "document_1 = Document(page_content=\"foo\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "document_2 = Document(page_content=\"bar\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "document_3 = Document(page_content=\"baz\", metadata={\"source\": \"https://example.com\"})\n",
+    "\n",
+    "documents = [document_1, document_2, document_3]\n",
+    "\n",
+    "vector_store.add_documents(documents=documents, ids=[\"1\", \"2\", \"3\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c738c3e0",
+   "metadata": {},
+   "source": "### Update items in vector store\n"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0aa8b71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "updated_document = Document(\n",
+    "    page_content=\"qux\", metadata={\"source\": \"https://another-example.com\"}\n",
+    ")\n",
+    "\n",
+    "# If the id is already exist, will update the document\n",
+    "vector_store.add_documents(document_id=\"1\", document=updated_document)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcf1b905",
+   "metadata": {},
+   "source": "### Delete items from vector store\n"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef61e188",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_store.delete(ids=[\"3\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3620501",
+   "metadata": {},
+   "source": [
+    "## Query vector store\n",
+    "\n",
+    "Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.\n",
+    "\n",
+    "### Query directly\n",
+    "\n",
+    "Performing a simple similarity search can be done as follows:\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0a16fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = vector_store.similarity_search(\n",
+    "    query=\"thud\", k=1, filter={\"source\": \"https://another-example.com\"}\n",
+    ")\n",
+    "for doc in results:\n",
+    "    print(f\"* {doc.page_content} [{doc.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ed9d733",
+   "metadata": {},
+   "source": "If you want to execute a similarity search and receive the corresponding scores you can run:\n"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5efd2eaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = vector_store.similarity_search_with_score(\n",
+    "    query=\"thud\", k=1, filter={\"source\": \"https://example.com\"}\n",
+    ")\n",
+    "for doc, score in results:\n",
+    "    print(f\"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c235cdc",
+   "metadata": {},
+   "source": [
+    "### Query by turning into retriever\n",
+    "\n",
+    "You can also transform the vector store into a retriever for easier usage in your chains.\n",
+    "\n",
+    "- TODO: Edit and then run code cell to generate output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3460093",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = vector_store.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 1})\n",
+    "retriever.invoke(\"thud\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "901c75dc",
+   "metadata": {},
+   "source": [
+    "## Usage for retrieval-augmented generation\n",
+    "\n",
+    "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
+    "\n",
+    "- [Tutorials](/docs/tutorials/)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "069f1b5f",
+   "metadata": {},
+   "source": [
+    "## Configuration\n",
+    "\n",
+    "### Connection Settings\n",
+    "| Parameter           | Default                 | Description                                            |\n",
+    "|---------------------|-------------------------|--------------------------------------------------------|\n",
+    "| `host`              | localhost               | Database server address                                |\n",
+    "| `port`              | 8888                    | Database connection port                               |\n",
+    "| `user`              | gaussdb                 | Database username                                      |\n",
+    "| `password`          | -                       | Complex password string                                |\n",
+    "| `database`          | postgres                | Default database name                                  |\n",
+    "| `min_connections`   | 1                       | Connection pool minimum size                           |\n",
+    "| `max_connections`   | 5                       | Connection pool maximum size                           |\n",
+    "| `table_name`        | langchain_docs          | Name of the table for storing vector data and metadata |\n",
+    "| `index_type`        | IndexType.HNSW          |Vector index algorithm type. Options: HNSW or IVFFLAT\\nDefault is HNSW.|\n",
+    "| `vector_type`       | VectorType.vector       |Type of vector representation to use. Default is Vector.|\n",
+    "| `distance_strategy` | DistanceStrategy.COSINE |Vector similarity metric to use for retrieval. Options: euclidean (L2 distance), cosine (angular distance, ideal for text embeddings), manhattan (L1 distance for sparse data), negative_inner_product (dot product for normalized vectors).\\n Default is cosine.|\n",
+    "|`embedding_dimension`| 1536                    |Dimensionality of the vector embeddings.|\n",
+    "\n",
+    "### Supported Combinations\n",
+    "\n",
+    "| Vector Type | Dimensions | Index Types  | Supported Distance Strategies         |\n",
+    "|-------------|------------|--------------|---------------------------------------|\n",
+    "| vector      | ≤2000      | HNSW/IVFFLAT | COSINE/EUCLIDEAN/MANHATTAN/INNER_PROD |\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a7b7b7c4f5a03e1",
+   "metadata": {},
+   "source": [
+    "## Performance Optimization\n",
+    "\n",
+    "### Index Tuning Guidelines\n",
+    "**HNSW Parameters**:\n",
+    "- `m`: 16-100 (balance between recall and memory)\n",
+    "- `ef_construction`: 64-1000 (must be > 2*m)\n",
+    "\n",
+    "**IVFFLAT Recommendations**:\n",
+    "```python\n",
+    "import math\n",
+    "\n",
+    "lists = min(\n",
+    "    int(math.sqrt(total_rows)) if total_rows > 1e6 else int(total_rows / 1000),\n",
+    "    2000,  # openGauss maximum\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "### Connection Pooling\n",
+    "```python\n",
+    "OpenGaussSettings(min_connections=3, max_connections=20)\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b581b499ffed641",
+   "metadata": {},
+   "source": [
+    "## Limitations\n",
+    "- `bit` and `sparsevec` vector types currently in development\n",
+    "- Maximum vector dimensions: 2000 for `vector` type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a27244f",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://python.langchain.com/api_reference/en/latest/vectorstores/opengauss.OpenGuass.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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/vectorstores/redis.ipynb

@@ -120,7 +120,7 @@
         "1. `redis://`  - Connection to Redis standalone, unencrypted\n",
         "2. `rediss://` - Connection to Redis standalone, with TLS encryption\n",
         "3. `redis+sentinel://`  - Connection to Redis server via Redis Sentinel, unencrypted\n",
-        "4. `rediss+sentinel://` - Connection to Redis server via Redis Sentinel, booth connections with TLS encryption\n",
+        "4. `rediss+sentinel://` - Connection to Redis server via Redis Sentinel, both connections with TLS encryption\n",
         "\n",
         "More information about additional connection parameters can be found in the [redis-py documentation](https://redis-py.readthedocs.io/en/stable/connections.html)."
       ]
@@ -187,7 +187,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "id": "b1b1eb90-5155-44ca-a8a7-b04b02d5e77c",
       "metadata": {},
       "outputs": [],
@@ -209,7 +209,7 @@
         "# connection to redis standalone at localhost, db 0, no password but with TLS support\n",
         "redis_url = \"rediss://localhost:6379\"\n",
         "# connection to redis sentinel at localhost and default port, db 0, no password\n",
-        "# but with TLS support for booth Sentinel and Redis server\n",
+        "# but with TLS support for both Sentinel and Redis server\n",
         "redis_url = \"rediss+sentinel://localhost\""
       ]
     },
@@ -254,7 +254,9 @@
       "cell_type": "markdown",
       "id": "7f98392b",
       "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
     },
     {
       "cell_type": "code",
@@ -899,7 +901,7 @@
           ]
         },
         {
-          "name": "stdin",
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Please enter your OpenAI API key:  ········\n"

docs/docs/integrations/vectorstores/ydb.ipynb

@@ -0,0 +1,491 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "683953b3",
+   "metadata": {},
+   "source": [
+    "# YDB\n",
+    "\n",
+    "> [YDB](https://ydb.tech/) is a versatile open source Distributed SQL Database that combines high availability and scalability with strong consistency and ACID transactions. It accommodates transactional (OLTP), analytical (OLAP), and streaming workloads simultaneously.\n",
+    "\n",
+    "This notebook shows how to use functionality related to the `YDB` vector store.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First, set up a local YDB with Docker:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da75f17c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! docker run -d -p 2136:2136 --name ydb-langchain -e YDB_USE_IN_MEMORY_PDISKS=true -h localhost ydbplatform/local-ydb:trunk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0acb2a8d",
+   "metadata": {},
+   "source": [
+    "You'll need to install `langchain-ydb` to use this integration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d454fb7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install -qU langchain-ydb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3df5501b",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "There are no credentials for this notebook, just make sure you have installed the packages as shown above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54d5276f",
+   "metadata": {},
+   "source": [
+    "If you want to get best in-class 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": "f6fd5b03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b87fe34",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
+    "\n",
+    "<EmbeddingTabs/>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60276097",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ovcharuk/opensource/langchain/.venv/lib/python3.13/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": [
+    "# | output: false\n",
+    "# | echo: false\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aac9563e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-03T08:33:31.554934Z",
+     "start_time": "2023-06-03T08:33:31.549590Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_ydb.vectorstores import YDB, YDBSearchStrategy, YDBSettings\n",
+    "\n",
+    "settings = YDBSettings(\n",
+    "    table=\"ydb_example\",\n",
+    "    strategy=YDBSearchStrategy.COSINE_SIMILARITY,\n",
+    ")\n",
+    "vector_store = YDB(embeddings, config=settings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32dd3f67",
+   "metadata": {},
+   "source": [
+    "## Manage vector store\n",
+    "\n",
+    "Once you have created your vector store, you can interact with it by adding and deleting different items.\n",
+    "\n",
+    "### Add items to vector store\n",
+    "\n",
+    "Prepare documents to work with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "944743ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from uuid import uuid4\n",
+    "\n",
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "document_1 = Document(\n",
+    "    page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
+    "    metadata={\"source\": \"tweet\"},\n",
+    ")\n",
+    "\n",
+    "document_2 = Document(\n",
+    "    page_content=\"The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.\",\n",
+    "    metadata={\"source\": \"news\"},\n",
+    ")\n",
+    "\n",
+    "document_3 = Document(\n",
+    "    page_content=\"Building an exciting new project with LangChain - come check it out!\",\n",
+    "    metadata={\"source\": \"tweet\"},\n",
+    ")\n",
+    "\n",
+    "document_4 = Document(\n",
+    "    page_content=\"Robbers broke into the city bank and stole $1 million in cash.\",\n",
+    "    metadata={\"source\": \"news\"},\n",
+    ")\n",
+    "\n",
+    "document_5 = Document(\n",
+    "    page_content=\"Wow! That was an amazing movie. I can't wait to see it again.\",\n",
+    "    metadata={\"source\": \"tweet\"},\n",
+    ")\n",
+    "\n",
+    "document_6 = Document(\n",
+    "    page_content=\"Is the new iPhone worth the price? Read this review to find out.\",\n",
+    "    metadata={\"source\": \"website\"},\n",
+    ")\n",
+    "\n",
+    "document_7 = Document(\n",
+    "    page_content=\"The top 10 soccer players in the world right now.\",\n",
+    "    metadata={\"source\": \"website\"},\n",
+    ")\n",
+    "\n",
+    "document_8 = Document(\n",
+    "    page_content=\"LangGraph is the best framework for building stateful, agentic applications!\",\n",
+    "    metadata={\"source\": \"tweet\"},\n",
+    ")\n",
+    "\n",
+    "document_9 = Document(\n",
+    "    page_content=\"The stock market is down 500 points today due to fears of a recession.\",\n",
+    "    metadata={\"source\": \"news\"},\n",
+    ")\n",
+    "\n",
+    "document_10 = Document(\n",
+    "    page_content=\"I have a bad feeling I am going to get deleted :(\",\n",
+    "    metadata={\"source\": \"tweet\"},\n",
+    ")\n",
+    "\n",
+    "documents = [\n",
+    "    document_1,\n",
+    "    document_2,\n",
+    "    document_3,\n",
+    "    document_4,\n",
+    "    document_5,\n",
+    "    document_6,\n",
+    "    document_7,\n",
+    "    document_8,\n",
+    "    document_9,\n",
+    "    document_10,\n",
+    "]\n",
+    "uuids = [str(uuid4()) for _ in range(len(documents))]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c616b7f4",
+   "metadata": {},
+   "source": [
+    "You can add items to your vector store by using the `add_documents` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3f632996",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Inserting data...: 100%|██████████| 10/10 [00:00<00:00, 14.67it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['947be6aa-d489-44c5-910e-62e4d58d2ffb',\n",
+       " '7a62904d-9db3-412b-83b6-f01b34dd7de3',\n",
+       " 'e5a49c64-c985-4ed7-ac58-5ffa31ade699',\n",
+       " '99cf4104-36ab-4bd5-b0da-e210d260e512',\n",
+       " '5810bcd0-b46e-443e-a663-e888c9e028d1',\n",
+       " '190c193d-844e-4dbb-9a4b-b8f5f16cfae6',\n",
+       " 'f8912944-f80a-4178-954e-4595bf59e341',\n",
+       " '34fc7b09-6000-42c9-95f7-7d49f430b904',\n",
+       " '0f6b6783-f300-4a4d-bb04-8025c4dfd409',\n",
+       " '46c37ba9-7cf2-4ac8-9bd1-d84e2cb1155c']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vector_store.add_documents(documents=documents, ids=uuids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18af81cc",
+   "metadata": {},
+   "source": [
+    "### Delete items from vector store\n",
+    "\n",
+    "You can delete items from your vector store by ID using the `delete` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "12b32762",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vector_store.delete(ids=[uuids[-1]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ada27577",
+   "metadata": {},
+   "source": [
+    "## Query vector store\n",
+    "\n",
+    "Once your vector store has been created and relevant documents have been added, you will likely want to query it during the execution of your chain or agent.\n",
+    "\n",
+    "### Query directly\n",
+    "\n",
+    "#### Similarity search\n",
+    "\n",
+    "A simple similarity search can be performed as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "015831a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]\n",
+      "* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = vector_store.similarity_search(\n",
+    "    \"LangChain provides abstractions to make working with LLMs easy\", k=2\n",
+    ")\n",
+    "for res in results:\n",
+    "    print(f\"* {res.page_content} [{res.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "623d3b9d",
+   "metadata": {},
+   "source": [
+    "#### Similarity search with score\n",
+    "\n",
+    "You can also perform a search with a score:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e7d43430",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* [SIM=0.595] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]\n",
+      "* [SIM=0.212] I had chocalate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]\n",
+      "* [SIM=0.118] Wow! That was an amazing movie. I can't wait to see it again. [{'source': 'tweet'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = vector_store.similarity_search_with_score(\"Will it be hot tomorrow?\", k=3)\n",
+    "for res, score in results:\n",
+    "    print(f\"* [SIM={score:.3f}] {res.page_content} [{res.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5a90c12",
+   "metadata": {},
+   "source": [
+    "### Filtering\n",
+    "\n",
+    "You can search with filters as described below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "169d01d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* I had chocalate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]\n",
+      "* Wow! That was an amazing movie. I can't wait to see it again. [{'source': 'tweet'}]\n",
+      "* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]\n",
+      "* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = vector_store.similarity_search_with_score(\n",
+    "    \"What did I eat for breakfast?\",\n",
+    "    k=4,\n",
+    "    filter={\"source\": \"tweet\"},\n",
+    ")\n",
+    "for res, _ in results:\n",
+    "    print(f\"* {res.page_content} [{res.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afacfd4e",
+   "metadata": {},
+   "source": [
+    "### Query by turning into retriever\n",
+    "\n",
+    "You can also transform the vector store into a retriever for easier usage in your chains.\n",
+    "\n",
+    "Here's how to transform your vector store into a retriever and then invoke the retriever with a simple query and filter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "97187188",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Robbers broke into the city bank and stole $1 million in cash. [{'source': 'news'}]\n",
+      "* The stock market is down 500 points today due to fears of a recession. [{'source': 'news'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "retriever = vector_store.as_retriever(\n",
+    "    search_kwargs={\"k\": 2},\n",
+    ")\n",
+    "results = retriever.invoke(\n",
+    "    \"Stealing from the bank is a crime\", filter={\"source\": \"news\"}\n",
+    ")\n",
+    "for res in results:\n",
+    "    print(f\"* {res.page_content} [{res.metadata}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57fade30",
+   "metadata": {},
+   "source": [
+    "## Usage for retrieval-augmented generation\n",
+    "\n",
+    "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
+    "\n",
+    "- [Tutorials](/docs/tutorials/)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02452d34",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `YDB` features and configurations head to the API reference:https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.ydb.YDB.html"
+   ]
+  }
+ ],
+ "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/vectorstores/yellowbrick.ipynb

@@ -413,7 +413,7 @@
     "Yellowbrick also supports indexing using the Locality-Sensitive Hashing approach. This is an approximate nearest-neighbor search technique, and allows one to trade off similarity search time at the expense of accuracy. The index introduces two new tunable parameters:\n",
     "\n",
     "- The number of hyperplanes, which is provided as an argument to `create_lsh_index(num_hyperplanes)`. The more documents, the more hyperplanes are needed. LSH is a form of dimensionality reduction. The original embeddings are transformed into lower dimensional vectors where the number of components is the same as the number of hyperplanes.\n",
-    "- The Hamming distance, an integer representing the breadth of the search. Smaller Hamming distances result in faster retreival but lower accuracy.\n",
+    "- The Hamming distance, an integer representing the breadth of the search. Smaller Hamming distances result in faster retrieval but lower accuracy.\n",
     "\n",
     "Here's how you can create an index on the embeddings we loaded into Yellowbrick. We'll also re-run the previous chat session, but this time the retrieval will use the index. Note that for such a small number of documents, you won't see the benefit of indexing in terms of performance."
    ]

docs/docs/tutorials/index.mdx

@@ -38,7 +38,7 @@ LangSmith allows you to closely trace, monitor and evaluate your LLM application
 It seamlessly integrates with LangChain, and you can use it to inspect and debug individual steps of your chains as you build.
 
 LangSmith documentation is hosted on a separate site.
-You can peruse [LangSmith tutorials here](https://docs.smith.langchain.com/tutorials/).
+You can peruse [LangSmith tutorials here](https://docs.smith.langchain.com/).
 
 ### Evaluation
 

docs/docs/versions/migrating_chains/map_rerank_docs_chain.ipynb

@@ -13,7 +13,7 @@
     "- Map a process to the set of documents, where the process includes generating a score;\n",
     "- Rank the results by score and return the maximum.\n",
     "\n",
-    "A common process in this scenario is question-answering using pieces of context from a document. Forcing the model to generate score along with its answer helps to select for answers generated only by relevant context.\n",
+    "A common process in this scenario is question-answering using pieces of context from a document. Forcing the model to generate a score along with its answer helps to select for answers generated only by relevant context.\n",
     "\n",
     "An [LangGraph](https://langchain-ai.github.io/langgraph/) implementation allows for the incorporation of [tool calling](/docs/concepts/tool_calling) and other features for this problem. Below we will go through both `MapRerankDocumentsChain` and a corresponding LangGraph implementation on a simple example for illustrative purposes."
    ]

docs/scripts/tool_feat_table.py

@@ -152,6 +152,16 @@ WEBBROWSING_TOOL_FEAT_TABLE = {
         "interactions": True,
         "pricing": "Free trial, with pay-as-you-go and flat rate plans after",
     },
+    "Hyperbrowser Browser Agent Tools": {
+        "link": "/docs/integrations/tools/hyperbrowser_browser_agent_tools",
+        "interactions": True,
+        "pricing": "Free trial, with flat rate plans and pre-paid credits after",
+    },
+    "Hyperbrowser Web Scraping Tools": {
+        "link": "/docs/integrations/tools/hyperbrowser_web_scraping_tools",
+        "interactions": False,
+        "pricing": "Free trial, with flat rate plans and pre-paid credits after",
+    },
 }
 
 DATABASE_TOOL_FEAT_TABLE = {

docs/scripts/vectorstore_feat_table.py

@@ -6,7 +6,7 @@ from langchain_astradb import AstraDBVectorStore
 from langchain_chroma import Chroma
 from langchain_community import vectorstores
 from langchain_core.vectorstores import VectorStore
-from langchain_couchbase import CouchbaseVectorStore
+from langchain_couchbase import CouchbaseSearchVectorStore
 from langchain_milvus import Milvus
 from langchain_mongodb import MongoDBAtlasVectorSearch
 from langchain_pinecone import PineconeVectorStore
@@ -28,7 +28,7 @@ from_partners = [
     ("PineconeVectorStore", PineconeVectorStore),
     ("Milvus", Milvus),
     ("MongoDBAtlasVectorSearch", MongoDBAtlasVectorSearch),
-    ("CouchbaseVectorStore", CouchbaseVectorStore),
+    ("CouchbaseSearchVectorStore", CouchbaseSearchVectorStore),
 ]
 
 VECTORSTORE_TEMPLATE = """\
@@ -140,6 +140,17 @@ def get_vectorstore_table():
             "Local/Cloud": "Local",
             "IDs in add Documents": True,
         },
+        "openGauss": {
+            "Delete by ID": True,
+            "Filtering": True,
+            "similarity_search_by_vector": True,
+            "similarity_search_with_score": True,
+            "asearch": False,
+            "Passes Standard Tests": True,
+            "Multi Tenancy": False,
+            "Local/Cloud": "Local",
+            "IDs in add Documents": True,
+        },
         "QdrantVectorStore": {
             "Delete by ID": True,
             "Filtering": True,
@@ -184,7 +195,7 @@ def get_vectorstore_table():
             "Local/Cloud": "Local",
             "IDs in add Documents": True,
         },
-        "CouchbaseVectorStore": {
+        "CouchbaseSearchVectorStore": {
             "Delete by ID": True,
             "Filtering": True,
             "similarity_search_by_vector": True,

docs/src/theme/ChatModelTabs.js

@@ -218,6 +218,13 @@ ${llmVarName} = ChatWatsonx(
       apiKeyName: "XAI_API_KEY",
       packageName: "langchain-xai",
     },
+    {
+      value: "perplexity",
+      label: "Perplexity",
+      model: "llama-3.1-sonar-small-128k-online",
+      apiKeyName: "PPLX_API_KEY",
+      packageName: "langchain-perplexity",
+    }
   ].map((item) => ({
     ...item,
     ...overrideParams?.[item.value],

docs/src/theme/FeatureTables.js

@@ -237,6 +237,17 @@ const FEATURE_TABLES = {
                 "local": false,
                 "apiLink": "https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html"
             },
+            {
+                "name": "ChatPerplexity",
+                "package": "langchain-perplexity",
+                "link": "perplexity",
+                "structured_output": true,
+                "tool_calling": false,
+                "json_mode": true,
+                "multimodal": true,
+                "local": false,
+                "apiLink": "https://python.langchain.com/api_reference/perplexity/chat_models/langchain_perplexity.chat_models.ChatPerplexity.html"
+            }
         ],
     },
     llms: {
@@ -1119,6 +1130,19 @@ const FEATURE_TABLES = {
                 local: true,
                 idsInAddDocuments: false,
             },
+            {
+                name: "openGauss",
+                link: "openGauss",
+                deleteById: true,
+                filtering: true,
+                searchByVector: true,
+                searchWithScore: true,
+                async: false,
+                passesStandardTests: true,
+                multiTenancy: false,
+                local: true,
+                idsInAddDocuments: true,
+            },
             {
                 name: "PGVector",
                 link: "pgvector",

libs/community/langchain_community/agent_toolkits/sql/base.py

@@ -196,7 +196,7 @@ def create_sql_agent(
             ]
             prompt = ChatPromptTemplate.from_messages(messages)
         agent = RunnableAgent(
-            runnable=create_openai_functions_agent(llm, tools, prompt),  # type: ignore
+            runnable=create_openai_functions_agent(llm, tools, prompt),  # type: ignore[arg-type]
             input_keys_arg=["input"],
             return_keys_arg=["output"],
             **kwargs,
@@ -211,9 +211,9 @@ def create_sql_agent(
             ]
             prompt = ChatPromptTemplate.from_messages(messages)
         if agent_type == "openai-tools":
-            runnable = create_openai_tools_agent(llm, tools, prompt)  # type: ignore
+            runnable = create_openai_tools_agent(llm, tools, prompt)  # type: ignore[arg-type]
         else:
-            runnable = create_tool_calling_agent(llm, tools, prompt)  # type: ignore
+            runnable = create_tool_calling_agent(llm, tools, prompt)  # type: ignore[arg-type]
         agent = RunnableMultiActionAgent(  # type: ignore[assignment]
             runnable=runnable,
             input_keys_arg=["input"],

libs/community/langchain_community/agents/openai_assistant/base.py

@@ -135,7 +135,7 @@ def _get_assistants_tool(
         Dict[str, Any]: A dictionary of tools that are converted into OpenAI tools.
     """
     if _is_assistants_builtin_tool(tool):
-        return tool  # type: ignore
+        return tool  # type: ignore[return-value]
     else:
         return convert_to_openai_tool(tool)
 
@@ -288,7 +288,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
         assistant = client.beta.assistants.create(
             name=name,
             instructions=instructions,
-            tools=[_get_assistants_tool(tool) for tool in tools],  # type: ignore
+            tools=[_get_assistants_tool(tool) for tool in tools],
             tool_resources=tool_resources,  # type: ignore[arg-type]
             model=model,
             extra_body=extra_body,
@@ -430,7 +430,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
         assistant = await async_client.beta.assistants.create(
             name=name,
             instructions=instructions,
-            tools=openai_tools,  # type: ignore
+            tools=openai_tools,
             tool_resources=tool_resources,  # type: ignore[arg-type]
             model=model,
         )

libs/community/langchain_community/cache.py

@@ -238,7 +238,7 @@ class InMemoryCache(BaseCache):
 Base = declarative_base()
 
 
-class FullLLMCache(Base):  # type: ignore
+class FullLLMCache(Base):  # type: ignore[misc,valid-type]
     """SQLite table for full LLM Cache (all generations)."""
 
     __tablename__ = "full_llm_cache"
@@ -261,7 +261,7 @@ class SQLAlchemyCache(BaseCache):
         """Look up based on prompt and llm_string."""
         stmt = (
             select(self.cache_schema.response)
-            .where(self.cache_schema.prompt == prompt)  # type: ignore
+            .where(self.cache_schema.prompt == prompt)
             .where(self.cache_schema.llm == llm_string)
             .order_by(self.cache_schema.idx)
         )
@@ -1348,17 +1348,14 @@ class CassandraSemanticCache(BaseCache):
             return await self.embedding.aembed_query(text=text)
 
         self._aget_embedding = _acache_embedding
-
+        kwargs = {}
         embedding_dimension: Union[int, Awaitable[int], None] = None
         if setup_mode == CassandraSetupMode.ASYNC:
             embedding_dimension = self._aget_embedding_dimension()
+            kwargs["async_setup"] = True
         elif setup_mode == CassandraSetupMode.SYNC:
             embedding_dimension = self._get_embedding_dimension()
 
-        kwargs = {}
-        if setup_mode == CassandraSetupMode.ASYNC:
-            kwargs["async_setup"] = True
-
         self.table = MetadataVectorCassandraTable(
             session=self.session,
             keyspace=self.keyspace,
@@ -1534,7 +1531,7 @@ class CassandraSemanticCache(BaseCache):
         await self.table.aclear()
 
 
-class FullMd5LLMCache(Base):  # type: ignore
+class FullMd5LLMCache(Base):  # type: ignore[misc,valid-type]
     """SQLite table for full LLM Cache (all generations)."""
 
     __tablename__ = "full_md5_llm_cache"
@@ -1586,7 +1583,7 @@ class SQLAlchemyMd5Cache(BaseCache):
     def _delete_previous(self, session: Session, prompt: str, llm_string: str) -> None:
         stmt = (
             delete(self.cache_schema)
-            .where(self.cache_schema.prompt_md5 == self.get_md5(prompt))  # type: ignore
+            .where(self.cache_schema.prompt_md5 == self.get_md5(prompt))
             .where(self.cache_schema.llm == llm_string)
             .where(self.cache_schema.prompt == prompt)
         )
@@ -1596,7 +1593,7 @@ class SQLAlchemyMd5Cache(BaseCache):
         prompt_pd5 = self.get_md5(prompt)
         stmt = (
             select(self.cache_schema.response)
-            .where(self.cache_schema.prompt_md5 == prompt_pd5)  # type: ignore
+            .where(self.cache_schema.prompt_md5 == prompt_pd5)
             .where(self.cache_schema.llm == llm_string)
             .where(self.cache_schema.prompt == prompt)
             .order_by(self.cache_schema.idx)
@@ -1799,7 +1796,7 @@ class _CachedAwaitable:
     def __await__(self) -> Generator:
         if self.result is _unset:
             self.result = yield from self.awaitable.__await__()
-        return self.result  # type: ignore
+        return self.result  # type: ignore[return-value]
 
 
 def _reawaitable(func: Callable) -> Callable:

libs/community/langchain_community/callbacks/comet_ml_callback.py

@@ -584,7 +584,7 @@ class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         )
         _custom_metrics = custom_metrics if custom_metrics else self.custom_metrics
 
-        self.__init__(  # type: ignore
+        self.__init__(  # type: ignore[misc]
             task_type=_task_type,
             workspace=_workspace,
             project_name=_project_name,

libs/community/langchain_community/callbacks/openai_info.py

@@ -218,12 +218,17 @@ def standardize_model_name(
         or model_name.startswith("gpt-3.5")
         or model_name.startswith("gpt-35")
         or model_name.startswith("o1-")
+        or model_name.startswith("o3-")
         or ("finetuned" in model_name and "legacy" not in model_name)
     ):
         return model_name + "-completion"
     if (
         token_type == TokenType.PROMPT_CACHED
-        and (model_name.startswith("gpt-4o") or model_name.startswith("o1"))
+        and (
+            model_name.startswith("gpt-4o")
+            or model_name.startswith("o1")
+            or model_name.startswith("o3")
+        )
         and not (model_name.startswith("gpt-4o-2024-05-13"))
     ):
         return model_name + "-cached"

libs/community/langchain_community/callbacks/wandb_callback.py

@@ -580,7 +580,7 @@ class WandbCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
             self.temp_dir.cleanup()
             self.reset_callback_meta()
         if reset:
-            self.__init__(  # type: ignore
+            self.__init__(  # type: ignore[misc]
                 job_type=job_type if job_type else self.job_type,
                 project=project if project else self.project,
                 entity=entity if entity else self.entity,

libs/community/langchain_community/chains/ernie_functions/base.py

@@ -352,7 +352,7 @@ def create_structured_output_runnable(
         class _OutputFormatter(BaseModel):
             """Output formatter. Should always be used to format your response to the user."""  # noqa: E501
 
-            output: output_schema  # type: ignore
+            output: output_schema  # type: ignore[valid-type]
 
         function = _OutputFormatter
         output_parser = output_parser or PydanticAttrOutputFunctionsParser(
@@ -537,7 +537,7 @@ def create_structured_output_chain(
         class _OutputFormatter(BaseModel):
             """Output formatter. Should always be used to format your response to the user."""  # noqa: E501
 
-            output: output_schema  # type: ignore
+            output: output_schema  # type: ignore[valid-type]
 
         function = _OutputFormatter
         output_parser = output_parser or PydanticAttrOutputFunctionsParser(

libs/community/langchain_community/chains/graph_qa/cypher.py

@@ -316,7 +316,7 @@ class GraphCypherQAChain(Chain):
                         MessagesPlaceholder(variable_name="function_response"),
                     ]
                 )
-                qa_chain = response_prompt | qa_llm | StrOutputParser()  # type: ignore
+                qa_chain = response_prompt | qa_llm | StrOutputParser()  # type: ignore[operator]
             except (NotImplementedError, AttributeError):
                 raise ValueError("Provided LLM does not support native tools/functions")
         else:
@@ -404,15 +404,15 @@ class GraphCypherQAChain(Chain):
             intermediate_steps.append({"context": context})
             if self.use_function_response:
                 function_response = get_function_response(question, context)
-                final_result = self.qa_chain.invoke(  # type: ignore
+                final_result = self.qa_chain.invoke(  # type: ignore[assignment]
                     {"question": question, "function_response": function_response},
                 )
             else:
-                result = self.qa_chain.invoke(  # type: ignore
+                result = self.qa_chain.invoke(
                     {"question": question, "context": context},
                     callbacks=callbacks,
                 )
-                final_result = result[self.qa_chain.output_key]  # type: ignore
+                final_result = result[self.qa_chain.output_key]  # type: ignore[union-attr]
 
         chain_result: Dict[str, Any] = {self.output_key: final_result}
         if self.return_intermediate_steps:

libs/community/langchain_community/chains/graph_qa/memgraph.py

@@ -225,11 +225,11 @@ class MemgraphQAChain(Chain):
                         MessagesPlaceholder(variable_name="function_response"),
                     ]
                 )
-                qa_chain = response_prompt | qa_llm | StrOutputParser()  # type: ignore
+                qa_chain = response_prompt | qa_llm | StrOutputParser()  # type: ignore[operator]
             except (NotImplementedError, AttributeError):
                 raise ValueError("Provided LLM does not support native tools/functions")
         else:
-            qa_chain = use_qa_llm_kwargs["prompt"] | qa_llm | StrOutputParser()  # type: ignore
+            qa_chain = use_qa_llm_kwargs["prompt"] | qa_llm | StrOutputParser()
 
         prompt = use_cypher_llm_kwargs["prompt"]
         llm_to_use = cypher_llm if cypher_llm is not None else llm
@@ -300,11 +300,11 @@ class MemgraphQAChain(Chain):
             intermediate_steps.append({"context": context})
             if self.use_function_response:
                 function_response = get_function_response(question, context)
-                result = self.qa_chain.invoke(  # type: ignore
+                result = self.qa_chain.invoke(
                     {"question": question, "function_response": function_response},
                 )
             else:
-                result = self.qa_chain.invoke(  # type: ignore
+                result = self.qa_chain.invoke(
                     {"question": question, "context": context},
                     callbacks=callbacks,
                 )

libs/community/langchain_community/chains/graph_qa/neptune_cypher.py

@@ -67,11 +67,11 @@ def extract_cypher(text: str) -> str:
 
 def use_simple_prompt(llm: BaseLanguageModel) -> bool:
     """Decides whether to use the simple prompt"""
-    if llm._llm_type and "anthropic" in llm._llm_type:  # type: ignore
+    if llm._llm_type and "anthropic" in llm._llm_type:  # type: ignore[attr-defined]
         return True
 
     # Bedrock anthropic
-    if hasattr(llm, "model_id") and "anthropic" in llm.model_id:  # type: ignore
+    if hasattr(llm, "model_id") and "anthropic" in llm.model_id:
         return True
 
     return False

libs/community/langchain_community/chains/pebblo_retrieval/base.py

@@ -313,8 +313,12 @@ class PebbloRetrievalQA(Chain):
         )
 
     @staticmethod
-    def _get_app_details(  # type: ignore
-        app_name: str, owner: str, description: str, llm: BaseLanguageModel, **kwargs
+    def _get_app_details(
+        app_name: str,
+        owner: str,
+        description: str,
+        llm: BaseLanguageModel,
+        **kwargs: Any,
     ) -> App:
         """Fetch app details. Internal method.
         Returns:

libs/community/langchain_community/chat_message_histories/cassandra.py

@@ -81,7 +81,7 @@ class CassandraChatMessageHistory(BaseChatMessageHistory):
         )
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """Retrieve all session messages from DB"""
         # The latest are returned, in chronological order
         rows = self.table.get_partition(