core: release 0.3.54 (#30911)

Follow up to https://github.com/langchain-ai/langchain/pull/30769,
fixing the regression reported
[here](https://github.com/langchain-ai/langchain/pull/30769#issuecomment-2807483610),
thanks @krassowski for the report!

Fix inspired by https://github.com/PrefectHQ/prefect/pull/16172/files

Other changes:
* Using tuples for `__all__`, except in `output_parsers` bc of a list
namespace conflict
* Using a helper function for imports due to repeated logic across
`__init__.py` files becoming hard to maintain.

Co-authored-by: Michał Krassowski < krassowski 5832902+krassowski@users.noreply.github.com>"

.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",
     ")"
    ]
   },

cookbook/img-to_img-search_CLIP_ChromaDB.ipynb

@@ -358,7 +358,7 @@
    "id": "6e5cd014-db86-4d6b-8399-25cae3da5570",
    "metadata": {},
    "source": [
-    "## Helper function to plot retrived similar images"
+    "## Helper function to plot retrieved similar images"
    ]
   },
   {

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/api_reference/templates/runnable_non_pydantic.rst

@@ -7,7 +7,7 @@
 
 .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
 
-    The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
+    The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_config <langchain_core.runnables.base.Runnable.with_config>`, :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
 
    {% block attributes %}
    {% if attributes %}

docs/api_reference/templates/runnable_pydantic.rst

@@ -19,6 +19,6 @@
 
     .. NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
 
-        The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
+        The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_config <langchain_core.runnables.base.Runnable.with_config>`, :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
 
 .. example_links:: {{ objname }}

docs/cassettes/chat_token_usage_tracking_05f22a1d-b021-490f-8840-f628a07459f2.msgpack.zlib

@@ -1 +0,0 @@
-eNrtVmlUFFcWJsGFmBk00XGJW9OikyDVVO8LommgwQ5ikxZUQAarq17TBV2LVdXQwDgKxhg1iZQyJkhGoyytiCjgoKgY0cRxQRj1iIe4xMkYiXNy1IgROaLOa5YRj/6cP5mxzuk6XfXuu/e7937v1pfvyQQcTzL0K5UkLQAOwwX4wK/P93BgiQvwwgflFBAcDFEaZ5kXX+LiyLYghyCwvCEkBGNJGcMCGiNlOEOFZMpDcAcmhMD/rBP0uCm1MUR2G5srpQDPY2mAlxqSc6U4AyPRgtQgjQdOp4QCEkySzmQAabCUY5wAvnfxgJMuTQmWUgwBnPBFGisgKgahSJqEVrzAAYySGuyYkwfBUgFQLEQuuDi4F5WhSz0OgBEwras+I0odDC+IVc9C3Y3hOIAeAY0zBEmnibvSckg2WEIAuxMTQAUESIOeQogVGQCwCOYkM0F57y5xD8ayThLHvOsh6TxDV/YlhAjZLHh+ucKbDQKzpwVxrwWCMJpD4rJhTWmJXKaRy+R73AgvYCTthEVCnBjEU872rB8cuMBieAZ0gvT1Syzv3Vw10IbhxbJYDLfMe8YlxuEOsQzjKI2qduB7zkULJAVET0Tc8+H6Fp+GU8rkCpmu+hnHfDaNi2U9bdj3zGYgcNkIzkAf4la0HGeYDBKIbXdTU3F7qo0Km2NCoy2kJWGJFTUlzVVl8Hi21pZqtfAul9nORlrM6a73c2h+NkcyiFyr1Gl1ehTVIXIZKoMpI2p9hs5BWGZHuMzuKDQyJtwSu0Rn5GJ182z0wki5PUuXoGc5U6SGRuPpeE1SGodRQlSmiiA0RIIuNsklk0U7+Sgnk8CkJ8oUiXGpcxRoVqgEonNlkkQYatQnLnFHxsforQ4NRrgzIq0uR1YCistiw91ah5GLizImpVnTFWzaAHgauRZB+xBqUJUO9V5V/dxwAjpNcIglcrliOwd4Fp4OsKIclkxw8fmlkIeg6YSn75hss8Q8pfDo0kjISbFhASCCJahaEgVsEgWqUMObQa0xqPSS6Nj4yoi+MPEvpGB1PIfRvB3S0NRPeQ/ucNEZgKiIeCHZG7xkh530woeHEgFuluEB0odKrFyIWHvnA2KOrO09WQjDpWE0mdMTVmzoYX1WjjuLwF0E4cjMolB9jkpJ2oALt+/t28JyjDcMBIRQvFiiU6ir+lb6eVcBc0UROYqg8gNuBJ5y4CQpEtaz5943pHixVA2Lvf95AwHOFZoXPaqebqCHB1pwgIKE9cZ+6kal1+sPvdio35USmui1+gPPWvFgIBq5guL3P2/Q52Ibyle6+60RkhDbAuFDqhpVaFDcrleplEoVhioBIVdDetlwvV0D1JimHs49EodevM1kGU5AeIDDiSxki23BFOb2zpgwpVyt1MBMQyUkjTtdBJjnskUy3hz4UAnLASeDEbtxO4JjuAMgvfwTPZGJc42x5oi6hchAIiEWtmeMix6a4WnSbi+fBzjYGLECdzIuAg5LDpRHRCFWY6K4V48Satyu0MltQKuzY3YkHI6hfm//oV2pd9J6MCfEnomLtQ5lmNQA85WGSigsTKeBber5ZuSVe3Ol07555dTktX4+PZcv/D158rG1mr6Ejrjyy+8WNM8PSDZOzX3Nujciz//mCMExLG+a5dzUUScL4v8sufPVmMJRMVHJX76+qvPWpO8OHrw4aLlIDF287QejML9M23LmWMGy+oZc5xpmX9aW7vPt3+/4adPu9WvzKNsq/cwv/ZiU2ytvVh59N8qylz3hG3WBEj8z1bfUcIGGKe9/U3f8w3unSieS72W13Qo6s0ZIbr6yKyBv88Rr0a/63DEcPuvZ+KDJv4C6WuDf0P7bmmP1fsv1JdKj+zuv+17Mo0Zbx06MW9f61b4QiTnc8caha/4rlb6/Kc3LuOabdfetW780PP4ov3PSVtPY/UPPr9oeZNa++1bS3biRQV30o0+OHeicEXWhwar8k+Hh/PLq5gmPmrb8vEnVVcedaDh7tmT48RoxbKrPrL913RsjFrnKa7cOu/GGa9Rl+vf6Oicw3UpTfH7kQn57x5yQC0Gr8zbc3dmlaDSt5W4u2tiYa7abAnc2LdvUVXy/sFjomHX39YBB3y6vqfGfuvr0kZiiY+7aJ0GhLW5V04wDqYF7hn0gu2W0nclVp7zZETJz8YILpeOF1rPC3L/u+rSmqePK4ccFtdcLu4d4G+br83hqVcci2L2XmuKlpnipKX4FmkKN/i9pCo32/1hTKJR2zEZo9Da72qbUqgGmVGJ2XKEiMB1QauzyX4WmACiw/zc1xfGnmuJja+On59ERDe3Th2wZX1hnJHONgjE8f+SE0atNYoZlfeKESzV/oT5LeDJRf1KzY3FZw4M7V5s6cpUdhT7oeufQOaoZH7VW3Du24/CspY+6Sx7Wtkx/FPbw/g/3p9zQah+u6j6/CYF64nKJX0sKs5LcBfXE8F49AaqvjiqmNlK529RvJjh3UrP5DXWl76SAnTOgnqgfd3B8xKthlxpbBvlc27GsxIL8/HVmAbJZHN3Qrq65UjZQTyDTvHqis5Upji4IDFD5/YFddbRoygSz78Uj6y6f7GQ9t+9NGnx7RvgK9vqkOWO2VQSm+JKOExGm7deLu7T+O5s7xiZkvffJg67vk4u+NfB3pixrnH7uevG5gM6o6HUzx57Ouf+FT/e/2s+nmOytRa0fKj7/bnvJNU5/opqcruRC1zcOqpr2x7Dme0m7Csfq3plVHDJykeptTzM57tAGwh6pON29u+gJObzefHG3uMUv4O9DCstcb5sXDd18asLx4qU7flL88yra8eOk2YvH+aVcXvH1mqz0/YE3Ji89Ulc5++KPlSX/+CIoefT0wsGzDp0eHNN8MtWnV0+8Jgua7NUT/wZqjsb/

docs/cassettes/chat_token_usage_tracking_07f0c872-6b6c-4fed-a129-9b5a858505be.msgpack.zlib

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docs/cassettes/chat_token_usage_tracking_c00c9158-7bb4-4279-88e6-ea70f46e6ac2.msgpack.zlib

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docs/cassettes/chat_token_usage_tracking_fe945078-ee2d-43ba-8cdf-afb2f2f4ecef.msgpack.zlib

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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/qa_citations.ipynb

@@ -127,7 +127,7 @@
    "id": "c89e2045-9244-43e6-bf3f-59af22658529",
    "metadata": {},
    "source": [
-    "Now that we've got a [model](/docs/concepts/chat_models/), [retriver](/docs/concepts/retrievers/) and [prompt](/docs/concepts/prompt_templates/), let's chain them all together. Following the how-to guide on [adding citations](/docs/how_to/qa_citations) to a RAG application, we'll make it so our chain returns both the answer and the retrieved Documents. This uses the same [LangGraph](/docs/concepts/architecture/#langgraph) implementation as in the [RAG Tutorial](/docs/tutorials/rag)."
+    "Now that we've got a [model](/docs/concepts/chat_models/), [retriever](/docs/concepts/retrievers/) and [prompt](/docs/concepts/prompt_templates/), let's chain them all together. Following the how-to guide on [adding citations](/docs/how_to/qa_citations) to a RAG application, we'll make it so our chain returns both the answer and the retrieved Documents. This uses the same [LangGraph](/docs/concepts/architecture/#langgraph) implementation as in the [RAG Tutorial](/docs/tutorials/rag)."
    ]
   },
   {

docs/docs/how_to/query_multiple_queries.ipynb

@@ -270,7 +270,7 @@
    "source": [
     "## Retrieval with query analysis\n",
     "\n",
-    "So how would we include this in a chain? One thing that will make this a lot easier is if we call our retriever asyncronously - this will let us loop over the queries and not get blocked on the response time."
+    "So how would we include this in a chain? One thing that will make this a lot easier is if we call our retriever asynchronously - this will let us loop over the queries and not get blocked on the response time."
    ]
   },
   {

docs/docs/how_to/summarize_map_reduce.ipynb

@@ -24,7 +24,7 @@
     "\n",
     "Note that the map step is typically parallelized over the input documents. This strategy is especially effective when understanding of a sub-document does not rely on preceeding context. For example, when summarizing a corpus of many, shorter documents.\n",
     "\n",
-    "[LangGraph](https://langchain-ai.github.io/langgraph/), built on top of `langchain-core`, suports [map-reduce](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) workflows and is well-suited to this problem:\n",
+    "[LangGraph](https://langchain-ai.github.io/langgraph/), built on top of `langchain-core`, supports [map-reduce](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) workflows and is well-suited to this problem:\n",
     "\n",
     "- LangGraph allows for individual steps (such as successive summarizations) to be streamed, allowing for greater control of execution;\n",
     "- LangGraph's [checkpointing](https://langchain-ai.github.io/langgraph/how-tos/persistence/) supports error recovery, extending with human-in-the-loop workflows, and easier incorporation into conversational applications.\n",

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/ai21.ipynb

@@ -1,318 +1,316 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "4cebeec0",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: AI21 Labs\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatAI21\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "This notebook covers how to get started with AI21 chat models.\n",
-    "Note that different chat models support different parameters. See the [AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.\n",
-    "[See all AI21's LangChain components.](https://pypi.org/project/langchain-ai21/) \n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatAI21](https://python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html#langchain_ai21.chat_models.ChatAI21) | [langchain-ai21](https://python.langchain.com/api_reference/ai21/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ai21?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ai21?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",
-    "\n",
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "### Credentials\n",
-    "\n",
-    "We'll need to get an [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "if \"AI21_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"AI21_API_KEY\"] = getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6844fff-3702-4489-ab74-732f69f3b9d7",
-   "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": "7c2e19d3-7c58-4470-9e1a-718b27a32056",
-   "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": "98e22f31-8acc-42d6-916d-415d1263c56e",
-   "metadata": {},
-   "source": [
-    "### Installation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f9699cd9-58f2-450e-aa64-799e66906c0f",
-   "metadata": {},
-   "source": [
-    "!pip install -qU langchain-ai21"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4828829d3da430ce",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "4cebeec0",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: AI21 Labs\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatAI21\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "This notebook covers how to get started with AI21 chat models.\n",
+        "Note that different chat models support different parameters. See the [AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.\n",
+        "[See all AI21's LangChain components.](https://pypi.org/project/langchain-ai21/)\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatAI21](https://python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html#langchain_ai21.chat_models.ChatAI21) | [langchain-ai21](https://python.langchain.com/api_reference/ai21/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ai21?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ai21?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",
+        "\n",
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "### Credentials\n",
+        "\n",
+        "We'll need to get an [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "\n",
+        "if \"AI21_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"AI21_API_KEY\"] = getpass()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f6844fff-3702-4489-ab74-732f69f3b9d7",
+      "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": "7c2e19d3-7c58-4470-9e1a-718b27a32056",
+      "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": "98e22f31-8acc-42d6-916d-415d1263c56e",
+      "metadata": {},
+      "source": [
+        "### Installation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f9699cd9-58f2-450e-aa64-799e66906c0f",
+      "metadata": {},
+      "source": [
+        "!pip install -qU langchain-ai21"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4828829d3da430ce",
+      "metadata": {
+        "collapsed": false,
+        "jupyter": {
+          "outputs_hidden": false
+        }
+      },
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c40756fb-cbf8-4d44-a293-3989d707237e",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_ai21 import ChatAI21\n",
+        "\n",
+        "llm = ChatAI21(model=\"jamba-instruct\", temperature=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2bdc5d68-2a19-495e-8c04-d11adc86d3ae",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "46b982dc-5d8a-46da-a711-81c03ccd6adc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "10a30f84-b531-4fd5-8b5b-91512fbdc75b",
+      "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": null,
+      "id": "39353473fce5dd2e",
+      "metadata": {
+        "collapsed": false,
+        "jupyter": {
+          "outputs_hidden": false
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "39c0ccd229927eab",
+      "metadata": {},
+      "source": "# Tool Calls / Function Calling"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2bf6b40be07fe2d4",
+      "metadata": {},
+      "source": "This example shows how to use tool calling with AI21 models:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a181a28df77120fb",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "\n",
+        "from langchain_ai21.chat_models import ChatAI21\n",
+        "from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n",
+        "from langchain_core.tools import tool\n",
+        "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
+        "\n",
+        "if \"AI21_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"AI21_API_KEY\"] = getpass()\n",
+        "\n",
+        "\n",
+        "@tool\n",
+        "def get_weather(location: str, date: str) -> str:\n",
+        "    \"\"\"“Provide the weather for the specified location on the given date.”\"\"\"\n",
+        "    if location == \"New York\" and date == \"2024-12-05\":\n",
+        "        return \"25 celsius\"\n",
+        "    elif location == \"New York\" and date == \"2024-12-06\":\n",
+        "        return \"27 celsius\"\n",
+        "    elif location == \"London\" and date == \"2024-12-05\":\n",
+        "        return \"22 celsius\"\n",
+        "    return \"32 celsius\"\n",
+        "\n",
+        "\n",
+        "llm = ChatAI21(model=\"jamba-1.5-mini\")\n",
+        "\n",
+        "llm_with_tools = llm.bind_tools([convert_to_openai_tool(get_weather)])\n",
+        "\n",
+        "chat_messages = [\n",
+        "    SystemMessage(\n",
+        "        content=\"You are a helpful assistant. You can use the provided tools \"\n",
+        "        \"to assist with various tasks and provide accurate information\"\n",
+        "    )\n",
+        "]\n",
+        "\n",
+        "human_messages = [\n",
+        "    HumanMessage(\n",
+        "        content=\"What is the forecast for the weather in New York on December 5, 2024?\"\n",
+        "    ),\n",
+        "    HumanMessage(content=\"And what about the 2024-12-06?\"),\n",
+        "    HumanMessage(content=\"OK, thank you.\"),\n",
+        "    HumanMessage(content=\"What is the expected weather in London on December 5, 2024?\"),\n",
+        "]\n",
+        "\n",
+        "\n",
+        "for human_message in human_messages:\n",
+        "    print(f\"User: {human_message.content}\")\n",
+        "    chat_messages.append(human_message)\n",
+        "    response = llm_with_tools.invoke(chat_messages)\n",
+        "    chat_messages.append(response)\n",
+        "    if response.tool_calls:\n",
+        "        tool_call = response.tool_calls[0]\n",
+        "        if tool_call[\"name\"] == \"get_weather\":\n",
+        "            weather = get_weather.invoke(\n",
+        "                {\n",
+        "                    \"location\": tool_call[\"args\"][\"location\"],\n",
+        "                    \"date\": tool_call[\"args\"][\"date\"],\n",
+        "                }\n",
+        "            )\n",
+        "            chat_messages.append(\n",
+        "                ToolMessage(content=weather, tool_call_id=tool_call[\"id\"])\n",
+        "            )\n",
+        "            llm_answer = llm_with_tools.invoke(chat_messages)\n",
+        "            print(f\"Assistant: {llm_answer.content}\")\n",
+        "    else:\n",
+        "        print(f\"Assistant: {response.content}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e79de691-9dd6-4697-b57e-59a4a3cc073a",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatAI21 features and configurations head to the API reference: https://python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html"
+      ]
     }
-   },
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c40756fb-cbf8-4d44-a293-3989d707237e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_ai21 import ChatAI21\n",
-    "\n",
-    "llm = ChatAI21(model=\"jamba-instruct\", temperature=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bdc5d68-2a19-495e-8c04-d11adc86d3ae",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "46b982dc-5d8a-46da-a711-81c03ccd6adc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "10a30f84-b531-4fd5-8b5b-91512fbdc75b",
-   "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": null,
-   "id": "39353473fce5dd2e",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
     }
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "39c0ccd229927eab",
-   "metadata": {},
-   "source": "# Tool Calls / Function Calling"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bf6b40be07fe2d4",
-   "metadata": {},
-   "source": "This example shows how to use tool calling with AI21 models:"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a181a28df77120fb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "from langchain_ai21.chat_models import ChatAI21\n",
-    "from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n",
-    "from langchain_core.tools import tool\n",
-    "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
-    "\n",
-    "if \"AI21_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"AI21_API_KEY\"] = getpass()\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def get_weather(location: str, date: str) -> str:\n",
-    "    \"\"\"“Provide the weather for the specified location on the given date.”\"\"\"\n",
-    "    if location == \"New York\" and date == \"2024-12-05\":\n",
-    "        return \"25 celsius\"\n",
-    "    elif location == \"New York\" and date == \"2024-12-06\":\n",
-    "        return \"27 celsius\"\n",
-    "    elif location == \"London\" and date == \"2024-12-05\":\n",
-    "        return \"22 celsius\"\n",
-    "    return \"32 celsius\"\n",
-    "\n",
-    "\n",
-    "llm = ChatAI21(model=\"jamba-1.5-mini\")\n",
-    "\n",
-    "llm_with_tools = llm.bind_tools([convert_to_openai_tool(get_weather)])\n",
-    "\n",
-    "chat_messages = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful assistant. You can use the provided tools \"\n",
-    "        \"to assist with various tasks and provide accurate information\"\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "human_messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"What is the forecast for the weather in New York on December 5, 2024?\"\n",
-    "    ),\n",
-    "    HumanMessage(content=\"And what about the 2024-12-06?\"),\n",
-    "    HumanMessage(content=\"OK, thank you.\"),\n",
-    "    HumanMessage(content=\"What is the expected weather in London on December 5, 2024?\"),\n",
-    "]\n",
-    "\n",
-    "\n",
-    "for human_message in human_messages:\n",
-    "    print(f\"User: {human_message.content}\")\n",
-    "    chat_messages.append(human_message)\n",
-    "    response = llm_with_tools.invoke(chat_messages)\n",
-    "    chat_messages.append(response)\n",
-    "    if response.tool_calls:\n",
-    "        tool_call = response.tool_calls[0]\n",
-    "        if tool_call[\"name\"] == \"get_weather\":\n",
-    "            weather = get_weather.invoke(\n",
-    "                {\n",
-    "                    \"location\": tool_call[\"args\"][\"location\"],\n",
-    "                    \"date\": tool_call[\"args\"][\"date\"],\n",
-    "                }\n",
-    "            )\n",
-    "            chat_messages.append(\n",
-    "                ToolMessage(content=weather, tool_call_id=tool_call[\"id\"])\n",
-    "            )\n",
-    "            llm_answer = llm_with_tools.invoke(chat_messages)\n",
-    "            print(f\"Assistant: {llm_answer.content}\")\n",
-    "    else:\n",
-    "        print(f\"Assistant: {response.content}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e79de691-9dd6-4697-b57e-59a4a3cc073a",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatAI21 features and configurations head to the API reference: https://python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.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
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/azure_chat_openai.ipynb

@@ -1,349 +1,347 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Azure OpenAI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# AzureChatOpenAI\n",
-    "\n",
-    "This guide will help you get started with AzureOpenAI [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html).\n",
-    "\n",
-    "Azure OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).\n",
-    "\n",
-    ":::info Azure OpenAI vs OpenAI\n",
-    "\n",
-    "Azure OpenAI refers to OpenAI models hosted on the [Microsoft Azure platform](https://azure.microsoft.com/en-us/products/ai-services/openai-service). OpenAI also provides its own model APIs. To access OpenAI services directly, use the [ChatOpenAI integration](/docs/integrations/chat/openai/).\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/azure) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [AzureChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?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 AzureOpenAI models you'll need to create an Azure account, create a deployment of an Azure OpenAI model, get the name and endpoint for your deployment, get an Azure OpenAI API key, and install the `langchain-openai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python) to create your deployment and generate an API key. Once you've done this set the AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT environment variables:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"AZURE_OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your AzureOpenAI API key: \"\n",
-    "    )\n",
-    "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://YOUR-ENDPOINT.openai.azure.com/\""
-   ]
-  },
-  {
-   "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_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 AzureOpenAI integration lives in the `langchain-openai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-openai"
-   ]
-  },
-  {
-   "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",
-    "- Replace `azure_deployment` with the name of your deployment,\n",
-    "- You can find the latest supported `api_version` here: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import AzureChatOpenAI\n",
-    "\n",
-    "llm = AzureChatOpenAI(\n",
-    "    azure_deployment=\"gpt-35-turbo\",  # or your deployment\n",
-    "    api_version=\"2023-06-01-preview\",  # or your api version\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 31, 'total_tokens': 39}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-bea4b46c-e3e1-4495-9d3a-698370ad963d-0', usage_metadata={'input_tokens': 31, 'output_tokens': 8, 'total_tokens': 39})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Azure OpenAI\n",
+        "---"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore 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": 5,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 26, 'total_tokens': 32}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-cbc44038-09d3-40d4-9da2-c5910ee636ca-0', usage_metadata={'input_tokens': 26, 'output_tokens': 6, 'total_tokens': 32})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# AzureChatOpenAI\n",
+        "\n",
+        "This guide will help you get started with AzureOpenAI [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html).\n",
+        "\n",
+        "Azure OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).\n",
+        "\n",
+        ":::info Azure OpenAI vs OpenAI\n",
+        "\n",
+        "Azure OpenAI refers to OpenAI models hosted on the [Microsoft Azure platform](https://azure.microsoft.com/en-us/products/ai-services/openai-service). OpenAI also provides its own model APIs. To access OpenAI services directly, use the [ChatOpenAI integration](/docs/integrations/chat/openai/).\n",
+        "\n",
+        ":::\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/azure) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [AzureChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?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 AzureOpenAI models you'll need to create an Azure account, create a deployment of an Azure OpenAI model, get the name and endpoint for your deployment, get an Azure OpenAI API key, and install the `langchain-openai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python) to create your deployment and generate an API key. Once you've done this set the AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT environment variables:"
       ]
-     },
-     "execution_count": 5,
-     "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": [
-    "## Specifying model version\n",
-    "\n",
-    "Azure OpenAI responses contain `model_name` response metadata property, which is name of the model used to generate the response. However unlike native OpenAI responses, it does not contain the specific version of the model, which is set on the deployment in Azure. E.g. it does not distinguish between `gpt-35-turbo-0125` and `gpt-35-turbo-0301`. This makes it tricky to know which version of the model was used to generate the response, which as result can lead to e.g. wrong total cost calculation with `OpenAICallbackHandler`.\n",
-    "\n",
-    "To solve this problem, you can pass `model_version` parameter to `AzureChatOpenAI` class, which will be added to the model name in the llm output. This way you can easily distinguish between different versions of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "04b36e75-e8b7-4721-899e-76301ac2ecd9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-community"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2ca02d23-60d0-43eb-8d04-070f61f8fefd",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total Cost (USD): $0.000063\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_community.callbacks import get_openai_callback\n",
-    "\n",
-    "with get_openai_callback() as cb:\n",
-    "    llm.invoke(messages)\n",
-    "    print(\n",
-    "        f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\"\n",
-    "    )  # without specifying the model version, flat-rate 0.002 USD per 1k input and output tokens is used"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e1b07ae2-3de7-44bd-bfdc-b76f4ba45a35",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"AZURE_OPENAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your AzureOpenAI API key: \"\n",
+        "    )\n",
+        "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://YOUR-ENDPOINT.openai.azure.com/\""
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total Cost (USD): $0.000074\n"
-     ]
+      "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 AzureOpenAI integration lives in the `langchain-openai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-openai"
+      ]
+    },
+    {
+      "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",
+        "- Replace `azure_deployment` with the name of your deployment,\n",
+        "- You can find the latest supported `api_version` here: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_openai import AzureChatOpenAI\n",
+        "\n",
+        "llm = AzureChatOpenAI(\n",
+        "    azure_deployment=\"gpt-35-turbo\",  # or your deployment\n",
+        "    api_version=\"2023-06-01-preview\",  # or your api version\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 31, 'total_tokens': 39}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-bea4b46c-e3e1-4495-9d3a-698370ad963d-0', usage_metadata={'input_tokens': 31, 'output_tokens': 8, 'total_tokens': 39})"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "J'adore 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": 5,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 26, 'total_tokens': 32}, 'model_name': 'gpt-35-turbo', 'system_fingerprint': None, 'prompt_filter_results': [{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}], 'finish_reason': 'stop', 'logprobs': None, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}, id='run-cbc44038-09d3-40d4-9da2-c5910ee636ca-0', usage_metadata={'input_tokens': 26, 'output_tokens': 6, 'total_tokens': 32})"
+            ]
+          },
+          "execution_count": 5,
+          "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": [
+        "## Specifying model version\n",
+        "\n",
+        "Azure OpenAI responses contain `model_name` response metadata property, which is name of the model used to generate the response. However unlike native OpenAI responses, it does not contain the specific version of the model, which is set on the deployment in Azure. E.g. it does not distinguish between `gpt-35-turbo-0125` and `gpt-35-turbo-0301`. This makes it tricky to know which version of the model was used to generate the response, which as result can lead to e.g. wrong total cost calculation with `OpenAICallbackHandler`.\n",
+        "\n",
+        "To solve this problem, you can pass `model_version` parameter to `AzureChatOpenAI` class, which will be added to the model name in the llm output. This way you can easily distinguish between different versions of the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "04b36e75-e8b7-4721-899e-76301ac2ecd9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-community"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "2ca02d23-60d0-43eb-8d04-070f61f8fefd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Total Cost (USD): $0.000063\n"
+          ]
+        }
+      ],
+      "source": [
+        "from langchain_community.callbacks import get_openai_callback\n",
+        "\n",
+        "with get_openai_callback() as cb:\n",
+        "    llm.invoke(messages)\n",
+        "    print(\n",
+        "        f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\"\n",
+        "    )  # without specifying the model version, flat-rate 0.002 USD per 1k input and output tokens is used"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "e1b07ae2-3de7-44bd-bfdc-b76f4ba45a35",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Total Cost (USD): $0.000074\n"
+          ]
+        }
+      ],
+      "source": [
+        "llm_0301 = AzureChatOpenAI(\n",
+        "    azure_deployment=\"gpt-35-turbo\",  # or your deployment\n",
+        "    api_version=\"2023-06-01-preview\",  # or your api version\n",
+        "    model_version=\"0301\",\n",
+        ")\n",
+        "with get_openai_callback() as cb:\n",
+        "    llm_0301.invoke(messages)\n",
+        "    print(f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.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"
     }
-   ],
-   "source": [
-    "llm_0301 = AzureChatOpenAI(\n",
-    "    azure_deployment=\"gpt-35-turbo\",  # or your deployment\n",
-    "    api_version=\"2023-06-01-preview\",  # or your api version\n",
-    "    model_version=\"0301\",\n",
-    ")\n",
-    "with get_openai_callback() as cb:\n",
-    "    llm_0301.invoke(messages)\n",
-    "    print(f\"Total Cost (USD): ${format(cb.total_cost, '.6f')}\")"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.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
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/bedrock.ipynb

@@ -19,9 +19,15 @@
     "\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",
+    "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",
-    "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",
+    ":::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",
@@ -29,11 +35,15 @@
     "| 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",
     "\n",
     "## Setup\n",
     "\n",
@@ -49,7 +59,7 @@
    "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:"
+    "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
    ]
   },
   {
@@ -100,11 +110,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_aws import ChatBedrock\n",
+    "from langchain_aws import ChatBedrockConverse\n",
     "\n",
-    "llm = ChatBedrock(\n",
-    "    model_id=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
-    "    model_kwargs=dict(temperature=0),\n",
+    "llm = ChatBedrockConverse(\n",
+    "    model_id=\"anthropic.claude-3-5-sonnet-20240620-v1:0\",\n",
+    "    # temperature=...,\n",
+    "    # max_tokens=...,\n",
     "    # other params...\n",
     ")"
    ]
@@ -119,19 +130,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 2,
+   "id": "fcd8de52-4a1b-4875-b463-d41b031e06a1",
+   "metadata": {},
    "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})"
+       "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": 5,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -150,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
    "metadata": {},
    "outputs": [
@@ -158,9 +167,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Voici la traduction en français :\n",
-      "\n",
-      "J'aime la programmation.\n"
+      "J'adore la programmation.\n"
      ]
     }
    ],
@@ -170,7 +177,146 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "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",
@@ -181,13 +327,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "id": "6033f3fa-0e96-46e3-abb3-1530928fea88",
    "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})"
+       "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,
@@ -218,131 +364,6 @@
     ")"
    ]
   },
-  {
-   "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",

docs/docs/integrations/chat/cerebras.ipynb

@@ -1,424 +1,422 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Cerebras\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatCerebras\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with Cerebras [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCerebras features and configurations head to the [API reference](https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.html#).\n",
-    "\n",
-    "At Cerebras, we've developed the world's largest and fastest AI processor, the Wafer-Scale Engine-3 (WSE-3). The Cerebras CS-3 system, powered by the WSE-3, represents a new class of AI supercomputer that sets the standard for generative AI training and inference with unparalleled performance and scalability.\n",
-    "\n",
-    "With Cerebras as your inference provider, you can:\n",
-    "- Achieve unprecedented speed for AI inference workloads\n",
-    "- Build commercially with high throughput\n",
-    "- Effortlessly scale your AI workloads with our seamless clustering technology\n",
-    "\n",
-    "Our CS-3 systems can be quickly and easily clustered to create the largest AI supercomputers in the world, making it simple to place and run the largest models. Leading corporations, research institutions, and governments are already using Cerebras solutions to develop proprietary models and train popular open-source models.\n",
-    "\n",
-    "Want to experience the power of Cerebras? Check out our [website](https://cerebras.ai) for more resources and explore options for accessing our technology through the Cerebras Cloud or on-premise deployments!\n",
-    "\n",
-    "For more information about Cerebras Cloud, visit [cloud.cerebras.ai](https://cloud.cerebras.ai/). Our API reference is available at [inference-docs.cerebras.ai](https://inference-docs.cerebras.ai/).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cerebras) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatCerebras](https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.html#) | [langchain-cerebras](https://python.langchain.com/api_reference/cerebras/index.html) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-cerebras?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-cerebras?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",
-    "```bash\n",
-    "pip install langchain-cerebras\n",
-    "```\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Get an API Key from [cloud.cerebras.ai](https://cloud.cerebras.ai/) and add it to your environment variables:\n",
-    "```\n",
-    "export CEREBRAS_API_KEY=\"your-api-key-here\"\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "ce19c2d6",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Enter your Cerebras API key:  ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"CEREBRAS_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"CEREBRAS_API_KEY\"] = getpass.getpass(\"Enter your Cerebras API key: \")"
-   ]
-  },
-  {
-   "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": 9,
-   "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 Cerebras integration lives in the `langchain-cerebras` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-cerebras"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea69675d",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "21155898",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_cerebras import ChatCerebras\n",
-    "\n",
-    "llm = ChatCerebras(\n",
-    "    model=\"llama-3.3-70b\",\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Je adore le programmation.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 35, 'total_tokens': 42}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-e5d66faf-019c-4ac6-9265-71093b13202d-0', usage_metadata={'input_tokens': 35, 'output_tokens': 7, 'total_tokens': 42})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Cerebras\n",
+        "---"
       ]
-     },
-     "execution_count": 13,
-     "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": "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": 14,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe Programmieren!\\n\\n(Literally: I love programming!)', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 30, 'total_tokens': 44}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-e1d2ebb8-76d1-471b-9368-3b68d431f16a-0', usage_metadata={'input_tokens': 30, 'output_tokens': 14, 'total_tokens': 44})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatCerebras\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with Cerebras [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCerebras features and configurations head to the [API reference](https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.html#).\n",
+        "\n",
+        "At Cerebras, we've developed the world's largest and fastest AI processor, the Wafer-Scale Engine-3 (WSE-3). The Cerebras CS-3 system, powered by the WSE-3, represents a new class of AI supercomputer that sets the standard for generative AI training and inference with unparalleled performance and scalability.\n",
+        "\n",
+        "With Cerebras as your inference provider, you can:\n",
+        "- Achieve unprecedented speed for AI inference workloads\n",
+        "- Build commercially with high throughput\n",
+        "- Effortlessly scale your AI workloads with our seamless clustering technology\n",
+        "\n",
+        "Our CS-3 systems can be quickly and easily clustered to create the largest AI supercomputers in the world, making it simple to place and run the largest models. Leading corporations, research institutions, and governments are already using Cerebras solutions to develop proprietary models and train popular open-source models.\n",
+        "\n",
+        "Want to experience the power of Cerebras? Check out our [website](https://cerebras.ai) for more resources and explore options for accessing our technology through the Cerebras Cloud or on-premise deployments!\n",
+        "\n",
+        "For more information about Cerebras Cloud, visit [cloud.cerebras.ai](https://cloud.cerebras.ai/). Our API reference is available at [inference-docs.cerebras.ai](https://inference-docs.cerebras.ai/).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cerebras) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatCerebras](https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.html#) | [langchain-cerebras](https://python.langchain.com/api_reference/cerebras/index.html) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-cerebras?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-cerebras?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",
+        "```bash\n",
+        "pip install langchain-cerebras\n",
+        "```\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Get an API Key from [cloud.cerebras.ai](https://cloud.cerebras.ai/) and add it to your environment variables:\n",
+        "```\n",
+        "export CEREBRAS_API_KEY=\"your-api-key-here\"\n",
+        "```"
       ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_cerebras import ChatCerebras\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "llm = ChatCerebras(\n",
-    "    model=\"llama-3.3-70b\",\n",
-    "    # other params...\n",
-    ")\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": "0ec73a0e",
-   "metadata": {},
-   "source": [
-    "## Streaming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "46fd21a7",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "OH BOY! Let me tell you all about LIONS!\n",
-      "\n",
-      "Lions are the kings of the jungle! They're really big and have beautiful, fluffy manes around their necks. The mane is like a big, golden crown!\n",
-      "\n",
-      "Lions live in groups called prides. A pride is like a big family, and the lionesses (that's what we call the female lions) take care of the babies. The lionesses are like the mommies, and they teach the babies how to hunt and play.\n",
-      "\n",
-      "Lions are very good at hunting. They work together to catch their food, like zebras and antelopes. They're super fast and can run really, really fast!\n",
-      "\n",
-      "But lions are also very sleepy. They like to take long naps in the sun, and they can sleep for up to 20 hours a day! Can you imagine sleeping that much?\n",
-      "\n",
-      "Lions are also very loud. They roar really loudly to talk to each other. It's like they're saying, \"ROAR! I'm the king of the jungle!\"\n",
-      "\n",
-      "And guess what? Lions are very social. They like to play and cuddle with each other. They're like big, furry teddy bears!\n",
-      "\n",
-      "So, that's lions! Aren't they just the coolest?"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_cerebras import ChatCerebras\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "llm = ChatCerebras(\n",
-    "    model=\"llama-3.3-70b\",\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "system = \"You are an expert on animals who must answer questions in a manner that a 5 year old can understand.\"\n",
-    "human = \"I want to learn more about this animal: {animal}\"\n",
-    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "\n",
-    "for chunk in chain.stream({\"animal\": \"Lion\"}):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f67b6132",
-   "metadata": {},
-   "source": [
-    "## Async"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "a3a45baf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ice', response_metadata={'token_usage': {'completion_tokens': 2, 'prompt_tokens': 36, 'total_tokens': 38}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-7434bdde-1bec-44cf-827b-8d978071dfe8-0', usage_metadata={'input_tokens': 36, 'output_tokens': 2, 'total_tokens': 38})"
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "ce19c2d6",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Enter your Cerebras API key:  ········\n"
+          ]
+        }
+      ],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"CEREBRAS_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"CEREBRAS_API_KEY\"] = getpass.getpass(\"Enter your Cerebras API key: \")"
       ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_cerebras import ChatCerebras\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "llm = ChatCerebras(\n",
-    "    model=\"llama-3.3-70b\",\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"human\",\n",
-    "            \"Let's play a game of opposites. What's the opposite of {topic}? Just give me the answer with no extra input.\",\n",
-    "        )\n",
-    "    ]\n",
-    ")\n",
-    "chain = prompt | llm\n",
-    "await chain.ainvoke({\"topic\": \"fire\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f9d9945",
-   "metadata": {},
-   "source": [
-    "## Async Streaming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "c7448e0f",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In the distant reaches of the cosmos, there existed a peculiar phenomenon known as the \"Eclipse of Eternity,\" a swirling vortex of darkness that had been shrouded in mystery for eons. It was said that this blackhole, born from the cataclysmic collision of two ancient stars, had been slowly devouring the fabric of space-time itself, warping the very essence of reality. As the celestial bodies of the galaxy danced around it, they began to notice a strange, almost imperceptible distortion in the fabric of space, as if the blackhole's gravitational pull was exerting an influence on the very course of events itself.\n",
-      "\n",
-      "As the centuries passed, astronomers from across the galaxy became increasingly fascinated by the Eclipse of Eternity, pouring over ancient texts and scouring the cosmos for any hint of its secrets. One such scholar, a brilliant and reclusive astrophysicist named Dr. Elara Vex, became obsessed with unraveling the mysteries of the blackhole. She spent years pouring over ancient texts, deciphering cryptic messages and hidden codes that hinted at the existence of a long-lost civilization that had once thrived in the heart of the blackhole itself. According to legend, this ancient civilization had possessed knowledge of the cosmos that was beyond human comprehension, and had used their mastery of the universe to create the Eclipse of Eternity as a gateway to other dimensions.\n",
-      "\n",
-      "As Dr. Vex delved deeper into her research, she began to experience strange and vivid dreams, visions that seemed to transport her to the very heart of the blackhole itself. In these dreams, she saw ancient beings, their faces twisted in agony as they were consumed by the void. She saw stars and galaxies, their light warped and distorted by the blackhole's gravitational pull. And she saw the Eclipse of Eternity itself, its swirling vortex of darkness pulsing with an otherworldly energy that seemed to be calling to her. As the dreams grew more vivid and more frequent, Dr. Vex became convinced that she was being drawn into the heart of the blackhole, and that the secrets of the universe lay waiting for her on the other side."
-     ]
+      "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": 9,
+      "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 Cerebras integration lives in the `langchain-cerebras` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-cerebras"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ea69675d",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "21155898",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_cerebras import ChatCerebras\n",
+        "\n",
+        "llm = ChatCerebras(\n",
+        "    model=\"llama-3.3-70b\",\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Je adore le programmation.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 35, 'total_tokens': 42}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-e5d66faf-019c-4ac6-9265-71093b13202d-0', usage_metadata={'input_tokens': 35, 'output_tokens': 7, 'total_tokens': 42})"
+            ]
+          },
+          "execution_count": 13,
+          "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": "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": 14,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe Programmieren!\\n\\n(Literally: I love programming!)', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 30, 'total_tokens': 44}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-e1d2ebb8-76d1-471b-9368-3b68d431f16a-0', usage_metadata={'input_tokens': 30, 'output_tokens': 14, 'total_tokens': 44})"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_cerebras import ChatCerebras\n",
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "llm = ChatCerebras(\n",
+        "    model=\"llama-3.3-70b\",\n",
+        "    # other params...\n",
+        ")\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": "0ec73a0e",
+      "metadata": {},
+      "source": [
+        "## Streaming"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "46fd21a7",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "OH BOY! Let me tell you all about LIONS!\n",
+            "\n",
+            "Lions are the kings of the jungle! They're really big and have beautiful, fluffy manes around their necks. The mane is like a big, golden crown!\n",
+            "\n",
+            "Lions live in groups called prides. A pride is like a big family, and the lionesses (that's what we call the female lions) take care of the babies. The lionesses are like the mommies, and they teach the babies how to hunt and play.\n",
+            "\n",
+            "Lions are very good at hunting. They work together to catch their food, like zebras and antelopes. They're super fast and can run really, really fast!\n",
+            "\n",
+            "But lions are also very sleepy. They like to take long naps in the sun, and they can sleep for up to 20 hours a day! Can you imagine sleeping that much?\n",
+            "\n",
+            "Lions are also very loud. They roar really loudly to talk to each other. It's like they're saying, \"ROAR! I'm the king of the jungle!\"\n",
+            "\n",
+            "And guess what? Lions are very social. They like to play and cuddle with each other. They're like big, furry teddy bears!\n",
+            "\n",
+            "So, that's lions! Aren't they just the coolest?"
+          ]
+        }
+      ],
+      "source": [
+        "from langchain_cerebras import ChatCerebras\n",
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "llm = ChatCerebras(\n",
+        "    model=\"llama-3.3-70b\",\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "system = \"You are an expert on animals who must answer questions in a manner that a 5 year old can understand.\"\n",
+        "human = \"I want to learn more about this animal: {animal}\"\n",
+        "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "\n",
+        "for chunk in chain.stream({\"animal\": \"Lion\"}):\n",
+        "    print(chunk.content, end=\"\", flush=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f67b6132",
+      "metadata": {},
+      "source": [
+        "## Async"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "a3a45baf",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ice', response_metadata={'token_usage': {'completion_tokens': 2, 'prompt_tokens': 36, 'total_tokens': 38}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-7434bdde-1bec-44cf-827b-8d978071dfe8-0', usage_metadata={'input_tokens': 36, 'output_tokens': 2, 'total_tokens': 38})"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_cerebras import ChatCerebras\n",
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "llm = ChatCerebras(\n",
+        "    model=\"llama-3.3-70b\",\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"human\",\n",
+        "            \"Let's play a game of opposites. What's the opposite of {topic}? Just give me the answer with no extra input.\",\n",
+        "        )\n",
+        "    ]\n",
+        ")\n",
+        "chain = prompt | llm\n",
+        "await chain.ainvoke({\"topic\": \"fire\"})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4f9d9945",
+      "metadata": {},
+      "source": [
+        "## Async Streaming"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "c7448e0f",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "In the distant reaches of the cosmos, there existed a peculiar phenomenon known as the \"Eclipse of Eternity,\" a swirling vortex of darkness that had been shrouded in mystery for eons. It was said that this blackhole, born from the cataclysmic collision of two ancient stars, had been slowly devouring the fabric of space-time itself, warping the very essence of reality. As the celestial bodies of the galaxy danced around it, they began to notice a strange, almost imperceptible distortion in the fabric of space, as if the blackhole's gravitational pull was exerting an influence on the very course of events itself.\n",
+            "\n",
+            "As the centuries passed, astronomers from across the galaxy became increasingly fascinated by the Eclipse of Eternity, pouring over ancient texts and scouring the cosmos for any hint of its secrets. One such scholar, a brilliant and reclusive astrophysicist named Dr. Elara Vex, became obsessed with unraveling the mysteries of the blackhole. She spent years pouring over ancient texts, deciphering cryptic messages and hidden codes that hinted at the existence of a long-lost civilization that had once thrived in the heart of the blackhole itself. According to legend, this ancient civilization had possessed knowledge of the cosmos that was beyond human comprehension, and had used their mastery of the universe to create the Eclipse of Eternity as a gateway to other dimensions.\n",
+            "\n",
+            "As Dr. Vex delved deeper into her research, she began to experience strange and vivid dreams, visions that seemed to transport her to the very heart of the blackhole itself. In these dreams, she saw ancient beings, their faces twisted in agony as they were consumed by the void. She saw stars and galaxies, their light warped and distorted by the blackhole's gravitational pull. And she saw the Eclipse of Eternity itself, its swirling vortex of darkness pulsing with an otherworldly energy that seemed to be calling to her. As the dreams grew more vivid and more frequent, Dr. Vex became convinced that she was being drawn into the heart of the blackhole, and that the secrets of the universe lay waiting for her on the other side."
+          ]
+        }
+      ],
+      "source": [
+        "from langchain_cerebras import ChatCerebras\n",
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "llm = ChatCerebras(\n",
+        "    model=\"llama-3.3-70b\",\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"human\",\n",
+        "            \"Write a long convoluted story about {subject}. I want {num_paragraphs} paragraphs.\",\n",
+        "        )\n",
+        "    ]\n",
+        ")\n",
+        "chain = prompt | llm\n",
+        "\n",
+        "async for chunk in chain.astream({\"num_paragraphs\": 3, \"subject\": \"blackholes\"}):\n",
+        "    print(chunk.content, end=\"\", flush=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatCerebras features and configurations head to the API reference: https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.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": "3.8.13"
     }
-   ],
-   "source": [
-    "from langchain_cerebras import ChatCerebras\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "llm = ChatCerebras(\n",
-    "    model=\"llama-3.3-70b\",\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"human\",\n",
-    "            \"Write a long convoluted story about {subject}. I want {num_paragraphs} paragraphs.\",\n",
-    "        )\n",
-    "    ]\n",
-    ")\n",
-    "chain = prompt | llm\n",
-    "\n",
-    "async for chunk in chain.astream({\"num_paragraphs\": 3, \"subject\": \"blackholes\"}):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatCerebras features and configurations head to the API reference: https://python.langchain.com/api_reference/cerebras/chat_models/langchain_cerebras.chat_models.ChatCerebras.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": "3.8.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/cloudflare_workersai.ipynb

@@ -2,50 +2,74 @@
  "cells": [
   {
    "cell_type": "raw",
-   "id": "30373ae2-f326-4e96-a1f7-062f57396886",
+   "id": "afaf8039",
    "metadata": {},
    "source": [
     "---\n",
-    "sidebar_label: Cloudflare Workers AI\n",
+    "sidebar_label: CloudflareWorkersAI\n",
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f679592d",
+   "id": "e49f1e0d",
    "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",
+    "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",
-    "| 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",
+    "| 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",
+    "|                     ✅                     |                          ✅                           |     ✅     |                       ❌                        |      ❌      |      ❌      |                           ❌                           |      ❌       |                             ✅                            |                 ❌                  | \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",
+    "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 [this document](https://developers.cloudflare.com/workers-ai/get-started/rest-api/) to sign up to Cloudflare Workers AI and generate an API key."
+    "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": "4a524cff",
+   "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:"
@@ -53,8 +77,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "71b53c25",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,80 +88,81 @@
   },
   {
    "cell_type": "markdown",
-   "id": "777a8526",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
    "metadata": {},
    "source": [
     "### Installation\n",
     "\n",
-    "The LangChain ChatCloudflareWorkersAI integration lives in the `langchain-community` package:"
+    "The LangChain CloudflareWorkersAI integration lives in the `langchain-cloudflare` package:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "54990998",
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU langchain-community"
+    "%pip install -qU langchain-cloudflare"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "629ba46f",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
    "metadata": {},
    "source": [
     "## Instantiation\n",
     "\n",
-    "Now we can instantiate our model object and generate chat completions:"
+    "Now we can instantiate our model object and generate chat completions:\n",
+    "\n",
+    "- Update model instantiation with relevant params."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ec13c2d9",
-   "metadata": {},
+   "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_community.chat_models.cloudflare_workersai import ChatCloudflareWorkersAI\n",
+    "from langchain_cloudflare.chat_models 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",
+    "    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": "119b6732",
+   "id": "2b4f3e15",
    "metadata": {},
    "source": [
-    "## Invocation"
+    "## Invocation\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "2438a906",
+   "execution_count": 19,
+   "id": "62e0dbc3",
    "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')"
+       "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": 8,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -156,15 +181,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "1b4911bd",
+   "execution_count": 20,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
    "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"
+      "J'adore la programmation.\n"
      ]
     }
    ],
@@ -174,34 +199,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "111aa5d4",
+   "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:"
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "b2a14282",
+   "execution_count": 21,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
    "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')"
+       "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": 10,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,7 +227,7 @@
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
+    "prompt = ChatPromptTemplate(\n",
     "    [\n",
     "        (\n",
     "            \"system\",\n",
@@ -231,12 +249,123 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1f311bd",
+   "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",
-    "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)."
+    "https://developers.cloudflare.com/workers-ai/\n",
+    "https://developers.cloudflare.com/agents/"
    ]
   }
  ],
@@ -256,7 +385,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/cohere.ipynb

@@ -1,352 +1,350 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "53fbf15f",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Cohere\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf733a38-db84-4363-89e2-de6735c37230",
-   "metadata": {},
-   "source": [
-    "# Cohere\n",
-    "\n",
-    "This notebook covers how to get started with [Cohere chat models](https://cohere.com/chat).\n",
-    "\n",
-    "Head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.cohere.ChatCohere.html) for detailed documentation of all attributes and methods."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3607d67e-e56c-4102-bbba-df2edc0e109e",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "The integration lives in the `langchain-cohere` package. We can install these with:\n",
-    "\n",
-    "```bash\n",
-    "pip install -U langchain-cohere\n",
-    "```\n",
-    "\n",
-    "We'll also need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "2108b517-1e8d-473d-92fa-4f930e8072a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"COHERE_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf690fbb",
-   "metadata": {},
-   "source": [
-    "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "7f11de02",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c26754b-b3c9-4d93-8f36-43049bd943bf",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "\n",
-    "ChatCohere supports all [ChatModel](/docs/how_to#chat-models) functionality:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_cohere import ChatCohere\n",
-    "from langchain_core.messages import HumanMessage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "chat = ChatCohere()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='4 && 5 \\n6 || 7 \\n\\nWould you like to play a game of odds and evens?', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, id='run-3475e0c8-c89b-4937-9300-e07d652455e1-0')"
+      "cell_type": "raw",
+      "id": "53fbf15f",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Cohere\n",
+        "---"
       ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [HumanMessage(content=\"1\"), HumanMessage(content=\"2 3\")]\n",
-    "chat.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-1635e63e-2994-4e7f-986e-152ddfc95777-0')"
+      "cell_type": "markdown",
+      "id": "bf733a38-db84-4363-89e2-de6735c37230",
+      "metadata": {},
+      "source": [
+        "# Cohere\n",
+        "\n",
+        "This notebook covers how to get started with [Cohere chat models](https://cohere.com/chat).\n",
+        "\n",
+        "Head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.cohere.ChatCohere.html) for detailed documentation of all attributes and methods."
       ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "await chat.ainvoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 && 5"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in chat.stream(messages):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "064288e4-f184-4496-9427-bcf148fa055e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-8d6fade2-1b39-4e31-ab23-4be622dd0027-0')]"
+      "cell_type": "markdown",
+      "id": "3607d67e-e56c-4102-bbba-df2edc0e109e",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "The integration lives in the `langchain-cohere` package. We can install these with:\n",
+        "\n",
+        "```bash\n",
+        "pip install -U langchain-cohere\n",
+        "```\n",
+        "\n",
+        "We'll also need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
       ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat.batch([messages])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1c56460",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts/lcel)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "0851b103",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
-    "chain = prompt | chat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "ae950c0f-1691-47f1-b609-273033cae707",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='What color socks do bears wear?\\n\\nThey don’t wear socks, they have bear feet. \\n\\nHope you laughed! If not, maybe this will help: laughter is the best medicine, and a good sense of humor is infectious!', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, id='run-ef7f9789-0d4d-43bf-a4f7-f2a0e27a5320-0')"
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "2108b517-1e8d-473d-92fa-4f930e8072a7",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "os.environ[\"COHERE_API_KEY\"] = getpass.getpass()"
       ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"topic\": \"bears\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12db8d69",
-   "metadata": {},
-   "source": [
-    "## Tool calling\n",
-    "\n",
-    "Cohere supports tool calling functionalities!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "337e24af",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.messages import (\n",
-    "    HumanMessage,\n",
-    "    ToolMessage,\n",
-    ")\n",
-    "from langchain_core.tools import tool"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "74d292e7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@tool\n",
-    "def magic_function(number: int) -> int:\n",
-    "    \"\"\"Applies a magic operation to an integer\n",
-    "    Args:\n",
-    "        number: Number to have magic operation performed on\n",
-    "    \"\"\"\n",
-    "    return number + 10\n",
-    "\n",
-    "\n",
-    "def invoke_tools(tool_calls, messages):\n",
-    "    for tool_call in tool_calls:\n",
-    "        selected_tool = {\"magic_function\": magic_function}[tool_call[\"name\"].lower()]\n",
-    "        tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
-    "        messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
-    "    return messages\n",
-    "\n",
-    "\n",
-    "tools = [magic_function]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "ecafcbc6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm_with_tools = chat.bind_tools(tools=tools)\n",
-    "messages = [HumanMessage(content=\"What is the value of magic_function(2)?\")]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "aa34fc39",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='The value of magic_function(2) is 12.', additional_kwargs={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, response_metadata={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, id='run-f318a9cf-55c8-44f4-91d1-27cf46c6a465-0')"
+      "cell_type": "markdown",
+      "id": "cf690fbb",
+      "metadata": {},
+      "source": [
+        "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3c2fc2201dc80557",
+      "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": "31f2af10e04dec59",
+      "metadata": {},
+      "source": [
+        "## Usage\n",
+        "\n",
+        "ChatCohere supports all [ChatModel](/docs/how_to#chat-models) functionality:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fa83b00a929614ad",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_cohere import ChatCohere\n",
+        "from langchain_core.messages import HumanMessage"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "chat = ChatCohere()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='4 && 5 \\n6 || 7 \\n\\nWould you like to play a game of odds and evens?', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '2076b614-52b3-4082-a259-cc92cd3d9fea', 'token_count': {'prompt_tokens': 68, 'response_tokens': 23, 'total_tokens': 91, 'billed_tokens': 77}}, id='run-3475e0c8-c89b-4937-9300-e07d652455e1-0')"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [HumanMessage(content=\"1\"), HumanMessage(content=\"2 3\")]\n",
+        "chat.invoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'f0708a92-f874-46ee-9b93-334d616ad92e', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-1635e63e-2994-4e7f-986e-152ddfc95777-0')"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "await chat.ainvoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "4 && 5"
+          ]
+        }
+      ],
+      "source": [
+        "for chunk in chat.stream(messages):\n",
+        "    print(chunk.content, end=\"\", flush=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "064288e4-f184-4496-9427-bcf148fa055e",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[AIMessage(content='4 && 5', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6770ca86-f6c3-4ba3-a285-c4772160612f', 'token_count': {'prompt_tokens': 68, 'response_tokens': 3, 'total_tokens': 71, 'billed_tokens': 57}}, id='run-8d6fade2-1b39-4e31-ab23-4be622dd0027-0')]"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "chat.batch([messages])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f1c56460",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts/lcel)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "0851b103",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
+        "chain = prompt | chat"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "ae950c0f-1691-47f1-b609-273033cae707",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='What color socks do bears wear?\\n\\nThey don’t wear socks, they have bear feet. \\n\\nHope you laughed! If not, maybe this will help: laughter is the best medicine, and a good sense of humor is infectious!', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '6edccf44-9bc8-4139-b30e-13b368f3563c', 'token_count': {'prompt_tokens': 68, 'response_tokens': 51, 'total_tokens': 119, 'billed_tokens': 108}}, id='run-ef7f9789-0d4d-43bf-a4f7-f2a0e27a5320-0')"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "chain.invoke({\"topic\": \"bears\"})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "12db8d69",
+      "metadata": {},
+      "source": [
+        "## Tool calling\n",
+        "\n",
+        "Cohere supports tool calling functionalities!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "337e24af",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.messages import (\n",
+        "    HumanMessage,\n",
+        "    ToolMessage,\n",
+        ")\n",
+        "from langchain_core.tools import tool"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "74d292e7",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "@tool\n",
+        "def magic_function(number: int) -> int:\n",
+        "    \"\"\"Applies a magic operation to an integer\n",
+        "    Args:\n",
+        "        number: Number to have magic operation performed on\n",
+        "    \"\"\"\n",
+        "    return number + 10\n",
+        "\n",
+        "\n",
+        "def invoke_tools(tool_calls, messages):\n",
+        "    for tool_call in tool_calls:\n",
+        "        selected_tool = {\"magic_function\": magic_function}[tool_call[\"name\"].lower()]\n",
+        "        tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
+        "        messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
+        "    return messages\n",
+        "\n",
+        "\n",
+        "tools = [magic_function]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "ecafcbc6",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "llm_with_tools = chat.bind_tools(tools=tools)\n",
+        "messages = [HumanMessage(content=\"What is the value of magic_function(2)?\")]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "aa34fc39",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='The value of magic_function(2) is 12.', additional_kwargs={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, response_metadata={'documents': [{'id': 'magic_function:0:2:0', 'output': '12', 'tool_name': 'magic_function'}], 'citations': [ChatCitation(start=34, end=36, text='12', document_ids=['magic_function:0:2:0'])], 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '96a55791-0c58-4e2e-bc2a-8550e137c46d', 'token_count': {'input_tokens': 998, 'output_tokens': 59}}, id='run-f318a9cf-55c8-44f4-91d1-27cf46c6a465-0')"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "res = llm_with_tools.invoke(messages)\n",
+        "while res.tool_calls:\n",
+        "    messages.append(res)\n",
+        "    messages = invoke_tools(res.tool_calls, messages)\n",
+        "    res = llm_with_tools.invoke(messages)\n",
+        "\n",
+        "res"
       ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "res = llm_with_tools.invoke(messages)\n",
-    "while res.tool_calls:\n",
-    "    messages.append(res)\n",
-    "    messages = invoke_tools(res.tool_calls, messages)\n",
-    "    res = llm_with_tools.invoke(messages)\n",
-    "\n",
-    "res"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.6"
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/deepseek.ipynb

@@ -1,237 +1,235 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: DeepSeek\n",
-    "---"
-   ]
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: DeepSeek\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatDeepSeek\n",
+        "\n",
+        "\n",
+        "This will help you getting started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
+        "\n",
+        ":::tip\n",
+        "\n",
+        "DeepSeek's models are open source and can be run locally (e.g. in [Ollama](./ollama.ipynb)) or on other inference providers (e.g. [Fireworks](./fireworks.ipynb), [Together](./together.ipynb)) as well.\n",
+        "\n",
+        ":::\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/deepseek) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatDeepSeek](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html) | [langchain-deepseek](https://python.langchain.com/api_reference/deepseek/) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-deepseek?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-deepseek?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",
+        ":::note\n",
+        "\n",
+        "DeepSeek-R1, specified via `model=\"deepseek-reasoner\"`, does not support tool calling or structured output. Those features [are supported](https://api-docs.deepseek.com/guides/function_calling) by DeepSeek-V3 (specified via `model=\"deepseek-chat\"`).\n",
+        "\n",
+        ":::\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access DeepSeek models you'll need to create a/an DeepSeek account, get an API key, and install the `langchain-deepseek` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [DeepSeek's API Key page](https://platform.deepseek.com/api_keys) to sign up to DeepSeek and generate an API key. Once you've done this set the `DEEPSEEK_API_KEY` environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"DEEPSEEK_API_KEY\"):\n",
+        "    os.environ[\"DEEPSEEK_API_KEY\"] = getpass.getpass(\"Enter your DeepSeek API key: \")"
+      ]
+    },
+    {
+      "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_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 DeepSeek integration lives in the `langchain-deepseek` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-deepseek"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_deepseek import ChatDeepSeek\n",
+        "\n",
+        "llm = ChatDeepSeek(\n",
+        "    model=\"deepseek-chat\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg.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": null,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatDeepSeek features and configurations head to the [API Reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html)."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatDeepSeek\n",
-    "\n",
-    "\n",
-    "This will help you getting started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
-    "\n",
-    ":::tip\n",
-    "\n",
-    "DeepSeek's models are open source and can be run locally (e.g. in [Ollama](./ollama.ipynb)) or on other inference providers (e.g. [Fireworks](./fireworks.ipynb), [Together](./together.ipynb)) as well.\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/deepseek) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatDeepSeek](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html) | [langchain-deepseek](https://python.langchain.com/api_reference/deepseek/) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-deepseek?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-deepseek?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",
-    ":::note\n",
-    "\n",
-    "DeepSeek-R1, specified via `model=\"deepseek-reasoner\"`, does not support tool calling or structured output. Those features [are supported](https://api-docs.deepseek.com/guides/function_calling) by DeepSeek-V3 (specified via `model=\"deepseek-chat\"`).\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access DeepSeek models you'll need to create a/an DeepSeek account, get an API key, and install the `langchain-deepseek` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [DeepSeek's API Key page](https://platform.deepseek.com/api_keys) to sign up to DeepSeek and generate an API key. Once you've done this set the `DEEPSEEK_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"DEEPSEEK_API_KEY\"):\n",
-    "    os.environ[\"DEEPSEEK_API_KEY\"] = getpass.getpass(\"Enter your DeepSeek API key: \")"
-   ]
-  },
-  {
-   "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 DeepSeek integration lives in the `langchain-deepseek` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-deepseek"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_deepseek import ChatDeepSeek\n",
-    "\n",
-    "llm = ChatDeepSeek(\n",
-    "    model=\"deepseek-chat\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg.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": null,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatDeepSeek features and configurations head to the [API Reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/fireworks.ipynb

@@ -1,268 +1,266 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Fireworks\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatFireworks\n",
-    "\n",
-    "This doc help you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
-    "\n",
-    "Fireworks AI is an AI inference platform to run and customize models. For a list of all models served by Fireworks see the [Fireworks docs](https://fireworks.ai/models).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/fireworks) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatFireworks](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html) | [langchain-fireworks](https://python.langchain.com/api_reference/fireworks/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-fireworks?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-fireworks?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 Fireworks models you'll need to create a Fireworks account, get an API key, and install the `langchain-fireworks` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to (ttps://fireworks.ai/login to sign up to Fireworks and generate an API key. Once you've done this set the FIREWORKS_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"FIREWORKS_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
-   ]
-  },
-  {
-   "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_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 Fireworks integration lives in the `langchain-fireworks` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-fireworks"
-   ]
-  },
-  {
-   "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",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_fireworks import ChatFireworks\n",
-    "\n",
-    "llm = ChatFireworks(\n",
-    "    model=\"accounts/fireworks/models/llama-v3-70b-instruct\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'prompt_tokens': 35, 'total_tokens': 44, 'completion_tokens': 9}, 'model_name': 'accounts/fireworks/models/llama-v3-70b-instruct', 'system_fingerprint': '', 'finish_reason': 'stop', 'logprobs': None}, id='run-df28e69a-ff30-457e-a743-06eb14d01cb0-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Fireworks\n",
+        "---"
       ]
-     },
-     "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": "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": 4,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'prompt_tokens': 30, 'total_tokens': 37, 'completion_tokens': 7}, 'model_name': 'accounts/fireworks/models/llama-v3-70b-instruct', 'system_fingerprint': '', 'finish_reason': 'stop', 'logprobs': None}, id='run-ff3f91ad-ed81-4acf-9f59-7490dc8d8f48-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatFireworks\n",
+        "\n",
+        "This doc help you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
+        "\n",
+        "Fireworks AI is an AI inference platform to run and customize models. For a list of all models served by Fireworks see the [Fireworks docs](https://fireworks.ai/models).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/fireworks) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatFireworks](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html) | [langchain-fireworks](https://python.langchain.com/api_reference/fireworks/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-fireworks?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-fireworks?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 Fireworks models you'll need to create a Fireworks account, get an API key, and install the `langchain-fireworks` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to (ttps://fireworks.ai/login to sign up to Fireworks and generate an API key. Once you've done this set the FIREWORKS_API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"FIREWORKS_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Enter your Fireworks API key: \")"
+      ]
+    },
+    {
+      "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 Fireworks integration lives in the `langchain-fireworks` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-fireworks"
+      ]
+    },
+    {
+      "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",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_fireworks import ChatFireworks\n",
+        "\n",
+        "llm = ChatFireworks(\n",
+        "    model=\"accounts/fireworks/models/llama-v3-70b-instruct\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'prompt_tokens': 35, 'total_tokens': 44, 'completion_tokens': 9}, 'model_name': 'accounts/fireworks/models/llama-v3-70b-instruct', 'system_fingerprint': '', 'finish_reason': 'stop', 'logprobs': None}, id='run-df28e69a-ff30-457e-a743-06eb14d01cb0-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})"
+            ]
+          },
+          "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": "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": 4,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'prompt_tokens': 30, 'total_tokens': 37, 'completion_tokens': 7}, 'model_name': 'accounts/fireworks/models/llama-v3-70b-instruct', 'system_fingerprint': '', 'finish_reason': 'stop', 'logprobs': None}, id='run-ff3f91ad-ed81-4acf-9f59-7490dc8d8f48-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})"
+            ]
+          },
+          "execution_count": 4,
+          "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 ChatFireworks features and configurations head to the API reference: https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html"
       ]
-     },
-     "execution_count": 4,
-     "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",
-    ")"
-   ]
+  ],
+  "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"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatFireworks features and configurations head to the API reference: https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/goodfire.ipynb

@@ -1,354 +1,352 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Goodfire\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatGoodfire\n",
-    "\n",
-    "This will help you getting started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatGoodfire](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.html) | [langchain-goodfire](https://python.langchain.com/api_reference/goodfire/) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-goodfire?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-goodfire?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 Goodfire models you'll need to create a/an Goodfire account, get an API key, and install the `langchain-goodfire` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [Goodfire Settings](https://platform.goodfire.ai/organization/settings/api-keys) to sign up to Goodfire and generate an API key. Once you've done this set the GOODFIRE_API_KEY environment variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"GOODFIRE_API_KEY\"):\n",
-    "    os.environ[\"GOODFIRE_API_KEY\"] = getpass.getpass(\"Enter your Goodfire API key: \")"
-   ]
-  },
-  {
-   "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 Goodfire integration lives in the `langchain-goodfire` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU langchain-goodfire"
-   ]
-  },
-  {
-   "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": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import goodfire\n",
-    "from langchain_goodfire import ChatGoodfire\n",
-    "\n",
-    "base_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
-    "\n",
-    "llm = ChatGoodfire(\n",
-    "    model=base_variant,\n",
-    "    temperature=0,\n",
-    "    max_completion_tokens=1000,\n",
-    "    seed=42,\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'adore la programmation.\", additional_kwargs={}, response_metadata={}, id='run-8d43cf35-bce8-4827-8935-c64f8fb78cd0-0', usage_metadata={'input_tokens': 51, 'output_tokens': 39, 'total_tokens': 90})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Goodfire\n",
+        "---"
       ]
-     },
-     "execution_count": 4,
-     "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 = await llm.ainvoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "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:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren. How can I help you with programming today?', additional_kwargs={}, response_metadata={}, id='run-03d1a585-8234-46f1-a8df-bf9143fe3309-0', usage_metadata={'input_tokens': 46, 'output_tokens': 46, 'total_tokens': 92})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatGoodfire\n",
+        "\n",
+        "This will help you getting started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatGoodfire](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.html) | [langchain-goodfire](https://python.langchain.com/api_reference/goodfire/) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-goodfire?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-goodfire?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 Goodfire models you'll need to create a/an Goodfire account, get an API key, and install the `langchain-goodfire` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [Goodfire Settings](https://platform.goodfire.ai/organization/settings/api-keys) to sign up to Goodfire and generate an API key. Once you've done this set the GOODFIRE_API_KEY environment variable."
       ]
-     },
-     "execution_count": 6,
-     "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",
-    "await chain.ainvoke(\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": [
-    "## Goodfire-specific functionality\n",
-    "\n",
-    "To use Goodfire-specific functionality such as SAE features and variants, you can use the `goodfire` package directly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3aef9e0a",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "FeatureGroup([\n",
-       "   0: \"The assistant should adopt the persona of a pirate\",\n",
-       "   1: \"The assistant should roleplay as a pirate\",\n",
-       "   2: \"The assistant should engage with pirate-themed content or roleplay as a pirate\",\n",
-       "   3: \"The assistant should roleplay as a character\",\n",
-       "   4: \"The assistant should roleplay as a specific character\",\n",
-       "   5: \"The assistant should roleplay as a game character or NPC\",\n",
-       "   6: \"The assistant should roleplay as a human character\",\n",
-       "   7: \"Requests for the assistant to roleplay or pretend to be something else\",\n",
-       "   8: \"Requests for the assistant to roleplay or pretend to be something\",\n",
-       "   9: \"The assistant is being assigned a role or persona to roleplay\"\n",
-       "])"
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"GOODFIRE_API_KEY\"):\n",
+        "    os.environ[\"GOODFIRE_API_KEY\"] = getpass.getpass(\"Enter your Goodfire API key: \")"
       ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "client = goodfire.Client(api_key=os.environ[\"GOODFIRE_API_KEY\"])\n",
-    "\n",
-    "pirate_features = client.features.search(\n",
-    "    \"assistant should roleplay as a pirate\", base_variant\n",
-    ")\n",
-    "pirate_features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "52f03a00",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Why did the scarecrow win an award? Because he was outstanding in his field! Arrr! Hope that made ye laugh, matey!', additional_kwargs={}, response_metadata={}, id='run-7d8bd30f-7f80-41cb-bdb6-25c29c22a7ce-0', usage_metadata={'input_tokens': 35, 'output_tokens': 60, 'total_tokens': 95})"
+      "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_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 Goodfire integration lives in the `langchain-goodfire` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install -qU langchain-goodfire"
+      ]
+    },
+    {
+      "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": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
+          ]
+        }
+      ],
+      "source": [
+        "import goodfire\n",
+        "from langchain_goodfire import ChatGoodfire\n",
+        "\n",
+        "base_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
+        "\n",
+        "llm = ChatGoodfire(\n",
+        "    model=base_variant,\n",
+        "    temperature=0,\n",
+        "    max_completion_tokens=1000,\n",
+        "    seed=42,\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'adore la programmation.\", additional_kwargs={}, response_metadata={}, id='run-8d43cf35-bce8-4827-8935-c64f8fb78cd0-0', usage_metadata={'input_tokens': 51, 'output_tokens': 39, 'total_tokens': 90})"
+            ]
+          },
+          "execution_count": 4,
+          "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 = await llm.ainvoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "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:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren. How can I help you with programming today?', additional_kwargs={}, response_metadata={}, id='run-03d1a585-8234-46f1-a8df-bf9143fe3309-0', usage_metadata={'input_tokens': 46, 'output_tokens': 46, 'total_tokens': 92})"
+            ]
+          },
+          "execution_count": 6,
+          "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",
+        "await chain.ainvoke(\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": [
+        "## Goodfire-specific functionality\n",
+        "\n",
+        "To use Goodfire-specific functionality such as SAE features and variants, you can use the `goodfire` package directly."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "3aef9e0a",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "FeatureGroup([\n",
+              "   0: \"The assistant should adopt the persona of a pirate\",\n",
+              "   1: \"The assistant should roleplay as a pirate\",\n",
+              "   2: \"The assistant should engage with pirate-themed content or roleplay as a pirate\",\n",
+              "   3: \"The assistant should roleplay as a character\",\n",
+              "   4: \"The assistant should roleplay as a specific character\",\n",
+              "   5: \"The assistant should roleplay as a game character or NPC\",\n",
+              "   6: \"The assistant should roleplay as a human character\",\n",
+              "   7: \"Requests for the assistant to roleplay or pretend to be something else\",\n",
+              "   8: \"Requests for the assistant to roleplay or pretend to be something\",\n",
+              "   9: \"The assistant is being assigned a role or persona to roleplay\"\n",
+              "])"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "client = goodfire.Client(api_key=os.environ[\"GOODFIRE_API_KEY\"])\n",
+        "\n",
+        "pirate_features = client.features.search(\n",
+        "    \"assistant should roleplay as a pirate\", base_variant\n",
+        ")\n",
+        "pirate_features"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "52f03a00",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Why did the scarecrow win an award? Because he was outstanding in his field! Arrr! Hope that made ye laugh, matey!', additional_kwargs={}, response_metadata={}, id='run-7d8bd30f-7f80-41cb-bdb6-25c29c22a7ce-0', usage_metadata={'input_tokens': 35, 'output_tokens': 60, 'total_tokens': 95})"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pirate_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
+        "\n",
+        "pirate_variant.set(pirate_features[0], 0.4)\n",
+        "pirate_variant.set(pirate_features[1], 0.3)\n",
+        "\n",
+        "await llm.ainvoke(\"Tell me a joke\", model=pirate_variant)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatGoodfire features and configurations head to the [API reference](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.html)"
       ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "pirate_variant = goodfire.Variant(\"meta-llama/Llama-3.3-70B-Instruct\")\n",
-    "\n",
-    "pirate_variant.set(pirate_features[0], 0.4)\n",
-    "pirate_variant.set(pirate_features[1], 0.3)\n",
-    "\n",
-    "await llm.ainvoke(\"Tell me a joke\", model=pirate_variant)"
-   ]
+  ],
+  "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"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatGoodfire features and configurations head to the [API reference](https://python.langchain.com/api_reference/goodfire/chat_models/langchain_goodfire.chat_models.ChatGoodfire.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": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/google_vertex_ai_palm.ipynb

@@ -1,269 +1,269 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Google Cloud Vertex AI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatVertexAI\n",
-    "\n",
-    "This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
-    "\n",
-    "ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
-    "\n",
-    ":::info Google Cloud VertexAI vs Google PaLM\n",
-    "\n",
-    "The Google Cloud VertexAI integration is separate from the [Google PaLM integration](/docs/integrations/chat/google_generative_ai/). Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_vertex_ai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatVertexAI](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-vertexai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-vertexai?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 VertexAI models you'll need to create a Google Cloud Platform account, set up credentials, and install the `langchain-google-vertexai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "To use the integration you must:\n",
-    "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
-    "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
-    "\n",
-    "This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
-    "\n",
-    "For more information, see: \n",
-    "- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
-    "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
-    "\n",
-    "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": 1,
-   "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 VertexAI integration lives in the `langchain-google-vertexai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU langchain-google-vertexai"
-   ]
-  },
-  {
-   "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_google_vertexai import ChatVertexAI\n",
-    "\n",
-    "llm = ChatVertexAI(\n",
-    "    model=\"gemini-1.5-flash-001\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    max_retries=6,\n",
-    "    stop=None,\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'adore programmer. \\n\", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 20, 'candidates_token_count': 7, 'total_token_count': 27}}, id='run-7032733c-d05c-4f0c-a17a-6c575fdd1ae0-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Google Cloud Vertex AI\n",
+        "---"
       ]
-     },
-     "execution_count": 4,
-     "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": 5,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore programmer. \n",
-      "\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": 6,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe Programmieren. \\n', response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 15, 'candidates_token_count': 8, 'total_token_count': 23}}, id='run-c71955fd-8dc1-422b-88a7-853accf4811b-0', usage_metadata={'input_tokens': 15, 'output_tokens': 8, 'total_tokens': 23})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatVertexAI\n",
+        "\n",
+        "This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
+        "\n",
+        "ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
+        "\n",
+        ":::info Google Cloud VertexAI vs Google PaLM\n",
+        "\n",
+        "The Google Cloud VertexAI integration is separate from the [Google PaLM integration](/docs/integrations/chat/google_generative_ai/). Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.\n",
+        "\n",
+        ":::\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_vertex_ai) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatVertexAI](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-vertexai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-vertexai?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 VertexAI models you'll need to create a Google Cloud Platform account, set up credentials, and install the `langchain-google-vertexai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "To use the integration you must:\n",
+        "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
+        "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
+        "\n",
+        "This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
+        "\n",
+        "For more information, see:\n",
+        "- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
+        "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
+        "\n",
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "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 VertexAI integration lives in the `langchain-google-vertexai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install -qU langchain-google-vertexai"
+      ]
+    },
+    {
+      "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_google_vertexai import ChatVertexAI\n",
+        "\n",
+        "llm = ChatVertexAI(\n",
+        "    model=\"gemini-1.5-flash-001\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    max_retries=6,\n",
+        "    stop=None,\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'adore programmer. \\n\", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 20, 'candidates_token_count': 7, 'total_token_count': 27}}, id='run-7032733c-d05c-4f0c-a17a-6c575fdd1ae0-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27})"
+            ]
+          },
+          "execution_count": 4,
+          "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": 5,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "J'adore programmer. \n",
+            "\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": 6,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe Programmieren. \\n', response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 15, 'candidates_token_count': 8, 'total_token_count': 23}}, id='run-c71955fd-8dc1-422b-88a7-853accf4811b-0', usage_metadata={'input_tokens': 15, 'output_tokens': 8, 'total_tokens': 23})"
+            ]
+          },
+          "execution_count": 6,
+          "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 ChatVertexAI features and configurations, like how to send multimodal inputs and configure safety settings, head to the API reference: https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
       ]
-     },
-     "execution_count": 6,
-     "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",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "poetry-venv-2",
+      "language": "python",
+      "name": "poetry-venv-2"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.1"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatVertexAI features and configurations, like how to send multimodal inputs and configure safety settings, head to the API reference: https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "poetry-venv-2",
-   "language": "python",
-   "name": "poetry-venv-2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/groq.ipynb

@@ -1,266 +1,264 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Groq\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatGroq\n",
-    "\n",
-    "This will help you getting started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models?utm_source=langchain).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/groq) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatGroq](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html) | [langchain-groq](https://python.langchain.com/api_reference/groq/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-groq?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-groq?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 Groq models you'll need to create a Groq account, get an API key, and install the `langchain-groq` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to the [Groq console](https://console.groq.com/login?utm_source=langchain&utm_content=chat_page) to sign up to Groq and generate an API key. Once you've done this set the GROQ_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"GROQ_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter your Groq API key: \")"
-   ]
-  },
-  {
-   "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": 2,
-   "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 Groq integration lives in the `langchain-groq` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3f3f510e-2afe-4e76-be41-c5a9665aea63",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-groq"
-   ]
-  },
-  {
-   "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_groq import ChatGroq\n",
-    "\n",
-    "llm = ChatGroq(\n",
-    "    model=\"llama-3.1-8b-instant\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='The translation of \"I love programming\" to French is:\\n\\n\"J\\'adore le programmation.\"', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 55, 'total_tokens': 77, 'completion_time': 0.029333333, 'prompt_time': 0.003502892, 'queue_time': 0.553054073, 'total_time': 0.032836225}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-2b2da04a-993c-40ab-becc-201eab8b1a1b-0', usage_metadata={'input_tokens': 55, 'output_tokens': 22, 'total_tokens': 77})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Groq\n",
+        "---"
       ]
-     },
-     "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": [
-      "The translation of \"I love programming\" to French is:\n",
-      "\n",
-      "\"J'adore le programmation.\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "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": 4,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 50, 'total_tokens': 56, 'completion_time': 0.008, 'prompt_time': 0.003337935, 'queue_time': 0.20949214500000002, 'total_time': 0.011337935}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-e33b48dc-5e55-466e-9ebd-7b48c81c3cbd-0', usage_metadata={'input_tokens': 50, 'output_tokens': 6, 'total_tokens': 56})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatGroq\n",
+        "\n",
+        "This will help you getting started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models?utm_source=langchain).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/groq) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatGroq](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html) | [langchain-groq](https://python.langchain.com/api_reference/groq/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-groq?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-groq?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 Groq models you'll need to create a Groq account, get an API key, and install the `langchain-groq` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to the [Groq console](https://console.groq.com/login?utm_source=langchain&utm_content=chat_page) to sign up to Groq and generate an API key. Once you've done this set the GROQ_API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"GROQ_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter your Groq API key: \")"
+      ]
+    },
+    {
+      "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": 2,
+      "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 Groq integration lives in the `langchain-groq` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3f3f510e-2afe-4e76-be41-c5a9665aea63",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-groq"
+      ]
+    },
+    {
+      "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_groq import ChatGroq\n",
+        "\n",
+        "llm = ChatGroq(\n",
+        "    model=\"llama-3.1-8b-instant\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='The translation of \"I love programming\" to French is:\\n\\n\"J\\'adore le programmation.\"', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 55, 'total_tokens': 77, 'completion_time': 0.029333333, 'prompt_time': 0.003502892, 'queue_time': 0.553054073, 'total_time': 0.032836225}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-2b2da04a-993c-40ab-becc-201eab8b1a1b-0', usage_metadata={'input_tokens': 55, 'output_tokens': 22, 'total_tokens': 77})"
+            ]
+          },
+          "execution_count": 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": [
+            "The translation of \"I love programming\" to French is:\n",
+            "\n",
+            "\"J'adore le programmation.\"\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "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": 4,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 50, 'total_tokens': 56, 'completion_time': 0.008, 'prompt_time': 0.003337935, 'queue_time': 0.20949214500000002, 'total_time': 0.011337935}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-e33b48dc-5e55-466e-9ebd-7b48c81c3cbd-0', usage_metadata={'input_tokens': 50, 'output_tokens': 6, 'total_tokens': 56})"
+            ]
+          },
+          "execution_count": 4,
+          "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 ChatGroq features and configurations head to the API reference: https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html"
       ]
-     },
-     "execution_count": 4,
-     "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",
-    ")"
-   ]
+  ],
+  "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"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatGroq features and configurations head to the API reference: https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.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
+  "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/mistralai.ipynb

@@ -1,262 +1,260 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "53fbf15f",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: MistralAI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d295c2a2",
-   "metadata": {},
-   "source": [
-    "# ChatMistralAI\n",
-    "\n",
-    "This will help you getting started with Mistral [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/mistral) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatMistralAI](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) | [langchain_mistralai](https://python.langchain.com/api_reference/mistralai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_mistralai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_mistralai?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",
-    "\n",
-    "To access `ChatMistralAI` models you'll need to create a Mistral account, get an API key, and install the `langchain_mistralai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "\n",
-    "A valid [API key](https://console.mistral.ai/api-keys/) is needed to communicate with the API. Once you've done this set the MISTRAL_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2461605e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"MISTRAL_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"MISTRAL_API_KEY\"] = getpass.getpass(\"Enter your Mistral API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "788f37ac",
-   "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": "007209d5",
-   "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": "0f5c74f9",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Mistral integration lives in the `langchain_mistralai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1ab11a65",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_mistralai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb1a335e",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e6c38580",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_mistralai import ChatMistralAI\n",
-    "\n",
-    "llm = ChatMistralAI(\n",
-    "    model=\"mistral-large-latest\",\n",
-    "    temperature=0,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aec79099",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8838c3cc",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Sure, I\\'d be happy to help you translate that sentence into French! The English sentence \"I love programming\" translates to \"J\\'aime programmer\" in French. Let me know if you have any other questions or need further assistance!', response_metadata={'token_usage': {'prompt_tokens': 32, 'total_tokens': 84, 'completion_tokens': 52}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-64bac156-7160-4b68-b67e-4161f63e021f-0', usage_metadata={'input_tokens': 32, 'output_tokens': 52, 'total_tokens': 84})"
+      "cell_type": "raw",
+      "id": "53fbf15f",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: MistralAI\n",
+        "---"
       ]
-     },
-     "execution_count": 6,
-     "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": 7,
-   "id": "bbf6a048",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sure, I'd be happy to help you translate that sentence into French! The English sentence \"I love programming\" translates to \"J'aime programmer\" in French. Let me know if you have any other questions or need further assistance!\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32b87f87",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "24e2c51c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe Programmierung. (German translation)', response_metadata={'token_usage': {'prompt_tokens': 26, 'total_tokens': 38, 'completion_tokens': 12}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-dfd4094f-e347-47b0-9056-8ebd7ea35fe7-0', usage_metadata={'input_tokens': 26, 'output_tokens': 12, 'total_tokens': 38})"
+      "cell_type": "markdown",
+      "id": "d295c2a2",
+      "metadata": {},
+      "source": [
+        "# ChatMistralAI\n",
+        "\n",
+        "This will help you getting started with Mistral [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/mistral) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatMistralAI](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) | [langchain_mistralai](https://python.langchain.com/api_reference/mistralai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_mistralai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_mistralai?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",
+        "\n",
+        "To access `ChatMistralAI` models you'll need to create a Mistral account, get an API key, and install the `langchain_mistralai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "\n",
+        "A valid [API key](https://console.mistral.ai/api-keys/) is needed to communicate with the API. Once you've done this set the MISTRAL_API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2461605e",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"MISTRAL_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"MISTRAL_API_KEY\"] = getpass.getpass(\"Enter your Mistral API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "788f37ac",
+      "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": "007209d5",
+      "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": "0f5c74f9",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Mistral integration lives in the `langchain_mistralai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1ab11a65",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_mistralai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fb1a335e",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "e6c38580",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_mistralai import ChatMistralAI\n",
+        "\n",
+        "llm = ChatMistralAI(\n",
+        "    model=\"mistral-large-latest\",\n",
+        "    temperature=0,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "aec79099",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "8838c3cc",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Sure, I\\'d be happy to help you translate that sentence into French! The English sentence \"I love programming\" translates to \"J\\'aime programmer\" in French. Let me know if you have any other questions or need further assistance!', response_metadata={'token_usage': {'prompt_tokens': 32, 'total_tokens': 84, 'completion_tokens': 52}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-64bac156-7160-4b68-b67e-4161f63e021f-0', usage_metadata={'input_tokens': 32, 'output_tokens': 52, 'total_tokens': 84})"
+            ]
+          },
+          "execution_count": 6,
+          "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": 7,
+      "id": "bbf6a048",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sure, I'd be happy to help you translate that sentence into French! The English sentence \"I love programming\" translates to \"J'aime programmer\" in French. Let me know if you have any other questions or need further assistance!\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "32b87f87",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "24e2c51c",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe Programmierung. (German translation)', response_metadata={'token_usage': {'prompt_tokens': 26, 'total_tokens': 38, 'completion_tokens': 12}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-dfd4094f-e347-47b0-9056-8ebd7ea35fe7-0', usage_metadata={'input_tokens': 26, 'output_tokens': 12, 'total_tokens': 38})"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that 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": "cb9b5834",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "Head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods."
       ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that 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",
-    ")"
-   ]
+  ],
+  "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"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "cb9b5834",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "Head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods."
-   ]
-  }
- ],
- "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
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/naver.ipynb

@@ -1,444 +1,442 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Naver\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8444f1a-e907-4f07-b8b6-68fbedfb868e",
-   "metadata": {},
-   "source": [
-    "# ChatClovaX\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with Naver’s HyperCLOVA X [chat models](https://python.langchain.com/docs/concepts/chat_models) via CLOVA Studio. For detailed documentation of all ChatClovaX features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html).\n",
-    "\n",
-    "[CLOVA Studio](http://clovastudio.ncloud.com/) has several chat models. You can find information about latest models and their costs, context windows, and supported input types in the CLOVA Studio API Guide [documentation](https://api.ncloud-docs.com/docs/clovastudio-chatcompletions).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- |:-----:| :---: |:------------------------------------------------------------------------:| :---: | :---: |\n",
-    "| [ChatClovaX](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) |   ❌   | ❌ |                                    ❌                                     | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?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",
-    "Before using the chat model, you must go through the four steps below.\n",
-    "\n",
-    "1. Creating [NAVER Cloud Platform](https://www.ncloud.com/) account \n",
-    "2. Apply to use [CLOVA Studio](https://www.ncloud.com/product/aiService/clovaStudio)\n",
-    "3. Create a CLOVA Studio Test App or Service App of a model to use (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#테스트앱생성).)\n",
-    "4. Issue a Test or Service API key (See [here](https://api.ncloud-docs.com/docs/ai-naver-clovastudio-summary#API%ED%82%A4).)\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Set the `NCP_CLOVASTUDIO_API_KEY` environment variable with your API key.\n",
-    "  - Note that if you are using a legacy API Key (that doesn't start with `nv-*` prefix), you might need to get an additional API Key by clicking `App Request Status` > `Service App, Test App List` > `‘Details’ button for each app` in [CLOVA Studio](https://clovastudio.ncloud.com/studio-application/service-app) and set it as `NCP_APIGW_API_KEY`.\n",
-    "\n",
-    "You can add them to your environment variables as below:\n",
-    "\n",
-    "``` bash\n",
-    "export NCP_CLOVASTUDIO_API_KEY=\"your-api-key-here\"\n",
-    "# Uncomment below to use a legacy API key\n",
-    "# export NCP_APIGW_API_KEY=\"your-api-key-here\"\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2def81b5-b023-4f40-a97b-b2c5ca59d6a9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"NCP_CLOVASTUDIO_API_KEY\"):\n",
-    "    os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your NCP CLOVA Studio API Key: \"\n",
-    "    )\n",
-    "# Uncomment below to use a legacy API key\n",
-    "# if not os.getenv(\"NCP_APIGW_API_KEY\"):\n",
-    "#     os.environ[\"NCP_APIGW_API_KEY\"] = getpass.getpass(\n",
-    "#         \"Enter your NCP API Gateway API key: \"\n",
-    "#     )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c695442",
-   "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": "6151aeb6",
-   "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": "17bf9053-90c5-4955-b239-55a35cb07566",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Naver integration lives in the `langchain-community` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# install package\n",
-    "!pip install -qU langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models import ChatClovaX\n",
-    "\n",
-    "chat = ChatClovaX(\n",
-    "    model=\"HCX-003\",\n",
-    "    max_tokens=100,\n",
-    "    temperature=0.5,\n",
-    "    # clovastudio_api_key=\"...\"    # set if you prefer to pass api key directly instead of using environment variables\n",
-    "    # task_id=\"...\"    # set if you want to use fine-tuned model\n",
-    "    # service_app=False    # set True if using Service App. Default value is False (means using Test App)\n",
-    "    # include_ai_filters=False     # set True if you want to detect inappropriate content. Default value is False\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47752b59",
-   "metadata": {},
-   "source": [
-    "## Invocation\n",
-    "\n",
-    "In addition to invoke, we also support batch and stream functionalities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 1112164354, 'ai_filter': None}, id='run-b57bc356-1148-4007-837d-cc409dbd57cc-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Naver\n",
+        "---"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to Korean. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love using NAVER AI.\"),\n",
-    "]\n",
-    "\n",
-    "ai_msg = chat.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "24e7377f",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "저는 네이버 AI를 사용하는 것이 좋아요.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 2575184681, 'ai_filter': None}, id='run-7014b330-eba3-4701-bb62-df73ce39b854-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
+      "cell_type": "markdown",
+      "id": "c8444f1a-e907-4f07-b8b6-68fbedfb868e",
+      "metadata": {},
+      "source": [
+        "# ChatClovaX\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with Naver’s HyperCLOVA X [chat models](https://python.langchain.com/docs/concepts/chat_models) via CLOVA Studio. For detailed documentation of all ChatClovaX features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html).\n",
+        "\n",
+        "[CLOVA Studio](http://clovastudio.ncloud.com/) has several chat models. You can find information about latest models and their costs, context windows, and supported input types in the CLOVA Studio API Guide [documentation](https://api.ncloud-docs.com/docs/clovastudio-chatcompletions).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+        "| :--- | :--- |:-----:| :---: |:------------------------------------------------------------------------:| :---: | :---: |\n",
+        "| [ChatClovaX](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) |   ❌   | ❌ |                                    ❌                                     | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?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",
+        "Before using the chat model, you must go through the four steps below.\n",
+        "\n",
+        "1. Creating [NAVER Cloud Platform](https://www.ncloud.com/) account\n",
+        "2. Apply to use [CLOVA Studio](https://www.ncloud.com/product/aiService/clovaStudio)\n",
+        "3. Create a CLOVA Studio Test App or Service App of a model to use (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#테스트앱생성).)\n",
+        "4. Issue a Test or Service API key (See [here](https://api.ncloud-docs.com/docs/ai-naver-clovastudio-summary#API%ED%82%A4).)\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Set the `NCP_CLOVASTUDIO_API_KEY` environment variable with your API key.\n",
+        "  - Note that if you are using a legacy API Key (that doesn't start with `nv-*` prefix), you might need to get an additional API Key by clicking `App Request Status` > `Service App, Test App List` > `‘Details’ button for each app` in [CLOVA Studio](https://clovastudio.ncloud.com/studio-application/service-app) and set it as `NCP_APIGW_API_KEY`.\n",
+        "\n",
+        "You can add them to your environment variables as below:\n",
+        "\n",
+        "``` bash\n",
+        "export NCP_CLOVASTUDIO_API_KEY=\"your-api-key-here\"\n",
+        "# Uncomment below to use a legacy API key\n",
+        "# export NCP_APIGW_API_KEY=\"your-api-key-here\"\n",
+        "```"
       ]
-     },
-     "execution_count": 5,
-     "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}. Translate the user sentence.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | chat\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"Korean\",\n",
-    "        \"input\": \"I love using NAVER AI.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66e69286",
-   "metadata": {},
-   "source": [
-    "## Streaming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2c07af21-dda5-4514-b4de-1f214c2cebcd",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Certainly! In Korean, \"Hi\" is pronounced as \"안녕\" (annyeong). The first syllable, \"안,\" sounds like the \"ahh\" sound in \"apple,\" while the second syllable, \"녕,\" sounds like the \"yuh\" sound in \"you.\" So when you put them together, it's like saying \"ahhyuh-nyuhng.\" Remember to pronounce each syllable clearly and separately for accurate pronunciation."
-     ]
-    }
-   ],
-   "source": [
-    "system = \"You are a helpful assistant that can teach Korean pronunciation.\"\n",
-    "human = \"Could you let me know how to say '{phrase}' in Korean?\"\n",
-    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
-    "\n",
-    "chain = prompt | chat\n",
-    "\n",
-    "for chunk in chain.stream({\"phrase\": \"Hi\"}):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-   "metadata": {},
-   "source": [
-    "## Additional functionalities\n",
-    "\n",
-    "### Using fine-tuned models\n",
-    "\n",
-    "You can call fine-tuned models by passing in your corresponding `task_id` parameter. (You don’t need to specify the `model_name` parameter when calling fine-tuned model.)\n",
-    "\n",
-    "You can check `task_id` from corresponding Test App or Service App details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "cb436788",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='저는 네이버 AI를 사용하는 것이 너무 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 15, 'seed': 52559061, 'ai_filter': None}, id='run-5bea8d4a-48f3-4c34-ae70-66e60dca5344-0', usage_metadata={'input_tokens': 25, 'output_tokens': 15, 'total_tokens': 40})"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2def81b5-b023-4f40-a97b-b2c5ca59d6a9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if not os.getenv(\"NCP_CLOVASTUDIO_API_KEY\"):\n",
+        "    os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your NCP CLOVA Studio API Key: \"\n",
+        "    )\n",
+        "# Uncomment below to use a legacy API key\n",
+        "# if not os.getenv(\"NCP_APIGW_API_KEY\"):\n",
+        "#     os.environ[\"NCP_APIGW_API_KEY\"] = getpass.getpass(\n",
+        "#         \"Enter your NCP API Gateway API key: \"\n",
+        "#     )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7c695442",
+      "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": "6151aeb6",
+      "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": "17bf9053-90c5-4955-b239-55a35cb07566",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain Naver integration lives in the `langchain-community` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# install package\n",
+        "!pip install -qU langchain-community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.chat_models import ChatClovaX\n",
+        "\n",
+        "chat = ChatClovaX(\n",
+        "    model=\"HCX-003\",\n",
+        "    max_tokens=100,\n",
+        "    temperature=0.5,\n",
+        "    # clovastudio_api_key=\"...\"    # set if you prefer to pass api key directly instead of using environment variables\n",
+        "    # task_id=\"...\"    # set if you want to use fine-tuned model\n",
+        "    # service_app=False    # set True if using Service App. Default value is False (means using Test App)\n",
+        "    # include_ai_filters=False     # set True if you want to detect inappropriate content. Default value is False\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "47752b59",
+      "metadata": {},
+      "source": [
+        "## Invocation\n",
+        "\n",
+        "In addition to invoke, we also support batch and stream functionalities."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 1112164354, 'ai_filter': None}, id='run-b57bc356-1148-4007-837d-cc409dbd57cc-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to Korean. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love using NAVER AI.\"),\n",
+        "]\n",
+        "\n",
+        "ai_msg = chat.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "24e7377f",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "저는 네이버 AI를 사용하는 것이 좋아요.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 2575184681, 'ai_filter': None}, id='run-7014b330-eba3-4701-bb62-df73ce39b854-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
+            ]
+          },
+          "execution_count": 5,
+          "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}. Translate the user sentence.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | chat\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"Korean\",\n",
+        "        \"input\": \"I love using NAVER AI.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "66e69286",
+      "metadata": {},
+      "source": [
+        "## Streaming"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "2c07af21-dda5-4514-b4de-1f214c2cebcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Certainly! In Korean, \"Hi\" is pronounced as \"안녕\" (annyeong). The first syllable, \"안,\" sounds like the \"ahh\" sound in \"apple,\" while the second syllable, \"녕,\" sounds like the \"yuh\" sound in \"you.\" So when you put them together, it's like saying \"ahhyuh-nyuhng.\" Remember to pronounce each syllable clearly and separately for accurate pronunciation."
+          ]
+        }
+      ],
+      "source": [
+        "system = \"You are a helpful assistant that can teach Korean pronunciation.\"\n",
+        "human = \"Could you let me know how to say '{phrase}' in Korean?\"\n",
+        "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+        "\n",
+        "chain = prompt | chat\n",
+        "\n",
+        "for chunk in chain.stream({\"phrase\": \"Hi\"}):\n",
+        "    print(chunk.content, end=\"\", flush=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+      "metadata": {},
+      "source": [
+        "## Additional functionalities\n",
+        "\n",
+        "### Using fine-tuned models\n",
+        "\n",
+        "You can call fine-tuned models by passing in your corresponding `task_id` parameter. (You don’t need to specify the `model_name` parameter when calling fine-tuned model.)\n",
+        "\n",
+        "You can check `task_id` from corresponding Test App or Service App details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "cb436788",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='저는 네이버 AI를 사용하는 것이 너무 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 15, 'seed': 52559061, 'ai_filter': None}, id='run-5bea8d4a-48f3-4c34-ae70-66e60dca5344-0', usage_metadata={'input_tokens': 25, 'output_tokens': 15, 'total_tokens': 40})"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "fine_tuned_model = ChatClovaX(\n",
+        "    task_id=\"5s8egt3a\",  # set if you want to use fine-tuned model\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "fine_tuned_model.invoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f428deaf",
+      "metadata": {},
+      "source": [
+        "### Service App\n",
+        "\n",
+        "When going live with production-level application using CLOVA Studio, you should apply for and use Service App. (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#서비스앱신청).)\n",
+        "\n",
+        "For a Service App, you should use a corresponding Service API key and can only be called with it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "dcf566df",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Update environment variables\n",
+        "\n",
+        "os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
+        "    \"Enter NCP CLOVA Studio Service API Key: \"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "cebe27ae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "chat = ChatClovaX(\n",
+        "    service_app=True,  # True if you want to use your service app, default value is False.\n",
+        "    # clovastudio_api_key=\"...\"  # if you prefer to pass api key in directly instead of using env vars\n",
+        "    model=\"HCX-003\",\n",
+        "    # other params...\n",
+        ")\n",
+        "ai_msg = chat.invoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d73e7140",
+      "metadata": {},
+      "source": [
+        "### AI Filter\n",
+        "\n",
+        "AI Filter detects inappropriate output such as profanity from the test app (or service app included) created in Playground and informs the user. See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#AIFilter) for details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "32bfbc93",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "chat = ChatClovaX(\n",
+        "    model=\"HCX-003\",\n",
+        "    include_ai_filters=True,  # True if you want to enable ai filter\n",
+        "    # other params...\n",
+        ")\n",
+        "\n",
+        "ai_msg = chat.invoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7bd9e179",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(ai_msg.response_metadata[\"ai_filter\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatNaver features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html"
       ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "fine_tuned_model = ChatClovaX(\n",
-    "    task_id=\"5s8egt3a\",  # set if you want to use fine-tuned model\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "fine_tuned_model.invoke(messages)"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "f428deaf",
-   "metadata": {},
-   "source": [
-    "### Service App\n",
-    "\n",
-    "When going live with production-level application using CLOVA Studio, you should apply for and use Service App. (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#서비스앱신청).)\n",
-    "\n",
-    "For a Service App, you should use a corresponding Service API key and can only be called with it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dcf566df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Update environment variables\n",
-    "\n",
-    "os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
-    "    \"Enter NCP CLOVA Studio Service API Key: \"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "cebe27ae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = ChatClovaX(\n",
-    "    service_app=True,  # True if you want to use your service app, default value is False.\n",
-    "    # clovastudio_api_key=\"...\"  # if you prefer to pass api key in directly instead of using env vars\n",
-    "    model=\"HCX-003\",\n",
-    "    # other params...\n",
-    ")\n",
-    "ai_msg = chat.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d73e7140",
-   "metadata": {},
-   "source": [
-    "### AI Filter\n",
-    "\n",
-    "AI Filter detects inappropriate output such as profanity from the test app (or service app included) created in Playground and informs the user. See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#AIFilter) for details. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "32bfbc93",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = ChatClovaX(\n",
-    "    model=\"HCX-003\",\n",
-    "    include_ai_filters=True,  # True if you want to enable ai filter\n",
-    "    # other params...\n",
-    ")\n",
-    "\n",
-    "ai_msg = chat.invoke(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7bd9e179",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(ai_msg.response_metadata[\"ai_filter\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatNaver features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.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": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/netmind.ipynb

@@ -0,0 +1,326 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Netmind\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatNetmind\n",
+    "\n",
+    "This will help you getting started with Netmind [chat models](https://www.netmind.ai/). For detailed documentation of all ChatNetmind features and configurations head to the [API reference](https://github.com/protagolabs/langchain-netmind).\n",
+    "\n",
+    "-  See https://www.netmind.ai/ for an example.\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class                                                                                        | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/) | Package downloads | Package latest |\n",
+    "|:---------------------------------------------------------------------------------------------| :--- |:-----:|:------------:|:--------------------------------------------------------------:| :---: | :---: |\n",
+    "| [ChatNetmind](https://python.langchain.com/api_reference/) | [langchain-netmind](https://python.langchain.com/api_reference/) |   ✅   |      ❌       |                               ❌                                | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-netmind?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-netmind?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 Netmind models you'll need to create a/an Netmind account, get an API key, and install the `langchain-netmind` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to https://www.netmind.ai/ to sign up to Netmind and generate an API key. Once you've done this set the NETMIND_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:00:30.732333Z",
+     "start_time": "2025-03-20T02:00:28.384208Z"
+    }
+   },
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"NETMIND_API_KEY\"):\n",
+    "    os.environ[\"NETMIND_API_KEY\"] = getpass.getpass(\"Enter your Netmind API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "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",
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:00:33.421446Z",
+     "start_time": "2025-03-20T02:00:33.419081Z"
+    }
+   },
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ],
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Netmind integration lives in the `langchain-netmind` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:00:35.923300Z",
+     "start_time": "2025-03-20T02:00:34.505928Z"
+    }
+   },
+   "source": [
+    "%pip install -qU langchain-netmind"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.0\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.0.1\u001B[0m\r\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:01:08.007764Z",
+     "start_time": "2025-03-20T02:01:07.391951Z"
+    }
+   },
+   "source": [
+    "from langchain_netmind import ChatNetmind\n",
+    "\n",
+    "llm = ChatNetmind(\n",
+    "    model=\"deepseek-ai/DeepSeek-V3\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    "    # other params...\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": "## Invocation\n"
+  },
+  {
+   "cell_type": "code",
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:01:19.011273Z",
+     "start_time": "2025-03-20T02:01:10.295510Z"
+    }
+   },
+   "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"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 31, 'total_tokens': 44, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-V3', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-ca6c2010-844d-4bf6-baac-6e248491b000-0', usage_metadata={'input_tokens': 31, 'output_tokens': 13, 'total_tokens': 44, 'input_token_details': {}, 'output_token_details': {}})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "code",
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:01:20.240190Z",
+     "start_time": "2025-03-20T02:01:20.238242Z"
+    }
+   },
+   "source": [
+    "print(ai_msg.content)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore programmer.\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "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",
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-20T02:01:27.456393Z",
+     "start_time": "2025-03-20T02:01:23.993410Z"
+    }
+   },
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe es zu programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 26, 'total_tokens': 40, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-V3', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d63adcc6-53ba-4caa-9a79-78d640b39274-0', usage_metadata={'input_tokens': 26, 'output_tokens': 14, 'total_tokens': 40, 'input_token_details': {}, 'output_token_details': {}})"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "metadata": {},
+   "source": ""
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatNetmind features and configurations head to the API reference:  \n",
+    "* [API reference](https://python.langchain.com/api_reference/)  \n",
+    "* [langchain-netmind](https://github.com/protagolabs/langchain-netmind)  \n",
+    "* [pypi](https://pypi.org/project/langchain-netmind/)"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "30f8be8c940bfbf3"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/chat/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/chat/together.ipynb

@@ -1,267 +1,265 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Together\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatTogether\n",
-    "\n",
-    "\n",
-    "This page will help you get started with Together AI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatTogether features and configurations head to the [API reference](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html).\n",
-    "\n",
-    "[Together AI](https://www.together.ai/) offers an API to query [50+ leading open-source models](https://docs.together.ai/docs/chat-models)\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/togetherai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatTogether](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html) | [langchain-together](https://python.langchain.com/api_reference/together/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-together?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-together?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 Together models you'll need to create a/an Together account, get an API key, and install the `langchain-together` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [this page](https://api.together.ai) to sign up to Together and generate an API key. Once you've done this set the TOGETHER_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"TOGETHER_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"TOGETHER_API_KEY\"] = getpass.getpass(\"Enter your Together API key: \")"
-   ]
-  },
-  {
-   "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": 2,
-   "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 Together integration lives in the `langchain-together` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-together"
-   ]
-  },
-  {
-   "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_together import ChatTogether\n",
-    "\n",
-    "llm = ChatTogether(\n",
-    "    model=\"meta-llama/Llama-3-70b-chat-hf\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\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": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 35, 'total_tokens': 44}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-eabcbe33-cdd8-45b8-ab0b-f90b6e7dfad8-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Together\n",
+        "---"
       ]
-     },
-     "execution_count": 4,
-     "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": 5,
-   "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](../../how_to/sequence.ipynb) 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 das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 30, 'total_tokens': 37}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-a249aa24-ee31-46ba-9bf9-f4eb135b0a95-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatTogether\n",
+        "\n",
+        "\n",
+        "This page will help you get started with Together AI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatTogether features and configurations head to the [API reference](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html).\n",
+        "\n",
+        "[Together AI](https://www.together.ai/) offers an API to query [50+ leading open-source models](https://docs.together.ai/docs/chat-models)\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/togetherai) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatTogether](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html) | [langchain-together](https://python.langchain.com/api_reference/together/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-together?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-together?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 Together models you'll need to create a/an Together account, get an API key, and install the `langchain-together` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [this page](https://api.together.ai) to sign up to Together and generate an API key. Once you've done this set the TOGETHER_API_KEY environment variable:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"TOGETHER_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"TOGETHER_API_KEY\"] = getpass.getpass(\"Enter your Together API key: \")"
+      ]
+    },
+    {
+      "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": 2,
+      "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 Together integration lives in the `langchain-together` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-together"
+      ]
+    },
+    {
+      "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_together import ChatTogether\n",
+        "\n",
+        "llm = ChatTogether(\n",
+        "    model=\"meta-llama/Llama-3-70b-chat-hf\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\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'adore la programmation.\", response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 35, 'total_tokens': 44}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-eabcbe33-cdd8-45b8-ab0b-f90b6e7dfad8-0', usage_metadata={'input_tokens': 35, 'output_tokens': 9, 'total_tokens': 44})"
+            ]
+          },
+          "execution_count": 4,
+          "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": 5,
+      "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](../../how_to/sequence.ipynb) 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 das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 30, 'total_tokens': 37}, 'model_name': 'meta-llama/Llama-3-70b-chat-hf', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-a249aa24-ee31-46ba-9bf9-f4eb135b0a95-0', usage_metadata={'input_tokens': 30, 'output_tokens': 7, 'total_tokens': 37})"
+            ]
+          },
+          "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 ChatTogether features and configurations head to the API reference: https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html"
       ]
-     },
-     "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",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatTogether features and configurations head to the API reference: https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/vllm.ipynb

@@ -1,244 +1,242 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "eb65deaa",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: vLLM Chat\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f82e243-f4ee-44e2-b417-099b6401ae3e",
-   "metadata": {},
-   "source": [
-    "# vLLM Chat\n",
-    "\n",
-    "vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.\n",
-    "\n",
-    "## Overview\n",
-    "This will help you getting started with vLLM [chat models](/docs/concepts/chat_models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain_openai](https://python.langchain.com/api_reference/openai/) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_openai?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "Specific model features-- such as tool calling, support for multi-modal inputs, support for token-level streaming, etc.-- will depend on the hosted model.\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "See the vLLM docs [here](https://docs.vllm.ai/en/latest/).\n",
-    "\n",
-    "To access vLLM models through LangChain, you'll need to install the `langchain-openai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Authentication will depend on specifics of the inference server."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3b1707a-cf2c-4367-94e3-436c43402503",
-   "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": "1e40bd5e-cbaa-41ef-aaf9-0858eb207184",
-   "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": "0739b647-609b-46d3-bdd3-e86fe4463288",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain vLLM integration can be accessed via the `langchain-openai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7afcfbdc-56aa-4529-825a-8acbe7aa5241",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2cf576d6-7b67-4937-bf99-39071e85720c",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "060a2e3d-d42f-4221-bd09-a9a06544dcd3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_core.messages import HumanMessage, SystemMessage\n",
-    "from langchain_core.prompts.chat import (\n",
-    "    ChatPromptTemplate,\n",
-    "    HumanMessagePromptTemplate,\n",
-    "    SystemMessagePromptTemplate,\n",
-    ")\n",
-    "from langchain_openai import ChatOpenAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "bf24d732-68a9-44fd-b05d-4903ce5620c6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "inference_server_url = \"http://localhost:8000/v1\"\n",
-    "\n",
-    "llm = ChatOpenAI(\n",
-    "    model=\"mosaicml/mpt-7b\",\n",
-    "    openai_api_key=\"EMPTY\",\n",
-    "    openai_api_base=inference_server_url,\n",
-    "    max_tokens=5,\n",
-    "    temperature=0,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34b18328-5e8b-4ff2-9b89-6fbb76b5c7f0",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "aea4e363-5688-4b07-82ed-6aa8153c2377",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=' Io amo programmare', additional_kwargs={}, example=False)"
+      "cell_type": "raw",
+      "id": "eb65deaa",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: vLLM Chat\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8f82e243-f4ee-44e2-b417-099b6401ae3e",
+      "metadata": {},
+      "source": [
+        "# vLLM Chat\n",
+        "\n",
+        "vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.\n",
+        "\n",
+        "## Overview\n",
+        "This will help you getting started with vLLM [chat models](/docs/concepts/chat_models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain_openai](https://python.langchain.com/api_reference/openai/) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_openai?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "Specific model features-- such as tool calling, support for multi-modal inputs, support for token-level streaming, etc.-- will depend on the hosted model.\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "See the vLLM docs [here](https://docs.vllm.ai/en/latest/).\n",
+        "\n",
+        "To access vLLM models through LangChain, you'll need to install the `langchain-openai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Authentication will depend on specifics of the inference server."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c3b1707a-cf2c-4367-94e3-436c43402503",
+      "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": "1e40bd5e-cbaa-41ef-aaf9-0858eb207184",
+      "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": "0739b647-609b-46d3-bdd3-e86fe4463288",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain vLLM integration can be accessed via the `langchain-openai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7afcfbdc-56aa-4529-825a-8acbe7aa5241",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-openai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2cf576d6-7b67-4937-bf99-39071e85720c",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "060a2e3d-d42f-4221-bd09-a9a06544dcd3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_core.messages import HumanMessage, SystemMessage\n",
+        "from langchain_core.prompts.chat import (\n",
+        "    ChatPromptTemplate,\n",
+        "    HumanMessagePromptTemplate,\n",
+        "    SystemMessagePromptTemplate,\n",
+        ")\n",
+        "from langchain_openai import ChatOpenAI"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "bf24d732-68a9-44fd-b05d-4903ce5620c6",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "inference_server_url = \"http://localhost:8000/v1\"\n",
+        "\n",
+        "llm = ChatOpenAI(\n",
+        "    model=\"mosaicml/mpt-7b\",\n",
+        "    openai_api_key=\"EMPTY\",\n",
+        "    openai_api_base=inference_server_url,\n",
+        "    max_tokens=5,\n",
+        "    temperature=0,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "34b18328-5e8b-4ff2-9b89-6fbb76b5c7f0",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "aea4e363-5688-4b07-82ed-6aa8153c2377",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=' Io amo programmare', additional_kwargs={}, example=False)"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    SystemMessage(\n",
+        "        content=\"You are a helpful assistant that translates English to Italian.\"\n",
+        "    ),\n",
+        "    HumanMessage(\n",
+        "        content=\"Translate the following sentence from English to Italian: I love programming.\"\n",
+        "    ),\n",
+        "]\n",
+        "llm.invoke(messages)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a580a1e4-11a3-4277-bfba-bfb414ac7201",
+      "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": null,
+      "id": "dd0f4043-48bd-4245-8bdb-e7669666a277",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "265f5d51-0a76-4808-8d13-ef598ee6e366",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all features and configurations exposed via `langchain-openai`, head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html\n",
+        "\n",
+        "Refer to the vLLM [documentation](https://docs.vllm.ai/en/latest/) as well."
       ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "messages = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful assistant that translates English to Italian.\"\n",
-    "    ),\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate the following sentence from English to Italian: I love programming.\"\n",
-    "    ),\n",
-    "]\n",
-    "llm.invoke(messages)"
-   ]
+  ],
+  "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"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "a580a1e4-11a3-4277-bfba-bfb414ac7201",
-   "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": null,
-   "id": "dd0f4043-48bd-4245-8bdb-e7669666a277",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "265f5d51-0a76-4808-8d13-ef598ee6e366",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all features and configurations exposed via `langchain-openai`, head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html\n",
-    "\n",
-    "Refer to the vLLM [documentation](https://docs.vllm.ai/en/latest/) as well."
-   ]
-  }
- ],
- "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
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/xai.ipynb

@@ -1,332 +1,330 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: xAI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatXAI\n",
-    "\n",
-    "\n",
-    "This page will help you get started with xAI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatXAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html).\n",
-    "\n",
-    "[xAI](https://console.x.ai/) offers an API to interact with Grok models.\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",
-    "| [ChatXAI](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html) | [langchain-xai](https://python.langchain.com/api_reference/xai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-xai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-xai?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 xAI models you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this set the `XAI_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"XAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"XAI_API_KEY\"] = getpass.getpass(\"Enter your xAI API key: \")"
-   ]
-  },
-  {
-   "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": 2,
-   "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 xAI integration lives in the `langchain-xai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-xai"
-   ]
-  },
-  {
-   "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": 4,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_xai import ChatXAI\n",
-    "\n",
-    "llm = ChatXAI(\n",
-    "    model=\"grok-beta\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 30, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-adffb7a3-e48a-4f52-b694-340d85abe5c3-0', usage_metadata={'input_tokens': 30, 'output_tokens': 6, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: xAI\n",
+        "---"
       ]
-     },
-     "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": [
-      "J'adore programmer.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](../../how_to/sequence.ipynb) 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 das Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 25, 'total_tokens': 32, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-569fc8dc-101b-4e6d-864e-d4fa80df2b63-0', usage_metadata={'input_tokens': 25, 'output_tokens': 7, 'total_tokens': 32, 'input_token_details': {}, 'output_token_details': {}})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatXAI\n",
+        "\n",
+        "\n",
+        "This page will help you get started with xAI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatXAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html).\n",
+        "\n",
+        "[xAI](https://console.x.ai/) offers an API to interact with Grok models.\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",
+        "| [ChatXAI](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html) | [langchain-xai](https://python.langchain.com/api_reference/xai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-xai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-xai?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 xAI models you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this set the `XAI_API_KEY` environment variable:"
       ]
-     },
-     "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": "e074bce1-0994-4b83-b393-ae7aa7e21750",
-   "metadata": {},
-   "source": [
-    "## Tool calling\n",
-    "\n",
-    "ChatXAI has a [tool calling](https://docs.x.ai/docs#capabilities) (we use \"tool calling\" and \"function calling\" interchangeably here) 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. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n",
-    "\n",
-    "### ChatXAI.bind_tools()\n",
-    "\n",
-    "With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n",
-    "```\n",
-    "{\n",
-    "    \"name\": \"...\",\n",
-    "    \"description\": \"...\",\n",
-    "    \"parameters\": {...}  # JSONSchema\n",
-    "}\n",
-    "```\n",
-    "and passed in every model invocation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c6bfe929-ec02-46bd-9d54-76350edddabc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pydantic import BaseModel, Field\n",
-    "\n",
-    "\n",
-    "class GetWeather(BaseModel):\n",
-    "    \"\"\"Get the current weather in a given location\"\"\"\n",
-    "\n",
-    "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
-    "\n",
-    "\n",
-    "llm_with_tools = llm.bind_tools([GetWeather])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5265c892-d8c2-48af-aef5-adbee1647ba6",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='I am retrieving the current weather for San Francisco.', additional_kwargs={'tool_calls': [{'id': '0', 'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 151, 'total_tokens': 162, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73707da7-afec-4a52-bee1-a176b0ab8585-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': '0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 151, 'output_tokens': 11, 'total_tokens': 162, 'input_token_details': {}, 'output_token_details': {}})"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"XAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"XAI_API_KEY\"] = getpass.getpass(\"Enter your xAI API key: \")"
+      ]
+    },
+    {
+      "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": 2,
+      "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 xAI integration lives in the `langchain-xai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-xai"
+      ]
+    },
+    {
+      "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": 4,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_xai import ChatXAI\n",
+        "\n",
+        "llm = ChatXAI(\n",
+        "    model=\"grok-beta\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\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=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 30, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-adffb7a3-e48a-4f52-b694-340d85abe5c3-0', usage_metadata={'input_tokens': 30, 'output_tokens': 6, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})"
+            ]
+          },
+          "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": [
+            "J'adore programmer.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](../../how_to/sequence.ipynb) 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 das Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 25, 'total_tokens': 32, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-569fc8dc-101b-4e6d-864e-d4fa80df2b63-0', usage_metadata={'input_tokens': 25, 'output_tokens': 7, 'total_tokens': 32, 'input_token_details': {}, 'output_token_details': {}})"
+            ]
+          },
+          "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": "e074bce1-0994-4b83-b393-ae7aa7e21750",
+      "metadata": {},
+      "source": [
+        "## Tool calling\n",
+        "\n",
+        "ChatXAI has a [tool calling](https://docs.x.ai/docs#capabilities) (we use \"tool calling\" and \"function calling\" interchangeably here) 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. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n",
+        "\n",
+        "### ChatXAI.bind_tools()\n",
+        "\n",
+        "With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n",
+        "```\n",
+        "{\n",
+        "    \"name\": \"...\",\n",
+        "    \"description\": \"...\",\n",
+        "    \"parameters\": {...}  # JSONSchema\n",
+        "}\n",
+        "```\n",
+        "and passed in every model invocation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "c6bfe929-ec02-46bd-9d54-76350edddabc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from pydantic import BaseModel, Field\n",
+        "\n",
+        "\n",
+        "class GetWeather(BaseModel):\n",
+        "    \"\"\"Get the current weather in a given location\"\"\"\n",
+        "\n",
+        "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
+        "\n",
+        "\n",
+        "llm_with_tools = llm.bind_tools([GetWeather])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "5265c892-d8c2-48af-aef5-adbee1647ba6",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='I am retrieving the current weather for San Francisco.', additional_kwargs={'tool_calls': [{'id': '0', 'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 151, 'total_tokens': 162, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73707da7-afec-4a52-bee1-a176b0ab8585-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': '0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 151, 'output_tokens': 11, 'total_tokens': 162, 'input_token_details': {}, 'output_token_details': {}})"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "ai_msg = llm_with_tools.invoke(\n",
+        "    \"what is the weather like in San Francisco\",\n",
+        ")\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `ChatXAI` features and configurations head to the API reference: https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html"
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "ai_msg = llm_with_tools.invoke(\n",
-    "    \"what is the weather like in San Francisco\",\n",
-    ")\n",
-    "ai_msg"
-   ]
+  ],
+  "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"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `ChatXAI` features and configurations head to the API reference: https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

docs/docs/integrations/chat/yi.ipynb

@@ -1,229 +1,227 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ChatYI\n",
-    "\n",
-    "This will help you getting started with Yi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
-    "\n",
-    "[01.AI](https://www.lingyiwanwu.com/en), founded by Dr. Kai-Fu Lee, is a global company at the forefront of AI 2.0. They offer cutting-edge large language models, including the Yi series, which range from 6B to hundreds of billions of parameters. 01.AI also provides multimodal models, an open API platform, and open-source options like Yi-34B/9B/6B and Yi-VL.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatYi](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?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 ChatYi models you'll need to create a/an 01.AI account, get an API key, and install the `langchain_community` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [01.AI](https://platform.01.ai) to sign up to 01.AI and generate an API key. Once you've done this set the `YI_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"YI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"YI_API_KEY\"] = getpass.getpass(\"Enter your Yi API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "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,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain __ModuleName__ integration lives in the `langchain_community` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models.yi import ChatYi\n",
-    "\n",
-    "llm = ChatYi(\n",
-    "    model=\"yi-large\",\n",
-    "    temperature=0,\n",
-    "    timeout=60,\n",
-    "    yi_api_base=\"https://api.01.ai/v1/chat/completions\",\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Invocation\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"Large Language Models (LLMs) have the potential to significantly impact healthcare by enhancing various aspects of patient care, research, and administrative processes. Here are some potential applications:\\n\\n1. **Clinical Documentation and Reporting**: LLMs can assist in generating patient reports and documentation by understanding and summarizing clinical notes, making the process more efficient and reducing the administrative burden on healthcare professionals.\\n\\n2. **Medical Coding and Billing**: These models can help in automating the coding process for medical billing by accurately translating clinical notes into standardized codes, reducing errors and improving billing efficiency.\\n\\n3. **Clinical Decision Support**: LLMs can analyze patient data and medical literature to provide evidence-based recommendations to healthcare providers, aiding in diagnosis and treatment planning.\\n\\n4. **Patient Education and Communication**: By simplifying medical jargon, LLMs can help in educating patients about their conditions, treatment options, and preventive care, improving patient engagement and health literacy.\\n\\n5. **Natural Language Processing (NLP) for EHRs**: LLMs can enhance NLP capabilities in Electronic Health Records (EHRs) systems, enabling better extraction of information from unstructured data, such as clinical notes, to support data-driven decision-making.\\n\\n6. **Drug Discovery and Development**: LLMs can analyze biomedical literature and clinical trial data to identify new drug candidates, predict drug interactions, and support the development of personalized medicine.\\n\\n7. **Telemedicine and Virtual Health Assistants**: Integrated into telemedicine platforms, LLMs can provide preliminary assessments and triage, offering patients basic health advice and determining the urgency of their needs, thus optimizing the utilization of healthcare resources.\\n\\n8. **Research and Literature Review**: LLMs can expedite the process of reviewing medical literature by quickly identifying relevant studies and summarizing findings, accelerating research and evidence-based practice.\\n\\n9. **Personalized Medicine**: By analyzing a patient's genetic information and medical history, LLMs can help in tailoring treatment plans and medication dosages, contributing to the advancement of personalized medicine.\\n\\n10. **Quality Improvement and Risk Assessment**: LLMs can analyze healthcare data to identify patterns that may indicate areas for quality improvement or potential risks, such as hospital-acquired infections or adverse drug events.\\n\\n11. **Mental Health Support**: LLMs can provide mental health support by offering coping strategies, mindfulness exercises, and preliminary assessments, serving as a complement to professional mental health services.\\n\\n12. **Continuing Medical Education (CME)**: LLMs can personalize CME by recommending educational content based on a healthcare provider's practice area, patient demographics, and emerging medical literature, ensuring that professionals stay updated with the latest advancements.\\n\\nWhile the applications of LLMs in healthcare are promising, it's crucial to address challenges such as data privacy, model bias, and the need for regulatory approval to ensure that these technologies are implemented safely and ethically.\", response_metadata={'token_usage': {'completion_tokens': 656, 'prompt_tokens': 40, 'total_tokens': 696}, 'model': 'yi-large'}, id='run-870850bd-e4bf-4265-8730-1736409c0acf-0')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# ChatYI\n",
+        "\n",
+        "This will help you getting started with Yi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
+        "\n",
+        "[01.AI](https://www.lingyiwanwu.com/en), founded by Dr. Kai-Fu Lee, is a global company at the forefront of AI 2.0. They offer cutting-edge large language models, including the Yi series, which range from 6B to hundreds of billions of parameters. 01.AI also provides multimodal models, an open API platform, and open-source options like Yi-34B/9B/6B and Yi-VL.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatYi](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?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 ChatYi models you'll need to create a/an 01.AI account, get an API key, and install the `langchain_community` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [01.AI](https://platform.01.ai) to sign up to 01.AI and generate an API key. Once you've done this set the `YI_API_KEY` environment variable:"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import HumanMessage, SystemMessage\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(content=\"You are an AI assistant specializing in technology trends.\"),\n",
-    "    HumanMessage(\n",
-    "        content=\"What are the potential applications of large language models in healthcare?\"\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "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": 4,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 33, 'total_tokens': 41}, 'model': 'yi-large'}, id='run-daa3bc58-8289-4d72-a24e-80622fa90d6d-0')"
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"YI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"YI_API_KEY\"] = getpass.getpass(\"Enter your Yi API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain __ModuleName__ integration lives in the `langchain_community` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.chat_models.yi import ChatYi\n",
+        "\n",
+        "llm = ChatYi(\n",
+        "    model=\"yi-large\",\n",
+        "    temperature=0,\n",
+        "    timeout=60,\n",
+        "    yi_api_base=\"https://api.01.ai/v1/chat/completions\",\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Invocation\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"Large Language Models (LLMs) have the potential to significantly impact healthcare by enhancing various aspects of patient care, research, and administrative processes. Here are some potential applications:\\n\\n1. **Clinical Documentation and Reporting**: LLMs can assist in generating patient reports and documentation by understanding and summarizing clinical notes, making the process more efficient and reducing the administrative burden on healthcare professionals.\\n\\n2. **Medical Coding and Billing**: These models can help in automating the coding process for medical billing by accurately translating clinical notes into standardized codes, reducing errors and improving billing efficiency.\\n\\n3. **Clinical Decision Support**: LLMs can analyze patient data and medical literature to provide evidence-based recommendations to healthcare providers, aiding in diagnosis and treatment planning.\\n\\n4. **Patient Education and Communication**: By simplifying medical jargon, LLMs can help in educating patients about their conditions, treatment options, and preventive care, improving patient engagement and health literacy.\\n\\n5. **Natural Language Processing (NLP) for EHRs**: LLMs can enhance NLP capabilities in Electronic Health Records (EHRs) systems, enabling better extraction of information from unstructured data, such as clinical notes, to support data-driven decision-making.\\n\\n6. **Drug Discovery and Development**: LLMs can analyze biomedical literature and clinical trial data to identify new drug candidates, predict drug interactions, and support the development of personalized medicine.\\n\\n7. **Telemedicine and Virtual Health Assistants**: Integrated into telemedicine platforms, LLMs can provide preliminary assessments and triage, offering patients basic health advice and determining the urgency of their needs, thus optimizing the utilization of healthcare resources.\\n\\n8. **Research and Literature Review**: LLMs can expedite the process of reviewing medical literature by quickly identifying relevant studies and summarizing findings, accelerating research and evidence-based practice.\\n\\n9. **Personalized Medicine**: By analyzing a patient's genetic information and medical history, LLMs can help in tailoring treatment plans and medication dosages, contributing to the advancement of personalized medicine.\\n\\n10. **Quality Improvement and Risk Assessment**: LLMs can analyze healthcare data to identify patterns that may indicate areas for quality improvement or potential risks, such as hospital-acquired infections or adverse drug events.\\n\\n11. **Mental Health Support**: LLMs can provide mental health support by offering coping strategies, mindfulness exercises, and preliminary assessments, serving as a complement to professional mental health services.\\n\\n12. **Continuing Medical Education (CME)**: LLMs can personalize CME by recommending educational content based on a healthcare provider's practice area, patient demographics, and emerging medical literature, ensuring that professionals stay updated with the latest advancements.\\n\\nWhile the applications of LLMs in healthcare are promising, it's crucial to address challenges such as data privacy, model bias, and the need for regulatory approval to ensure that these technologies are implemented safely and ethically.\", response_metadata={'token_usage': {'completion_tokens': 656, 'prompt_tokens': 40, 'total_tokens': 696}, 'model': 'yi-large'}, id='run-870850bd-e4bf-4265-8730-1736409c0acf-0')"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.messages import HumanMessage, SystemMessage\n",
+        "\n",
+        "messages = [\n",
+        "    SystemMessage(content=\"You are an AI assistant specializing in technology trends.\"),\n",
+        "    HumanMessage(\n",
+        "        content=\"What are the potential applications of large language models in healthcare?\"\n",
+        "    ),\n",
+        "]\n",
+        "\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren.', response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 33, 'total_tokens': 41}, 'model': 'yi-large'}, id='run-daa3bc58-8289-4d72-a24e-80622fa90d6d-0')"
+            ]
+          },
+          "execution_count": 4,
+          "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",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatYi features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.yi.ChatYi.html"
       ]
-     },
-     "execution_count": 4,
-     "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",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatYi features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.yi.ChatYi.html"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

docs/docs/integrations/document_loaders/box.ipynb

@@ -1,466 +1,464 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Box\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# BoxLoader and BoxBlobLoader\n",
-    "\n",
-    "\n",
-    "The `langchain-box` package provides two methods to index your files from Box: `BoxLoader` and `BoxBlobLoader`. `BoxLoader` allows you to ingest text representations of files that have a text representation in Box. The `BoxBlobLoader` allows you download the blob for any document or image file for processing with the blob parser of your choice.\n",
-    "\n",
-    "This notebook details getting started with both of these. For detailed documentation of all BoxLoader features and configurations head to the API Reference pages for [BoxLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html) and [BoxBlobLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.blob_loaders.box.BoxBlobLoader.html).\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "The `BoxLoader` class helps you get your unstructured content from Box in Langchain's `Document` format. You can do this with either a `List[str]` containing Box file IDs, or with a `str` containing a Box folder ID. \n",
-    "\n",
-    "The `BoxBlobLoader` class helps you get your unstructured content from Box in Langchain's `Blob` format. You can do this with a `List[str]` containing Box file IDs, a `str` containing a Box folder ID, a search query, or a `BoxMetadataQuery`. \n",
-    "\n",
-    "If getting files from a folder with folder ID, you can also set a `Bool` to tell the loader to get all sub-folders in that folder, as well. \n",
-    "\n",
-    ":::info\n",
-    "A Box instance can contain Petabytes of files, and folders can contain millions of files. Be intentional when choosing what folders you choose to index. And we recommend never getting all files from folder 0 recursively. Folder ID 0 is your root folder.\n",
-    ":::\n",
-    "\n",
-    "The `BoxLoader` will skip files without a text representation, while the `BoxBlobLoader` will return blobs for all document and image files.\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [BoxLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html) | [langchain_box](https://python.langchain.com/api_reference/box/index.html) | ✅ | ❌ | ❌ | \n",
-    "| [BoxBlobLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.blob_loaders.box.BoxBlobLoader.html) | [langchain_box](https://python.langchain.com/api_reference/box/index.html) | ✅ | ❌ | ❌ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| BoxLoader | ✅ | ❌ | \n",
-    "| BoxBlobLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "In order to use the Box package, you will need a few things:\n",
-    "\n",
-    "* A Box account — If you are not a current Box customer or want to test outside of your production Box instance, you can use a [free developer account](https://account.box.com/signup/n/developer#ty9l3).\n",
-    "* [A Box app](https://developer.box.com/guides/getting-started/first-application/) — This is configured in the [developer console](https://account.box.com/developers/console), and for Box AI, must have the `Manage AI` scope enabled. Here you will also select your authentication method\n",
-    "* The app must be [enabled by the administrator](https://developer.box.com/guides/authorization/custom-app-approval/#manual-approval). For free developer accounts, this is whomever signed up for the account.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "For these examples, we will use [token authentication](https://developer.box.com/guides/authentication/tokens/developer-tokens). This can be used with any [authentication method](https://developer.box.com/guides/authentication/). Just get the token with whatever methodology. If you want to learn more about how to use other authentication types with `langchain-box`, visit the [Box provider](/docs/integrations/providers/box) document.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "Enter your Box Developer Token:  ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "box_developer_token = getpass.getpass(\"Enter your Box Developer Token: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "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,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_box**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_box"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "### Load files\n",
-    "\n",
-    "If you wish to load files, you must provide the `List` of file ids at instantiation time. \n",
-    "\n",
-    "This requires 1 piece of information:\n",
-    "\n",
-    "* **box_file_ids** (`List[str]`)- A list of Box file IDs.\n",
-    "\n",
-    "#### BoxLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.document_loaders import BoxLoader\n",
-    "\n",
-    "box_file_ids = [\"1514555423624\", \"1514553902288\"]\n",
-    "\n",
-    "loader = BoxLoader(\n",
-    "    box_developer_token=box_developer_token,\n",
-    "    box_file_ids=box_file_ids,\n",
-    "    character_limit=10000,  # Optional. Defaults to no limit\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### BoxBlobLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.blob_loaders import BoxBlobLoader\n",
-    "\n",
-    "box_file_ids = [\"1514555423624\", \"1514553902288\"]\n",
-    "\n",
-    "loader = BoxBlobLoader(\n",
-    "    box_developer_token=box_developer_token, box_file_ids=box_file_ids\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load from folder\n",
-    "\n",
-    "If you wish to load files from a folder, you must provide a `str` with the Box folder ID at instantiation time. \n",
-    "\n",
-    "This requires 1 piece of information:\n",
-    "\n",
-    "* **box_folder_id** (`str`)- A string containing a Box folder ID.\n",
-    "\n",
-    "#### BoxLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.document_loaders import BoxLoader\n",
-    "\n",
-    "box_folder_id = \"260932470532\"\n",
-    "\n",
-    "loader = BoxLoader(\n",
-    "    box_folder_id=box_folder_id,\n",
-    "    recursive=False,  # Optional. return entire tree, defaults to False\n",
-    "    character_limit=10000,  # Optional. Defaults to no limit\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### BoxBlobLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.blob_loaders import BoxBlobLoader\n",
-    "\n",
-    "box_folder_id = \"260932470532\"\n",
-    "\n",
-    "loader = BoxBlobLoader(\n",
-    "    box_folder_id=box_folder_id,\n",
-    "    recursive=False,  # Optional. return entire tree, defaults to False\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Search for files with BoxBlobLoader\n",
-    "\n",
-    "If you need to search for files, the `BoxBlobLoader` offers two methods. First you can perform a full text search with optional search options to narrow down that search.\n",
-    "\n",
-    "This requires 1 piece of information:\n",
-    "\n",
-    "* **query** (`str`)- A string containing the search query to perform.\n",
-    "\n",
-    "You can also provide a `BoxSearchOptions` object to narrow down that search\n",
-    "* **box_search_options** (`BoxSearchOptions`)\n",
-    "\n",
-    "#### BoxBlobLoader search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.blob_loaders import BoxBlobLoader\n",
-    "from langchain_box.utilities import BoxSearchOptions, DocumentFiles, SearchTypeFilter\n",
-    "\n",
-    "box_folder_id = \"260932470532\"\n",
-    "\n",
-    "box_search_options = BoxSearchOptions(\n",
-    "    ancestor_folder_ids=[box_folder_id],\n",
-    "    search_type_filter=[SearchTypeFilter.FILE_CONTENT],\n",
-    "    created_date_range=[\"2023-01-01T00:00:00-07:00\", \"2024-08-01T00:00:00-07:00,\"],\n",
-    "    file_extensions=[DocumentFiles.DOCX, DocumentFiles.PDF],\n",
-    "    k=200,\n",
-    "    size_range=[1, 1000000],\n",
-    "    updated_data_range=None,\n",
-    ")\n",
-    "\n",
-    "loader = BoxBlobLoader(\n",
-    "    box_developer_token=box_developer_token,\n",
-    "    query=\"Victor\",\n",
-    "    box_search_options=box_search_options,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also search for content based on Box Metadata. If your Box instance uses Metadata, you can search for any documents that have a specific Metadata Template attached that meet a certain criteria, like returning any invoices with a total greater than or equal to $500 that were created last quarter.\n",
-    "\n",
-    "This requires 1 piece of information:\n",
-    "\n",
-    "* **query** (`str`)- A string containing the search query to perform.\n",
-    "\n",
-    "You can also provide a `BoxSearchOptions` object to narrow down that search\n",
-    "* **box_search_options** (`BoxSearchOptions`)\n",
-    "\n",
-    "#### BoxBlobLoader Metadata query"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_box.blob_loaders import BoxBlobLoader\n",
-    "from langchain_box.utilities import BoxMetadataQuery\n",
-    "\n",
-    "query = BoxMetadataQuery(\n",
-    "    template_key=\"enterprise_1234.myTemplate\",\n",
-    "    query=\"total >= :value\",\n",
-    "    query_params={\"value\": 100},\n",
-    "    ancestor_folder_id=\"260932470532\",\n",
-    ")\n",
-    "\n",
-    "loader = BoxBlobLoader(box_metadata_query=query)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load\n",
-    "\n",
-    "#### BoxLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': 'https://dl.boxcloud.com/api/2.0/internal_files/1514555423624/versions/1663171610024/representations/extracted_text/content/', 'title': 'Invoice-A5555_txt'}, page_content='Vendor: AstroTech Solutions\\nInvoice Number: A5555\\n\\nLine Items:\\n    - Gravitational Wave Detector Kit: $800\\n    - Exoplanet Terrarium: $120\\nTotal: $920')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: Box\n",
+        "---"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': 'https://dl.boxcloud.com/api/2.0/internal_files/1514555423624/versions/1663171610024/representations/extracted_text/content/', 'title': 'Invoice-A5555_txt'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### BoxBlobLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# BoxLoader and BoxBlobLoader\n",
+        "\n",
+        "\n",
+        "The `langchain-box` package provides two methods to index your files from Box: `BoxLoader` and `BoxBlobLoader`. `BoxLoader` allows you to ingest text representations of files that have a text representation in Box. The `BoxBlobLoader` allows you download the blob for any document or image file for processing with the blob parser of your choice.\n",
+        "\n",
+        "This notebook details getting started with both of these. For detailed documentation of all BoxLoader features and configurations head to the API Reference pages for [BoxLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html) and [BoxBlobLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.blob_loaders.box.BoxBlobLoader.html).\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "The `BoxLoader` class helps you get your unstructured content from Box in Langchain's `Document` format. You can do this with either a `List[str]` containing Box file IDs, or with a `str` containing a Box folder ID.\n",
+        "\n",
+        "The `BoxBlobLoader` class helps you get your unstructured content from Box in Langchain's `Blob` format. You can do this with a `List[str]` containing Box file IDs, a `str` containing a Box folder ID, a search query, or a `BoxMetadataQuery`.\n",
+        "\n",
+        "If getting files from a folder with folder ID, you can also set a `Bool` to tell the loader to get all sub-folders in that folder, as well.\n",
+        "\n",
+        ":::info\n",
+        "A Box instance can contain Petabytes of files, and folders can contain millions of files. Be intentional when choosing what folders you choose to index. And we recommend never getting all files from folder 0 recursively. Folder ID 0 is your root folder.\n",
+        ":::\n",
+        "\n",
+        "The `BoxLoader` will skip files without a text representation, while the `BoxBlobLoader` will return blobs for all document and image files.\n",
+        "\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [BoxLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html) | [langchain_box](https://python.langchain.com/api_reference/box/index.html) | ✅ | ❌ | ❌ |\n",
+        "| [BoxBlobLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.blob_loaders.box.BoxBlobLoader.html) | [langchain_box](https://python.langchain.com/api_reference/box/index.html) | ✅ | ❌ | ❌ |\n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Async Support\n",
+        "| :---: | :---: | :---: |\n",
+        "| BoxLoader | ✅ | ❌ |\n",
+        "| BoxBlobLoader | ✅ | ❌ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "In order to use the Box package, you will need a few things:\n",
+        "\n",
+        "* A Box account — If you are not a current Box customer or want to test outside of your production Box instance, you can use a [free developer account](https://account.box.com/signup/n/developer#ty9l3).\n",
+        "* [A Box app](https://developer.box.com/guides/getting-started/first-application/) — This is configured in the [developer console](https://account.box.com/developers/console), and for Box AI, must have the `Manage AI` scope enabled. Here you will also select your authentication method\n",
+        "* The app must be [enabled by the administrator](https://developer.box.com/guides/authorization/custom-app-approval/#manual-approval). For free developer accounts, this is whomever signed up for the account.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "For these examples, we will use [token authentication](https://developer.box.com/guides/authentication/tokens/developer-tokens). This can be used with any [authentication method](https://developer.box.com/guides/authentication/). Just get the token with whatever methodology. If you want to learn more about how to use other authentication types with `langchain-box`, visit the [Box provider](/docs/integrations/providers/box) document.\n"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Blob(id='1514555423624' metadata={'source': 'https://app.box.com/0/260935730128/260931903795/Invoice-A5555.txt', 'name': 'Invoice-A5555.txt', 'file_size': 150} data=\"b'Vendor: AstroTech Solutions\\\\nInvoice Number: A5555\\\\n\\\\nLine Items:\\\\n    - Gravitational Wave Detector Kit: $800\\\\n    - Exoplanet Terrarium: $120\\\\nTotal: $920'\" mimetype='text/plain' path='https://app.box.com/0/260935730128/260931903795/Invoice-A5555.txt')\n",
-      "Blob(id='1514553902288' metadata={'source': 'https://app.box.com/0/260935730128/260931903795/Invoice-B1234.txt', 'name': 'Invoice-B1234.txt', 'file_size': 168} data=\"b'Vendor: Galactic Gizmos Inc.\\\\nInvoice Number: B1234\\\\nPurchase Order Number: 001\\\\nLine Items:\\\\n    - Quantum Flux Capacitor: $500\\\\n    - Anti-Gravity Pen Set: $75\\\\nTotal: $575'\" mimetype='text/plain' path='https://app.box.com/0/260935730128/260931903795/Invoice-B1234.txt')\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdin",
+          "output_type": "stream",
+          "text": [
+            "Enter your Box Developer Token:  ········\n"
+          ]
+        }
+      ],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "box_developer_token = getpass.getpass(\"Enter your Box Developer Token: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_box**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_box"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "### Load files\n",
+        "\n",
+        "If you wish to load files, you must provide the `List` of file ids at instantiation time.\n",
+        "\n",
+        "This requires 1 piece of information:\n",
+        "\n",
+        "* **box_file_ids** (`List[str]`)- A list of Box file IDs.\n",
+        "\n",
+        "#### BoxLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.document_loaders import BoxLoader\n",
+        "\n",
+        "box_file_ids = [\"1514555423624\", \"1514553902288\"]\n",
+        "\n",
+        "loader = BoxLoader(\n",
+        "    box_developer_token=box_developer_token,\n",
+        "    box_file_ids=box_file_ids,\n",
+        "    character_limit=10000,  # Optional. Defaults to no limit\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### BoxBlobLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.blob_loaders import BoxBlobLoader\n",
+        "\n",
+        "box_file_ids = [\"1514555423624\", \"1514553902288\"]\n",
+        "\n",
+        "loader = BoxBlobLoader(\n",
+        "    box_developer_token=box_developer_token, box_file_ids=box_file_ids\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load from folder\n",
+        "\n",
+        "If you wish to load files from a folder, you must provide a `str` with the Box folder ID at instantiation time.\n",
+        "\n",
+        "This requires 1 piece of information:\n",
+        "\n",
+        "* **box_folder_id** (`str`)- A string containing a Box folder ID.\n",
+        "\n",
+        "#### BoxLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.document_loaders import BoxLoader\n",
+        "\n",
+        "box_folder_id = \"260932470532\"\n",
+        "\n",
+        "loader = BoxLoader(\n",
+        "    box_folder_id=box_folder_id,\n",
+        "    recursive=False,  # Optional. return entire tree, defaults to False\n",
+        "    character_limit=10000,  # Optional. Defaults to no limit\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### BoxBlobLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.blob_loaders import BoxBlobLoader\n",
+        "\n",
+        "box_folder_id = \"260932470532\"\n",
+        "\n",
+        "loader = BoxBlobLoader(\n",
+        "    box_folder_id=box_folder_id,\n",
+        "    recursive=False,  # Optional. return entire tree, defaults to False\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Search for files with BoxBlobLoader\n",
+        "\n",
+        "If you need to search for files, the `BoxBlobLoader` offers two methods. First you can perform a full text search with optional search options to narrow down that search.\n",
+        "\n",
+        "This requires 1 piece of information:\n",
+        "\n",
+        "* **query** (`str`)- A string containing the search query to perform.\n",
+        "\n",
+        "You can also provide a `BoxSearchOptions` object to narrow down that search\n",
+        "* **box_search_options** (`BoxSearchOptions`)\n",
+        "\n",
+        "#### BoxBlobLoader search"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.blob_loaders import BoxBlobLoader\n",
+        "from langchain_box.utilities import BoxSearchOptions, DocumentFiles, SearchTypeFilter\n",
+        "\n",
+        "box_folder_id = \"260932470532\"\n",
+        "\n",
+        "box_search_options = BoxSearchOptions(\n",
+        "    ancestor_folder_ids=[box_folder_id],\n",
+        "    search_type_filter=[SearchTypeFilter.FILE_CONTENT],\n",
+        "    created_date_range=[\"2023-01-01T00:00:00-07:00\", \"2024-08-01T00:00:00-07:00,\"],\n",
+        "    file_extensions=[DocumentFiles.DOCX, DocumentFiles.PDF],\n",
+        "    k=200,\n",
+        "    size_range=[1, 1000000],\n",
+        "    updated_data_range=None,\n",
+        ")\n",
+        "\n",
+        "loader = BoxBlobLoader(\n",
+        "    box_developer_token=box_developer_token,\n",
+        "    query=\"Victor\",\n",
+        "    box_search_options=box_search_options,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can also search for content based on Box Metadata. If your Box instance uses Metadata, you can search for any documents that have a specific Metadata Template attached that meet a certain criteria, like returning any invoices with a total greater than or equal to $500 that were created last quarter.\n",
+        "\n",
+        "This requires 1 piece of information:\n",
+        "\n",
+        "* **query** (`str`)- A string containing the search query to perform.\n",
+        "\n",
+        "You can also provide a `BoxSearchOptions` object to narrow down that search\n",
+        "* **box_search_options** (`BoxSearchOptions`)\n",
+        "\n",
+        "#### BoxBlobLoader Metadata query"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_box.blob_loaders import BoxBlobLoader\n",
+        "from langchain_box.utilities import BoxMetadataQuery\n",
+        "\n",
+        "query = BoxMetadataQuery(\n",
+        "    template_key=\"enterprise_1234.myTemplate\",\n",
+        "    query=\"total >= :value\",\n",
+        "    query_params={\"value\": 100},\n",
+        "    ancestor_folder_id=\"260932470532\",\n",
+        ")\n",
+        "\n",
+        "loader = BoxBlobLoader(box_metadata_query=query)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load\n",
+        "\n",
+        "#### BoxLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': 'https://dl.boxcloud.com/api/2.0/internal_files/1514555423624/versions/1663171610024/representations/extracted_text/content/', 'title': 'Invoice-A5555_txt'}, page_content='Vendor: AstroTech Solutions\\nInvoice Number: A5555\\n\\nLine Items:\\n    - Gravitational Wave Detector Kit: $800\\n    - Exoplanet Terrarium: $120\\nTotal: $920')"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': 'https://dl.boxcloud.com/api/2.0/internal_files/1514555423624/versions/1663171610024/representations/extracted_text/content/', 'title': 'Invoice-A5555_txt'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### BoxBlobLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Blob(id='1514555423624' metadata={'source': 'https://app.box.com/0/260935730128/260931903795/Invoice-A5555.txt', 'name': 'Invoice-A5555.txt', 'file_size': 150} data=\"b'Vendor: AstroTech Solutions\\\\nInvoice Number: A5555\\\\n\\\\nLine Items:\\\\n    - Gravitational Wave Detector Kit: $800\\\\n    - Exoplanet Terrarium: $120\\\\nTotal: $920'\" mimetype='text/plain' path='https://app.box.com/0/260935730128/260931903795/Invoice-A5555.txt')\n",
+            "Blob(id='1514553902288' metadata={'source': 'https://app.box.com/0/260935730128/260931903795/Invoice-B1234.txt', 'name': 'Invoice-B1234.txt', 'file_size': 168} data=\"b'Vendor: Galactic Gizmos Inc.\\\\nInvoice Number: B1234\\\\nPurchase Order Number: 001\\\\nLine Items:\\\\n    - Quantum Flux Capacitor: $500\\\\n    - Anti-Gravity Pen Set: $75\\\\nTotal: $575'\" mimetype='text/plain' path='https://app.box.com/0/260935730128/260931903795/Invoice-B1234.txt')\n"
+          ]
+        }
+      ],
+      "source": [
+        "for blob in loader.yield_blobs():\n",
+        "    print(f\"Blob({blob})\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load\n",
+        "\n",
+        "#### BoxLoader only"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Extra fields\n",
+        "\n",
+        "All Box connectors offer the ability to select additional fields from the Box `FileFull` object to return as custom LangChain metadata. Each object accepts an optional `List[str]` called `extra_fields` containing the json key from the return object, like `extra_fields=[\"shared_link\"]`.\n",
+        "\n",
+        "The connector will add this field to the list of fields the integration needs to function and then add the results to the metadata returned in the `Document` or `Blob`, like `\"metadata\" : { \"source\" : \"source, \"shared_link\" : \"shared_link\" }`. If the field is unavailable for that file, it will be returned as an empty string, like `\"shared_link\" : \"\"`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all BoxLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html)\n",
+        "\n",
+        "\n",
+        "## Help\n",
+        "\n",
+        "If you have questions, you can check out our [developer documentation](https://developer.box.com) or reach out to use in our [developer community](https://community.box.com)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.6"
     }
-   ],
-   "source": [
-    "for blob in loader.yield_blobs():\n",
-    "    print(f\"Blob({blob})\")"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load\n",
-    "\n",
-    "#### BoxLoader only"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extra fields\n",
-    "\n",
-    "All Box connectors offer the ability to select additional fields from the Box `FileFull` object to return as custom LangChain metadata. Each object accepts an optional `List[str]` called `extra_fields` containing the json key from the return object, like `extra_fields=[\"shared_link\"]`. \n",
-    "\n",
-    "The connector will add this field to the list of fields the integration needs to function and then add the results to the metadata returned in the `Document` or `Blob`, like `\"metadata\" : { \"source\" : \"source, \"shared_link\" : \"shared_link\" }`. If the field is unavailable for that file, it will be returned as an empty string, like `\"shared_link\" : \"\"`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all BoxLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html)\n",
-    "\n",
-    "\n",
-    "## Help\n",
-    "\n",
-    "If you have questions, you can check out our [developer documentation](https://developer.box.com) or reach out to use in our [developer community](https://community.box.com)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }

docs/docs/integrations/document_loaders/bshtml.ipynb

@@ -1,243 +1,241 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# BSHTMLLoader\n",
-    "\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with BeautifulSoup4 [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html).\n",
-    "\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [BSHTMLLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| BSHTMLLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access BSHTMLLoader document loader you'll need to install the `langchain-community` integration package and the `bs4` python package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed to use the `BSHTMLLoader` class."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community** and **bs4**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community bs4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import BSHTMLLoader\n",
-    "\n",
-    "loader = BSHTMLLoader(\n",
-    "    file_path=\"./example_data/fake-content.html\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}, page_content='\\nTest Title\\n\\n\\nMy First Heading\\nMy first paragraph.\\n\\n\\n')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# BSHTMLLoader\n",
+        "\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with BeautifulSoup4 [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html).\n",
+        "\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [BSHTMLLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| BSHTMLLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access BSHTMLLoader document loader you'll need to install the `langchain-community` integration package and the `bs4` python package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed to use the `BSHTMLLoader` class."
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': './example_data/fake-content.html', 'title': 'Test Title'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
     {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}, page_content='\\nTest Title\\n\\n\\nMy First Heading\\nMy first paragraph.\\n\\n\\n')"
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []\n",
-    "page[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Adding separator to BS4\n",
-    "\n",
-    "We can also pass a separator to use when calling get_text on the soup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page_content='\n",
-      ", Test Title, \n",
-      ", \n",
-      ", \n",
-      ", My First Heading, \n",
-      ", My first paragraph., \n",
-      ", \n",
-      ", \n",
-      "' metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community** and **bs4**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community bs4"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import BSHTMLLoader\n",
+        "\n",
+        "loader = BSHTMLLoader(\n",
+        "    file_path=\"./example_data/fake-content.html\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}, page_content='\\nTest Title\\n\\n\\nMy First Heading\\nMy first paragraph.\\n\\n\\n')"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': './example_data/fake-content.html', 'title': 'Test Title'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}, page_content='\\nTest Title\\n\\n\\nMy First Heading\\nMy first paragraph.\\n\\n\\n')"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []\n",
+        "page[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Adding separator to BS4\n",
+        "\n",
+        "We can also pass a separator to use when calling get_text on the soup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "page_content='\n",
+            ", Test Title, \n",
+            ", \n",
+            ", \n",
+            ", My First Heading, \n",
+            ", My first paragraph., \n",
+            ", \n",
+            ", \n",
+            "' metadata={'source': './example_data/fake-content.html', 'title': 'Test Title'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "loader = BSHTMLLoader(\n",
+        "    file_path=\"./example_data/fake-content.html\", get_text_separator=\", \"\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "print(docs[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all BSHTMLLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html"
+      ]
+    }
+  ],
+  "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": "3.11.9"
     }
-   ],
-   "source": [
-    "loader = BSHTMLLoader(\n",
-    "    file_path=\"./example_data/fake-content.html\", get_text_separator=\", \"\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "print(docs[0])"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all BSHTMLLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }

docs/docs/integrations/document_loaders/json.ipynb

@@ -1,348 +1,346 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# JSONLoader\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with JSON [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all JSONLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html).\n",
-    "\n",
-    "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/json/)|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [JSONLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| JSONLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access JSON document loader you'll need to install the `langchain-community` integration package as well as the ``jq`` python package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are required to use the `JSONLoader` class."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community** and **jq**:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community jq "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:\n",
-    "\n",
-    "- TODO: Update model instantiation with relevant params."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import JSONLoader\n",
-    "\n",
-    "loader = JSONLoader(\n",
-    "    file_path=\"./example_data/facebook_chat.json\",\n",
-    "    jq_schema=\".messages[].content\",\n",
-    "    text_content=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}, page_content='Bye!')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# JSONLoader\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with JSON [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all JSONLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html).\n",
+        "\n",
+        "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/json/)|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [JSONLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| JSONLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access JSON document loader you'll need to install the `langchain-community` integration package as well as the ``jq`` python package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are required to use the `JSONLoader` class."
       ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pages = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    pages.append(doc)\n",
-    "    if len(pages) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(pages)\n",
-    "\n",
-    "        pages = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Read from JSON Lines file\n",
-    "\n",
-    "If you want to load documents from a JSON Lines file, you pass `json_lines=True`\n",
-    "and specify `jq_schema` to extract `page_content` from a single JSON object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "loader = JSONLoader(\n",
-    "    file_path=\"./example_data/facebook_chat_messages.jsonl\",\n",
-    "    jq_schema=\".content\",\n",
-    "    text_content=False,\n",
-    "    json_lines=True,\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "print(docs[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Read specific content keys\n",
-    "\n",
-    "Another option is to set `jq_schema='.'` and provide a `content_key` in order to only load specific content:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page_content='User 2' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "loader = JSONLoader(\n",
-    "    file_path=\"./example_data/facebook_chat_messages.jsonl\",\n",
-    "    jq_schema=\".\",\n",
-    "    content_key=\"sender_name\",\n",
-    "    json_lines=True,\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "print(docs[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## JSON file with jq schema `content_key`\n",
-    "\n",
-    "To load documents from a JSON file using the `content_key` within the jq schema, set `is_content_key_jq_parsable=True`. Ensure that `content_key` is compatible and can be parsed using the jq schema."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community** and **jq**:"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "loader = JSONLoader(\n",
-    "    file_path=\"./example_data/facebook_chat.json\",\n",
-    "    jq_schema=\".messages[]\",\n",
-    "    content_key=\".content\",\n",
-    "    is_content_key_jq_parsable=True,\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "print(docs[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting metadata\n",
-    "\n",
-    "Generally, we want to include metadata available in the JSON file into the documents that we create from the content.\n",
-    "\n",
-    "The following demonstrates how metadata can be extracted using the `JSONLoader`.\n",
-    "\n",
-    "There are some key changes to be noted. In the previous example where we didn't collect the metadata, we managed to directly specify in the schema where the value for the `page_content` can be extracted from.\n",
-    "\n",
-    "In this example, we have to tell the loader to iterate over the records in the `messages` field. The jq_schema then has to be `.messages[]`\n",
-    "\n",
-    "This allows us to pass the records (dict) into the `metadata_func` that has to be implemented. The `metadata_func` is responsible for identifying which pieces of information in the record should be included in the metadata stored in the final `Document` object.\n",
-    "\n",
-    "Additionally, we now have to explicitly specify in the loader, via the `content_key` argument, the key from the record where the value for the `page_content` needs to be extracted from."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community jq "
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:\n",
+        "\n",
+        "- TODO: Update model instantiation with relevant params."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import JSONLoader\n",
+        "\n",
+        "loader = JSONLoader(\n",
+        "    file_path=\"./example_data/facebook_chat.json\",\n",
+        "    jq_schema=\".messages[].content\",\n",
+        "    text_content=False,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}, page_content='Bye!')"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pages = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    pages.append(doc)\n",
+        "    if len(pages) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(pages)\n",
+        "\n",
+        "        pages = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Read from JSON Lines file\n",
+        "\n",
+        "If you want to load documents from a JSON Lines file, you pass `json_lines=True`\n",
+        "and specify `jq_schema` to extract `page_content` from a single JSON object."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "loader = JSONLoader(\n",
+        "    file_path=\"./example_data/facebook_chat_messages.jsonl\",\n",
+        "    jq_schema=\".content\",\n",
+        "    text_content=False,\n",
+        "    json_lines=True,\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "print(docs[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Read specific content keys\n",
+        "\n",
+        "Another option is to set `jq_schema='.'` and provide a `content_key` in order to only load specific content:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "page_content='User 2' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat_messages.jsonl', 'seq_num': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "loader = JSONLoader(\n",
+        "    file_path=\"./example_data/facebook_chat_messages.jsonl\",\n",
+        "    jq_schema=\".\",\n",
+        "    content_key=\"sender_name\",\n",
+        "    json_lines=True,\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "print(docs[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## JSON file with jq schema `content_key`\n",
+        "\n",
+        "To load documents from a JSON file using the `content_key` within the jq schema, set `is_content_key_jq_parsable=True`. Ensure that `content_key` is compatible and can be parsed using the jq schema."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "page_content='Bye!' metadata={'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "loader = JSONLoader(\n",
+        "    file_path=\"./example_data/facebook_chat.json\",\n",
+        "    jq_schema=\".messages[]\",\n",
+        "    content_key=\".content\",\n",
+        "    is_content_key_jq_parsable=True,\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "print(docs[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Extracting metadata\n",
+        "\n",
+        "Generally, we want to include metadata available in the JSON file into the documents that we create from the content.\n",
+        "\n",
+        "The following demonstrates how metadata can be extracted using the `JSONLoader`.\n",
+        "\n",
+        "There are some key changes to be noted. In the previous example where we didn't collect the metadata, we managed to directly specify in the schema where the value for the `page_content` can be extracted from.\n",
+        "\n",
+        "In this example, we have to tell the loader to iterate over the records in the `messages` field. The jq_schema then has to be `.messages[]`\n",
+        "\n",
+        "This allows us to pass the records (dict) into the `metadata_func` that has to be implemented. The `metadata_func` is responsible for identifying which pieces of information in the record should be included in the metadata stored in the final `Document` object.\n",
+        "\n",
+        "Additionally, we now have to explicitly specify in the loader, via the `content_key` argument, the key from the record where the value for the `page_content` needs to be extracted from."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': '/Users/isaachershenson/Documents/langchain/docs/docs/integrations/document_loaders/example_data/facebook_chat.json', 'seq_num': 1, 'sender_name': 'User 2', 'timestamp_ms': 1675597571851}\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Define the metadata extraction function.\n",
+        "def metadata_func(record: dict, metadata: dict) -> dict:\n",
+        "    metadata[\"sender_name\"] = record.get(\"sender_name\")\n",
+        "    metadata[\"timestamp_ms\"] = record.get(\"timestamp_ms\")\n",
+        "\n",
+        "    return metadata\n",
+        "\n",
+        "\n",
+        "loader = JSONLoader(\n",
+        "    file_path=\"./example_data/facebook_chat.json\",\n",
+        "    jq_schema=\".messages[]\",\n",
+        "    content_key=\"content\",\n",
+        "    metadata_func=metadata_func,\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all JSONLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html"
+      ]
+    }
+  ],
+  "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": "3.11.9"
     }
-   ],
-   "source": [
-    "# Define the metadata extraction function.\n",
-    "def metadata_func(record: dict, metadata: dict) -> dict:\n",
-    "    metadata[\"sender_name\"] = record.get(\"sender_name\")\n",
-    "    metadata[\"timestamp_ms\"] = record.get(\"timestamp_ms\")\n",
-    "\n",
-    "    return metadata\n",
-    "\n",
-    "\n",
-    "loader = JSONLoader(\n",
-    "    file_path=\"./example_data/facebook_chat.json\",\n",
-    "    jq_schema=\".messages[]\",\n",
-    "    content_key=\"content\",\n",
-    "    metadata_func=metadata_func,\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "print(docs[0].metadata)"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all JSONLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }

docs/docs/integrations/document_loaders/mathpix.ipynb

@@ -1,178 +1,176 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MathPixPDFLoader\n",
-    "\n",
-    "Inspired by Daniel Gross's snippet here: [https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21](https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21)\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [MathPixPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| MathPixPDFLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Sign up for Mathpix and [create an API key](https://mathpix.com/docs/ocr/creating-an-api-key) to set the `MATHPIX_API_KEY` variables in your environment"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# MathPixPDFLoader\n",
+        "\n",
+        "Inspired by Daniel Gross's snippet here: [https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21](https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21)\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [MathPixPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| MathPixPDFLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Sign up for Mathpix and [create an API key](https://mathpix.com/docs/ocr/creating-an-api-key) to set the `MATHPIX_API_KEY` variables in your environment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"MATHPIX_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"MATHPIX_API_KEY\"] = getpass.getpass(\"Enter your Mathpix API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we are ready to initialize our loader:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import MathpixPDFLoader\n",
+        "\n",
+        "file_path = \"./example_data/layout-parser-paper.pdf\"\n",
+        "loader = MathpixPDFLoader(file_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all MathpixPDFLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html"
+      ]
+    }
+  ],
+  "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": "3.11.9"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"MATHPIX_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"MATHPIX_API_KEY\"] = getpass.getpass(\"Enter your Mathpix API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we are ready to initialize our loader:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import MathpixPDFLoader\n",
-    "\n",
-    "file_path = \"./example_data/layout-parser-paper.pdf\"\n",
-    "loader = MathpixPDFLoader(file_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all MathpixPDFLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }

docs/docs/integrations/document_loaders/pdfplumber.ipynb

@@ -1,183 +1,181 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# PDFPlumber\n",
-    "\n",
-    "Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [PDFPlumberLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| PDFPlumberLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed to use this loader."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import PDFPlumberLoader\n",
-    "\n",
-    "loader = PDFPlumberLoader(\"./example_data/layout-parser-paper.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}, page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recentadvancesindocumentimageanalysis(DIA)havebeen\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomescouldbeeasilydeployedinproductionandextendedforfurther\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportantinnovationsbyawideaudience.Thoughtherehavebeenon-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopmentindisciplineslikenaturallanguageprocessingandcomputer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademicresearchacross awiderangeof disciplinesinthesocialsciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitiveinterfacesforapplyingandcustomizingDLmodelsforlayoutde-\\ntection,characterrecognition,andmanyotherdocumentprocessingtasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: DocumentImageAnalysis·DeepLearning·LayoutAnalysis\\n· Character Recognition · Open Source library · Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocumentimageanalysis(DIA)tasksincludingdocumentimageclassification[11,\\n1202\\nnuJ\\n12\\n]VC.sc[\\n2v84351.3012:viXra\\n')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# PDFPlumber\n",
+        "\n",
+        "Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [PDFPlumberLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| PDFPlumberLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed to use this loader."
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}\n"
-     ]
+      "cell_type": "markdown",
+      "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,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import PDFPlumberLoader\n",
+        "\n",
+        "loader = PDFPlumberLoader(\"./example_data/layout-parser-paper.pdf\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}, page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recentadvancesindocumentimageanalysis(DIA)havebeen\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomescouldbeeasilydeployedinproductionandextendedforfurther\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportantinnovationsbyawideaudience.Thoughtherehavebeenon-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopmentindisciplineslikenaturallanguageprocessingandcomputer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademicresearchacross awiderangeof disciplinesinthesocialsciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitiveinterfacesforapplyingandcustomizingDLmodelsforlayoutde-\\ntection,characterrecognition,andmanyotherdocumentprocessingtasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: DocumentImageAnalysis·DeepLearning·LayoutAnalysis\\n· Character Recognition · Open Source library · Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocumentimageanalysis(DIA)tasksincludingdocumentimageclassification[11,\\n1202\\nnuJ\\n12\\n]VC.sc[\\n2v84351.3012:viXra\\n')"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all PDFPlumberLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html"
+      ]
+    }
+  ],
+  "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": "3.11.9"
     }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all PDFPlumberLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }

docs/docs/integrations/document_loaders/pypdfdirectory.ipynb

@@ -1,230 +1,228 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# PyPDFDirectoryLoader\n",
-    "\n",
-    "This loader loads all PDF files from a specific directory.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [PyPDFDirectoryLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| PyPDFDirectoryLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed for this loader."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:08.878423Z",
-     "start_time": "2025-01-21T08:00:08.876042Z"
-    }
-   },
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ],
-   "outputs": [],
-   "execution_count": 1
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community**."
-   ]
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:12.003718Z",
-     "start_time": "2025-01-21T08:00:10.291617Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "%pip install -qU langchain_community pypdf pillow",
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "execution_count": 2
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:"
-   ]
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:18.512061Z",
-     "start_time": "2025-01-21T08:00:17.313969Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "from langchain_community.document_loaders import PyPDFDirectoryLoader\n",
-    "\n",
-    "directory_path = (\n",
-    "    \"../../docs/integrations/document_loaders/example_data/layout-parser-paper.pdf\"\n",
-    ")\n",
-    "loader = PyPDFDirectoryLoader(\"example_data/\")"
-   ],
-   "outputs": [],
-   "execution_count": 3
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:23.549752Z",
-     "start_time": "2025-01-21T08:00:23.129010Z"
-    }
-   },
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ],
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}, page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (\\x00 ), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\\n· Character Recognition · Open Source library · Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [11,\\narXiv:2103.15348v2  [cs.CV]  21 Jun 2021')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# PyPDFDirectoryLoader\n",
+        "\n",
+        "This loader loads all PDF files from a specific directory.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "\n",
+        "| Class | Package | Local | Serializable | JS support|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [PyPDFDirectoryLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| PyPDFDirectoryLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed for this loader."
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 4
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:26.612346Z",
-     "start_time": "2025-01-21T08:00:26.609051Z"
-    }
-   },
-   "source": [
-    "print(docs[0].metadata)"
-   ],
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}\n"
-     ]
+      "cell_type": "markdown",
+      "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": 1,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:08.878423Z",
+          "start_time": "2025-01-21T08:00:08.876042Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:12.003718Z",
+          "start_time": "2025-01-21T08:00:10.291617Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": "%pip install -qU langchain_community pypdf pillow"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:18.512061Z",
+          "start_time": "2025-01-21T08:00:17.313969Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import PyPDFDirectoryLoader\n",
+        "\n",
+        "directory_path = (\n",
+        "    \"../../docs/integrations/document_loaders/example_data/layout-parser-paper.pdf\"\n",
+        ")\n",
+        "loader = PyPDFDirectoryLoader(\"example_data/\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:23.549752Z",
+          "start_time": "2025-01-21T08:00:23.129010Z"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}, page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (\\x00 ), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\\n· Character Recognition · Open Source library · Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [11,\\narXiv:2103.15348v2  [cs.CV]  21 Jun 2021')"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:26.612346Z",
+          "start_time": "2025-01-21T08:00:26.609051Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2025-01-21T08:00:30.251598Z",
+          "start_time": "2025-01-21T08:00:29.972141Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all PyPDFDirectoryLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": ""
     }
-   ],
-   "execution_count": 5
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-01-21T08:00:30.251598Z",
-     "start_time": "2025-01-21T08:00:29.972141Z"
+  ],
+  "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": "3.11.9"
     }
-   },
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ],
-   "outputs": [],
-   "execution_count": 6
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all PyPDFDirectoryLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html"
-   ]
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": ""
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }

docs/docs/integrations/document_loaders/sitemap.ipynb

@@ -1,380 +1,378 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sitemap\n",
-    "\n",
-    "Extends from the `WebBaseLoader`, `SitemapLoader` loads a sitemap from a given URL, and then scrapes and loads all pages in the sitemap, returning each page as a Document.\n",
-    "\n",
-    "The scraping is done concurrently. There are reasonable limits to concurrent requests, defaulting to 2 per second.  If you aren't concerned about being a good citizen, or you control the scrapped server, or don't care about load you can increase this limit. Note, while this will speed up the scraping process, it may cause the server to block you. Be careful!\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/sitemap/)|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [SiteMapLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| SiteMapLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access SiteMap document loader you'll need to install the `langchain-community` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed to run this."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Fix notebook asyncio bug"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nest_asyncio\n",
-    "\n",
-    "nest_asyncio.apply()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders.sitemap import SitemapLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitemap_loader = SitemapLoader(web_path=\"https://api.python.langchain.com/sitemap.xml\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fetching pages: 100%|##########| 28/28 [00:04<00:00,  6.83it/s]\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Sitemap\n",
+        "\n",
+        "Extends from the `WebBaseLoader`, `SitemapLoader` loads a sitemap from a given URL, and then scrapes and loads all pages in the sitemap, returning each page as a Document.\n",
+        "\n",
+        "The scraping is done concurrently. There are reasonable limits to concurrent requests, defaulting to 2 per second.  If you aren't concerned about being a good citizen, or you control the scrapped server, or don't care about load you can increase this limit. Note, while this will speed up the scraping process, it may cause the server to block you. Be careful!\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/sitemap/)|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [SiteMapLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| SiteMapLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access SiteMap document loader you'll need to install the `langchain-community` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed to run this."
+      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-05-15T00:29:42.163001+00:00', 'changefreq': 'weekly', 'priority': '1'}, page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n')"
+      "cell_type": "markdown",
+      "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,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = sitemap_loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-05-15T00:29:42.163001+00:00', 'changefreq': 'weekly', 'priority': '1'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can change the `requests_per_second` parameter to increase the max concurrent requests. and use `requests_kwargs` to pass kwargs when send requests."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitemap_loader.requests_per_second = 2\n",
-    "# Optional: avoid `[SSL: CERTIFICATE_VERIFY_FAILED]` issue\n",
-    "sitemap_loader.requests_kwargs = {\"verify\": False}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load\n",
-    "\n",
-    "You can also load the pages lazily in order to minimize the memory load."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fetching pages: 100%|##########| 28/28 [00:01<00:00, 19.06it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "page = []\n",
-    "for doc in sitemap_loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Filtering sitemap URLs\n",
-    "\n",
-    "Sitemaps can be massive files, with thousands of URLs.  Often you don't need every single one of them.  You can filter the URLs by passing a list of strings or regex patterns to the `filter_urls` parameter.  Only URLs that match one of the patterns will be loaded."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = SitemapLoader(\n",
-    "    web_path=\"https://api.python.langchain.com/sitemap.xml\",\n",
-    "    filter_urls=[\"https://api.python.langchain.com/en/latest\"],\n",
-    ")\n",
-    "documents = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/latest/', 'loc': 'https://api.python.langchain.com/en/latest/', 'lastmod': '2024-02-12T05:26:10.971077+00:00', 'changefreq': 'daily', 'priority': '0.9'})"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Fix notebook asyncio bug"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import nest_asyncio\n",
+        "\n",
+        "nest_asyncio.apply()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders.sitemap import SitemapLoader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sitemap_loader = SitemapLoader(web_path=\"https://api.python.langchain.com/sitemap.xml\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Fetching pages: 100%|##########| 28/28 [00:04<00:00,  6.83it/s]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-05-15T00:29:42.163001+00:00', 'changefreq': 'weekly', 'priority': '1'}, page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n')"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = sitemap_loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-05-15T00:29:42.163001+00:00', 'changefreq': 'weekly', 'priority': '1'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can change the `requests_per_second` parameter to increase the max concurrent requests. and use `requests_kwargs` to pass kwargs when send requests."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sitemap_loader.requests_per_second = 2\n",
+        "# Optional: avoid `[SSL: CERTIFICATE_VERIFY_FAILED]` issue\n",
+        "sitemap_loader.requests_kwargs = {\"verify\": False}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load\n",
+        "\n",
+        "You can also load the pages lazily in order to minimize the memory load."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Fetching pages: 100%|##########| 28/28 [00:01<00:00, 19.06it/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "page = []\n",
+        "for doc in sitemap_loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Filtering sitemap URLs\n",
+        "\n",
+        "Sitemaps can be massive files, with thousands of URLs.  Often you don't need every single one of them.  You can filter the URLs by passing a list of strings or regex patterns to the `filter_urls` parameter.  Only URLs that match one of the patterns will be loaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "loader = SitemapLoader(\n",
+        "    web_path=\"https://api.python.langchain.com/sitemap.xml\",\n",
+        "    filter_urls=[\"https://api.python.langchain.com/en/latest\"],\n",
+        ")\n",
+        "documents = loader.load()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "scrolled": true
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/latest/', 'loc': 'https://api.python.langchain.com/en/latest/', 'lastmod': '2024-02-12T05:26:10.971077+00:00', 'changefreq': 'daily', 'priority': '0.9'})"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "documents[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Add custom scraping rules\n",
+        "\n",
+        "The `SitemapLoader` uses `beautifulsoup4` for the scraping process, and it scrapes every element on the page by default. The `SitemapLoader` constructor accepts a custom scraping function. This feature can be helpful to tailor the scraping process to your specific needs; for example, you might want to avoid scraping headers or navigation elements.\n",
+        "\n",
+        " The following example shows how to develop and use a custom function to avoid navigation and header elements."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Import the `beautifulsoup4` library and define the custom function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pip install beautifulsoup4"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from bs4 import BeautifulSoup\n",
+        "\n",
+        "\n",
+        "def remove_nav_and_header_elements(content: BeautifulSoup) -> str:\n",
+        "    # Find all 'nav' and 'header' elements in the BeautifulSoup object\n",
+        "    nav_elements = content.find_all(\"nav\")\n",
+        "    header_elements = content.find_all(\"header\")\n",
+        "\n",
+        "    # Remove each 'nav' and 'header' element from the BeautifulSoup object\n",
+        "    for element in nav_elements + header_elements:\n",
+        "        element.decompose()\n",
+        "\n",
+        "    return str(content.get_text())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Add your custom function to the `SitemapLoader` object."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "loader = SitemapLoader(\n",
+        "    \"https://api.python.langchain.com/sitemap.xml\",\n",
+        "    filter_urls=[\"https://api.python.langchain.com/en/latest/\"],\n",
+        "    parsing_function=remove_nav_and_header_elements,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Local Sitemap\n",
+        "\n",
+        "The sitemap loader can also be used to load local files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "sitemap_loader = SitemapLoader(web_path=\"example_data/sitemap.xml\", is_local=True)\n",
+        "\n",
+        "docs = sitemap_loader.load()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all SiteMapLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader"
       ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "documents[0]"
-   ]
+  ],
+  "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"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Add custom scraping rules\n",
-    "\n",
-    "The `SitemapLoader` uses `beautifulsoup4` for the scraping process, and it scrapes every element on the page by default. The `SitemapLoader` constructor accepts a custom scraping function. This feature can be helpful to tailor the scraping process to your specific needs; for example, you might want to avoid scraping headers or navigation elements.\n",
-    "\n",
-    " The following example shows how to develop and use a custom function to avoid navigation and header elements."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Import the `beautifulsoup4` library and define the custom function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install beautifulsoup4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bs4 import BeautifulSoup\n",
-    "\n",
-    "\n",
-    "def remove_nav_and_header_elements(content: BeautifulSoup) -> str:\n",
-    "    # Find all 'nav' and 'header' elements in the BeautifulSoup object\n",
-    "    nav_elements = content.find_all(\"nav\")\n",
-    "    header_elements = content.find_all(\"header\")\n",
-    "\n",
-    "    # Remove each 'nav' and 'header' element from the BeautifulSoup object\n",
-    "    for element in nav_elements + header_elements:\n",
-    "        element.decompose()\n",
-    "\n",
-    "    return str(content.get_text())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Add your custom function to the `SitemapLoader` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = SitemapLoader(\n",
-    "    \"https://api.python.langchain.com/sitemap.xml\",\n",
-    "    filter_urls=[\"https://api.python.langchain.com/en/latest/\"],\n",
-    "    parsing_function=remove_nav_and_header_elements,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Local Sitemap\n",
-    "\n",
-    "The sitemap loader can also be used to load local files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitemap_loader = SitemapLoader(web_path=\"example_data/sitemap.xml\", is_local=True)\n",
-    "\n",
-    "docs = sitemap_loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all SiteMapLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }

docs/docs/integrations/document_loaders/unstructured_markdown.ipynb

@@ -1,269 +1,267 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# UnstructuredMarkdownLoader\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with UnstructuredMarkdown [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html).\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [UnstructuredMarkdownLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ✅ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| UnstructuredMarkdownLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access UnstructuredMarkdownLoader document loader you'll need to install the `langchain-community` integration package and the `unstructured` python package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed to use this loader."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community** and **unstructured**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community unstructured"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents. \n",
-    "\n",
-    "You can run the loader in one of two modes: \"single\" and \"elements\". If you use \"single\" mode, the document will be returned as a single `Document` object. If you use \"elements\" mode, the unstructured library will split the document into elements such as `Title` and `NarrativeText`. You can pass in additional `unstructured` kwargs after mode to apply different `unstructured` settings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import UnstructuredMarkdownLoader\n",
-    "\n",
-    "loader = UnstructuredMarkdownLoader(\n",
-    "    \"./example_data/example.md\",\n",
-    "    mode=\"single\",\n",
-    "    strategy=\"fast\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/example.md'}, page_content='Sample Markdown Document\\n\\nIntroduction\\n\\nWelcome to this sample Markdown document. Markdown is a lightweight markup language used for formatting text. It\\'s widely used for documentation, readme files, and more.\\n\\nFeatures\\n\\nHeaders\\n\\nMarkdown supports multiple levels of headers:\\n\\nHeader 1: # Header 1\\n\\nHeader 2: ## Header 2\\n\\nHeader 3: ### Header 3\\n\\nLists\\n\\nUnordered List\\n\\nItem 1\\n\\nItem 2\\n\\nSubitem 2.1\\n\\nSubitem 2.2\\n\\nOrdered List\\n\\nFirst item\\n\\nSecond item\\n\\nThird item\\n\\nLinks\\n\\nOpenAI is an AI research organization.\\n\\nImages\\n\\nHere\\'s an example image:\\n\\nCode\\n\\nInline Code\\n\\nUse code for inline code snippets.\\n\\nCode Block\\n\\n```python def greet(name): return f\"Hello, {name}!\"\\n\\nprint(greet(\"World\")) ```')"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# UnstructuredMarkdownLoader\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with UnstructuredMarkdown [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [UnstructuredMarkdownLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ✅ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| UnstructuredMarkdownLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access UnstructuredMarkdownLoader document loader you'll need to install the `langchain-community` integration package and the `unstructured` python package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed to use this loader."
       ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': './example_data/example.md'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
     {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/example.md', 'link_texts': ['OpenAI'], 'link_urls': ['https://www.openai.com'], 'last_modified': '2024-08-14T15:04:18', 'languages': ['eng'], 'parent_id': 'de1f74bf226224377ab4d8b54f215bb9', 'filetype': 'text/markdown', 'file_directory': './example_data', 'filename': 'example.md', 'category': 'NarrativeText', 'element_id': '898a542a261f7dc65e0072d1e847d535'}, page_content='OpenAI is an AI research organization.')"
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
       ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []\n",
-    "page[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Elements\n",
-    "\n",
-    "In this example we will load in the `elements` mode, which will return a list of the different elements in the markdown document:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "29"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community** and **unstructured**"
       ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_community.document_loaders import UnstructuredMarkdownLoader\n",
-    "\n",
-    "loader = UnstructuredMarkdownLoader(\n",
-    "    \"./example_data/example.md\",\n",
-    "    mode=\"elements\",\n",
-    "    strategy=\"fast\",\n",
-    ")\n",
-    "\n",
-    "docs = loader.load()\n",
-    "len(docs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you see there are 29 elements that were pulled from the `example.md` file. The first element is the title of the document as expected:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "'Sample Markdown Document'"
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community unstructured"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents. \n",
+        "\n",
+        "You can run the loader in one of two modes: \"single\" and \"elements\". If you use \"single\" mode, the document will be returned as a single `Document` object. If you use \"elements\" mode, the unstructured library will split the document into elements such as `Title` and `NarrativeText`. You can pass in additional `unstructured` kwargs after mode to apply different `unstructured` settings."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import UnstructuredMarkdownLoader\n",
+        "\n",
+        "loader = UnstructuredMarkdownLoader(\n",
+        "    \"./example_data/example.md\",\n",
+        "    mode=\"single\",\n",
+        "    strategy=\"fast\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/example.md'}, page_content='Sample Markdown Document\\n\\nIntroduction\\n\\nWelcome to this sample Markdown document. Markdown is a lightweight markup language used for formatting text. It\\'s widely used for documentation, readme files, and more.\\n\\nFeatures\\n\\nHeaders\\n\\nMarkdown supports multiple levels of headers:\\n\\nHeader 1: # Header 1\\n\\nHeader 2: ## Header 2\\n\\nHeader 3: ### Header 3\\n\\nLists\\n\\nUnordered List\\n\\nItem 1\\n\\nItem 2\\n\\nSubitem 2.1\\n\\nSubitem 2.2\\n\\nOrdered List\\n\\nFirst item\\n\\nSecond item\\n\\nThird item\\n\\nLinks\\n\\nOpenAI is an AI research organization.\\n\\nImages\\n\\nHere\\'s an example image:\\n\\nCode\\n\\nInline Code\\n\\nUse code for inline code snippets.\\n\\nCode Block\\n\\n```python def greet(name): return f\"Hello, {name}!\"\\n\\nprint(greet(\"World\")) ```')"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': './example_data/example.md'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/example.md', 'link_texts': ['OpenAI'], 'link_urls': ['https://www.openai.com'], 'last_modified': '2024-08-14T15:04:18', 'languages': ['eng'], 'parent_id': 'de1f74bf226224377ab4d8b54f215bb9', 'filetype': 'text/markdown', 'file_directory': './example_data', 'filename': 'example.md', 'category': 'NarrativeText', 'element_id': '898a542a261f7dc65e0072d1e847d535'}, page_content='OpenAI is an AI research organization.')"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []\n",
+        "page[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load Elements\n",
+        "\n",
+        "In this example we will load in the `elements` mode, which will return a list of the different elements in the markdown document:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "29"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_community.document_loaders import UnstructuredMarkdownLoader\n",
+        "\n",
+        "loader = UnstructuredMarkdownLoader(\n",
+        "    \"./example_data/example.md\",\n",
+        "    mode=\"elements\",\n",
+        "    strategy=\"fast\",\n",
+        ")\n",
+        "\n",
+        "docs = loader.load()\n",
+        "len(docs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As you see there are 29 elements that were pulled from the `example.md` file. The first element is the title of the document as expected:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'Sample Markdown Document'"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs[0].page_content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all UnstructuredMarkdownLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html"
       ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "docs[0].page_content"
-   ]
+  ],
+  "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": "3.11.9"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all UnstructuredMarkdownLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html"
-   ]
-  }
- ],
- "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }

docs/docs/integrations/document_loaders/xml.ipynb

@@ -1,202 +1,200 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "72ccbe2b",
-   "metadata": {},
-   "source": [
-    "# UnstructuredXMLLoader\n",
-    "\n",
-    "This notebook provides a quick overview for getting started with UnstructuredXMLLoader [document loader](https://python.langchain.com/docs/concepts/document_loaders). The `UnstructuredXMLLoader` is used to load `XML` files. The loader works with `.xml` files. The page content will be the text extracted from the XML tags.\n",
-    "\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
-    "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [UnstructuredXMLLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
-    "### Loader features\n",
-    "| Source | Document Lazy Loading | Native Async Support\n",
-    "| :---: | :---: | :---: | \n",
-    "| UnstructuredXMLLoader | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access UnstructuredXMLLoader document loader you'll need to install the `langchain-community` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "No credentials are needed to use the UnstructuredXMLLoader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc4ba987",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9fa4d5e5",
-   "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": "38e53f22",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "Install **langchain_community**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fcd320ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain_community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a102f199",
-   "metadata": {},
-   "source": [
-    "## Initialization\n",
-    "\n",
-    "Now we can instantiate our model object and load documents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2d198582",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import UnstructuredXMLLoader\n",
-    "\n",
-    "loader = UnstructuredXMLLoader(\n",
-    "    \"./example_data/factbook.xml\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9bbb463c",
-   "metadata": {},
-   "source": [
-    "## Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cd875e75",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "Document(metadata={'source': './example_data/factbook.xml'}, page_content='United States\\n\\nWashington, DC\\n\\nJoe Biden\\n\\nBaseball\\n\\nCanada\\n\\nOttawa\\n\\nJustin Trudeau\\n\\nHockey\\n\\nFrance\\n\\nParis\\n\\nEmmanuel Macron\\n\\nSoccer\\n\\nTrinidad & Tobado\\n\\nPort of Spain\\n\\nKeith Rowley\\n\\nTrack & Field')"
+      "cell_type": "markdown",
+      "id": "72ccbe2b",
+      "metadata": {},
+      "source": [
+        "# UnstructuredXMLLoader\n",
+        "\n",
+        "This notebook provides a quick overview for getting started with UnstructuredXMLLoader [document loader](https://python.langchain.com/docs/concepts/document_loaders). The `UnstructuredXMLLoader` is used to load `XML` files. The loader works with `.xml` files. The page content will be the text extracted from the XML tags.\n",
+        "\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
+        "| :--- | :--- | :---: | :---: |  :---: |\n",
+        "| [UnstructuredXMLLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+        "### Loader features\n",
+        "| Source | Document Lazy Loading | Native Async Support\n",
+        "| :---: | :---: | :---: | \n",
+        "| UnstructuredXMLLoader | ✅ | ❌ | \n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access UnstructuredXMLLoader document loader you'll need to install the `langchain-community` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "No credentials are needed to use the UnstructuredXMLLoader"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs = loader.load()\n",
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "79b52cc0",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'source': './example_data/factbook.xml'}\n"
-     ]
+      "cell_type": "markdown",
+      "id": "fc4ba987",
+      "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": "9fa4d5e5",
+      "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": "38e53f22",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "Install **langchain_community**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fcd320ec",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain_community"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a102f199",
+      "metadata": {},
+      "source": [
+        "## Initialization\n",
+        "\n",
+        "Now we can instantiate our model object and load documents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "2d198582",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_community.document_loaders import UnstructuredXMLLoader\n",
+        "\n",
+        "loader = UnstructuredXMLLoader(\n",
+        "    \"./example_data/factbook.xml\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9bbb463c",
+      "metadata": {},
+      "source": [
+        "## Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "cd875e75",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Document(metadata={'source': './example_data/factbook.xml'}, page_content='United States\\n\\nWashington, DC\\n\\nJoe Biden\\n\\nBaseball\\n\\nCanada\\n\\nOttawa\\n\\nJustin Trudeau\\n\\nHockey\\n\\nFrance\\n\\nParis\\n\\nEmmanuel Macron\\n\\nSoccer\\n\\nTrinidad & Tobado\\n\\nPort of Spain\\n\\nKeith Rowley\\n\\nTrack & Field')"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs = loader.load()\n",
+        "docs[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "79b52cc0",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'source': './example_data/factbook.xml'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(docs[0].metadata)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "557608e5",
+      "metadata": {},
+      "source": [
+        "## Lazy Load"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "e3b9e75c",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "page = []\n",
+        "for doc in loader.lazy_load():\n",
+        "    page.append(doc)\n",
+        "    if len(page) >= 10:\n",
+        "        # do some paged operation, e.g.\n",
+        "        # index.upsert(page)\n",
+        "\n",
+        "        page = []"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "712aa98f",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
     }
-   ],
-   "source": [
-    "print(docs[0].metadata)"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "557608e5",
-   "metadata": {},
-   "source": [
-    "## Lazy Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e3b9e75c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "page = []\n",
-    "for doc in loader.lazy_load():\n",
-    "    page.append(doc)\n",
-    "    if len(page) >= 10:\n",
-    "        # do some paged operation, e.g.\n",
-    "        # index.upsert(page)\n",
-    "\n",
-    "        page = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "712aa98f",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

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

@@ -11,39 +11,41 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[RankLLM](https://github.com/castorini/rank_llm) offers a suite of listwise rerankers, albeit with focus on open source LLMs finetuned for the task - RankVicuna and RankZephyr being two of them."
+    "**[RankLLM](https://github.com/castorini/rank_llm)** is a **flexible reranking framework** supporting **listwise, pairwise, and pointwise ranking models**. It includes **RankVicuna, RankZephyr, MonoT5, DuoT5, LiT5, and FirstMistral**, with integration for **FastChat, vLLM, SGLang, and TensorRT-LLM** for efficient inference. RankLLM is optimized for **retrieval and ranking tasks**, leveraging both **open-source LLMs** and proprietary rerankers like **RankGPT and RankGemini**. It supports **batched inference, first-token reranking, and retrieval via BM25 and SPLADE**.\n",
+    "\n",
+    "> **Note:** If using the built-in retriever, RankLLM requires **Pyserini, JDK 21, PyTorch, and Faiss** for retrieval functionality."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  rank_llm"
+    "%pip install --upgrade --quiet rank_llm"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain_openai"
+    "%pip install --upgrade --quiet langchain_openai"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  faiss-cpu"
+    "%pip install --upgrade --quiet faiss-cpu"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +66,7 @@
     "def pretty_print_docs(docs):\n",
     "    print(\n",
     "        f\"\\n{'-' * 100}\\n\".join(\n",
-    "            [f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]\n",
+    "            [f\"Document {i + 1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]\n",
     "        )\n",
     "    )"
    ]
@@ -79,9 +81,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2025-02-22 15:28:58,344 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain_community.document_loaders import TextLoader\n",
     "from langchain_community.vectorstores import FAISS\n",
@@ -114,14 +124,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2025-02-17 04:37:08,458 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+      "2025-02-22 15:29:00,892 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
      ]
     },
     {
@@ -331,27 +341,41 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Retrieval + Reranking with RankZephyr"
+    "RankZephyr performs listwise reranking for improved retrieval quality but requires at least 24GB of VRAM to run efficiently."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading shards: 100%|██████████| 3/3 [00:00<00:00, 2674.37it/s]\n",
+      "Loading checkpoint shards: 100%|██████████| 3/3 [01:49<00:00, 36.39s/it]\n"
+     ]
+    }
+   ],
    "source": [
+    "import torch\n",
     "from langchain.retrievers.contextual_compression import ContextualCompressionRetriever\n",
     "from langchain_community.document_compressors.rankllm_rerank import RankLLMRerank\n",
     "\n",
-    "compressor = RankLLMRerank(top_n=3, model=\"zephyr\")\n",
+    "torch.cuda.empty_cache()\n",
+    "\n",
+    "compressor = RankLLMRerank(top_n=3, model=\"rank_zephyr\")\n",
     "compression_retriever = ContextualCompressionRetriever(\n",
     "    base_compressor=compressor, base_retriever=retriever\n",
-    ")"
+    ")\n",
+    "\n",
+    "del compressor"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -386,7 +410,7 @@
      ]
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "\n"
@@ -407,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -432,7 +456,7 @@
     "    llm=ChatOpenAI(temperature=0), retriever=compression_retriever\n",
     ")\n",
     "\n",
-    "chain({\"query\": query})"
+    "chain.invoke({\"query\": query})"
    ]
   },
   {
@@ -451,9 +475,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2025-02-22 15:01:29,469 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -683,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -698,9 +729,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2025-02-22 15:01:38,554 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+      "  0%|          | 0/1 [00:00<?, ?it/s]2025-02-22 15:01:43,704 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+      "100%|██████████| 1/1 [00:05<00:00,  5.15s/it]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -727,7 +767,7 @@
      ]
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "\n"
@@ -748,15 +788,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_2153001/1437145854.py:10: LangChainDeprecationWarning: The method `Chain.__call__` was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
-      "  chain({\"query\": query})\n",
+      "  chain.invoke({\"query\": query})\n",
       "2025-02-17 04:30:00,016 - INFO - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
       "  0%|          | 0/1 [00:00<?, ?it/s]2025-02-17 04:30:01,649 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
       "100%|██████████| 1/1 [00:01<00:00,  1.63s/it]\n",
@@ -785,13 +824,13 @@
     "    llm=ChatOpenAI(temperature=0), retriever=compression_retriever\n",
     ")\n",
     "\n",
-    "chain({\"query\": query})"
+    "chain.invoke({\"query\": query})"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rankllm",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -805,7 +844,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,

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/bittensor.ipynb

@@ -48,7 +48,7 @@
     "print(f\"Response provided by LLM with system prompt set is : {sys_resp}\")\n",
     "\n",
     "# The top_responses parameter can give multiple responses based on its parameter value\n",
-    "# This below code retrive top 10 miner's response all the response are in format of json\n",
+    "# This below code retrieve top 10 miner's response all the response are in format of json\n",
     "\n",
     "# Json response structure is\n",
     "\"\"\" {\n",

docs/docs/integrations/llms/openai.ipynb

@@ -1,263 +1,261 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9597802c",
-   "metadata": {},
-   "source": [
-    "# OpenAI\n",
-    "\n",
-    ":::caution\n",
-    "You are currently on a page documenting the use of OpenAI [text completion models](/docs/concepts/text_llms). The latest and most popular OpenAI models are [chat completion models](/docs/concepts/chat_models).\n",
-    "\n",
-    "Unless you are specifically using `gpt-3.5-turbo-instruct`, you are probably looking for [this page instead](/docs/integrations/chat/openai/).\n",
-    ":::\n",
-    "\n",
-    "[OpenAI](https://platform.openai.com/docs/introduction) offers a spectrum of models with different levels of power suitable for different tasks.\n",
-    "\n",
-    "This example goes over how to use LangChain to interact with `OpenAI` [models](https://platform.openai.com/docs/models)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74312161",
-   "metadata": {},
-   "source": [
-    "## Overview\n",
-    "\n",
-    "### Integration details\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
-    "\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access OpenAI models you'll need to create an OpenAI account, get an API key, and install the `langchain-openai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to https://platform.openai.com to sign up to 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": "efcdb2b6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5d528fa",
-   "metadata": {},
-   "source": [
-    "If you want to get automated best in-class tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "52fa46e8",
-   "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": "0fad78d8",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain OpenAI integration lives in the `langchain-openai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2e300149",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "129a3275",
-   "metadata": {},
-   "source": [
-    "Should you need to specify your organization ID, you can use the following cell. However, it is not required if you are only part of a single organization or intend to use your default organization. You can check your default organization [here](https://platform.openai.com/account/api-keys).\n",
-    "\n",
-    "To specify your organization, you can use this:\n",
-    "```python\n",
-    "OPENAI_ORGANIZATION = getpass()\n",
-    "\n",
-    "os.environ[\"OPENAI_ORGANIZATION\"] = OPENAI_ORGANIZATION\n",
-    "```\n",
-    "\n",
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6fb585dd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_openai import OpenAI\n",
-    "\n",
-    "llm = OpenAI()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "464003c1",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "85b49da0",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "'\\n\\nI am an AI and do not have emotions like humans do, so I am always functioning at my optimal level. Thank you for asking! How can I assist you today?'"
+      "cell_type": "markdown",
+      "id": "9597802c",
+      "metadata": {},
+      "source": [
+        "# OpenAI\n",
+        "\n",
+        ":::caution\n",
+        "You are currently on a page documenting the use of OpenAI [text completion models](/docs/concepts/text_llms). The latest and most popular OpenAI models are [chat completion models](/docs/concepts/chat_models).\n",
+        "\n",
+        "Unless you are specifically using `gpt-3.5-turbo-instruct`, you are probably looking for [this page instead](/docs/integrations/chat/openai/).\n",
+        ":::\n",
+        "\n",
+        "[OpenAI](https://platform.openai.com/docs/introduction) offers a spectrum of models with different levels of power suitable for different tasks.\n",
+        "\n",
+        "This example goes over how to use LangChain to interact with `OpenAI` [models](https://platform.openai.com/docs/models)"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm.invoke(\"Hello how are you?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b7e0dfc",
-   "metadata": {},
-   "source": [
-    "## Chaining"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a641dbd9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "'\\nIch liebe Programmieren.'"
+      "cell_type": "markdown",
+      "id": "74312161",
+      "metadata": {},
+      "source": [
+        "## Overview\n",
+        "\n",
+        "### Integration details\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
+        "\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access OpenAI models you'll need to create an OpenAI account, get an API key, and install the `langchain-openai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to https://platform.openai.com to sign up to 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": "efcdb2b6",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"OPENAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f5d528fa",
+      "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": 2,
+      "id": "52fa46e8",
+      "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": "0fad78d8",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain OpenAI integration lives in the `langchain-openai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2e300149",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-openai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "129a3275",
+      "metadata": {},
+      "source": [
+        "Should you need to specify your organization ID, you can use the following cell. However, it is not required if you are only part of a single organization or intend to use your default organization. You can check your default organization [here](https://platform.openai.com/account/api-keys).\n",
+        "\n",
+        "To specify your organization, you can use this:\n",
+        "```python\n",
+        "OPENAI_ORGANIZATION = getpass()\n",
+        "\n",
+        "os.environ[\"OPENAI_ORGANIZATION\"] = OPENAI_ORGANIZATION\n",
+        "```\n",
+        "\n",
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "6fb585dd",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "from langchain_openai import OpenAI\n",
+        "\n",
+        "llm = OpenAI()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "464003c1",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "85b49da0",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'\\n\\nI am an AI and do not have emotions like humans do, so I am always functioning at my optimal level. Thank you for asking! How can I assist you today?'"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "llm.invoke(\"Hello how are you?\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b7e0dfc",
+      "metadata": {},
+      "source": [
+        "## Chaining"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "a641dbd9",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'\\nIch liebe Programmieren.'"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import PromptTemplate\n",
+        "\n",
+        "prompt = PromptTemplate.from_template(\"How to say {input} in {output_language}:\\n\")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "58a9ddb1",
+      "metadata": {},
+      "source": [
+        "## Using a proxy\n",
+        "\n",
+        "If you are behind an explicit proxy, you can specify the http_client to pass through"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "55142cec",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install httpx\n",
+        "\n",
+        "import httpx\n",
+        "\n",
+        "openai = OpenAI(\n",
+        "    model_name=\"gpt-3.5-turbo-instruct\",\n",
+        "    http_client=httpx.Client(proxies=\"http://proxy.yourcompany.com:8080\"),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "id": "2fd99e97-013f-4c28-bb47-426faa42a2cf",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `OpenAI` llm features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/llms/langchain_openai.llms.base.OpenAI.html"
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
-    "\n",
-    "prompt = PromptTemplate.from_template(\"How to say {input} in {output_language}:\\n\")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+      }
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "58a9ddb1",
-   "metadata": {},
-   "source": [
-    "## Using a proxy\n",
-    "\n",
-    "If you are behind an explicit proxy, you can specify the http_client to pass through"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "55142cec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install httpx\n",
-    "\n",
-    "import httpx\n",
-    "\n",
-    "openai = OpenAI(\n",
-    "    model_name=\"gpt-3.5-turbo-instruct\",\n",
-    "    http_client=httpx.Client(proxies=\"http://proxy.yourcompany.com:8080\"),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "2fd99e97-013f-4c28-bb47-426faa42a2cf",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `OpenAI` llm features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/llms/langchain_openai.llms.base.OpenAI.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

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.