x

This reverts commit 08574848cc.

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,8 +1,8 @@
 Thank you for contributing to LangChain!
 
 - [ ] **PR title**: "package: description"
-  - Where "package" is whichever of langchain, community, core, etc. is being modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI changes.
-  - Example: "community: add foobar LLM"
+  - Where "package" is whichever of langchain, core, etc. is being modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI changes.
+  - Example: "core: add foobar LLM"
 
 
 - [ ] **PR message**: ***Delete this entire checklist*** and replace with
@@ -24,6 +24,5 @@ Additional guidelines:
 - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests.
 - Most PRs should not touch more than one package.
 - Changes should be backwards compatible.
-- If you are adding something to community, do not re-import it in langchain.
 
 If no one reviews your PR within a few days, please @-mention one of baskaryan, eyurtsev, ccurme, vbarda, hwchase17.

.github/scripts/check_diff.py

@@ -16,7 +16,6 @@ LANGCHAIN_DIRS = [
     "libs/core",
     "libs/text-splitters",
     "libs/langchain",
-    "libs/community",
 ]
 
 # when set to True, we are ignoring core dependents
@@ -38,8 +37,8 @@ IGNORED_PARTNERS = [
 ]
 
 PY_312_MAX_PACKAGES = [
-    "libs/partners/huggingface",  # https://github.com/pytorch/pytorch/issues/130249
     "libs/partners/voyageai",
+    "libs/partners/chroma", # https://github.com/chroma-core/chroma/issues/4382
 ]
 
 
@@ -134,12 +133,6 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     elif dir_ == "libs/langchain" and job == "extended-tests":
         py_versions = ["3.9", "3.13"]
 
-    elif dir_ == "libs/community" and job == "extended-tests":
-        py_versions = ["3.9", "3.12"]
-
-    elif dir_ == "libs/community" and job == "compile-integration-tests":
-        # community integration deps are slow in 3.12
-        py_versions = ["3.9", "3.11"]
     elif dir_ == ".":
         # unable to install with 3.13 because tokenizers doesn't support 3.13 yet
         py_versions = ["3.9", "3.12"]
@@ -184,11 +177,6 @@ def _get_pydantic_test_configs(
         else "0"
     )
 
-    custom_mins = {
-        # depends on pydantic-settings 2.4 which requires pydantic 2.7
-        "libs/community": 7,
-    }
-
     max_pydantic_minor = min(
         int(dir_max_pydantic_minor),
         int(core_max_pydantic_minor),
@@ -196,7 +184,6 @@ def _get_pydantic_test_configs(
     min_pydantic_minor = max(
         int(dir_min_pydantic_minor),
         int(core_min_pydantic_minor),
-        custom_mins.get(dir_, 0),
     )
 
     configs = [

.github/scripts/get_min_versions.py

@@ -22,7 +22,6 @@ import re
 
 MIN_VERSION_LIBS = [
     "langchain-core",
-    "langchain-community",
     "langchain",
     "langchain-text-splitters",
     "numpy",
@@ -35,7 +34,6 @@ SKIP_IF_PULL_REQUEST = [
     "langchain-core",
     "langchain-text-splitters",
     "langchain",
-    "langchain-community",
 ]
 
 

.github/scripts/prep_api_docs_build.py

@@ -20,6 +20,8 @@ def get_target_dir(package_name: str) -> Path:
     base_path = Path("langchain/libs")
     if package_name_short == "experimental":
         return base_path / "experimental"
+    if package_name_short == "community":
+        return base_path / "community"
     return base_path / "partners" / package_name_short
 
 
@@ -69,7 +71,7 @@ def main():
         clean_target_directories([
             p
             for p in package_yaml["packages"]
-            if p["repo"].startswith("langchain-ai/")
+            if (p["repo"].startswith("langchain-ai/") or p.get("include_in_api_ref"))
             and p["repo"] != "langchain-ai/langchain"
         ])
 
@@ -78,7 +80,7 @@ def main():
             p
             for p in package_yaml["packages"]
             if not p.get("disabled", False)
-            and p["repo"].startswith("langchain-ai/")
+            and (p["repo"].startswith("langchain-ai/") or p.get("include_in_api_ref"))
             and p["repo"] != "langchain-ai/langchain"
         ])
 

.github/workflows/.codespell-exclude

@@ -1,4 +1,3 @@
-libs/community/langchain_community/llms/yuan2.py
 "NotIn": "not in",
 - `/checkin`: Check-in
 docs/docs/integrations/providers/trulens.mdx

.github/workflows/_integration_test.yml

@@ -34,11 +34,6 @@ jobs:
         shell: bash
         run: uv sync --group test --group test_integration
 
-      - name: Install deps outside pyproject
-        if: ${{ startsWith(inputs.working-directory, 'libs/community/') }}
-        shell: bash
-        run: VIRTUAL_ENV=.venv uv pip install "boto3<2" "google-cloud-aiplatform<2"
-
       - name: Run integration tests
         shell: bash
         env:

.github/workflows/_release.yml

@@ -395,8 +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/_test_doc_imports.yml

@@ -30,7 +30,7 @@ jobs:
 
       - name: Install langchain editable
         run: |
-          VIRTUAL_ENV=.venv uv pip install langchain-experimental -e libs/core libs/langchain libs/community
+          VIRTUAL_ENV=.venv uv pip install langchain-experimental langchain-community -e libs/core libs/langchain
 
       - name: Check doc imports
         shell: bash

.github/workflows/api_doc_build.yml

@@ -26,7 +26,20 @@ jobs:
         id: get-unsorted-repos
         uses: mikefarah/yq@master
         with:
-          cmd: yq '.packages[].repo' langchain/libs/packages.yml
+          cmd: |
+            yq '
+              .packages[]
+              | select(
+                  (
+                    (.repo | test("^langchain-ai/"))
+                    and
+                    (.repo != "langchain-ai/langchain")
+                  )
+                  or
+                  (.include_in_api_ref // false)
+                )
+              | .repo
+            ' langchain/libs/packages.yml
 
       - name: Parse YAML and checkout repos
         env:
@@ -38,11 +51,9 @@ jobs:
           
           # Checkout each unique repository that is in langchain-ai org
           for repo in $REPOS; do
-            if [[ "$repo" != "langchain-ai/langchain" && "$repo" == langchain-ai/* ]]; then
-              REPO_NAME=$(echo $repo | cut -d'/' -f2)
-              echo "Checking out $repo to $REPO_NAME"
-              git clone --depth 1 https://github.com/$repo.git $REPO_NAME
-            fi
+            REPO_NAME=$(echo $repo | cut -d'/' -f2)
+            echo "Checking out $repo to $REPO_NAME"
+            git clone --depth 1 https://github.com/$repo.git $REPO_NAME
           done
 
       - name: Setup python ${{ env.PYTHON_VERSION }}

.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/run_notebooks.yml

@@ -61,6 +61,7 @@ jobs:
         env:
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
+          GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

.gitignore

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

.pre-commit-config.yaml

@@ -7,12 +7,6 @@ repos:
       entry: make -C libs/core format
       files: ^libs/core/
       pass_filenames: false
-    - id: community
-      name: format community
-      language: system
-      entry: make -C libs/community format
-      files: ^libs/community/
-      pass_filenames: false
     - id: langchain
       name: format langchain
       language: system

README.md

@@ -17,6 +17,7 @@
 [![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)
 [<img src="https://github.com/codespaces/badge.svg" title="Open in Github Codespace" width="150" height="20">](https://codespaces.new/langchain-ai/langchain)
 [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
+[![CodSpeed Badge](https://img.shields.io/endpoint?url=https://codspeed.io/badge.json)](https://codspeed.io/langchain-ai/langchain)
 
 > [!NOTE]
 > Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).

cookbook/advanced_rag_eval.ipynb

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

docs/api_reference/conf.py

@@ -11,6 +11,7 @@
 import json
 import os
 import sys
+from datetime import datetime
 from pathlib import Path
 
 import toml
@@ -104,7 +105,7 @@ def skip_private_members(app, what, name, obj, skip, options):
 # -- Project information -----------------------------------------------------
 
 project = "🦜🔗 LangChain"
-copyright = "2023, LangChain Inc"
+copyright = f"{datetime.now().year}, LangChain Inc"
 author = "LangChain, Inc"
 
 html_favicon = "_static/img/brand/favicon.png"
@@ -275,3 +276,7 @@ if os.environ.get("READTHEDOCS", "") == "True":
     html_context["READTHEDOCS"] = True
 
 master_doc = "index"
+
+# If a signature’s length in characters exceeds 60,
+# each parameter within the signature will be displayed on an individual logical line
+maximum_signature_line_length = 60

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

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

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docs/cassettes/configure_42647fb7.msgpack.zlib

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docs/cassettes/configure_48b45337.msgpack.zlib

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docs/cassettes/configure_4f83245c.msgpack.zlib

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docs/cassettes/configure_5cf53202.msgpack.zlib

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

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docs/cassettes/configure_71248a9f.msgpack.zlib

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docs/cassettes/configure_7ba735f4.msgpack.zlib

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docs/cassettes/configure_927297a1.msgpack.zlib

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docs/cassettes/configure_97538c23.msgpack.zlib

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docs/cassettes/configure_9a9ea077.msgpack.zlib

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docs/cassettes/configure_9f6a7c6c.msgpack.zlib

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docs/cassettes/configure_c09fac15.msgpack.zlib

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docs/cassettes/configure_e4ee9fbc.msgpack.zlib

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docs/cassettes/configure_e75ae678.msgpack.zlib

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docs/cassettes/configure_f33f3cf2.msgpack.zlib

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docs/cassettes/multimodal_inputs_0f68cce7-350b-4cde-bc40-d3a169551fc3.msgpack.zlib

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docs/cassettes/multimodal_inputs_cd22ea82-2f93-46f9-9f7a-6aaf479fcaa9.msgpack.zlib

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docs/cassettes/sql_qa_11.msgpack.zlib

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docs/cassettes/sql_qa_39.msgpack.zlib

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docs/cassettes/tools_chain_4f5325ca-e5dc-4d1a-ba36-b085a029c90a.msgpack.zlib

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docs/cassettes/tools_chain_68f30343-14ef-48f1-badd-b6a03977316d.msgpack.zlib

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docs/cassettes/tools_chain_6be83879-9da3-4dd9-b147-a79f76affd7a.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/cassettes/tools_chain_f7dbb240-809e-4e41-8f63-1a4636e8e26d.msgpack.zlib

@@ -1 +0,0 @@
-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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/multimodality.mdx

@@ -15,7 +15,10 @@
 * [Messages](/docs/concepts/messages)
 :::
  
-Multimodal support is still relatively new and less common, model providers have not yet standardized on the "best" way to define the API. As such, LangChain's multimodal abstractions are lightweight and flexible, designed to accommodate different model providers' APIs and interaction patterns, but are **not** standardized across models.
+LangChain supports multimodal data as input to chat models:
+
+1. Following provider-specific formats
+2. Adhering to a cross-provider standard (see [how-to guides](/docs/how_to/#multimodal) for detail)
 
 ### How to use multimodal models
 
@@ -26,38 +29,85 @@ Multimodal support is still relatively new and less common, model providers have
 
 #### Inputs
 
-Some models can accept multimodal inputs, such as images, audio, video, or files. The types of multimodal inputs supported depend on the model provider. For instance, [Google's Gemini](/docs/integrations/chat/google_generative_ai/) supports documents like PDFs as inputs.
+Some models can accept multimodal inputs, such as images, audio, video, or files.
+The types of multimodal inputs supported depend on the model provider. For instance,
+[OpenAI](/docs/integrations/chat/openai/),
+[Anthropic](/docs/integrations/chat/anthropic/), and
+[Google Gemini](/docs/integrations/chat/google_generative_ai/)
+support documents like PDFs as inputs.
 
-Most chat models that support **multimodal inputs** also accept those values in OpenAI's content blocks format. So far this is restricted to image inputs. For models like Gemini which support video and other bytes input, the APIs also support the native, model-specific representations.
-
-The gist of passing multimodal inputs to a chat model is to use content blocks that specify a type and corresponding data. For example, to pass an image to a chat model:
+The gist of passing multimodal inputs to a chat model is to use content blocks that
+specify a type and corresponding data. For example, to pass an image to a chat model
+as URL:
 
 ```python
 from langchain_core.messages import HumanMessage
 
 message = HumanMessage(
     content=[
-        {"type": "text", "text": "describe the weather in this image"},
+        {"type": "text", "text": "Describe the weather in this image:"},
+        {
+            "type": "image",
+            "source_type": "url",
+            "url": "https://...",
+        },
+    ],
+)
+response = model.invoke([message])
+```
+
+We can also pass the image as in-line data:
+
+```python
+from langchain_core.messages import HumanMessage
+
+message = HumanMessage(
+    content=[
+        {"type": "text", "text": "Describe the weather in this image:"},
+        {
+            "type": "image",
+            "source_type": "base64",
+            "data": "<base64 string>",
+            "mime_type": "image/jpeg",
+        },
+    ],
+)
+response = model.invoke([message])
+```
+
+To pass a PDF file as in-line data (or URL, as supported by providers such as
+Anthropic), just change `"type"` to `"file"` and `"mime_type"` to `"application/pdf"`.
+
+See the [how-to guides](/docs/how_to/#multimodal) for more detail.
+
+Most chat models that support multimodal **image** inputs also accept those values in
+OpenAI's [Chat Completions format](https://platform.openai.com/docs/guides/images?api-mode=chat):
+
+```python
+from langchain_core.messages import HumanMessage
+
+message = HumanMessage(
+    content=[
+        {"type": "text", "text": "Describe the weather in this image:"},
         {"type": "image_url", "image_url": {"url": image_url}},
     ],
 )
 response = model.invoke([message])
 ```
 
-:::caution
-The exact format of the content blocks may vary depending on the model provider. Please refer to the chat model's
-integration documentation for the correct format. Find the integration in the [chat model integration table](/docs/integrations/chat/).
-:::
+Otherwise, chat models will typically accept the native, provider-specific content
+block format. See [chat model integrations](/docs/integrations/chat/) for detail
+on specific providers.
+
 
 #### Outputs
 
-Virtually no popular chat models support multimodal outputs at the time of writing (October 2024). 
+Some chat models support multimodal outputs, such as images and audio. Multimodal
+outputs will appear as part of the [AIMessage](/docs/concepts/messages/#aimessage)
+response object. See for example:
 
-The only exception is OpenAI's chat model ([gpt-4o-audio-preview](/docs/integrations/chat/openai/)), which can generate audio outputs.
-
-Multimodal outputs will appear as part of the [AIMessage](/docs/concepts/messages/#aimessage) response object.
-
-Please see the [ChatOpenAI](/docs/integrations/chat/openai/) for more information on how to use multimodal outputs.
+- Generating [audio outputs](/docs/integrations/chat/openai/#audio-generation-preview) with OpenAI;
+- Generating [image outputs](/docs/integrations/chat/google_generative_ai/#multimodal-usage) with Google Gemini.
 
 #### Tools
 

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/concepts/vectorstores.mdx

@@ -66,7 +66,7 @@ This API works with a list of [Document](https://python.langchain.com/api_refere
 from langchain_core.documents import Document
 
 document_1 = Document(
-    page_content="I had chocalate chip pancakes and scrambled eggs for breakfast this morning.",
+    page_content="I had chocolate chip pancakes and scrambled eggs for breakfast this morning.",
     metadata={"source": "tweet"},
 )
 

docs/docs/contributing/how_to/code/setup.mdx

@@ -13,23 +13,33 @@ Install `uv`: **[documentation on how to install it](https://docs.astral.sh/uv/g
 
 This repository contains multiple packages:
 - `langchain-core`: Base interfaces for key abstractions as well as logic for combining them in chains (LangChain Expression Language).
-- `langchain-community`: Third-party integrations of various components.
 - `langchain`: Chains, agents, and retrieval logic that makes up the cognitive architecture of your applications.
-- `langchain-experimental`: Components and chains that are experimental, either in the sense that the techniques are novel and still being tested, or they require giving the LLM more access than would be possible in most production systems.
 - Partner integrations: Partner packages in `libs/partners` that are independently version controlled.
 
+:::note
+
+Some LangChain packages live outside the monorepo, see for example
+[langchain-community](https://github.com/langchain-ai/langchain-community) for various
+third-party integrations and
+[langchain-experimental](https://github.com/langchain-ai/langchain-experimental) for
+abstractions that are experimental (either in the sense that the techniques are novel
+and still being tested, or they require giving the LLM more access than would be
+possible in most production systems).
+
+:::
+
 Each of these has its own development environment. Docs are run from the top-level makefile, but development
 is split across separate test & release flows.
 
-For this quickstart, start with langchain-community:
+For this quickstart, start with `langchain`:
 
 ```bash
-cd libs/community
+cd libs/langchain
 ```
 
 ## Local Development Dependencies
 
-Install langchain-community development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):
+Install development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):
 
 ```bash
 uv sync
@@ -62,22 +72,15 @@ make docker_tests
 
 There are also [integration tests and code-coverage](../testing.mdx) available.
 
-### Only develop langchain_core or langchain_community
+### Developing langchain_core
 
-If you are only developing `langchain_core` or `langchain_community`, you can simply install the dependencies for the respective projects and run tests:
+If you are only developing `langchain_core`, you can simply install the dependencies for the project and run tests:
 
 ```bash
 cd libs/core
 make test
 ```
 
-Or:
-
-```bash
-cd libs/community
-make test
-```
-
 ## Formatting and Linting
 
 Run these locally before submitting a PR; the CI system will check also.

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/configure.ipynb

@@ -336,70 +336,6 @@
     "chain.with_config(configurable={\"llm_temperature\": 0.9}).invoke({\"x\": 0})"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "fb9637d0",
-   "metadata": {},
-   "source": [
-    "### With HubRunnables\n",
-    "\n",
-    "This is useful to allow for switching of prompts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9a9ea077",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ChatPromptValue(messages=[HumanMessage(content=\"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: foo \\nContext: bar \\nAnswer:\")])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.runnables.hub import HubRunnable\n",
-    "\n",
-    "prompt = HubRunnable(\"rlm/rag-prompt\").configurable_fields(\n",
-    "    owner_repo_commit=ConfigurableField(\n",
-    "        id=\"hub_commit\",\n",
-    "        name=\"Hub Commit\",\n",
-    "        description=\"The Hub commit to pull from\",\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "prompt.invoke({\"question\": \"foo\", \"context\": \"bar\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "f33f3cf2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ChatPromptValue(messages=[HumanMessage(content=\"[INST]<<SYS>> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.<</SYS>> \\nQuestion: foo \\nContext: bar \\nAnswer: [/INST]\")])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "prompt.with_config(configurable={\"hub_commit\": \"rlm/rag-prompt-llama\"}).invoke(\n",
-    "    {\"question\": \"foo\", \"context\": \"bar\"}\n",
-    ")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "79d51519",

docs/docs/how_to/graph_constructing.ipynb

@@ -167,7 +167,7 @@
     "She was, in 1906, the first woman to become a professor at the University of Paris.\n",
     "\"\"\"\n",
     "documents = [Document(page_content=text)]\n",
-    "graph_documents = llm_transformer.convert_to_graph_documents(documents)\n",
+    "graph_documents = await llm_transformer.aconvert_to_graph_documents(documents)\n",
     "print(f\"Nodes:{graph_documents[0].nodes}\")\n",
     "print(f\"Relationships:{graph_documents[0].relationships}\")"
    ]
@@ -205,7 +205,7 @@
     "    allowed_nodes=[\"Person\", \"Country\", \"Organization\"],\n",
     "    allowed_relationships=[\"NATIONALITY\", \"LOCATED_IN\", \"WORKED_AT\", \"SPOUSE\"],\n",
     ")\n",
-    "graph_documents_filtered = llm_transformer_filtered.convert_to_graph_documents(\n",
+    "graph_documents_filtered = await llm_transformer_filtered.aconvert_to_graph_documents(\n",
     "    documents\n",
     ")\n",
     "print(f\"Nodes:{graph_documents_filtered[0].nodes}\")\n",
@@ -245,7 +245,9 @@
     "    allowed_nodes=[\"Person\", \"Country\", \"Organization\"],\n",
     "    allowed_relationships=allowed_relationships,\n",
     ")\n",
-    "graph_documents_filtered = llm_transformer_tuple.convert_to_graph_documents(documents)\n",
+    "graph_documents_filtered = await llm_transformer_tuple.aconvert_to_graph_documents(\n",
+    "    documents\n",
+    ")\n",
     "print(f\"Nodes:{graph_documents_filtered[0].nodes}\")\n",
     "print(f\"Relationships:{graph_documents_filtered[0].relationships}\")"
    ]
@@ -289,7 +291,9 @@
     "    allowed_relationships=[\"NATIONALITY\", \"LOCATED_IN\", \"WORKED_AT\", \"SPOUSE\"],\n",
     "    node_properties=[\"born_year\"],\n",
     ")\n",
-    "graph_documents_props = llm_transformer_props.convert_to_graph_documents(documents)\n",
+    "graph_documents_props = await llm_transformer_props.aconvert_to_graph_documents(\n",
+    "    documents\n",
+    ")\n",
     "print(f\"Nodes:{graph_documents_props[0].nodes}\")\n",
     "print(f\"Relationships:{graph_documents_props[0].relationships}\")"
    ]

docs/docs/how_to/index.mdx

@@ -50,6 +50,7 @@ See [supported integrations](/docs/integrations/chat/) for details on getting st
 - [How to: force a specific tool call](/docs/how_to/tool_choice)
 - [How to: work with local models](/docs/how_to/local_llms)
 - [How to: init any model in one line](/docs/how_to/chat_models_universal_init/)
+- [How to: pass multimodal data directly to models](/docs/how_to/multimodal_inputs/)
 
 ### Messages
 
@@ -67,6 +68,7 @@ See [supported integrations](/docs/integrations/chat/) for details on getting st
 - [How to: use few shot examples in chat models](/docs/how_to/few_shot_examples_chat/)
 - [How to: partially format prompt templates](/docs/how_to/prompts_partial)
 - [How to: compose prompts together](/docs/how_to/prompts_composition)
+- [How to: use multimodal prompts](/docs/how_to/multimodal_prompts/)
 
 ### Example selectors
 
@@ -170,7 +172,7 @@ Indexing is the process of keeping your vectorstore in-sync with the underlying
 
 ### Tools
 
-LangChain [Tools](/docs/concepts/tools) contain a description of the tool (to pass to the language model) as well as the implementation of the function to call. Refer [here](/docs/integrations/tools/) for a list of pre-buit tools. 
+LangChain [Tools](/docs/concepts/tools) contain a description of the tool (to pass to the language model) as well as the implementation of the function to call. Refer [here](/docs/integrations/tools/) for a list of pre-built tools. 
 
 - [How to: create tools](/docs/how_to/custom_tools)
 - [How to: use built-in tools and toolkits](/docs/how_to/tools_builtin)
@@ -351,7 +353,7 @@ 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/multimodal_inputs.ipynb

@@ -5,120 +5,165 @@
    "id": "4facdf7f-680e-4d28-908b-2b8408e2a741",
    "metadata": {},
    "source": [
-    "# How to pass multimodal data directly to models\n",
+    "# How to pass multimodal data to models\n",
     "\n",
-    "Here we demonstrate how to pass [multimodal](/docs/concepts/multimodality/) input directly to models. \n",
-    "We currently expect all input to be passed in the same format as [OpenAI expects](https://platform.openai.com/docs/guides/vision).\n",
-    "For other model providers that support multimodal input, we have added logic inside the class to convert to the expected format.\n",
+    "Here we demonstrate how to pass [multimodal](/docs/concepts/multimodality/) input directly to models.\n",
     "\n",
-    "In this example we will ask a [model](/docs/concepts/chat_models/#multimodality) to describe an image."
+    "LangChain supports multimodal data as input to chat models:\n",
+    "\n",
+    "1. Following provider-specific formats\n",
+    "2. Adhering to a cross-provider standard\n",
+    "\n",
+    "Below, we demonstrate the cross-provider standard. See [chat model integrations](/docs/integrations/chat/) for detail\n",
+    "on native formats for specific providers.\n",
+    "\n",
+    ":::note\n",
+    "\n",
+    "Most chat models that support multimodal **image** inputs also accept those values in\n",
+    "OpenAI's [Chat Completions format](https://platform.openai.com/docs/guides/images?api-mode=chat):\n",
+    "\n",
+    "```python\n",
+    "{\n",
+    "    \"type\": \"image_url\",\n",
+    "    \"image_url\": {\"url\": image_url},\n",
+    "}\n",
+    "```\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e30a4ff0-ab38-41a7-858c-a93f99bb2f1b",
+   "metadata": {},
+   "source": [
+    "## Images\n",
+    "\n",
+    "Many providers will accept images passed in-line as base64 data. Some will additionally accept an image from a URL directly.\n",
+    "\n",
+    "### Images from base64 data\n",
+    "\n",
+    "To pass images in-line, format them as content blocks of the following form:\n",
+    "\n",
+    "```python\n",
+    "{\n",
+    "    \"type\": \"image\",\n",
+    "    \"source_type\": \"base64\",\n",
+    "    \"mime_type\": \"image/jpeg\",  # or image/png, etc.\n",
+    "    \"data\": \"<base64 data string>\",\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "Example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "0d9fd81a-b7f0-445a-8e3d-cfc2d31fdd59",
+   "execution_count": 10,
+   "id": "1fcf7b27-1cc3-420a-b920-0420b5892e20",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The image shows a beautiful clear day with bright blue skies and wispy cirrus clouds stretching across the horizon. The clouds are thin and streaky, creating elegant patterns against the blue backdrop. The lighting suggests it's during the day, possibly late afternoon given the warm, golden quality of the light on the grass. The weather appears calm with no signs of wind (the grass looks relatively still) and no indication of rain. It's the kind of perfect, mild weather that's ideal for walking along the wooden boardwalk through the marsh grass.\n"
+     ]
+    }
+   ],
    "source": [
-    "image_url = \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\""
+    "import base64\n",
+    "\n",
+    "import httpx\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "# Fetch image data\n",
+    "image_url = \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\"\n",
+    "image_data = base64.b64encode(httpx.get(image_url).content).decode(\"utf-8\")\n",
+    "\n",
+    "\n",
+    "# Pass to LLM\n",
+    "llm = init_chat_model(\"anthropic:claude-3-5-sonnet-latest\")\n",
+    "\n",
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Describe the weather in this image:\",\n",
+    "        },\n",
+    "        # highlight-start\n",
+    "        {\n",
+    "            \"type\": \"image\",\n",
+    "            \"source_type\": \"base64\",\n",
+    "            \"data\": image_data,\n",
+    "            \"mime_type\": \"image/jpeg\",\n",
+    "        },\n",
+    "        # highlight-end\n",
+    "    ],\n",
+    "}\n",
+    "response = llm.invoke([message])\n",
+    "print(response.text())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee2b678a-01dd-40c1-81ff-ddac22be21b7",
+   "metadata": {},
+   "source": [
+    "See [LangSmith trace](https://smith.langchain.com/public/eab05a31-54e8-4fc9-911f-56805da67bef/r) for more detail.\n",
+    "\n",
+    "### Images from a URL\n",
+    "\n",
+    "Some providers (including [OpenAI](/docs/integrations/chat/openai/),\n",
+    "[Anthropic](/docs/integrations/chat/anthropic/), and\n",
+    "[Google Gemini](/docs/integrations/chat/google_generative_ai/)) will also accept images from URLs directly.\n",
+    "\n",
+    "To pass images as URLs, format them as content blocks of the following form:\n",
+    "\n",
+    "```python\n",
+    "{\n",
+    "    \"type\": \"image\",\n",
+    "    \"source_type\": \"url\",\n",
+    "    \"url\": \"https://...\",\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "Example:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "fb896ce9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.messages import HumanMessage\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4fca4da7",
-   "metadata": {},
-   "source": [
-    "The most commonly supported way to pass in images is to pass it in as a byte string.\n",
-    "This should work for most model integrations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9ca1040c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import base64\n",
-    "\n",
-    "import httpx\n",
-    "\n",
-    "image_data = base64.b64encode(httpx.get(image_url).content).decode(\"utf-8\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ec680b6b",
+   "id": "99d27f8f-ae78-48bc-9bf2-3cef35213ec7",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The weather in the image appears to be clear and pleasant. The sky is mostly blue with scattered, light clouds, suggesting a sunny day with minimal cloud cover. There is no indication of rain or strong winds, and the overall scene looks bright and calm. The lush green grass and clear visibility further indicate good weather conditions.\n"
+      "The weather in this image appears to be pleasant and clear. The sky is mostly blue with a few scattered, light clouds, and there is bright sunlight illuminating the green grass and plants. There are no signs of rain or stormy conditions, suggesting it is a calm, likely warm day—typical of spring or summer.\n"
      ]
     }
    ],
    "source": [
-    "message = HumanMessage(\n",
-    "    content=[\n",
-    "        {\"type\": \"text\", \"text\": \"describe the weather in this image\"},\n",
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
     "        {\n",
-    "            \"type\": \"image_url\",\n",
-    "            \"image_url\": {\"url\": f\"data:image/jpeg;base64,{image_data}\"},\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Describe the weather in this image:\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"type\": \"image\",\n",
+    "            # highlight-start\n",
+    "            \"source_type\": \"url\",\n",
+    "            \"url\": image_url,\n",
+    "            # highlight-end\n",
     "        },\n",
     "    ],\n",
-    ")\n",
-    "response = model.invoke([message])\n",
-    "print(response.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8656018e-c56d-47d2-b2be-71e87827f90a",
-   "metadata": {},
-   "source": [
-    "We can feed the image URL directly in a content block of type \"image_url\". Note that only some model providers support this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a8819cf3-5ddc-44f0-889a-19ca7b7fe77e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The weather in the image appears to be clear and sunny. The sky is mostly blue with a few scattered clouds, suggesting good visibility and a likely pleasant temperature. The bright sunlight is casting distinct shadows on the grass and vegetation, indicating it is likely daytime, possibly late morning or early afternoon. The overall ambiance suggests a warm and inviting day, suitable for outdoor activities.\n"
-     ]
-    }
-   ],
-   "source": [
-    "message = HumanMessage(\n",
-    "    content=[\n",
-    "        {\"type\": \"text\", \"text\": \"describe the weather in this image\"},\n",
-    "        {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n",
-    "    ],\n",
-    ")\n",
-    "response = model.invoke([message])\n",
-    "print(response.content)"
+    "}\n",
+    "response = llm.invoke([message])\n",
+    "print(response.text())"
    ]
   },
   {
@@ -126,12 +171,12 @@
    "id": "1c470309",
    "metadata": {},
    "source": [
-    "We can also pass in multiple images."
+    "We can also pass in multiple images:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "325fb4ca",
    "metadata": {},
    "outputs": [
@@ -139,20 +184,460 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Yes, the two images are the same. They both depict a wooden boardwalk extending through a grassy field under a blue sky with light clouds. The scenery, lighting, and composition are identical.\n"
+      "Yes, these two images are the same. They depict a wooden boardwalk going through a grassy field under a blue sky with some clouds. The colors, composition, and elements in both images are identical.\n"
      ]
     }
    ],
    "source": [
-    "message = HumanMessage(\n",
-    "    content=[\n",
-    "        {\"type\": \"text\", \"text\": \"are these two images the same?\"},\n",
-    "        {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n",
-    "        {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n",
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\"type\": \"text\", \"text\": \"Are these two images the same?\"},\n",
+    "        {\"type\": \"image\", \"source_type\": \"url\", \"url\": image_url},\n",
+    "        {\"type\": \"image\", \"source_type\": \"url\", \"url\": image_url},\n",
     "    ],\n",
-    ")\n",
-    "response = model.invoke([message])\n",
-    "print(response.content)"
+    "}\n",
+    "response = llm.invoke([message])\n",
+    "print(response.text())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d72b83e6-8d21-448e-b5df-d5b556c3ccc8",
+   "metadata": {},
+   "source": [
+    "## Documents (PDF)\n",
+    "\n",
+    "Some providers (including [OpenAI](/docs/integrations/chat/openai/),\n",
+    "[Anthropic](/docs/integrations/chat/anthropic/), and\n",
+    "[Google Gemini](/docs/integrations/chat/google_generative_ai/)) will accept PDF documents.\n",
+    "\n",
+    "### Documents from base64 data\n",
+    "\n",
+    "To pass documents in-line, format them as content blocks of the following form:\n",
+    "\n",
+    "```python\n",
+    "{\n",
+    "    \"type\": \"file\",\n",
+    "    \"source_type\": \"base64\",\n",
+    "    \"mime_type\": \"application/pdf\",\n",
+    "    \"data\": \"<base64 data string>\",\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "Example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6c1455a9-699a-4702-a7e0-7f6eaec76a21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This document appears to be a sample PDF file that contains Lorem ipsum placeholder text. It begins with a title \"Sample PDF\" followed by the subtitle \"This is a simple PDF file. Fun fun fun.\"\n",
+      "\n",
+      "The rest of the document consists of several paragraphs of Lorem ipsum text, which is a commonly used placeholder text in design and publishing. The text is formatted in a clean, readable layout with consistent paragraph spacing. The document appears to be a single page containing four main paragraphs of this placeholder text.\n",
+      "\n",
+      "The Lorem ipsum text, while appearing to be Latin, is actually scrambled Latin-like text that is used primarily to demonstrate the visual form of a document or typeface without the distraction of meaningful content. It's commonly used in publishing and graphic design when the actual content is not yet available but the layout needs to be demonstrated.\n",
+      "\n",
+      "The document has a professional, simple layout with generous margins and clear paragraph separation, making it an effective example of basic PDF formatting and structure.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import base64\n",
+    "\n",
+    "import httpx\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "# Fetch PDF data\n",
+    "pdf_url = \"https://pdfobject.com/pdf/sample.pdf\"\n",
+    "pdf_data = base64.b64encode(httpx.get(pdf_url).content).decode(\"utf-8\")\n",
+    "\n",
+    "\n",
+    "# Pass to LLM\n",
+    "llm = init_chat_model(\"anthropic:claude-3-5-sonnet-latest\")\n",
+    "\n",
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Describe the document:\",\n",
+    "        },\n",
+    "        # highlight-start\n",
+    "        {\n",
+    "            \"type\": \"file\",\n",
+    "            \"source_type\": \"base64\",\n",
+    "            \"data\": pdf_data,\n",
+    "            \"mime_type\": \"application/pdf\",\n",
+    "        },\n",
+    "        # highlight-end\n",
+    "    ],\n",
+    "}\n",
+    "response = llm.invoke([message])\n",
+    "print(response.text())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efb271da-8fdd-41b5-9f29-be6f8c76f49b",
+   "metadata": {},
+   "source": [
+    "### Documents from a URL\n",
+    "\n",
+    "Some providers (specifically [Anthropic](/docs/integrations/chat/anthropic/))\n",
+    "will also accept documents from URLs directly.\n",
+    "\n",
+    "To pass documents as URLs, format them as content blocks of the following form:\n",
+    "\n",
+    "```python\n",
+    "{\n",
+    "    \"type\": \"file\",\n",
+    "    \"source_type\": \"url\",\n",
+    "    \"url\": \"https://...\",\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "Example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "55e1d937-3b22-4deb-b9f0-9e688f0609dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This document appears to be a sample PDF file with both text and an image. It begins with a title \"Sample PDF\" followed by the text \"This is a simple PDF file. Fun fun fun.\" The rest of the document contains Lorem ipsum placeholder text arranged in several paragraphs. The content is shown both as text and as an image of the formatted PDF, with the same content displayed in a clean, formatted layout with consistent spacing and typography. The document consists of a single page containing this sample text.\n"
+     ]
+    }
+   ],
+   "source": [
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Describe the document:\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"type\": \"file\",\n",
+    "            # highlight-start\n",
+    "            \"source_type\": \"url\",\n",
+    "            \"url\": pdf_url,\n",
+    "            # highlight-end\n",
+    "        },\n",
+    "    ],\n",
+    "}\n",
+    "response = llm.invoke([message])\n",
+    "print(response.text())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e661c26-e537-4721-8268-42c0861cb1e6",
+   "metadata": {},
+   "source": [
+    "## Audio\n",
+    "\n",
+    "Some providers (including [OpenAI](/docs/integrations/chat/openai/) and\n",
+    "[Google Gemini](/docs/integrations/chat/google_generative_ai/)) will accept audio inputs.\n",
+    "\n",
+    "### Audio from base64 data\n",
+    "\n",
+    "To pass audio in-line, format them as content blocks of the following form:\n",
+    "\n",
+    "```python\n",
+    "{\n",
+    "    \"type\": \"audio\",\n",
+    "    \"source_type\": \"base64\",\n",
+    "    \"mime_type\": \"audio/wav\",  # or appropriate mime-type\n",
+    "    \"data\": \"<base64 data string>\",\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "Example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a0b91b29-dbd6-4c94-8f24-05471adc7598",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The audio appears to consist primarily of bird sounds, specifically bird vocalizations like chirping and possibly other bird songs.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import base64\n",
+    "\n",
+    "import httpx\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "# Fetch audio data\n",
+    "audio_url = \"https://upload.wikimedia.org/wikipedia/commons/3/3d/Alcal%C3%A1_de_Henares_%28RPS_13-04-2024%29_canto_de_ruise%C3%B1or_%28Luscinia_megarhynchos%29_en_el_Soto_del_Henares.wav\"\n",
+    "audio_data = base64.b64encode(httpx.get(audio_url).content).decode(\"utf-8\")\n",
+    "\n",
+    "\n",
+    "# Pass to LLM\n",
+    "llm = init_chat_model(\"google_genai:gemini-2.0-flash-001\")\n",
+    "\n",
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Describe this audio:\",\n",
+    "        },\n",
+    "        # highlight-start\n",
+    "        {\n",
+    "            \"type\": \"audio\",\n",
+    "            \"source_type\": \"base64\",\n",
+    "            \"data\": audio_data,\n",
+    "            \"mime_type\": \"audio/wav\",\n",
+    "        },\n",
+    "        # highlight-end\n",
+    "    ],\n",
+    "}\n",
+    "response = llm.invoke([message])\n",
+    "print(response.text())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92f55a6c-2e4a-4175-8444-8b9aacd6a13e",
+   "metadata": {},
+   "source": [
+    "## Provider-specific parameters\n",
+    "\n",
+    "Some providers will support or require additional fields on content blocks containing multimodal data.\n",
+    "For example, Anthropic lets you specify [caching](/docs/integrations/chat/anthropic/#prompt-caching) of\n",
+    "specific content to reduce token consumption.\n",
+    "\n",
+    "To use these fields, you can:\n",
+    "\n",
+    "1. Store them on directly on the content block; or\n",
+    "2. Use the native format supported by each provider (see [chat model integrations](/docs/integrations/chat/) for detail).\n",
+    "\n",
+    "We show three examples below.\n",
+    "\n",
+    "### Example: Anthropic prompt caching"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "83593b9d-a8d3-4c99-9dac-64e0a9d397cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The image shows a beautiful, clear day with partly cloudy skies. The sky is a vibrant blue with wispy, white cirrus clouds stretching across it. The lighting suggests it's during daylight hours, possibly late afternoon or early evening given the warm, golden quality of the light on the grass. The weather appears calm with no signs of wind (the grass looks relatively still) and no threatening weather conditions. It's the kind of perfect weather you'd want for a walk along this wooden boardwalk through the marshland or grassland area.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'input_tokens': 1586,\n",
+       " 'output_tokens': 117,\n",
+       " 'total_tokens': 1703,\n",
+       " 'input_token_details': {'cache_read': 0, 'cache_creation': 1582}}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm = init_chat_model(\"anthropic:claude-3-5-sonnet-latest\")\n",
+    "\n",
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Describe the weather in this image:\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"type\": \"image\",\n",
+    "            \"source_type\": \"url\",\n",
+    "            \"url\": image_url,\n",
+    "            # highlight-next-line\n",
+    "            \"cache_control\": {\"type\": \"ephemeral\"},\n",
+    "        },\n",
+    "    ],\n",
+    "}\n",
+    "response = llm.invoke([message])\n",
+    "print(response.text())\n",
+    "response.usage_metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9bbf578e-794a-4dc0-a469-78c876ccd4a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clear blue skies, wispy clouds.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'input_tokens': 1716,\n",
+       " 'output_tokens': 12,\n",
+       " 'total_tokens': 1728,\n",
+       " 'input_token_details': {'cache_read': 1582, 'cache_creation': 0}}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next_message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Summarize that in 5 words.\",\n",
+    "        }\n",
+    "    ],\n",
+    "}\n",
+    "response = llm.invoke([message, response, next_message])\n",
+    "print(response.text())\n",
+    "response.usage_metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "915b9443-5964-43b8-bb08-691c1ba59065",
+   "metadata": {},
+   "source": [
+    "### Example: Anthropic citations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ea7707a1-5660-40a1-a10f-0df48a028689",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'citations': [{'cited_text': 'Sample PDF\\r\\nThis is a simple PDF file. Fun fun fun.\\r\\n',\n",
+       "    'document_index': 0,\n",
+       "    'document_title': None,\n",
+       "    'end_page_number': 2,\n",
+       "    'start_page_number': 1,\n",
+       "    'type': 'page_location'}],\n",
+       "  'text': 'Simple PDF file: fun fun',\n",
+       "  'type': 'text'}]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Generate a 5 word summary of this document.\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"type\": \"file\",\n",
+    "            \"source_type\": \"base64\",\n",
+    "            \"data\": pdf_data,\n",
+    "            \"mime_type\": \"application/pdf\",\n",
+    "            # highlight-next-line\n",
+    "            \"citations\": {\"enabled\": True},\n",
+    "        },\n",
+    "    ],\n",
+    "}\n",
+    "response = llm.invoke([message])\n",
+    "response.content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e26991eb-e769-41f4-b6e0-63d81f2c7d67",
+   "metadata": {},
+   "source": [
+    "### Example: OpenAI file names\n",
+    "\n",
+    "OpenAI requires that PDF documents be associated with file names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ae076c9b-ff8f-461d-9349-250f396c9a25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The document is a sample PDF file containing placeholder text. It consists of one page, titled \"Sample PDF\". The content is a mixture of English and the commonly used filler text \"Lorem ipsum dolor sit amet...\" and its extensions, which are often used in publishing and web design as generic text to demonstrate font, layout, and other visual elements.\n",
+      "\n",
+      "**Key points about the document:**\n",
+      "- Length: 1 page\n",
+      "- Purpose: Demonstrative/sample content\n",
+      "- Content: No substantive or meaningful information, just demonstration text in paragraph form\n",
+      "- Language: English (with the Latin-like \"Lorem Ipsum\" text used for layout purposes)\n",
+      "\n",
+      "There are no charts, tables, diagrams, or images on the page—only plain text. The document serves as an example of what a PDF file looks like rather than providing actual, useful content.\n"
+     ]
+    }
+   ],
+   "source": [
+    "llm = init_chat_model(\"openai:gpt-4.1\")\n",
+    "\n",
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Describe the document:\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"type\": \"file\",\n",
+    "            \"source_type\": \"base64\",\n",
+    "            \"data\": pdf_data,\n",
+    "            \"mime_type\": \"application/pdf\",\n",
+    "            # highlight-next-line\n",
+    "            \"filename\": \"my-file\",\n",
+    "        },\n",
+    "    ],\n",
+    "}\n",
+    "response = llm.invoke([message])\n",
+    "print(response.text())"
    ]
   },
   {
@@ -167,16 +652,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "cd22ea82-2f93-46f9-9f7a-6aaf479fcaa9",
+   "execution_count": 4,
+   "id": "0f68cce7-350b-4cde-bc40-d3a169551fc3",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[{'name': 'weather_tool', 'args': {'weather': 'sunny'}, 'id': 'call_BSX4oq4SKnLlp2WlzDhToHBr'}]\n"
-     ]
+     "data": {
+      "text/plain": [
+       "[{'name': 'weather_tool',\n",
+       "  'args': {'weather': 'sunny'},\n",
+       "  'id': 'toolu_01G6JgdkhwggKcQKfhXZQPjf',\n",
+       "  'type': 'tool_call'}]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -191,16 +682,17 @@
     "    pass\n",
     "\n",
     "\n",
-    "model_with_tools = model.bind_tools([weather_tool])\n",
+    "llm_with_tools = llm.bind_tools([weather_tool])\n",
     "\n",
-    "message = HumanMessage(\n",
-    "    content=[\n",
-    "        {\"type\": \"text\", \"text\": \"describe the weather in this image\"},\n",
-    "        {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n",
+    "message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\"type\": \"text\", \"text\": \"Describe the weather in this image:\"},\n",
+    "        {\"type\": \"image\", \"source_type\": \"url\", \"url\": image_url},\n",
     "    ],\n",
-    ")\n",
-    "response = model_with_tools.invoke([message])\n",
-    "print(response.tool_calls)"
+    "}\n",
+    "response = llm_with_tools.invoke([message])\n",
+    "response.tool_calls"
    ]
   }
  ],
@@ -220,7 +712,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/multimodal_prompts.ipynb

@@ -9,157 +9,148 @@
     "\n",
     "Here we demonstrate how to use prompt templates to format [multimodal](/docs/concepts/multimodality/) inputs to models. \n",
     "\n",
-    "In this example we will ask a [model](/docs/concepts/chat_models/#multimodality) to describe an image."
+    "To use prompt templates in the context of multimodal data, we can templatize elements of the corresponding content block.\n",
+    "For example, below we define a prompt that takes a URL for an image as a parameter:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "0d9fd81a-b7f0-445a-8e3d-cfc2d31fdd59",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import base64\n",
-    "\n",
-    "import httpx\n",
-    "\n",
-    "image_url = \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\"\n",
-    "image_data = base64.b64encode(httpx.get(image_url).content).decode(\"utf-8\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "id": "2671f995",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "4ee35e4f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = ChatPromptTemplate.from_messages(\n",
+    "# Define prompt\n",
+    "prompt = ChatPromptTemplate(\n",
     "    [\n",
-    "        (\"system\", \"Describe the image provided\"),\n",
-    "        (\n",
-    "            \"user\",\n",
-    "            [\n",
+    "        {\n",
+    "            \"role\": \"system\",\n",
+    "            \"content\": \"Describe the image provided.\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [\n",
     "                {\n",
-    "                    \"type\": \"image_url\",\n",
-    "                    \"image_url\": {\"url\": \"data:image/jpeg;base64,{image_data}\"},\n",
-    "                }\n",
+    "                    \"type\": \"image\",\n",
+    "                    \"source_type\": \"url\",\n",
+    "                    # highlight-next-line\n",
+    "                    \"url\": \"{image_url}\",\n",
+    "                },\n",
     "            ],\n",
-    "        ),\n",
+    "        },\n",
     "    ]\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "089f75c2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = prompt | model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "02744b06",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The image depicts a sunny day with a beautiful blue sky filled with scattered white clouds. The sky has varying shades of blue, ranging from a deeper hue near the horizon to a lighter, almost pale blue higher up. The white clouds are fluffy and scattered across the expanse of the sky, creating a peaceful and serene atmosphere. The lighting and cloud patterns suggest pleasant weather conditions, likely during the daytime hours on a mild, sunny day in an outdoor natural setting.\n"
-     ]
-    }
-   ],
-   "source": [
-    "response = chain.invoke({\"image_data\": image_data})\n",
-    "print(response.content)"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "id": "e9b9ebf6",
+   "id": "f75d2e26-5b9a-4d5f-94a7-7f98f5666f6d",
    "metadata": {},
    "source": [
-    "We can also pass in multiple images."
+    "Let's use this prompt to pass an image to a [chat model](/docs/concepts/chat_models/#multimodality):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "02190ee3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"compare the two pictures provided\"),\n",
-    "        (\n",
-    "            \"user\",\n",
-    "            [\n",
-    "                {\n",
-    "                    \"type\": \"image_url\",\n",
-    "                    \"image_url\": {\"url\": \"data:image/jpeg;base64,{image_data1}\"},\n",
-    "                },\n",
-    "                {\n",
-    "                    \"type\": \"image_url\",\n",
-    "                    \"image_url\": {\"url\": \"data:image/jpeg;base64,{image_data2}\"},\n",
-    "                },\n",
-    "            ],\n",
-    "        ),\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "42af057b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = prompt | model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "513abe00",
+   "execution_count": 2,
+   "id": "5df2e558-321d-4cf7-994e-2815ac37e704",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The two images provided are identical. Both images feature a wooden boardwalk path extending through a lush green field under a bright blue sky with some clouds. The perspective, colors, and elements in both images are exactly the same.\n"
+      "This image shows a beautiful wooden boardwalk cutting through a lush green wetland or marsh area. The boardwalk extends straight ahead toward the horizon, creating a strong leading line through the composition. On either side, tall green grasses sway in what appears to be a summer or late spring setting. The sky is particularly striking, with wispy cirrus clouds streaking across a vibrant blue background. In the distance, you can see a tree line bordering the wetland area. The lighting suggests this may be during \"golden hour\" - either early morning or late afternoon - as there's a warm, gentle quality to the light that's illuminating the scene. The wooden planks of the boardwalk appear well-maintained and provide safe passage through what would otherwise be difficult terrain to traverse. It's the kind of scene you might find in a nature preserve or wildlife refuge designed to give visitors access to observe wetland ecosystems while protecting the natural environment.\n"
      ]
     }
    ],
    "source": [
-    "response = chain.invoke({\"image_data1\": image_data, \"image_data2\": image_data})\n",
-    "print(response.content)"
+    "from langchain.chat_models import init_chat_model\n",
+    "\n",
+    "llm = init_chat_model(\"anthropic:claude-3-5-sonnet-latest\")\n",
+    "\n",
+    "url = \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\"\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "response = chain.invoke({\"image_url\": url})\n",
+    "print(response.text())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4cfdc50-4a9f-4888-93b4-af697366b0f3",
+   "metadata": {},
+   "source": [
+    "Note that we can templatize arbitrary elements of the content block:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "53c88ebb-dd57-40c8-8542-b2c916706653",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        {\n",
+    "            \"role\": \"system\",\n",
+    "            \"content\": \"Describe the image provided.\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [\n",
+    "                {\n",
+    "                    \"type\": \"image\",\n",
+    "                    \"source_type\": \"base64\",\n",
+    "                    \"mime_type\": \"{image_mime_type}\",\n",
+    "                    \"data\": \"{image_data}\",\n",
+    "                    \"cache_control\": {\"type\": \"{cache_type}\"},\n",
+    "                },\n",
+    "            ],\n",
+    "        },\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "25e4829e-0073-49a8-9669-9f43e5778383",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This image shows a beautiful wooden boardwalk cutting through a lush green marsh or wetland area. The boardwalk extends straight ahead toward the horizon, creating a strong leading line in the composition. The surrounding vegetation consists of tall grass and reeds in vibrant green hues, with some bushes and trees visible in the background. The sky is particularly striking, featuring a bright blue color with wispy white clouds streaked across it. The lighting suggests this photo was taken during the \"golden hour\" - either early morning or late afternoon - giving the scene a warm, peaceful quality. The raised wooden path provides accessible access through what would otherwise be difficult terrain to traverse, allowing visitors to experience and appreciate this natural environment.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import base64\n",
+    "\n",
+    "import httpx\n",
+    "\n",
+    "image_data = base64.b64encode(httpx.get(url).content).decode(\"utf-8\")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "response = chain.invoke(\n",
+    "    {\n",
+    "        \"image_data\": image_data,\n",
+    "        \"image_mime_type\": \"image/jpeg\",\n",
+    "        \"cache_type\": \"ephemeral\",\n",
+    "    }\n",
+    ")\n",
+    "print(response.text())"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ea8152c3",
+   "id": "424defe8-d85c-4e45-a88d-bf6f910d5ebb",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -181,7 +172,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/prompts_composition.ipynb

@@ -100,7 +100,7 @@
    "id": "8554bae5",
    "metadata": {},
    "source": [
-    "A chat prompt is made up a of a list of messages. Similarly to the above example, we can concatenate chat prompt templates. Each new element is a new message in the final prompt.\n",
+    "A chat prompt is made up of a list of messages. Similarly to the above example, we can concatenate chat prompt templates. Each new element is a new message in the final prompt.\n",
     "\n",
     "First, let's initialize the a [`ChatPromptTemplate`](https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html) with a [`SystemMessage`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.system.SystemMessage.html)."
    ]

docs/docs/how_to/tool_calling_parallel.ipynb

@@ -6,9 +6,9 @@
    "source": [
     "# How to disable parallel tool calling\n",
     "\n",
-    ":::info OpenAI-specific\n",
+    ":::info Provider-specific\n",
     "\n",
-    "This API is currently only supported by OpenAI.\n",
+    "This API is currently only supported by OpenAI and Anthropic.\n",
     "\n",
     ":::\n",
     "\n",
@@ -55,12 +55,12 @@
     "import os\n",
     "from getpass import getpass\n",
     "\n",
-    "from langchain_openai import ChatOpenAI\n",
+    "from langchain.chat_models import init_chat_model\n",
     "\n",
     "if \"OPENAI_API_KEY\" not in os.environ:\n",
     "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
     "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)"
+    "llm = init_chat_model(\"openai:gpt-4.1-mini\")"
    ]
   },
   {
@@ -121,7 +121,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/how_to/tools_chain.ipynb

@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "90187d07",
    "metadata": {},
    "outputs": [],
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "d7009e1a",
    "metadata": {},
    "outputs": [
@@ -99,7 +99,7 @@
      "output_type": "stream",
      "text": [
       "multiply\n",
-      "multiply(first_int: int, second_int: int) -> int - Multiply two integers together.\n",
+      "Multiply two integers together.\n",
       "{'first_int': {'title': 'First Int', 'type': 'integer'}, 'second_int': {'title': 'Second Int', 'type': 'integer'}}\n"
      ]
     }
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "be77e780",
    "metadata": {},
    "outputs": [
@@ -122,7 +122,7 @@
        "20"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "9bce8935-1465-45ac-8a93-314222c753c4",
    "metadata": {},
    "outputs": [],
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "3bfe2cdc-7d72-457c-a9a1-5fa1e0bcde55",
    "metadata": {},
    "outputs": [],
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "68f30343-14ef-48f1-badd-b6a03977316d",
    "metadata": {},
    "outputs": [
@@ -204,10 +204,11 @@
       "text/plain": [
        "[{'name': 'multiply',\n",
        "  'args': {'first_int': 5, 'second_int': 42},\n",
-       "  'id': 'call_cCP9oA3tRz7HDrjFn1FdmDaG'}]"
+       "  'id': 'call_8QIg4QVFVAEeC1orWAgB2036',\n",
+       "  'type': 'tool_call'}]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -237,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "id": "4f5325ca-e5dc-4d1a-ba36-b085a029c90a",
    "metadata": {},
    "outputs": [
@@ -247,7 +248,7 @@
        "92"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -274,58 +275,31 @@
    "source": [
     "## Agents\n",
     "\n",
-    "Chains are great when we know the specific sequence of tool usage needed for any user input. But for certain use cases, how many times we use tools depends on the input. In these cases, we want to let the model itself decide how many times to use tools and in what order. [Agents](/docs/tutorials/agents) let us do just this.\n",
+    "Chains are great when we know the specific sequence of tool usage needed for any user input. But for certain use cases, how many times we use tools depends on the input. In these cases, we want to let the model itself decide how many times to use tools and in what order. [Agents](/docs/concepts/agents/) let us do just this.\n",
     "\n",
-    "LangChain comes with a number of built-in agents that are optimized for different use cases. Read about all the [agent types here](/docs/concepts/agents).\n",
-    "\n",
-    "We'll use the [tool calling agent](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.tool_calling_agent.base.create_tool_calling_agent.html), which is generally the most reliable kind and the recommended one for most use cases.\n",
+    "We'll demonstrate a simple example using a LangGraph agent. See [this tutorial](/docs/tutorials/agents) for more detail.\n",
     "\n",
     "![agent](../../static/img/tool_agent.svg)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "21723cf4-9421-4a8d-92a6-eeeb8f4367f1",
+   "execution_count": null,
+   "id": "86789cfb-f441-4453-adf8-961eeceb00bc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain import hub\n",
-    "from langchain.agents import AgentExecutor, create_tool_calling_agent"
+    "!pip install -qU langgraph"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "6be83879-9da3-4dd9-b147-a79f76affd7a",
+   "execution_count": 9,
+   "id": "21723cf4-9421-4a8d-92a6-eeeb8f4367f1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m System Message \u001b[0m================================\n",
-      "\n",
-      "You are a helpful assistant\n",
-      "\n",
-      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
-      "\n",
-      "\u001b[33;1m\u001b[1;3m{chat_history}\u001b[0m\n",
-      "\n",
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "\u001b[33;1m\u001b[1;3m{input}\u001b[0m\n",
-      "\n",
-      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
-      "\n",
-      "\u001b[33;1m\u001b[1;3m{agent_scratchpad}\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Get the prompt to use - can be replaced with any prompt that includes variables \"agent_scratchpad\" and \"input\"!\n",
-    "prompt = hub.pull(\"hwchase17/openai-tools-agent\")\n",
-    "prompt.pretty_print()"
+    "from langgraph.prebuilt import create_react_agent"
    ]
   },
   {
@@ -338,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "id": "95c86d32-ee45-4c87-a28c-14eff19b49e9",
    "metadata": {},
    "outputs": [],
@@ -360,24 +334,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "17b09ac6-c9b7-4340-a8a0-3d3061f7888c",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Construct the tool calling agent\n",
-    "agent = create_tool_calling_agent(llm, tools, prompt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "675091d2-cac9-45c4-a5d7-b760ee6c1986",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create an agent executor by passing in the agent and tools\n",
-    "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
+    "agent = create_react_agent(llm, tools)"
    ]
   },
   {
@@ -390,62 +353,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "f7dbb240-809e-4e41-8f63-1a4636e8e26d",
+   "execution_count": 13,
+   "id": "71c84594-d420-4703-8bdd-ca4eb7efefb6",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
+      "Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result.\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  exponentiate (call_EHGS8gnEVNCJQ9rVOk11KCQH)\n",
+      " Call ID: call_EHGS8gnEVNCJQ9rVOk11KCQH\n",
+      "  Args:\n",
+      "    base: 3\n",
+      "    exponent: 5\n",
+      "  add (call_s2cxOrXEKqI6z7LWbMUG6s8c)\n",
+      " Call ID: call_s2cxOrXEKqI6z7LWbMUG6s8c\n",
+      "  Args:\n",
+      "    first_int: 12\n",
+      "    second_int: 3\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: add\n",
       "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `exponentiate` with `{'base': 3, 'exponent': 5}`\n",
+      "15\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  multiply (call_25v5JEfDWuKNgmVoGBan0d7J)\n",
+      " Call ID: call_25v5JEfDWuKNgmVoGBan0d7J\n",
+      "  Args:\n",
+      "    first_int: 243\n",
+      "    second_int: 15\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: multiply\n",
       "\n",
+      "3645\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  exponentiate (call_x1yKEeBPrFYmCp2z5Kn8705r)\n",
+      " Call ID: call_x1yKEeBPrFYmCp2z5Kn8705r\n",
+      "  Args:\n",
+      "    base: 3645\n",
+      "    exponent: 2\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: exponentiate\n",
       "\n",
-      "\u001b[0m\u001b[38;5;200m\u001b[1;3m243\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `add` with `{'first_int': 12, 'second_int': 3}`\n",
+      "13286025\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "\n",
-      "\u001b[0m\u001b[33;1m\u001b[1;3m15\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `multiply` with `{'first_int': 243, 'second_int': 15}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m3645\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `exponentiate` with `{'base': 405, 'exponent': 2}`\n",
-      "\n",
-      "\n",
-      "\u001b[0m\u001b[38;5;200m\u001b[1;3m13286025\u001b[0m\u001b[32;1m\u001b[1;3mThe result of taking 3 to the fifth power is 243. \n",
-      "\n",
-      "The sum of twelve and three is 15. \n",
-      "\n",
-      "Multiplying 243 by 15 gives 3645. \n",
-      "\n",
-      "Finally, squaring 3645 gives 13286025.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "The final result of taking 3 to the fifth power, multiplying it by the sum of twelve and three, and then squaring the whole result is **13,286,025**.\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'input': 'Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result',\n",
-       " 'output': 'The result of taking 3 to the fifth power is 243. \\n\\nThe sum of twelve and three is 15. \\n\\nMultiplying 243 by 15 gives 3645. \\n\\nFinally, squaring 3645 gives 13286025.'}"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_executor.invoke(\n",
-    "    {\n",
-    "        \"input\": \"Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result\"\n",
-    "    }\n",
-    ")"
+    "# Use the agent\n",
+    "\n",
+    "query = (\n",
+    "    \"Take 3 to the fifth power and multiply that by the sum of twelve and \"\n",
+    "    \"three, then square the whole result.\"\n",
+    ")\n",
+    "input_message = {\"role\": \"user\", \"content\": query}\n",
+    "\n",
+    "for step in agent.stream({\"messages\": [input_message]}, stream_mode=\"values\"):\n",
+    "    step[\"messages\"][-1].pretty_print()"
    ]
   },
   {
@@ -473,7 +446,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

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/caches/singlestore_semantic_cache.ipynb

@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# SingleStoreSemanticCache\n",
+    "\n",
+    "This example demonstrates how to get started with the SingleStore semantic cache.\n",
+    "\n",
+    "### Integration Overview\n",
+    "\n",
+    "`SingleStoreSemanticCache` leverages `SingleStoreVectorStore` to cache LLM responses directly in a SingleStore database, enabling efficient semantic retrieval and reuse of results.\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "\n",
+    "| Class | Package | JS support |\n",
+    "| :--- | :--- |  :---: |\n",
+    "| SingleStoreSemanticCache | langchain_singlestore | ❌ | "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "## Installation\n",
+    "\n",
+    "This cache lives in the `langchain-singlestore` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-singlestore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c5f2839-4020-424e-9fc9-07777eede442",
+   "metadata": {},
+   "source": [
+    "## Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.globals import set_llm_cache\n",
+    "from langchain_singlestore import SingleStoreSemanticCache\n",
+    "\n",
+    "set_llm_cache(\n",
+    "    SingleStoreSemanticCache(\n",
+    "        embedding=YourEmbeddings(),\n",
+    "        host=\"root:pass@localhost:3306/db\",\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cddda8ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "# The first time, it is not yet in cache, so it should take longer\n",
+    "llm.invoke(\"Tell me a joke\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c474168f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "# The second time, while not a direct hit, the question is semantically similar to the original question,\n",
+    "# so it uses the cached result!\n",
+    "llm.invoke(\"Tell me one joke\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain-singlestore-BD1RbQ07-py3.11",
+   "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/chat/bedrock.ipynb

@@ -1,378 +1,401 @@
 {
-  "cells": [
-    {
-      "cell_type": "raw",
-      "id": "afaf8039",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "sidebar_label: AWS Bedrock\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "e49f1e0d",
-      "metadata": {},
-      "source": [
-        "# ChatBedrock\n",
-        "\n",
-        "This doc will help you get started with AWS Bedrock [chat models](/docs/concepts/chat_models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
-        "\n",
-        "For more information on which models are accessible via Bedrock, head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/models-features.html).\n",
-        "\n",
-        "For detailed documentation of all ChatBedrock features and configurations head to the [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html).\n",
-        "\n",
-        "## Overview\n",
-        "### Integration details\n",
-        "\n",
-        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/bedrock) | Package downloads | Package latest |\n",
-        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-        "| [ChatBedrock](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?style=flat-square&label=%20) |\n",
-        "\n",
-        "### Model features\n",
-        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-        "| ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ |\n",
-        "\n",
-        "## Setup\n",
-        "\n",
-        "To access Bedrock models you'll need to create an AWS account, set up the Bedrock API service, get an access key ID and secret key, and install the `langchain-aws` integration package.\n",
-        "\n",
-        "### Credentials\n",
-        "\n",
-        "Head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/setting-up.html) to sign up to AWS and setup your credentials. You'll also need to turn on model access for your account, which you can do by following [these instructions](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-      "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-      "metadata": {},
-      "source": [
-        "### Installation\n",
-        "\n",
-        "The LangChain Bedrock integration lives in the `langchain-aws` package:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install -qU langchain-aws"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-      "metadata": {},
-      "source": [
-        "## Instantiation\n",
-        "\n",
-        "Now we can instantiate our model object and generate chat completions:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain_aws import ChatBedrock\n",
-        "\n",
-        "llm = ChatBedrock(\n",
-        "    model_id=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
-        "    model_kwargs=dict(temperature=0),\n",
-        "    # other params...\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "2b4f3e15",
-      "metadata": {},
-      "source": [
-        "## Invocation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "62e0dbc3",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", additional_kwargs={'usage': {'prompt_tokens': 29, 'completion_tokens': 21, 'total_tokens': 50}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 29, 'completion_tokens': 21, 'total_tokens': 50}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, id='run-fdb07dc3-ff72-430d-b22b-e7824b15c766-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})"
-            ]
-          },
-          "execution_count": 5,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "messages = [\n",
-        "    (\n",
-        "        \"system\",\n",
-        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-        "    ),\n",
-        "    (\"human\", \"I love programming.\"),\n",
-        "]\n",
-        "ai_msg = llm.invoke(messages)\n",
-        "ai_msg"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Voici la traduction en français :\n",
-            "\n",
-            "J'aime la programmation.\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(ai_msg.content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-      "metadata": {},
-      "source": [
-        "## Chaining\n",
-        "\n",
-        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={'usage': {'prompt_tokens': 23, 'completion_tokens': 11, 'total_tokens': 34}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, response_metadata={'usage': {'prompt_tokens': 23, 'completion_tokens': 11, 'total_tokens': 34}, 'stop_reason': 'end_turn', 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0'}, id='run-5ad005ce-9f31-4670-baa0-9373d418698a-0', usage_metadata={'input_tokens': 23, 'output_tokens': 11, 'total_tokens': 34})"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from langchain_core.prompts import ChatPromptTemplate\n",
-        "\n",
-        "prompt = ChatPromptTemplate.from_messages(\n",
-        "    [\n",
-        "        (\n",
-        "            \"system\",\n",
-        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-        "        ),\n",
-        "        (\"human\", \"{input}\"),\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "chain = prompt | llm\n",
-        "chain.invoke(\n",
-        "    {\n",
-        "        \"input_language\": \"English\",\n",
-        "        \"output_language\": \"German\",\n",
-        "        \"input\": \"I love programming.\",\n",
-        "    }\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
-      "metadata": {},
-      "source": [
-        "## Bedrock Converse API\n",
-        "\n",
-        "AWS has recently released the Bedrock Converse API which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). To improve reliability the ChatBedrock integration will switch to using the Bedrock Converse API as soon as it has feature parity with the existing Bedrock API. Until then a separate [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) integration has been released.\n",
-        "\n",
-        "We recommend using `ChatBedrockConverse` for users who do not need to use custom models.\n",
-        "\n",
-        "You can use it like so:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "ae728e59-94d4-40cf-9d24-25ad8723fc59",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", response_metadata={'ResponseMetadata': {'RequestId': '4fcbfbe9-f916-4df2-b0bd-ea1147b550aa', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 21 Aug 2024 17:23:49 GMT', 'content-type': 'application/json', 'content-length': '243', 'connection': 'keep-alive', 'x-amzn-requestid': '4fcbfbe9-f916-4df2-b0bd-ea1147b550aa'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': 672}}, id='run-77ee9810-e32b-45dc-9ccb-6692253b1f45-0', usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50})"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from langchain_aws import ChatBedrockConverse\n",
-        "\n",
-        "llm = ChatBedrockConverse(\n",
-        "    model=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
-        "    temperature=0,\n",
-        "    max_tokens=None,\n",
-        "    # other params...\n",
-        ")\n",
-        "\n",
-        "llm.invoke(messages)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "4da16f3e-e80b-48c0-8036-c1cc5f7c8c05",
-      "metadata": {},
-      "source": [
-        "### Streaming\n",
-        "\n",
-        "Note that `ChatBedrockConverse` emits content blocks while streaming:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "7794b32e-d8de-4973-bf0f-39807dc745f0",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "content=[] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'Vo', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'ici', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' la', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' tra', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'duction', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' en', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' français', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' :', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': '\\n\\nJ', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': \"'\", 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'a', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'ime', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' la', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': ' programm', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': 'ation', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'type': 'text', 'text': '.', 'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[{'index': 0}] id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[] response_metadata={'stopReason': 'end_turn'} id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8'\n",
-            "content=[] response_metadata={'metrics': {'latencyMs': 713}} id='run-2c92c5af-d771-4cc2-98d9-c11bbd30a1d8' usage_metadata={'input_tokens': 29, 'output_tokens': 21, 'total_tokens': 50}\n"
-          ]
-        }
-      ],
-      "source": [
-        "for chunk in llm.stream(messages):\n",
-        "    print(chunk)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0ef05abb-9c04-4dc3-995e-f857779644d5",
-      "metadata": {},
-      "source": [
-        "An output parser can be used to filter to text, if desired:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "2a4e743f-ea7d-4e5a-9b12-f9992362de8b",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "|Vo|ici| la| tra|duction| en| français| :|\n",
-            "\n",
-            "J|'|a|ime| la| programm|ation|.||||"
-          ]
-        }
-      ],
-      "source": [
-        "from langchain_core.output_parsers import StrOutputParser\n",
-        "\n",
-        "chain = llm | StrOutputParser()\n",
-        "\n",
-        "for chunk in chain.stream(messages):\n",
-        "    print(chunk, end=\"|\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-      "metadata": {},
-      "source": [
-        "## API reference\n",
-        "\n",
-        "For detailed documentation of all ChatBedrock features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html\n",
-        "\n",
-        "For detailed documentation of all ChatBedrockConverse features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.4"
-    }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: AWS Bedrock\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatBedrock\n",
+    "\n",
+    "This doc will help you get started with AWS Bedrock [chat models](/docs/concepts/chat_models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
+    "\n",
+    "AWS Bedrock maintains a [Converse API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html) which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html).\n",
+    "\n",
+    ":::info\n",
+    "\n",
+    "We recommend the Converse API for users who do not need to use custom models. It can be accessed using [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html).\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "For detailed documentation of all Bedrock features and configurations head to the [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/bedrock) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatBedrock](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?style=flat-square&label=%20) |\n",
+    "| [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "\n",
+    "The below apply to both `ChatBedrock` and `ChatBedrockConverse`.\n",
+    "\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Bedrock models you'll need to create an AWS account, set up the Bedrock API service, get an access key ID and secret key, and install the `langchain-aws` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/setting-up.html) to sign up to AWS and setup your credentials. You'll also need to turn on model access for your account, which you can do by following [these instructions](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Bedrock integration lives in the `langchain-aws` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-aws"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_aws import ChatBedrockConverse\n",
+    "\n",
+    "llm = ChatBedrockConverse(\n",
+    "    model_id=\"anthropic.claude-3-5-sonnet-20240620-v1:0\",\n",
+    "    # temperature=...,\n",
+    "    # max_tokens=...,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fcd8de52-4a1b-4875-b463-d41b031e06a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={'ResponseMetadata': {'RequestId': 'b07d1630-06f2-44b1-82bf-e82538dd2215', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 16 Apr 2025 19:35:34 GMT', 'content-type': 'application/json', 'content-length': '206', 'connection': 'keep-alive', 'x-amzn-requestid': 'b07d1630-06f2-44b1-82bf-e82538dd2215'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': [488]}, 'model_name': 'anthropic.claude-3-5-sonnet-20240620-v1:0'}, id='run-d09ed928-146a-4336-b1fd-b63c9e623494-0', usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore la programmation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4da16f3e-e80b-48c0-8036-c1cc5f7c8c05",
+   "metadata": {},
+   "source": [
+    "### Streaming\n",
+    "\n",
+    "Note that `ChatBedrockConverse` emits content blocks while streaming:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "605e04fa-1a76-47ac-8c92-fe128659663e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content=[] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[{'type': 'text', 'text': 'J', 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[{'type': 'text', 'text': \"'adore la\", 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[{'type': 'text', 'text': ' programmation.', 'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[{'index': 0}] additional_kwargs={} response_metadata={} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[] additional_kwargs={} response_metadata={'stopReason': 'end_turn'} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd'\n",
+      "content=[] additional_kwargs={} response_metadata={'metrics': {'latencyMs': 600}, 'model_name': 'anthropic.claude-3-5-sonnet-20240620-v1:0'} id='run-d0e0836e-7146-4c3d-97c7-ad23dac6febd' usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in llm.stream(messages):\n",
+    "    print(chunk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ef05abb-9c04-4dc3-995e-f857779644d5",
+   "metadata": {},
+   "source": [
+    "You can filter to text using the [.text()](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.text) method on the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2a4e743f-ea7d-4e5a-9b12-f9992362de8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|J|'adore la| programmation.||||"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in llm.stream(messages):\n",
+    "    print(chunk.text(), end=\"|\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a77519e5-897d-41a0-a9bb-55300fa79efc",
+   "metadata": {},
+   "source": [
+    "## Prompt caching\n",
+    "\n",
+    "Bedrock supports [caching](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html) of elements of your prompts, including messages and tools. This allows you to re-use large documents, instructions, [few-shot documents](/docs/concepts/few_shot_prompting/), and other data to reduce latency and costs.\n",
+    "\n",
+    ":::note\n",
+    "\n",
+    "Not all models support prompt caching. See supported models [here](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html#prompt-caching-models).\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "To enable caching on an element of a prompt, mark its associated content block using the `cachePoint` key. See example below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d5f63d01-85e8-4797-a2be-0fea747a6049",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First invocation:\n",
+      "{'cache_creation': 1528, 'cache_read': 0}\n",
+      "\n",
+      "Second:\n",
+      "{'cache_creation': 0, 'cache_read': 1528}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "from langchain_aws import ChatBedrockConverse\n",
+    "\n",
+    "llm = ChatBedrockConverse(model=\"us.anthropic.claude-3-7-sonnet-20250219-v1:0\")\n",
+    "\n",
+    "# Pull LangChain readme\n",
+    "get_response = requests.get(\n",
+    "    \"https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md\"\n",
+    ")\n",
+    "readme = get_response.text\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\n",
+    "                \"type\": \"text\",\n",
+    "                \"text\": \"What's LangChain, according to its README?\",\n",
+    "            },\n",
+    "            {\n",
+    "                \"type\": \"text\",\n",
+    "                \"text\": f\"{readme}\",\n",
+    "            },\n",
+    "            {\n",
+    "                \"cachePoint\": {\"type\": \"default\"},\n",
+    "            },\n",
+    "        ],\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "response_1 = llm.invoke(messages)\n",
+    "response_2 = llm.invoke(messages)\n",
+    "\n",
+    "usage_1 = response_1.usage_metadata[\"input_token_details\"]\n",
+    "usage_2 = response_2.usage_metadata[\"input_token_details\"]\n",
+    "\n",
+    "print(f\"First invocation:\\n{usage_1}\")\n",
+    "print(f\"\\nSecond:\\n{usage_2}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b550667-af5b-4557-b84f-c8f865dad6cb",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6033f3fa-0e96-46e3-abb3-1530928fea88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Here's the German translation:\\n\\nIch liebe das Programmieren.\", additional_kwargs={}, response_metadata={'ResponseMetadata': {'RequestId': '1de3d7c0-8062-4f7e-bb8a-8f725b97a8b0', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Wed, 16 Apr 2025 19:32:51 GMT', 'content-type': 'application/json', 'content-length': '243', 'connection': 'keep-alive', 'x-amzn-requestid': '1de3d7c0-8062-4f7e-bb8a-8f725b97a8b0'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': [719]}, 'model_name': 'anthropic.claude-3-5-sonnet-20240620-v1:0'}, id='run-7021fcd7-704e-496b-a92e-210139614402-0', usage_metadata={'input_tokens': 23, 'output_tokens': 19, 'total_tokens': 42, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatBedrock features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html\n",
+    "\n",
+    "For detailed documentation of all ChatBedrockConverse features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }

docs/docs/integrations/chat/cloudflare_workersai.ipynb

@@ -1,262 +1,393 @@
 {
-  "cells": [
-    {
-      "cell_type": "raw",
-      "id": "30373ae2-f326-4e96-a1f7-062f57396886",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "sidebar_label: Cloudflare Workers AI\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "f679592d",
-      "metadata": {},
-      "source": [
-        "# ChatCloudflareWorkersAI\n",
-        "\n",
-        "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all available Cloudflare WorkersAI models head to the [API reference](https://developers.cloudflare.com/workers-ai/).\n",
-        "\n",
-        "\n",
-        "## Overview\n",
-        "### Integration details\n",
-        "\n",
-        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cloudflare_workersai) | Package downloads | Package latest |\n",
-        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-        "| ChatCloudflareWorkersAI | langchain-community| ❌ | ❌ | ✅ | ❌  | ❌ |\n",
-        "\n",
-        "### Model features\n",
-        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-        "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |\n",
-        "\n",
-        "## Setup\n",
-        "\n",
-        "- To access Cloudflare Workers AI models you'll need to create a Cloudflare account, get an account number and API key, and install the `langchain-community` package.\n",
-        "\n",
-        "\n",
-        "### Credentials\n",
-        "\n",
-        "\n",
-        "Head to [this document](https://developers.cloudflare.com/workers-ai/get-started/rest-api/) to sign up to Cloudflare Workers AI and generate an API key."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "4a524cff",
-      "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "71b53c25",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "777a8526",
-      "metadata": {},
-      "source": [
-        "### Installation\n",
-        "\n",
-        "The LangChain ChatCloudflareWorkersAI integration lives in the `langchain-community` package:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "54990998",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install -qU langchain-community"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "629ba46f",
-      "metadata": {},
-      "source": [
-        "## Instantiation\n",
-        "\n",
-        "Now we can instantiate our model object and generate chat completions:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "ec13c2d9",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain_community.chat_models.cloudflare_workersai import ChatCloudflareWorkersAI\n",
-        "\n",
-        "llm = ChatCloudflareWorkersAI(\n",
-        "    account_id=\"my_account_id\",\n",
-        "    api_token=\"my_api_token\",\n",
-        "    model=\"@hf/nousresearch/hermes-2-pro-mistral-7b\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "119b6732",
-      "metadata": {},
-      "source": [
-        "## Invocation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "2438a906",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "2024-11-07 15:55:14 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to French. Translate the user sentence.\\nrole: user, content: I love programming.', 'tools': None}\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content='{\\'result\\': {\\'response\\': \\'Je suis un assistant virtuel qui peut traduire l\\\\\\'anglais vers le français. La phrase que vous avez dite est : \"J\\\\\\'aime programmer.\" En français, cela se traduit par : \"J\\\\\\'adore programmer.\"\\'}, \\'success\\': True, \\'errors\\': [], \\'messages\\': []}', additional_kwargs={}, response_metadata={}, id='run-838fd398-8594-4ca5-9055-03c72993caf6-0')"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "messages = [\n",
-        "    (\n",
-        "        \"system\",\n",
-        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-        "    ),\n",
-        "    (\"human\", \"I love programming.\"),\n",
-        "]\n",
-        "ai_msg = llm.invoke(messages)\n",
-        "ai_msg"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "1b4911bd",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "{'result': {'response': 'Je suis un assistant virtuel qui peut traduire l\\'anglais vers le français. La phrase que vous avez dite est : \"J\\'aime programmer.\" En français, cela se traduit par : \"J\\'adore programmer.\"'}, 'success': True, 'errors': [], 'messages': []}\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(ai_msg.content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "111aa5d4",
-      "metadata": {},
-      "source": [
-        "## Chaining\n",
-        "\n",
-        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "b2a14282",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "2024-11-07 15:55:24 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to German.\\nrole: user, content: I love programming.', 'tools': None}\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content=\"{'result': {'response': 'role: system, content: Das ist sehr nett zu hören! Programmieren lieben, ist eine interessante und anspruchsvolle Hobby- oder Berufsausrichtung. Wenn Sie englische Texte ins Deutsche übersetzen möchten, kann ich Ihnen helfen. Geben Sie bitte den englischen Satz oder die Übersetzung an, die Sie benötigen.'}, 'success': True, 'errors': [], 'messages': []}\", additional_kwargs={}, response_metadata={}, id='run-0d3be9a6-3d74-4dde-b49a-4479d6af00ef-0')"
-            ]
-          },
-          "execution_count": 10,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from langchain_core.prompts import ChatPromptTemplate\n",
-        "\n",
-        "prompt = ChatPromptTemplate.from_messages(\n",
-        "    [\n",
-        "        (\n",
-        "            \"system\",\n",
-        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-        "        ),\n",
-        "        (\"human\", \"{input}\"),\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "chain = prompt | llm\n",
-        "chain.invoke(\n",
-        "    {\n",
-        "        \"input_language\": \"English\",\n",
-        "        \"output_language\": \"German\",\n",
-        "        \"input\": \"I love programming.\",\n",
-        "    }\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "e1f311bd",
-      "metadata": {},
-      "source": [
-        "## API reference\n",
-        "\n",
-        "For detailed documentation on `ChatCloudflareWorkersAI` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.cloudflare_workersai.html)."
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.4"
-    }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: CloudflareWorkersAI\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatCloudflareWorkersAI\n",
+    "\n",
+    "\n",
+    "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCloudflareWorkersAI features and configurations head to the [API reference](https://python.langchain.com/docs/integrations/chat/cloudflare_workersai/).\n",
+    "\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cloudflare) | Package downloads | Package latest |\n",
+    "| :--- | :--- |:-----:|:------------:|:------------------------------------------------------------------------:| :---: | :---: |\n",
+    "| [ChatCloudflareWorkersAI](https://python.langchain.com/docs/integrations/chat/cloudflare_workersai/) | [langchain-cloudflare](https://pypi.org/project/langchain-cloudflare/) |   ✅   |      ❌       |                                     ❌                                     | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-cloudflare?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-cloudflare?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "|:-----------------------------------------:|:----------------------------------------------------:|:---------:|:----------------------------------------------:|:-----------:|:-----------:|:-----------------------------------------------------:|:------------:|:------------------------------------------------------:|:----------------------------------:|\n",
+    "|                     ✅                     |                          ✅                           |     ✅     |                       ❌                        |      ❌      |      ❌      |                           ❌                           |      ❌       |                             ✅                            |                 ❌                  | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access CloudflareWorkersAI models you'll need to create a/an CloudflareWorkersAI account, get an API key, and install the `langchain-cloudflare` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "\n",
+    "Head to https://www.cloudflare.com/developer-platform/products/workers-ai/ to sign up to CloudflareWorkersAI and generate an API key. Once you've done this set the CF_API_KEY environment variable and the CF_ACCOUNT_ID environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"CF_API_KEY\"):\n",
+    "    os.environ[\"CF_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your CloudflareWorkersAI API key: \"\n",
+    "    )\n",
+    "\n",
+    "if not os.getenv(\"CF_ACCOUNT_ID\"):\n",
+    "    os.environ[\"CF_ACCOUNT_ID\"] = getpass.getpass(\n",
+    "        \"Enter your CloudflareWorkersAI account ID: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain CloudflareWorkersAI integration lives in the `langchain-cloudflare` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-cloudflare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:\n",
+    "\n",
+    "- Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-07T17:48:31.193773Z",
+     "start_time": "2025-04-07T17:48:31.179196Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_cloudflare.chat_models import ChatCloudflareWorkersAI\n",
+    "\n",
+    "llm = ChatCloudflareWorkersAI(\n",
+    "    model=\"@cf/meta/llama-3.3-70b-instruct-fp8-fast\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=1024,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 37, 'completion_tokens': 9, 'total_tokens': 46}, 'model_name': '@cf/meta/llama-3.3-70b-instruct-fp8-fast'}, id='run-995d1970-b6be-49f3-99ae-af4cdba02304-0', usage_metadata={'input_tokens': 37, 'output_tokens': 9, 'total_tokens': 46})"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore la programmation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 32, 'completion_tokens': 7, 'total_tokens': 39}, 'model_name': '@cf/meta/llama-3.3-70b-instruct-fp8-fast'}, id='run-d1b677bc-194e-4473-90f1-aa65e8e46d50-0', usage_metadata={'input_tokens': 32, 'output_tokens': 7, 'total_tokens': 39})"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "metadata": {},
+   "source": [
+    "## Structured Outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "91cae406-14d7-46c9-b942-2d1476588423",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'setup': 'Why did the cat join a band?',\n",
+       " 'punchline': 'Because it wanted to be the purr-cussionist',\n",
+       " 'rating': '8'}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "json_schema = {\n",
+    "    \"title\": \"joke\",\n",
+    "    \"description\": \"Joke to tell user.\",\n",
+    "    \"type\": \"object\",\n",
+    "    \"properties\": {\n",
+    "        \"setup\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"description\": \"The setup of the joke\",\n",
+    "        },\n",
+    "        \"punchline\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"description\": \"The punchline to the joke\",\n",
+    "        },\n",
+    "        \"rating\": {\n",
+    "            \"type\": \"integer\",\n",
+    "            \"description\": \"How funny the joke is, from 1 to 10\",\n",
+    "            \"default\": None,\n",
+    "        },\n",
+    "    },\n",
+    "    \"required\": [\"setup\", \"punchline\"],\n",
+    "}\n",
+    "structured_llm = llm.with_structured_output(json_schema)\n",
+    "\n",
+    "structured_llm.invoke(\"Tell me a joke about cats\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbfc0c43-e76b-446e-bbb1-d351640bb7be",
+   "metadata": {},
+   "source": [
+    "## Bind tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0765265e-4d00-4030-bf48-7e8d8c9af2ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'validate_user',\n",
+       "  'args': {'user_id': '123',\n",
+       "   'addresses': '[\"123 Fake St in Boston MA\", \"234 Pretend Boulevard in Houston TX\"]'},\n",
+       "  'id': '31ec7d6a-9ce5-471b-be64-8ea0492d1387',\n",
+       "  'type': 'tool_call'}]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def validate_user(user_id: int, addresses: List[str]) -> bool:\n",
+    "    \"\"\"Validate user using historical addresses.\n",
+    "\n",
+    "    Args:\n",
+    "        user_id (int): the user ID.\n",
+    "        addresses (List[str]): Previous addresses as a list of strings.\n",
+    "    \"\"\"\n",
+    "    return True\n",
+    "\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools([validate_user])\n",
+    "\n",
+    "result = llm_with_tools.invoke(\n",
+    "    \"Could you validate user 123? They previously lived at \"\n",
+    "    \"123 Fake St in Boston MA and 234 Pretend Boulevard in \"\n",
+    "    \"Houston TX.\"\n",
+    ")\n",
+    "result.tool_calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "https://developers.cloudflare.com/workers-ai/\n",
+    "https://developers.cloudflare.com/agents/"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }

docs/docs/integrations/chat/google_generative_ai.ipynb

@@ -1,35 +1,26 @@
 {
  "cells": [
   {
-   "cell_type": "raw",
-   "id": "afaf8039",
+   "cell_type": "markdown",
+   "id": "d982c99f",
    "metadata": {},
    "source": [
     "---\n",
-    "sidebar_label: Google AI\n",
+    "sidebar_label: Google Gemini\n",
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e49f1e0d",
+   "id": "56a6d990",
    "metadata": {},
    "source": [
     "# ChatGoogleGenerativeAI\n",
     "\n",
-    "This docs will help you get started with Google AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html).\n",
+    "Access Google's Generative AI models, including the Gemini family, directly via the Gemini API or experiment rapidly using Google AI Studio. The `langchain-google-genai` package provides the LangChain integration for these models. This is often the best starting point for individual developers.\n",
     "\n",
-    "Google AI offers a number of different chat models. For information on the latest models, their features, context windows, etc. head to the [Google AI docs](https://ai.google.dev/gemini-api/docs/models/gemini).\n",
+    "For information on the latest models, their features, context windows, etc. head to the [Google AI docs](https://ai.google.dev/gemini-api/docs/models/gemini). All examples use the `gemini-2.0-flash` model. Gemini 2.5 Pro and 2.5 Flash can be used via  `gemini-2.5-pro-preview-03-25` and `gemini-2.5-flash-preview-04-17`. All model ids can be found in the [Gemini API docs](https://ai.google.dev/gemini-api/docs/models).\n",
     "\n",
-    ":::info Google AI vs Google Cloud Vertex AI\n",
-    "\n",
-    "Google's Gemini models are accessible through Google AI and through Google Cloud Vertex AI. Using Google AI just requires a Google account and an API key. Using Google Cloud Vertex AI requires a Google Cloud account (with term agreements and billing) but offers enterprise features like customer encryption key, virtual private cloud, and more.\n",
-    "\n",
-    "To learn more about the key features of the two APIs see the [Google docs](https://cloud.google.com/vertex-ai/generative-ai/docs/migrate/migrate-google-ai#google-ai).\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "## Overview\n",
     "### Integration details\n",
     "\n",
     "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_generativeai) | Package downloads | Package latest |\n",
@@ -37,23 +28,46 @@
     "| [ChatGoogleGenerativeAI](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html) | [langchain-google-genai](https://python.langchain.com/api_reference/google_genai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-genai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-genai?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",
-    "## Setup\n",
+    "### Setup\n",
     "\n",
-    "To access Google AI models you'll need to create a Google Acount account, get a Google AI API key, and install the `langchain-google-genai` integration package.\n",
+    "To access Google AI models you'll need to create a Google Account, get a Google AI API key, and install the `langchain-google-genai` integration package.\n",
     "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to https://ai.google.dev/gemini-api/docs/api-key to generate a Google AI API key. Once you've done this set the GOOGLE_API_KEY environment variable:"
+    "**1. Installation:**"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "id": "8d12ce35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -U langchain-google-genai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60be0b38",
+   "metadata": {},
+   "source": [
+    "**2. Credentials:**\n",
+    "\n",
+    "Head to [https://ai.google.dev/gemini-api/docs/api-key](https://ai.google.dev/gemini-api/docs/api-key) (or via Google AI Studio) to generate a Google AI API key.\n",
+    "\n",
+    "### Chat Models\n",
+    "\n",
+    "Use the `ChatGoogleGenerativeAI` class to interact with Google's chat models. See the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html) for full details.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fb18c875",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +80,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "id": "f050e8db",
    "metadata": {},
    "source": [
     "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
@@ -75,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "id": "82cb346f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,27 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain Google AI integration lives in the `langchain-google-genai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-google-genai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "id": "273cefa0",
    "metadata": {},
    "source": [
     "## Instantiation\n",
@@ -115,15 +109,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "execution_count": 4,
+   "id": "7d3dc0b3",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_google_genai import ChatGoogleGenerativeAI\n",
     "\n",
     "llm = ChatGoogleGenerativeAI(\n",
-    "    model=\"gemini-2.0-flash-001\",\n",
+    "    model=\"gemini-2.0-flash\",\n",
     "    temperature=0,\n",
     "    max_tokens=None,\n",
     "    timeout=None,\n",
@@ -134,7 +128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b4f3e15",
+   "id": "343a8c13",
    "metadata": {},
    "source": [
     "## Invocation"
@@ -142,19 +136,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 5,
+   "id": "82c5708c",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash-001', 'safety_ratings': []}, id='run-61cff164-40be-4f88-a2df-cca58297502f-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27, 'input_token_details': {'cache_read': 0}})"
+       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash', 'safety_ratings': []}, id='run-3b28d4b8-8a62-4e6c-ad4e-b53e6e825749-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27, 'input_token_details': {'cache_read': 0}})"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -173,8 +165,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "execution_count": 6,
+   "id": "49d2d0c2",
    "metadata": {},
    "outputs": [
     {
@@ -191,7 +183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "id": "ee3f6e1d",
    "metadata": {},
    "source": [
     "## Chaining\n",
@@ -201,17 +193,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "execution_count": 7,
+   "id": "3c8407ee",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash-001', 'safety_ratings': []}, id='run-dd2f8fb9-62d9-4b84-9c97-ed9c34cda313-0', usage_metadata={'input_tokens': 15, 'output_tokens': 7, 'total_tokens': 22, 'input_token_details': {'cache_read': 0}})"
+       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash', 'safety_ratings': []}, id='run-e5561c6b-2beb-4411-9210-4796b576a7cd-0', usage_metadata={'input_tokens': 15, 'output_tokens': 7, 'total_tokens': 22, 'input_token_details': {'cache_read': 0}})"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -241,22 +233,164 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41c2ff10-a3ba-4f40-b3aa-7a395854849e",
+   "id": "bdae9742",
    "metadata": {},
    "source": [
-    "## Image generation\n",
+    "## Multimodal Usage\n",
     "\n",
-    "Some Gemini models (specifically `gemini-2.0-flash-exp`) support image generation capabilities.\n",
+    "Gemini models can accept multimodal inputs (text, images, audio, video) and, for some models, generate multimodal outputs.\n",
     "\n",
-    "### Text to image\n",
+    "### Image Input\n",
     "\n",
-    "See a simple usage example below:"
+    "Provide image inputs along with text using a `HumanMessage` with a list content format. The `gemini-2.0-flash` model can handle images."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "7589e14d-8d1b-4c82-965f-5558d80cb677",
+   "execution_count": null,
+   "id": "6833fe5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "# Example using a public URL (remains the same)\n",
+    "message_url = HumanMessage(\n",
+    "    content=[\n",
+    "        {\n",
+    "            \"type\": \"text\",\n",
+    "            \"text\": \"Describe the image at the URL.\",\n",
+    "        },\n",
+    "        {\"type\": \"image_url\", \"image_url\": \"https://picsum.photos/seed/picsum/200/300\"},\n",
+    "    ]\n",
+    ")\n",
+    "result_url = llm.invoke([message_url])\n",
+    "print(f\"Response for URL image: {result_url.content}\")\n",
+    "\n",
+    "# Example using a local image file encoded in base64\n",
+    "image_file_path = \"/Users/philschmid/projects/google-gemini/langchain/docs/static/img/agents_vs_chains.png\"\n",
+    "\n",
+    "with open(image_file_path, \"rb\") as image_file:\n",
+    "    encoded_image = base64.b64encode(image_file.read()).decode(\"utf-8\")\n",
+    "\n",
+    "message_local = HumanMessage(\n",
+    "    content=[\n",
+    "        {\"type\": \"text\", \"text\": \"Describe the local image.\"},\n",
+    "        {\"type\": \"image_url\", \"image_url\": f\"data:image/png;base64,{encoded_image}\"},\n",
+    "    ]\n",
+    ")\n",
+    "result_local = llm.invoke([message_local])\n",
+    "print(f\"Response for local image: {result_local.content}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b422382",
+   "metadata": {},
+   "source": [
+    "Other supported `image_url` formats:\n",
+    "- A Google Cloud Storage URI (`gs://...`). Ensure the service account has access.\n",
+    "- A PIL Image object (the library handles encoding).\n",
+    "\n",
+    "### Audio Input\n",
+    "\n",
+    "Provide audio file inputs along with text. Use a model like `gemini-2.0-flash`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3461836",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "# Ensure you have an audio file named 'example_audio.mp3' or provide the correct path.\n",
+    "audio_file_path = \"example_audio.mp3\"\n",
+    "audio_mime_type = \"audio/mpeg\"\n",
+    "\n",
+    "\n",
+    "with open(audio_file_path, \"rb\") as audio_file:\n",
+    "    encoded_audio = base64.b64encode(audio_file.read()).decode(\"utf-8\")\n",
+    "\n",
+    "message = HumanMessage(\n",
+    "    content=[\n",
+    "        {\"type\": \"text\", \"text\": \"Transcribe the audio.\"},\n",
+    "        {\n",
+    "            \"type\": \"media\",\n",
+    "            \"data\": encoded_audio,  # Use base64 string directly\n",
+    "            \"mime_type\": audio_mime_type,\n",
+    "        },\n",
+    "    ]\n",
+    ")\n",
+    "response = llm.invoke([message])  # Uncomment to run\n",
+    "print(f\"Response for audio: {response.content}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d898e27",
+   "metadata": {},
+   "source": [
+    "### Video Input\n",
+    "\n",
+    "Provide video file inputs along with text. Use a model like `gemini-2.0-flash`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3046e74b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "# Ensure you have a video file named 'example_video.mp4' or provide the correct path.\n",
+    "video_file_path = \"example_video.mp4\"\n",
+    "video_mime_type = \"video/mp4\"\n",
+    "\n",
+    "\n",
+    "with open(video_file_path, \"rb\") as video_file:\n",
+    "    encoded_video = base64.b64encode(video_file.read()).decode(\"utf-8\")\n",
+    "\n",
+    "message = HumanMessage(\n",
+    "    content=[\n",
+    "        {\"type\": \"text\", \"text\": \"Describe the first few frames of the video.\"},\n",
+    "        {\n",
+    "            \"type\": \"media\",\n",
+    "            \"data\": encoded_video,  # Use base64 string directly\n",
+    "            \"mime_type\": video_mime_type,\n",
+    "        },\n",
+    "    ]\n",
+    ")\n",
+    "response = llm.invoke([message])  # Uncomment to run\n",
+    "print(f\"Response for video: {response.content}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2df11d89",
+   "metadata": {},
+   "source": [
+    "### Image Generation (Multimodal Output)\n",
+    "\n",
+    "The `gemini-2.0-flash` model can generate text and images inline (image generation is experimental). You need to specify the desired `response_modalities`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0b7180f",
    "metadata": {},
    "outputs": [
     {
@@ -266,17 +400,12 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "metadata": {
-      "image/png": {
-       "width": 300
-      }
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "import base64\n",
-    "from io import BytesIO\n",
     "\n",
     "from IPython.display import Image, display\n",
     "from langchain_google_genai import ChatGoogleGenerativeAI\n",
@@ -301,7 +430,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b14c0d87-cf7e-4d88-bda1-2ab40ec0350a",
+   "id": "14bf00f1",
    "metadata": {},
    "source": [
     "### Image and text to image\n",
@@ -311,8 +440,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "0f4ed7a5-980c-4b54-b743-0b988909744c",
+   "execution_count": null,
+   "id": "d65e195c",
    "metadata": {},
    "outputs": [
     {
@@ -322,11 +451,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "metadata": {
-      "image/png": {
-       "width": 300
-      }
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -349,7 +474,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a62669d8-becd-495f-8f4a-82d7c5d87969",
+   "id": "43b54d3f",
    "metadata": {},
    "source": [
     "You can also represent an input image and query in a single message by encoding the base64 data in the [data URI scheme](https://en.wikipedia.org/wiki/Data_URI_scheme):"
@@ -357,8 +482,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "6241da43-e210-43bc-89af-b3c480ea06e9",
+   "execution_count": null,
+   "id": "0dfc7e1e",
    "metadata": {},
    "outputs": [
     {
@@ -368,11 +493,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "metadata": {
-      "image/png": {
-       "width": 300
-      }
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -403,7 +524,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfe228d3-6773-4283-9788-87bdf6912b1c",
+   "id": "789818d7",
    "metadata": {},
    "source": [
     "You can also use LangGraph to manage the conversation history for you as in [this tutorial](/docs/tutorials/chatbot/)."
@@ -411,7 +532,313 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "id": "b037e2dc",
+   "metadata": {},
+   "source": [
+    "## Tool Calling\n",
+    "\n",
+    "You can equip the model with tools to call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0d759f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'name': 'get_weather', 'args': {'location': 'San Francisco'}, 'id': 'a6248087-74c5-4b7c-9250-f335e642927c', 'type': 'tool_call'}]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"OK. It's sunny in San Francisco.\", additional_kwargs={}, response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'model_name': 'gemini-2.0-flash', 'safety_ratings': []}, id='run-ac5bb52c-e244-4c72-9fbc-fb2a9cd7a72e-0', usage_metadata={'input_tokens': 29, 'output_tokens': 11, 'total_tokens': 40, 'input_token_details': {'cache_read': 0}})"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.tools import tool\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "\n",
+    "# Define the tool\n",
+    "@tool(description=\"Get the current weather in a given location\")\n",
+    "def get_weather(location: str) -> str:\n",
+    "    return \"It's sunny.\"\n",
+    "\n",
+    "\n",
+    "# Initialize the model and bind the tool\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\")\n",
+    "llm_with_tools = llm.bind_tools([get_weather])\n",
+    "\n",
+    "# Invoke the model with a query that should trigger the tool\n",
+    "query = \"What's the weather in San Francisco?\"\n",
+    "ai_msg = llm_with_tools.invoke(query)\n",
+    "\n",
+    "# Check the tool calls in the response\n",
+    "print(ai_msg.tool_calls)\n",
+    "\n",
+    "# Example tool call message would be needed here if you were actually running the tool\n",
+    "from langchain_core.messages import ToolMessage\n",
+    "\n",
+    "tool_message = ToolMessage(\n",
+    "    content=get_weather(*ai_msg.tool_calls[0][\"args\"]),\n",
+    "    tool_call_id=ai_msg.tool_calls[0][\"id\"],\n",
+    ")\n",
+    "llm_with_tools.invoke([ai_msg, tool_message])  # Example of passing tool result back"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91d42b86",
+   "metadata": {},
+   "source": [
+    "## Structured Output\n",
+    "\n",
+    "Force the model to respond with a specific structure using Pydantic models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7457dbe4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name='Abraham Lincoln' height_m=1.93\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "\n",
+    "# Define the desired structure\n",
+    "class Person(BaseModel):\n",
+    "    \"\"\"Information about a person.\"\"\"\n",
+    "\n",
+    "    name: str = Field(..., description=\"The person's name\")\n",
+    "    height_m: float = Field(..., description=\"The person's height in meters\")\n",
+    "\n",
+    "\n",
+    "# Initialize the model\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", temperature=0)\n",
+    "structured_llm = llm.with_structured_output(Person)\n",
+    "\n",
+    "# Invoke the model with a query asking for structured information\n",
+    "result = structured_llm.invoke(\n",
+    "    \"Who was the 16th president of the USA, and how tall was he in meters?\"\n",
+    ")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90d4725e",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "## Token Usage Tracking\n",
+    "\n",
+    "Access token usage information from the response metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "edcc003e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prompt engineering is the art and science of crafting effective text prompts to elicit desired and accurate responses from large language models.\n",
+      "\n",
+      "Usage Metadata:\n",
+      "{'input_tokens': 10, 'output_tokens': 24, 'total_tokens': 34, 'input_token_details': {'cache_read': 0}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\")\n",
+    "\n",
+    "result = llm.invoke(\"Explain the concept of prompt engineering in one sentence.\")\n",
+    "\n",
+    "print(result.content)\n",
+    "print(\"\\nUsage Metadata:\")\n",
+    "print(result.usage_metadata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28950dbc",
+   "metadata": {},
+   "source": [
+    "## Built-in tools\n",
+    "\n",
+    "Google Gemini supports a variety of built-in tools ([google search](https://ai.google.dev/gemini-api/docs/grounding/search-suggestions), [code execution](https://ai.google.dev/gemini-api/docs/code-execution?lang=python)), which can be bound to the model in the usual way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd074816",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The next total solar eclipse visible in the United States will occur on August 23, 2044. However, the path of totality will only pass through Montana, North Dakota, and South Dakota.\n",
+      "\n",
+      "For a total solar eclipse that crosses a significant portion of the continental U.S., you'll have to wait until August 12, 2045. This eclipse will start in California and end in Florida.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.ai.generativelanguage_v1beta.types import Tool as GenAITool\n",
+    "\n",
+    "resp = llm.invoke(\n",
+    "    \"When is the next total solar eclipse in US?\",\n",
+    "    tools=[GenAITool(google_search={})],\n",
+    ")\n",
+    "\n",
+    "print(resp.content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "6964be2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Executable code: print(2*2)\n",
+      "\n",
+      "Code execution result: 4\n",
+      "\n",
+      "2*2 is 4.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/philschmid/projects/google-gemini/langchain/.venv/lib/python3.9/site-packages/langchain_google_genai/chat_models.py:580: UserWarning: \n",
+      "        ⚠️ Warning: Output may vary each run.  \n",
+      "        - 'executable_code': Always present.  \n",
+      "        - 'execution_result' & 'image_url': May be absent for some queries.  \n",
+      "\n",
+      "        Validate before using in production.\n",
+      "\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.ai.generativelanguage_v1beta.types import Tool as GenAITool\n",
+    "\n",
+    "resp = llm.invoke(\n",
+    "    \"What is 2*2, use python\",\n",
+    "    tools=[GenAITool(code_execution={})],\n",
+    ")\n",
+    "\n",
+    "for c in resp.content:\n",
+    "    if isinstance(c, dict):\n",
+    "        if c[\"type\"] == \"code_execution_result\":\n",
+    "            print(f\"Code execution result: {c['code_execution_result']}\")\n",
+    "        elif c[\"type\"] == \"executable_code\":\n",
+    "            print(f\"Executable code: {c['executable_code']}\")\n",
+    "    else:\n",
+    "        print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a27e6ff4",
+   "metadata": {},
+   "source": [
+    "## Native Async\n",
+    "\n",
+    "Use asynchronous methods for non-blocking calls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c6803e57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Async Invoke Result: The sky is blue due to a phenomenon called **Rayle...\n",
+      "\n",
+      "Async Stream Result:\n",
+      "The thread is free, it does not wait,\n",
+      "For answers slow, or tasks of fate.\n",
+      "A promise made, a future bright,\n",
+      "It moves ahead, with all its might.\n",
+      "\n",
+      "A callback waits, a signal sent,\n",
+      "When data's read, or job is spent.\n",
+      "Non-blocking code, a graceful dance,\n",
+      "Responsive apps, a fleeting glance.\n",
+      "\n",
+      "Async Batch Results: ['1 + 1 = 2', '2 + 2 = 4']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\")\n",
+    "\n",
+    "\n",
+    "async def run_async_calls():\n",
+    "    # Async invoke\n",
+    "    result_ainvoke = await llm.ainvoke(\"Why is the sky blue?\")\n",
+    "    print(\"Async Invoke Result:\", result_ainvoke.content[:50] + \"...\")\n",
+    "\n",
+    "    # Async stream\n",
+    "    print(\"\\nAsync Stream Result:\")\n",
+    "    async for chunk in llm.astream(\n",
+    "        \"Write a short poem about asynchronous programming.\"\n",
+    "    ):\n",
+    "        print(chunk.content, end=\"\", flush=True)\n",
+    "    print(\"\\n\")\n",
+    "\n",
+    "    # Async batch\n",
+    "    results_abatch = await llm.abatch([\"What is 1+1?\", \"What is 2+2?\"])\n",
+    "    print(\"Async Batch Results:\", [res.content for res in results_abatch])\n",
+    "\n",
+    "\n",
+    "await run_async_calls()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99204b32",
    "metadata": {},
    "source": [
     "## Safety Settings\n",
@@ -421,8 +848,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "238b2f96-e573-4fac-bbf2-7e52ad926833",
+   "execution_count": null,
+   "id": "d4c14039",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -442,7 +869,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5805d40c-deb8-4924-8e72-a294a0482fc9",
+   "id": "dea38fb1",
    "metadata": {},
    "source": [
     "For an enumeration of the categories and thresholds available, see Google's [safety setting types](https://ai.google.dev/api/python/google/generativeai/types/SafetySettingDict)."
@@ -450,7 +877,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "id": "d6d0e853",
    "metadata": {},
    "source": [
     "## API reference\n",
@@ -461,7 +888,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -475,7 +902,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/litellm.ipynb

@@ -19,18 +19,50 @@
     "id": "5bcea387"
    },
    "source": [
-    "# ChatLiteLLM\n",
+    "# ChatLiteLLM and ChatLiteLLMRouter\n",
     "\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.\n",
     "\n",
+    "This integration contains two main classes:\n",
+    "\n",
+    "- ```ChatLiteLLM```: The main Langchain wrapper for basic usage of LiteLLM ([docs](https://docs.litellm.ai/docs/)).\n",
+    "- ```ChatLiteLLMRouter```: A ```ChatLiteLLM``` wrapper that leverages LiteLLM's Router ([docs](https://docs.litellm.ai/docs/routing))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ddb7fd3",
+   "metadata": {},
+   "source": [
+    "## Table of Contents\n",
+    "1. [Overview](#overview)\n",
+    "   - [Integration Details](#integration-details)\n",
+    "   - [Model Features](#model-features)\n",
+    "2. [Setup](#setup)\n",
+    "3. [Credentials](#credentials)\n",
+    "4. [Installation](#installation)\n",
+    "5. [Instantiation](#instantiation)\n",
+    "   - [ChatLiteLLM](#chatlitellm)\n",
+    "   - [ChatLiteLLMRouter](#chatlitellmrouter)\n",
+    "6. [Invocation](#invocation)\n",
+    "7. [Async and Streaming Functionality](#async-and-streaming-functionality)\n",
+    "8. [API Reference](#api-reference)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37be6ef8",
+   "metadata": {},
+   "source": [
     "## 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",
+    "| [ChatLiteLLM](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellm) | [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",
+    "| [ChatLiteLLMRouter](https://python.langchain.com/docs/integrations/chat/litellm/#chatlitellmrouter) | [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",
@@ -38,7 +70,7 @@
     "| ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ |\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."
+    "To access ```ChatLiteLLM``` and ```ChatLiteLLMRouter``` 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."
    ]
   },
   {
@@ -53,23 +85,23 @@
     "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",
+    "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."
+    "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,
+   "execution_count": null,
    "id": "7595eddf",
    "metadata": {
     "id": "7595eddf"
    },
    "outputs": [],
    "source": [
-    "## set ENV variables\n",
+    "## Set ENV variables\n",
     "import os\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-key\"\n",
@@ -85,7 +117,7 @@
    "source": [
     "### Installation\n",
     "\n",
-    "The LangChain LiteLLM integration lives in the `langchain-litellm` package:"
+    "The LangChain LiteLLM integration is available in the `langchain-litellm` package:"
    ]
   },
   {
@@ -107,13 +139,21 @@
     "id": "bc1182b4"
    },
    "source": [
-    "## Instantiation\n",
-    "Now we can instantiate our model object and generate chat completions:"
+    "## Instantiation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d439241a",
+   "metadata": {},
+   "source": [
+    "### ChatLiteLLM\n",
+    "You can instantiate a ```ChatLiteLLM``` model by providing a ```model``` name [supported by LiteLLM](https://docs.litellm.ai/docs/providers)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
    "metadata": {
     "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
@@ -121,9 +161,52 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_litellm.chat_models import ChatLiteLLM\n",
+    "from langchain_litellm import ChatLiteLLM\n",
     "\n",
-    "llm = ChatLiteLLM(model=\"gpt-3.5-turbo\")"
+    "llm = ChatLiteLLM(model=\"gpt-4.1-nano\", temperature=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d0ed306",
+   "metadata": {},
+   "source": [
+    "### ChatLiteLLMRouter\n",
+    "You can also leverage LiteLLM's routing capabilities by defining your model list as specified [here](https://docs.litellm.ai/docs/routing)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d26393a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_litellm import ChatLiteLLMRouter\n",
+    "from litellm import Router\n",
+    "\n",
+    "model_list = [\n",
+    "    {\n",
+    "        \"model_name\": \"gpt-4.1\",\n",
+    "        \"litellm_params\": {\n",
+    "            \"model\": \"azure/gpt-4.1\",\n",
+    "            \"api_key\": \"<your-api-key>\",\n",
+    "            \"api_version\": \"2024-10-21\",\n",
+    "            \"api_base\": \"https://<your-endpoint>.openai.azure.com/\",\n",
+    "        },\n",
+    "    },\n",
+    "    {\n",
+    "        \"model_name\": \"gpt-4o\",\n",
+    "        \"litellm_params\": {\n",
+    "            \"model\": \"azure/gpt-4o\",\n",
+    "            \"api_key\": \"<your-api-key>\",\n",
+    "            \"api_version\": \"2024-10-21\",\n",
+    "            \"api_base\": \"https://<your-endpoint>.openai.azure.com/\",\n",
+    "        },\n",
+    "    },\n",
+    "]\n",
+    "litellm_router = Router(model_list=model_list)\n",
+    "llm = ChatLiteLLMRouter(router=litellm_router, model_name=\"gpt-4.1\", temperature=0.1)"
    ]
   },
   {
@@ -133,7 +216,8 @@
     "id": "63d98454"
    },
    "source": [
-    "## Invocation"
+    "## Invocation\n",
+    "Whether you've instantiated a `ChatLiteLLM` or a `ChatLiteLLMRouter`, you can now use the ChatModel through Langchain's API."
    ]
   },
   {
@@ -171,7 +255,8 @@
     "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c"
    },
    "source": [
-    "## `ChatLiteLLM` also supports async and streaming functionality:"
+    "## Async and Streaming Functionality\n",
+    "`ChatLiteLLM` and `ChatLiteLLMRouter` also support async and streaming functionality:"
    ]
   },
   {
@@ -212,7 +297,7 @@
    },
    "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"
+    "For detailed documentation of all `ChatLiteLLM` and `ChatLiteLLMRouter` features and configurations, head to the API reference: https://github.com/Akshay-Dongare/langchain-litellm"
    ]
   }
  ],

docs/docs/integrations/chat/litellm_router.ipynb

@@ -1,218 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "59148044",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: LiteLLM Router\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "247da7a6",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "bf733a38-db84-4363-89e2-de6735c37230",
-   "metadata": {},
-   "source": [
-    "# ChatLiteLLMRouter\n",
-    "\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 Router I/O library. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_community.chat_models import ChatLiteLLMRouter\n",
-    "from langchain_core.messages import HumanMessage\n",
-    "from litellm import Router"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model_list = [\n",
-    "    {\n",
-    "        \"model_name\": \"gpt-4\",\n",
-    "        \"litellm_params\": {\n",
-    "            \"model\": \"azure/gpt-4-1106-preview\",\n",
-    "            \"api_key\": \"<your-api-key>\",\n",
-    "            \"api_version\": \"2023-05-15\",\n",
-    "            \"api_base\": \"https://<your-endpoint>.openai.azure.com/\",\n",
-    "        },\n",
-    "    },\n",
-    "    {\n",
-    "        \"model_name\": \"gpt-35-turbo\",\n",
-    "        \"litellm_params\": {\n",
-    "            \"model\": \"azure/gpt-35-turbo\",\n",
-    "            \"api_key\": \"<your-api-key>\",\n",
-    "            \"api_version\": \"2023-05-15\",\n",
-    "            \"api_base\": \"https://<your-endpoint>.openai.azure.com/\",\n",
-    "        },\n",
-    "    },\n",
-    "]\n",
-    "litellm_router = Router(model_list=model_list)\n",
-    "chat = ChatLiteLLMRouter(router=litellm_router, model_name=\"gpt-35-turbo\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'aime programmer.\")"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
-    "    )\n",
-    "]\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
-   "metadata": {},
-   "source": [
-    "## `ChatLiteLLMRouter` also supports async and streaming functionality:"
-   ]
-  },
-  {
-   "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'adore programmer.\", generation_info={'finish_reason': 'stop'}, message=AIMessage(content=\"J'adore programmer.\"))]], llm_output={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 19, 'total_tokens': 25}, 'model_name': None}, run=[RunInfo(run_id=UUID('75003ec9-1e2b-43b7-a216-10dcc0f75e00'))])"
-      ]
-     },
-     "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",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore programmer."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore programmer.\")"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat = ChatLiteLLMRouter(\n",
-    "    router=litellm_router,\n",
-    "    model_name=\"gpt-35-turbo\",\n",
-    "    streaming=True,\n",
-    "    verbose=True,\n",
-    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    ")\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c253883f",
-   "metadata": {},
-   "outputs": [],
-   "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/chat/naver.ipynb

@@ -17,21 +17,21 @@
       "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",
+        "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://guide.ncloud-docs.com/docs/clovastudio-dev-langchain).\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",
+        "[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 Guide [documentation](https://guide.ncloud-docs.com/docs/clovastudio-model).\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",
+        "| [ChatClovaX](https://guide.ncloud-docs.com/docs/clovastudio-dev-langchain#HyperCLOVAX%EB%AA%A8%EB%8D%B8%EC%9D%B4%EC%9A%A9) | [langchain-naver](https://pypi.org/project/langchain-naver/) |   ❌   | ❌ |                                    ❌                                     | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_naver?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_naver?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\n",
         "\n",
@@ -39,26 +39,23 @@
         "\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",
+        "3. Create a CLOVA Studio Test App or Service App of a model to use (See [here](https://guide.ncloud-docs.com/docs/clovastudio-playground-testapp).)\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",
+        "Set the `CLOVASTUDIO_API_KEY` environment variable with your 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",
+        "export CLOVASTUDIO_API_KEY=\"your-api-key-here\"\n",
         "```"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "id": "2def81b5-b023-4f40-a97b-b2c5ca59d6a9",
       "metadata": {},
       "outputs": [],
@@ -66,22 +63,19 @@
         "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",
-        "#     )"
+        "if not os.getenv(\"CLOVASTUDIO_API_KEY\"):\n",
+        "    os.environ[\"CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
+        "        \"Enter your CLOVA Studio 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:"
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
     },
     {
       "cell_type": "code",
@@ -101,7 +95,7 @@
       "source": [
         "### Installation\n",
         "\n",
-        "The LangChain Naver integration lives in the `langchain-community` package:"
+        "The LangChain Naver integration lives in the `langchain-naver` package:"
       ]
     },
     {
@@ -112,7 +106,7 @@
       "outputs": [],
       "source": [
         "# install package\n",
-        "!pip install -qU langchain-community"
+        "%pip install -qU langchain-naver"
       ]
     },
     {
@@ -127,21 +121,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 3,
       "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
       "metadata": {},
       "outputs": [],
       "source": [
-        "from langchain_community.chat_models import ChatClovaX\n",
+        "from langchain_naver import ChatClovaX\n",
         "\n",
         "chat = ChatClovaX(\n",
-        "    model=\"HCX-003\",\n",
-        "    max_tokens=100,\n",
+        "    model=\"HCX-005\",\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",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
         "    # other params...\n",
         ")"
       ]
@@ -153,12 +145,12 @@
       "source": [
         "## Invocation\n",
         "\n",
-        "In addition to invoke, we also support batch and stream functionalities."
+        "In addition to invoke, `ChatClovaX` also support batch and stream functionalities."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 4,
       "id": "62e0dbc3",
       "metadata": {
         "tags": []
@@ -167,10 +159,10 @@
         {
           "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})"
+              "AIMessage(content='네이버 인공지능을 사용하는 것을 정말 좋아합니다.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 28, 'total_tokens': 39, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'HCX-005', 'system_fingerprint': None, 'id': 'b70c26671cd247a0864115bacfb5fc12', 'finish_reason': 'stop', 'logprobs': None}, id='run-3faf6a8d-d5da-49ad-9fbb-7b56ed23b484-0', usage_metadata={'input_tokens': 28, 'output_tokens': 11, 'total_tokens': 39, 'input_token_details': {}, 'output_token_details': {}})"
             ]
           },
-          "execution_count": 3,
+          "execution_count": 4,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -190,7 +182,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 5,
       "id": "24e7377f",
       "metadata": {},
       "outputs": [
@@ -198,7 +190,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "저는 네이버 AI를 사용하는 것이 좋아요.\n"
+            "네이버 인공지능을 사용하는 것을 정말 좋아합니다.\n"
           ]
         }
       ],
@@ -218,17 +210,17 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 6,
       "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})"
+              "AIMessage(content='저는 네이버 인공지능을 사용하는 것을 좋아합니다.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 28, 'total_tokens': 38, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'HCX-005', 'system_fingerprint': None, 'id': 'b7a826d17fcf4fee8386fca2ebc63284', 'finish_reason': 'stop', 'logprobs': None}, id='run-35957816-3325-4d9c-9441-e40704912be6-0', usage_metadata={'input_tokens': 28, 'output_tokens': 10, 'total_tokens': 38, 'input_token_details': {}, 'output_token_details': {}})"
             ]
           },
-          "execution_count": 5,
+          "execution_count": 6,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -266,7 +258,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 7,
       "id": "2c07af21-dda5-4514-b4de-1f214c2cebcd",
       "metadata": {},
       "outputs": [
@@ -274,7 +266,7 @@
           "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."
+            "In Korean, the informal way of saying 'hi' is \"안녕\" (annyeong). If you're addressing someone older or showing more respect, you would use \"안녕하세요\" (annjeonghaseyo). Both phrases are used as greetings similar to 'hello'. Remember, pronunciation is key so make sure to pronounce each syllable clearly: 안-녀-엉 (an-nyeo-eong) and 안-녕-하-세-요 (an-nyeong-ha-se-yo)."
           ]
         }
       ],
@@ -298,115 +290,37 @@
         "\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",
+        "You can call fine-tuned models by passing the `task_id` to the `model` parameter as: `ft:{task_id}`.\n",
         "\n",
         "You can check `task_id` from corresponding Test App or Service App details."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": null,
       "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})"
+              "AIMessage(content='네이버 인공지능을 사용하는 것을 정말 좋아합니다.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 28, 'total_tokens': 39, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'HCX-005', 'system_fingerprint': None, 'id': '2222d6d411a948c883aac1e03ca6cebe', 'finish_reason': 'stop', 'logprobs': None}, id='run-9696d7e2-7afa-4bb4-9c03-b95fcf678ab8-0', usage_metadata={'input_tokens': 28, 'output_tokens': 11, 'total_tokens': 39, 'input_token_details': {}, 'output_token_details': {}})"
             ]
           },
-          "execution_count": 7,
+          "execution_count": 10,
           "metadata": {},
           "output_type": "execute_result"
         }
       ],
       "source": [
         "fine_tuned_model = ChatClovaX(\n",
-        "    task_id=\"5s8egt3a\",  # set if you want to use fine-tuned model\n",
+        "    model=\"ft:a1b2c3d4\",  # set as `ft:{task_id}` with your fine-tuned model's task id\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",
@@ -414,13 +328,13 @@
       "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"
+        "For detailed documentation of all ChatClovaX features and configurations head to the [API reference](https://guide.ncloud-docs.com/docs/clovastudio-dev-langchain)"
       ]
     }
   ],
   "metadata": {
     "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
+      "display_name": "Python 3",
       "language": "python",
       "name": "python3"
     },
@@ -434,7 +348,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.3"
+      "version": "3.12.8"
     }
   },
   "nbformat": 4,

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",
@@ -1342,6 +1413,23 @@
     "second_output_message = llm.invoke(history)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "90c18d18-b25c-4509-a639-bd652b92f518",
+   "metadata": {},
+   "source": [
+    "## Flex processing\n",
+    "\n",
+    "OpenAI offers a variety of [service tiers](https://platform.openai.com/docs/guides/flex-processing). The \"flex\" tier offers cheaper pricing for requests, with the trade-off that responses may take longer and resources might not always be available. This approach is best suited for non-critical tasks, including model testing, data enhancement, or jobs that can be run asynchronously.\n",
+    "\n",
+    "To use it, initialize the model with `service_tier=\"flex\"`:\n",
+    "```python\n",
+    "llm = ChatOpenAI(model=\"o4-mini\", service_tier=\"flex\")\n",
+    "```\n",
+    "\n",
+    "Note that this is a beta feature that is only available for a subset of models. See OpenAI [docs](https://platform.openai.com/docs/guides/flex-processing) for more detail."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a796d728-971b-408b-88d5-440015bbb941",
@@ -1349,7 +1437,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "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"
+    "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)."
    ]
   }
  ],

docs/docs/integrations/chat/predictionguard.ipynb

@@ -57,8 +57,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-08T19:44:51.390231Z",
-     "start_time": "2024-11-08T19:44:51.387945Z"
+     "end_time": "2025-04-21T18:23:30.746350Z",
+     "start_time": "2025-04-21T18:23:30.744744Z"
     }
    },
    "cell_type": "code",
@@ -70,7 +70,7 @@
    ],
    "id": "fa57fba89276da13",
    "outputs": [],
-   "execution_count": 1
+   "execution_count": 2
   },
   {
    "metadata": {},
@@ -82,12 +82,25 @@
    "id": "87dc1742af7b053"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T18:23:33.359278Z",
+     "start_time": "2025-04-21T18:23:32.853207Z"
+    }
+   },
    "cell_type": "code",
    "source": "%pip install -qU langchain-predictionguard",
    "id": "b816ae8553cba021",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -103,13 +116,13 @@
    "metadata": {
     "id": "2xe8JEUwA7_y",
     "ExecuteTime": {
-     "end_time": "2024-11-08T19:44:53.950653Z",
-     "start_time": "2024-11-08T19:44:53.488694Z"
+     "end_time": "2025-04-21T18:23:39.812675Z",
+     "start_time": "2025-04-21T18:23:39.666881Z"
     }
    },
    "source": "from langchain_predictionguard import ChatPredictionGuard",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 4
   },
   {
    "cell_type": "code",
@@ -117,8 +130,8 @@
    "metadata": {
     "id": "Ua7Mw1N4HcER",
     "ExecuteTime": {
-     "end_time": "2024-11-08T19:44:54.890695Z",
-     "start_time": "2024-11-08T19:44:54.502846Z"
+     "end_time": "2025-04-21T18:23:41.590296Z",
+     "start_time": "2025-04-21T18:23:41.253237Z"
     }
    },
    "source": [
@@ -126,7 +139,7 @@
     "chat = ChatPredictionGuard(model=\"Hermes-3-Llama-3.1-8B\")"
    ],
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 5
   },
   {
    "metadata": {},
@@ -221,6 +234,132 @@
    ],
    "execution_count": 6
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Tool Calling\n",
+    "\n",
+    "Prediction Guard has a tool calling API that lets you describe tools and their arguments, which enables the model return a JSON object with a tool to call and the inputs to that tool. Tool-calling is very useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n"
+   ],
+   "id": "1227780d6e6728ba"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### ChatPredictionGuard.bind_tools()\n",
+    "\n",
+    "Using `ChatPredictionGuard.bind_tools()`, you can pass in Pydantic classes, dict schemas, and Langchain tools as tools to the model, which are then reformatted to allow for use by the model."
+   ],
+   "id": "23446aa52e01d1ba"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "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",
+    "class GetPopulation(BaseModel):\n",
+    "    \"\"\"Get the current population 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 = chat.bind_tools(\n",
+    "    [GetWeather, GetPopulation]\n",
+    "    # strict = True  # enforce tool args schema is respected\n",
+    ")"
+   ],
+   "id": "135efb0bfc5916c1"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T18:42:41.834079Z",
+     "start_time": "2025-04-21T18:42:40.289095Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ai_msg = llm_with_tools.invoke(\n",
+    "    \"Which city is hotter today and which is bigger: LA or NY?\"\n",
+    ")\n",
+    "ai_msg"
+   ],
+   "id": "8136f19a8836cd58",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'chatcmpl-tool-b1204a3c70b44cd8802579df48df0c8c', 'type': 'function', 'index': 0, 'function': {'name': 'GetWeather', 'arguments': '{\"location\": \"Los Angeles, CA\"}'}}, {'id': 'chatcmpl-tool-e299116c05bf4ce498cd6042928ae080', 'type': 'function', 'index': 0, 'function': {'name': 'GetWeather', 'arguments': '{\"location\": \"New York, NY\"}'}}, {'id': 'chatcmpl-tool-19502a60f30348669ffbac00ff503388', 'type': 'function', 'index': 0, 'function': {'name': 'GetPopulation', 'arguments': '{\"location\": \"Los Angeles, CA\"}'}}, {'id': 'chatcmpl-tool-4b8d56ef067f447795d9146a56e43510', 'type': 'function', 'index': 0, 'function': {'name': 'GetPopulation', 'arguments': '{\"location\": \"New York, NY\"}'}}]}, response_metadata={}, id='run-4630cfa9-4e95-42dd-8e4a-45db78180a10-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'Los Angeles, CA'}, 'id': 'chatcmpl-tool-b1204a3c70b44cd8802579df48df0c8c', 'type': 'tool_call'}, {'name': 'GetWeather', 'args': {'location': 'New York, NY'}, 'id': 'chatcmpl-tool-e299116c05bf4ce498cd6042928ae080', 'type': 'tool_call'}, {'name': 'GetPopulation', 'args': {'location': 'Los Angeles, CA'}, 'id': 'chatcmpl-tool-19502a60f30348669ffbac00ff503388', 'type': 'tool_call'}, {'name': 'GetPopulation', 'args': {'location': 'New York, NY'}, 'id': 'chatcmpl-tool-4b8d56ef067f447795d9146a56e43510', 'type': 'tool_call'}])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### AIMessage.tool_calls\n",
+    "\n",
+    "Notice that the AIMessage has a tool_calls attribute. This contains in a standardized ToolCall format that is model-provider agnostic."
+   ],
+   "id": "84f405c45a35abe5"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T18:43:00.429453Z",
+     "start_time": "2025-04-21T18:43:00.426399Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "ai_msg.tool_calls",
+   "id": "bdcee85475019719",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'GetWeather',\n",
+       "  'args': {'location': 'Los Angeles, CA'},\n",
+       "  'id': 'chatcmpl-tool-b1204a3c70b44cd8802579df48df0c8c',\n",
+       "  'type': 'tool_call'},\n",
+       " {'name': 'GetWeather',\n",
+       "  'args': {'location': 'New York, NY'},\n",
+       "  'id': 'chatcmpl-tool-e299116c05bf4ce498cd6042928ae080',\n",
+       "  'type': 'tool_call'},\n",
+       " {'name': 'GetPopulation',\n",
+       "  'args': {'location': 'Los Angeles, CA'},\n",
+       "  'id': 'chatcmpl-tool-19502a60f30348669ffbac00ff503388',\n",
+       "  'type': 'tool_call'},\n",
+       " {'name': 'GetPopulation',\n",
+       "  'args': {'location': 'New York, NY'},\n",
+       "  'id': 'chatcmpl-tool-4b8d56ef067f447795d9146a56e43510',\n",
+       "  'type': 'tool_call'}]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 8
+  },
   {
    "cell_type": "markdown",
    "id": "ff1b51a8",

docs/docs/integrations/document_loaders/astradb.ipynb

@@ -14,7 +14,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Cassandra and made conveniently available through an easy-to-use JSON API."
+    "> [DataStax Astra DB](https://docs.datastax.com/en/astra-db-serverless/index.html) is a serverless \n",
+    "> AI-ready database built on `Apache Cassandra®` and made conveniently available \n",
+    "> through an easy-to-use JSON API."
    ]
   },
   {
@@ -34,33 +36,46 @@
     "id": "juAmbgoWD17u"
    },
    "source": [
-    "The AstraDB Document Loader returns a list of Langchain Documents from an AstraDB database.\n",
+    "The Astra DB Document Loader returns a list of Langchain `Document` objects read from an Astra DB collection.\n",
     "\n",
-    "The Loader takes the following parameters:\n",
+    "The loader takes the following parameters:\n",
     "\n",
-    "* `api_endpoint`: AstraDB API endpoint. Looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n",
-    "* `token`: AstraDB token. Looks like `AstraCS:6gBhNmsk135....`\n",
+    "* `api_endpoint`: Astra DB API endpoint. Looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n",
+    "* `token`: Astra DB token. Looks like `AstraCS:aBcD0123...`\n",
     "* `collection_name` : AstraDB collection name\n",
-    "* `namespace`: (Optional) AstraDB namespace\n",
+    "* `namespace`: (Optional) AstraDB namespace (called _keyspace_ in Astra DB)\n",
     "* `filter_criteria`: (Optional) Filter used in the find query\n",
     "* `projection`: (Optional) Projection used in the find query\n",
-    "* `find_options`: (Optional) Options used in the find query\n",
-    "* `nb_prefetched`: (Optional) Number of documents pre-fetched by the loader\n",
+    "* `limit`: (Optional) Maximum number of documents to retrieve\n",
     "* `extraction_function`: (Optional) A function to convert the AstraDB document to the LangChain `page_content` string. Defaults to `json.dumps`\n",
     "\n",
-    "The following metadata is set to the LangChain Documents metadata output:\n",
+    "The loader sets the following metadata for the documents it reads:\n",
     "\n",
     "```python\n",
-    "{\n",
-    "    metadata : {\n",
-    "        \"namespace\": \"...\", \n",
-    "        \"api_endpoint\": \"...\", \n",
-    "        \"collection\": \"...\"\n",
-    "    }\n",
+    "metadata={\n",
+    "    \"namespace\": \"...\", \n",
+    "    \"api_endpoint\": \"...\", \n",
+    "    \"collection\": \"...\"\n",
     "}\n",
     "```"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"langchain-astradb>=0.6,<0.7\""
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -71,24 +86,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.document_loaders import AstraDBLoader"
+    "from langchain_astradb import AstraDBLoader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[**API Reference:** `AstraDBLoader`](https://python.langchain.com/api_reference/astradb/document_loaders/langchain_astradb.document_loaders.AstraDBLoader.html#langchain_astradb.document_loaders.AstraDBLoader)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:41:22.643335Z",
      "start_time": "2024-01-08T12:40:57.759116Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "ASTRA_DB_API_ENDPOINT =  https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com\n",
+      "ASTRA_DB_APPLICATION_TOKEN =  ········\n"
+     ]
+    }
+   ],
    "source": [
     "from getpass import getpass\n",
     "\n",
@@ -98,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:42:25.395162Z",
@@ -112,19 +146,22 @@
     "    token=ASTRA_DB_APPLICATION_TOKEN,\n",
     "    collection_name=\"movie_reviews\",\n",
     "    projection={\"title\": 1, \"reviewtext\": 1},\n",
-    "    find_options={\"limit\": 10},\n",
+    "    limit=10,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:42:30.236489Z",
      "start_time": "2024-01-08T12:42:29.612133Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "outputs": [],
    "source": [
@@ -133,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-01-08T12:42:31.369394Z",
@@ -144,10 +181,10 @@
     {
      "data": {
       "text/plain": [
-       "Document(page_content='{\"_id\": \"659bdffa16cbc4586b11a423\", \"title\": \"Dangerous Men\", \"reviewtext\": \"\\\\\"Dangerous Men,\\\\\" the picture\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\"}', metadata={'namespace': 'default_keyspace', 'api_endpoint': 'https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com', 'collection': 'movie_reviews'})"
+       "Document(metadata={'namespace': 'default_keyspace', 'api_endpoint': 'https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com', 'collection': 'movie_reviews'}, page_content='{\"_id\": \"659bdffa16cbc4586b11a423\", \"title\": \"Dangerous Men\", \"reviewtext\": \"\\\\\"Dangerous Men,\\\\\" the picture\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\"}')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -179,7 +216,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/browserbase.ipynb

@@ -49,7 +49,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.document_loaders import BrowserbaseLoader"
+    "import os\n",
+    "\n",
+    "from langchain_community.document_loaders import BrowserbaseLoader\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "BROWSERBASE_API_KEY = os.getenv(\"BROWSERBASE_API_KEY\")\n",
+    "BROWSERBASE_PROJECT_ID = os.getenv(\"BROWSERBASE_PROJECT_ID\")"
    ]
   },
   {
@@ -59,6 +66,8 @@
    "outputs": [],
    "source": [
     "loader = BrowserbaseLoader(\n",
+    "    api_key=BROWSERBASE_API_KEY,\n",
+    "    project_id=BROWSERBASE_PROJECT_ID,\n",
     "    urls=[\n",
     "        \"https://example.com\",\n",
     "    ],\n",
@@ -78,52 +87,11 @@
     "\n",
     "- `urls` Required. A list of URLs to fetch.\n",
     "- `text_content` Retrieve only text content. Default is `False`.\n",
-    "- `api_key` Optional. Browserbase API key. Default is `BROWSERBASE_API_KEY` env variable.\n",
-    "- `project_id` Optional. Browserbase Project ID. Default is `BROWSERBASE_PROJECT_ID` env variable.\n",
+    "- `api_key` Browserbase API key. Default is `BROWSERBASE_API_KEY` env variable.\n",
+    "- `project_id` Browserbase Project ID. Default is `BROWSERBASE_PROJECT_ID` env variable.\n",
     "- `session_id` Optional. Provide an existing Session ID.\n",
     "- `proxy` Optional. Enable/Disable Proxies."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading images\n",
-    "\n",
-    "You can also load screenshots of webpages (as bytes) for multi-modal models.\n",
-    "\n",
-    "Full example using GPT-4V:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from browserbase import Browserbase\n",
-    "from browserbase.helpers.gpt4 import GPT4VImage, GPT4VImageDetail\n",
-    "from langchain_core.messages import HumanMessage\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "chat = ChatOpenAI(model=\"gpt-4-vision-preview\", max_tokens=256)\n",
-    "browser = Browserbase()\n",
-    "\n",
-    "screenshot = browser.screenshot(\"https://browserbase.com\")\n",
-    "\n",
-    "result = chat.invoke(\n",
-    "    [\n",
-    "        HumanMessage(\n",
-    "            content=[\n",
-    "                {\"type\": \"text\", \"text\": \"What color is the logo?\"},\n",
-    "                GPT4VImage(screenshot, GPT4VImageDetail.auto),\n",
-    "            ]\n",
-    "        )\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "print(result.content)"
-   ]
   }
  ],
  "metadata": {

docs/docs/integrations/document_loaders/oracleadb_loader.ipynb

@@ -36,10 +36,7 @@
     "pip install oracledb"
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    }
+    "collapsed": false
    }
   },
   {
@@ -51,10 +48,7 @@
     "from settings import s"
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    }
+    "collapsed": false
    }
   },
   {
@@ -97,16 +91,14 @@
     "doc_2 = doc_loader_2.load()"
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "is_executing": true
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "markdown",
    "source": [
-    "With TLS authentication, wallet_location and wallet_password are not required."
+    "With TLS authentication, wallet_location and wallet_password are not required.\n",
+    "Bind variable option is provided by argument \"parameters\"."
    ],
    "metadata": {
     "collapsed": false
@@ -117,6 +109,8 @@
    "execution_count": null,
    "outputs": [],
    "source": [
+    "SQL_QUERY = \"select channel_id, channel_desc from sh.channels where channel_desc = :1 fetch first 5 rows only\"\n",
+    "\n",
     "doc_loader_3 = OracleAutonomousDatabaseLoader(\n",
     "    query=SQL_QUERY,\n",
     "    user=s.USERNAME,\n",
@@ -124,6 +118,7 @@
     "    schema=s.SCHEMA,\n",
     "    config_dir=s.CONFIG_DIR,\n",
     "    tns_name=s.TNS_NAME,\n",
+    "    parameters=[\"Direct Sales\"],\n",
     ")\n",
     "doc_3 = doc_loader_3.load()\n",
     "\n",
@@ -133,6 +128,7 @@
     "    password=s.PASSWORD,\n",
     "    schema=s.SCHEMA,\n",
     "    connection_string=s.CONNECTION_STRING,\n",
+    "    parameters=[\"Direct Sales\"],\n",
     ")\n",
     "doc_4 = doc_loader_4.load()"
    ],

docs/docs/integrations/document_loaders/singlestore.ipynb

@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: SingleStore\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SingleStoreLoader\n",
+    "\n",
+    "The `SingleStoreLoader` allows you to load documents directly from a SingleStore database table. It is part of the `langchain-singlestore` integration package.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "### Integration Details\n",
+    "\n",
+    "| Class | Package | JS Support |\n",
+    "| :--- | :--- | :---: |\n",
+    "| `SingleStoreLoader` | `langchain_singlestore` | ❌ |\n",
+    "\n",
+    "### Features\n",
+    "- Load documents lazily to handle large datasets efficiently.\n",
+    "- Supports native asynchronous operations.\n",
+    "- Easily configurable to work with different database schemas.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To use the `SingleStoreLoader`, you need to install the `langchain-singlestore` package. Follow the installation instructions below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Install **langchain_singlestore**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_singlestore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "\n",
+    "To initialize `SingleStoreLoader`, you need to provide connection parameters for the SingleStore database and specify the table and fields to load documents from.\n",
+    "\n",
+    "### Required Parameters:\n",
+    "- **host** (`str`): Hostname, IP address, or URL for the database.\n",
+    "- **table_name** (`str`): Name of the table to query. Defaults to `embeddings`.\n",
+    "- **content_field** (`str`): Field containing document content. Defaults to `content`.\n",
+    "- **metadata_field** (`str`): Field containing document metadata. Defaults to `metadata`.\n",
+    "\n",
+    "### Optional Parameters:\n",
+    "- **id_field** (`str`): Field containing document IDs. Defaults to `id`.\n",
+    "\n",
+    "### Connection Pool Parameters:\n",
+    "- **pool_size** (`int`): Number of active connections in the pool. Defaults to `5`.\n",
+    "- **max_overflow** (`int`): Maximum connections beyond `pool_size`. Defaults to `10`.\n",
+    "- **timeout** (`float`): Connection timeout in seconds. Defaults to `30`.\n",
+    "\n",
+    "### Additional Options:\n",
+    "- **pure_python** (`bool`): Enables pure Python mode.\n",
+    "- **local_infile** (`bool`): Allows local file uploads.\n",
+    "- **charset** (`str`): Character set for string values.\n",
+    "- **ssl_key**, **ssl_cert**, **ssl_ca** (`str`): Paths to SSL files.\n",
+    "- **ssl_disabled** (`bool`): Disables SSL.\n",
+    "- **ssl_verify_cert** (`bool`): Verifies server's certificate.\n",
+    "- **ssl_verify_identity** (`bool`): Verifies server's identity.\n",
+    "- **autocommit** (`bool`): Enables autocommits.\n",
+    "- **results_type** (`str`): Structure of query results (e.g., `tuples`, `dicts`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_singlestore.document_loaders import SingleStoreLoader\n",
+    "\n",
+    "loader = SingleStoreLoader(\n",
+    "    host=\"127.0.0.1:3306/db\",\n",
+    "    table_name=\"documents\",\n",
+    "    content_field=\"content\",\n",
+    "    metadata_field=\"metadata\",\n",
+    "    id_field=\"id\",\n",
+    ")"
+   ]
+  },
+  {
+   "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 SingleStore Document Loader features and configurations head to the github page: [https://github.com/singlestore-labs/langchain-singlestore/](https://github.com/singlestore-labs/langchain-singlestore/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/integrations/llm_caching.ipynb

@@ -1214,9 +1214,7 @@
    "source": [
     "### Connecting to the DB\n",
     "\n",
-    "The Cassandra caches shown in this page can be used with Cassandra as well as other derived databases, such as Astra DB, which use the CQL (Cassandra Query Language) protocol.\n",
-    "\n",
-    "> DataStax [Astra DB](https://docs.datastax.com/en/astra-serverless/docs/vector-search/quickstart.html) is a managed serverless database built on Cassandra, offering the same interface and strengths.\n",
+    "The Cassandra caches shown in this page can be used with Cassandra as well as other derived databases that can use the CQL (Cassandra Query Language) protocol, such as DataStax Astra DB.\n",
     "\n",
     "Depending on whether you connect to a Cassandra cluster or to Astra DB through CQL, you will provide different parameters when instantiating the cache (through initialization of a CassIO connection)."
    ]
@@ -1517,6 +1515,12 @@
    "source": [
     "You can easily use [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) as an LLM cache, with either the \"exact\" or the \"semantic-based\" cache.\n",
     "\n",
+    "> [DataStax Astra DB](https://docs.datastax.com/en/astra-db-serverless/index.html) is a serverless \n",
+    "> AI-ready database built on `Apache Cassandra®` and made conveniently available \n",
+    "> through an easy-to-use JSON API.\n",
+    "\n",
+    "_This approach differs from the `Cassandra` caches mentioned above in that it natively uses the HTTP Data API. The Data API is specific to Astra DB. Keep in mind that the storage format will also differ._\n",
+    "\n",
     "Make sure you have a running database (it must be a Vector-enabled database to use the Semantic cache) and get the required credentials on your Astra dashboard:\n",
     "\n",
     "- the API Endpoint looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n",
@@ -2525,7 +2529,17 @@
    "source": [
     "## `SingleStoreDB` semantic cache\n",
     "\n",
-    "You can use [SingleStoreDB](https://python.langchain.com/docs/integrations/vectorstores/singlestoredb/) as a semantic cache to cache prompts and responses."
+    "You can use [SingleStore](https://python.langchain.com/docs/integrations/vectorstores/singlestore/) as a semantic cache to cache prompts and responses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "596e15e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-singlestore"
    ]
   },
   {
@@ -2535,11 +2549,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.cache import SingleStoreDBSemanticCache\n",
     "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_singlestore.cache import SingleStoreSemanticCache\n",
     "\n",
     "set_llm_cache(\n",
-    "    SingleStoreDBSemanticCache(\n",
+    "    SingleStoreSemanticCache(\n",
     "        embedding=OpenAIEmbeddings(),\n",
     "        host=\"root:pass@localhost:3306/db\",\n",
     "    )\n",
@@ -3102,8 +3116,8 @@
     "|------------|---------|\n",
     "| langchain_astradb.cache | [AstraDBCache](https://python.langchain.com/api_reference/astradb/cache/langchain_astradb.cache.AstraDBCache.html) |\n",
     "| langchain_astradb.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/astradb/cache/langchain_astradb.cache.AstraDBSemanticCache.html) |\n",
-    "| langchain_community.cache | [AstraDBCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBCache.html) |\n",
-    "| langchain_community.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBSemanticCache.html) |\n",
+    "| langchain_community.cache | [AstraDBCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBCache.html) (deprecated since `langchain-community==0.0.28`) |\n",
+    "| langchain_community.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBSemanticCache.html) (deprecated since `langchain-community==0.0.28`) |\n",
     "| langchain_community.cache | [AzureCosmosDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AzureCosmosDBSemanticCache.html) |\n",
     "| langchain_community.cache | [CassandraCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.CassandraCache.html) |\n",
     "| langchain_community.cache | [CassandraSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.CassandraSemanticCache.html) |\n",
@@ -3150,7 +3164,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,