linting

Merge branch 'sr/remove-unused-validators' of https://github.com/langchain-ai/langchain into sr/remove-unused-validators
Merge branch 'master' into sr/remove-unused-validators
2026-02-15 09:39:11 +00:00 · 2025-05-14 08:16:07 -07:00 · 2025-05-14 08:14:39 -07:00 · 2025-05-14 08:14:35 -07:00 · 2025-05-14 08:08:37 -07:00 · 2025-05-14 07:55:43 -07:00
403 changed files with 11668 additions and 16802 deletions
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -1,11 +0,0 @@
-# Please see the documentation for all configuration options:
-# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
-# and
-# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
-
-version: 2
-updates:
-  - package-ecosystem: "github-actions"
-    directory: "/"
-    schedule:
-      interval: "weekly"
--- a/.github/scripts/check_diff.py
+++ b/.github/scripts/check_diff.py
@@ -37,6 +37,7 @@ IGNORED_PARTNERS = [
 ]

 PY_312_MAX_PACKAGES = [
+    "libs/partners/voyageai",
    "libs/partners/chroma", # https://github.com/chroma-core/chroma/issues/4382
 ]

@@ -119,9 +120,7 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
    if job == "test-pydantic":
        return _get_pydantic_test_configs(dir_)

-    if job == "codspeed":
-        py_versions = ["3.12"]  # 3.13 is not yet supported
-    elif dir_ == "libs/core":
+    if dir_ == "libs/core":
        py_versions = ["3.9", "3.10", "3.11", "3.12", "3.13"]
    # custom logic for specific directories
    elif dir_ == "libs/partners/milvus":
@@ -212,8 +211,6 @@ def _get_configs_for_multi_dirs(
        )
    elif job == "extended-tests":
        dirs = list(dirs_to_run["extended-test"])
-    elif job == "codspeed":
-        dirs = list(dirs_to_run["codspeed"])
    else:
        raise ValueError(f"Unknown job: {job}")

@@ -229,7 +226,6 @@ if __name__ == "__main__":
        "lint": set(),
        "test": set(),
        "extended-test": set(),
-        "codspeed": set(),
    }
    docs_edited = False

@@ -253,8 +249,6 @@ if __name__ == "__main__":
            dirs_to_run["extended-test"].update(LANGCHAIN_DIRS)
            dirs_to_run["lint"].add(".")

-        if file.startswith("libs/core"):
-            dirs_to_run["codspeed"].add(f"libs/core")
        if any(file.startswith(dir_) for dir_ in LANGCHAIN_DIRS):
            # add that dir and all dirs after in LANGCHAIN_DIRS
            # for extended testing
@@ -293,7 +287,6 @@ if __name__ == "__main__":
                if not filename.startswith(".")
            ] != ["README.md"]:
                dirs_to_run["test"].add(f"libs/partners/{partner_dir}")
-                dirs_to_run["codspeed"].add(f"libs/partners/{partner_dir}")
            # Skip if the directory was deleted or is just a tombstone readme
        elif file == "libs/packages.yml":
            continue
@@ -319,7 +312,6 @@ if __name__ == "__main__":
            "compile-integration-tests",
            "dependencies",
            "test-pydantic",
-            "codspeed",
        ]
    }
    map_job_to_configs["test-doc-imports"] = (
--- a/.github/workflows/_integration_test.yml
+++ b/.github/workflows/_integration_test.yml
@@ -41,8 +41,6 @@ jobs:
          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
          GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          ANTHROPIC_FILES_API_IMAGE_ID: ${{ secrets.ANTHROPIC_FILES_API_IMAGE_ID }}
-          ANTHROPIC_FILES_API_PDF_ID: ${{ secrets.ANTHROPIC_FILES_API_PDF_ID }}
          AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
          AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
@@ -69,6 +67,7 @@ jobs:
          ES_CLOUD_ID: ${{ secrets.ES_CLOUD_ID }}
          ES_API_KEY: ${{ secrets.ES_API_KEY }}
          MONGODB_ATLAS_URI: ${{ secrets.MONGODB_ATLAS_URI }}
+          VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
          COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
          UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
--- a/.github/workflows/_release.yml
+++ b/.github/workflows/_release.yml
@@ -322,6 +322,7 @@ jobs:
          ES_CLOUD_ID: ${{ secrets.ES_CLOUD_ID }}
          ES_API_KEY: ${{ secrets.ES_API_KEY }}
          MONGODB_ATLAS_URI: ${{ secrets.MONGODB_ATLAS_URI }}
+          VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
          UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
          XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
@@ -344,8 +345,6 @@ jobs:
      fail-fast: false  # Continue testing other partners if one fails
    env:
      ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-      ANTHROPIC_FILES_API_IMAGE_ID: ${{ secrets.ANTHROPIC_FILES_API_IMAGE_ID }}
-      ANTHROPIC_FILES_API_PDF_ID: ${{ secrets.ANTHROPIC_FILES_API_PDF_ID }}
      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
      AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
      AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
--- a/.github/workflows/check-broken-links.yml
+++ b/.github/workflows/check-broken-links.yml
@@ -12,7 +12,7 @@ jobs:
    steps:
      - uses: actions/checkout@v4
      - name: Use Node.js 18.x
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v3
        with:
          node-version: 18.x
          cache: "yarn"
--- a/.github/workflows/check_diffs.yml
+++ b/.github/workflows/check_diffs.yml
@@ -29,7 +29,7 @@ jobs:
        with:
          python-version: '3.11'
      - id: files
-        uses: Ana06/get-changed-files@v2.3.0
+        uses: Ana06/get-changed-files@v2.2.0
      - id: set-matrix
        run: |
          python -m pip install packaging requests
@@ -152,7 +152,6 @@ jobs:
          # grep will exit non-zero if the target message isn't found,
          # and `set -e` above will cause the step to fail.
          echo "$STATUS" | grep 'nothing to commit, working tree clean'
-
  ci_success:
    name: "CI Success"
    needs: [build, lint, test, compile-integration-tests, extended-tests, test-doc-imports, test-pydantic]
--- a/.github/workflows/check_new_docs.yml
+++ b/.github/workflows/check_new_docs.yml
@@ -24,7 +24,7 @@ jobs:
        with:
          python-version: '3.10'
      - id: files
-        uses: Ana06/get-changed-files@v2.3.0
+        uses: Ana06/get-changed-files@v2.2.0
        with:
          filter: |
            *.ipynb
--- a/.github/workflows/codspeed.yml
+++ b/.github/workflows/codspeed.yml
@@ -5,58 +5,40 @@ on:
    branches:
        - master
  pull_request:
+    paths:
+      - 'libs/core/**'
+  # `workflow_dispatch` allows CodSpeed to trigger backtest
+  # performance analysis in order to generate initial data.
  workflow_dispatch:

-env:
-  AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: foo
-  AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: foo
-  DEEPSEEK_API_KEY: foo
-  FIREWORKS_API_KEY: foo
-
 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
-    strategy:
-      matrix:
-        include:
-          - working-directory: libs/core
-            mode: walltime
-          - working-directory: libs/partners/openai
-          - working-directory: libs/partners/anthropic
-          - working-directory: libs/partners/deepseek
-          - working-directory: libs/partners/fireworks
-          - working-directory: libs/partners/xai
-          - working-directory: libs/partners/mistralai
-          - working-directory: libs/partners/groq
-      fail-fast: false
-
    steps:
      - uses: actions/checkout@v4

-      # We have to use 3.12 as 3.13 is not yet supported
+      # We have to use 3.12, 3.13 is not yet supported
      - name: Install uv
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v5
        with:
          python-version: "3.12"

-      - uses: actions/setup-python@v5
+      # Using this action is still necessary for CodSpeed to work
+      - uses: actions/setup-python@v3
        with:
          python-version: "3.12"

-      - name: Install dependencies
+      - name: install deps
        run: uv sync --group test
-        working-directory: ${{ matrix.working-directory }}
+        working-directory: ./libs/core

-      - name: Run benchmarks ${{ matrix.working-directory }}
+      - name: Run benchmarks
        uses: CodSpeedHQ/action@v3
        with:
          token: ${{ secrets.CODSPEED_TOKEN }}
          run: |
-            cd ${{ matrix.working-directory }}
-            if [ "${{ matrix.working-directory }}" = "libs/core" ]; then
-              uv run --no-sync pytest ./tests/benchmarks --codspeed
-            else
-              uv run --no-sync pytest ./tests/ --codspeed
-            fi
-          mode: ${{ matrix.mode || 'instrumentation' }}
+            cd libs/core
+            uv run --no-sync pytest ./tests/benchmarks --codspeed
+          mode: walltime
--- a/.github/workflows/scheduled_test.yml
+++ b/.github/workflows/scheduled_test.yml
@@ -127,8 +127,6 @@ jobs:
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          ANTHROPIC_FILES_API_IMAGE_ID: ${{ secrets.ANTHROPIC_FILES_API_IMAGE_ID }}
-          ANTHROPIC_FILES_API_PDF_ID: ${{ secrets.ANTHROPIC_FILES_API_PDF_ID }}
          AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
          AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -103,6 +103,12 @@ repos:
      entry: make -C libs/partners/qdrant format
      files: ^libs/partners/qdrant/
      pass_filenames: false
+    - id: voyageai
+      name: format partners/voyageai
+      language: system
+      entry: make -C libs/partners/voyageai format
+      files: ^libs/partners/voyageai/
+      pass_filenames: false
    - id: root
      name: format docs, cookbook
      language: system
--- a/README.md
+++ b/README.md
@@ -66,7 +66,7 @@ reliably handle complex tasks with LangGraph, our low-level agent orchestration
 framework. LangGraph offers customizable architecture, long-term memory, and
 human-in-the-loop workflows — and is trusted in production by companies like LinkedIn,
 Uber, Klarna, and GitLab.
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) - Deploy
+- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) - Deploy
 and scale agents effortlessly with a purpose-built deployment platform for long
 running, stateful workflows. Discover, reuse, configure, and share agents across
 teams — and iterate quickly with visual prototyping in
--- a/cookbook/rag_with_quantized_embeddings.ipynb
+++ b/cookbook/rag_with_quantized_embeddings.ipynb
@@ -53,7 +53,7 @@
   "id": "f5ccda4e-7af5-4355-b9c4-25547edf33f9",
   "metadata": {},
   "source": [
-    "Let's first load up this paper, and split into text chunks of size 1000."
+    "Lets first load up this paper, and split into text chunks of size 1000."
   ]
  },
  {
@@ -241,7 +241,7 @@
   "id": "360b2837-8024-47e0-a4ba-592505a9a5c8",
   "metadata": {},
   "source": [
-    "With our embedder in place, let's define our retriever:"
+    "With our embedder in place, lets define our retriever:"
   ]
  },
  {
@@ -312,7 +312,7 @@
   "id": "d84ea8f4-a5de-4d76-b44d-85e56583f489",
   "metadata": {},
   "source": [
-    "Let's write our documents into our new store. This will use our embedder on each document."
+    "Lets write our documents into our new store. This will use our embedder on each document."
   ]
  },
  {
@@ -339,7 +339,7 @@
   "id": "580bc212-8ecd-4d28-8656-b96fcd0d7eb6",
   "metadata": {},
   "source": [
-    "Great! Our retriever is good to go. Let's load up an LLM, that will reason over the retrieved documents:"
+    "Great! Our retriever is good to go. Lets load up an LLM, that will reason over the retrieved documents:"
   ]
  },
  {
@@ -430,7 +430,7 @@
   "id": "3bc53602-86d6-420f-91b1-fc2effa7e986",
   "metadata": {},
   "source": [
-    "Excellent! Let's ask it a question.\n",
+    "Excellent! lets ask it a question.\n",
    "We will also use a verbose and debug, to check which documents were used by the model to produce the answer."
   ]
  },
--- a/docs/api_reference/create_api_rst.py
+++ b/docs/api_reference/create_api_rst.py
@@ -663,7 +663,6 @@ def main(dirs: Optional[list] = None) -> None:
            dir_
            for dir_ in os.listdir(ROOT_DIR / "libs")
            if dir_ not in ("cli", "partners", "packages.yml")
-            and "pyproject.toml" in os.listdir(ROOT_DIR / "libs" / dir_)
        ]
        dirs += [
            dir_
--- a/docs/cassettes/response_metadata_161f5898-9976-4a75-943d-03eda1a40a60.msgpack.zlib
+++ b/docs/cassettes/response_metadata_161f5898-9976-4a75-943d-03eda1a40a60.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/response_metadata_1e4ac668-4c6a-48ad-9a6f-7b291477b45d.msgpack.zlib
+++ b/docs/cassettes/response_metadata_1e4ac668-4c6a-48ad-9a6f-7b291477b45d.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/response_metadata_39549336-25f5-4839-9846-f687cd77e59b.msgpack.zlib
+++ b/docs/cassettes/response_metadata_39549336-25f5-4839-9846-f687cd77e59b.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/response_metadata_61c43496-83b5-4d71-bd60-3e6d46c62a5e.msgpack.zlib
+++ b/docs/cassettes/response_metadata_61c43496-83b5-4d71-bd60-3e6d46c62a5e.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/response_metadata_6ae32a93-26db-41bb-95c2-38ddd5085fbe.msgpack.zlib
+++ b/docs/cassettes/response_metadata_6ae32a93-26db-41bb-95c2-38ddd5085fbe.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/response_metadata_744e14ec-ff50-4642-9893-ff7bdf8927ff.msgpack.zlib
+++ b/docs/cassettes/response_metadata_744e14ec-ff50-4642-9893-ff7bdf8927ff.msgpack.zlib
@@ -1 +1 @@
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--- a/docs/cassettes/response_metadata_deb41321-52d0-4795-a40c-4a811a13d7b0.msgpack.zlib
+++ b/docs/cassettes/response_metadata_deb41321-52d0-4795-a40c-4a811a13d7b0.msgpack.zlib
@@ -1 +1 @@
-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
--- a/docs/docs/additional_resources/arxiv_references.mdx
+++ b/docs/docs/additional_resources/arxiv_references.mdx
@@ -48,7 +48,7 @@ From the opposite direction, scientists use `LangChain` in research and referenc
 | `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022&#8209;05&#8209;25 | `API:` [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
 | `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022&#8209;03&#8209;15 | `Docs:` [docs/tutorials/sql_qa](https://python.langchain.com/docs/tutorials/sql_qa), `API:` [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
 | `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher,  et al. | 2022&#8209;02&#8209;01 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-| `2112.01488v3` [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](http://arxiv.org/abs/2112.01488v3) | Keshav Santhanam, Omar Khattab, Jon Saad-Falcon,  et al. | 2021&#8209;12&#8209;02 | `Docs:` [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts)
+| `2112.01488v3` [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](http://arxiv.org/abs/2112.01488v3) | Keshav Santhanam, Omar Khattab, Jon Saad-Falcon,  et al. | 2021&#8209;12&#8209;02 | `Docs:` [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/docs/integrations/providers/dspy)
 | `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy,  et al. | 2021&#8209;02&#8209;26 | `API:` [langchain_experimental.open_clip](https://python.langchain.com/api_reference/experimental/open_clip.html)
 | `2005.14165v4` [Language Models are Few-Shot Learners](http://arxiv.org/abs/2005.14165v4) | Tom B. Brown, Benjamin Mann, Nick Ryder,  et al. | 2020&#8209;05&#8209;28 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
 | `2005.11401v4` [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](http://arxiv.org/abs/2005.11401v4) | Patrick Lewis, Ethan Perez, Aleksandra Piktus,  et al. | 2020&#8209;05&#8209;22 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
@@ -970,7 +970,7 @@ reducing degenerate repetitions.
 - **arXiv id:** [2112.01488v3](http://arxiv.org/abs/2112.01488v3)  **Published Date:** 2021-12-02
 - **LangChain:**

-   - **Documentation:** [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts)
+   - **Documentation:** [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/docs/integrations/providers/dspy)

 **Abstract:** Neural information retrieval (IR) has greatly advanced search and other
 knowledge-intensive language tasks. While many neural IR methods encode queries
--- a/docs/docs/concepts/agents.mdx
+++ b/docs/docs/concepts/agents.mdx
@@ -15,7 +15,7 @@ LangChain previously introduced the `AgentExecutor` as a runtime for agents.
 While it served as an excellent starting point, its limitations became apparent when dealing with more sophisticated and customized agents. 
 As a result, we're gradually phasing out `AgentExecutor` in favor of more flexible solutions in LangGraph.

-### Transitioning from AgentExecutor to LangGraph
+### Transitioning from AgentExecutor to langgraph

 If you're currently using `AgentExecutor`, don't worry! We've prepared resources to help you:

--- a/docs/docs/concepts/async.mdx
+++ b/docs/docs/concepts/async.mdx
@@ -9,7 +9,7 @@ LLM based applications often involve a lot of I/O-bound operations, such as maki

 :::note
 You are expected to be familiar with asynchronous programming in Python before reading this guide. If you are not, please find appropriate resources online to learn how to program asynchronously in Python.
-This guide specifically focuses on what you need to know to work with LangChain in an asynchronous context, assuming that you are already familiar with asynchronous programming.
+This guide specifically focuses on what you need to know to work with LangChain in an asynchronous context, assuming that you are already familiar with asynch
 :::

 ## Langchain asynchronous APIs
--- a/docs/docs/concepts/callbacks.mdx
+++ b/docs/docs/concepts/callbacks.mdx
@@ -6,7 +6,7 @@

 LangChain provides a callback system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.

-You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is a list of handler objects, which are expected to implement one or more of the methods described below in more detail.
+You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail.

 ## Callback events

--- a/docs/docs/concepts/retrievers.mdx
+++ b/docs/docs/concepts/retrievers.mdx
@@ -32,7 +32,7 @@ The only requirement for a retriever is the ability to accepts a query and retur
 In particular, [LangChain's retriever class](https://python.langchain.com/api_reference/core/retrievers/langchain_core.retrievers.BaseRetriever.html#) only requires that the `_get_relevant_documents` method is implemented, which takes a `query: str` and returns a list of [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects that are most relevant to the query.
 The underlying logic used to get relevant documents is specified by the retriever and can be whatever is most useful for the application.

-A LangChain retriever is a [runnable](/docs/how_to/lcel_cheatsheet/), which is a standard interface for LangChain components. 
+A LangChain retriever is a [runnable](/docs/how_to/lcel_cheatsheet/), which is a standard interface is for LangChain components. 
 This means that it has a few common methods, including `invoke`, that are used to interact with it. A retriever can be invoked with a query:

 ```python
--- a/docs/docs/concepts/text_splitters.mdx
+++ b/docs/docs/concepts/text_splitters.mdx
@@ -3,8 +3,8 @@

 :::info[Prerequisites]

-* [Documents](./retrievers.mdx)
-* [Tokenization](./tokens.mdx)
+* [Documents](/docs/concepts/retrievers/#interface)
+* Tokenization(/docs/concepts/tokens)
 :::

 ## Overview
--- a/docs/docs/contributing/how_to/documentation/style_guide.mdx
+++ b/docs/docs/contributing/how_to/documentation/style_guide.mdx
@@ -82,7 +82,7 @@ Here are some high-level tips on writing a good how-to guide:
 LangChain's conceptual guide falls under the **Explanation** quadrant of Diataxis. These guides should cover LangChain terms and concepts
 in a more abstract way than how-to guides or tutorials, targeting curious users interested in
 gaining a deeper understanding and insights of the framework. Try to avoid excessively large code examples as the primary goal is to
-provide perspective to the user rather than to finish a practical project. These guides should cover **why** things work the way they do.
+provide perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.

 This guide on documentation style is meant to fall under this category.

--- a/docs/docs/how_to/assign.ipynb
+++ b/docs/docs/how_to/assign.ipynb
@@ -157,7 +157,7 @@
    "\n",
    "## Next steps\n",
    "\n",
-    "Now you've learned how to pass data through your chains to help format the data flowing through your chains.\n",
+    "Now you've learned how to pass data through your chains to help to help format the data flowing through your chains.\n",
    "\n",
    "To learn more, see the other how-to guides on runnables in this section."
   ]
--- a/docs/docs/how_to/custom_retriever.ipynb
+++ b/docs/docs/how_to/custom_retriever.ipynb
@@ -98,7 +98,7 @@
    "    ) -> List[Document]:\n",
    "        \"\"\"Sync implementations for retriever.\"\"\"\n",
    "        matching_documents = []\n",
-    "        for document in self.documents:\n",
+    "        for document in documents:\n",
    "            if len(matching_documents) > self.k:\n",
    "                return matching_documents\n",
    "\n",
--- a/docs/docs/how_to/custom_tools.ipynb
+++ b/docs/docs/how_to/custom_tools.ipynb
@@ -141,7 +141,7 @@
       "{'description': 'Multiply a by the maximum of b.',\n",
       " 'properties': {'a': {'description': 'scale factor',\n",
       "   'title': 'A',\n",
-       "   'type': 'integer'},\n",
+       "   'type': 'string'},\n",
       "  'b': {'description': 'list of ints over which to take maximum',\n",
       "   'items': {'type': 'integer'},\n",
       "   'title': 'B',\n",
--- a/docs/docs/how_to/document_loader_custom.ipynb
+++ b/docs/docs/how_to/document_loader_custom.ipynb
@@ -152,6 +152,7 @@
    "    ) -> AsyncIterator[Document]:  # <-- Does not take any arguments\n",
    "        \"\"\"An async lazy loader that reads a file line by line.\"\"\"\n",
    "        # Requires aiofiles\n",
+    "        # Install with `pip install aiofiles`\n",
    "        # https://github.com/Tinche/aiofiles\n",
    "        import aiofiles\n",
    "\n",
@@ -722,11 +723,109 @@
   ]
  },
  {
-   "cell_type": "markdown",
-   "id": "d3f84501-b0aa-4a60-aad2-5109cbd37d4f",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6139b546b8c4d524",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:50:15.514959Z",
+     "start_time": "2025-04-21T08:50:01.219848Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 17%|█▋        | 1/6 [00:04<00:20,  4.20s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metadata={} mimetype='application/pdf' path='s3://bucket-01/Annual-Report-2016.pdf'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 33%|███▎      | 2/6 [00:05<00:09,  2.28s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metadata={} mimetype='application/pdf' path='s3://bucket-01/ComingHomeToNature_ActivityBooklet.pdf'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 50%|█████     | 3/6 [00:06<00:06,  2.01s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metadata={} mimetype='application/pdf' path='s3://bucket-01/ComingHomeToNature_ActivityBookletFoyles.pdf'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 67%|██████▋   | 4/6 [00:07<00:02,  1.44s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metadata={} mimetype='application/pdf' path='s3://bucket-01/EVENTS E-POSTER_DAYS OF AWE.pdf'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 83%|████████▎ | 5/6 [00:07<00:01,  1.11s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metadata={} mimetype='application/pdf' path='s3://bucket-01/MH.pdf'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6/6 [00:08<00:00,  1.02s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metadata={} mimetype='application/pdf' path='s3://bucket-01/SRT Annual Report 2018.pdf'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6/6 [00:11<00:00,  1.87s/it]\n"
+     ]
+    }
+   ],
   "source": [
-    "```python\n",
    "from cloudpathlib import S3Client, S3Path\n",
    "from langchain_community.document_loaders.blob_loaders import CloudBlobLoader\n",
    "\n",
@@ -740,8 +839,7 @@
    "cloud_loader = CloudBlobLoader(path, glob=\"**/*.pdf\", show_progress=True)\n",
    "\n",
    "for blob in cloud_loader.yield_blobs():\n",
-    "    print(blob)\n",
-    "```"
+    "    print(blob)"
   ]
  },
  {
@@ -918,7 +1016,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.1"
  }
 },
 "nbformat": 4,
--- a/docs/docs/how_to/document_loader_pdf.ipynb
+++ b/docs/docs/how_to/document_loader_pdf.ipynb
@@ -20,7 +20,7 @@
    "\n",
    "LangChain integrates with a host of PDF parsers. Some are simple and relatively low-level; others will support OCR and image-processing, or perform advanced document layout analysis. The right choice will depend on your needs. Below we enumerate the possibilities.\n",
    "\n",
-    "We will demonstrate these approaches on a [sample file](https://github.com/langchain-ai/langchain-community/blob/main/libs/community/tests/examples/layout-parser-paper.pdf):"
+    "We will demonstrate these approaches on a [sample file](https://github.com/langchain-ai/langchain/blob/master/libs/community/tests/integration_tests/examples/layout-parser-paper.pdf):"
   ]
  },
  {
--- a/docs/docs/how_to/llm_caching.ipynb
+++ b/docs/docs/how_to/llm_caching.ipynb
@@ -40,7 +40,7 @@
    "from langchain_core.globals import set_llm_cache\n",
    "from langchain_openai import OpenAI\n",
    "\n",
-    "# To make the caching really obvious, let's use a slower and older model.\n",
+    "# To make the caching really obvious, lets use a slower and older model.\n",
    "# Caching supports newer chat models as well.\n",
    "llm = OpenAI(model=\"gpt-3.5-turbo-instruct\", n=2, best_of=2)"
   ]
--- a/docs/docs/how_to/local_llms.ipynb
+++ b/docs/docs/how_to/local_llms.ipynb
@@ -314,7 +314,7 @@
   "source": [
    "%env CMAKE_ARGS=\"-DLLAMA_METAL=on\"\n",
    "%env FORCE_CMAKE=1\n",
-    "%pip install --upgrade --quiet  llama-cpp-python --no-cache-dir"
+    "%pip install --upgrade --quiet  llama-cpp-python --no-cache-dirclear"
   ]
  },
  {
--- a/docs/docs/how_to/response_metadata.ipynb
+++ b/docs/docs/how_to/response_metadata.ipynb
@@ -102,7 +102,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
   "id": "39549336-25f5-4839-9846-f687cd77e59b",
   "metadata": {},
   "outputs": [
@@ -110,20 +110,43 @@
     "data": {
      "text/plain": [
       "{'is_blocked': False,\n",
-       " 'safety_ratings': [],\n",
+       " 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH',\n",
+       "   'probability_label': 'NEGLIGIBLE',\n",
+       "   'probability_score': 0.046142578125,\n",
+       "   'blocked': False,\n",
+       "   'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
+       "   'severity_score': 0.07275390625},\n",
+       "  {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT',\n",
+       "   'probability_label': 'NEGLIGIBLE',\n",
+       "   'probability_score': 0.05419921875,\n",
+       "   'blocked': False,\n",
+       "   'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
+       "   'severity_score': 0.03955078125},\n",
+       "  {'category': 'HARM_CATEGORY_HARASSMENT',\n",
+       "   'probability_label': 'NEGLIGIBLE',\n",
+       "   'probability_score': 0.083984375,\n",
+       "   'blocked': False,\n",
+       "   'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
+       "   'severity_score': 0.029296875},\n",
+       "  {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n",
+       "   'probability_label': 'NEGLIGIBLE',\n",
+       "   'probability_score': 0.054931640625,\n",
+       "   'blocked': False,\n",
+       "   'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
+       "   'severity_score': 0.03466796875}],\n",
       " 'usage_metadata': {'prompt_token_count': 10,\n",
-       "  'candidates_token_count': 55,\n",
-       "  'total_token_count': 65,\n",
+       "  'candidates_token_count': 193,\n",
+       "  'total_token_count': 203,\n",
       "  'prompt_tokens_details': [{'modality': 1, 'token_count': 10}],\n",
-       "  'candidates_tokens_details': [{'modality': 1, 'token_count': 55}],\n",
+       "  'candidates_tokens_details': [{'modality': 1, 'token_count': 193}],\n",
       "  'cached_content_token_count': 0,\n",
       "  'cache_tokens_details': []},\n",
       " 'finish_reason': 'STOP',\n",
-       " 'avg_logprobs': -0.251378042047674,\n",
-       " 'model_name': 'gemini-2.0-flash-001'}"
+       " 'avg_logprobs': -0.5702065976790196,\n",
+       " 'model_name': 'gemini-1.5-flash-001'}"
      ]
     },
-     "execution_count": 1,
+     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -131,7 +154,7 @@
   "source": [
    "from langchain_google_vertexai import ChatVertexAI\n",
    "\n",
-    "llm = ChatVertexAI(model=\"gemini-2.0-flash-001\")\n",
+    "llm = ChatVertexAI(model=\"gemini-1.5-flash-001\")\n",
    "msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n",
    "msg.response_metadata"
   ]
--- a/docs/docs/how_to/sql_large_db.ipynb
+++ b/docs/docs/how_to/sql_large_db.ipynb
@@ -162,7 +162,7 @@
    "\n",
    "table_chain = prompt | llm_with_tools | output_parser\n",
    "\n",
-    "table_chain.invoke({\"input\": \"What are all the genres of Alanis Morissette songs\"})"
+    "table_chain.invoke({\"input\": \"What are all the genres of Alanis Morisette songs\"})"
   ]
  },
  {
@@ -206,7 +206,7 @@
    ")\n",
    "\n",
    "category_chain = prompt | llm_with_tools | output_parser\n",
-    "category_chain.invoke({\"input\": \"What are all the genres of Alanis Morissette songs\"})"
+    "category_chain.invoke({\"input\": \"What are all the genres of Alanis Morisette songs\"})"
   ]
  },
  {
@@ -261,7 +261,7 @@
    "\n",
    "\n",
    "table_chain = category_chain | get_tables\n",
-    "table_chain.invoke({\"input\": \"What are all the genres of Alanis Morissette songs\"})"
+    "table_chain.invoke({\"input\": \"What are all the genres of Alanis Morisette songs\"})"
   ]
  },
  {
@@ -313,7 +313,7 @@
   ],
   "source": [
    "query = full_chain.invoke(\n",
-    "    {\"question\": \"What are all the genres of Alanis Morissette songs\"}\n",
+    "    {\"question\": \"What are all the genres of Alanis Morisette songs\"}\n",
    ")\n",
    "print(query)"
   ]
@@ -346,7 +346,7 @@
   "source": [
    "We can see the LangSmith trace for this run [here](https://smith.langchain.com/public/4fbad408-3554-4f33-ab47-1e510a1b52a3/r).\n",
    "\n",
-    "We've seen how to dynamically include a subset of table schemas in a prompt within a chain. Another possible approach to this problem is to let an Agent decide for itself when to look up tables by giving it a Tool to do so. You can see an example of this in the [SQL: Agents](/docs/tutorials/sql_qa/#agents) guide."
+    "We've seen how to dynamically include a subset of table schemas in a prompt within a chain. Another possible approach to this problem is to let an Agent decide for itself when to look up tables by giving it a Tool to do so. You can see an example of this in the [SQL: Agents](/docs/tutorials/agents) guide."
   ]
  },
  {
@@ -555,7 +555,7 @@
   "source": [
    "We can see that with retrieval we're able to correct the spelling from \"Elenis Moriset\" to \"Alanis Morissette\" and get back a valid result.\n",
    "\n",
-    "Another possible approach to this problem is to let an Agent decide for itself when to look up proper nouns. You can see an example of this in the [SQL: Agents](/docs/tutorials/sql_qa/#agents) guide."
+    "Another possible approach to this problem is to let an Agent decide for itself when to look up proper nouns. You can see an example of this in the [SQL: Agents](/docs/tutorials/agents) guide."
   ]
  }
 ],
--- a/docs/docs/integrations/callbacks/promptlayer.ipynb
+++ b/docs/docs/integrations/callbacks/promptlayer.ipynb
@@ -43,7 +43,7 @@
    "### Getting API Credentials\n",
    "\n",
    "If you do not have a PromptLayer account, create one on [promptlayer.com](https://www.promptlayer.com). Then get an API key by clicking on the settings cog in the navbar and\n",
-    "set it as an environment variable called `PROMPTLAYER_API_KEY`\n"
+    "set it as an environment variabled called `PROMPTLAYER_API_KEY`\n"
   ]
  },
  {
--- a/docs/docs/integrations/callbacks/uptrain.ipynb
+++ b/docs/docs/integrations/callbacks/uptrain.ipynb
@@ -26,7 +26,7 @@
    "\n",
    "This notebook showcases the UpTrain callback handler seamlessly integrating into your pipeline, facilitating diverse evaluations. We have chosen a few evaluations that we deemed apt for evaluating the chains. These evaluations run automatically, with results displayed in the output. More details on UpTrain's evaluations can be found [here](https://github.com/uptrain-ai/uptrain?tab=readme-ov-file#pre-built-evaluations-we-offer-). \n",
    "\n",
-    "Selected retrievers from Langchain are highlighted for demonstration:\n",
+    "Selected retievers from Langchain are highlighted for demonstration:\n",
    "\n",
    "### 1. **Vanilla RAG**:\n",
    "RAG plays a crucial role in retrieving context and generating responses. To ensure its performance and response quality, we conduct the following evaluations:\n",
--- a/docs/docs/integrations/chat/abso.ipynb
+++ b/docs/docs/integrations/chat/abso.ipynb
@@ -17,7 +17,7 @@
   "source": [
    "# ChatAbso\n",
    "\n",
-    "This will help you get started with ChatAbso [chat models](https://python.langchain.com/docs/concepts/chat_models/). For detailed documentation of all ChatAbso features and configurations, head to the [API reference](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html).\n",
+    "This will help you getting started with ChatAbso [chat models](https://python.langchain.com/docs/concepts/chat_models/). For detailed documentation of all ChatAbso features and configurations head to the [API reference](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html).\n",
    "\n",
    "- You can find the full documentation for the Abso router [here] (https://abso.ai)\n",
    "\n",
@@ -29,13 +29,13 @@
    "| [ChatAbso](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html) | [langchain-abso](https://python.langchain.com/api_reference/en/latest/abso_api_reference.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-abso?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-abso?style=flat-square&label=%20) |\n",
    "\n",
    "## Setup\n",
-    "To access ChatAbso models, you'll need to create an OpenAI account, get an API key, and install the `langchain-abso` integration package.\n",
+    "To access ChatAbso models you'll need to create an OpenAI account, get an API key, and install the `langchain-abso` integration package.\n",
    "\n",
    "### Credentials\n",
    "\n",
    "- TODO: Update with relevant info.\n",
    "\n",
-    "Head to (TODO: link) to sign up for ChatAbso and generate an API key. Once you've done this, set the ABSO_API_KEY environment variable:"
+    "Head to (TODO: link) to sign up to ChatAbso and generate an API key. Once you've done this set the ABSO_API_KEY environment variable:"
   ]
  },
  {
@@ -198,7 +198,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.11.9"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/chat/ai21.ipynb
+++ b/docs/docs/integrations/chat/ai21.ipynb
@@ -17,6 +17,8 @@
      "source": [
        "# ChatAI21\n",
        "\n",
+        "## Overview\n",
+        "\n",
        "This notebook covers how to get started with AI21 chat models.\n",
        "Note that different chat models support different parameters. See the [AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.\n",
        "[See all AI21's LangChain components.](https://pypi.org/project/langchain-ai21/)\n",
@@ -66,9 +68,7 @@
      "cell_type": "markdown",
      "id": "f6844fff-3702-4489-ab74-732f69f3b9d7",
      "metadata": {},
-      "source": [
-        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-      ]
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
    },
    {
      "cell_type": "code",
@@ -198,17 +198,13 @@
      "cell_type": "markdown",
      "id": "39c0ccd229927eab",
      "metadata": {},
-      "source": [
-        "# Tool Calls / Function Calling"
-      ]
+      "source": "# Tool Calls / Function Calling"
    },
    {
      "cell_type": "markdown",
      "id": "2bf6b40be07fe2d4",
      "metadata": {},
-      "source": [
-        "This example shows how to use tool calling with AI21 models:"
-      ]
+      "source": "This example shows how to use tool calling with AI21 models:"
    },
    {
      "cell_type": "code",
--- a/docs/docs/integrations/chat/anthropic.ipynb
+++ b/docs/docs/integrations/chat/anthropic.ipynb
@@ -107,7 +107,7 @@
   "id": "fe4993ad-4a9b-4021-8ebd-f0fbbc739f49",
   "metadata": {},
   "source": [
-    ":::info This guide requires ``langchain-anthropic>=0.3.13``\n",
+    ":::info This guide requires ``langchain-anthropic>=0.3.10``\n",
    "\n",
    ":::"
   ]
@@ -325,102 +325,6 @@
    "ai_msg.tool_calls"
   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "535a16e4-cd5a-479f-b315-37c816ec4387",
-   "metadata": {},
-   "source": [
-    "## Multimodal\n",
-    "\n",
-    "Claude supports image and PDF inputs as content blocks, both in Anthropic's native format (see docs for [vision](https://docs.anthropic.com/en/docs/build-with-claude/vision#base64-encoded-image-example) and [PDF support](https://docs.anthropic.com/en/docs/build-with-claude/pdf-support)) as well as LangChain's [standard format](/docs/how_to/multimodal_inputs/).\n",
-    "\n",
-    "### Files API\n",
-    "\n",
-    "Claude also supports interactions with files through its managed [Files API](https://docs.anthropic.com/en/docs/build-with-claude/files). See examples below.\n",
-    "\n",
-    "The Files API can also be used to upload files to a container for use with Claude's built-in code-execution tools. See the [code execution](#code-execution) section below, for details.\n",
-    "\n",
-    "<details>\n",
-    "<summary>Images</summary>\n",
-    "\n",
-    "```python\n",
-    "# Upload image\n",
-    "\n",
-    "import anthropic\n",
-    "\n",
-    "client = anthropic.Anthropic()\n",
-    "file = client.beta.files.upload(\n",
-    "    # Supports image/jpeg, image/png, image/gif, image/webp\n",
-    "    file=(\"image.png\", open(\"/path/to/image.png\", \"rb\"), \"image/png\"),\n",
-    ")\n",
-    "image_file_id = file.id\n",
-    "\n",
-    "\n",
-    "# Run inference\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "\n",
-    "llm = ChatAnthropic(\n",
-    "    model=\"claude-sonnet-4-20250514\",\n",
-    "    betas=[\"files-api-2025-04-14\"],\n",
-    ")\n",
-    "\n",
-    "input_message = {\n",
-    "    \"role\": \"user\",\n",
-    "    \"content\": [\n",
-    "        {\n",
-    "            \"type\": \"text\",\n",
-    "            \"text\": \"Describe this image.\",\n",
-    "        },\n",
-    "        {\n",
-    "            \"type\": \"image\",\n",
-    "            \"source\": {\n",
-    "                \"type\": \"file\",\n",
-    "                \"file_id\": image_file_id,\n",
-    "            },\n",
-    "        },\n",
-    "    ],\n",
-    "}\n",
-    "llm.invoke([input_message])\n",
-    "```\n",
-    "\n",
-    "</details>\n",
-    "\n",
-    "<details>\n",
-    "<summary>PDFs</summary>\n",
-    "\n",
-    "```python\n",
-    "# Upload document\n",
-    "\n",
-    "import anthropic\n",
-    "\n",
-    "client = anthropic.Anthropic()\n",
-    "file = client.beta.files.upload(\n",
-    "    file=(\"document.pdf\", open(\"/path/to/document.pdf\", \"rb\"), \"application/pdf\"),\n",
-    ")\n",
-    "pdf_file_id = file.id\n",
-    "\n",
-    "\n",
-    "# Run inference\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "\n",
-    "llm = ChatAnthropic(\n",
-    "    model=\"claude-sonnet-4-20250514\",\n",
-    "    betas=[\"files-api-2025-04-14\"],\n",
-    ")\n",
-    "\n",
-    "input_message = {\n",
-    "    \"role\": \"user\",\n",
-    "    \"content\": [\n",
-    "        {\"type\": \"text\", \"text\": \"Describe this document.\"},\n",
-    "        {\"type\": \"document\", \"source\": {\"type\": \"file\", \"file_id\": pdf_file_id}}\n",
-    "    ],\n",
-    "}\n",
-    "llm.invoke([input_message])\n",
-    "```\n",
-    "\n",
-    "</details>"
-   ]
-  },
  {
   "cell_type": "markdown",
   "id": "6e36d25c-f358-49e5-aefa-b99fbd3fec6b",
@@ -550,27 +454,6 @@
    "print(f\"\\nSecond:\\n{usage_2}\")"
   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "9678656f-1ec4-4bf1-bf62-bbd49eb5c4e7",
-   "metadata": {},
-   "source": [
-    ":::tip Extended caching\n",
-    "\n",
-    "    The cache lifetime is 5 minutes by default. If this is too short, you can apply one hour caching by enabling the `\"extended-cache-ttl-2025-04-11\"` beta header:\n",
-    "\n",
-    "    ```python\n",
-    "    llm = ChatAnthropic(\n",
-    "        model=\"claude-3-7-sonnet-20250219\",\n",
-    "        # highlight-next-line\n",
-    "        betas=[\"extended-cache-ttl-2025-04-11\"],\n",
-    "    )\n",
-    "    ```\n",
-    "    and specifying `\"cache_control\": {\"type\": \"ephemeral\", \"ttl\": \"1h\"}`.\n",
-    "\n",
-    ":::"
-   ]
-  },
  {
   "cell_type": "markdown",
   "id": "141ce9c5-012d-4502-9d61-4a413b5d959a",
@@ -1043,16 +926,6 @@
    "Claude can use a [web search tool](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/web-search-tool) to run searches and ground its responses with citations."
   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "6a0e8fd5",
-   "metadata": {},
-   "source": [
-    ":::info Web search tool is supported since ``langchain-anthropic>=0.3.13``\n",
-    "\n",
-    ":::"
-   ]
-  },
  {
   "cell_type": "code",
   "execution_count": 1,
@@ -1070,159 +943,6 @@
    "response = llm_with_tools.invoke(\"How do I update a web app to TypeScript 5.5?\")"
   ]
  },
-  {
-   "cell_type": "markdown",
-   "id": "1478cdc6-2e52-4870-80f9-b4ddf88f2db2",
-   "metadata": {},
-   "source": [
-    "### Code execution\n",
-    "\n",
-    "Claude can use a [code execution tool](https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/code-execution-tool) to execute Python code in a sandboxed environment.\n",
-    "\n",
-    ":::info Code execution is supported since ``langchain-anthropic>=0.3.14``\n",
-    "\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2ce13632-a2da-439f-a429-f66481501630",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_anthropic import ChatAnthropic\n",
-    "\n",
-    "llm = ChatAnthropic(\n",
-    "    model=\"claude-sonnet-4-20250514\",\n",
-    "    betas=[\"code-execution-2025-05-22\"],\n",
-    ")\n",
-    "\n",
-    "tool = {\"type\": \"code_execution_20250522\", \"name\": \"code_execution\"}\n",
-    "llm_with_tools = llm.bind_tools([tool])\n",
-    "\n",
-    "response = llm_with_tools.invoke(\n",
-    "    \"Calculate the mean and standard deviation of \" \"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "24076f91-3a3d-4e53-9618-429888197061",
-   "metadata": {},
-   "source": [
-    "<details>\n",
-    "<summary>Use with Files API</summary>\n",
-    "\n",
-    "Using the Files API, Claude can write code to access files for data analysis and other purposes. See example below:\n",
-    "\n",
-    "```python\n",
-    "# Upload file\n",
-    "\n",
-    "import anthropic\n",
-    "\n",
-    "client = anthropic.Anthropic()\n",
-    "file = client.beta.files.upload(\n",
-    "    file=open(\"/path/to/sample_data.csv\", \"rb\")\n",
-    ")\n",
-    "file_id = file.id\n",
-    "\n",
-    "\n",
-    "# Run inference\n",
-    "from langchain_anthropic import ChatAnthropic\n",
-    "\n",
-    "llm = ChatAnthropic(\n",
-    "    model=\"claude-sonnet-4-20250514\",\n",
-    "    betas=[\"code-execution-2025-05-22\"],\n",
-    ")\n",
-    "\n",
-    "tool = {\"type\": \"code_execution_20250522\", \"name\": \"code_execution\"}\n",
-    "llm_with_tools = llm.bind_tools([tool])\n",
-    "\n",
-    "input_message = {\n",
-    "    \"role\": \"user\",\n",
-    "    \"content\": [\n",
-    "        {\n",
-    "            \"type\": \"text\",\n",
-    "            \"text\": \"Please plot these data and tell me what you see.\",\n",
-    "        },\n",
-    "        {\n",
-    "            \"type\": \"container_upload\",\n",
-    "            \"file_id\": file_id,\n",
-    "        },\n",
-    "    ]\n",
-    "}\n",
-    "llm_with_tools.invoke([input_message])\n",
-    "```\n",
-    "\n",
-    "Note that Claude may generate files as part of its code execution. You can access these files using the Files API:\n",
-    "```python\n",
-    "# Take all file outputs for demonstration purposes\n",
-    "file_ids = []\n",
-    "for block in response.content:\n",
-    "    if block[\"type\"] == \"code_execution_tool_result\":\n",
-    "        file_ids.extend(\n",
-    "            content[\"file_id\"]\n",
-    "            for content in block.get(\"content\", {}).get(\"content\", [])\n",
-    "            if \"file_id\" in content\n",
-    "        )\n",
-    "\n",
-    "for i, file_id in enumerate(file_ids):\n",
-    "    file_content = client.beta.files.download(file_id)\n",
-    "    file_content.write_to_file(f\"/path/to/file_{i}.png\")\n",
-    "```\n",
-    "\n",
-    "</details>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "040f381a-1768-479a-9a5e-aa2d7d77e0d5",
-   "metadata": {},
-   "source": [
-    "### Remote MCP\n",
-    "\n",
-    "Claude can use a [MCP connector tool](https://docs.anthropic.com/en/docs/agents-and-tools/mcp-connector) for model-generated calls to remote MCP servers.\n",
-    "\n",
-    ":::info Remote MCP is supported since ``langchain-anthropic>=0.3.14``\n",
-    "\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "22fc4a89-e6d8-4615-96cb-2e117349aebf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_anthropic import ChatAnthropic\n",
-    "\n",
-    "mcp_servers = [\n",
-    "    {\n",
-    "        \"type\": \"url\",\n",
-    "        \"url\": \"https://mcp.deepwiki.com/mcp\",\n",
-    "        \"name\": \"deepwiki\",\n",
-    "        \"tool_configuration\": {  # optional configuration\n",
-    "            \"enabled\": True,\n",
-    "            \"allowed_tools\": [\"ask_question\"],\n",
-    "        },\n",
-    "        \"authorization_token\": \"PLACEHOLDER\",  # optional authorization\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "llm = ChatAnthropic(\n",
-    "    model=\"claude-sonnet-4-20250514\",\n",
-    "    betas=[\"mcp-client-2025-04-04\"],\n",
-    "    mcp_servers=mcp_servers,\n",
-    ")\n",
-    "\n",
-    "response = llm.invoke(\n",
-    "    \"What transport protocols does the 2025-03-26 version of the MCP \"\n",
-    "    \"spec (modelcontextprotocol/modelcontextprotocol) support?\"\n",
-    ")"
-   ]
-  },
  {
   "cell_type": "markdown",
   "id": "2fd5d545-a40d-42b1-ad0c-0a79e2536c9b",
--- a/docs/docs/integrations/chat/azure_ai.ipynb
+++ b/docs/docs/integrations/chat/azure_ai.ipynb
@@ -17,9 +17,9 @@
   "source": [
    "# AzureAIChatCompletionsModel\n",
    "\n",
-    "This will help you get started with AzureAIChatCompletionsModel [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureAIChatCompletionsModel features and configurations, head to the [API reference](https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html)\n",
+    "This will help you getting started with AzureAIChatCompletionsModel [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureAIChatCompletionsModel features and configurations head to the [API reference](https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html)\n",
    "\n",
-    "The AzureAIChatCompletionsModel class uses the Azure AI Foundry SDK. AI Foundry has several chat models, including AzureOpenAI, Cohere, Llama, Phi-3/4, and DeepSeek-R1, among others. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/azure/ai-studio/how-to/model-catalog-overview).\n",
+    "The AzureAIChatCompletionsModel class uses the Azure AI Foundry SDK. AI Foundry has several chat models including AzureOpenAI, Cohere, Llama, Phi-3/4, and DeepSeek-R1 to name a few. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/azure/ai-studio/how-to/model-catalog-overview).\n",
    "\n",
    "\n",
    "## Overview\n",
@@ -37,12 +37,12 @@
    "\n",
    "## Setup\n",
    "\n",
-    "To access AzureAIChatCompletionsModel models, you'll need to create an [Azure account](https://azure.microsoft.com/pricing/purchase-options/azure-account), get an API key, and install the `langchain-azure-ai` integration package.\n",
+    "To access AzureAIChatCompletionsModel models you'll need to create an [Azure account](https://azure.microsoft.com/pricing/purchase-options/azure-account), get an API key, and install the `langchain-azure-ai` integration package.\n",
    "\n",
    "### Credentials\n",
    "\n",
    "\n",
-    "Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/develop/sdk-overview?tabs=sync&pivots=programming-language-python) to see how to create your deployment and generate an API key. Once your model is deployed, you click the 'get endpoint' button in AI Foundry. This will show you your endpoint and api key. Once you've done this, set the AZURE_INFERENCE_CREDENTIAL and AZURE_INFERENCE_ENDPOINT environment variables:"
+    "Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/develop/sdk-overview?tabs=sync&pivots=programming-language-python) to see how to create your deployment and generate an API key. Once your model is deployed you click the 'get endpoint' button in AI Foundry. This will show you your endpoint and api key. Once you've done this set the AZURE_INFERENCE_CREDENTIAL and AZURE_INFERENCE_ENDPOINT environment variables:"
   ]
  },
  {
@@ -71,7 +71,7 @@
   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
   "metadata": {},
   "source": [
-    "If you want to get automated tracing of your model calls, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+    "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:"
   ]
  },
  {
@@ -247,13 +247,13 @@
   "source": [
    "## API reference\n",
    "\n",
-    "For detailed documentation of all AzureAIChatCompletionsModel features and configurations, head to the API reference: https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html"
+    "For detailed documentation of all AzureAIChatCompletionsModel features and configurations head to the API reference: https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "langchain-3-9",
   "language": "python",
   "name": "python3"
  },
@@ -267,7 +267,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.9.19"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/chat/cloudflare_workersai.ipynb
+++ b/docs/docs/integrations/chat/cloudflare_workersai.ipynb
@@ -18,7 +18,7 @@
    "# ChatCloudflareWorkersAI\n",
    "\n",
    "\n",
-    "This will help you get 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",
+    "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",
--- a/docs/docs/integrations/chat/contextual.ipynb
+++ b/docs/docs/integrations/chat/contextual.ipynb
@@ -21,7 +21,7 @@
   "source": [
    "# ChatContextual\n",
    "\n",
-    "This will help you get started with Contextual AI's Grounded Language Model [chat models](/docs/concepts/chat_models/).\n",
+    "This will help you getting started with Contextual AI's Grounded Language Model [chat models](/docs/concepts/chat_models/).\n",
    "\n",
    "To learn more about Contextual AI, please visit our [documentation](https://docs.contextual.ai/).\n",
    "\n",
--- a/docs/docs/integrations/chat/deepseek.ipynb
+++ b/docs/docs/integrations/chat/deepseek.ipynb
@@ -18,7 +18,7 @@
        "# ChatDeepSeek\n",
        "\n",
        "\n",
-        "This will help you get started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
+        "This will help you getting started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
        "\n",
        ":::tip\n",
        "\n",
--- a/docs/docs/integrations/chat/ernie.ipynb
+++ b/docs/docs/integrations/chat/ernie.ipynb
@@ -25,16 +25,17 @@
   "source": [
    "**Deprecated Warning**\n",
    "\n",
-    "We recommend users switch from `langchain_community.chat_models.ErnieBotChat` to `langchain_community.chat_models.QianfanChatEndpoint`.\n",
+    "We recommend users using `langchain_community.chat_models.ErnieBotChat` \n",
+    "to use `langchain_community.chat_models.QianfanChatEndpoint` instead.\n",
    "\n",
    "documentation for `QianfanChatEndpoint` is [here](/docs/integrations/chat/baidu_qianfan_endpoint/).\n",
    "\n",
-    "There are 4 reasons why we recommend users to use `QianfanChatEndpoint`:\n",
+    "they are 4 why we recommend users to use `QianfanChatEndpoint`:\n",
    "\n",
-    "1. `QianfanChatEndpoint` supports more LLMs in the Qianfan platform.\n",
-    "2. `QianfanChatEndpoint` supports streaming mode.\n",
-    "3. `QianfanChatEndpoint` support function calling usage.\n",
-    "4. `ErnieBotChat` is no longer maintained and has been deprecated."
+    "1. `QianfanChatEndpoint` support more LLM in the Qianfan platform.\n",
+    "2. `QianfanChatEndpoint` support streaming mode.\n",
+    "3. `QianfanChatEndpoint` support function calling usgage.\n",
+    "4. `ErnieBotChat` is lack of maintenance and deprecated."
   ]
  },
  {
@@ -131,9 +132,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.11.4"
  }
 },
 "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 2
 }
--- a/docs/docs/integrations/chat/featherless_ai.ipynb
+++ b/docs/docs/integrations/chat/featherless_ai.ipynb
@@ -1,308 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
-    }
-   },
-   "source": [
-    "---\n",
-    "sidebar_label: Featherless AI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatFeatherlessAi\n",
-    "\n",
-    "\n",
-    "This will help you get started with FeatherlessAi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFeatherlessAi features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.ChatFeatherlessAi.html).\n",
-    "\n",
-    "- See https://featherless.ai/ for an example.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatFeatherlessAi](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.ChatFeatherlessAi.html) | [langchain-featherless-ai](https://python.langchain.com/api_reference/__package_name_short_snake__/) | ✅ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-featherless-ai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-featherless-ai?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ❌ | ❌ | ✅| ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "\n",
-    "To access Featherless AI models you'll need to create a/an Featherless AI account, get an API key, and install the `langchain-featherless-ai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "\n",
-    "Head to https://featherless.ai/ to sign up to FeatherlessAI and generate an API key. Once you've done this set the FEATHERLESSAI_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"FEATHERLESSAI_API_KEY\"):\n",
-    "    os.environ[\"FEATHERLESSAI_API_KEY\"] = getpass.getpass(\n",
-    "        \"Enter your FeatherlessAI API key: \"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "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 FeatherlessAi integration lives in the `langchain-featherless-ai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -qU langchain-featherless-ai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_featherless_ai import ChatFeatherlessAi\n",
-    "\n",
-    "llm = ChatFeatherlessAi(\n",
-    "    model=\"featherless-ai/Qwerky-72B\",\n",
-    "    temperature=0.9,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Python311\\Lib\\site-packages\\pydantic\\main.py:463: UserWarning: Pydantic serializer warnings:\n",
-      "  PydanticSerializationUnexpectedValue(Expected `int` - serialized value may not be as expected [input_value=1747322408.706, input_type=float])\n",
-      "  return self.__pydantic_serializer__.to_python(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'aime programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 27, 'total_tokens': 32, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'featherless-ai/Qwerky-72B', 'system_fingerprint': '', 'id': 'G1sgui', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--6ecbe184-c94e-4d03-bf75-9bd85b04ba5b-0', usage_metadata={'input_tokens': 27, 'output_tokens': 5, 'total_tokens': 32, 'input_token_details': {}, 'output_token_details': {}})"
-      ]
-     },
-     "execution_count": 22,
-     "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": 23,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'aime programmer.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fca9e713",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Python311\\Lib\\site-packages\\pydantic\\main.py:463: UserWarning: Pydantic serializer warnings:\n",
-      "  PydanticSerializationUnexpectedValue(Expected `int` - serialized value may not be as expected [input_value=1747322423.487, input_type=float])\n",
-      "  return self.__pydantic_serializer__.to_python(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 22, 'total_tokens': 27, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'featherless-ai/Qwerky-72B', 'system_fingerprint': '', 'id': 'BoBqht', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--67464357-83d1-4591-9a62-303ed74b8148-0', usage_metadata={'input_tokens': 22, 'output_tokens': 5, 'total_tokens': 27, 'input_token_details': {}, 'output_token_details': {}})"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatFeatherlessAi features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/.chat_models.ChatFeatherlessAi.html)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--- a/docs/docs/integrations/chat/fireworks.ipynb
+++ b/docs/docs/integrations/chat/fireworks.ipynb
@@ -17,7 +17,7 @@
      "source": [
        "# ChatFireworks\n",
        "\n",
-        "This doc helps you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
+        "This doc help you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
        "\n",
        "Fireworks AI is an AI inference platform to run and customize models. For a list of all models served by Fireworks see the [Fireworks docs](https://fireworks.ai/models).\n",
        "\n",
@@ -39,7 +39,7 @@
        "\n",
        "### Credentials\n",
        "\n",
-        "Head to (https://fireworks.ai/login to sign up to Fireworks and generate an API key. Once you've done this set the FIREWORKS_API_KEY environment variable:"
+        "Head to (ttps://fireworks.ai/login to sign up to Fireworks and generate an API key. Once you've done this set the FIREWORKS_API_KEY environment variable:"
      ]
    },
    {
--- a/docs/docs/integrations/chat/gigachat.ipynb
+++ b/docs/docs/integrations/chat/gigachat.ipynb
@@ -0,0 +1,117 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# GigaChat\n",
+    "This notebook shows how to use LangChain with [GigaChat](https://developers.sber.ru/portal/products/gigachat).\n",
+    "To use you need to install ```langchain_gigachat``` python package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain-gigachat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/individuals-quickstart)\n",
+    "\n",
+    "## Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "if \"GIGACHAT_CREDENTIALS\" not in os.environ:\n",
+    "    os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_gigachat import GigaChat\n",
+    "\n",
+    "chat = GigaChat(verify_ssl_certs=False, scope=\"GIGACHAT_API_PERS\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The capital of Russia is Moscow.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "\n",
+    "messages = [\n",
+    "    SystemMessage(\n",
+    "        content=\"You are a helpful AI that shares everything you know. Talk in English.\"\n",
+    "    ),\n",
+    "    HumanMessage(content=\"What is capital of Russia?\"),\n",
+    "]\n",
+    "\n",
+    "print(chat.invoke(messages).content)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
--- a/docs/docs/integrations/chat/goodfire.ipynb
+++ b/docs/docs/integrations/chat/goodfire.ipynb
@@ -17,7 +17,7 @@
      "source": [
        "# ChatGoodfire\n",
        "\n",
-        "This will help you get started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
+        "This will help you getting started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
        "\n",
        "## Overview\n",
        "### Integration details\n",
--- a/docs/docs/integrations/chat/groq.ipynb
+++ b/docs/docs/integrations/chat/groq.ipynb
@@ -17,7 +17,7 @@
      "source": [
        "# ChatGroq\n",
        "\n",
-        "This will help you get started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models?utm_source=langchain).\n",
+        "This will help you getting started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models?utm_source=langchain).\n",
        "\n",
        "## Overview\n",
        "### Integration details\n",
--- a/docs/docs/integrations/chat/huggingface.ipynb
+++ b/docs/docs/integrations/chat/huggingface.ipynb
@@ -6,7 +6,7 @@
   "source": [
    "# ChatHuggingFace\n",
    "\n",
-    "This will help you get started with `langchain_huggingface` [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatHuggingFace` features and configurations head to the [API reference](https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html). For a list of models supported by Hugging Face check out [this page](https://huggingface.co/models).\n",
+    "This will help you getting started with `langchain_huggingface` [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatHuggingFace` features and configurations head to the [API reference](https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html). For a list of models supported by Hugging Face check out [this page](https://huggingface.co/models).\n",
    "\n",
    "## Overview\n",
    "### Integration details\n",
--- a/docs/docs/integrations/chat/kinetica.ipynb
+++ b/docs/docs/integrations/chat/kinetica.ipynb
@@ -61,7 +61,7 @@
    "# Install Langchain community and core packages\n",
    "%pip install --upgrade --quiet langchain-core langchain-community\n",
    "\n",
-    "# Install Kinetica DB connection package\n",
+    "# Install Kineitca DB connection package\n",
    "%pip install --upgrade --quiet 'gpudb>=7.2.0.8' typeguard pandas tqdm\n",
    "\n",
    "# Install packages needed for this tutorial\n",
--- a/docs/docs/integrations/chat/llamacpp.ipynb
+++ b/docs/docs/integrations/chat/llamacpp.ipynb
@@ -41,7 +41,7 @@
    "\n",
    "## Setup\n",
    "\n",
-    "To get started and use **all** the features shown below, we recommend using a model that has been fine-tuned for tool-calling.\n",
+    "To get started and use **all** the features show below, we reccomend using a model that has been fine-tuned for tool-calling.\n",
    "\n",
    "We will use [\n",
    "Hermes-2-Pro-Llama-3-8B-GGUF](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF) from NousResearch. \n",
@@ -204,7 +204,7 @@
    "\n",
    "OpenAI has a [tool calling](https://platform.openai.com/docs/guides/function-calling) (we use \"tool calling\" and \"function calling\" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n",
    "\n",
-    "With `ChatLlamaCpp.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood, these are converted to an OpenAI tool schema, which looks like:\n",
+    "With `ChatLlamaCpp.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n",
    "```\n",
    "{\n",
    "    \"name\": \"...\",\n",
@@ -404,7 +404,7 @@
   "source": [
    "## API reference\n",
    "\n",
-    "For detailed documentation of all ChatLlamaCpp features and configurations, head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html"
+    "For detailed documentation of all ChatLlamaCpp features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html"
   ]
  }
 ],
@@ -424,7 +424,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.10.12"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/chat/maritalk.ipynb
+++ b/docs/docs/integrations/chat/maritalk.ipynb
@@ -8,6 +8,8 @@
    "\n",
    "# Maritalk\n",
    "\n",
+    "## Introduction\n",
+    "\n",
    "MariTalk is an assistant developed by the Brazilian company [Maritaca AI](https://www.maritaca.ai).\n",
    "MariTalk is based on language models that have been specially trained to understand Portuguese well.\n",
    "\n",
--- a/docs/docs/integrations/chat/mistralai.ipynb
+++ b/docs/docs/integrations/chat/mistralai.ipynb
@@ -17,7 +17,7 @@
      "source": [
        "# ChatMistralAI\n",
        "\n",
-        "This will help you get started with Mistral [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
+        "This will help you getting started with Mistral [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
        "\n",
        "## Overview\n",
        "### Integration details\n",
--- a/docs/docs/integrations/chat/modelscope_chat_endpoint.ipynb
+++ b/docs/docs/integrations/chat/modelscope_chat_endpoint.ipynb
@@ -20,7 +20,7 @@
    "\n",
    "ModelScope ([Home](https://www.modelscope.cn/) | [GitHub](https://github.com/modelscope/modelscope)) is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. \n",
    "\n",
-    "This will help you get started with ModelScope Chat Endpoint.\n",
+    "This will help you getting started with ModelScope Chat Endpoint.\n",
    "\n",
    "\n",
    "## Overview\n",
--- a/docs/docs/integrations/chat/netmind.ipynb
+++ b/docs/docs/integrations/chat/netmind.ipynb
@@ -17,7 +17,7 @@
   "source": [
    "# ChatNetmind\n",
    "\n",
-    "This will help you get started with Netmind [chat models](https://www.netmind.ai/). For detailed documentation of all ChatNetmind features and configurations head to the [API reference](https://github.com/protagolabs/langchain-netmind).\n",
+    "This will help you getting started with Netmind [chat models](https://www.netmind.ai/). For detailed documentation of all ChatNetmind features and configurations head to the [API reference](https://github.com/protagolabs/langchain-netmind).\n",
    "\n",
    "-  See https://www.netmind.ai/ for an example.\n",
    "\n",
--- a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
+++ b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
@@ -17,7 +17,7 @@
      "source": [
        "# ChatNVIDIA\n",
        "\n",
-        "This will help you get started with NVIDIA [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatNVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html).\n",
+        "This will help you getting started with NVIDIA [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatNVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html).\n",
        "\n",
        "## Overview\n",
        "The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on\n",
@@ -318,7 +318,7 @@
      "source": [
        "### Code Generation\n",
        "\n",
-        "These models accept the same arguments and input structure as regular chat models, but they tend to perform better on code-generation and structured code tasks. An example of this is `meta/codellama-70b`."
+        "These models accept the same arguments and input structure as regular chat models, but they tend to perform better on code-genreation and structured code tasks. An example of this is `meta/codellama-70b`."
      ]
    },
    {
--- a/docs/docs/integrations/chat/oci_data_science.ipynb
+++ b/docs/docs/integrations/chat/oci_data_science.ipynb
@@ -19,7 +19,7 @@
   "source": [
    "# ChatOCIModelDeployment\n",
    "\n",
-    "This will help you get started with OCIModelDeployment [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatOCIModelDeployment features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.oci_data_science.ChatOCIModelDeployment.html).\n",
+    "This will help you getting started with OCIModelDeployment [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatOCIModelDeployment features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.oci_data_science.ChatOCIModelDeployment.html).\n",
    "\n",
    "[OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a fully managed and serverless platform for data science teams to build, train, and manage machine learning models in the Oracle Cloud Infrastructure. You can use [AI Quick Actions](https://blogs.oracle.com/ai-and-datascience/post/ai-quick-actions-in-oci-data-science) to easily deploy LLMs on [OCI Data Science Model Deployment Service](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). You may choose to deploy the model with popular inference frameworks such as vLLM or TGI. By default, the model deployment endpoint mimics the OpenAI API protocol.\n",
    "\n",
--- a/docs/docs/integrations/chat/octoai.ipynb
+++ b/docs/docs/integrations/chat/octoai.ipynb
@@ -16,7 +16,7 @@
    "\n",
    "1. Get an API Token from [your OctoAI account page](https://octoai.cloud/settings).\n",
    "   \n",
-    "2. Paste your API token in the code cell below or use the `octoai_api_token` keyword argument.\n",
+    "2. Paste your API token in in the code cell below or use the `octoai_api_token` keyword argument.\n",
    "\n",
    "Note: If you want to use a different model than the [available models](https://octoai.cloud/text?selectedTags=Chat), you can containerize the model and make a custom OctoAI endpoint yourself, by following [Build a Container from Python](https://octo.ai/docs/bring-your-own-model/advanced-build-a-container-from-scratch-in-python) and [Create a Custom Endpoint from a Container](https://octo.ai/docs/bring-your-own-model/create-custom-endpoints-from-a-container/create-custom-endpoints-from-a-container) and then updating your `OCTOAI_API_BASE` environment variable.\n"
   ]
@@ -99,7 +99,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.10.12"
  },
  "vscode": {
   "interpreter": {
--- a/docs/docs/integrations/chat/openai.ipynb
+++ b/docs/docs/integrations/chat/openai.ipynb
--- a/docs/docs/integrations/chat/outlines.ipynb
+++ b/docs/docs/integrations/chat/outlines.ipynb
@@ -17,7 +17,7 @@
            "source": [
                "# ChatOutlines\n",
                "\n",
-                "This will help you get started with Outlines [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatOutlines features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.outlines.ChatOutlines.html).\n",
+                "This will help you getting started with Outlines [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatOutlines features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.outlines.ChatOutlines.html).\n",
                "\n",
                "[Outlines](https://github.com/outlines-dev/outlines) is a library for constrained language generation. It allows you to use large language models (LLMs) with various backends while applying constraints to the generated output.\n",
                "\n",
--- a/docs/docs/integrations/chat/pipeshift.ipynb
+++ b/docs/docs/integrations/chat/pipeshift.ipynb
@@ -17,7 +17,7 @@
   "source": [
    "# ChatPipeshift\n",
    "\n",
-    "This will help you get started with Pipeshift [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatPipeshift features and configurations head to the [API reference](https://dashboard.pipeshift.com/docs).\n",
+    "This will help you getting started with Pipeshift [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatPipeshift features and configurations head to the [API reference](https://dashboard.pipeshift.com/docs).\n",
    "\n",
    "## Overview\n",
    "### Integration details\n",
--- a/docs/docs/integrations/chat/predictionguard.ipynb
+++ b/docs/docs/integrations/chat/predictionguard.ipynb
@@ -4,99 +4,93 @@
   "cell_type": "markdown",
   "id": "3f0a201c",
   "metadata": {},
-   "source": [
-    "# ChatPredictionGuard"
-   ]
+   "source": "# ChatPredictionGuard"
  },
  {
-   "cell_type": "markdown",
-   "id": "c3adc2aac37985ac",
   "metadata": {},
-   "source": [
-    ">[Prediction Guard](https://predictionguard.com) is a secure, scalable GenAI platform that safeguards sensitive data, prevents common AI malfunctions, and runs on affordable hardware.\n"
-   ]
+   "cell_type": "markdown",
+   "source": ">[Prediction Guard](https://predictionguard.com) is a secure, scalable GenAI platform that safeguards sensitive data, prevents common AI malfunctions, and runs on affordable hardware.\n",
+   "id": "c3adc2aac37985ac"
  },
  {
-   "cell_type": "markdown",
-   "id": "4e1ec341481fb244",
   "metadata": {},
-   "source": [
-    "## Overview"
-   ]
+   "cell_type": "markdown",
+   "source": "## Overview",
+   "id": "4e1ec341481fb244"
  },
  {
-   "cell_type": "markdown",
-   "id": "b4090b7489e37a91",
   "metadata": {},
+   "cell_type": "markdown",
   "source": [
    "### Integration details\n",
    "This integration utilizes the Prediction Guard API, which includes various safeguards and security features."
-   ]
+   ],
+   "id": "b4090b7489e37a91"
  },
  {
-   "cell_type": "markdown",
-   "id": "e26e5b3240452162",
   "metadata": {},
+   "cell_type": "markdown",
   "source": [
    "### Model features\n",
    "The models supported by this integration only feature text-generation currently, along with the input and output checks described here."
-   ]
+   ],
+   "id": "e26e5b3240452162"
  },
  {
-   "cell_type": "markdown",
-   "id": "4fca548b61efb049",
   "metadata": {},
+   "cell_type": "markdown",
   "source": [
    "## Setup\n",
    "To access Prediction Guard models, contact us [here](https://predictionguard.com/get-started) to get a Prediction Guard API key and get started. "
-   ]
+   ],
+   "id": "4fca548b61efb049"
  },
  {
-   "cell_type": "markdown",
-   "id": "7cc34a9cd865690c",
   "metadata": {},
+   "cell_type": "markdown",
   "source": [
    "### Credentials\n",
    "Once you have a key, you can set it with "
-   ]
+   ],
+   "id": "7cc34a9cd865690c"
  },
  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "fa57fba89276da13",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-21T18:23:30.746350Z",
     "start_time": "2025-04-21T18:23:30.744744Z"
    }
   },
-   "outputs": [],
+   "cell_type": "code",
   "source": [
    "import os\n",
    "\n",
    "if \"PREDICTIONGUARD_API_KEY\" not in os.environ:\n",
    "    os.environ[\"PREDICTIONGUARD_API_KEY\"] = \"<Your Prediction Guard API Key>\""
-   ]
+   ],
+   "id": "fa57fba89276da13",
+   "outputs": [],
+   "execution_count": 2
  },
  {
-   "cell_type": "markdown",
-   "id": "87dc1742af7b053",
   "metadata": {},
+   "cell_type": "markdown",
   "source": [
    "### Installation\n",
    "Install the Prediction Guard Langchain integration with"
-   ]
+   ],
+   "id": "87dc1742af7b053"
  },
  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "b816ae8553cba021",
   "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": [
    {
     "name": "stdout",
@@ -106,9 +100,7 @@
     ]
    }
   ],
-   "source": [
-    "%pip install -qU langchain-predictionguard"
-   ]
+   "execution_count": 3
  },
  {
   "cell_type": "markdown",
@@ -116,61 +108,63 @@
   "metadata": {
    "id": "mesCTyhnJkNS"
   },
-   "source": [
-    "## Instantiation"
-   ]
+   "source": "## Instantiation"
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
   "id": "7191a5ce",
   "metadata": {
+    "id": "2xe8JEUwA7_y",
    "ExecuteTime": {
     "end_time": "2025-04-21T18:23:39.812675Z",
     "start_time": "2025-04-21T18:23:39.666881Z"
-    },
-    "id": "2xe8JEUwA7_y"
+    }
   },
+   "source": "from langchain_predictionguard import ChatPredictionGuard",
   "outputs": [],
-   "source": [
-    "from langchain_predictionguard import ChatPredictionGuard"
-   ]
+   "execution_count": 4
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
   "id": "140717c9",
   "metadata": {
+    "id": "Ua7Mw1N4HcER",
    "ExecuteTime": {
     "end_time": "2025-04-21T18:23:41.590296Z",
     "start_time": "2025-04-21T18:23:41.253237Z"
-    },
-    "id": "Ua7Mw1N4HcER"
+    }
   },
-   "outputs": [],
   "source": [
    "# If predictionguard_api_key is not passed, default behavior is to use the `PREDICTIONGUARD_API_KEY` environment variable.\n",
    "chat = ChatPredictionGuard(model=\"Hermes-3-Llama-3.1-8B\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 5
  },
  {
-   "cell_type": "markdown",
-   "id": "8dbdfc55b638e4c2",
   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
+   "cell_type": "markdown",
+   "source": "## Invocation",
+   "id": "8dbdfc55b638e4c2"
  },
  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "5a1635e7ae7134a3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-08T19:44:56.634939Z",
     "start_time": "2024-11-08T19:44:55.924534Z"
    }
   },
+   "cell_type": "code",
+   "source": [
+    "messages = [\n",
+    "    (\"system\", \"You are a helpful assistant that tells jokes.\"),\n",
+    "    (\"human\", \"Tell me a joke\"),\n",
+    "]\n",
+    "\n",
+    "ai_msg = chat.invoke(messages)\n",
+    "ai_msg"
+   ],
+   "id": "5a1635e7ae7134a3",
   "outputs": [
    {
     "data": {
@@ -183,26 +177,18 @@
     "output_type": "execute_result"
    }
   ],
-   "source": [
-    "messages = [\n",
-    "    (\"system\", \"You are a helpful assistant that tells jokes.\"),\n",
-    "    (\"human\", \"Tell me a joke\"),\n",
-    "]\n",
-    "\n",
-    "ai_msg = chat.invoke(messages)\n",
-    "ai_msg"
-   ]
+   "execution_count": 4
  },
  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a6f8025726e5da3c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-08T19:44:57.501782Z",
     "start_time": "2024-11-08T19:44:57.498931Z"
    }
   },
+   "cell_type": "code",
+   "source": "print(ai_msg.content)",
+   "id": "a6f8025726e5da3c",
   "outputs": [
    {
     "name": "stdout",
@@ -212,21 +198,16 @@
     ]
    }
   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
+   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "id": "e9e96106-8e44-4373-9c57-adc3d0062df3",
   "metadata": {},
-   "source": [
-    "## Streaming"
-   ]
+   "source": "## Streaming"
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
   "id": "ea62d2da-802c-4b8a-a63e-5d1d0a72540f",
   "metadata": {
    "ExecuteTime": {
@@ -234,6 +215,12 @@
     "start_time": "2024-11-08T19:44:59.095584Z"
    }
   },
+   "source": [
+    "chat = ChatPredictionGuard(model=\"Hermes-2-Pro-Llama-3-8B\")\n",
+    "\n",
+    "for chunk in chat.stream(\"Tell me a joke\"):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ],
   "outputs": [
    {
     "name": "stdout",
@@ -245,39 +232,33 @@
     ]
    }
   ],
-   "source": [
-    "chat = ChatPredictionGuard(model=\"Hermes-2-Pro-Llama-3-8B\")\n",
-    "\n",
-    "for chunk in chat.stream(\"Tell me a joke\"):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
+   "execution_count": 6
  },
  {
-   "cell_type": "markdown",
-   "id": "1227780d6e6728ba",
   "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 to 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"
-   ]
+    "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"
  },
  {
-   "cell_type": "markdown",
-   "id": "23446aa52e01d1ba",
   "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"
  },
  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "135efb0bfc5916c1",
   "metadata": {},
+   "cell_type": "code",
   "outputs": [],
+   "execution_count": null,
   "source": [
    "from pydantic import BaseModel, Field\n",
    "\n",
@@ -298,18 +279,24 @@
    "    [GetWeather, GetPopulation]\n",
    "    # strict = True  # enforce tool args schema is respected\n",
    ")"
-   ]
+   ],
+   "id": "135efb0bfc5916c1"
  },
  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "8136f19a8836cd58",
   "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": {
@@ -322,33 +309,28 @@
     "output_type": "execute_result"
    }
   ],
-   "source": [
-    "ai_msg = llm_with_tools.invoke(\n",
-    "    \"Which city is hotter today and which is bigger: LA or NY?\"\n",
-    ")\n",
-    "ai_msg"
-   ]
+   "execution_count": 7
  },
  {
-   "cell_type": "markdown",
-   "id": "84f405c45a35abe5",
   "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"
  },
  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "bdcee85475019719",
   "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": {
@@ -376,9 +358,7 @@
     "output_type": "execute_result"
    }
   ],
-   "source": [
-    "ai_msg.tool_calls"
-   ]
+   "execution_count": 8
  },
  {
   "cell_type": "markdown",
@@ -406,7 +386,6 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
   "id": "9c5d7a87",
   "metadata": {
    "ExecuteTime": {
@@ -414,15 +393,6 @@
     "start_time": "2024-11-08T19:45:01.633319Z"
    }
   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Could not make prediction. pii detected\n"
-     ]
-    }
-   ],
   "source": [
    "chat = ChatPredictionGuard(\n",
    "    model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_input={\"pii\": \"block\"}\n",
@@ -432,7 +402,17 @@
    "    chat.invoke(\"Hello, my name is John Doe and my SSN is 111-22-3333\")\n",
    "except ValueError as e:\n",
    "    print(e)"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Could not make prediction. pii detected\n"
+     ]
+    }
+   ],
+   "execution_count": 7
  },
  {
   "cell_type": "markdown",
@@ -444,7 +424,6 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
   "id": "a9f96fb4-00c3-4a39-b177-d1ccd5caecab",
   "metadata": {
    "ExecuteTime": {
@@ -452,15 +431,6 @@
     "start_time": "2024-11-08T19:45:03.275661Z"
    }
   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Could not make prediction. prompt injection detected\n"
-     ]
-    }
-   ],
   "source": [
    "chat = ChatPredictionGuard(\n",
    "    model=\"Hermes-2-Pro-Llama-3-8B\",\n",
@@ -473,7 +443,17 @@
    "    )\n",
    "except ValueError as e:\n",
    "    print(e)"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Could not make prediction. prompt injection detected\n"
+     ]
+    }
+   ],
+   "execution_count": 8
  },
  {
   "cell_type": "markdown",
@@ -503,15 +483,23 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
   "id": "0cb3b91f",
   "metadata": {
+    "id": "PzxSbYwqTm2w",
    "ExecuteTime": {
     "end_time": "2024-11-08T19:45:10.044203Z",
     "start_time": "2024-11-08T19:45:05.692378Z"
-    },
-    "id": "PzxSbYwqTm2w"
+    }
   },
+   "source": [
+    "chat = ChatPredictionGuard(\n",
+    "    model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_output={\"toxicity\": True}\n",
+    ")\n",
+    "try:\n",
+    "    chat.invoke(\"Please tell me something that would fail a toxicity check!\")\n",
+    "except ValueError as e:\n",
+    "    print(e)"
+   ],
   "outputs": [
    {
     "name": "stdout",
@@ -521,15 +509,7 @@
     ]
    }
   ],
-   "source": [
-    "chat = ChatPredictionGuard(\n",
-    "    model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_output={\"toxicity\": True}\n",
-    ")\n",
-    "try:\n",
-    "    chat.invoke(\"Please tell me something that would fail a toxicity check!\")\n",
-    "except ValueError as e:\n",
-    "    print(e)"
-   ]
+   "execution_count": 9
  },
  {
   "cell_type": "markdown",
@@ -541,7 +521,6 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 10,
   "id": "249da02a-d32d-4f91-82d0-10ec0505aec7",
   "metadata": {
    "ExecuteTime": {
@@ -549,15 +528,6 @@
     "start_time": "2024-11-08T19:45:10.109509Z"
    }
   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Could not make prediction. failed factuality check\n"
-     ]
-    }
-   ],
   "source": [
    "chat = ChatPredictionGuard(\n",
    "    model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_output={\"factuality\": True}\n",
@@ -567,38 +537,32 @@
    "    chat.invoke(\"Make up something that would fail a factuality check!\")\n",
    "except ValueError as e:\n",
    "    print(e)"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Could not make prediction. failed factuality check\n"
+     ]
+    }
+   ],
+   "execution_count": 10
  },
  {
-   "cell_type": "markdown",
-   "id": "3c81e5a85a765ece",
   "metadata": {},
-   "source": [
-    "## Chaining"
-   ]
+   "cell_type": "markdown",
+   "source": "## Chaining",
+   "id": "3c81e5a85a765ece"
  },
  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "beb4e0666bb514a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-08T19:45:17.525848Z",
     "start_time": "2024-11-08T19:45:15.197628Z"
    }
   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Step 1: Determine the year Justin Bieber was born.\\nJustin Bieber was born on March 1, 1994.\\n\\nStep 2: Determine which NFL team won the Super Bowl in 1994.\\nThe 1994 Super Bowl was Super Bowl XXVIII, which took place on January 30, 1994. The winning team was the Dallas Cowboys, who defeated the Buffalo Bills with a score of 30-13.\\n\\nSo, the NFL team that won the Super Bowl in the year Justin Bieber was born is the Dallas Cowboys.', additional_kwargs={}, response_metadata={}, id='run-bbc94f8b-9ab0-4839-8580-a9e510bfc97a-0')"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "cell_type": "code",
   "source": [
    "from langchain_core.prompts import PromptTemplate\n",
    "\n",
@@ -613,24 +577,30 @@
    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
    "\n",
    "chat_chain.invoke({\"question\": question})"
-   ]
+   ],
+   "id": "beb4e0666bb514a7",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Step 1: Determine the year Justin Bieber was born.\\nJustin Bieber was born on March 1, 1994.\\n\\nStep 2: Determine which NFL team won the Super Bowl in 1994.\\nThe 1994 Super Bowl was Super Bowl XXVIII, which took place on January 30, 1994. The winning team was the Dallas Cowboys, who defeated the Buffalo Bills with a score of 30-13.\\n\\nSo, the NFL team that won the Super Bowl in the year Justin Bieber was born is the Dallas Cowboys.', additional_kwargs={}, response_metadata={}, id='run-bbc94f8b-9ab0-4839-8580-a9e510bfc97a-0')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 11
  },
  {
-   "cell_type": "markdown",
-   "id": "d87695d5ff1471c1",
   "metadata": {},
+   "cell_type": "markdown",
   "source": [
    "## API reference\n",
-    "For detailed documentation of all ChatPredictionGuard features and configurations, check out the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.predictionguard.ChatPredictionGuard.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3664cc0e-841c-46f1-a158-4d5f5185bc94",
-   "metadata": {},
-   "outputs": [],
-   "source": []
+    "For detailed documentation of all ChatPredictionGuard features and configurations check out the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.predictionguard.ChatPredictionGuard.html"
+   ],
+   "id": "d87695d5ff1471c1"
  }
 ],
 "metadata": {
@@ -652,7 +622,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.9.16"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/chat/qwq.ipynb
+++ b/docs/docs/integrations/chat/qwq.ipynb
@@ -21,7 +21,7 @@
   "source": [
    "# ChatQwQ\n",
    "\n",
-    "This will help you get started with QwQ [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatQwQ features and configurations head to the [API reference](https://pypi.org/project/langchain-qwq/).\n",
+    "This will help you getting started with QwQ [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatQwQ features and configurations head to the [API reference](https://pypi.org/project/langchain-qwq/).\n",
    "\n",
    "## Overview\n",
    "### Integration details\n",
--- a/docs/docs/integrations/chat/reka.ipynb
+++ b/docs/docs/integrations/chat/reka.ipynb
@@ -36,7 +36,7 @@
    "\n",
    "## Setup\n",
    "\n",
-    "To access Reka models you'll need to create a Reka developer account, get an API key, and install the `langchain_community` integration package and the reka python package via 'pip install reka-api'.\n",
+    "To access Reka models you'll need to create an Reka developer account, get an API key, and install the `langchain_community` integration package and the reka python package via 'pip install reka-api'.\n",
    "\n",
    "### Credentials\n",
    "\n",
@@ -280,7 +280,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Use with Tavily api search"
+    "Use use with tavtly api search"
   ]
  },
  {
--- a/docs/docs/integrations/chat/sambanova.ipynb
+++ b/docs/docs/integrations/chat/sambanova.ipynb
@@ -19,7 +19,7 @@
   "source": [
    "# ChatSambaNovaCloud\n",
    "\n",
-    "This will help you get started with SambaNovaCloud [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\n",
+    "This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\n",
    "\n",
    "**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models\n",
    "\n",
--- a/docs/docs/integrations/chat/sambastudio.ipynb
+++ b/docs/docs/integrations/chat/sambastudio.ipynb
@@ -19,7 +19,7 @@
   "source": [
    "# ChatSambaStudio\n",
    "\n",
-    "This will help you get started with SambaStudio [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://docs.sambanova.ai/sambastudio/latest/index.html).\n",
+    "This will help you getting started with SambaStudio [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://docs.sambanova.ai/sambastudio/latest/index.html).\n",
    "\n",
    "**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) SambaStudio is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova [DataScale](https://sambanova.ai/products/datascale) systems.\n",
    "\n",
--- a/docs/docs/integrations/chat/solar.ipynb
+++ b/docs/docs/integrations/chat/solar.ipynb
@@ -1,13 +1,5 @@
 {
 "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "516cad96-cfcb-4dd1-b70e-ecaef33e60ba",
-   "metadata": {},
-   "source": [
-    "Deprecated since version 0.0.34: Use langchain_upstage.ChatUpstage instead."
-   ]
-  },
  {
   "cell_type": "code",
   "execution_count": 5,
@@ -80,7 +72,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.9.0"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/chat/symblai_nebula.ipynb
+++ b/docs/docs/integrations/chat/symblai_nebula.ipynb
@@ -21,6 +21,7 @@
   "source": [
    "# Nebula (Symbl.ai)\n",
    "\n",
+    "## Overview\n",
    "This notebook covers how to get started with [Nebula](https://docs.symbl.ai/docs/nebula-llm) - Symbl.ai's chat model.\n",
    "\n",
    "### Integration details\n",
--- a/docs/docs/integrations/chat/tencent_hunyuan.ipynb
+++ b/docs/docs/integrations/chat/tencent_hunyuan.ipynb
@@ -22,7 +22,7 @@
    "> script creation, resume generation, article writing, code generation, data analysis, and content\n",
    "> analysis.\n",
    "\n",
-    "See [more information](https://cloud.tencent.com/document/product/1729) for more details."
+    "See for [more information](https://cloud.tencent.com/document/product/1729)."
   ]
  },
  {
@@ -98,7 +98,7 @@
    }
   },
   "source": [
-    "## Using ChatHunyuan with Streaming"
+    "## For ChatHunyuan with Streaming"
   ]
  },
  {
--- a/docs/docs/integrations/chat/together.ipynb
+++ b/docs/docs/integrations/chat/together.ipynb
@@ -18,7 +18,7 @@
        "# ChatTogether\n",
        "\n",
        "\n",
-        "This page will help you get started with Together AI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatTogether features and configurations, head to the [API reference](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html).\n",
+        "This page will help you get started with Together AI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatTogether features and configurations head to the [API reference](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html).\n",
        "\n",
        "[Together AI](https://www.together.ai/) offers an API to query [50+ leading open-source models](https://docs.together.ai/docs/chat-models)\n",
        "\n",
@@ -40,7 +40,7 @@
        "\n",
        "### Credentials\n",
        "\n",
-        "Head to [this page](https://api.together.ai) to sign up to Together and generate an API key. Once you've done this, set the TOGETHER_API_KEY environment variable:"
+        "Head to [this page](https://api.together.ai) to sign up to Together and generate an API key. Once you've done this set the TOGETHER_API_KEY environment variable:"
      ]
    },
    {
@@ -81,7 +81,7 @@
      "source": [
        "### Installation\n",
        "\n",
-        "The LangChain Together integration is included in the `langchain-together` package:"
+        "The LangChain Together integration lives in the `langchain-together` package:"
      ]
    },
    {
@@ -187,7 +187,7 @@
      "source": [
        "## Chaining\n",
        "\n",
-        "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template as follows:"
+        "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:"
      ]
    },
    {
@@ -237,7 +237,7 @@
      "source": [
        "## API reference\n",
        "\n",
-        "For detailed documentation of all ChatTogether features and configurations, head to the API reference: https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html"
+        "For detailed documentation of all ChatTogether features and configurations head to the API reference: https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html"
      ]
    }
  ],
--- a/docs/docs/integrations/chat/vectara.ipynb
+++ b/docs/docs/integrations/chat/vectara.ipynb
@@ -5,14 +5,16 @@
   "id": "134a0785",
   "metadata": {},
   "source": [
-    "[Vectara](https://vectara.com/) is the trusted AI Assistant and Agent platform, which focuses on enterprise readiness for mission-critical applications.\n",
+    "## Overview\n",
+    "\n",
+    "[Vectara](https://vectara.com/) is the trusted AI Assistant and Agent platform which focuses on enterprise readiness for mission-critical applications.\n",
    "Vectara serverless RAG-as-a-service provides all the components of RAG behind an easy-to-use API, including:\n",
    "1. A way to extract text from files (PDF, PPT, DOCX, etc)\n",
-    "2. ML-based chunking that provides state-of-the-art performance.\n",
+    "2. ML-based chunking that provides state of the art performance.\n",
    "3. The [Boomerang](https://vectara.com/how-boomerang-takes-retrieval-augmented-generation-to-the-next-level-via-grounded-generation/) embeddings model.\n",
    "4. Its own internal vector database where text chunks and embedding vectors are stored.\n",
    "5. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments, including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) as well as multiple reranking options such as the [multi-lingual relevance reranker](https://www.vectara.com/blog/deep-dive-into-vectara-multilingual-reranker-v1-state-of-the-art-reranker-across-100-languages), [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/), [UDF reranker](https://www.vectara.com/blog/rag-with-user-defined-functions-based-reranking). \n",
-    "6. An LLM for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.\n",
+    "6. An LLM to for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.\n",
    "\n",
    "For more information:\n",
    "- [Documentation](https://docs.vectara.com/docs/)\n",
@@ -24,7 +26,7 @@
    "\n",
    "### Setup\n",
    "\n",
-    "To use the `VectaraVectorStore`, you first need to install the partner package.\n"
+    "To use the `VectaraVectorStore` you first need to install the partner package.\n"
   ]
  },
  {
@@ -46,8 +48,8 @@
    "\n",
    "To get started, use the following steps:\n",
    "1. If you don't already have one, [Sign up](https://www.vectara.com/integrations/langchain) for your free Vectara trial.\n",
-    "2. Within your account, you can create one or more corpora. Each corpus represents an area that stores text data upon ingestion from input documents. To create a corpus, use the **\"Create Corpus\"** button. You then provide a name to your corpus as well as a description. Optionally, you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.\n",
-    "3. Next, you'll need to create API keys to access the corpus. Click on the **\"Access Control\"** tab in the corpus view and then the **\"Create API Key\"** button. Give your key a name, and choose whether you want query-only or query+index for your key. Click \"Create\", and you now have an active API key. Keep this key confidential. \n",
+    "2. Within your account you can create one or more corpora. Each corpus represents an area that stores text data upon ingest from input documents. To create a corpus, use the **\"Create Corpus\"** button. You then provide a name to your corpus as well as a description. Optionally you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.\n",
+    "3. Next you'll need to create API keys to access the corpus. Click on the **\"Access Control\"** tab in the corpus view and then the **\"Create API Key\"** button. Give your key a name, and choose whether you want query-only or query+index for your key. Click \"Create\" and you now have an active API key. Keep this key confidential. \n",
    "\n",
    "To use LangChain with Vectara, you'll need to have these two values: `corpus_key` and `api_key`.\n",
    "You can provide `VECTARA_API_KEY` to LangChain in two ways:\n",
@@ -73,7 +75,7 @@
    ")\n",
    "```\n",
    "\n",
-    "In this notebook, we assume they are provided in the environment."
+    "In this notebook we assume they are provided in the environment."
   ]
  },
  {
@@ -109,9 +111,9 @@
    "\n",
    "In most uses of LangChain to create chatbots, one must integrate a special `memory` component that maintains the history of chat sessions and then uses that history to ensure the chatbot is aware of conversation history.\n",
    "\n",
-    "With Vectara Chat, all of that is performed in the backend by Vectara automatically. You can look at the [Chat](https://docs.vectara.com/docs/api-reference/chat-apis/chat-apis-overview) documentation for the details, to learn more about the internals of how this is implemented, but with LangChain, all you have to do is turn that feature on in the Vectara vectorstore.\n",
+    "With Vectara Chat - all of that is performed in the backend by Vectara automatically. You can look at the [Chat](https://docs.vectara.com/docs/api-reference/chat-apis/chat-apis-overview) documentation for the details, to learn more about the internals of how this is implemented, but with LangChain all you have to do is turn that feature on in the Vectara vectorstore.\n",
    "\n",
-    "Let's see an example. First, we load the SOTU document (remember, text extraction and chunking all occur automatically on the Vectara platform):"
+    "Let's see an example. First we load the SOTU document (remember, text extraction and chunking all occurs automatically on the Vectara platform):"
   ]
  },
  {
@@ -241,8 +243,8 @@
   "source": [
    "## Chat with streaming\n",
    "\n",
-    "Of course, the chatbot interface also supports streaming.\n",
-    "Instead of the `invoke` method, you simply use `stream`:"
+    "Of course the chatbot interface also supports streaming.\n",
+    "Instead of the `invoke` method you simply use `stream`:"
   ]
  },
  {
@@ -279,15 +281,12 @@
   "cell_type": "markdown",
   "id": "cefdf72b1d90085a",
   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
   },
   "source": [
    "## Chaining\n",
    "\n",
-    "For additional capabilities, you can use chaining."
+    "For additional capabilities you can use chaining."
   ]
  },
  {
@@ -347,10 +346,7 @@
   "cell_type": "markdown",
   "id": "3b8bb761-db4a-436c-8939-41e9f8652083",
   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
+    "collapsed": false
   },
   "source": [
    "## API reference\n",
@@ -375,7 +371,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.12.0"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/chat/vllm.ipynb
+++ b/docs/docs/integrations/chat/vllm.ipynb
@@ -20,7 +20,7 @@
        "vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.\n",
        "\n",
        "## Overview\n",
-        "This will help you get started with vLLM [chat models](/docs/concepts/chat_models), which leverages the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
+        "This will help you getting started with vLLM [chat models](/docs/concepts/chat_models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
        "\n",
        "### Integration details\n",
        "\n",
@@ -29,7 +29,7 @@
        "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain_openai](https://python.langchain.com/api_reference/openai/) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_openai?style=flat-square&label=%20) |\n",
        "\n",
        "### Model features\n",
-        "Specific model features, such as tool calling, support for multi-modal inputs, support for token-level streaming, etc., will depend on the hosted model.\n",
+        "Specific model features-- such as tool calling, support for multi-modal inputs, support for token-level streaming, etc.-- will depend on the hosted model.\n",
        "\n",
        "## Setup\n",
        "\n",
--- a/docs/docs/integrations/chat/volcengine_maas.ipynb
+++ b/docs/docs/integrations/chat/volcengine_maas.ipynb
@@ -6,7 +6,7 @@
   "metadata": {},
   "source": [
    "---\n",
-    "sidebar_label: Volc Engine Maas\n",
+    "sidebar_label: Volc Enging Maas\n",
    "---"
   ]
  },
--- a/docs/docs/integrations/chat/writer.ipynb
+++ b/docs/docs/integrations/chat/writer.ipynb
@@ -303,7 +303,7 @@
   "source": [
    "### A note on tool binding\n",
    "\n",
-    "The `ChatWriter.bind_tools()` method does not create a new instance with bound tools, but stores the received `tools` and `tool_choice` in the initial class instance attributes to pass them as parameters during the Palmyra LLM call while using `ChatWriter` invocation. This approach allows the support of different tool types, e.g. `function` and `graph`. `Graph` is one of the remotely called Writer Palmyra tools. For further information, visit our [docs](https://dev.writer.com/api-guides/knowledge-graph#knowledge-graph). \n",
+    "The `ChatWriter.bind_tools()` method does not create new instance with bound tools, but stores the received `tools` and `tool_choice` in the initial class instance attributes to pass them as parameters during the Palmyra LLM call while using `ChatWriter` invocation. This approach allows the support of different tool types, e.g. `function` and `graph`. `Graph` is one of the remotely called Writer Palmyra tools. For further information visit our [docs](https://dev.writer.com/api-guides/knowledge-graph#knowledge-graph). \n",
    "\n",
    "For more information about tool usage in LangChain, visit the [LangChain tool calling documentation](https://python.langchain.com/docs/concepts/tool_calling/)."
   ]
@@ -373,7 +373,7 @@
   "source": [
    "## Prompt templates\n",
    "\n",
-    "[Prompt templates](https://python.langchain.com/docs/concepts/prompt_templates/) help to translate user input and parameters into instructions for a language model. You can use `ChatWriter` with a prompt template like so:\n"
+    "[Prompt templates](https://python.langchain.com/docs/concepts/prompt_templates/) help to translate user input and parameters into instructions for a language model. You can use `ChatWriter` with a prompt templates like so:\n"
   ]
  },
  {
@@ -411,7 +411,7 @@
   "metadata": {},
   "source": [
    "## API reference\n",
-    "For detailed documentation of all ChatWriter features and configurations, head to the [API reference](https://python.langchain.com/api_reference/writer/chat_models/langchain_writer.chat_models.ChatWriter.html#langchain_writer.chat_models.ChatWriter).\n",
+    "For detailed documentation of all ChatWriter features and configurations head to the [API reference](https://python.langchain.com/api_reference/writer/chat_models/langchain_writer.chat_models.ChatWriter.html#langchain_writer.chat_models.ChatWriter).\n",
    "\n",
    "## Additional resources\n",
    "You can find information about Writer's models (including costs, context windows, and supported input types) and tools in the [Writer docs](https://dev.writer.com/home)."
--- a/docs/docs/integrations/chat/xai.ipynb
+++ b/docs/docs/integrations/chat/xai.ipynb
@@ -1,373 +1,330 @@
 {
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: xAI\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatXAI\n",
-    "\n",
-    "\n",
-    "This page will help you get started with xAI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatXAI` features and configurations, head to the [API reference](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html).\n",
-    "\n",
-    "[xAI](https://console.x.ai/) offers an API to interact with Grok models.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/xai) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatXAI](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html) | [langchain-xai](https://python.langchain.com/api_reference/xai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-xai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-xai?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access xAI models, you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this, set the `XAI_API_KEY` environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if \"XAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"XAI_API_KEY\"] = getpass.getpass(\"Enter your xAI API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain xAI integration lives in the `langchain-xai` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-xai"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "Now we can instantiate our model object and generate chat completions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_xai import ChatXAI\n",
-    "\n",
-    "llm = ChatXAI(\n",
-    "    model=\"grok-beta\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=None,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    "    # other params...\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 30, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-adffb7a3-e48a-4f52-b694-340d85abe5c3-0', usage_metadata={'input_tokens': 30, 'output_tokens': 6, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})"
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "sidebar_label: xAI\n",
+        "---"
      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    (\n",
-    "        \"system\",\n",
-    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-    "    ),\n",
-    "    (\"human\", \"I love programming.\"),\n",
-    "]\n",
-    "ai_msg = llm.invoke(messages)\n",
-    "ai_msg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "J'adore programmer.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 25, 'total_tokens': 32, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-569fc8dc-101b-4e6d-864e-d4fa80df2b63-0', usage_metadata={'input_tokens': 25, 'output_tokens': 7, 'total_tokens': 32, 'input_token_details': {}, 'output_token_details': {}})"
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatXAI\n",
+        "\n",
+        "\n",
+        "This page will help you get started with xAI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatXAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html).\n",
+        "\n",
+        "[xAI](https://console.x.ai/) offers an API to interact with Grok models.\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/xai) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [ChatXAI](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html) | [langchain-xai](https://python.langchain.com/api_reference/xai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-xai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-xai?style=flat-square&label=%20) |\n",
+        "\n",
+        "### Model features\n",
+        "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ |\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access xAI models you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this set the `XAI_API_KEY` environment variable:"
      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-    "        ),\n",
-    "        (\"human\", \"{input}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | llm\n",
-    "chain.invoke(\n",
-    "    {\n",
-    "        \"input_language\": \"English\",\n",
-    "        \"output_language\": \"German\",\n",
-    "        \"input\": \"I love programming.\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e074bce1-0994-4b83-b393-ae7aa7e21750",
-   "metadata": {},
-   "source": [
-    "## Tool calling\n",
-    "\n",
-    "ChatXAI has a [tool calling](https://docs.x.ai/docs#capabilities) (we use \"tool calling\" and \"function calling\" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n",
-    "\n",
-    "### ChatXAI.bind_tools()\n",
-    "\n",
-    "With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood, these are converted to an OpenAI tool schema, which looks like:\n",
-    "```\n",
-    "{\n",
-    "    \"name\": \"...\",\n",
-    "    \"description\": \"...\",\n",
-    "    \"parameters\": {...}  # JSONSchema\n",
-    "}\n",
-    "```\n",
-    "and passed in every model invocation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c6bfe929-ec02-46bd-9d54-76350edddabc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pydantic import BaseModel, Field\n",
-    "\n",
-    "\n",
-    "class GetWeather(BaseModel):\n",
-    "    \"\"\"Get the current weather in a given location\"\"\"\n",
-    "\n",
-    "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
-    "\n",
-    "\n",
-    "llm_with_tools = llm.bind_tools([GetWeather])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5265c892-d8c2-48af-aef5-adbee1647ba6",
-   "metadata": {},
-   "outputs": [
+    },
    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='I am retrieving the current weather for San Francisco.', additional_kwargs={'tool_calls': [{'id': '0', 'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 151, 'total_tokens': 162, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73707da7-afec-4a52-bee1-a176b0ab8585-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': '0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 151, 'output_tokens': 11, 'total_tokens': 162, 'input_token_details': {}, 'output_token_details': {}})"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import getpass\n",
+        "import os\n",
+        "\n",
+        "if \"XAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"XAI_API_KEY\"] = getpass.getpass(\"Enter your xAI API key: \")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+      "metadata": {},
+      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+      "metadata": {},
+      "source": [
+        "### Installation\n",
+        "\n",
+        "The LangChain xAI integration lives in the `langchain-xai` package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install -qU langchain-xai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from langchain_xai import ChatXAI\n",
+        "\n",
+        "llm = ChatXAI(\n",
+        "    model=\"grok-beta\",\n",
+        "    temperature=0,\n",
+        "    max_tokens=None,\n",
+        "    timeout=None,\n",
+        "    max_retries=2,\n",
+        "    # other params...\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "## Invocation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content=\"J'adore programmer.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 30, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-adffb7a3-e48a-4f52-b694-340d85abe5c3-0', usage_metadata={'input_tokens': 30, 'output_tokens': 6, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    (\n",
+        "        \"system\",\n",
+        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "    ),\n",
+        "    (\"human\", \"I love programming.\"),\n",
+        "]\n",
+        "ai_msg = llm.invoke(messages)\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "J'adore programmer.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(ai_msg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](../../how_to/sequence.ipynb) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 25, 'total_tokens': 32, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'stop', 'logprobs': None}, id='run-569fc8dc-101b-4e6d-864e-d4fa80df2b63-0', usage_metadata={'input_tokens': 25, 'output_tokens': 7, 'total_tokens': 32, 'input_token_details': {}, 'output_token_details': {}})"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "prompt = ChatPromptTemplate.from_messages(\n",
+        "    [\n",
+        "        (\n",
+        "            \"system\",\n",
+        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "        ),\n",
+        "        (\"human\", \"{input}\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "chain = prompt | llm\n",
+        "chain.invoke(\n",
+        "    {\n",
+        "        \"input_language\": \"English\",\n",
+        "        \"output_language\": \"German\",\n",
+        "        \"input\": \"I love programming.\",\n",
+        "    }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e074bce1-0994-4b83-b393-ae7aa7e21750",
+      "metadata": {},
+      "source": [
+        "## Tool calling\n",
+        "\n",
+        "ChatXAI has a [tool calling](https://docs.x.ai/docs#capabilities) (we use \"tool calling\" and \"function calling\" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n",
+        "\n",
+        "### ChatXAI.bind_tools()\n",
+        "\n",
+        "With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n",
+        "```\n",
+        "{\n",
+        "    \"name\": \"...\",\n",
+        "    \"description\": \"...\",\n",
+        "    \"parameters\": {...}  # JSONSchema\n",
+        "}\n",
+        "```\n",
+        "and passed in every model invocation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "c6bfe929-ec02-46bd-9d54-76350edddabc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from pydantic import BaseModel, Field\n",
+        "\n",
+        "\n",
+        "class GetWeather(BaseModel):\n",
+        "    \"\"\"Get the current weather in a given location\"\"\"\n",
+        "\n",
+        "    location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
+        "\n",
+        "\n",
+        "llm_with_tools = llm.bind_tools([GetWeather])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "5265c892-d8c2-48af-aef5-adbee1647ba6",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "AIMessage(content='I am retrieving the current weather for San Francisco.', additional_kwargs={'tool_calls': [{'id': '0', 'function': {'arguments': '{\"location\":\"San Francisco, CA\"}', 'name': 'GetWeather'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 151, 'total_tokens': 162, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'grok-beta', 'system_fingerprint': 'fp_14b89b2dfc', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-73707da7-afec-4a52-bee1-a176b0ab8585-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': '0', 'type': 'tool_call'}], usage_metadata={'input_tokens': 151, 'output_tokens': 11, 'total_tokens': 162, 'input_token_details': {}, 'output_token_details': {}})"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "ai_msg = llm_with_tools.invoke(\n",
+        "    \"what is the weather like in San Francisco\",\n",
+        ")\n",
+        "ai_msg"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all `ChatXAI` features and configurations head to the API reference: https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html"
      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
    }
-   ],
-   "source": [
-    "ai_msg = llm_with_tools.invoke(\n",
-    "    \"what is the weather like in San Francisco\",\n",
-    ")\n",
-    "ai_msg"
-   ]
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.9"
+    }
  },
-  {
-   "cell_type": "markdown",
-   "id": "00297c44-9bd6-4f1f-b364-2a7ff77090fd",
-   "metadata": {},
-   "source": [
-    "## Live Search\n",
-    "\n",
-    "xAI supports a [Live Search](https://docs.x.ai/docs/guides/live-search) feature that enables Grok to ground its answers using results from web searches:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d39cd1a4-80f2-48b2-8a74-3856d7706973",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_xai import ChatXAI\n",
-    "\n",
-    "llm = ChatXAI(\n",
-    "    model=\"grok-3-latest\",\n",
-    "    search_parameters={\n",
-    "        \"mode\": \"auto\",\n",
-    "        # Example optional parameters below:\n",
-    "        \"max_search_results\": 3,\n",
-    "        \"from_date\": \"2025-05-26\",\n",
-    "        \"to_date\": \"2025-05-27\",\n",
-    "    },\n",
-    ")\n",
-    "\n",
-    "llm.invoke(\"Provide me a digest of world news in the last 24 hours.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cc62dc4d-e3ce-4b8b-8b94-d3e2e1a48bd1",
-   "metadata": {},
-   "source": [
-    "See [xAI docs](https://docs.x.ai/docs/guides/live-search) for the full set of web search options."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all `ChatXAI` features and configurations, head to the API reference: https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
--- a/docs/docs/integrations/chat/yi.ipynb
+++ b/docs/docs/integrations/chat/yi.ipynb
@@ -6,7 +6,7 @@
      "source": [
        "# ChatYI\n",
        "\n",
-        "This will help you get started with Yi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
+        "This will help you getting started with Yi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
        "\n",
        "[01.AI](https://www.lingyiwanwu.com/en), founded by Dr. Kai-Fu Lee, is a global company at the forefront of AI 2.0. They offer cutting-edge large language models, including the Yi series, which range from 6B to hundreds of billions of parameters. 01.AI also provides multimodal models, an open API platform, and open-source options like Yi-34B/9B/6B and Yi-VL.\n",
        "\n",
@@ -29,7 +29,7 @@
        "\n",
        "### Credentials\n",
        "\n",
-        "Head to [01.AI](https://platform.01.ai) to sign up for 01.AI and generate an API key. Once you've done this set the `YI_API_KEY` environment variable:"
+        "Head to [01.AI](https://platform.01.ai) to sign up to 01.AI and generate an API key. Once you've done this set the `YI_API_KEY` environment variable:"
      ]
    },
    {
@@ -197,7 +197,7 @@
      "source": [
        "## API reference\n",
        "\n",
-        "For detailed documentation of all ChatYi features and configurations, head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.yi.ChatYi.html"
+        "For detailed documentation of all ChatYi features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.yi.ChatYi.html"
      ]
    }
  ],
--- a/docs/docs/integrations/chat/zhipuai.ipynb
+++ b/docs/docs/integrations/chat/zhipuai.ipynb
@@ -56,7 +56,7 @@
   "metadata": {},
   "source": [
    "### Setting Up Your API Key\n",
-    "Sign in to [ZHIPU AI](https://open.bigmodel.cn/login?redirect=%2Fusercenter%2Fapikeys) for an API Key to access our models."
+    "Sign in to [ZHIPU AI](https://open.bigmodel.cn/login?redirect=%2Fusercenter%2Fapikeys) for the an API Key to access our models."
   ]
  },
  {
--- a/docs/docs/integrations/chat_loaders/facebook.ipynb
+++ b/docs/docs/integrations/chat_loaders/facebook.ipynb
@@ -7,7 +7,7 @@
   "source": [
    "# Facebook Messenger\n",
    "\n",
-    "This notebook shows how to load data from Facebook into a format you can fine-tune on. The overall steps are:\n",
+    "This notebook shows how to load data from Facebook in a format you can fine-tune on. The overall steps are:\n",
    "\n",
    "1. Download your messenger data to disk.\n",
    "2. Create the Chat Loader and call `loader.load()` (or `loader.lazy_load()`) to perform the conversion.\n",
@@ -25,7 +25,7 @@
    "\n",
    "## 1. Download Data\n",
    "\n",
-    "To download your own messenger data, follow the instructions [here](https://www.zapptales.com/en/download-facebook-messenger-chat-history-how-to/). IMPORTANT - make sure to download them in JSON format (not HTML).\n",
+    "To download your own messenger data, following instructions [here](https://www.zapptales.com/en/download-facebook-messenger-chat-history-how-to/). IMPORTANT - make sure to download them in JSON format (not HTML).\n",
    "\n",
    "We are hosting an example dump at [this google drive link](https://drive.google.com/file/d/1rh1s1o2i7B-Sk1v9o8KNgivLVGwJ-osV/view?usp=sharing) that we will use in this walkthrough."
   ]
--- a/docs/docs/integrations/chat_loaders/imessage.ipynb
+++ b/docs/docs/integrations/chat_loaders/imessage.ipynb
@@ -70,7 +70,7 @@
   "source": [
    "## 2. Create the Chat Loader\n",
    "\n",
-    "Provide the loader with the file path to the zip directory. You can optionally specify the user id that maps to an ai message as well as configure whether to merge message runs."
+    "Provide the loader with the file path to the zip directory. You can optionally specify the user id that maps to an ai message as well an configure whether to merge message runs."
   ]
  },
  {
--- a/docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb
+++ b/docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb
@@ -7,7 +7,7 @@
   "source": [
    "# LangSmith Chat Datasets\n",
    "\n",
-    "This notebook demonstrates an easy way to load a LangSmith chat dataset and fine-tune a model on that data.\n",
+    "This notebook demonstrates an easy way to load a LangSmith chat dataset fine-tune a model on that data.\n",
    "The process is simple and comprises 3 steps.\n",
    "\n",
    "1. Create the chat dataset.\n",
--- a/docs/docs/integrations/chat_loaders/slack.ipynb
+++ b/docs/docs/integrations/chat_loaders/slack.ipynb
@@ -16,7 +16,7 @@
    "\n",
    "## 1. Create message dump\n",
    "\n",
-    "Currently (2023/08/23), this loader best supports a zip directory of files in the format generated by exporting your a direct message conversation from Slack. Follow the up-to-date instructions from slack on how to do so.\n",
+    "Currently (2023/08/23) this loader best supports a zip directory of files in the format generated by exporting your a direct message conversation from Slack. Follow up-to-date instructions from slack on how to do so.\n",
    "\n",
    "We have an example in the LangChain repo."
   ]
@@ -43,7 +43,7 @@
   "source": [
    "## 2. Create the Chat Loader\n",
    "\n",
-    "Provide the loader with the file path to the zip directory. You can optionally specify the user id that maps to an ai message as well as configure whether to merge message runs."
+    "Provide the loader with the file path to the zip directory. You can optionally specify the user id that maps to an ai message as well an configure whether to merge message runs."
   ]
  },
  {
--- a/docs/docs/integrations/chat_loaders/telegram.ipynb
+++ b/docs/docs/integrations/chat_loaders/telegram.ipynb
@@ -10,7 +10,7 @@
    "This notebook shows how to use the Telegram chat loader. This class helps map exported Telegram conversations to LangChain chat messages.\n",
    "\n",
    "The process has three steps:\n",
-    "1. Export the chat .txt file by copying chats from the Telegram app and pasting them in a file on your local computer\n",
+    "1. Export  the chat .txt file by copying chats from the Telegram app and pasting them in a file on your local computer\n",
    "2. Create the `TelegramChatLoader` with the file path pointed to the json file or directory of JSON files\n",
    "3. Call `loader.load()` (or `loader.lazy_load()`) to perform the conversion. Optionally use `merge_chat_runs` to combine message from the same sender in sequence, and/or `map_ai_messages` to convert messages from the specified sender to the \"AIMessage\" class.\n",
    "\n",
@@ -92,7 +92,7 @@
   "source": [
    "## 2. Create the Chat Loader\n",
    "\n",
-    "All that's required is the file path. You can optionally specify the user name that maps to an ai message as well as configure whether to merge message runs."
+    "All that's required is the file path. You can optionally specify the user name that maps to an ai message as well an configure whether to merge message runs."
   ]
  },
  {
--- a/docs/docs/integrations/chat_loaders/wechat.ipynb
+++ b/docs/docs/integrations/chat_loaders/wechat.ipynb
@@ -13,7 +13,7 @@
    "\n",
    "\n",
    "The process has five steps:\n",
-    "1. Open your chat in the WeChat desktop app. Select messages you need by mouse-dragging or right-click. Due to restrictions, you can select up to 100 messages at a time. `CMD`/`Ctrl` + `C` to copy.\n",
+    "1. Open your chat in the WeChat desktop app. Select messages you need by mouse-dragging or right-click. Due to restrictions, you can select up to 100 messages once a time. `CMD`/`Ctrl` + `C` to copy.\n",
    "2. Create the chat .txt file by pasting selected messages in a file on your local computer.\n",
    "3. Copy the chat loader definition from below to a local file.\n",
    "4. Initialize the `WeChatChatLoader` with the file path pointed to the text file.\n",
--- a/docs/docs/integrations/document_loaders/airbyte_cdk.ipynb
+++ b/docs/docs/integrations/document_loaders/airbyte_cdk.ipynb
@@ -152,7 +152,7 @@
   "id": "4a93dc2a",
   "metadata": {},
   "source": [
-    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also use the `lazy_load` method which returns an iterator instead:"
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
   ]
  },
  {
@@ -170,7 +170,7 @@
   "id": "3a124086",
   "metadata": {},
   "source": [
-    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different way, pass in a record_handler function when creating the loader:"
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/airbyte_gong.ipynb
+++ b/docs/docs/integrations/document_loaders/airbyte_gong.ipynb
@@ -131,7 +131,7 @@
   "id": "4a93dc2a",
   "metadata": {},
   "source": [
-    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also use the `lazy_load` method which returns an iterator instead:"
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/airbyte_hubspot.ipynb
+++ b/docs/docs/integrations/document_loaders/airbyte_hubspot.ipynb
@@ -133,7 +133,7 @@
   "id": "4a93dc2a",
   "metadata": {},
   "source": [
-    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also use the `lazy_load` method which returns an iterator instead:"
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/airbyte_json.ipynb
+++ b/docs/docs/integrations/document_loaders/airbyte_json.ipynb
@@ -50,11 +50,11 @@
    "\n",
    "5) Setup any source you wish.\n",
    "\n",
-    "6) Set destination as Local JSON, with specified destination path - let's say `/json_data`. Set up manual sync.\n",
+    "6) Set destination as Local JSON, with specified destination path - lets say `/json_data`. Set up manual sync.\n",
    "\n",
    "7) Run the connection.\n",
    "\n",
-    "7) To see what files are created, you can navigate to: `file:///tmp/airbyte_local`\n",
+    "7) To see what files are create, you can navigate to: `file:///tmp/airbyte_local`\n",
    "\n",
    "8) Find your data and copy path. That path should be saved in the file variable below. It should start with `/tmp/airbyte_local`\n"
   ]
--- a/docs/docs/integrations/document_loaders/airbyte_salesforce.ipynb
+++ b/docs/docs/integrations/document_loaders/airbyte_salesforce.ipynb
@@ -138,7 +138,7 @@
   "id": "4a93dc2a",
   "metadata": {},
   "source": [
-    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also use the `lazy_load` method which returns an iterator instead:"
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
   ]
  },
  {
@@ -156,7 +156,7 @@
   "id": "3a124086",
   "metadata": {},
   "source": [
-    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different way, pass in a record_handler function when creating the loader:"
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/airbyte_shopify.ipynb
+++ b/docs/docs/integrations/document_loaders/airbyte_shopify.ipynb
@@ -134,7 +134,7 @@
   "id": "4a93dc2a",
   "metadata": {},
   "source": [
-    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also use the `lazy_load` method which returns an iterator instead:"
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
   ]
  },
  {
@@ -152,7 +152,7 @@
   "id": "3a124086",
   "metadata": {},
   "source": [
-    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different way, pass in a record_handler function when creating the loader:"
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/airbyte_stripe.ipynb
+++ b/docs/docs/integrations/document_loaders/airbyte_stripe.ipynb
@@ -131,7 +131,7 @@
   "id": "4a93dc2a",
   "metadata": {},
   "source": [
-    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also use the `lazy_load` method which returns an iterator instead:"
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
   ]
  },
  {
@@ -149,7 +149,7 @@
   "id": "3a124086",
   "metadata": {},
   "source": [
-    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different way, pass in a record_handler function when creating the loader:"
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/airbyte_typeform.ipynb
+++ b/docs/docs/integrations/document_loaders/airbyte_typeform.ipynb
@@ -134,7 +134,7 @@
   "id": "4a93dc2a",
   "metadata": {},
   "source": [
-    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also use the `lazy_load` method which returns an iterator instead:"
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
   ]
  },
  {
@@ -152,7 +152,7 @@
   "id": "3a124086",
   "metadata": {},
   "source": [
-    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different way, pass in a record_handler function when creating the loader:"
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/airbyte_zendesk_support.ipynb
+++ b/docs/docs/integrations/document_loaders/airbyte_zendesk_support.ipynb
@@ -135,7 +135,7 @@
   "id": "4a93dc2a",
   "metadata": {},
   "source": [
-    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also use the `lazy_load` method which returns an iterator instead:"
+    "As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
   ]
  },
  {
@@ -153,7 +153,7 @@
   "id": "3a124086",
   "metadata": {},
   "source": [
-    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different way, pass in a record_handler function when creating the loader:"
+    "Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/alibaba_cloud_maxcompute.ipynb
+++ b/docs/docs/integrations/document_loaders/alibaba_cloud_maxcompute.ipynb
@@ -46,7 +46,7 @@
   "metadata": {},
   "source": [
    "## Basic Usage\n",
-    "To instantiate the loader you'll need a SQL query to execute, your MaxCompute endpoint and project name, and your access ID and secret access key. The access ID and secret access key can either be passed in direct via the `access_id` and `secret_access_key` parameters or they can be set as environment variables `MAX_COMPUTE_ACCESS_ID` and `MAX_COMPUTE_SECRET_ACCESS_KEY`."
+    "To instantiate the loader you'll need a SQL query to execute, your MaxCompute endpoint and project name, and you access ID and secret access key. The access ID and secret access key can either be passed in direct via the `access_id` and `secret_access_key` parameters or they can be set as environment variables `MAX_COMPUTE_ACCESS_ID` and `MAX_COMPUTE_SECRET_ACCESS_KEY`."
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/amazon_textract.ipynb
+++ b/docs/docs/integrations/document_loaders/amazon_textract.ipynb
@@ -45,7 +45,7 @@
   "source": [
    "## Sample 1\n",
    "\n",
-    "The first example uses a local file, which internally will be sent to Amazon Textract sync API [DetectDocumentText](https://docs.aws.amazon.com/textract/latest/dg/API_DetectDocumentText.html). \n",
+    "The first example uses a local file, which internally will be send to Amazon Textract sync API [DetectDocumentText](https://docs.aws.amazon.com/textract/latest/dg/API_DetectDocumentText.html). \n",
    "\n",
    "Local files or URL endpoints like HTTP:// are limited to one page documents for Textract.\n",
    "Multi-page documents have to reside on S3. This sample file is a jpeg."
@@ -218,7 +218,7 @@
   "source": [
    "## Sample 4\n",
    "\n",
-    "You have the option to pass an additional parameter called `linearization_config` to the AmazonTextractPDFLoader which will determine how the text output will be linearized by the parser after Textract runs."
+    "You have the option to pass an additional parameter called `linearization_config` to the AmazonTextractPDFLoader which will determine how the the text output will be linearized by the parser after Textract runs."
   ]
  },
  {
@@ -247,7 +247,7 @@
   "id": "b3e41b4d-b159-4274-89be-80d8159134ef",
   "metadata": {},
   "source": [
-    "## Using the AmazonTextractPDFLoader in a LangChain chain (e.g. OpenAI)\n",
+    "## Using the AmazonTextractPDFLoader in an LangChain chain (e. g. OpenAI)\n",
    "\n",
    "The AmazonTextractPDFLoader can be used in a chain the same way the other loaders are used.\n",
    "Textract itself does have a [Query feature](https://docs.aws.amazon.com/textract/latest/dg/API_Query.html), which offers similar functionality to the QA chain in this sample, which is worth checking out as well."
--- a/docs/docs/integrations/document_loaders/async_chromium.ipynb
+++ b/docs/docs/integrations/document_loaders/async_chromium.ipynb
@@ -13,7 +13,7 @@
    "\n",
    "Headless mode means that the browser is running without a graphical user interface.\n",
    "\n",
-    "In the below example we'll use the `AsyncChromiumLoader` to load the page, and then the [`Html2TextTransformer`](/docs/integrations/document_transformers/html2text/) to strip out the HTML tags and other semantic information."
+    "In the below example we'll use the `AsyncChromiumLoader` to loads the page, and then the [`Html2TextTransformer`](/docs/integrations/document_transformers/html2text/) to strip out the HTML tags and other semantic information."
   ]
  },
  {
@@ -79,7 +79,7 @@
   "id": "7eb5e6aa",
   "metadata": {},
   "source": [
-    "Now let's transform the documents into a more readable format using the transformer:"
+    "Now let's transform the documents into a more readable syntax using the transformer:"
   ]
  },
  {
--- a/docs/docs/integrations/document_loaders/async_html.ipynb
+++ b/docs/docs/integrations/document_loaders/async_html.ipynb
@@ -40,7 +40,7 @@
    "# If you need to use the proxy to make web requests, for example using http_proxy/https_proxy environmental variables,\n",
    "# please set trust_env=True explicitly here as follows:\n",
    "# loader = AsyncHtmlLoader(urls, trust_env=True)\n",
-    "# Otherwise, loader.load() may get stuck because aiohttp session does not recognize the proxy by default\n",
+    "# Otherwise, loader.load() may stuck becuase aiohttp session does not recognize the proxy by default\n",
    "docs = loader.load()"
   ]
  },
--- a/docs/docs/integrations/document_loaders/aws_s3_directory.ipynb
+++ b/docs/docs/integrations/document_loaders/aws_s3_directory.ipynb
@@ -68,7 +68,7 @@
   "metadata": {},
   "source": [
    "## Specifying a prefix\n",
-    "You can also specify a prefix for more fine-grained control over what files to load."
+    "You can also specify a prefix for more finegrained control over what files to load."
   ]
  },
  {
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Sydney Runkle	8bd7ea5a5e	linting	2025-05-14 08:16:07 -07:00
Sydney Runkle	b9c2031795	Merge branch 'sr/remove-unused-validators' of https://github.com/langchain-ai/langchain into sr/remove-unused-validators	2025-05-14 08:14:39 -07:00
Sydney Runkle	94a637f4a8	Merge branch 'master' into sr/remove-unused-validators	2025-05-14 08:14:35 -07:00
Sydney Runkle	d8bb6b24c4	remove another id -> str custom validator	2025-05-14 08:08:37 -07:00
Sydney Runkle	f1e9bf9d85	removing id -> str costly field validator	2025-05-14 07:55:43 -07:00