core: release 0.3.63 (#31419)

Release core 0.3.63

Small update just to expand the list of well known tools. This is
necessary while the logic lives in langchain-core.

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>

.github/dependabot.yml

@@ -0,0 +1,11 @@
+# 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"

.github/scripts/check_diff.py

@@ -37,7 +37,6 @@ IGNORED_PARTNERS = [
 ]
 
 PY_312_MAX_PACKAGES = [
-    "libs/partners/voyageai",
     "libs/partners/chroma", # https://github.com/chroma-core/chroma/issues/4382
 ]
 
@@ -120,7 +119,9 @@ 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 dir_ == "libs/core":
+    if job == "codspeed":
+        py_versions = ["3.12"]  # 3.13 is not yet supported
+    elif 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":
@@ -211,6 +212,8 @@ 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}")
 
@@ -226,6 +229,7 @@ if __name__ == "__main__":
         "lint": set(),
         "test": set(),
         "extended-test": set(),
+        "codspeed": set(),
     }
     docs_edited = False
 
@@ -249,6 +253,8 @@ 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
@@ -287,6 +293,7 @@ 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
@@ -312,6 +319,7 @@ if __name__ == "__main__":
             "compile-integration-tests",
             "dependencies",
             "test-pydantic",
+            "codspeed",
         ]
     }
     map_job_to_configs["test-doc-imports"] = (

.github/workflows/_integration_test.yml

@@ -41,6 +41,8 @@ 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 }}
@@ -67,7 +69,6 @@ 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 }}

.github/workflows/_release.yml

@@ -322,7 +322,6 @@ 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 }}
@@ -345,6 +344,8 @@ 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 }}

.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@v3
+        uses: actions/setup-node@v4
         with:
           node-version: 18.x
           cache: "yarn"

.github/workflows/check_diffs.yml

@@ -29,7 +29,7 @@ jobs:
         with:
           python-version: '3.11'
       - id: files
-        uses: Ana06/get-changed-files@v2.2.0
+        uses: Ana06/get-changed-files@v2.3.0
       - id: set-matrix
         run: |
           python -m pip install packaging requests
@@ -152,6 +152,7 @@ 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]

.github/workflows/check_new_docs.yml

@@ -24,7 +24,7 @@ jobs:
         with:
           python-version: '3.10'
       - id: files
-        uses: Ana06/get-changed-files@v2.2.0
+        uses: Ana06/get-changed-files@v2.3.0
         with:
           filter: |
             *.ipynb

.github/workflows/codspeed.yml

@@ -5,40 +5,61 @@ 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_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
+  AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
+  AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
+  AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
+  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, 3.13 is not yet supported
+      # We have to use 3.12 as 3.13 is not yet supported
       - name: Install uv
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: "3.12"
 
-      # Using this action is still necessary for CodSpeed to work
-      - uses: actions/setup-python@v3
+      - uses: actions/setup-python@v5
         with:
           python-version: "3.12"
 
-      - name: install deps
+      - name: Install dependencies
         run: uv sync --group test
-        working-directory: ./libs/core
+        working-directory: ${{ matrix.working-directory }}
 
-      - name: Run benchmarks
+      - name: Run benchmarks ${{ matrix.working-directory }}
         uses: CodSpeedHQ/action@v3
         with:
           token: ${{ secrets.CODSPEED_TOKEN }}
           run: |
-            cd libs/core
-            uv run --no-sync pytest ./tests/benchmarks --codspeed
-          mode: walltime
+            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' }}
+

.github/workflows/scheduled_test.yml

@@ -127,6 +127,8 @@ 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 }}

.pre-commit-config.yaml

@@ -103,12 +103,6 @@ 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

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

docs/api_reference/create_api_rst.py

@@ -663,6 +663,7 @@ 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_

docs/cassettes/response_metadata_161f5898-9976-4a75-943d-03eda1a40a60.msgpack.zlib

@@ -1 +1 @@
-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

docs/cassettes/response_metadata_1e4ac668-4c6a-48ad-9a6f-7b291477b45d.msgpack.zlib

@@ -1 +1 @@
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docs/cassettes/response_metadata_39549336-25f5-4839-9846-f687cd77e59b.msgpack.zlib

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docs/cassettes/response_metadata_61c43496-83b5-4d71-bd60-3e6d46c62a5e.msgpack.zlib

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docs/cassettes/response_metadata_6ae32a93-26db-41bb-95c2-38ddd5085fbe.msgpack.zlib

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docs/cassettes/response_metadata_744e14ec-ff50-4642-9893-ff7bdf8927ff.msgpack.zlib

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docs/cassettes/response_metadata_deb41321-52d0-4795-a40c-4a811a13d7b0.msgpack.zlib

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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 | 2022‑05‑25 | `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 | 2022‑03‑15 | `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. | 2022‑02‑01 | `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. | 2021‑12‑02 | `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)
+| `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. | 2021‑12‑02 | `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)
 | `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. | 2021‑02‑26 | `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. | 2020‑05‑28 | `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. | 2020‑05‑22 | `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), [docs/integrations/providers/dspy](https://python.langchain.com/docs/integrations/providers/dspy)
+   - **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)
 
 **Abstract:** Neural information retrieval (IR) has greatly advanced search and other
 knowledge-intensive language tasks. While many neural IR methods encode queries

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:
 

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 asynch
+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.
 :::
 
 ## Langchain asynchronous APIs

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 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 a list of handler objects, which are expected to implement one or more of the methods described below in more detail.
 
 ## Callback events
 

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 is for LangChain components. 
+A LangChain retriever is a [runnable](/docs/how_to/lcel_cheatsheet/), which is a standard interface 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

docs/docs/concepts/text_splitters.mdx

@@ -3,8 +3,8 @@
 
 :::info[Prerequisites]
 
-* [Documents](/docs/concepts/retrievers/#interface)
-* Tokenization(/docs/concepts/tokens)
+* [Documents](./retrievers.mdx)
+* [Tokenization](./tokens.mdx)
 :::
 
 ## Overview

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 documents:\n",
+    "        for document in self.documents:\n",
     "            if len(matching_documents) > self.k:\n",
     "                return matching_documents\n",
     "\n",

docs/docs/how_to/document_loader_custom.ipynb

@@ -67,9 +67,34 @@
     "When implementing a document loader do **NOT** provide parameters via the `lazy_load` or `alazy_load` methods.\n",
     "\n",
     "All configuration is expected to be passed through the initializer (__init__). This was a design choice made by LangChain to make sure that once a document loader has been instantiated it has all the information needed to load documents.\n",
-    ":::\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "520edbbabde7df6e",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
     "\n",
+    "Install **langchain-core** and **langchain_community**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "936bd5fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain_core langchain_community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a93f17a87d323bdd",
+   "metadata": {},
+   "source": [
     "### Implementation\n",
     "\n",
     "Let's create an example of a standard document loader that loads a file and creates a document from each line in the file."
@@ -77,9 +102,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "20f128c1-1a2c-43b9-9e7b-cf9b3a86d1db",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:56.764714Z",
+     "start_time": "2025-04-21T08:49:56.623508Z"
+    },
     "tags": []
    },
    "outputs": [],
@@ -122,7 +151,7 @@
     "        self,\n",
     "    ) -> AsyncIterator[Document]:  # <-- Does not take any arguments\n",
     "        \"\"\"An async lazy loader that reads a file line by line.\"\"\"\n",
-    "        # Requires aiofiles (install with pip)\n",
+    "        # Requires aiofiles\n",
     "        # https://github.com/Tinche/aiofiles\n",
     "        import aiofiles\n",
     "\n",
@@ -151,9 +180,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "b1751198-c6dd-4149-95bd-6370ce8fa06f",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:56.776521Z",
+     "start_time": "2025-04-21T08:49:56.773511Z"
+    },
     "tags": []
    },
    "outputs": [],
@@ -167,9 +200,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "id": "c5210428",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q aiofiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "71ef1482-f9de-4852-b5a4-0938f350612e",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:57.972675Z",
+     "start_time": "2025-04-21T08:49:57.969411Z"
+    },
     "tags": []
    },
    "outputs": [
@@ -179,10 +226,12 @@
      "text": [
       "\n",
       "<class 'langchain_core.documents.base.Document'>\n",
-      "page_content='meow meow🐱 \\n' metadata={'line_number': 0, 'source': './meow.txt'}\n",
+      "page_content='meow meow🐱 \n",
+      "' metadata={'line_number': 0, 'source': './meow.txt'}\n",
       "\n",
       "<class 'langchain_core.documents.base.Document'>\n",
-      "page_content=' meow meow🐱 \\n' metadata={'line_number': 1, 'source': './meow.txt'}\n",
+      "page_content=' meow meow🐱 \n",
+      "' metadata={'line_number': 1, 'source': './meow.txt'}\n",
       "\n",
       "<class 'langchain_core.documents.base.Document'>\n",
       "page_content=' meow😻😻' metadata={'line_number': 2, 'source': './meow.txt'}\n"
@@ -199,9 +248,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "1588e78c-e81a-4d40-b36c-634242c84a6a",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.028989Z",
+     "start_time": "2025-04-21T08:49:58.021972Z"
+    },
     "tags": []
    },
    "outputs": [
@@ -211,10 +264,12 @@
      "text": [
       "\n",
       "<class 'langchain_core.documents.base.Document'>\n",
-      "page_content='meow meow🐱 \\n' metadata={'line_number': 0, 'source': './meow.txt'}\n",
+      "page_content='meow meow🐱 \n",
+      "' metadata={'line_number': 0, 'source': './meow.txt'}\n",
       "\n",
       "<class 'langchain_core.documents.base.Document'>\n",
-      "page_content=' meow meow🐱 \\n' metadata={'line_number': 1, 'source': './meow.txt'}\n",
+      "page_content=' meow meow🐱 \n",
+      "' metadata={'line_number': 1, 'source': './meow.txt'}\n",
       "\n",
       "<class 'langchain_core.documents.base.Document'>\n",
       "page_content=' meow😻😻' metadata={'line_number': 2, 'source': './meow.txt'}\n"
@@ -245,21 +300,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "df5ad46a-9e00-4073-8505-489fc4f3799e",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.078111Z",
+     "start_time": "2025-04-21T08:49:58.071421Z"
+    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='meow meow🐱 \\n', metadata={'line_number': 0, 'source': './meow.txt'}),\n",
-       " Document(page_content=' meow meow🐱 \\n', metadata={'line_number': 1, 'source': './meow.txt'}),\n",
-       " Document(page_content=' meow😻😻', metadata={'line_number': 2, 'source': './meow.txt'})]"
+       "[Document(metadata={'line_number': 0, 'source': './meow.txt'}, page_content='meow meow🐱 \\n'),\n",
+       " Document(metadata={'line_number': 1, 'source': './meow.txt'}, page_content=' meow meow🐱 \\n'),\n",
+       " Document(metadata={'line_number': 2, 'source': './meow.txt'}, page_content=' meow😻😻')]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -286,9 +345,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "209f6a91-2f15-4cb2-9237-f79fc9493b82",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.124363Z",
+     "start_time": "2025-04-21T08:49:58.120782Z"
+    },
     "tags": []
    },
    "outputs": [],
@@ -313,9 +376,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "b1275c59-06d4-458f-abd2-fcbad0bde442",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.172506Z",
+     "start_time": "2025-04-21T08:49:58.167416Z"
+    },
     "tags": []
    },
    "outputs": [],
@@ -326,21 +393,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "56a3d707-2086-413b-ae82-50e92ddb27f6",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.218426Z",
+     "start_time": "2025-04-21T08:49:58.214684Z"
+    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='meow meow🐱 \\n', metadata={'line_number': 1, 'source': './meow.txt'}),\n",
-       " Document(page_content=' meow meow🐱 \\n', metadata={'line_number': 2, 'source': './meow.txt'}),\n",
-       " Document(page_content=' meow😻😻', metadata={'line_number': 3, 'source': './meow.txt'})]"
+       "[Document(metadata={'line_number': 1, 'source': './meow.txt'}, page_content='meow meow🐱 \\n'),\n",
+       " Document(metadata={'line_number': 2, 'source': './meow.txt'}, page_content=' meow meow🐱 \\n'),\n",
+       " Document(metadata={'line_number': 3, 'source': './meow.txt'}, page_content=' meow😻😻')]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -359,20 +430,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "20d03092-ba35-47d7-b612-9d1631c261cd",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.267755Z",
+     "start_time": "2025-04-21T08:49:58.264369Z"
+    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='some data from memory\\n', metadata={'line_number': 1, 'source': None}),\n",
-       " Document(page_content='meow', metadata={'line_number': 2, 'source': None})]"
+       "[Document(metadata={'line_number': 1, 'source': None}, page_content='some data from memory\\n'),\n",
+       " Document(metadata={'line_number': 2, 'source': None}, page_content='meow')]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -394,9 +469,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "a9e92e0e-c8da-401c-b8c6-f0676004cf58",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.330432Z",
+     "start_time": "2025-04-21T08:49:58.327223Z"
+    },
     "tags": []
    },
    "outputs": [],
@@ -406,9 +485,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "6b559d30-8b0c-4e45-86b1-e4602d9aaa7e",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.383905Z",
+     "start_time": "2025-04-21T08:49:58.380658Z"
+    },
     "tags": []
    },
    "outputs": [
@@ -418,7 +501,7 @@
        "'utf-8'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -429,9 +512,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "id": "2f7b145a-9c6f-47f9-9487-1f4b25aff46f",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.443829Z",
+     "start_time": "2025-04-21T08:49:58.440222Z"
+    },
     "tags": []
    },
    "outputs": [
@@ -441,7 +528,7 @@
        "b'meow meow\\xf0\\x9f\\x90\\xb1 \\n meow meow\\xf0\\x9f\\x90\\xb1 \\n meow\\xf0\\x9f\\x98\\xbb\\xf0\\x9f\\x98\\xbb'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -452,9 +539,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "id": "9b9482fa-c49c-42cd-a2ef-80bc93214631",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.498609Z",
+     "start_time": "2025-04-21T08:49:58.494903Z"
+    },
     "tags": []
    },
    "outputs": [
@@ -464,7 +555,7 @@
        "'meow meow🐱 \\n meow meow🐱 \\n meow😻😻'"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -475,19 +566,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "id": "04cc7a81-290e-4ef8-b7e1-d885fcc59ece",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.551353Z",
+     "start_time": "2025-04-21T08:49:58.547518Z"
+    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<contextlib._GeneratorContextManager at 0x743f34324450>"
+       "<contextlib._GeneratorContextManager at 0x74b8d42e9940>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -498,9 +593,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "ec8de0ab-51d7-4e41-82c9-3ce0a6fdc2cd",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.599576Z",
+     "start_time": "2025-04-21T08:49:58.596567Z"
+    },
     "tags": []
    },
    "outputs": [
@@ -510,7 +609,7 @@
        "{'foo': 'bar'}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,9 +620,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "id": "19eae991-ae48-43c2-8952-7347cdb76a34",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.649634Z",
+     "start_time": "2025-04-21T08:49:58.646313Z"
+    },
     "tags": []
    },
    "outputs": [
@@ -533,7 +636,7 @@
        "'./meow.txt'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -551,65 +654,50 @@
     "\n",
     "While a parser encapsulates the logic needed to parse binary data into documents, *blob loaders* encapsulate the logic that's necessary to load blobs from a given storage location.\n",
     "\n",
-    "At the moment, `LangChain` only supports `FileSystemBlobLoader`.\n",
+    "At the moment, `LangChain` supports `FileSystemBlobLoader` and `CloudBlobLoader`.\n",
     "\n",
     "You can use the `FileSystemBlobLoader` to load blobs and then use the parser to parse them."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "id": "c093becb-2e84-4329-89e3-956a3bd765e5",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:49:58.718259Z",
+     "start_time": "2025-04-21T08:49:58.705367Z"
+    },
     "tags": []
    },
    "outputs": [],
    "source": [
     "from langchain_community.document_loaders.blob_loaders import FileSystemBlobLoader\n",
     "\n",
-    "blob_loader = FileSystemBlobLoader(path=\".\", glob=\"*.mdx\", show_progress=True)"
+    "filesystem_blob_loader = FileSystemBlobLoader(\n",
+    "    path=\".\", glob=\"*.mdx\", show_progress=True\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "77739dab-2a1e-4b64-8daa-fee8aa029972",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45e85d3f63224bb59db02a40ae2e3268",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/8 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "page_content='# Microsoft Office\\n' metadata={'line_number': 1, 'source': 'office_file.mdx'}\n",
-      "page_content='# Markdown\\n' metadata={'line_number': 1, 'source': 'markdown.mdx'}\n",
-      "page_content='# JSON\\n' metadata={'line_number': 1, 'source': 'json.mdx'}\n",
-      "page_content='---\\n' metadata={'line_number': 1, 'source': 'pdf.mdx'}\n",
-      "page_content='---\\n' metadata={'line_number': 1, 'source': 'index.mdx'}\n",
-      "page_content='# File Directory\\n' metadata={'line_number': 1, 'source': 'file_directory.mdx'}\n",
-      "page_content='# CSV\\n' metadata={'line_number': 1, 'source': 'csv.mdx'}\n",
-      "page_content='# HTML\\n' metadata={'line_number': 1, 'source': 'html.mdx'}\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "21b91bad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40be670b",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "parser = MyParser()\n",
-    "for blob in blob_loader.yield_blobs():\n",
+    "for blob in filesystem_blob_loader.yield_blobs():\n",
     "    for doc in parser.lazy_parse(blob):\n",
     "        print(doc)\n",
     "        break"
@@ -620,56 +708,104 @@
    "id": "f016390c-d38b-4261-946d-34eefe546df7",
    "metadata": {},
    "source": [
-    "### Generic Loader\n",
-    "\n",
-    "LangChain has a `GenericLoader` abstraction which composes a `BlobLoader` with a `BaseBlobParser`.\n",
-    "\n",
-    "`GenericLoader` is meant to provide standardized classmethods that make it easy to use existing `BlobLoader` implementations. At the moment, only the `FileSystemBlobLoader` is supported."
+    "Or, you can use `CloudBlobLoader` to load blobs from a cloud storage location (Supports s3://, az://, gs://, file:// schemes)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "1de74daf-70ee-4616-9089-d28e26b16851",
+   "execution_count": null,
+   "id": "8210714e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -q 'cloudpathlib[s3]'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3f84501-b0aa-4a60-aad2-5109cbd37d4f",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "from cloudpathlib import S3Client, S3Path\n",
+    "from langchain_community.document_loaders.blob_loaders import CloudBlobLoader\n",
+    "\n",
+    "client = S3Client(no_sign_request=True)\n",
+    "client.set_as_default_client()\n",
+    "\n",
+    "path = S3Path(\n",
+    "    \"s3://bucket-01\", client=client\n",
+    ")  # Supports s3://, az://, gs://, file:// schemes.\n",
+    "\n",
+    "cloud_loader = CloudBlobLoader(path, glob=\"**/*.pdf\", show_progress=True)\n",
+    "\n",
+    "for blob in cloud_loader.yield_blobs():\n",
+    "    print(blob)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40c361ba4cd30164",
+   "metadata": {},
+   "source": [
+    "### Generic Loader\n",
+    "\n",
+    "LangChain has a `GenericLoader` abstraction which composes a `BlobLoader` with a `BaseBlobParser`.\n",
+    "\n",
+    "`GenericLoader` is meant to provide standardized classmethods that make it easy to use existing `BlobLoader` implementations. At the moment, the `FileSystemBlobLoader` and `CloudBlobLoader` are supported. See example below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "5dfb2be02fe662c5",
    "metadata": {
-    "tags": []
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:50:16.244917Z",
+     "start_time": "2025-04-21T08:50:15.527562Z"
+    }
    },
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5f1f6810a71a4909ac9fe1e8f8cb9e0a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/8 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 7/7 [00:00<00:00, 1224.82it/s]"
+     ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "page_content='# Microsoft Office\\n' metadata={'line_number': 1, 'source': 'office_file.mdx'}\n",
-      "page_content='\\n' metadata={'line_number': 2, 'source': 'office_file.mdx'}\n",
-      "page_content='>[The Microsoft Office](https://www.office.com/) suite of productivity software includes Microsoft Word, Microsoft Excel, Microsoft PowerPoint, Microsoft Outlook, and Microsoft OneNote. It is available for Microsoft Windows and macOS operating systems. It is also available on Android and iOS.\\n' metadata={'line_number': 3, 'source': 'office_file.mdx'}\n",
-      "page_content='\\n' metadata={'line_number': 4, 'source': 'office_file.mdx'}\n",
-      "page_content='This covers how to load commonly used file formats including `DOCX`, `XLSX` and `PPTX` documents into a document format that we can use downstream.\\n' metadata={'line_number': 5, 'source': 'office_file.mdx'}\n",
+      "page_content='# Text embedding models\n",
+      "' metadata={'line_number': 1, 'source': 'embed_text.mdx'}\n",
+      "page_content='\n",
+      "' metadata={'line_number': 2, 'source': 'embed_text.mdx'}\n",
+      "page_content=':::info\n",
+      "' metadata={'line_number': 3, 'source': 'embed_text.mdx'}\n",
+      "page_content='Head to [Integrations](/docs/integrations/text_embedding/) for documentation on built-in integrations with text embedding model providers.\n",
+      "' metadata={'line_number': 4, 'source': 'embed_text.mdx'}\n",
+      "page_content=':::\n",
+      "' metadata={'line_number': 5, 'source': 'embed_text.mdx'}\n",
       "... output truncated for demo purposes\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     }
    ],
    "source": [
     "from langchain_community.document_loaders.generic import GenericLoader\n",
     "\n",
-    "loader = GenericLoader.from_filesystem(\n",
-    "    path=\".\", glob=\"*.mdx\", show_progress=True, parser=MyParser()\n",
+    "generic_loader_filesystem = GenericLoader(\n",
+    "    blob_loader=filesystem_blob_loader, blob_parser=parser\n",
     ")\n",
-    "\n",
-    "for idx, doc in enumerate(loader.lazy_load()):\n",
+    "for idx, doc in enumerate(generic_loader_filesystem.lazy_load()):\n",
     "    if idx < 5:\n",
     "        print(doc)\n",
     "\n",
@@ -690,9 +826,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 28,
    "id": "23633102-dc44-4fed-a4e1-8159489101c8",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:50:34.841862Z",
+     "start_time": "2025-04-21T08:50:34.838375Z"
+    },
     "tags": []
    },
    "outputs": [],
@@ -709,37 +849,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 29,
    "id": "dc95be85-4a29-4c6f-a260-08afa3c95538",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-21T08:50:34.901734Z",
+     "start_time": "2025-04-21T08:50:34.888098Z"
+    },
     "tags": []
    },
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4320598ea3b44a52b1873e1c801db312",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/8 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 7/7 [00:00<00:00, 814.86it/s]"
+     ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "page_content='# Microsoft Office\\n' metadata={'line_number': 1, 'source': 'office_file.mdx'}\n",
-      "page_content='\\n' metadata={'line_number': 2, 'source': 'office_file.mdx'}\n",
-      "page_content='>[The Microsoft Office](https://www.office.com/) suite of productivity software includes Microsoft Word, Microsoft Excel, Microsoft PowerPoint, Microsoft Outlook, and Microsoft OneNote. It is available for Microsoft Windows and macOS operating systems. It is also available on Android and iOS.\\n' metadata={'line_number': 3, 'source': 'office_file.mdx'}\n",
-      "page_content='\\n' metadata={'line_number': 4, 'source': 'office_file.mdx'}\n",
-      "page_content='This covers how to load commonly used file formats including `DOCX`, `XLSX` and `PPTX` documents into a document format that we can use downstream.\\n' metadata={'line_number': 5, 'source': 'office_file.mdx'}\n",
+      "page_content='# Text embedding models\n",
+      "' metadata={'line_number': 1, 'source': 'embed_text.mdx'}\n",
+      "page_content='\n",
+      "' metadata={'line_number': 2, 'source': 'embed_text.mdx'}\n",
+      "page_content=':::info\n",
+      "' metadata={'line_number': 3, 'source': 'embed_text.mdx'}\n",
+      "page_content='Head to [Integrations](/docs/integrations/text_embedding/) for documentation on built-in integrations with text embedding model providers.\n",
+      "' metadata={'line_number': 4, 'source': 'embed_text.mdx'}\n",
+      "page_content=':::\n",
+      "' metadata={'line_number': 5, 'source': 'embed_text.mdx'}\n",
       "... output truncated for demo purposes\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -769,7 +918,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

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/blob/master/libs/community/tests/integration_tests/examples/layout-parser-paper.pdf):"
+    "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):"
    ]
   },
   {

docs/docs/how_to/response_metadata.ipynb

@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "39549336-25f5-4839-9846-f687cd77e59b",
    "metadata": {},
    "outputs": [
@@ -110,43 +110,20 @@
      "data": {
       "text/plain": [
        "{'is_blocked': False,\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",
+       " 'safety_ratings': [],\n",
        " 'usage_metadata': {'prompt_token_count': 10,\n",
-       "  'candidates_token_count': 193,\n",
-       "  'total_token_count': 203,\n",
+       "  'candidates_token_count': 55,\n",
+       "  'total_token_count': 65,\n",
        "  'prompt_tokens_details': [{'modality': 1, 'token_count': 10}],\n",
-       "  'candidates_tokens_details': [{'modality': 1, 'token_count': 193}],\n",
+       "  'candidates_tokens_details': [{'modality': 1, 'token_count': 55}],\n",
        "  'cached_content_token_count': 0,\n",
        "  'cache_tokens_details': []},\n",
        " 'finish_reason': 'STOP',\n",
-       " 'avg_logprobs': -0.5702065976790196,\n",
-       " 'model_name': 'gemini-1.5-flash-001'}"
+       " 'avg_logprobs': -0.251378042047674,\n",
+       " 'model_name': 'gemini-2.0-flash-001'}"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -154,7 +131,7 @@
    "source": [
     "from langchain_google_vertexai import ChatVertexAI\n",
     "\n",
-    "llm = ChatVertexAI(model=\"gemini-1.5-flash-001\")\n",
+    "llm = ChatVertexAI(model=\"gemini-2.0-flash-001\")\n",
     "msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n",
     "msg.response_metadata"
    ]

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 Morisette songs\"})"
+    "table_chain.invoke({\"input\": \"What are all the genres of Alanis Morissette 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 Morisette songs\"})"
+    "category_chain.invoke({\"input\": \"What are all the genres of Alanis Morissette 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 Morisette songs\"})"
+    "table_chain.invoke({\"input\": \"What are all the genres of Alanis Morissette songs\"})"
    ]
   },
   {
@@ -313,7 +313,7 @@
    ],
    "source": [
     "query = full_chain.invoke(\n",
-    "    {\"question\": \"What are all the genres of Alanis Morisette songs\"}\n",
+    "    {\"question\": \"What are all the genres of Alanis Morissette 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/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/sql_qa/#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/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/sql_qa/#agents) guide."
    ]
   }
  ],

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 variabled called `PROMPTLAYER_API_KEY`\n"
+    "set it as an environment variable called `PROMPTLAYER_API_KEY`\n"
    ]
   },
   {

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 retievers from Langchain are highlighted for demonstration:\n",
+    "Selected retrievers 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",

docs/docs/integrations/chat/abso.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "# ChatAbso\n",
     "\n",
-    "This will help you getting started with ChatAbso [chat models](https://python.langchain.com/docs/concepts/chat_models/). For detailed documentation of all ChatAbso features and configurations head to the [API reference](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_abso.chat_models.ChatAbso.html).\n",
+    "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",
     "\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 to 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 for 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": "3.11.9"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/ai21.ipynb

@@ -17,8 +17,6 @@
       "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",
@@ -68,7 +66,9 @@
       "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",
@@ -198,13 +198,17 @@
       "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",

docs/docs/integrations/chat/anthropic.ipynb

@@ -107,7 +107,7 @@
    "id": "fe4993ad-4a9b-4021-8ebd-f0fbbc739f49",
    "metadata": {},
    "source": [
-    ":::info This guide requires ``langchain-anthropic>=0.3.10``\n",
+    ":::info This guide requires ``langchain-anthropic>=0.3.13``\n",
     "\n",
     ":::"
    ]
@@ -325,6 +325,102 @@
     "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",
@@ -454,6 +550,27 @@
     "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",
@@ -926,6 +1043,16 @@
     "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,
@@ -943,6 +1070,159 @@
     "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",

docs/docs/integrations/chat/azure_ai.ipynb

@@ -17,9 +17,9 @@
    "source": [
     "# AzureAIChatCompletionsModel\n",
     "\n",
-    "This will help you getting started with AzureAIChatCompletionsModel [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureAIChatCompletionsModel features and configurations head to the [API reference](https://python.langchain.com/api_reference/azure_ai/chat_models/langchain_azure_ai.chat_models.AzureAIChatCompletionsModel.html)\n",
+    "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",
     "\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",
+    "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",
     "\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": "langchain-3-9",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -267,7 +267,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/cloudflare_workersai.ipynb

@@ -18,7 +18,7 @@
     "# ChatCloudflareWorkersAI\n",
     "\n",
     "\n",
-    "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCloudflareWorkersAI features and configurations head to the [API reference](https://python.langchain.com/docs/integrations/chat/cloudflare_workersai/).\n",
+    "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",
     "\n",
     "\n",
     "## Overview\n",
@@ -41,7 +41,7 @@
     "### Credentials\n",
     "\n",
     "\n",
-    "Head to https://www.cloudflare.com/developer-platform/products/workers-ai/ to sign up to CloudflareWorkersAI and generate an API key. Once you've done this set the CF_API_KEY environment variable and the CF_ACCOUNT_ID environment variable:"
+    "Head to https://www.cloudflare.com/developer-platform/products/workers-ai/ to sign up to CloudflareWorkersAI and generate an API key. Once you've done this set the CF_AI_API_KEY environment variable and the CF_ACCOUNT_ID environment variable:"
    ]
   },
   {
@@ -56,8 +56,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "if not os.getenv(\"CF_API_KEY\"):\n",
-    "    os.environ[\"CF_API_KEY\"] = getpass.getpass(\n",
+    "if not os.getenv(\"CF_AI_API_KEY\"):\n",
+    "    os.environ[\"CF_AI_API_KEY\"] = getpass.getpass(\n",
     "        \"Enter your CloudflareWorkersAI API key: \"\n",
     "    )\n",
     "\n",

docs/docs/integrations/chat/contextual.ipynb

@@ -21,7 +21,7 @@
    "source": [
     "# ChatContextual\n",
     "\n",
-    "This will help you getting started with Contextual AI's Grounded Language Model [chat models](/docs/concepts/chat_models/).\n",
+    "This will help you get 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",

docs/docs/integrations/chat/deepseek.ipynb

@@ -18,7 +18,7 @@
         "# ChatDeepSeek\n",
         "\n",
         "\n",
-        "This will help you getting started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
+        "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",
         "\n",
         ":::tip\n",
         "\n",

docs/docs/integrations/chat/ernie.ipynb

@@ -25,17 +25,16 @@
    "source": [
     "**Deprecated Warning**\n",
     "\n",
-    "We recommend users using `langchain_community.chat_models.ErnieBotChat` \n",
-    "to use `langchain_community.chat_models.QianfanChatEndpoint` instead.\n",
+    "We recommend users switch from `langchain_community.chat_models.ErnieBotChat` to `langchain_community.chat_models.QianfanChatEndpoint`.\n",
     "\n",
     "documentation for `QianfanChatEndpoint` is [here](/docs/integrations/chat/baidu_qianfan_endpoint/).\n",
     "\n",
-    "they are 4 why we recommend users to use `QianfanChatEndpoint`:\n",
+    "There are 4 reasons why we recommend users to use `QianfanChatEndpoint`:\n",
     "\n",
-    "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."
+    "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."
    ]
   },
   {
@@ -132,9 +131,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/integrations/chat/featherless_ai.ipynb

@@ -0,0 +1,308 @@
+{
+ "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
+}

docs/docs/integrations/chat/gigachat.ipynb

@@ -1,117 +0,0 @@
-{
- "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
-}

docs/docs/integrations/chat/goodfire.ipynb

@@ -17,7 +17,7 @@
       "source": [
         "# ChatGoodfire\n",
         "\n",
-        "This will help you getting started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
+        "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",
         "\n",
         "## Overview\n",
         "### Integration details\n",

docs/docs/integrations/chat/groq.ipynb

@@ -17,7 +17,7 @@
       "source": [
         "# ChatGroq\n",
         "\n",
-        "This will help you getting started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models?utm_source=langchain).\n",
+        "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",
         "\n",
         "## Overview\n",
         "### Integration details\n",

docs/docs/integrations/chat/huggingface.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# ChatHuggingFace\n",
     "\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",
+    "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",
     "\n",
     "## Overview\n",
     "### Integration details\n",

docs/docs/integrations/chat/llamacpp.ipynb

@@ -41,7 +41,7 @@
     "\n",
     "## Setup\n",
     "\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",
+    "To get started and use **all** the features shown below, we recommend 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 schemas, 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 schema, 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": "3.10.12"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/maritalk.ipynb

@@ -8,8 +8,6 @@
     "\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",

docs/docs/integrations/chat/mistralai.ipynb

@@ -17,7 +17,7 @@
       "source": [
         "# ChatMistralAI\n",
         "\n",
-        "This will help you getting started with Mistral [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
+        "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",
         "\n",
         "## Overview\n",
         "### Integration details\n",

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 getting started with ModelScope Chat Endpoint.\n",
+    "This will help you get started with ModelScope Chat Endpoint.\n",
     "\n",
     "\n",
     "## Overview\n",

docs/docs/integrations/chat/netmind.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "# ChatNetmind\n",
     "\n",
-    "This will help you getting started with Netmind [chat models](https://www.netmind.ai/). For detailed documentation of all ChatNetmind features and configurations head to the [API reference](https://github.com/protagolabs/langchain-netmind).\n",
+    "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",
     "\n",
     "-  See https://www.netmind.ai/ for an example.\n",
     "\n",

docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb

@@ -17,7 +17,7 @@
       "source": [
         "# ChatNVIDIA\n",
         "\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",
+        "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",
         "\n",
         "## Overview\n",
         "The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on\n",

docs/docs/integrations/chat/oci_data_science.ipynb

@@ -19,7 +19,7 @@
    "source": [
     "# ChatOCIModelDeployment\n",
     "\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",
+    "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",
     "\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",

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 in the code cell below or use the `octoai_api_token` keyword argument.\n",
+    "2. Paste your API token 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": "3.10.12"
+   "version": "3.12.10"
   },
   "vscode": {
    "interpreter": {

docs/docs/integrations/chat/openai.ipynb

@@ -915,6 +915,175 @@
     "response_2.text()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "34ad0015-688c-4274-be55-93268b44f558",
+   "metadata": {},
+   "source": [
+    "#### Code interpreter\n",
+    "\n",
+    "OpenAI implements a [code interpreter](https://platform.openai.com/docs/guides/tools-code-interpreter) tool to support the sandboxed generation and execution of code.\n",
+    "\n",
+    "Example use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "34826aae-6d48-4b84-bc00-89594a87d461",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"o4-mini\", use_responses_api=True)\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools(\n",
+    "    [\n",
+    "        {\n",
+    "            \"type\": \"code_interpreter\",\n",
+    "            # Create a new container\n",
+    "            \"container\": {\"type\": \"auto\"},\n",
+    "        }\n",
+    "    ]\n",
+    ")\n",
+    "response = llm_with_tools.invoke(\n",
+    "    \"Write and run code to answer the question: what is 3^3?\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b4d92b9-941f-4d54-93a5-b0c73afd66b2",
+   "metadata": {},
+   "source": [
+    "Note that the above command created a new container. We can also specify an existing container ID:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d8c82895-5011-4062-a1bb-278ec91321e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tool_outputs = response.additional_kwargs[\"tool_outputs\"]\n",
+    "assert len(tool_outputs) == 1\n",
+    "# highlight-next-line\n",
+    "container_id = tool_outputs[0][\"container_id\"]\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools(\n",
+    "    [\n",
+    "        {\n",
+    "            \"type\": \"code_interpreter\",\n",
+    "            # Use an existing container\n",
+    "            # highlight-next-line\n",
+    "            \"container\": container_id,\n",
+    "        }\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8db30501-522c-4915-963d-d60539b5c16e",
+   "metadata": {},
+   "source": [
+    "#### Remote MCP\n",
+    "\n",
+    "OpenAI implements a [remote MCP](https://platform.openai.com/docs/guides/tools-remote-mcp) tool that allows for model-generated calls to MCP servers.\n",
+    "\n",
+    "Example use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7044a87b-8b99-49e8-8ca4-e2a8ae49f65a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"o4-mini\", use_responses_api=True)\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools(\n",
+    "    [\n",
+    "        {\n",
+    "            \"type\": \"mcp\",\n",
+    "            \"server_label\": \"deepwiki\",\n",
+    "            \"server_url\": \"https://mcp.deepwiki.com/mcp\",\n",
+    "            \"require_approval\": \"never\",\n",
+    "        }\n",
+    "    ]\n",
+    ")\n",
+    "response = llm_with_tools.invoke(\n",
+    "    \"What transport protocols does the 2025-03-26 version of the MCP \"\n",
+    "    \"spec (modelcontextprotocol/modelcontextprotocol) support?\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ed7494e-425d-4bdf-ab83-3164757031dd",
+   "metadata": {},
+   "source": [
+    "<details>\n",
+    "<summary>MCP Approvals</summary>\n",
+    "\n",
+    "OpenAI will at times request approval before sharing data with a remote MCP server.\n",
+    "\n",
+    "In the above command, we instructed the model to never require approval. We can also configure the model to always request approval, or to always request approval for specific tools:\n",
+    "\n",
+    "```python\n",
+    "llm_with_tools = llm.bind_tools(\n",
+    "    [\n",
+    "        {\n",
+    "            \"type\": \"mcp\",\n",
+    "            \"server_label\": \"deepwiki\",\n",
+    "            \"server_url\": \"https://mcp.deepwiki.com/mcp\",\n",
+    "            \"require_approval\": {\n",
+    "                \"always\": {\n",
+    "                    \"tool_names\": [\"read_wiki_structure\"]\n",
+    "                }\n",
+    "            }\n",
+    "        }\n",
+    "    ]\n",
+    ")\n",
+    "response = llm_with_tools.invoke(\n",
+    "    \"What transport protocols does the 2025-03-26 version of the MCP \"\n",
+    "    \"spec (modelcontextprotocol/modelcontextprotocol) support?\"\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "Responses may then include blocks with type `\"mcp_approval_request\"`.\n",
+    "\n",
+    "To submit approvals for an approval request, structure it into a content block in an input message:\n",
+    "\n",
+    "```python\n",
+    "approval_message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": [\n",
+    "        {\n",
+    "            \"type\": \"mcp_approval_response\",\n",
+    "            \"approve\": True,\n",
+    "            \"approval_request_id\": output[\"id\"],\n",
+    "        }\n",
+    "        for output in response.additional_kwargs[\"tool_outputs\"]\n",
+    "        if output[\"type\"] == \"mcp_approval_request\"\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "next_response = llm_with_tools.invoke(\n",
+    "    [approval_message],\n",
+    "    # continue existing thread\n",
+    "    previous_response_id=response.response_metadata[\"id\"]\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "</details>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6fda05f0-4b81-4709-9407-f316d760ad50",

docs/docs/integrations/chat/outlines.ipynb

@@ -17,7 +17,7 @@
             "source": [
                 "# ChatOutlines\n",
                 "\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",
+                "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",
                 "\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",

docs/docs/integrations/chat/pipeshift.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "# ChatPipeshift\n",
     "\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",
+    "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",
     "\n",
     "## Overview\n",
     "### Integration details\n",

docs/docs/integrations/chat/predictionguard.ipynb

@@ -4,93 +4,99 @@
    "cell_type": "markdown",
    "id": "3f0a201c",
    "metadata": {},
-   "source": "# ChatPredictionGuard"
+   "source": [
+    "# ChatPredictionGuard"
+   ]
   },
   {
-   "metadata": {},
    "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"
+   "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"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "## Overview",
-   "id": "4e1ec341481fb244"
+   "id": "4e1ec341481fb244",
+   "metadata": {},
+   "source": [
+    "## Overview"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "b4090b7489e37a91",
+   "metadata": {},
    "source": [
     "### Integration details\n",
     "This integration utilizes the Prediction Guard API, which includes various safeguards and security features."
-   ],
-   "id": "b4090b7489e37a91"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "e26e5b3240452162",
+   "metadata": {},
    "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"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "4fca548b61efb049",
+   "metadata": {},
    "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"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "7cc34a9cd865690c",
+   "metadata": {},
    "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"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "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
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "87dc1742af7b053",
+   "metadata": {},
    "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",
@@ -100,7 +106,9 @@
      ]
     }
    ],
-   "execution_count": 3
+   "source": [
+    "%pip install -qU langchain-predictionguard"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -108,63 +116,61 @@
    "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": [],
-   "execution_count": 4
+   "source": [
+    "from langchain_predictionguard import ChatPredictionGuard"
+   ]
   },
   {
    "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
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "## Invocation",
-   "id": "8dbdfc55b638e4c2"
+   "id": "8dbdfc55b638e4c2",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
   },
   {
+   "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": {
@@ -177,18 +183,26 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 4
+   "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"
+   ]
   },
   {
+   "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",
@@ -198,16 +212,21 @@
      ]
     }
    ],
-   "execution_count": 5
+   "source": [
+    "print(ai_msg.content)"
+   ]
   },
   {
    "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": {
@@ -215,12 +234,6 @@
      "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",
@@ -232,33 +245,39 @@
      ]
     }
    ],
-   "execution_count": 6
+   "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)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "1227780d6e6728ba",
+   "metadata": {},
    "source": [
     "## Tool Calling\n",
     "\n",
-    "Prediction Guard has a tool calling API that lets you describe tools and their arguments, which enables the model return a JSON object with a tool to call and the inputs to that tool. Tool-calling is very useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n"
-   ],
-   "id": "1227780d6e6728ba"
+    "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"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "23446aa52e01d1ba",
+   "metadata": {},
    "source": [
     "### ChatPredictionGuard.bind_tools()\n",
     "\n",
     "Using `ChatPredictionGuard.bind_tools()`, you can pass in Pydantic classes, dict schemas, and Langchain tools as tools to the model, which are then reformatted to allow for use by the model."
-   ],
-   "id": "23446aa52e01d1ba"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "id": "135efb0bfc5916c1",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from pydantic import BaseModel, Field\n",
     "\n",
@@ -279,24 +298,18 @@
     "    [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": {
@@ -309,28 +322,33 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 7
+   "source": [
+    "ai_msg = llm_with_tools.invoke(\n",
+    "    \"Which city is hotter today and which is bigger: LA or NY?\"\n",
+    ")\n",
+    "ai_msg"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "84f405c45a35abe5",
+   "metadata": {},
    "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": {
@@ -358,7 +376,9 @@
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 8
+   "source": [
+    "ai_msg.tool_calls"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -386,6 +406,7 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
    "id": "9c5d7a87",
    "metadata": {
     "ExecuteTime": {
@@ -393,16 +414,6 @@
      "start_time": "2024-11-08T19:45:01.633319Z"
     }
    },
-   "source": [
-    "chat = ChatPredictionGuard(\n",
-    "    model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_input={\"pii\": \"block\"}\n",
-    ")\n",
-    "\n",
-    "try:\n",
-    "    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",
@@ -412,7 +423,16 @@
      ]
     }
    ],
-   "execution_count": 7
+   "source": [
+    "chat = ChatPredictionGuard(\n",
+    "    model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_input={\"pii\": \"block\"}\n",
+    ")\n",
+    "\n",
+    "try:\n",
+    "    chat.invoke(\"Hello, my name is John Doe and my SSN is 111-22-3333\")\n",
+    "except ValueError as e:\n",
+    "    print(e)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -424,6 +444,7 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
    "id": "a9f96fb4-00c3-4a39-b177-d1ccd5caecab",
    "metadata": {
     "ExecuteTime": {
@@ -431,6 +452,15 @@
      "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",
@@ -443,17 +473,7 @@
     "    )\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",
@@ -483,23 +503,15 @@
   },
   {
    "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",
@@ -509,7 +521,15 @@
      ]
     }
    ],
-   "execution_count": 9
+   "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)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -521,6 +541,7 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
    "id": "249da02a-d32d-4f91-82d0-10ec0505aec7",
    "metadata": {
     "ExecuteTime": {
@@ -528,16 +549,6 @@
      "start_time": "2024-11-08T19:45:10.109509Z"
     }
    },
-   "source": [
-    "chat = ChatPredictionGuard(\n",
-    "    model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_output={\"factuality\": True}\n",
-    ")\n",
-    "\n",
-    "try:\n",
-    "    chat.invoke(\"Make up something that would fail a factuality check!\")\n",
-    "except ValueError as e:\n",
-    "    print(e)"
-   ],
    "outputs": [
     {
      "name": "stdout",
@@ -547,22 +558,47 @@
      ]
     }
    ],
-   "execution_count": 10
+   "source": [
+    "chat = ChatPredictionGuard(\n",
+    "    model=\"Hermes-2-Pro-Llama-3-8B\", predictionguard_output={\"factuality\": True}\n",
+    ")\n",
+    "\n",
+    "try:\n",
+    "    chat.invoke(\"Make up something that would fail a factuality check!\")\n",
+    "except ValueError as e:\n",
+    "    print(e)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "## Chaining",
-   "id": "3c81e5a85a765ece"
+   "id": "3c81e5a85a765ece",
+   "metadata": {},
+   "source": [
+    "## Chaining"
+   ]
   },
   {
+   "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"
     }
    },
-   "cell_type": "code",
+   "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"
+    }
+   ],
    "source": [
     "from langchain_core.prompts import PromptTemplate\n",
     "\n",
@@ -577,30 +613,24 @@
     "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
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "d87695d5ff1471c1",
+   "metadata": {},
    "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"
-   ],
-   "id": "d87695d5ff1471c1"
+    "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": []
   }
  ],
  "metadata": {
@@ -622,7 +652,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/qwq.ipynb

@@ -21,7 +21,7 @@
    "source": [
     "# ChatQwQ\n",
     "\n",
-    "This will help you getting started with QwQ [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatQwQ features and configurations head to the [API reference](https://pypi.org/project/langchain-qwq/).\n",
+    "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",
     "\n",
     "## Overview\n",
     "### Integration details\n",

docs/docs/integrations/chat/sambanova.ipynb

@@ -19,7 +19,7 @@
    "source": [
     "# ChatSambaNovaCloud\n",
     "\n",
-    "This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\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",
     "\n",
     "**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models\n",
     "\n",

docs/docs/integrations/chat/sambastudio.ipynb

@@ -19,7 +19,7 @@
    "source": [
     "# ChatSambaStudio\n",
     "\n",
-    "This will help you getting started with SambaStudio [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://docs.sambanova.ai/sambastudio/latest/index.html).\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",
     "\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",

docs/docs/integrations/chat/solar.ipynb

@@ -1,5 +1,13 @@
 {
  "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,
@@ -72,7 +80,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/symblai_nebula.ipynb

@@ -21,7 +21,6 @@
    "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",

docs/docs/integrations/chat/vectara.ipynb

@@ -5,16 +5,14 @@
    "id": "134a0785",
    "metadata": {},
    "source": [
-    "## 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](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 to for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.\n",
+    "6. An LLM for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.\n",
     "\n",
     "For more information:\n",
     "- [Documentation](https://docs.vectara.com/docs/)\n",
@@ -26,7 +24,7 @@
     "\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"
    ]
   },
   {
@@ -48,8 +46,8 @@
     "\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 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",
+    "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",
     "\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",
@@ -75,7 +73,7 @@
     ")\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."
    ]
   },
   {
@@ -111,9 +109,9 @@
     "\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 occurs automatically on the Vectara platform):"
+    "Let's see an example. First, we load the SOTU document (remember, text extraction and chunking all occur automatically on the Vectara platform):"
    ]
   },
   {
@@ -243,8 +241,8 @@
    "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`:"
    ]
   },
   {
@@ -281,12 +279,15 @@
    "cell_type": "markdown",
    "id": "cefdf72b1d90085a",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "## Chaining\n",
     "\n",
-    "For additional capabilities you can use chaining."
+    "For additional capabilities, you can use chaining."
    ]
   },
   {
@@ -346,7 +347,10 @@
    "cell_type": "markdown",
    "id": "3b8bb761-db4a-436c-8939-41e9f8652083",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "## API reference\n",
@@ -371,7 +375,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,

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 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",
+        "This will help you get 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",

docs/docs/integrations/chat/xai.ipynb

@@ -1,330 +1,373 @@
 {
-  "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": [
-        {
-          "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"
-      ]
-    }
-  ],
-  "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"
-    }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: xAI\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "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": [
+    {
+     "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 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': {}})"
+      ]
+     },
+     "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": "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
 }

docs/docs/integrations/chat/yi.ipynb

@@ -6,7 +6,7 @@
       "source": [
         "# ChatYI\n",
         "\n",
-        "This will help you getting started with Yi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
+        "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",
         "\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",

docs/docs/integrations/document_loaders/blockchain.ipynb

@@ -9,15 +9,6 @@
     "# Blockchain"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5WjXERXzFEhg"
-   },
-   "source": [
-    "## Overview"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",

docs/docs/integrations/document_loaders/confluence.ipynb

@@ -6,20 +6,54 @@
    "source": [
     "# Confluence\n",
     "\n",
-    ">[Confluence](https://www.atlassian.com/software/confluence) is a wiki collaboration platform that saves and organizes all of the project-related material. `Confluence` is a knowledge base that primarily handles content management activities. \n",
+    "[Confluence](https://www.atlassian.com/software/confluence) is a wiki collaboration platform designed to save and organize all project-related materials. As a knowledge base, Confluence primarily serves content management activities.\n",
     "\n",
-    "A loader for `Confluence` pages.\n",
+    "This loader allows you to fetch and process Confluence pages into `Document` objects.\n",
     "\n",
+    "---\n",
     "\n",
-    "This currently supports `username/api_key`, `Oauth2 login`, `cookies`. Additionally, on-prem installations also support `token` authentication. \n",
+    "## Authentication Methods\n",
     "\n",
+    "The following authentication methods are supported:\n",
     "\n",
-    "Specify a list `page_id`-s and/or `space_key` to load in the corresponding pages into Document objects, if both are specified the union of both sets will be returned.\n",
+    "- `username/api_key`\n",
+    "- `OAuth2 login`\n",
+    "- `cookies`\n",
+    "- On-premises installations: `token` authentication\n",
     "\n",
+    "---\n",
     "\n",
-    "You can also specify a boolean `include_attachments` to include attachments, this is set to False by default, if set to True all attachments will be downloaded and ConfluenceReader will extract the text from the attachments and add it to the Document object. Currently supported attachment types are: `PDF`, `PNG`, `JPEG/JPG`, `SVG`, `Word` and `Excel`.\n",
+    "## Page Selection\n",
     "\n",
-    "Hint: `space_key` and `page_id` can both be found in the URL of a page in Confluence - https://yoursite.atlassian.com/wiki/spaces/<space_key>/pages/<page_id>\n"
+    "You can specify which pages to load using:\n",
+    "\n",
+    "- **page_ids** (*list*):  \n",
+    "  A list of `page_id` values to load the corresponding pages.\n",
+    "\n",
+    "- **space_key** (*string*):  \n",
+    "  A string of `space_key` value to load all pages within the specified confluence space.\n",
+    "\n",
+    "If both `page_ids` and `space_key` are provided, the loader will return the union of pages from both lists.\n",
+    "\n",
+    "*Hint:* Both `space_key` and `page_id` can be found in the URL of a Confluence page:  \n",
+    "`https://yoursite.atlassian.com/wiki/spaces/{space_key}/pages/{page_id}`\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Attachments\n",
+    "\n",
+    "You may include attachments in the loaded `Document` objects by setting the boolean parameter **include_attachments** to `True` (default: `False`). When enabled, all attachments are downloaded and their text content is extracted and added to the Document.\n",
+    "\n",
+    "**Currently supported attachment types:**\n",
+    "\n",
+    "- PDF (`.pdf`)\n",
+    "- PNG (`.png`)\n",
+    "- JPEG/JPG (`.jpeg`, `.jpg`)\n",
+    "- SVG (`.svg`)\n",
+    "- Word (`.doc`, `.docx`)\n",
+    "- Excel (`.xls`, `.xlsx`)\n",
+    "\n",
+    "---"
    ]
   },
   {
@@ -70,9 +104,14 @@
     "from langchain_community.document_loaders import ConfluenceLoader\n",
     "\n",
     "loader = ConfluenceLoader(\n",
-    "    url=\"https://yoursite.atlassian.com/wiki\", username=\"me\", api_key=\"12345\"\n",
+    "    url=\"https://yoursite.atlassian.com/wiki\",\n",
+    "    username=\"<your-confluence-username>\",\n",
+    "    api_key=\"<your-api-token>\",\n",
+    "    space_key=\"<your-space-key>\",\n",
+    "    include_attachments=True,\n",
+    "    limit=50,\n",
     ")\n",
-    "documents = loader.load(space_key=\"SPACE\", include_attachments=True, limit=50)"
+    "documents = loader.load()"
    ]
   },
   {
@@ -95,10 +134,15 @@
    "source": [
     "from langchain_community.document_loaders import ConfluenceLoader\n",
     "\n",
-    "loader = ConfluenceLoader(url=\"https://yoursite.atlassian.com/wiki\", token=\"12345\")\n",
-    "documents = loader.load(\n",
-    "    space_key=\"SPACE\", include_attachments=True, limit=50, max_pages=50\n",
-    ")"
+    "loader = ConfluenceLoader(\n",
+    "    url=\"https://confluence.yoursite.com/\",\n",
+    "    token=\"<your-personal-access-token>\",\n",
+    "    space_key=\"<your-space-key>\",\n",
+    "    include_attachments=True,\n",
+    "    limit=50,\n",
+    "    max_pages=50,\n",
+    ")\n",
+    "documents = loader.load()"
    ]
   }
  ],

docs/docs/integrations/document_loaders/google_firestore.ipynb

@@ -57,7 +57,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install -upgrade --quiet langchain-google-firestore"
+    "%pip install --upgrade --quiet langchain-google-firestore"
    ]
   },
   {

docs/docs/integrations/document_loaders/mintbase.ipynb

@@ -9,15 +9,6 @@
     "# Near Blockchain"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5WjXERXzFEhg"
-   },
-   "source": [
-    "## Overview"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",

docs/docs/integrations/document_loaders/scrapfly.ipynb

@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ScrapFly\n",
     "[ScrapFly](https://scrapfly.io/) is a web scraping API with headless browser capabilities, proxies, and anti-bot bypass. It allows for extracting web page data into accessible LLM markdown or text."
    ]
   },

docs/docs/integrations/document_loaders/scrapingant.ipynb

@@ -9,7 +9,6 @@
     "---\n",
     "\n",
     "# ScrapingAnt\n",
-    "## Overview\n",
     "[ScrapingAnt](https://scrapingant.com/) is a web scraping API with headless browser capabilities, proxies, and anti-bot bypass. It allows for extracting web page data into accessible LLM markdown.\n",
     "\n",
     "This particular integration uses only Markdown extraction feature, but don't hesitate to [reach out to us](mailto:support@scrapingant.com) if you need more features provided by ScrapingAnt, but not yet implemented in this integration.\n",

docs/docs/integrations/document_loaders/unstructured_pdfloader.ipynb

@@ -6,8 +6,6 @@
       "source": [
         "# UnstructuredPDFLoader\n",
         "\n",
-        "## Overview\n",
-        "\n",
         "[Unstructured](https://unstructured-io.github.io/unstructured/) supports a common interface for working with unstructured or semi-structured file formats, such as Markdown or PDF. LangChain's [UnstructuredPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.UnstructuredPDFLoader.html) integrates with Unstructured to parse PDF documents into LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects.\n",
         "\n",
         "Please see [this page](/docs/integrations/providers/unstructured/) for more information on installing system requirements.\n",
@@ -34,7 +32,9 @@
     {
       "cell_type": "markdown",
       "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",

docs/docs/integrations/document_loaders/zeroxpdfloader.ipynb

@@ -6,7 +6,6 @@
    "source": [
     "# ZeroxPDFLoader\n",
     "\n",
-    "## Overview\n",
     "`ZeroxPDFLoader` is a document loader that leverages the [Zerox](https://github.com/getomni-ai/zerox) library. Zerox converts PDF documents into images, processes them using a vision-capable language model, and generates a structured Markdown representation. This loader allows for asynchronous operations and provides page-level document extraction.\n",
     "\n",
     "### Integration details\n",

docs/docs/integrations/graphs/memgraph.ipynb

@@ -65,14 +65,16 @@
     "\n",
     "from langchain_core.prompts import PromptTemplate\n",
     "from langchain_memgraph.chains.graph_qa import MemgraphQAChain\n",
-    "from langchain_memgraph.graphs.memgraph import Memgraph\n",
+    "from langchain_memgraph.graphs.memgraph import MemgraphLangChain\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "url = os.environ.get(\"MEMGRAPH_URI\", \"bolt://localhost:7687\")\n",
     "username = os.environ.get(\"MEMGRAPH_USERNAME\", \"\")\n",
     "password = os.environ.get(\"MEMGRAPH_PASSWORD\", \"\")\n",
     "\n",
-    "graph = Memgraph(url=url, username=username, password=password, refresh_schema=False)"
+    "graph = MemgraphLangChain(\n",
+    "    url=url, username=username, password=password, refresh_schema=False\n",
+    ")"
    ]
   },
   {

docs/docs/integrations/llms/gigachat.ipynb

@@ -1,118 +0,0 @@
-{
- "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 ```gigachat``` python package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "is_executing": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet  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": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_community.llms import GigaChat\n",
-    "\n",
-    "llm = GigaChat(verify_ssl_certs=False, scope=\"GIGACHAT_API_PERS\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The capital of Russia is Moscow.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.chains import LLMChain\n",
-    "from langchain_core.prompts import PromptTemplate\n",
-    "\n",
-    "template = \"What is capital of {country}?\"\n",
-    "\n",
-    "prompt = PromptTemplate.from_template(template)\n",
-    "\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "\n",
-    "generated = llm_chain.invoke(input={\"country\": \"Russia\"})\n",
-    "print(generated[\"text\"])"
-   ]
-  }
- ],
- "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
-}

docs/docs/integrations/llms/nvidia_ai_endpoints.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# NVIDIA\n",
     "\n",
-    "This will help you getting started with NVIDIA [models](/docs/concepts/text_llms). For detailed documentation of all `NVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.chat_models.NVIDIA.html).\n",
+    "This will help you get started with NVIDIA [models](/docs/concepts/text_llms). For detailed documentation of all `NVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.chat_models.NVIDIA.html).\n",
     "\n",
     "## Overview\n",
     "The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on \n",

docs/docs/integrations/llms/outlines.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Outlines\n",
     "\n",
-    "This will help you getting started with Outlines LLM. For detailed documentation of all Outlines features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.outlines.Outlines.html).\n",
+    "This will help you get started with Outlines LLM. For detailed documentation of all Outlines features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.outlines.Outlines.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",

docs/docs/integrations/providers/dataforseo.mdx

@@ -32,7 +32,7 @@ For a detailed walkthrough of this wrapper, see [this notebook](/docs/integratio
 You can also load this wrapper as a Tool to use with an Agent:
 
 ```python
-from langchain.agents import load_tools
+from langchain_community.agent_toolkits.load_tools import load_tools
 tools = load_tools(["dataforseo-api-search"])
 ```
 

docs/docs/integrations/providers/featherless-ai.mdx

@@ -0,0 +1,12 @@
+# Featherless AI
+
+[Featherless AI](https://featherless.ai/) is a serverless AI inference platform that offers access to over 4300+ open-source models. Our goal is to make all AI models available for serverless inference. We provide inference via API to a continually expanding library of open-weight models. 
+
+# Installation and Setup
+`pip install langchain-featherless-ai`
+1. Sign up for an account at [Featherless](https://featherless.ai/register)
+2. Subscribe to a plan and get your API key from [API Keys](https://featherless.ai/account/api-keys)
+3. Set up your API key as an environment variable(`FEATHERLESSAI_API_KEY`)
+
+# Model catalog
+Visit our model catalog for an overview of all our models: https://featherless.ai/models 

docs/docs/integrations/providers/golden.mdx

@@ -27,7 +27,7 @@ For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integ
 You can also easily load this wrapper as a Tool (to use with an Agent).
 You can do this with:
 ```python
-from langchain.agents import load_tools
+from langchain_community.agent_toolkits.load_tools import load_tools
 tools = load_tools(["golden-query"])
 ```
 

docs/docs/integrations/providers/google.mdx

@@ -880,7 +880,7 @@ from langchain_community.tools import GoogleSearchRun, GoogleSearchResults
 
 Agent Loading:
 ```python
-from langchain.agents import load_tools
+from langchain_community.agent_toolkits.load_tools import load_tools
 tools = load_tools(["google-search"])
 ```
 
@@ -1313,7 +1313,7 @@ from langchain_community.tools import GoogleSearchRun, GoogleSearchResults
 
 Agent Loading:
 ```python
-from langchain.agents import load_tools
+from langchain_community.agent_toolkits.load_tools import load_tools
 tools = load_tools(["google-search"])
 ```
 

docs/docs/integrations/providers/google_serper.mdx

@@ -67,7 +67,7 @@ For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integ
 You can also easily load this wrapper as a Tool (to use with an Agent).
 You can do this with:
 ```python
-from langchain.agents import load_tools
+from langchain_community.agent_toolkits.load_tools import load_tools
 tools = load_tools(["google-serper"])
 ```
 

docs/docs/integrations/providers/groq.mdx

@@ -1,6 +1,6 @@
 # Groq
 
->[Groq](https://groq.com)developed the world's first Language Processing Unit™, or `LPU`. 
+>[Groq](https://groq.com) developed the world's first Language Processing Unit™, or `LPU`. 
 > The `Groq LPU` has a deterministic, single core streaming architecture that sets the standard 
 > for GenAI inference speed with predictable and repeatable performance for any given workload.
 >

docs/docs/integrations/providers/memgraph.mdx

@@ -14,7 +14,7 @@ and use them to retrieve relevant information from the database.
 
 ```python
 from langchain_memgraph.chains.graph_qa import MemgraphQAChain
-from langchain_memgraph.graphs.memgraph import Memgraph
+from langchain_memgraph.graphs.memgraph import MemgraphLangChain
 ```
 
 See a [usage example](/docs/integrations/graphs/memgraph)
@@ -24,7 +24,7 @@ See a [usage example](/docs/integrations/graphs/memgraph)
 You can use the integration to construct a knowledge graph from unstructured data.
 
 ```python
-from langchain_memgraph.graphs.memgraph import Memgraph
+from langchain_memgraph.graphs.memgraph import MemgraphLangChain
 from langchain_experimental.graph_transformers import LLMGraphTransformer
 ```
 

docs/docs/integrations/providers/ragatouille.ipynb

@@ -68,7 +68,7 @@
    "source": [
     "## Document Compressor\n",
     "\n",
-    "We can also use RAGatouille off-the-shelf as a reranker. This will allow us to use ColBERT to rerank retrieved results from any generic retriever. The benefits of this are that we can do this on top of any existing index, so that we don't need to create a new idex. We can do this by using the [document compressor](/docs/how_to/contextual_compression) abstraction in LangChain."
+    "We can also use RAGatouille off-the-shelf as a reranker. This will allow us to use ColBERT to rerank retrieved results from any generic retriever. The benefits of this are that we can do this on top of any existing index, so that we don't need to create a new index. We can do this by using the [document compressor](/docs/how_to/contextual_compression) abstraction in LangChain."
    ]
   },
   {

docs/docs/integrations/providers/salute_devices.mdx

@@ -1,37 +0,0 @@
-# Salute Devices
-
-Salute Devices provides GigaChat LLM's models.
-
-For more info how to get access to GigaChat [follow here](https://developers.sber.ru/docs/ru/gigachat/api/integration).
-
-## Installation and Setup
-
-GigaChat package can be installed via pip from PyPI:
-
-```bash
-pip install langchain-gigachat
-```
-
-## LLMs
-
-See a [usage example](/docs/integrations/llms/gigachat).
-
-```python
-from langchain_community.llms import GigaChat
-```
-
-## Chat models
-
-See a [usage example](/docs/integrations/chat/gigachat).
-
-```python
-from langchain_gigachat.chat_models import GigaChat
-```
-
-## Embeddings
-
-See a [usage example](/docs/integrations/text_embedding/gigachat).
-
-```python
-from langchain_gigachat.embeddings import GigaChatEmbeddings
-```

docs/docs/integrations/providers/searchapi.mdx

@@ -73,7 +73,7 @@ You can also easily load this wrapper as a Tool (to use with an Agent).
 You can do this with:
 
 ```python
-from langchain.agents import load_tools
+from langchain_community.agent_toolkits.load_tools import load_tools
 tools = load_tools(["searchapi"])
 ```
 

docs/docs/integrations/providers/searx.mdx

@@ -52,7 +52,7 @@ You can also load this wrapper as a Tool (to use with an Agent).
 You can do this with:
 
 ```python
-from langchain.agents import load_tools
+from langchain_community.agent_toolkits.load_tools import load_tools
 tools = load_tools(["searx-search"],
                     searx_host="http://localhost:8888",
                     engines=["github"])

docs/docs/integrations/providers/serpapi.mdx

@@ -24,7 +24,7 @@ For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integ
 You can also easily load this wrapper as a Tool (to use with an Agent).
 You can do this with:
 ```python
-from langchain.agents import load_tools
+from langchain_community.agent_toolkits.load_tools import load_tools
 tools = load_tools(["serpapi"])
 ```
 

docs/docs/integrations/providers/stackexchange.mdx

@@ -29,7 +29,7 @@ For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integ
 You can also easily load this wrapper as a Tool (to use with an Agent).
 You can do this with:
 ```python
-from langchain.agents import load_tools
+from langchain_community.agent_toolkits.load_tools import load_tools
 tools = load_tools(["stackexchange"])
 ```
 

docs/docs/integrations/providers/valyu.ipynb

@@ -4,13 +4,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# ValyuContext\n",
-    "\n",
+    "# Valyu Deep Search\n",
     ">[Valyu](https://www.valyu.network/) allows AI applications and agents to search the internet and proprietary data sources for relevant LLM ready information.\n",
     "\n",
     "This notebook goes over how to use Valyu in LangChain.\n",
     "\n",
-    "First, get an Valyu API key and add it as an environment variable. Get $10 free credit  by [signing up here](https://exchange.valyu.network/).\n",
+    "First, get an Valyu API key and add it as an environment variable. Get $10 free credit  by [signing up here](https://platform.valyu.network/).\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -43,12 +42,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_valyu import ValyuContextRetriever\n",
+    "from langchain_valyu import ValyuRetriever\n",
     "\n",
     "valyu_api_key = \"YOUR API KEY\"\n",
     "\n",
-    "# Create a new instance of the ValyuContextRetriever\n",
-    "valyu_retriever = ValyuContextRetriever(valyu_api_key=valyu_api_key)\n",
+    "# Create a new instance of the ValyuRetriever\n",
+    "valyu_retriever = ValyuRetriever(\n",
+    "    k=5,\n",
+    "    search_type=\"all\",\n",
+    "    relevance_threshold=0.5,\n",
+    "    max_price=20.0,\n",
+    "    start_date=\"2024-01-01\",\n",
+    "    end_date=\"2024-12-31\",\n",
+    "    valyu_api_key=valyu_api_key,\n",
+    ")\n",
     "\n",
     "# Search for a query and save the results\n",
     "docs = valyu_retriever.invoke(\"What are the benefits of renewable energy?\")\n",
@@ -84,9 +91,10 @@
     "    query=\"What are agentic search-enhanced large reasoning models?\",\n",
     "    search_type=\"all\",\n",
     "    max_num_results=5,\n",
-    "    similarity_threshold=0.4,\n",
-    "    query_rewrite=False,\n",
+    "    relevance_threshold=0.5,\n",
     "    max_price=20.0,\n",
+    "    start_date=\"2024-01-01\",\n",
+    "    end_date=\"2024-12-31\",\n",
     ")\n",
     "\n",
     "print(\"Search Results:\", search_results)"

docs/docs/integrations/providers/wolfram_alpha.mdx

@@ -32,7 +32,7 @@ For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integ
 You can also easily load this wrapper as a Tool (to use with an Agent).
 You can do this with:
 ```python
-from langchain.agents import load_tools
+from langchain_community.agent_toolkits.load_tools import load_tools
 tools = load_tools(["wolfram-alpha"])
 ```
 

docs/docs/integrations/providers/xai.ipynb

@@ -45,7 +45,9 @@
    "id": "8304b4d9",
    "metadata": {},
    "source": [
-    "## Example"
+    "## Example\n",
+    "\n",
+    "See [ChatXAI docs](/docs/integrations/chat/xai) for detail and supported features."
    ]
   },
   {

docs/docs/integrations/retrievers/azure_ai_search.ipynb

@@ -21,7 +21,7 @@
     "\n",
     "`AzureAISearchRetriever` is an integration module that returns documents from an unstructured query. It's based on the BaseRetriever class and it targets the 2023-11-01 stable REST API version of Azure AI Search, which means it supports vector indexing and queries.\n",
     "\n",
-    "This guide will help you getting started with the Azure AI Search [retriever](/docs/concepts/retrievers). For detailed documentation of all `AzureAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html).\n",
+    "This guide will help you get started with the Azure AI Search [retriever](/docs/concepts/retrievers). For detailed documentation of all `AzureAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html).\n",
     "\n",
     "`AzureAISearchRetriever` replaces `AzureCognitiveSearchRetriever`, which will soon be deprecated. We recommend switching to the newer version that's based on the most recent stable version of the search APIs.\n",
     "\n",

docs/docs/integrations/retrievers/bedrock.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "# Bedrock (Knowledge Bases) Retriever\n",
     "\n",
-    "This guide will help you getting started with the AWS Knowledge Bases [retriever](/docs/concepts/retrievers).\n",
+    "This guide will help you get started with the AWS Knowledge Bases [retriever](/docs/concepts/retrievers).\n",
     "\n",
     "[Knowledge Bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an Amazon Web Services (AWS) offering which lets you quickly build RAG applications by using your private data to customize FM response.\n",
     "\n",

docs/docs/integrations/retrievers/box.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "# BoxRetriever\n",
     "\n",
-    "This will help you getting started with the Box [retriever](/docs/concepts/retrievers). For detailed documentation of all BoxRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html).\n",
+    "This will help you get started with the Box [retriever](/docs/concepts/retrievers). For detailed documentation of all BoxRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html).\n",
     "\n",
     "# Overview\n",
     "\n",

docs/docs/integrations/retrievers/cognee.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "# CogneeRetriever\n",
     "\n",
-    "This will help you getting started with the Cognee [retriever](/docs/concepts/retrievers). For detailed documentation of all CogneeRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.cognee.CogneeRetriever.html).\n",
+    "This will help you get started with the Cognee [retriever](/docs/concepts/retrievers). For detailed documentation of all CogneeRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.cognee.CogneeRetriever.html).\n",
     "\n",
     "### Integration details\n",
     "\n",

docs/docs/integrations/retrievers/dappier.ipynb

@@ -23,7 +23,7 @@
    "source": [
     "# DappierRetriever\n",
     "\n",
-    "This will help you getting started with the Dappier [retriever](https://python.langchain.com/docs/concepts/retrievers/). For detailed documentation of all DappierRetriever features and configurations head to the [API reference](https://python.langchain.com/en/latest/retrievers/langchain_dappier.retrievers.Dappier.DappierRetriever.html).\n",
+    "This will help you get started with the Dappier [retriever](https://python.langchain.com/docs/concepts/retrievers/). For detailed documentation of all DappierRetriever features and configurations head to the [API reference](https://python.langchain.com/en/latest/retrievers/langchain_dappier.retrievers.Dappier.DappierRetriever.html).\n",
     "\n",
     "### Setup\n",
     "\n",