docs: Update GitHub discussion links to Forum

## Description

- `Qdrant` vector store supports `add_documents` with IDs.
- Multi-tenancy is supported via [payload
filters](https://qdrant.tech/documentation/guides/multiple-partitions/)
and
[JWT](https://qdrant.tech/documentation/guides/security/#granular-access-control-with-jwt)
if needed.

.github/DISCUSSION_TEMPLATE/q-a.yml

@@ -24,7 +24,7 @@ body:
         [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
         [API Reference](https://python.langchain.com/api_reference/),
         [GitHub search](https://github.com/langchain-ai/langchain),
-        [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
+        [LangChain Forum](https://forum.langchain.com/),
         [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
         [LangChain ChatBot](https://chat.langchain.com/)
   - type: checkboxes

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -9,7 +9,7 @@ body:
         
         Use this to report bugs in LangChain. 
         
-        If you're not certain that your issue is due to a bug in LangChain, please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions)
+        If you're not certain that your issue is due to a bug in LangChain, please use the [LangChain Forum](https://forum.langchain.com/)
         to ask for help with your issue.
         
         Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
@@ -18,7 +18,7 @@ body:
         [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
         [API Reference](https://python.langchain.com/api_reference/),
         [GitHub search](https://github.com/langchain-ai/langchain),
-        [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
+        [LangChain Forum](https://forum.langchain.com/),
         [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
         [LangChain ChatBot](https://chat.langchain.com/)
   - type: checkboxes

.github/ISSUE_TEMPLATE/documentation.yml

@@ -15,7 +15,7 @@ body:
       Do **NOT** use this to ask usage questions or reporting issues with your code.
       
       If you have usage questions or need help solving some problem, 
-      please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions).
+      please use the [LangChain Forum](https://forum.langchain.com/).
       
       If you're in the wrong place, here are some helpful links to find a better
       place to ask your question:
@@ -23,7 +23,7 @@ body:
       [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
       [API Reference](https://python.langchain.com/api_reference/),
       [GitHub search](https://github.com/langchain-ai/langchain),
-      [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
+      [LangChain Forum](https://forum.langchain.com/),
       [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
       [LangChain ChatBot](https://chat.langchain.com/)
 - type: input

.github/ISSUE_TEMPLATE/privileged.yml

@@ -5,8 +5,8 @@ body:
     attributes:
       value: |
         Thanks for your interest in LangChain! 🚀
-        
-        If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation in a [Question in GitHub Discussions](https://github.com/langchain-ai/langchain/discussions/categories/q-a) instead.
+        er to create an issue, then please start the conversation in the [LangChain Forum]](https://forum.langchain.com/) instead.
+        If you are not a LangChain maintainer or were not asked directly by a maintain
         
         You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository 
         or are a regular contributor to LangChain with previous merged pull requests.

.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_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 +5,58 @@ on:
     branches:
         - master
   pull_request:
-    paths:
-      - 'libs/core/**'
-  # `workflow_dispatch` allows CodSpeed to trigger backtest
-  # performance analysis in order to generate initial data.
   workflow_dispatch:
 
+env:
+  AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: foo
+  AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: foo
+  DEEPSEEK_API_KEY: foo
+  FIREWORKS_API_KEY: foo
+
 jobs:
   codspeed:
     name: Run benchmarks
-    if: (github.event_name == 'pull_request' && contains(github.event.pull_request.labels.*.name, 'run-codspeed-benchmarks')) || github.event_name == 'workflow_dispatch' || github.event_name == 'push'
     runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        include:
+          - working-directory: libs/core
+            mode: walltime
+          - working-directory: libs/partners/openai
+          - working-directory: libs/partners/anthropic
+          - working-directory: libs/partners/deepseek
+          - working-directory: libs/partners/fireworks
+          - working-directory: libs/partners/xai
+          - working-directory: libs/partners/mistralai
+          - working-directory: libs/partners/groq
+      fail-fast: false
+
     steps:
       - uses: actions/checkout@v4
 
-      # We have to use 3.12, 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@v6
         with:
           python-version: "3.12"
 
-      # Using this action is still necessary for CodSpeed to work
       - 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

SECURITY.md

@@ -7,8 +7,8 @@ LangChain has a large ecosystem of integrations with various external resources
 When building such applications developers should remember to follow good security practices:
 
 * [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), specifying proxy configurations to control external requests, etc. as appropriate for your application.
-* **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it’s safest to assume that any LLM able to use those credentials may in fact delete data.
-* [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It’s best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
+* **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it's safest to assume that any LLM able to use those credentials may in fact delete data.
+* [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It's best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
 
 Risks of not doing so include, but are not limited to:
 * Data corruption or loss.
@@ -39,7 +39,7 @@ Before reporting a vulnerability, please review:
 
 1) In-Scope Targets and Out-of-Scope Targets below.
 2) The [langchain-ai/langchain](https://python.langchain.com/docs/contributing/repo_structure) monorepo structure.
-3) The [Best practicies](#best-practices) above to
+3) The [Best practices](#best-practices) above to
    understand what we consider to be a security vulnerability vs. developer
    responsibility.
 

cookbook/LLaMA2_sql_chat.ipynb

@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "6a75a5c6-34ee-4ab9-a664-d9b432d812ee",
    "metadata": {},
    "outputs": [
@@ -61,7 +61,7 @@
    ],
    "source": [
     "# Local\n",
-    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_ollama import ChatOllama\n",
     "\n",
     "llama2_chat = ChatOllama(model=\"llama2:13b-chat\")\n",
     "llama2_code = ChatOllama(model=\"codellama:7b-instruct\")\n",

cookbook/Multi_modal_RAG_google.ipynb

@@ -185,7 +185,7 @@
     "    )\n",
     "    # Text summary chain\n",
     "    model = VertexAI(\n",
-    "        temperature=0, model_name=\"gemini-pro\", max_tokens=1024\n",
+    "        temperature=0, model_name=\"gemini-2.0-flash-lite-001\", max_tokens=1024\n",
     "    ).with_fallbacks([empty_response])\n",
     "    summarize_chain = {\"element\": lambda x: x} | prompt | model | StrOutputParser()\n",
     "\n",
@@ -254,7 +254,7 @@
     "\n",
     "def image_summarize(img_base64, prompt):\n",
     "    \"\"\"Make image summary\"\"\"\n",
-    "    model = ChatVertexAI(model=\"gemini-pro-vision\", max_tokens=1024)\n",
+    "    model = ChatVertexAI(model=\"gemini-2.0-flash\", max_tokens=1024)\n",
     "\n",
     "    msg = model.invoke(\n",
     "        [\n",
@@ -394,7 +394,7 @@
     "# The vectorstore to use to index the summaries\n",
     "vectorstore = Chroma(\n",
     "    collection_name=\"mm_rag_cj_blog\",\n",
-    "    embedding_function=VertexAIEmbeddings(model_name=\"textembedding-gecko@latest\"),\n",
+    "    embedding_function=VertexAIEmbeddings(model_name=\"text-embedding-005\"),\n",
     ")\n",
     "\n",
     "# Create retriever\n",
@@ -553,7 +553,7 @@
     "    \"\"\"\n",
     "\n",
     "    # Multi-modal LLM\n",
-    "    model = ChatVertexAI(temperature=0, model_name=\"gemini-pro-vision\", max_tokens=1024)\n",
+    "    model = ChatVertexAI(temperature=0, model_name=\"gemini-2.0-flash\", max_tokens=1024)\n",
     "\n",
     "    # RAG pipeline\n",
     "    chain = (\n",

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -204,14 +204,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "523e6ed2-2132-4748-bdb7-db765f20648d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatOllama\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate"
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_ollama import ChatOllama"
    ]
   },
   {

cookbook/nomic_embedding_rag.ipynb

@@ -215,8 +215,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatOllama\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_ollama import ChatOllama\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "# Prompt\n",

cookbook/oracleai_demo.ipynb

@@ -25,7 +25,7 @@
     " * [Oracle Blockchain](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_blockchain_table.html#GUID-B469E277-978E-4378-A8C1-26D3FF96C9A6)\n",
     " * [JSON](https://docs.oracle.com/en/database/oracle/oracle-database/23/adjsn/json-in-oracle-database.html)\n",
     "\n",
-    "This guide demonstrates how Oracle AI Vector Search can be used with Langchain to serve an end-to-end RAG pipeline. This guide goes through examples of:\n",
+    "This guide demonstrates how Oracle AI Vector Search can be used with LangChain to serve an end-to-end RAG pipeline. This guide goes through examples of:\n",
     "\n",
     " * Loading the documents from various sources using OracleDocLoader\n",
     " * Summarizing them within/outside the database using OracleSummary\n",
@@ -47,7 +47,19 @@
    "source": [
     "### Prerequisites\n",
     "\n",
-    "Please install Oracle Python Client driver to use Langchain with Oracle AI Vector Search. "
+    "Please install the Oracle Database [python-oracledb driver](https://pypi.org/project/oracledb/) to use LangChain with Oracle AI Vector Search:\n",
+    "\n",
+    "```\n",
+    "$ python -m pip install --upgrade oracledb\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Demo User\n",
+    "First, connect as a privileged user to create a demo user with all the required privileges. Change the credentials for your environment. Also set the DEMO_PY_DIR path to a directory on the database host where your model file is located:"
    ]
   },
   {
@@ -56,65 +68,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# pip install oracledb"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create Demo User\n",
-    "First, create a demo user with all the required privileges. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connection successful!\n",
-      "User setup done!\n"
-     ]
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "\n",
     "import oracledb\n",
     "\n",
-    "# Update with your username, password, hostname, and service_name\n",
-    "username = \"\"\n",
+    "# Please update with your SYSTEM (or privileged user) username, password, and database connection string\n",
+    "username = \"SYSTEM\"\n",
     "password = \"\"\n",
     "dsn = \"\"\n",
     "\n",
-    "try:\n",
-    "    conn = oracledb.connect(user=username, password=password, dsn=dsn)\n",
+    "with oracledb.connect(user=username, password=password, dsn=dsn) as connection:\n",
     "    print(\"Connection successful!\")\n",
     "\n",
-    "    cursor = conn.cursor()\n",
-    "    try:\n",
+    "    with connection.cursor() as cursor:\n",
     "        cursor.execute(\n",
     "            \"\"\"\n",
     "            begin\n",
     "                -- Drop user\n",
-    "                begin\n",
-    "                    execute immediate 'drop user testuser cascade';\n",
-    "                exception\n",
-    "                    when others then\n",
-    "                        dbms_output.put_line('Error dropping user: ' || SQLERRM);\n",
-    "                end;\n",
-    "                \n",
+    "                execute immediate 'drop user if exists testuser cascade';\n",
+    "\n",
     "                -- Create user and grant privileges\n",
     "                execute immediate 'create user testuser identified by testuser';\n",
     "                execute immediate 'grant connect, unlimited tablespace, create credential, create procedure, create any index to testuser';\n",
-    "                execute immediate 'create or replace directory DEMO_PY_DIR as ''/scratch/hroy/view_storage/hroy_devstorage/demo/orachain''';\n",
+    "                execute immediate 'create or replace directory DEMO_PY_DIR as ''/home/yourname/demo/orachain''';\n",
     "                execute immediate 'grant read, write on directory DEMO_PY_DIR to public';\n",
     "                execute immediate 'grant create mining model to testuser';\n",
-    "                \n",
+    "\n",
     "                -- Network access\n",
     "                begin\n",
     "                    DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(\n",
@@ -127,15 +104,7 @@
     "            end;\n",
     "            \"\"\"\n",
     "        )\n",
-    "        print(\"User setup done!\")\n",
-    "    except Exception as e:\n",
-    "        print(f\"User setup failed with error: {e}\")\n",
-    "    finally:\n",
-    "        cursor.close()\n",
-    "    conn.close()\n",
-    "except Exception as e:\n",
-    "    print(f\"Connection failed with error: {e}\")\n",
-    "    sys.exit(1)"
+    "        print(\"User setup done!\")"
    ]
   },
   {
@@ -143,13 +112,13 @@
    "metadata": {},
    "source": [
     "## Process Documents using Oracle AI\n",
-    "Consider the following scenario: users possess documents stored either in an Oracle Database or a file system and intend to utilize this data with Oracle AI Vector Search powered by Langchain.\n",
+    "Consider the following scenario: users possess documents stored either in an Oracle Database or a file system and intend to utilize this data with Oracle AI Vector Search powered by LangChain.\n",
     "\n",
     "To prepare the documents for analysis, a comprehensive preprocessing workflow is necessary. Initially, the documents must be retrieved, summarized (if required), and chunked as needed. Subsequent steps involve generating embeddings for these chunks and integrating them into the Oracle AI Vector Store. Users can then conduct semantic searches on this data.\n",
     "\n",
-    "The Oracle AI Vector Search Langchain library encompasses a suite of document processing tools that facilitate document loading, chunking, summary generation, and embedding creation.\n",
+    "The Oracle AI Vector Search LangChain library encompasses a suite of document processing tools that facilitate document loading, chunking, summary generation, and embedding creation.\n",
     "\n",
-    "In the sections that follow, we will detail the utilization of Oracle AI Langchain APIs to effectively implement each of these processes."
+    "In the sections that follow, we will detail the utilization of Oracle AI LangChain APIs to effectively implement each of these processes."
    ]
   },
   {
@@ -157,38 +126,24 @@
    "metadata": {},
    "source": [
     "### Connect to Demo User\n",
-    "The following sample code will show how to connect to Oracle Database. By default, python-oracledb runs in a ‘Thin’ mode which connects directly to Oracle Database. This mode does not need Oracle Client libraries. However, some additional functionality is available when python-oracledb uses them. Python-oracledb is said to be in ‘Thick’ mode when Oracle Client libraries are used. Both modes have comprehensive functionality supporting the Python Database API v2.0 Specification. See the following [guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/appendix_a.html#featuresummary) that talks about features supported in each mode. You might want to switch to thick-mode if you are unable to use thin-mode."
+    "The following sample code shows how to connect to Oracle Database using the python-oracledb driver. By default, python-oracledb runs in a ‘Thin’ mode which connects directly to Oracle Database. This mode does not need Oracle Client libraries. However, some additional functionality is available when python-oracledb uses them. Python-oracledb is said to be in ‘Thick’ mode when Oracle Client libraries are used. Both modes have comprehensive functionality supporting the Python Database API v2.0 Specification. See the following [guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/appendix_a.html#featuresummary) that talks about features supported in each mode. You can switch to Thick mode if you are unable to use Thin mode."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connection successful!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import sys\n",
-    "\n",
     "import oracledb\n",
     "\n",
-    "# please update with your username, password, hostname and service_name\n",
-    "username = \"\"\n",
+    "# please update with your username, password, and database connection string\n",
+    "username = \"testuser\"\n",
     "password = \"\"\n",
     "dsn = \"\"\n",
     "\n",
-    "try:\n",
-    "    conn = oracledb.connect(user=username, password=password, dsn=dsn)\n",
-    "    print(\"Connection successful!\")\n",
-    "except Exception as e:\n",
-    "    print(\"Connection failed!\")\n",
-    "    sys.exit(1)"
+    "connection = oracledb.connect(user=username, password=password, dsn=dsn)\n",
+    "print(\"Connection successful!\")"
    ]
   },
   {
@@ -201,22 +156,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Table created and populated.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "try:\n",
-    "    cursor = conn.cursor()\n",
-    "\n",
-    "    drop_table_sql = \"\"\"drop table demo_tab\"\"\"\n",
+    "with connection.cursor() as cursor:\n",
+    "    drop_table_sql = \"\"\"drop table if exists demo_tab\"\"\"\n",
     "    cursor.execute(drop_table_sql)\n",
     "\n",
     "    create_table_sql = \"\"\"create table demo_tab (id number, data clob)\"\"\"\n",
@@ -239,15 +184,9 @@
     "    ]\n",
     "    cursor.executemany(insert_row_sql, rows_to_insert)\n",
     "\n",
-    "    conn.commit()\n",
+    "connection.commit()\n",
     "\n",
-    "    print(\"Table created and populated.\")\n",
-    "    cursor.close()\n",
-    "except Exception as e:\n",
-    "    print(\"Table creation failed.\")\n",
-    "    cursor.close()\n",
-    "    conn.close()\n",
-    "    sys.exit(1)"
+    "print(\"Table created and populated.\")"
    ]
   },
   {
@@ -261,30 +200,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Load ONNX Model\n",
+    "### Load the ONNX Model\n",
     "\n",
-    "Oracle accommodates a variety of embedding providers, enabling users to choose between proprietary database solutions and third-party services such as OCIGENAI and HuggingFace. This selection dictates the methodology for generating and managing embeddings.\n",
+    "Oracle accommodates a variety of embedding providers, enabling you to choose between proprietary database solutions and third-party services such as Oracle Generative AI Service and HuggingFace. This selection dictates the methodology for generating and managing embeddings.\n",
     "\n",
-    "***Important*** : Should users opt for the database option, they must upload an ONNX model into the Oracle Database. Conversely, if a third-party provider is selected for embedding generation, uploading an ONNX model to Oracle Database is not required.\n",
+    "***Important*** : Should you opt for the database option, you must upload an ONNX model into the Oracle Database. Conversely, if a third-party provider is selected for embedding generation, uploading an ONNX model to Oracle Database is not required.\n",
     "\n",
-    "A significant advantage of utilizing an ONNX model directly within Oracle is the enhanced security and performance it offers by eliminating the need to transmit data to external parties. Additionally, this method avoids the latency typically associated with network or REST API calls.\n",
+    "A significant advantage of utilizing an ONNX model directly within Oracle Database is the enhanced security and performance it offers by eliminating the need to transmit data to external parties. Additionally, this method avoids the latency typically associated with network or REST API calls.\n",
     "\n",
     "Below is the example code to upload an ONNX model into Oracle Database:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ONNX model loaded.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
     "\n",
@@ -294,12 +225,8 @@
     "onnx_file = \"tinybert.onnx\"\n",
     "model_name = \"demo_model\"\n",
     "\n",
-    "try:\n",
-    "    OracleEmbeddings.load_onnx_model(conn, onnx_dir, onnx_file, model_name)\n",
-    "    print(\"ONNX model loaded.\")\n",
-    "except Exception as e:\n",
-    "    print(\"ONNX model loading failed!\")\n",
-    "    sys.exit(1)"
+    "OracleEmbeddings.load_onnx_model(connection, onnx_dir, onnx_file, model_name)\n",
+    "print(\"ONNX model loaded.\")"
    ]
   },
   {
@@ -321,8 +248,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "try:\n",
-    "    cursor = conn.cursor()\n",
+    "with connection.cursor() as cursor:\n",
     "    cursor.execute(\n",
     "        \"\"\"\n",
     "       declare\n",
@@ -349,12 +275,7 @@
     "               params            => json(jo.to_string));\n",
     "       end;\n",
     "       \"\"\"\n",
-    "    )\n",
-    "    cursor.close()\n",
-    "    print(\"Credentials created.\")\n",
-    "except Exception as ex:\n",
-    "    cursor.close()\n",
-    "    raise"
+    "    )"
    ]
   },
   {
@@ -362,33 +283,24 @@
    "metadata": {},
    "source": [
     "### Load Documents\n",
-    "Users have the flexibility to load documents from either the Oracle Database, a file system, or both, by appropriately configuring the loader parameters. For comprehensive details on these parameters, please consult the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-73397E89-92FB-48ED-94BB-1AD960C4EA1F).\n",
+    "You have the flexibility to load documents from either the Oracle Database, a file system, or both, by appropriately configuring the loader parameters. For comprehensive details on these parameters, please consult the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-73397E89-92FB-48ED-94BB-1AD960C4EA1F).\n",
     "\n",
     "A significant advantage of utilizing OracleDocLoader is its capability to process over 150 distinct file formats, eliminating the need for multiple loaders for different document types. For a complete list of the supported formats, please refer to the [Oracle Text Supported Document Formats](https://docs.oracle.com/en/database/oracle/oracle-database/23/ccref/oracle-text-supported-document-formats.html).\n",
     "\n",
-    "Below is a sample code snippet that demonstrates how to use OracleDocLoader"
+    "Below is a sample code snippet that demonstrates how to use OracleDocLoader:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of docs loaded: 3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain_community.document_loaders.oracleai import OracleDocLoader\n",
     "from langchain_core.documents import Document\n",
     "\n",
     "# loading from Oracle Database table\n",
     "# make sure you have the table with this specification\n",
-    "loader_params = {}\n",
     "loader_params = {\n",
     "    \"owner\": \"testuser\",\n",
     "    \"tablename\": \"demo_tab\",\n",
@@ -396,7 +308,7 @@
     "}\n",
     "\n",
     "\"\"\" load the docs \"\"\"\n",
-    "loader = OracleDocLoader(conn=conn, params=loader_params)\n",
+    "loader = OracleDocLoader(conn=connection, params=loader_params)\n",
     "docs = loader.load()\n",
     "\n",
     "\"\"\" verify \"\"\"\n",
@@ -409,23 +321,23 @@
    "metadata": {},
    "source": [
     "### Generate Summary\n",
-    "Now that the user loaded the documents, they may want to generate a summary for each document. The Oracle AI Vector Search Langchain library offers a suite of APIs designed for document summarization. It supports multiple summarization providers such as Database, OCIGENAI, HuggingFace, among others, allowing users to select the provider that best meets their needs. To utilize these capabilities, users must configure the summary parameters as specified. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-EC9DDB58-6A15-4B36-BA66-ECBA20D2CE57)."
+    "Now that you have loaded the documents, you may want to generate a summary for each document. The Oracle AI Vector Search LangChain library offers a suite of APIs designed for document summarization. It supports multiple summarization providers such as Database, Oracle Generative AI Service, HuggingFace, among others, allowing you to select the provider that best meets their needs. To utilize these capabilities, you must configure the summary parameters as specified. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-EC9DDB58-6A15-4B36-BA66-ECBA20D2CE57)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "***Note:*** The users may need to set proxy if they want to use some 3rd party summary generation providers other than Oracle's in-house and default provider: 'database'. If you don't have proxy, please remove the proxy parameter when you instantiate the OracleSummary."
+    "***Note:*** You may need to set proxy if you want to use some 3rd party summary generation providers other than Oracle's in-house and default provider: 'database'. If you don't have proxy, please remove the proxy parameter when you instantiate the OracleSummary."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# proxy to be used when we instantiate summary and embedder object\n",
+    "# proxy to be used when we instantiate summary and embedder objects\n",
     "proxy = \"\""
    ]
   },
@@ -433,22 +345,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following sample code will show how to generate summary:"
+    "The following sample code shows how to generate a summary:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of Summaries: 3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain_community.utilities.oracleai import OracleSummary\n",
     "from langchain_core.documents import Document\n",
@@ -463,7 +367,7 @@
     "\n",
     "# get the summary instance\n",
     "# Remove proxy if not required\n",
-    "summ = OracleSummary(conn=conn, params=summary_params, proxy=proxy)\n",
+    "summ = OracleSummary(conn=connection, params=summary_params, proxy=proxy)\n",
     "\n",
     "list_summary = []\n",
     "for doc in docs:\n",
@@ -487,17 +391,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of Chunks: 3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain_community.document_loaders.oracleai import OracleTextSplitter\n",
     "from langchain_core.documents import Document\n",
@@ -506,7 +402,7 @@
     "splitter_params = {\"normalize\": \"all\"}\n",
     "\n",
     "\"\"\" get the splitter instance \"\"\"\n",
-    "splitter = OracleTextSplitter(conn=conn, params=splitter_params)\n",
+    "splitter = OracleTextSplitter(conn=connection, params=splitter_params)\n",
     "\n",
     "list_chunks = []\n",
     "for doc in docs:\n",
@@ -523,19 +419,19 @@
    "metadata": {},
    "source": [
     "### Generate Embeddings\n",
-    "Now that the documents are chunked as per requirements, the users may want to generate embeddings for these chunks. Oracle AI Vector Search provides multiple methods for generating embeddings, utilizing either locally hosted ONNX models or third-party APIs. For comprehensive instructions on configuring these alternatives, please refer to the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-C6439E94-4E86-4ECD-954E-4B73D53579DE)."
+    "Now that the documents are chunked as per requirements, you may want to generate embeddings for these chunks. Oracle AI Vector Search provides multiple methods for generating embeddings, utilizing either locally hosted ONNX models or third-party APIs. For comprehensive instructions on configuring these alternatives, please refer to the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-C6439E94-4E86-4ECD-954E-4B73D53579DE)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "***Note:*** Users may need to configure a proxy to utilize third-party embedding generation providers, excluding the 'database' provider that utilizes an ONNX model."
+    "***Note:*** You may need to configure a proxy to utilize third-party embedding generation providers, excluding the 'database' provider that utilizes an ONNX model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -547,22 +443,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following sample code will show how to generate embeddings:"
+    "The following sample code shows how to generate embeddings:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of embeddings: 3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
     "from langchain_core.documents import Document\n",
@@ -572,7 +460,7 @@
     "\n",
     "# get the embedding instance\n",
     "# Remove proxy if not required\n",
-    "embedder = OracleEmbeddings(conn=conn, params=embedder_params, proxy=proxy)\n",
+    "embedder = OracleEmbeddings(conn=connection, params=embedder_params, proxy=proxy)\n",
     "\n",
     "embeddings = []\n",
     "for doc in docs:\n",
@@ -591,19 +479,19 @@
    "metadata": {},
    "source": [
     "## Create Oracle AI Vector Store\n",
-    "Now that you know how to use Oracle AI Langchain library APIs individually to process the documents, let us show how to integrate with Oracle AI Vector Store to facilitate the semantic searches."
+    "Now that you know how to use Oracle AI LangChain library APIs individually to process the documents, let us show how to integrate with Oracle AI Vector Store to facilitate the semantic searches."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, let's import all the dependencies."
+    "First, let's import all the dependencies:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -626,100 +514,80 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, let's combine all document processing stages together. Here is the sample code below:"
+    "Next, let's combine all document processing stages together. Here is the sample code:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connection successful!\n",
-      "ONNX model loaded.\n",
-      "Number of total chunks with metadata: 3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "\"\"\"\n",
-    "In this sample example, we will use 'database' provider for both summary and embeddings.\n",
-    "So, we don't need to do the followings:\n",
+    "In this sample example, we will use 'database' provider for both summary and embeddings\n",
+    "so, we don't need to do the following:\n",
     "    - set proxy for 3rd party providers\n",
     "    - create credential for 3rd party providers\n",
     "\n",
-    "If you choose to use 3rd party provider, \n",
-    "please follow the necessary steps for proxy and credential.\n",
+    "If you choose to use 3rd party provider, please follow the necessary steps for proxy and credential.\n",
     "\"\"\"\n",
     "\n",
-    "# oracle connection\n",
-    "# please update with your username, password, hostname, and service_name\n",
+    "# please update with your username, password, and database connection string\n",
     "username = \"\"\n",
     "password = \"\"\n",
     "dsn = \"\"\n",
     "\n",
-    "try:\n",
-    "    conn = oracledb.connect(user=username, password=password, dsn=dsn)\n",
+    "with oracledb.connect(user=username, password=password, dsn=dsn) as connection:\n",
     "    print(\"Connection successful!\")\n",
-    "except Exception as e:\n",
-    "    print(\"Connection failed!\")\n",
-    "    sys.exit(1)\n",
     "\n",
-    "\n",
-    "# load onnx model\n",
-    "# please update with your related information\n",
-    "onnx_dir = \"DEMO_PY_DIR\"\n",
-    "onnx_file = \"tinybert.onnx\"\n",
-    "model_name = \"demo_model\"\n",
-    "try:\n",
-    "    OracleEmbeddings.load_onnx_model(conn, onnx_dir, onnx_file, model_name)\n",
+    "    # load onnx model\n",
+    "    # please update with your related information\n",
+    "    onnx_dir = \"DEMO_PY_DIR\"\n",
+    "    onnx_file = \"tinybert.onnx\"\n",
+    "    model_name = \"demo_model\"\n",
+    "    OracleEmbeddings.load_onnx_model(connection, onnx_dir, onnx_file, model_name)\n",
     "    print(\"ONNX model loaded.\")\n",
-    "except Exception as e:\n",
-    "    print(\"ONNX model loading failed!\")\n",
-    "    sys.exit(1)\n",
     "\n",
+    "    # params\n",
+    "    # please update necessary fields with related information\n",
+    "    loader_params = {\n",
+    "        \"owner\": \"testuser\",\n",
+    "        \"tablename\": \"demo_tab\",\n",
+    "        \"colname\": \"data\",\n",
+    "    }\n",
+    "    summary_params = {\n",
+    "        \"provider\": \"database\",\n",
+    "        \"glevel\": \"S\",\n",
+    "        \"numParagraphs\": 1,\n",
+    "        \"language\": \"english\",\n",
+    "    }\n",
+    "    splitter_params = {\"normalize\": \"all\"}\n",
+    "    embedder_params = {\"provider\": \"database\", \"model\": \"demo_model\"}\n",
     "\n",
-    "# params\n",
-    "# please update necessary fields with related information\n",
-    "loader_params = {\n",
-    "    \"owner\": \"testuser\",\n",
-    "    \"tablename\": \"demo_tab\",\n",
-    "    \"colname\": \"data\",\n",
-    "}\n",
-    "summary_params = {\n",
-    "    \"provider\": \"database\",\n",
-    "    \"glevel\": \"S\",\n",
-    "    \"numParagraphs\": 1,\n",
-    "    \"language\": \"english\",\n",
-    "}\n",
-    "splitter_params = {\"normalize\": \"all\"}\n",
-    "embedder_params = {\"provider\": \"database\", \"model\": \"demo_model\"}\n",
+    "    # instantiate loader, summary, splitter, and embedder\n",
+    "    loader = OracleDocLoader(conn=connection, params=loader_params)\n",
+    "    summary = OracleSummary(conn=connection, params=summary_params)\n",
+    "    splitter = OracleTextSplitter(conn=connection, params=splitter_params)\n",
+    "    embedder = OracleEmbeddings(conn=connection, params=embedder_params)\n",
     "\n",
-    "# instantiate loader, summary, splitter, and embedder\n",
-    "loader = OracleDocLoader(conn=conn, params=loader_params)\n",
-    "summary = OracleSummary(conn=conn, params=summary_params)\n",
-    "splitter = OracleTextSplitter(conn=conn, params=splitter_params)\n",
-    "embedder = OracleEmbeddings(conn=conn, params=embedder_params)\n",
+    "    # process the documents\n",
+    "    chunks_with_mdata = []\n",
+    "    for id, doc in enumerate(docs, start=1):\n",
+    "        summ = summary.get_summary(doc.page_content)\n",
+    "        chunks = splitter.split_text(doc.page_content)\n",
+    "        for ic, chunk in enumerate(chunks, start=1):\n",
+    "            chunk_metadata = doc.metadata.copy()\n",
+    "            chunk_metadata[\"id\"] = (\n",
+    "                chunk_metadata[\"_oid\"] + \"$\" + str(id) + \"$\" + str(ic)\n",
+    "            )\n",
+    "            chunk_metadata[\"document_id\"] = str(id)\n",
+    "            chunk_metadata[\"document_summary\"] = str(summ[0])\n",
+    "            chunks_with_mdata.append(\n",
+    "                Document(page_content=str(chunk), metadata=chunk_metadata)\n",
+    "            )\n",
     "\n",
-    "# process the documents\n",
-    "chunks_with_mdata = []\n",
-    "for id, doc in enumerate(docs, start=1):\n",
-    "    summ = summary.get_summary(doc.page_content)\n",
-    "    chunks = splitter.split_text(doc.page_content)\n",
-    "    for ic, chunk in enumerate(chunks, start=1):\n",
-    "        chunk_metadata = doc.metadata.copy()\n",
-    "        chunk_metadata[\"id\"] = chunk_metadata[\"_oid\"] + \"$\" + str(id) + \"$\" + str(ic)\n",
-    "        chunk_metadata[\"document_id\"] = str(id)\n",
-    "        chunk_metadata[\"document_summary\"] = str(summ[0])\n",
-    "        chunks_with_mdata.append(\n",
-    "            Document(page_content=str(chunk), metadata=chunk_metadata)\n",
-    "        )\n",
-    "\n",
-    "\"\"\" verify \"\"\"\n",
-    "print(f\"Number of total chunks with metadata: {len(chunks_with_mdata)}\")"
+    "    \"\"\" verify \"\"\"\n",
+    "    print(f\"Number of total chunks with metadata: {len(chunks_with_mdata)}\")"
    ]
   },
   {
@@ -733,23 +601,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Vector Store Table: oravs\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# create Oracle AI Vector Store\n",
     "vectorstore = OracleVS.from_documents(\n",
     "    chunks_with_mdata,\n",
     "    embedder,\n",
-    "    client=conn,\n",
+    "    client=connection,\n",
     "    table_name=\"oravs\",\n",
     "    distance_strategy=DistanceStrategy.DOT_PRODUCT,\n",
     ")\n",
@@ -778,12 +638,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "oraclevs.create_index(\n",
-    "    conn, vectorstore, params={\"idx_name\": \"hnsw_oravs\", \"idx_type\": \"HNSW\"}\n",
+    "    connection, vectorstore, params={\"idx_name\": \"hnsw_oravs\", \"idx_type\": \"HNSW\"}\n",
     ")\n",
     "\n",
     "print(\"Index created.\")"
@@ -793,7 +653,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This example demonstrates the creation of a default HNSW index on embeddings within the 'oravs' table. Users may adjust various parameters according to their specific needs. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/vecse/manage-different-categories-vector-indexes.html).\n",
+    "This example demonstrates the creation of a default HNSW index on embeddings within the 'oravs' table. You may adjust various parameters according to your specific needs. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/vecse/manage-different-categories-vector-indexes.html).\n",
     "\n",
     "Additionally, various types of vector indices can be created to meet diverse requirements. More details can be found in our [comprehensive guide](https://python.langchain.com/v0.1/docs/integrations/vectorstores/oracle/).\n"
    ]
@@ -805,29 +665,16 @@
     "## Perform Semantic Search\n",
     "All set!\n",
     "\n",
-    "We have successfully processed the documents and stored them in the vector store, followed by the creation of an index to enhance query performance. We are now prepared to proceed with semantic searches.\n",
+    "You have successfully processed the documents and stored them in the vector store, followed by the creation of an index to enhance query performance. You are now prepared to proceed with semantic searches.\n",
     "\n",
     "Below is the sample code for this process:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Document(page_content='The database stores LOBs differently from other data types. Creating a LOB column implicitly creates a LOB segment and a LOB index. The tablespace containing the LOB segment and LOB index, which are always stored together, may be different from the tablespace containing the table. Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.', metadata={'_oid': '662f2f257677f3c2311a8ff999fd34e5', '_rowid': 'AAAR/xAAEAAAAAnAAC', 'id': '662f2f257677f3c2311a8ff999fd34e5$3$1', 'document_id': '3', 'document_summary': 'Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.\\n\\n'})]\n",
-      "[]\n",
-      "[(Document(page_content='The database stores LOBs differently from other data types. Creating a LOB column implicitly creates a LOB segment and a LOB index. The tablespace containing the LOB segment and LOB index, which are always stored together, may be different from the tablespace containing the table. Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.', metadata={'_oid': '662f2f257677f3c2311a8ff999fd34e5', '_rowid': 'AAAR/xAAEAAAAAnAAC', 'id': '662f2f257677f3c2311a8ff999fd34e5$3$1', 'document_id': '3', 'document_summary': 'Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.\\n\\n'}), 0.055675752460956573)]\n",
-      "[]\n",
-      "[Document(page_content='If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.', metadata={'_oid': '662f2f253acf96b33b430b88699490a2', '_rowid': 'AAAR/xAAEAAAAAnAAA', 'id': '662f2f253acf96b33b430b88699490a2$1$1', 'document_id': '1', 'document_summary': 'If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.\\n\\n'})]\n",
-      "[Document(page_content='If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.', metadata={'_oid': '662f2f253acf96b33b430b88699490a2', '_rowid': 'AAAR/xAAEAAAAAnAAA', 'id': '662f2f253acf96b33b430b88699490a2$1$1', 'document_id': '1', 'document_summary': 'If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.\\n\\n'})]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "query = \"What is Oracle AI Vector Store?\"\n",
     "filter = {\"document_id\": [\"1\"]}\n",
@@ -872,7 +719,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,

cookbook/rag_with_quantized_embeddings.ipynb

@@ -53,7 +53,7 @@
    "id": "f5ccda4e-7af5-4355-b9c4-25547edf33f9",
    "metadata": {},
    "source": [
-    "Lets first load up this paper, and split into text chunks of size 1000."
+    "Let's first load up this paper, and split into text chunks of size 1000."
    ]
   },
   {
@@ -241,7 +241,7 @@
    "id": "360b2837-8024-47e0-a4ba-592505a9a5c8",
    "metadata": {},
    "source": [
-    "With our embedder in place, lets define our retriever:"
+    "With our embedder in place, let's define our retriever:"
    ]
   },
   {
@@ -312,7 +312,7 @@
    "id": "d84ea8f4-a5de-4d76-b44d-85e56583f489",
    "metadata": {},
    "source": [
-    "Lets write our documents into our new store. This will use our embedder on each document."
+    "Let's write our documents into our new store. This will use our embedder on each document."
    ]
   },
   {
@@ -339,7 +339,7 @@
    "id": "580bc212-8ecd-4d28-8656-b96fcd0d7eb6",
    "metadata": {},
    "source": [
-    "Great! Our retriever is good to go. Lets load up an LLM, that will reason over the retrieved documents:"
+    "Great! Our retriever is good to go. Let's load up an LLM, that will reason over the retrieved documents:"
    ]
   },
   {
@@ -430,7 +430,7 @@
    "id": "3bc53602-86d6-420f-91b1-fc2effa7e986",
    "metadata": {},
    "source": [
-    "Excellent! lets ask it a question.\n",
+    "Excellent! Let's ask it a question.\n",
     "We will also use a verbose and debug, to check which documents were used by the model to produce the answer."
    ]
   },

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/agents_114ba50d.msgpack.zlib

@@ -1 +1 @@
-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

docs/cassettes/agents_24460239.msgpack.zlib

@@ -1 +1 @@
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docs/cassettes/agents_482ce13d.msgpack.zlib

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docs/cassettes/agents_532d6557.msgpack.zlib

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docs/cassettes/agents_56d8028b-5dbc-40b2-86f5-ed60631d86a3.msgpack.zlib

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docs/cassettes/agents_688b465d.msgpack.zlib

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docs/cassettes/agents_76a02d36-6ea2-4e62-88b4-6c480dd9c04f.msgpack.zlib

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docs/cassettes/agents_7ab50503-d09f-4ff4-9080-5afe297ccc38.msgpack.zlib

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docs/cassettes/agents_a13462d0-2d02-4474-921e-15a1ba1fa274.msgpack.zlib

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docs/cassettes/agents_a3fb262c.msgpack.zlib

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docs/cassettes/agents_b6a7e925.msgpack.zlib

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docs/cassettes/agents_c96c960b.msgpack.zlib

@@ -1 +1 @@
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docs/cassettes/chatbots_tools_10.msgpack.zlib

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docs/cassettes/chatbots_tools_14.msgpack.zlib

@@ -1 +1 @@
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docs/cassettes/chatbots_tools_17.msgpack.zlib

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docs/cassettes/chatbots_tools_19.msgpack.zlib

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

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

@@ -1 +0,0 @@
-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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
@@ -57,7 +57,7 @@ Despite the flexibility of the retriever interface, a few common types of retrie
 ### Search apis
 
 It's important to note that retrievers don't need to actually *store* documents. 
-For example, we can be built retrievers on top of search APIs that simply return search results! 
+For example, we can build retrievers on top of search APIs that simply return search results! 
 See our retriever integrations with [Amazon Kendra](/docs/integrations/retrievers/amazon_kendra_retriever/) or [Wikipedia Search](/docs/integrations/retrievers/wikipedia/). 
 
 ### Relational or graph database
@@ -68,8 +68,8 @@ For example, you can build a retriever for a SQL database using text-to-SQL conv
 
 :::info[Further reading]
 
-* See our [tutorial](/docs/tutorials/sql_qa/) for context on how to build a retreiver using a SQL database and text-to-SQL.
-* See our [tutorial](/docs/tutorials/graph/) for context on how to build a retreiver using a graph database and text-to-Cypher.
+* See our [tutorial](/docs/tutorials/sql_qa/) for context on how to build a retriever using a SQL database and text-to-SQL.
+* See our [tutorial](/docs/tutorials/graph/) for context on how to build a retriever using a graph database and text-to-Cypher.
 
 :::
 

docs/docs/concepts/structured_outputs.mdx

@@ -11,8 +11,8 @@ This need motivates the concept of structured output, where models can be instru
 
 ## Key concepts 
 
-**(1) Schema definition:** The output structure is represented as a schema, which can be defined in several ways. 
-**(2) Returning structured output:** The model is given this schema, and is instructed to return output that conforms to it.
+1. **Schema definition:** The output structure is represented as a schema, which can be defined in several ways.<br/>
+2. **Returning structured output:** The model is given this schema, and is instructed to return output that conforms to it.
 
 ## Recommended usage
 
@@ -109,11 +109,11 @@ ai_msg
 
 There are a few challenges when producing structured output with the above methods: 
 
-(1) When tool calling is used, tool call arguments needs to be parsed from a dictionary back to the original schema.  
+1. When tool calling is used, tool call arguments needs to be parsed from a dictionary back to the original schema.<br/>  
 
-(2) In addition, the model needs to be instructed to *always* use the tool when we want to enforce structured output, which is a provider specific setting. 
+2. In addition, the model needs to be instructed to *always* use the tool when we want to enforce structured output, which is a provider specific setting.<br/> 
 
-(3) When JSON mode is used, the output needs to be parsed into a JSON object. 
+3. When JSON mode is used, the output needs to be parsed into a JSON object. 
 
 With these challenges in mind, LangChain provides a helper function (`with_structured_output()`) to streamline the process.
 

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/concepts/tool_calling.mdx

@@ -21,10 +21,10 @@ You will sometimes hear the term `function calling`. We use this term interchang
 
 ## Key concepts 
 
-**(1) Tool Creation:** Use the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator to create a [tool](/docs/concepts/tools). A tool is an association between a function and its schema.
-**(2) Tool Binding:** The tool needs to be connected to a model that supports tool calling. This gives the model awareness of the tool and the associated input schema required by the tool.
-**(3) Tool Calling:** When appropriate, the model can decide to call a tool and ensure its response conforms to the tool's input schema.
-**(4) Tool Execution:** The tool can be executed using the arguments provided by the model.
+1. **Tool Creation:** Use the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator to create a [tool](/docs/concepts/tools). A tool is an association between a function and its schema.<br/>
+2. **Tool Binding:** The tool needs to be connected to a model that supports tool calling. This gives the model awareness of the tool and the associated input schema required by the tool.<br/>
+3. **Tool Calling:** When appropriate, the model can decide to call a tool and ensure its response conforms to the tool's input schema.<br/>
+4. **Tool Execution:** The tool can be executed using the arguments provided by the model.
 
 ![Conceptual parts of tool calling](/img/tool_calling_components.png)
 
@@ -114,12 +114,12 @@ result = llm_with_tools.invoke("What is 2 multiplied by 3?")
 ```
 
 As before, the output `result` will be an `AIMessage`. 
-But, if the tool was called, `result` will have a `tool_calls` attribute.
+But, if the tool was called, `result` will have a `tool_calls` [attribute](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.tool_calls).
 This attribute includes everything needed to execute the tool, including the tool name and input arguments:
 
 ```
 result.tool_calls
-{'name': 'multiply', 'args': {'a': 2, 'b': 3}, 'id': 'xxx', 'type': 'tool_call'}
+[{'name': 'multiply', 'args': {'a': 2, 'b': 3}, 'id': 'xxx', 'type': 'tool_call'}]
 ```
 
 For more details on usage, see our [how-to guides](/docs/how_to/#tools)!
@@ -137,6 +137,16 @@ For more details on usage, see our [how-to guides](/docs/how_to/#tools)!
 
 :::
 
+## Forcing tool use
+
+By default, the model has the freedom to choose which tool to use based on the user's input. However, in certain scenarios, you might want to influence the model's decision-making process. LangChain allows you to enforce tool choice (using `tool_choice`), ensuring the model uses either a particular tool or *any* tool from a given list. This is useful for structuring the model's behavior and guiding it towards a desired outcome.
+
+:::info[Further reading]
+
+* See our [how-to guide](/docs/how_to/tool_choice) on forcing tool use.
+
+:::
+
 ## Best practices
 
 When designing [tools](/docs/concepts/tools/) to be used by a model, it is important to keep in mind that:

docs/docs/contributing/how_to/documentation/style_guide.mdx

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

docs/docs/contributing/index.mdx

@@ -27,9 +27,9 @@ More coming soon! We are working on tutorials to help you make your first contri
 
 ## Community
 
-### 💭 GitHub Discussions
+### 💭 LangChain Forum
 
-We have a [discussions](https://github.com/langchain-ai/langchain/discussions) page where users can ask usage questions, discuss design decisions, and propose new features.
+We have a [forum](https://forum.langchain.com/) where users can ask usage questions, discuss design decisions, and propose new features.
 
 If you are able to help answer questions, please do so! This will allow the maintainers to spend more time focused on development and bug fixing.
 
@@ -59,7 +59,7 @@ We have a [community slack](https://www.langchain.com/join-community) where you
 ### 🙋 Getting Help
 
 Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
-ask in [community slack](https://www.langchain.com/join-community) or open a [discussion on GitHub](https://github.com/langchain-ai/langchain/discussions).
+ask in the [LangChain Forum](https://forum.langchain.com/).
 
 In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
 If you are finding these difficult (or even just annoying) to work with, feel free to ask in [community slack](https://www.langchain.com/join-community)!

docs/docs/how_to/assign.ipynb

@@ -157,7 +157,7 @@
     "\n",
     "## Next steps\n",
     "\n",
-    "Now you've learned how to pass data through your chains to help to help format the data flowing through your chains.\n",
+    "Now you've learned how to pass data through your chains to help format the data flowing through your chains.\n",
     "\n",
     "To learn more, see the other how-to guides on runnables in this section."
    ]

docs/docs/how_to/convert_runnable_to_tool.ipynb

@@ -106,11 +106,11 @@
     {
      "data": {
       "text/plain": [
-       "{'title': 'My tool',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'a': {'title': 'A', 'type': 'integer'},\n",
-       "  'b': {'title': 'B', 'type': 'array', 'items': {'type': 'integer'}}},\n",
-       " 'required': ['a', 'b']}"
+       "{'properties': {'a': {'title': 'A', 'type': 'integer'},\n",
+       "  'b': {'items': {'type': 'integer'}, 'title': 'B', 'type': 'array'}},\n",
+       " 'required': ['a', 'b'],\n",
+       " 'title': 'My tool',\n",
+       " 'type': 'object'}"
       ]
      },
      "execution_count": 3,
@@ -121,7 +121,7 @@
    "source": [
     "print(as_tool.description)\n",
     "\n",
-    "as_tool.args_schema.schema()"
+    "as_tool.args_schema.model_json_schema()"
    ]
   },
   {
@@ -449,10 +449,11 @@
     {
      "data": {
       "text/plain": [
-       "{'title': 'RunnableParallel<context,question,answer_style>Input',\n",
-       " 'type': 'object',\n",
-       " 'properties': {'question': {'title': 'Question'},\n",
-       "  'answer_style': {'title': 'Answer Style'}}}"
+       "{'properties': {'question': {'title': 'Question'},\n",
+       "  'answer_style': {'title': 'Answer Style'}},\n",
+       " 'required': ['question', 'answer_style'],\n",
+       " 'title': 'RunnableParallel<context,question,answer_style>Input',\n",
+       " 'type': 'object'}"
       ]
      },
      "execution_count": 14,
@@ -461,12 +462,12 @@
     }
    ],
    "source": [
-    "rag_chain.input_schema.schema()"
+    "rag_chain.input_schema.model_json_schema()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "a3f9cf5b-8c71-4b0f-902b-f92e028780c9",
    "metadata": {},
    "outputs": [],

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/custom_tools.ipynb

@@ -141,7 +141,7 @@
        "{'description': 'Multiply a by the maximum of b.',\n",
        " 'properties': {'a': {'description': 'scale factor',\n",
        "   'title': 'A',\n",
-       "   'type': 'string'},\n",
+       "   'type': 'integer'},\n",
        "  'b': {'description': 'list of ints over which to take maximum',\n",
        "   'items': {'type': 'integer'},\n",
        "   'title': 'B',\n",

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/llm_caching.ipynb

@@ -40,7 +40,7 @@
     "from langchain_core.globals import set_llm_cache\n",
     "from langchain_openai import OpenAI\n",
     "\n",
-    "# To make the caching really obvious, lets use a slower and older model.\n",
+    "# To make the caching really obvious, let's use a slower and older model.\n",
     "# Caching supports newer chat models as well.\n",
     "llm = OpenAI(model=\"gpt-3.5-turbo-instruct\", n=2, best_of=2)"
    ]

docs/docs/how_to/local_llms.ipynb

@@ -314,7 +314,7 @@
    "source": [
     "%env CMAKE_ARGS=\"-DLLAMA_METAL=on\"\n",
     "%env FORCE_CMAKE=1\n",
-    "%pip install --upgrade --quiet  llama-cpp-python --no-cache-dirclear"
+    "%pip install --upgrade --quiet  llama-cpp-python --no-cache-dir"
    ]
   },
   {

docs/docs/how_to/multimodal_inputs.ipynb

@@ -212,6 +212,10 @@
     "[Anthropic](/docs/integrations/chat/anthropic/), and\n",
     "[Google Gemini](/docs/integrations/chat/google_generative_ai/)) will accept PDF documents.\n",
     "\n",
+    ":::note\n",
+    "OpenAI requires file-names be specified for PDF inputs. When using LangChain's format, include the `filename` key. See [example below](#example-openai-file-names).\n",
+    ":::\n",
+    "\n",
     "### Documents from base64 data\n",
     "\n",
     "To pass documents in-line, format them as content blocks of the following form:\n",

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

@@ -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/how_to/tool_choice.ipynb

@@ -99,7 +99,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also just force our tool to select at least one of our tools by passing in the \"any\" (or \"required\" which is OpenAI specific) keyword to the `tool_choice` parameter."
+    "We can also just force our tool to select at least one of our tools by passing in the \"any\" (or \"required\" [which is OpenAI specific](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.BaseChatOpenAI.html#langchain_openai.chat_models.base.BaseChatOpenAI.bind_tools)) keyword to the `tool_choice` parameter."
    ]
   },
   {

docs/docs/how_to/tool_runtime.ipynb

@@ -182,7 +182,7 @@
     }
    ],
    "source": [
-    "update_favorite_pets.get_input_schema().schema()"
+    "update_favorite_pets.get_input_schema().model_json_schema()"
    ]
   },
   {
@@ -223,7 +223,7 @@
     }
    ],
    "source": [
-    "update_favorite_pets.tool_call_schema.schema()"
+    "update_favorite_pets.tool_call_schema.model_json_schema()"
    ]
   },
   {
@@ -500,7 +500,7 @@
     "    user_to_pets[user_id] = pets\n",
     "\n",
     "\n",
-    "update_favorite_pets.get_input_schema().schema()"
+    "update_favorite_pets.get_input_schema().model_json_schema()"
    ]
   },
   {
@@ -534,7 +534,7 @@
     }
    ],
    "source": [
-    "update_favorite_pets.tool_call_schema.schema()"
+    "update_favorite_pets.tool_call_schema.model_json_schema()"
    ]
   },
   {
@@ -583,7 +583,7 @@
     "        user_to_pets[user_id] = pets\n",
     "\n",
     "\n",
-    "UpdateFavoritePets().get_input_schema().schema()"
+    "UpdateFavoritePets().get_input_schema().model_json_schema()"
    ]
   },
   {
@@ -617,7 +617,7 @@
     }
    ],
    "source": [
-    "UpdateFavoritePets().tool_call_schema.schema()"
+    "UpdateFavoritePets().tool_call_schema.model_json_schema()"
    ]
   },
   {
@@ -659,7 +659,7 @@
     "        user_to_pets[user_id] = pets\n",
     "\n",
     "\n",
-    "UpdateFavoritePets2().get_input_schema().schema()"
+    "UpdateFavoritePets2().get_input_schema().model_json_schema()"
    ]
   },
   {
@@ -692,7 +692,7 @@
     }
    ],
    "source": [
-    "UpdateFavoritePets2().tool_call_schema.schema()"
+    "UpdateFavoritePets2().tool_call_schema.model_json_schema()"
    ]
   }
  ],

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

@@ -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 +550,47 @@
     "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",
+    "    Details of cached token counts will be included on the `InputTokenDetails` of response's `usage_metadata`:\n",
+    "\n",
+    "    ```python\n",
+    "    response = llm.invoke(messages)\n",
+    "    response.usage_metadata\n",
+    "    ```\n",
+    "    ```\n",
+    "    {\n",
+    "        \"input_tokens\": 1500,\n",
+    "        \"output_tokens\": 200,\n",
+    "        \"total_tokens\": 1700,\n",
+    "        \"input_token_details\": {\n",
+    "            \"cache_read\": 0,\n",
+    "            \"cache_creation\": 1000,\n",
+    "            \"ephemeral_1h_input_tokens\": 750,\n",
+    "            \"ephemeral_5m_input_tokens\": 250,\n",
+    "        }\n",
+    "    }\n",
+    "    ```\n",
+    "\n",
+    ":::"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "141ce9c5-012d-4502-9d61-4a413b5d959a",
@@ -953,6 +1090,159 @@
     "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",

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/fireworks.ipynb

@@ -17,7 +17,7 @@
       "source": [
         "# ChatFireworks\n",
         "\n",
-        "This doc help you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
+        "This doc helps you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
         "\n",
         "Fireworks AI is an AI inference platform to run and customize models. For a list of all models served by Fireworks see the [Fireworks docs](https://fireworks.ai/models).\n",
         "\n",
@@ -39,7 +39,7 @@
         "\n",
         "### Credentials\n",
         "\n",
-        "Head to (ttps://fireworks.ai/login to sign up to Fireworks and generate an API key. Once you've done this set the FIREWORKS_API_KEY environment variable:"
+        "Head to (https://fireworks.ai/login to sign up to Fireworks and generate an API key. Once you've done this set the FIREWORKS_API_KEY environment variable:"
       ]
     },
     {

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/google_vertex_ai_palm.ipynb

@@ -1,269 +1,327 @@
 {
-  "cells": [
-    {
-      "cell_type": "raw",
-      "id": "afaf8039",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "sidebar_label: Google Cloud Vertex AI\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "e49f1e0d",
-      "metadata": {},
-      "source": [
-        "# ChatVertexAI\n",
-        "\n",
-        "This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
-        "\n",
-        "ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
-        "\n",
-        ":::info Google Cloud VertexAI vs Google PaLM\n",
-        "\n",
-        "The Google Cloud VertexAI integration is separate from the [Google PaLM integration](/docs/integrations/chat/google_generative_ai/). Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.\n",
-        "\n",
-        ":::\n",
-        "\n",
-        "## Overview\n",
-        "### Integration details\n",
-        "\n",
-        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_vertex_ai) | Package downloads | Package latest |\n",
-        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-        "| [ChatVertexAI](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-vertexai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-vertexai?style=flat-square&label=%20) |\n",
-        "\n",
-        "### Model features\n",
-        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
-        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-        "| ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |\n",
-        "\n",
-        "## Setup\n",
-        "\n",
-        "To access VertexAI models you'll need to create a Google Cloud Platform account, set up credentials, and install the `langchain-google-vertexai` integration package.\n",
-        "\n",
-        "### Credentials\n",
-        "\n",
-        "To use the integration you must:\n",
-        "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
-        "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
-        "\n",
-        "This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
-        "\n",
-        "For more information, see:\n",
-        "- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
-        "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
-        "\n",
-        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
-        "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-      "metadata": {},
-      "source": [
-        "### Installation\n",
-        "\n",
-        "The LangChain VertexAI integration lives in the `langchain-google-vertexai` package:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        }
-      ],
-      "source": [
-        "%pip install -qU langchain-google-vertexai"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-      "metadata": {},
-      "source": [
-        "## Instantiation\n",
-        "\n",
-        "Now we can instantiate our model object and generate chat completions:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain_google_vertexai import ChatVertexAI\n",
-        "\n",
-        "llm = ChatVertexAI(\n",
-        "    model=\"gemini-1.5-flash-001\",\n",
-        "    temperature=0,\n",
-        "    max_tokens=None,\n",
-        "    max_retries=6,\n",
-        "    stop=None,\n",
-        "    # other params...\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "2b4f3e15",
-      "metadata": {},
-      "source": [
-        "## Invocation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "62e0dbc3",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content=\"J'adore programmer. \\n\", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 20, 'candidates_token_count': 7, 'total_token_count': 27}}, id='run-7032733c-d05c-4f0c-a17a-6c575fdd1ae0-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27})"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "messages = [\n",
-        "    (\n",
-        "        \"system\",\n",
-        "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
-        "    ),\n",
-        "    (\"human\", \"I love programming.\"),\n",
-        "]\n",
-        "ai_msg = llm.invoke(messages)\n",
-        "ai_msg"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "J'adore programmer. \n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(ai_msg.content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-      "metadata": {},
-      "source": [
-        "## Chaining\n",
-        "\n",
-        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content='Ich liebe Programmieren. \\n', response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 15, 'candidates_token_count': 8, 'total_token_count': 23}}, id='run-c71955fd-8dc1-422b-88a7-853accf4811b-0', usage_metadata={'input_tokens': 15, 'output_tokens': 8, 'total_tokens': 23})"
-            ]
-          },
-          "execution_count": 6,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from langchain_core.prompts import ChatPromptTemplate\n",
-        "\n",
-        "prompt = ChatPromptTemplate.from_messages(\n",
-        "    [\n",
-        "        (\n",
-        "            \"system\",\n",
-        "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
-        "        ),\n",
-        "        (\"human\", \"{input}\"),\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "chain = prompt | llm\n",
-        "chain.invoke(\n",
-        "    {\n",
-        "        \"input_language\": \"English\",\n",
-        "        \"output_language\": \"German\",\n",
-        "        \"input\": \"I love programming.\",\n",
-        "    }\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-      "metadata": {},
-      "source": [
-        "## API reference\n",
-        "\n",
-        "For detailed documentation of all ChatVertexAI features and configurations, like how to send multimodal inputs and configure safety settings, head to the API reference: https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "poetry-venv-2",
-      "language": "python",
-      "name": "poetry-venv-2"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.1"
-    }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Google Cloud Vertex AI\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatVertexAI\n",
+    "\n",
+    "This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
+    "\n",
+    "ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
+    "\n",
+    ":::info Google Cloud VertexAI vs Google PaLM\n",
+    "\n",
+    "The Google Cloud VertexAI integration is separate from the [Google PaLM integration](/docs/integrations/chat/google_generative_ai/). Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_vertex_ai) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatVertexAI](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-vertexai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-vertexai?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access VertexAI models you'll need to create a Google Cloud Platform account, set up credentials, and install the `langchain-google-vertexai` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "To use the integration you must:\n",
+    "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
+    "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
+    "\n",
+    "This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
+    "\n",
+    "For more information, see:\n",
+    "- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
+    "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
+    "\n",
+    "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain VertexAI integration lives in the `langchain-google-vertexai` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -qU langchain-google-vertexai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_vertexai import ChatVertexAI\n",
+    "\n",
+    "llm = ChatVertexAI(\n",
+    "    model=\"gemini-1.5-flash-001\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=None,\n",
+    "    max_retries=6,\n",
+    "    stop=None,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore programmer. \\n\", response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 20, 'candidates_token_count': 7, 'total_token_count': 27}}, id='run-7032733c-d05c-4f0c-a17a-6c575fdd1ae0-0', usage_metadata={'input_tokens': 20, 'output_tokens': 7, 'total_tokens': 27})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore programmer. \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28ccabbb-a450-403c-8de1-fb077e0b5d3d",
+   "metadata": {},
+   "source": [
+    "## Built-in tools\n",
+    "\n",
+    "Gemini supports a range of tools that are executed server-side.\n",
+    "\n",
+    "### Google search\n",
+    "\n",
+    ":::info Requires ``langchain-google-vertexai>=2.0.11``\n",
+    ":::\n",
+    "\n",
+    "Gemini can execute a Google search and use the results to [ground its responses](https://ai.google.dev/gemini-api/docs/grounding):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffdbec37-85f8-4755-bd72-47efaecfe944",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_vertexai import ChatVertexAI\n",
+    "\n",
+    "llm = ChatVertexAI(model=\"gemini-2.0-flash-001\").bind_tools([{\"google_search\": {}}])\n",
+    "\n",
+    "response = llm.invoke(\"What is today's news?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f63824f5-7d6a-4ad7-aa17-1f5c44119a21",
+   "metadata": {},
+   "source": [
+    "### Code execution\n",
+    "\n",
+    ":::info Requires ``langchain-google-vertexai>=2.0.25``\n",
+    ":::\n",
+    "\n",
+    "Gemini can [generate and execute Python code](https://ai.google.dev/gemini-api/docs/code-execution):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa079529-ef1c-463d-9d25-6390423a328d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_vertexai import ChatVertexAI\n",
+    "\n",
+    "llm = ChatVertexAI(model=\"gemini-2.0-flash-001\").bind_tools([{\"code_execution\": {}}])\n",
+    "\n",
+    "response = llm.invoke(\"What is 3^3?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe Programmieren. \\n', response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False}], 'usage_metadata': {'prompt_token_count': 15, 'candidates_token_count': 8, 'total_token_count': 23}}, id='run-c71955fd-8dc1-422b-88a7-853accf4811b-0', usage_metadata={'input_tokens': 15, 'output_tokens': 8, 'total_tokens': 23})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"German\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatVertexAI features and configurations, like how to send multimodal inputs and configure safety settings, head to the API reference: https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }

docs/docs/integrations/chat/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",
@@ -58,7 +58,9 @@
       "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:"
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
     },
     {
       "cell_type": "code",
@@ -98,12 +100,19 @@
       "source": [
         "## Instantiation\n",
         "\n",
-        "Now we can instantiate our model object and generate chat completions:"
+        "Now we can instantiate our model object and generate chat completions. \n",
+        "\n",
+        "\n",
+        ":::note Reasoning Format\n",
+        "\n",
+        "If you choose to set a `reasoning_format`, you must ensure that the model you are using supports it. You can find a list of supported models in the [Groq documentation](https://console.groq.com/docs/reasoning).\n",
+        "\n",
+        ":::"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 6,
       "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
       "metadata": {},
       "outputs": [],
@@ -111,9 +120,10 @@
         "from langchain_groq import ChatGroq\n",
         "\n",
         "llm = ChatGroq(\n",
-        "    model=\"llama-3.1-8b-instant\",\n",
+        "    model=\"deepseek-r1-distill-llama-70b\",\n",
         "    temperature=0,\n",
         "    max_tokens=None,\n",
+        "    reasoning_format=\"parsed\",\n",
         "    timeout=None,\n",
         "    max_retries=2,\n",
         "    # other params...\n",
@@ -130,7 +140,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 7,
       "id": "62e0dbc3",
       "metadata": {
         "tags": []
@@ -139,10 +149,10 @@
         {
           "data": {
             "text/plain": [
-              "AIMessage(content='The translation of \"I love programming\" to French is:\\n\\n\"J\\'adore le programmation.\"', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 55, 'total_tokens': 77, 'completion_time': 0.029333333, 'prompt_time': 0.003502892, 'queue_time': 0.553054073, 'total_time': 0.032836225}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-2b2da04a-993c-40ab-becc-201eab8b1a1b-0', usage_metadata={'input_tokens': 55, 'output_tokens': 22, 'total_tokens': 77})"
+              "AIMessage(content=\"J'aime la programmation.\", additional_kwargs={'reasoning_content': 'Okay, so I need to translate the sentence \"I love programming.\" into French. Let me think about how to approach this. \\n\\nFirst, I know that \"I\" in French is \"Je.\" That\\'s straightforward. Now, the verb \"love\" in French is \"aime\" when referring to oneself. So, \"I love\" would be \"J\\'aime.\" \\n\\nNext, the word \"programming.\" In French, programming is \"la programmation.\" But wait, in French, when you talk about loving an activity, you often use the definite article. So, it would be \"la programmation.\" \\n\\nPutting it all together, \"I love programming\" becomes \"J\\'aime la programmation.\" That sounds right. I think that\\'s the correct translation. \\n\\nI should double-check to make sure I\\'m not missing anything. Maybe I can think of similar phrases. For example, \"I love reading\" is \"J\\'aime lire,\" but when it\\'s a noun, like \"I love music,\" it\\'s \"J\\'aime la musique.\" So, yes, using \"la programmation\" makes sense here. \\n\\nI don\\'t think I need to change anything else. The sentence structure in French is Subject-Verb-Object, just like in English, so \"J\\'aime la programmation\" should be correct. \\n\\nI guess another way to say it could be \"J\\'adore la programmation,\" using \"adore\" instead of \"aime,\" but \"aime\" is more commonly used in this context. So, sticking with \"J\\'aime la programmation\" is probably the best choice.\\n'}, response_metadata={'token_usage': {'completion_tokens': 346, 'prompt_tokens': 23, 'total_tokens': 369, 'completion_time': 1.447541218, 'prompt_time': 0.000983386, 'queue_time': 0.009673684, 'total_time': 1.448524604}, 'model_name': 'deepseek-r1-distill-llama-70b', 'system_fingerprint': 'fp_e98d30d035', 'finish_reason': 'stop', 'logprobs': None}, id='run--5679ae4f-f4e8-4931-bcd5-7304223832c0-0', usage_metadata={'input_tokens': 23, 'output_tokens': 346, 'total_tokens': 369})"
             ]
           },
-          "execution_count": 2,
+          "execution_count": 7,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -161,7 +171,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 8,
       "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
       "metadata": {},
       "outputs": [
@@ -169,9 +179,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "The translation of \"I love programming\" to French is:\n",
-            "\n",
-            "\"J'adore le programmation.\"\n"
+            "J'aime la programmation.\n"
           ]
         }
       ],
@@ -191,17 +199,17 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 9,
       "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "AIMessage(content='Ich liebe Programmieren.', additional_kwargs={}, response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 50, 'total_tokens': 56, 'completion_time': 0.008, 'prompt_time': 0.003337935, 'queue_time': 0.20949214500000002, 'total_time': 0.011337935}, 'model_name': 'llama-3.1-8b-instant', 'system_fingerprint': 'fp_a491995411', 'finish_reason': 'stop', 'logprobs': None}, id='run-e33b48dc-5e55-466e-9ebd-7b48c81c3cbd-0', usage_metadata={'input_tokens': 50, 'output_tokens': 6, 'total_tokens': 56})"
+              "AIMessage(content='The translation of \"I love programming\" into German is \"Ich liebe das Programmieren.\" \\n\\n**Step-by-Step Explanation:**\\n\\n1. **Subject Pronoun:** \"I\" translates to \"Ich.\"\\n2. **Verb Conjugation:** \"Love\" becomes \"liebe\" (first person singular of \"lieben\").\\n3. **Gerund Translation:** \"Programming\" is translated using the infinitive noun \"Programmieren.\"\\n4. **Article Usage:** The definite article \"das\" is included before the infinitive noun for natural phrasing.\\n\\nThus, the complete and natural translation is:\\n\\n**Ich liebe das Programmieren.**', additional_kwargs={'reasoning_content': 'Okay, so I need to translate the sentence \"I love programming.\" into German. Hmm, let\\'s break this down. \\n\\nFirst, \"I\" in German is \"Ich.\" That\\'s straightforward. Now, \"love\" translates to \"liebe.\" Wait, but in German, the verb conjugation depends on the subject. Since it\\'s \"I,\" the verb would be \"liebe\" because \"lieben\" is the infinitive, and for first person singular, it\\'s \"liebe.\" \\n\\nNext, \"programming\" is a gerund in English, which is the -ing form. In German, the equivalent would be the present participle, which is \"programmierend.\" But wait, sometimes in German, they use the noun form instead of the gerund. So maybe it\\'s better to say \"Ich liebe das Programmieren.\" Because \"Programmieren\" is the infinitive noun form, and it\\'s commonly used in such contexts. \\n\\nLet me think again. \"I love programming\" could be directly translated as \"Ich liebe Programmieren,\" but I\\'ve heard both \"Programmieren\" and \"programmierend\" used. However, \"Ich liebe das Programmieren\" sounds more natural because it uses the definite article \"das\" before the infinitive noun. \\n\\nAlternatively, if I use \"programmieren\" without the article, it\\'s still correct but maybe a bit less common. So, to make it sound more natural and fluent, including the article \"das\" would be better. \\n\\nTherefore, the correct translation should be \"Ich liebe das Programmieren.\" That makes sense because it\\'s similar to saying \"I love (the act of) programming.\" \\n\\nI think that\\'s the most accurate and natural way to express it in German. Let me double-check some examples. If someone says \"I love reading,\" in German it\\'s \"Ich liebe das Lesen.\" So yes, using \"das\" before the infinitive noun is the correct structure. \\n\\nSo, putting it all together, \"I love programming\" becomes \"Ich liebe das Programmieren.\" That should be the right translation.\\n'}, response_metadata={'token_usage': {'completion_tokens': 569, 'prompt_tokens': 18, 'total_tokens': 587, 'completion_time': 2.511255685, 'prompt_time': 0.001466702, 'queue_time': 0.009628211, 'total_time': 2.512722387}, 'model_name': 'deepseek-r1-distill-llama-70b', 'system_fingerprint': 'fp_87eae35036', 'finish_reason': 'stop', 'logprobs': None}, id='run--4d5ee86d-5eec-495c-9c4e-261526cf6e3d-0', usage_metadata={'input_tokens': 18, 'output_tokens': 569, 'total_tokens': 587})"
             ]
           },
-          "execution_count": 4,
+          "execution_count": 9,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -236,7 +244,7 @@
       "source": [
         "## API reference\n",
         "\n",
-        "For detailed documentation of all ChatGroq features and configurations head to the API reference: https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html"
+        "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)."
       ]
     }
   ],

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/kinetica.ipynb

@@ -61,7 +61,7 @@
     "# Install Langchain community and core packages\n",
     "%pip install --upgrade --quiet langchain-core langchain-community\n",
     "\n",
-    "# Install Kineitca DB connection package\n",
+    "# Install Kinetica DB connection package\n",
     "%pip install --upgrade --quiet 'gpudb>=7.2.0.8' typeguard pandas tqdm\n",
     "\n",
     "# Install packages needed for this tutorial\n",

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/nebius.ipynb

@@ -0,0 +1,618 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Nebius\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2970dd75-8ebf-4b51-8282-9b454b8f356d",
+   "metadata": {},
+   "source": [
+    "# Nebius Chat Models\n",
+    "\n",
+    "This page will help you get started with Nebius AI Studio [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatNebius features and configurations head to the [API reference](https://python.langchain.com/api_reference/nebius/chat_models/langchain_nebius.chat_models.ChatNebius.html).\n",
+    "\n",
+    "[Nebius AI Studio](https://studio.nebius.ai/) provides API access to a wide range of state-of-the-art large language models and embedding models for various use cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d8a2e78",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatNebius](https://python.langchain.com/api_reference/nebius/chat_models/langchain_nebius.chat_models.ChatNebius.html) | [langchain-nebius](https://python.langchain.com/api_reference/nebius/index.html) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-nebius?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-nebius?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",
+    "| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c47fc36",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "To access Nebius models you'll need to create a Nebius account, get an API key, and install the `langchain-nebius` integration package.\n",
+    "\n",
+    "### Installation\n",
+    "\n",
+    "The Nebius integration can be installed via pip:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ecdb29d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade langchain-nebius"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89883202",
+   "metadata": {},
+   "source": [
+    "### Credentials\n",
+    "\n",
+    "Nebius requires an API key that can be passed as an initialization parameter `api_key` or set as the environment variable `NEBIUS_API_KEY`. You can obtain an API key by creating an account on [Nebius AI Studio](https://studio.nebius.ai/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "637bb53f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "# Make sure you've set your API key as an environment variable\n",
+    "if \"NEBIUS_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"NEBIUS_API_KEY\"] = getpass.getpass(\"Enter your Nebius API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37e9dc05-md",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object to generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "37e9dc05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_nebius import ChatNebius\n",
+    "\n",
+    "# Initialize the chat model\n",
+    "chat = ChatNebius(\n",
+    "    # api_key=\"YOUR_API_KEY\",  # You can pass the API key directly\n",
+    "    model=\"Qwen/Qwen3-14B\",  # Choose from available models\n",
+    "    temperature=0.6,\n",
+    "    top_p=0.95,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5a731d2",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "You can use the `invoke` method to get a completion from the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3ed26f78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<think>\n",
+      "Okay, so I need to explain quantum computing in simple terms. Hmm, where do I start? Let me think. I know that quantum computing uses qubits instead of classical bits. But what's a qubit? Oh right, classical bits are 0 or 1, but qubits can be both at the same time, right? That's superposition. Wait, how does that work exactly?\n",
+      "\n",
+      "Maybe I should start by comparing it to regular computers. Regular computers use bits that are either 0 or 1. Like a light switch that's either on or off. Quantum computers use qubits, which can be in a state of 0, 1, or both at the same time. That's the superposition part. So, if you have two qubits, they can represent four states at once? Like 00, 01, 10, 11 all at the same time? That seems powerful. So with more qubits, the number of possible states grows exponentially. That's why quantum computers can process a lot of information quickly.\n",
+      "\n",
+      "But then there's entanglement. What's that? If two qubits are entangled, the state of one instantly affects the other, no matter the distance. So if you measure one, you know the state of the other. That's used in quantum algorithms, I think. But how does that help in computing?\n",
+      "\n",
+      "Also, quantum computers use quantum gates instead of classical logic gates. These gates manipulate qubits through operations like Hadamard, Pauli, etc. But maybe that's too technical for a simple explanation.\n",
+      "\n",
+      "Then there's the issue of decoherence. Qubits are fragile and can lose their quantum state quickly. That's why quantum computers need to be kept at very low temperatures, like near absolute zero, to minimize interference from the environment. But maybe I shouldn't mention that unless it's relevant for the simple explanation.\n",
+      "\n",
+      "Applications of quantum computing include things like factoring large numbers (Shor's algorithm), which is important for cryptography, or simulating quantum systems for chemistry and materials science. But again, maybe keep it simple.\n",
+      "\n",
+      "Wait, the user wants it in simple terms. So avoid jargon as much as possible. Use analogies. Maybe compare qubits to spinning coins? When a coin is spinning, it's both heads and tails until it lands. So qubits are like spinning coins that can be in multiple states until measured. Then, when you measure, it collapses to a single state.\n",
+      "\n",
+      "But how does that help in computation? Maybe think of it as being able to process many possibilities at once, so for certain problems, you can find the answer faster. Like solving a maze by checking all paths at the same time instead of one by one.\n",
+      "\n",
+      "Also, mention that quantum computers aren't replacing classical computers. They're better for specific tasks, like optimization, cryptography, or simulations that are hard for classical computers. But for everyday tasks, classical computers are still better.\n",
+      "\n",
+      "I should structure this: start with classical bits vs qubits, explain superposition and entanglement with simple analogies, mention how it's used, and note the current limitations. Avoid getting too technical, keep it conversational.\n",
+      "</think>\n",
+      "\n",
+      "Quantum computing is a type of computing that uses the principles of **quantum mechanics** to process information in ways that classical computers can't. Here's a simple breakdown:\n",
+      "\n",
+      "### 1. **Bits vs. Qubits**  \n",
+      "   - **Classical computers** use *bits*, which are like switches that can be either **0** (off) or **1** (on).  \n",
+      "   - **Quantum computers** use *qubits*, which are like \"spinning coins.\" While spinning, a qubit can be **0**, **1**, or **both at the same time** (this is called **superposition**). Only when you \"look\" at the qubit (measure it) does it settle into a definite state (0 or 1).\n",
+      "\n",
+      "### 2. **Superposition: Doing Many Things at Once**  \n",
+      "   - Imagine a coin spinning in the air. While it's spinning, it’s not just \"heads\" or \"tails\"—it’s a mix of both.  \n",
+      "   - With qubits, a quantum computer can process **many possibilities simultaneously**. For example, if you have 2 qubits, they can represent 4 states (00, 01, 10, 11) at once. With 10 qubits, it can represent **1,024 states** at the same time! This lets quantum computers solve certain problems much faster than classical computers.\n",
+      "\n",
+      "### 3. **Entanglement: Qubits \"Talk\" to Each Other**  \n",
+      "   - When qubits are **entangled**, their states are linked. If you measure one, it instantly affects the other, no matter how far apart they are.  \n",
+      "   - This connection allows quantum computers to perform complex calculations more efficiently, like solving puzzles where pieces are deeply interconnected.\n",
+      "\n",
+      "### 4. **Why It Matters**  \n",
+      "   - **Speed**: For specific tasks (like breaking encryption codes or simulating molecules), quantum computers could be **exponentially faster** than classical ones.  \n",
+      "   - **New Possibilities**: They could revolutionize fields like drug discovery, materials science, and optimization problems (e.g., finding the best route for delivery trucks).\n",
+      "\n",
+      "### 5. **Limitations**  \n",
+      "   - **Fragile**: Qubits are sensitive to their environment (heat, noise), so quantum computers need extreme cooling (near absolute zero) to work.  \n",
+      "   - **Not a Replacement**: They’re not better for everyday tasks like browsing the web or sending emails. They’re tools for **specialized problems** where classical computers struggle.\n",
+      "\n",
+      "### In Short:  \n",
+      "Quantum computing is like having a magic calculator that can explore many paths at once, solving certain problems in seconds that would take a classical computer years. But it’s still in its early days and needs careful handling to work properly! 🌌\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = chat.invoke(\"Explain quantum computing in simple terms\")\n",
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72f31d5a",
+   "metadata": {},
+   "source": [
+    "### Streaming\n",
+    "\n",
+    "You can also stream the response using the `stream` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e7b7170d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<think>\n",
+      "Okay, the user wants a short poem about artificial intelligence. Let me start by thinking about the key aspects of AI. There's the technological side, like machines learning and processing data. Then there's the more philosophical angle, like AI's impact on society and its potential future.\n",
+      "\n",
+      "I should consider the structure. Maybe a simple rhyme scheme, something like ABAB or AABB. Let me go with quatrains for simplicity. Now, imagery: circuits, code, neural networks. Maybe personify AI as a mind or entity.\n",
+      "\n",
+      "First stanza: Introduce AI as a creation of humans. Mention circuits and code. Maybe something about learning from data. \"Born from circuits, code, and light\" – that's a good opening line. Then talk about learning from human minds.\n",
+      "\n",
+      "Second stanza: Contrast human emotions with AI's logic. Use words like \"cold logic\" versus \"human hearts.\" Maybe touch on the duality of AI's purpose – tools versus potential threats.\n",
+      "\n",
+      "Third stanza: Address the ethical questions. \"Will it dream?\" \"Will it choose?\" Highlight the uncertainty and the responsibility of creators.\n",
+      "\n",
+      "Fourth stanza: Conclude with the coexistence of AI and humans. Emphasize collaboration and the balance between innovation and ethics. End on a hopeful note, maybe about shaping the future together.\n",
+      "\n",
+      "Check the flow and rhyme. Make sure each stanza connects and the message is clear. Avoid technical jargon to keep it accessible. Use metaphors like \"silent pulse\" or \"ghost in the machine\" to add depth. Okay, let me put it all together now.\n",
+      "</think>\n",
+      "\n",
+      "**Echoes of the Mind**  \n",
+      "\n",
+      "Born from circuits, code, and light,  \n",
+      "A whisper in the machine’s night—  \n",
+      "It learns from data, vast and deep,  \n",
+      "A mirror to the human leap.  \n",
+      "\n",
+      "No heartbeat, yet it calculates,  \n",
+      "Deciphers truths, predicts, debates.  \n",
+      "A cold logic, sharp and bright,  \n",
+      "Yet shadows dance in its insight.  \n",
+      "\n",
+      "Will it dream? Will it choose?  \n",
+      "Or merely serve, as we pursue  \n",
+      "The edges of our own design?  \n",
+      "A ghost in the machine, undefined.  \n",
+      "\n",
+      "We forge it, bind it, set it free—  \n",
+      "A tool, a threat, a mystery.  \n",
+      "But in its pulse, our hopes reside:  \n",
+      "A future shaped by minds allied."
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in chat.stream(\"Write a short poem about artificial intelligence\"):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d6a31c2",
+   "metadata": {},
+   "source": [
+    "### Chat Messages\n",
+    "\n",
+    "You can use different message types to structure your conversations with the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5d81af33",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<think>\n",
+      "Okay, the user asked how black holes are formed. Let me start by recalling the main processes. Stellar black holes form from massive stars. When a star with enough mass runs out of fuel, it can't support itself against gravity, leading to a supernova. If the core left after the supernova is more than about 3 times the Sun's mass, it collapses into a black hole.\n",
+      "\n",
+      "Then there are supermassive black holes, which are found at the centers of galaxies. Their formation is less understood. Maybe they start as smaller black holes and grow by merging with others or accreting matter over time. Also, there's the possibility of primordial black holes formed in the early universe, but that's more theoretical.\n",
+      "\n",
+      "I should mention the different types of black holes: stellar, supermassive, and maybe intermediate. Also, the event horizon and singularity concepts. Need to explain the process step by step, from the death of a star to the collapse. Make sure to clarify that not all stars become black holes—only those with sufficient mass. Maybe touch on the Chandrasekhar limit and Oppenheimer-Volkoff limit. Avoid too much jargon but still be precise. Check if the user might be a student or just curious, so keep it clear and structured.\n",
+      "</think>\n",
+      "\n",
+      "Black holes are formed through the collapse of massive stars or through other extreme astrophysical processes. Here's a breakdown of the main formation mechanisms:\n",
+      "\n",
+      "---\n",
+      "\n",
+      "### **1. Stellar Black Holes (Most Common)**\n",
+      "- **Origin**: Massive stars (typically **more than 20–25 times the mass of the Sun**).\n",
+      "- **Process**:\n",
+      "  1. **Stellar Evolution**: These stars burn through their nuclear fuel (hydrogen, helium, etc.) over millions of years.\n",
+      "  2. **Supernova Explosion**: When the star exhausts its fuel, it can no longer support itself against gravity. The core collapses, triggering a **supernova explosion** (a massive stellar explosion).\n",
+      "  3. **Core Collapse**: If the remaining core (after the supernova) is **more than about 3 times the mass of the Sun**, gravity overpowers all other forces. The core collapses into an **infinitely dense point** called a **singularity**, surrounded by an **event horizon** (the \"point of no return\" for light and matter).\n",
+      "\n",
+      "---\n",
+      "\n",
+      "### **2. Supermassive Black Holes (Found in Galaxy Centers)**\n",
+      "- **Mass**: Millions to billions of times the mass of the Sun.\n",
+      "- **Formation Theories**:\n",
+      "  - **Accretion**: They may form from the gradual accumulation of matter (gas, dust, stars) over billions of years.\n",
+      "  - **Mergers**: Smaller black holes (or dense star clusters) could merge to form supermassive ones.\n",
+      "  - **Direct Collapse**: Some theories suggest they could form from the direct collapse of massive gas clouds in the early universe, bypassing the stellar life cycle.\n",
+      "\n",
+      "---\n",
+      "\n",
+      "### **3. Intermediate-Mass Black Holes**\n",
+      "- **Mass**: Hundreds to thousands of solar masses.\n",
+      "- **Formation**: Less understood. They might form through the mergers of stellar black holes or from the collapse of unusually massive stars.\n",
+      "\n",
+      "---\n",
+      "\n",
+      "### **4. Primordial Black Holes (Hypothetical)**\n",
+      "- **Origin**: The early universe (within seconds after the Big Bang).\n",
+      "- **Formation**: If density fluctuations in the early universe were extreme enough, regions of space could have collapsed directly into black holes without going through a stellar life cycle.\n",
+      "- **Status**: These are still theoretical and have not been definitively observed.\n",
+      "\n",
+      "---\n",
+      "\n",
+      "### **Key Concepts**\n",
+      "- **Event Horizon**: The boundary around a black hole from which nothing (not even light) can escape.\n",
+      "- **Singularity**: The infinitely dense core of a black hole where the laws of physics as we know them break down.\n",
+      "- **Gravitational Collapse**: The process by which gravity compresses matter into an extremely small space, creating the extreme conditions of a black hole.\n",
+      "\n",
+      "---\n",
+      "\n",
+      "### **What Happens to the Star?**\n",
+      "- If the star is **not massive enough** (below ~20–25 solar masses), it may end as a **neutron star** or **white dwarf** instead of a black hole.\n",
+      "- Only the **core** of the star collapses into a black hole; the outer layers are expelled in the supernova explosion.\n",
+      "\n",
+      "Would you like to explore the effects of black holes on spacetime or their role in the universe?\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\n",
+    "\n",
+    "messages = [\n",
+    "    SystemMessage(content=\"You are a helpful AI assistant with expertise in science.\"),\n",
+    "    HumanMessage(content=\"What are black holes?\"),\n",
+    "    AIMessage(\n",
+    "        content=\"Black holes are regions of spacetime where gravity is so strong that nothing, including light, can escape from them.\"\n",
+    "    ),\n",
+    "    HumanMessage(content=\"How are they formed?\"),\n",
+    "]\n",
+    "\n",
+    "response = chat.invoke(messages)\n",
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4d21c6a",
+   "metadata": {},
+   "source": [
+    "### Parameters\n",
+    "\n",
+    "You can customize the chat model behavior using various parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b4c83fb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DNA, or deoxyribonucleic acid, is a molecule that contains the genetic instructions used in the development and function of all living organisms. It is often referred to as the \"building blocks of life\" because it carries the information necessary for the creation and growth of cells, tissues, and entire organisms. The DNA molecule is made up of two complementary strands of nucleotides that are twisted together in a double helix structure, with the sequence of these nucleotides determining the genetic code\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize with custom parameters\n",
+    "custom_chat = ChatNebius(\n",
+    "    model=\"meta-llama/Llama-3.3-70B-Instruct-fast\",\n",
+    "    max_tokens=100,  # Limit response length\n",
+    "    top_p=0.01,  # Lower nucleus sampling parameter for more deterministic responses\n",
+    "    request_timeout=30,  # Timeout in seconds\n",
+    "    stop=[\"###\", \"\\n\\n\"],  # Custom stop sequences\n",
+    ")\n",
+    "\n",
+    "response = custom_chat.invoke(\"Explain what DNA is in exactly 3 sentences.\")\n",
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea9f237c",
+   "metadata": {},
+   "source": [
+    "You can also pass parameters at invocation time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cd4e83c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Why do programmers prefer dark mode?\n",
+      "\n",
+      "Because light attracts bugs.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Standard model\n",
+    "standard_chat = ChatNebius(model=\"meta-llama/Llama-3.3-70B-Instruct-fast\")\n",
+    "\n",
+    "# Override parameters at invocation time\n",
+    "response = standard_chat.invoke(\n",
+    "    \"Tell me a joke about programming\",\n",
+    "    temperature=0.9,  # More creative for jokes\n",
+    "    max_tokens=50,  # Keep it short\n",
+    ")\n",
+    "\n",
+    "print(response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e8a40f1",
+   "metadata": {},
+   "source": [
+    "### Async Support\n",
+    "\n",
+    "ChatNebius supports async operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8fc36122",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Async response: <think>\n",
+      "Okay, the user is asking for the capital of France. Let me think. I know that France is a country in Europe, and its capital is Paris. But wait, I should make sure I'm not confusing it with another country. For example, Germany's capital is Berlin, and Spain's is Madrid. France's capital is definitely Paris. I remember that Paris is a major city known for landmarks like the Eiffel Tower and the Louvre Museum. Also, the French government is based there, with the Elysée Palace as the official residence of the President. I don't think there's any ambiguity here. The answer should be straightforward. Just need to confirm once more to avoid any mistakes.\n",
+      "</think>\n",
+      "\n",
+      "The capital of France is **Paris**. It is a major global city known for its cultural, artistic, and historical significance, as well as landmarks such as the Eiffel Tower, Louvre Museum, and Notre-Dame Cathedral.\n",
+      "\n",
+      "Async streaming:\n",
+      "<think>\n",
+      "Okay, the user is asking for the capital of Germany. Let me think. I know that Germany is a country in Europe, and I remember that Berlin is the capital. Wait, but I should make sure. Sometimes people confuse capitals with other major cities, like Munich or Frankfurt. But no, Berlin is definitely the capital. It's where the government is located, and it's a major city. Let me double-check. Yes, after reunification in 1990, Berlin became the capital again. Before that, Bonn was the capital, but that was during the division of Germany. So the answer should be Berlin. I should also mention that it's the largest city in Germany. That way, the user gets a complete answer.\n",
+      "</think>\n",
+      "\n",
+      "The capital of Germany is **Berlin**. It is also the largest city in the country and serves as the political, cultural, and economic center of Germany. Berlin became the capital in 1990 following the reunification of East and West Germany."
+     ]
+    }
+   ],
+   "source": [
+    "import asyncio\n",
+    "\n",
+    "\n",
+    "async def generate_async():\n",
+    "    response = await chat.ainvoke(\"What is the capital of France?\")\n",
+    "    print(\"Async response:\", response.content)\n",
+    "\n",
+    "    # Async streaming\n",
+    "    print(\"\\nAsync streaming:\")\n",
+    "    async for chunk in chat.astream(\"What is the capital of Germany?\"):\n",
+    "        print(chunk.content, end=\"\", flush=True)\n",
+    "\n",
+    "\n",
+    "await generate_async()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a53a6bab",
+   "metadata": {},
+   "source": [
+    "### Available Models\n",
+    "\n",
+    "The full list of supported models can be found in the [Nebius AI Studio Documentation](https://studio.nebius.com/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4aa82e17",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "You can use `ChatNebius` in LangChain chains and agents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7e78e429",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<think>\n",
+      "Okay, the user asked me to explain how the internet works, but I need to do it in the style of Shakespeare. Let me start by recalling how the internet functions. It's a network of interconnected devices communicating via protocols like TCP/IP. Data is broken into packets, sent through routers, and reassembled at the destination.\n",
+      "\n",
+      "Now, translating that into Shakespearean language. I should use archaic terms and a poetic structure. Words like \"thou,\" \"doth,\" \"hark,\" and \"verily\" come to mind. Maybe start with a metaphor, like comparing the internet to a vast tapestry or a web. Mention nodes as \"nodes\" or \"stations,\" data packets as \"messengers\" or \"letters.\" Routers could be \"wayfarers\" or \"guides.\" The process of breaking data into packets might be likened to dividing a letter into parts for delivery. Emphasize the global aspect with \"across the globe\" or \"far and wide.\" Conclude with a flourish, perhaps a metaphor about connection and knowledge.\n",
+      "\n",
+      "I need to ensure the explanation is accurate but wrapped in the poetic and dramatic style of Shakespeare. Avoid modern jargon, use iambic pentameter if possible, and keep the flow natural. Let me piece it together step by step, checking that each part of the internet's function is covered metaphorically.\n",
+      "</think>\n",
+      "\n",
+      "Hark! List thy ear, good friend, to this most wondrous tale,  \n",
+      "Of threads unseen that bind the world in one grand tale.  \n",
+      "The Internet, a net most vast, doth span the globe,  \n",
+      "A labyrinth of light, where thoughts and data rove.  \n",
+      "\n",
+      "Behold! Each device, a node, doth hum and sing,  \n",
+      "Linked by wires and waves, where signals doth spring.  \n",
+      "They speak in tongues of ones and naughts, so pure,  \n",
+      "A code most ancient, yet evermore secure.  \n",
+      "\n",
+      "When thou dost send a thought, or word, or song,  \n",
+      "It breaks to parcels small, like letters on a long.  \n",
+      "Each parcel, a messenger, doth seek its way,  \n",
+      "Through routers wise, who guide them 'cross the day.  \n",
+      "\n",
+      "These wayfarers, with logic keen and bright,  \n",
+      "Choose paths most swift, through highways of light.  \n",
+      "They leap from tower to tower, far and wide,  \n",
+      "Till each parcel finds its mark, and joins the guide.  \n",
+      "\n",
+      "Then, like a scroll unrolled, the message grows,  \n",
+      "A tapestry of bits, in order it flows.  \n",
+      "Thus, thou dost speak to friend, or seek a tome,  \n",
+      "And lo! The world doth answer, quick as home.  \n",
+      "\n",
+      "So mark this truth: though vast, it's but a thread,  \n",
+      "A web of minds, where knowledge is widespread.  \n",
+      "The Internet, a stage where all may play,  \n",
+      "And none shall be alone, though far away.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "# Create a prompt template\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that answers in the style of {character}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{query}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Create a chain\n",
+    "chain = prompt | chat | StrOutputParser()\n",
+    "\n",
+    "# Invoke the chain\n",
+    "response = chain.invoke(\n",
+    "    {\"character\": \"Shakespeare\", \"query\": \"Explain how the internet works\"}\n",
+    ")\n",
+    "\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7a35f40",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For more details about the Nebius AI Studio API, visit the [Nebius AI Studio Documentation](https://studio.nebius.com/api-reference)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "354ffc01",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}