.

Integrate Bandit for security analysis, suppress warnings for specific issues, and address potential vulnerabilities such as hardcoded passwords and SQL injection risks. Adjust documentation and formatting for clarity.

.github/CODEOWNERS

@@ -1,2 +1,3 @@
-/.github/   @baskaryan @ccurme
+/.github/   @baskaryan @ccurme @eyurtsev
+/libs/core/ @eyurtsev
 /libs/packages.yml   @ccurme

.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/_compile_integration_test.yml

@@ -12,6 +12,9 @@ on:
         type: string
         description: "Python version to use"
 
+permissions:
+  contents: read
+
 env:
   UV_FROZEN: "true"
 

.github/workflows/_integration_test.yml

@@ -12,6 +12,9 @@ on:
         type: string
         description: "Python version to use"
 
+permissions:
+  contents: read
+
 env:
   UV_FROZEN: "true"
 
@@ -41,6 +44,8 @@ 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 +72,6 @@ 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/_lint.yml

@@ -12,6 +12,9 @@ on:
         type: string
         description: "Python version to use"
 
+permissions:
+  contents: read
+
 env:
   WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
 

.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/_test.yml

@@ -12,6 +12,9 @@ on:
         type: string
         description: "Python version to use"
 
+permissions:
+  contents: read
+
 env:
   UV_FROZEN: "true"
   UV_NO_SYNC: "true"

.github/workflows/_test_doc_imports.yml

@@ -8,6 +8,9 @@ on:
         type: string
         description: "Python version to use"
 
+permissions:
+  contents: read
+
 env:
   UV_FROZEN: "true"
 

.github/workflows/_test_pydantic.yml

@@ -17,6 +17,9 @@ on:
         type: string
         description: "Pydantic version to test."
 
+permissions:
+  contents: read
+
 env:
   UV_FROZEN: "true"
   UV_NO_SYNC: "true"

.github/workflows/check-broken-links.yml

@@ -5,6 +5,9 @@ on:
   schedule:
     - cron:  '0 13 * * *'
 
+permissions:
+  contents: read
+
 jobs:
   check-links:
     if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'

.github/workflows/check_core_versions.yml

@@ -6,6 +6,9 @@ on:
       - 'libs/core/pyproject.toml'
       - 'libs/core/langchain_core/version.py'
 
+permissions:
+  contents: read
+
 jobs:
   check_version_equality:
     runs-on: ubuntu-latest

.github/workflows/check_diffs.yml

@@ -16,6 +16,9 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
+permissions:
+  contents: read
+
 env:
   UV_FROZEN: "true"
   UV_NO_SYNC: "true"
@@ -29,7 +32,7 @@ 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 +155,7 @@ 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

@@ -15,6 +15,9 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
+permissions:
+  contents: read
+
 jobs:
   build:
     runs-on: ubuntu-latest
@@ -24,7 +27,7 @@ jobs:
         with:
           python-version: '3.10'
       - id: files
-        uses: Ana06/get-changed-files@v2.2.0
+        uses: Ana06/get-changed-files@v2.3.0
         with:
           filter: |
             *.ipynb

.github/workflows/codspeed.yml

@@ -5,40 +5,61 @@ on:
     branches:
         - master
   pull_request:
-    paths:
-      - 'libs/core/**'
-  # `workflow_dispatch` allows CodSpeed to trigger backtest
-  # performance analysis in order to generate initial data.
   workflow_dispatch:
 
+permissions:
+  contents: read
+
+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/people.yml

@@ -11,7 +11,8 @@ jobs:
   langchain-people:
     if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     runs-on: ubuntu-latest
-    permissions: write-all
+    permissions:
+      contents: write
     steps:
       - name: Dump GitHub context
         env:
@@ -23,4 +24,4 @@ jobs:
         run: mkdir -p /home/runner/work/_temp/_github_home && printf "[safe]\n\tdirectory = /github/workspace" > /home/runner/work/_temp/_github_home/.gitconfig
       - uses: ./.github/actions/people
         with:
-          token: ${{ secrets.LANGCHAIN_PEOPLE_GITHUB_TOKEN }}
+          token: ${{ secrets.LANGCHAIN_PEOPLE_GITHUB_TOKEN }}

.github/workflows/run_notebooks.yml

@@ -14,6 +14,9 @@ on:
   schedule:
     - cron: '0 13 * * *'
 
+permissions:
+  contents: read
+
 env:
   UV_FROZEN: "true"
 

.github/workflows/scheduled_test.yml

@@ -12,6 +12,9 @@ on:
   schedule:
     - cron:  '0 13 * * *'
 
+permissions:
+  contents: read
+
 env:
   POETRY_VERSION: "1.8.4"
   UV_FROZEN: "true"
@@ -127,6 +130,8 @@ 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/tutorials/docs.mdx

@@ -4,7 +4,7 @@ This tutorial will guide you through making a simple documentation edit, like co
 
 ### **Prerequisites**
 - GitHub account.
-- Familiarity with GitHub pull requests (basic understanding).
+- Familiarity with [GitHub pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests) (basic understanding).
 
 ---
 

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/streamlit.md

@@ -1,7 +1,7 @@
 # Streamlit
 
 > **[Streamlit](https://streamlit.io/) is a faster way to build and share data apps.**
-> Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No front‑end experience required.
+> Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No front-end experience required.
 > See more examples at [streamlit.io/generative-ai](https://streamlit.io/generative-ai).
 
 [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/streamlit-agent?quickstart=1)

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",
@@ -756,7 +893,7 @@
    "source": [
     "## Citations\n",
     "\n",
-    "Anthropic supports a [citations](https://docs.anthropic.com/en/docs/build-with-claude/citations) feature that lets Claude attach context to its answers based on source documents supplied by the user. When [document content blocks](https://docs.anthropic.com/en/docs/build-with-claude/citations#document-types) with `\"citations\": {\"enabled\": True}` are included in a query, Claude may generate citations in its response.\n",
+    "Anthropic supports a [citations](https://docs.anthropic.com/en/docs/build-with-claude/citations) feature that lets Claude attach context to its answers based on source documents supplied by the user. When [document](https://docs.anthropic.com/en/docs/build-with-claude/citations#document-types) or `search result` content blocks with `\"citations\": {\"enabled\": True}` are included in a query, Claude may generate citations in its response.\n",
     "\n",
     "### Simple example\n",
     "\n",
@@ -826,6 +963,156 @@
     "response.content"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4ca82106-69b3-4266-bf23-b2ffba873ee2",
+   "metadata": {},
+   "source": [
+    "### In tool results (agentic RAG)\n",
+    "\n",
+    ":::info Requires ``langchain-anthropic>=0.3.17``\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "Claude supports a [search_result](https://docs.anthropic.com/en/docs/build-with-claude/search-results) content block representing citable results from queries against a knowledge base or other custom source. These content blocks can be passed to claude both top-line (as in the above example) and within a tool result. This allows Claude to cite elements of its response using the result of a tool call.\n",
+    "\n",
+    "To pass search results in response to tool calls, define a tool that returns a list of `search_result` content blocks in Anthropic's native format. For example:\n",
+    "```python\n",
+    "def retrieval_tool(query: str) -> list[dict]:\n",
+    "    \"\"\"Access my knowledge base.\"\"\"\n",
+    "\n",
+    "    # Run a search (e.g., with a LangChain vector store)\n",
+    "    results = vector_store.similarity_search(query=query, k=2)\n",
+    "\n",
+    "    # Package results into search_result blocks\n",
+    "    return [\n",
+    "        {\n",
+    "            \"type\": \"search_result\",\n",
+    "            # Customize fields as desired, using document metadata or otherwise\n",
+    "            \"title\": \"My Document Title\",\n",
+    "            \"source\": \"Source description or provenance\",\n",
+    "            \"citations\": {\"enabled\": True},\n",
+    "            \"content\": [{\"type\": \"text\", \"text\": doc.page_content}],\n",
+    "        }\n",
+    "        for doc in results\n",
+    "    ]\n",
+    "```\n",
+    "\n",
+    "We also need to specify the `search-results-2025-06-09` beta when instantiating ChatAnthropic. You can see an end-to-end example below.\n",
+    "\n",
+    "<details>\n",
+    "<summary>End to end example with LangGraph</summary>\n",
+    "\n",
+    "Here we demonstrate an end-to-end example in which we populate a LangChain [vector store](/docs/concepts/vectorstores/) with sample documents and equip Claude with a tool that queries those documents.\n",
+    "The tool here takes a search query and a `category` string literal, but any valid tool signature can be used.\n",
+    "\n",
+    "```python\n",
+    "from typing import Literal\n",
+    "\n",
+    "from langchain.chat_models import init_chat_model\n",
+    "from langchain.embeddings import init_embeddings\n",
+    "from langchain_core.documents import Document\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "\n",
+    "# Set up vector store\n",
+    "embeddings = init_embeddings(\"openai:text-embedding-3-small\")\n",
+    "vector_store = InMemoryVectorStore(embeddings)\n",
+    "\n",
+    "document_1 = Document(\n",
+    "    id=\"1\",\n",
+    "    page_content=(\n",
+    "        \"To request vacation days, submit a leave request form through the \"\n",
+    "        \"HR portal. Approval will be sent by email.\"\n",
+    "    ),\n",
+    "    metadata={\n",
+    "        \"category\": \"HR Policy\",\n",
+    "        \"doc_title\": \"Leave Policy\",\n",
+    "        \"provenance\": \"Leave Policy - page 1\",\n",
+    "    },\n",
+    ")\n",
+    "document_2 = Document(\n",
+    "    id=\"2\",\n",
+    "    page_content=\"Managers will review vacation requests within 3 business days.\",\n",
+    "    metadata={\n",
+    "        \"category\": \"HR Policy\",\n",
+    "        \"doc_title\": \"Leave Policy\",\n",
+    "        \"provenance\": \"Leave Policy - page 2\",\n",
+    "    },\n",
+    ")\n",
+    "document_3 = Document(\n",
+    "    id=\"3\",\n",
+    "    page_content=(\n",
+    "        \"Employees with over 6 months tenure are eligible for 20 paid vacation days \"\n",
+    "        \"per year.\"\n",
+    "    ),\n",
+    "    metadata={\n",
+    "        \"category\": \"Benefits Policy\",\n",
+    "        \"doc_title\": \"Benefits Guide 2025\",\n",
+    "        \"provenance\": \"Benefits Policy - page 1\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "documents = [document_1, document_2, document_3]\n",
+    "vector_store.add_documents(documents=documents)\n",
+    "\n",
+    "\n",
+    "# Define tool\n",
+    "async def retrieval_tool(\n",
+    "    query: str, category: Literal[\"HR Policy\", \"Benefits Policy\"]\n",
+    ") -> list[dict]:\n",
+    "    \"\"\"Access my knowledge base.\"\"\"\n",
+    "\n",
+    "    def _filter_function(doc: Document) -> bool:\n",
+    "        return doc.metadata.get(\"category\") == category\n",
+    "\n",
+    "    results = vector_store.similarity_search(\n",
+    "        query=query, k=2, filter=_filter_function\n",
+    "    )\n",
+    "\n",
+    "    return [\n",
+    "        {\n",
+    "            \"type\": \"search_result\",\n",
+    "            \"title\": doc.metadata[\"doc_title\"],\n",
+    "            \"source\": doc.metadata[\"provenance\"],\n",
+    "            \"citations\": {\"enabled\": True},\n",
+    "            \"content\": [{\"type\": \"text\", \"text\": doc.page_content}],\n",
+    "        }\n",
+    "        for doc in results\n",
+    "    ]\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create agent\n",
+    "llm = init_chat_model(\n",
+    "    \"anthropic:claude-3-5-haiku-latest\",\n",
+    "    betas=[\"search-results-2025-06-09\"],\n",
+    ")\n",
+    "\n",
+    "checkpointer = InMemorySaver()\n",
+    "agent = create_react_agent(llm, [retrieval_tool], checkpointer=checkpointer)\n",
+    "\n",
+    "\n",
+    "# Invoke on a query\n",
+    "config = {\"configurable\": {\"thread_id\": \"session_1\"}}\n",
+    "\n",
+    "input_message = {\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": \"How do I request vacation days?\",\n",
+    "}\n",
+    "async for step in agent.astream(\n",
+    "    {\"messages\": [input_message]},\n",
+    "    config,\n",
+    "    stream_mode=\"values\",\n",
+    "):\n",
+    "    step[\"messages\"][-1].pretty_print()\n",
+    "```\n",
+    "\n",
+    "</details>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "69956596-0e6c-492b-934d-c08ed3c9de9a",
@@ -953,6 +1240,159 @@
     "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)."
       ]
     }
   ],