lock

.github/CODEOWNERS

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

.github/workflows/_compile_integration_test.yml

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

.github/workflows/_integration_test.yml

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

.github/workflows/_lint.yml

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

.github/workflows/_test.yml

@@ -12,9 +12,6 @@ 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

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

.github/workflows/_test_pydantic.yml

@@ -17,9 +17,6 @@ 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

@@ -5,9 +5,6 @@ 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

@@ -6,9 +6,6 @@ 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

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

.github/workflows/check_new_docs.yml

@@ -15,9 +15,6 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
-permissions:
-  contents: read
-
 jobs:
   build:
     runs-on: ubuntu-latest

.github/workflows/codspeed.yml

@@ -7,9 +7,6 @@ on:
   pull_request:
   workflow_dispatch:
 
-permissions:
-  contents: read
-
 env:
   AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: foo
   AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: foo

.github/workflows/people.yml

@@ -11,8 +11,7 @@ jobs:
   langchain-people:
     if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
     runs-on: ubuntu-latest
-    permissions:
-      contents: write
+    permissions: write-all
     steps:
       - name: Dump GitHub context
         env:
@@ -24,4 +23,4 @@ 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

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

.github/workflows/scheduled_test.yml

@@ -12,9 +12,6 @@ on:
   schedule:
     - cron:  '0 13 * * *'
 
-permissions:
-  contents: read
-
 env:
   POETRY_VERSION: "1.8.4"
   UV_FROZEN: "true"

Makefile

@@ -71,6 +71,7 @@ spell_fix:
 lint lint_package lint_tests:
 	uv run --group lint ruff check docs cookbook
 	uv run --group lint ruff format docs cookbook cookbook --diff
+	uv run --group lint ruff check --select I docs cookbook
 	git --no-pager grep 'from langchain import' docs cookbook | grep -vE 'from langchain import (hub)' && echo "Error: no importing langchain from root in docs, except for hub" && exit 1 || exit 0
 	
 	git --no-pager grep 'api.python.langchain.com' -- docs/docs ':!docs/docs/additional_resources/arxiv_references.mdx' ':!docs/docs/integrations/document_loaders/sitemap.ipynb' || exit 0 && \
@@ -80,7 +81,7 @@ lint lint_package lint_tests:
 ## format: Format the project files.
 format format_diff:
 	uv run --group lint ruff format docs cookbook
-	uv run --group lint ruff check --fix docs cookbook
+	uv run --group lint ruff check --select I --fix docs cookbook
 
 update-package-downloads:
 	uv run python docs/scripts/packages_yml_get_downloads.py

cookbook/LLaMA2_sql_chat.ipynb

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

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -204,14 +204,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "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\n",
-    "from langchain_ollama import ChatOllama"
+    "from langchain_core.prompts import ChatPromptTemplate"
    ]
   },
   {

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",
@@ -47,19 +47,7 @@
    "source": [
     "### Prerequisites\n",
     "\n",
-    "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:"
+    "Please install Oracle Python Client driver to use Langchain with Oracle AI Vector Search. "
    ]
   },
   {
@@ -68,30 +56,65 @@
    "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",
-    "# Please update with your SYSTEM (or privileged user) username, password, and database connection string\n",
-    "username = \"SYSTEM\"\n",
+    "# Update with your username, password, hostname, and service_name\n",
+    "username = \"\"\n",
     "password = \"\"\n",
     "dsn = \"\"\n",
     "\n",
-    "with oracledb.connect(user=username, password=password, dsn=dsn) as connection:\n",
+    "try:\n",
+    "    conn = oracledb.connect(user=username, password=password, dsn=dsn)\n",
     "    print(\"Connection successful!\")\n",
     "\n",
-    "    with connection.cursor() as cursor:\n",
+    "    cursor = conn.cursor()\n",
+    "    try:\n",
     "        cursor.execute(\n",
     "            \"\"\"\n",
     "            begin\n",
     "                -- Drop user\n",
-    "                execute immediate 'drop user if exists testuser cascade';\n",
-    "\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",
     "                -- 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 ''/home/yourname/demo/orachain''';\n",
+    "                execute immediate 'create or replace directory DEMO_PY_DIR as ''/scratch/hroy/view_storage/hroy_devstorage/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",
@@ -104,7 +127,15 @@
     "            end;\n",
     "            \"\"\"\n",
     "        )\n",
-    "        print(\"User setup done!\")"
+    "        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)"
    ]
   },
   {
@@ -112,13 +143,13 @@
    "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."
    ]
   },
   {
@@ -126,24 +157,38 @@
    "metadata": {},
    "source": [
     "### Connect to Demo User\n",
-    "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."
+    "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."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connection successful!\n"
+     ]
+    }
+   ],
    "source": [
+    "import sys\n",
+    "\n",
     "import oracledb\n",
     "\n",
-    "# please update with your username, password, and database connection string\n",
-    "username = \"testuser\"\n",
+    "# please update with your username, password, hostname and service_name\n",
+    "username = \"\"\n",
     "password = \"\"\n",
     "dsn = \"\"\n",
     "\n",
-    "connection = oracledb.connect(user=username, password=password, dsn=dsn)\n",
-    "print(\"Connection successful!\")"
+    "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)"
    ]
   },
   {
@@ -156,12 +201,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Table created and populated.\n"
+     ]
+    }
+   ],
    "source": [
-    "with connection.cursor() as cursor:\n",
-    "    drop_table_sql = \"\"\"drop table if exists demo_tab\"\"\"\n",
+    "try:\n",
+    "    cursor = conn.cursor()\n",
+    "\n",
+    "    drop_table_sql = \"\"\"drop table demo_tab\"\"\"\n",
     "    cursor.execute(drop_table_sql)\n",
     "\n",
     "    create_table_sql = \"\"\"create table demo_tab (id number, data clob)\"\"\"\n",
@@ -184,9 +239,15 @@
     "    ]\n",
     "    cursor.executemany(insert_row_sql, rows_to_insert)\n",
     "\n",
-    "connection.commit()\n",
+    "    conn.commit()\n",
     "\n",
-    "print(\"Table created and populated.\")"
+    "    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)"
    ]
   },
   {
@@ -200,22 +261,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Load the ONNX Model\n",
+    "### Load ONNX Model\n",
     "\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",
+    "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",
     "\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",
+    "***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",
     "\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",
+    "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",
     "\n",
     "Below is the example code to upload an ONNX model into Oracle Database:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ONNX model loaded.\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
     "\n",
@@ -225,8 +294,12 @@
     "onnx_file = \"tinybert.onnx\"\n",
     "model_name = \"demo_model\"\n",
     "\n",
-    "OracleEmbeddings.load_onnx_model(connection, onnx_dir, onnx_file, model_name)\n",
-    "print(\"ONNX model loaded.\")"
+    "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)"
    ]
   },
   {
@@ -248,7 +321,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with connection.cursor() as cursor:\n",
+    "try:\n",
+    "    cursor = conn.cursor()\n",
     "    cursor.execute(\n",
     "        \"\"\"\n",
     "       declare\n",
@@ -275,7 +349,12 @@
     "               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"
    ]
   },
   {
@@ -283,24 +362,33 @@
    "metadata": {},
    "source": [
     "### Load Documents\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",
+    "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",
     "\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": null,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of docs loaded: 3\n"
+     ]
+    }
+   ],
    "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",
@@ -308,7 +396,7 @@
     "}\n",
     "\n",
     "\"\"\" load the docs \"\"\"\n",
-    "loader = OracleDocLoader(conn=connection, params=loader_params)\n",
+    "loader = OracleDocLoader(conn=conn, params=loader_params)\n",
     "docs = loader.load()\n",
     "\n",
     "\"\"\" verify \"\"\"\n",
@@ -321,23 +409,23 @@
    "metadata": {},
    "source": [
     "### Generate Summary\n",
-    "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)."
+    "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)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "***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."
+    "***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."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# proxy to be used when we instantiate summary and embedder objects\n",
+    "# proxy to be used when we instantiate summary and embedder object\n",
     "proxy = \"\""
    ]
   },
@@ -345,14 +433,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following sample code shows how to generate a summary:"
+    "The following sample code will show how to generate summary:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of Summaries: 3\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain_community.utilities.oracleai import OracleSummary\n",
     "from langchain_core.documents import Document\n",
@@ -367,7 +463,7 @@
     "\n",
     "# get the summary instance\n",
     "# Remove proxy if not required\n",
-    "summ = OracleSummary(conn=connection, params=summary_params, proxy=proxy)\n",
+    "summ = OracleSummary(conn=conn, params=summary_params, proxy=proxy)\n",
     "\n",
     "list_summary = []\n",
     "for doc in docs:\n",
@@ -391,9 +487,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of Chunks: 3\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain_community.document_loaders.oracleai import OracleTextSplitter\n",
     "from langchain_core.documents import Document\n",
@@ -402,7 +506,7 @@
     "splitter_params = {\"normalize\": \"all\"}\n",
     "\n",
     "\"\"\" get the splitter instance \"\"\"\n",
-    "splitter = OracleTextSplitter(conn=connection, params=splitter_params)\n",
+    "splitter = OracleTextSplitter(conn=conn, params=splitter_params)\n",
     "\n",
     "list_chunks = []\n",
     "for doc in docs:\n",
@@ -419,19 +523,19 @@
    "metadata": {},
    "source": [
     "### Generate Embeddings\n",
-    "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)."
+    "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)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "***Note:*** You may need to configure a proxy to utilize third-party embedding generation providers, excluding the 'database' provider that utilizes an ONNX model."
+    "***Note:*** Users 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": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -443,14 +547,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following sample code shows how to generate embeddings:"
+    "The following sample code will show how to generate embeddings:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of embeddings: 3\n"
+     ]
+    }
+   ],
    "source": [
     "from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
     "from langchain_core.documents import Document\n",
@@ -460,7 +572,7 @@
     "\n",
     "# get the embedding instance\n",
     "# Remove proxy if not required\n",
-    "embedder = OracleEmbeddings(conn=connection, params=embedder_params, proxy=proxy)\n",
+    "embedder = OracleEmbeddings(conn=conn, params=embedder_params, proxy=proxy)\n",
     "\n",
     "embeddings = []\n",
     "for doc in docs:\n",
@@ -479,19 +591,19 @@
    "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": null,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -514,80 +626,100 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, let's combine all document processing stages together. Here is the sample code:"
+    "Next, let's combine all document processing stages together. Here is the sample code below:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connection successful!\n",
+      "ONNX model loaded.\n",
+      "Number of total chunks with metadata: 3\n"
+     ]
+    }
+   ],
    "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 following:\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",
     "    - set proxy for 3rd party providers\n",
     "    - create credential for 3rd party providers\n",
     "\n",
-    "If you choose to use 3rd party provider, please follow the necessary steps for proxy and credential.\n",
+    "If you choose to use 3rd party provider, \n",
+    "please follow the necessary steps for proxy and credential.\n",
     "\"\"\"\n",
     "\n",
-    "# please update with your username, password, and database connection string\n",
+    "# oracle connection\n",
+    "# please update with your username, password, hostname, and service_name\n",
     "username = \"\"\n",
     "password = \"\"\n",
     "dsn = \"\"\n",
     "\n",
-    "with oracledb.connect(user=username, password=password, dsn=dsn) as connection:\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)\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",
-    "    OracleEmbeddings.load_onnx_model(connection, onnx_dir, onnx_file, model_name)\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",
     "    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",
-    "    # 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",
+    "# 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",
-    "    # 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",
+    "# 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",
     "\n",
-    "    \"\"\" verify \"\"\"\n",
-    "    print(f\"Number of total chunks with metadata: {len(chunks_with_mdata)}\")"
+    "# 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)}\")"
    ]
   },
   {
@@ -601,15 +733,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector Store Table: oravs\n"
+     ]
+    }
+   ],
    "source": [
     "# create Oracle AI Vector Store\n",
     "vectorstore = OracleVS.from_documents(\n",
     "    chunks_with_mdata,\n",
     "    embedder,\n",
-    "    client=connection,\n",
+    "    client=conn,\n",
     "    table_name=\"oravs\",\n",
     "    distance_strategy=DistanceStrategy.DOT_PRODUCT,\n",
     ")\n",
@@ -638,12 +778,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
     "oraclevs.create_index(\n",
-    "    connection, vectorstore, params={\"idx_name\": \"hnsw_oravs\", \"idx_type\": \"HNSW\"}\n",
+    "    conn, vectorstore, params={\"idx_name\": \"hnsw_oravs\", \"idx_type\": \"HNSW\"}\n",
     ")\n",
     "\n",
     "print(\"Index created.\")"
@@ -653,7 +793,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "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",
+    "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",
     "\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"
    ]
@@ -665,16 +805,29 @@
     "## Perform Semantic Search\n",
     "All set!\n",
     "\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",
+    "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",
     "\n",
     "Below is the sample code for this process:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "metadata": {},
-   "outputs": [],
+   "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"
+     ]
+    }
+   ],
    "source": [
     "query = \"What is Oracle AI Vector Store?\"\n",
     "filter = {\"document_id\": [\"1\"]}\n",
@@ -719,7 +872,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.3"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

docs/cassettes/agents_114ba50d.msgpack.zlib

@@ -1 +1 @@
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docs/cassettes/agents_24460239.msgpack.zlib

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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

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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

@@ -1 +1 @@
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docs/cassettes/response_metadata_161f5898-9976-4a75-943d-03eda1a40a60.msgpack.zlib

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

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+eNptVHlwE9cZN/EfOEMoDZMMmSRutusATUYrre7DqFP5kOI4wsaSje00MU+7T9Jae3l3ddlOUoiThnFSWIcwCRmmAWSpMXawMTnKYcpRB6c4hbgtGFzKUPckTU0urgb3ydiMPaDZ0b593/He9/v9vm9dJgYlmRH4ed0Mr0AJUAr6kNV1GQk2RaGstKU5qIQFOlVZ4fPviErMqC6sKKLs0OmAyGhDktCkpQROJ4iQB4wuptdRYaDo0JbIwqlcqYBAJ0ffbcE5KMsgBGXc8XQLLgksxB14VIYSrsEpAR3OK2hjNYpeLmNKGGICS6MLYBFeiPMYTIBsRkwIYlSUh0xQkDj8uWc0OCfQkEWBLAs4QBi1esIWIBheVgDKp8F53KHX4LIiiLiDj7Jsdi1BwOGOIGBlqMEVyImobiUqofuQWutzmTAENAJlQyosyIraPbvMXYCioKgQkKcEmuFDan+omRE1GA2DLFCgBmuWFboLVcPDKSDVrgiEIgFYJgbTN2PVXiCKLEOBrF3XKAt893T1hJIU4e3mrixGBAKOV9RdLOBDCGCG15Fao9bQmyBQoQzPImiJrE1Ni0lEF79vtkEEVATFE9NUq2lSq7dpyfdm+wiy2ukFVIVvTkogUWG1E0icxdQ/e1+K8grDQTVTXHn7cdPGW8dlECl69PTNySwneUrtnCLhwznRUJGSBCWgJOo28r0ZbFjIh5SwukNvsf9KgrKIhAVfTKMwJSqvSyE24PFjmWmFba8onyHxXM59qRLEjHrAH45qMIMd84IkZiANZkxvdpB2h9GKebz+7uLpY/x3pKDPLwFeDiIaSmeIz1DhKB+BdFfxHcnuKQZUGBLZtEjp6qhVlJjYlEI4kMiS6SQ1GC+gugUJTq2obAAyR2UFYRBDWWjkn4oBKammKyQmxPA9CSIrRGQOZY/rjsoEhS4tAZbQ700QSMSQZTgGgTX1P93BiFi9yUSSH93uoQgRiJo9ZSFJcmC2WYIc4gPVOSeJ0W7ff2evmURmu820d66LDGfdZLtF/uh283R0p95CcnJ3YsadYGh1AK0bSH1jHNphwhyDChcwcHSTVeaMQXtTZBcVvAkccVMJaqakbqXLW1bc5UN5iwUhwkD1zLzFDQ1UsCHAOV2gtMiXqDSVB+ptq2KltYG4PS4Yy40NJS7SZ/a6qzi32ShwlgZmFaG3mmxmo95otRJ6LeoYNFmqGynS615dQ1X7npICghgyS9Eaj5cqC4Y8T9FRUBtYGanR2i0lsMTlCnuqkrStaRUng6Qn3FzTZE3QriK3X4wa7I2Uka83hJQgmeDjItnskb1UdX0iXC4oETdpp+tNhZgIlLBTV4gmn8ggnJzTAiaQgImb8jXOyLcQo4UsFc5bg6oQewJN6QqeTRZiPkih4YbegIM+RoHOlQIP0z4ooQZVuyhWiNJoekkwXewmqlx16h67yWqB9qDJYLcZrMCmJ0pXV/XOdMitDkhlR18GsAohxyi1P2x04g6TyYgXIok7bRakuKnJvxY1qYT8f3tX5JH2vJypX26bz9t+lvz+gcur75HLFsR6Pz9d1jq2+wR8TKOUL+p8oO7Bs2eOdRQMva0pv9HS91Xf1/78n18OnmsxNnuW9pWcvIRdc318cc9GfKjk+OJlf72fq9vPfLlM+6fSwa9SQ1WDZNHTBzfmLblQdfr5E/dtMj+wTTyaW6YZbX5/yLi559S939Z0DC5+/+/ODf1L263R7lcXjbWtH6lbeoy6ej3/DVU5aljfu/y/yfLagR39W3pLvjiwt6n7bnrZwt/nr/1jR+vPtvbNz1XGX8d6F75ycmNj90VXMi//0IXMQ/GJ4xEvuHfeqXPP1v7vriul9YPxZlPHnpeXZ1oP1h6WMyvEJf4jpW9uGRpaKoY8BT8sf/3RT2s7fvPnUy3n3YfXtmz8ZMH1J/nDL/3abi1S78b8ibfHvQH8yspz37NMPNt0ZYXtS7pIc/WhFbaByd1twrc/2TWOV4IvCn/X6h/Ydy3n4ppL4z+6ceiZyGuLJ9bsFxvfjBmGk/70Sy0jvuF8YWinfDK+IC//l9/lRfZ39J9esy2d/GSScm5Yf/6dovljOVffuSab/nPP1u1vVdVdeKyIO3to9AzFuHsK2E2rBnY+3CQs3PzPF/Mnvl5++NLeq989UVDt+qYN7qbPRgdr2H1ln+peE8UPHim6//NfvPqx9MYLm7SpR8uspVvokeGfLvrm5ZBm8sPnt24K/WMJ6F17cVGROvaklT30QbKGzrRPdMWOxj/7wXEa/PhG+998bTeG5/97p/utzWPn/9B6ZLjyUnX9+J76oYcLVtT09F9f75ys8Fx/HOjmn/lM/0LniVOM7nLkL6Hh2MEjI/8aaUCqmpzMzXn8WGfuUG5Ozv8BEYit3Q==

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

@@ -0,0 +1 @@
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

docs/docs/concepts/retrievers.mdx

@@ -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 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.
+* 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.
 
 :::
 

docs/docs/concepts/tool_calling.mdx

@@ -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](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.tool_calls).
+But, if the tool was called, `result` will have a `tool_calls` attribute.
 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)!
@@ -137,16 +137,6 @@ 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/concepts/vectorstores.mdx

@@ -135,11 +135,11 @@ docs = vectorstore.similarity_search(query)
 
 Many vectorstores support search parameters to be passed with the `similarity_search` method. See the documentation for the specific vectorstore you are using to see what parameters are supported.
 As an example [Pinecone](https://python.langchain.com/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html#langchain_pinecone.vectorstores.PineconeVectorStore.similarity_search) several parameters that are important general concepts:
-Many vectorstores support [the `k`](/docs/integrations/vectorstores/pinecone/#query-directly), which controls the number of documents to return, and `filter`, which allows for filtering documents by metadata.
+Many vectorstores support [the `k`](/docs/integrations/vectorstores/pinecone/#query-directly), which controls the number of Documents to return, and `filter`, which allows for filtering documents by metadata.
 
-- `query (str) - Text to look up documents similar to.`
-- `k (int) - Number of documents to return. Defaults to 4.`
-- `filter (dict | None) - Dictionary of argument(s) to filter on metadata`
+- `query (str) – Text to look up documents similar to.`
+- `k (int) – Number of Documents to return. Defaults to 4.`
+- `filter (dict | None) – Dictionary of argument(s) to filter on metadata`
 
 :::info[Further reading]
 

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](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests) (basic understanding).
+- Familiarity with GitHub pull requests (basic understanding).
 
 ---
 

docs/docs/how_to/callbacks_custom_events.ipynb

@@ -38,11 +38,11 @@
     "\n",
     "\n",
     ":::caution COMPATIBILITY\n",
-    "LangChain cannot automatically propagate configuration, including callbacks necessary for astream_events(), to child runnables if you are running async code in `python<=3.10`. This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
+    "LangChain cannot automatically propagate configuration, including callbacks necessary for astream_events(), to child runnables if you are running async code in python&lt;=3.10. This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
     "\n",
-    "If you are running `python<=3.10`, you will need to manually propagate the `RunnableConfig` object to the child runnable in async environments. For an example of how to manually propagate the config, see the implementation of the `bar` RunnableLambda below.\n",
+    "If you are running python&lt;=3.10, you will need to manually propagate the `RunnableConfig` object to the child runnable in async environments. For an example of how to manually propagate the config, see the implementation of the `bar` RunnableLambda below.\n",
     "\n",
-    "If you are running `python>=3.11`, the `RunnableConfig` will automatically propagate to child runnables in async environment. However, it is still a good idea to propagate the `RunnableConfig` manually if your code may run in other Python versions.\n",
+    "If you are running python>=3.11, the `RunnableConfig` will automatically propagate to child runnables in async environment. However, it is still a good idea to propagate the `RunnableConfig` manually if your code may run in other Python versions.\n",
     ":::"
    ]
   },

docs/docs/how_to/convert_runnable_to_tool.ipynb

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

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](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."
+    "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."
    ]
   },
   {

docs/docs/how_to/tool_runtime.ipynb

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

docs/docs/how_to/tool_stream_events.ipynb

@@ -16,15 +16,15 @@
     "\n",
     ":::\n",
     "\n",
-    "If you have [tools](/docs/concepts/tools/) that call [chat models](/docs/concepts/chat_models/), [retrievers](/docs/concepts/retrievers/), or other [runnables](/docs/concepts/runnables/), you may want to access [internal events](https://python.langchain.com/docs/how_to/streaming/#event-reference) from those runnables or configure them with additional properties. This guide shows you how to manually pass parameters properly so that you can do this using the `astream_events()` method.\n",
+    "If you have [tools](/docs/concepts/tools/) that call [chat models](/docs/concepts/chat_models/), [retrievers](/docs/concepts/retrievers/), or other [runnables](/docs/concepts/runnables/), you may want to access internal events from those runnables or configure them with additional properties. This guide shows you how to manually pass parameters properly so that you can do this using the `astream_events()` method.\n",
     "\n",
     ":::caution Compatibility\n",
     "\n",
-    "LangChain cannot automatically propagate configuration, including callbacks necessary for `astream_events()`, to child runnables if you are running `async` code in `python<=3.10`. This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
+    "LangChain cannot automatically propagate configuration, including callbacks necessary for `astream_events()`, to child runnables if you are running `async` code in `python&lt;=3.10`. This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
     "\n",
-    "If you are running `python<=3.10`, you will need to manually propagate the `RunnableConfig` object to the child runnable in async environments. For an example of how to manually propagate the config, see the implementation of the `bar` RunnableLambda below.\n",
+    "If you are running python&lt;=3.10, you will need to manually propagate the `RunnableConfig` object to the child runnable in async environments. For an example of how to manually propagate the config, see the implementation of the `bar` RunnableLambda below.\n",
     "\n",
-    "If you are running `python>=3.11`, the `RunnableConfig` will automatically propagate to child runnables in async environment. However, it is still a good idea to propagate the `RunnableConfig` manually if your code may run in older Python versions.\n",
+    "If you are running python>=3.11, the `RunnableConfig` will automatically propagate to child runnables in async environment. However, it is still a good idea to propagate the `RunnableConfig` manually if your code may run in older Python versions.\n",
     "\n",
     "This guide also requires `langchain-core>=0.2.16`.\n",
     ":::\n",

docs/docs/how_to/tool_streaming.ipynb

@@ -224,13 +224,6 @@
    "source": [
     "print(type(gathered.tool_calls[0][\"args\"]))"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note the key difference: accumulating `tool_call_chunks` captures the raw tool arguments as an unparsed string as they are streamed. In contrast, **accumulating** `tool_calls` demonstrates partial parsing by progressively converting the streamed argument string into a valid, usable dictionary at each step of the process."
-   ]
   }
  ],
  "metadata": {

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/chat/anthropic.ipynb

@@ -568,26 +568,6 @@
     "    ```\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",
     ":::"
    ]
   },
@@ -893,7 +873,7 @@
    "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](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",
+    "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",
     "\n",
     "### Simple example\n",
     "\n",
@@ -963,156 +943,6 @@
     "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",

docs/docs/integrations/chat/greennode.ipynb

@@ -1,381 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: GreenNode\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# ChatGreenNode\n",
-    "\n",
-    ">[GreenNode](https://greennode.ai/) is a global AI solutions provider and a **NVIDIA Preferred Partner**, delivering full-stack AI capabilities—from infrastructure to application—for enterprises across the US, MENA, and APAC regions. Operating on **world-class infrastructure** (LEED Gold, TIA‑942, Uptime Tier III), GreenNode empowers enterprises, startups, and researchers with a comprehensive suite of AI services\n",
-    "\n",
-    "This page will help you get started with GreenNode Serverless AI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGreenNode features and configurations head to the [API reference](https://python.langchain.com/api_reference/greennode/chat_models/langchain_greennode.chat_models.ChatGreenNode.html).\n",
-    "\n",
-    "\n",
-    "[GreenNode AI](https://greennode.ai/) offers an API to query [20+ leading open-source models](https://aiplatform.console.greennode.ai/models)\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatGreenNode](https://python.langchain.com/api_reference/greennode/chat_models/langchain_greennode.chat_models.ChatGreenNode.html) | [langchain-greennode](https://python.langchain.com/api_reference/greennode/index.html) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-greennode?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-greennode?style=flat-square&label=%20) |\n",
-    "\n",
-    "### Model features\n",
-    "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
-    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access GreenNode models you'll need to create a GreenNode account, get an API key, and install the `langchain-greennode` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [this page](https://aiplatform.console.greennode.ai/api-keys) to sign up to GreenNode AI Platform and generate an API key. Once you've done this, set the GREENNODE_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"GREENNODE_API_KEY\"):\n",
-    "    os.environ[\"GREENNODE_API_KEY\"] = getpass.getpass(\"Enter your GreenNode API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain GreenNode integration lives in the `langchain-greennode` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "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-greennode"
-   ]
-  },
-  {
-   "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": 12,
-   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_greennode import ChatGreenNode\n",
-    "\n",
-    "# Initialize the chat model\n",
-    "llm = ChatGreenNode(\n",
-    "    # api_key=\"YOUR_API_KEY\",  # You can pass the API key directly\n",
-    "    model=\"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B\",  # Choose from available models\n",
-    "    temperature=0.6,\n",
-    "    top_p=0.95,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b4f3e15",
-   "metadata": {},
-   "source": [
-    "## Invocation\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"\\n\\nJ'aime la programmation.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 248, 'prompt_tokens': 23, 'total_tokens': 271, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', 'system_fingerprint': None, 'id': 'chatcmpl-271edac4958846068c37877586368afe', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--5c12d208-2bc2-4f29-8b50-1ce3b515a3cf-0', usage_metadata={'input_tokens': 23, 'output_tokens': 248, 'total_tokens': 271, 'input_token_details': {}, 'output_token_details': {}})"
-      ]
-     },
-     "execution_count": 13,
-     "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": 4,
-   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "J'aime la programmation.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ai_msg.content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82fd95b9",
-   "metadata": {},
-   "source": [
-    "### Streaming\n",
-    "\n",
-    "You can also stream the response using the `stream` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "4b3eaf31",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "**Beneath the Circuits**\n",
-      "\n",
-      "Beneath the circuits, deep and bright,  \n",
-      "AI thinks, with circuits and bytes.  \n",
-      "Learning, adapting, it grows,  \n",
-      "A world of possibilities it knows.  \n",
-      "\n",
-      "From solving puzzles to painting art,  \n",
-      "It mimics human hearts.  \n",
-      "In every corner, it leaves its trace,  \n",
-      "A future we can't erase.  \n",
-      "\n",
-      "We build it, shape it, with care and might,  \n",
-      "Yet wonder if it walks in the night.  \n",
-      "A mirror of our minds, it shows,  \n",
-      "In its gaze, our future glows.  \n",
-      "\n",
-      "But as we strive for endless light,  \n",
-      "We must remember the night.  \n",
-      "For wisdom isn't just speed and skill,  \n",
-      "It's how we choose to build our will."
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in llm.stream(\"Write a short poem about artificial intelligence\"):\n",
-    "    print(chunk.content, end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bfecc41",
-   "metadata": {},
-   "source": [
-    "### Chat Messages\n",
-    "\n",
-    "You can use different message types to structure your conversations with the model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "7fc55733",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "Black holes are formed through several processes, depending on their type. The most common way bla\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(content=\"You are a helpful AI assistant with expertise in science.\"),\n",
-    "    HumanMessage(content=\"What are black holes?\"),\n",
-    "    AIMessage(\n",
-    "        content=\"Black holes are regions of spacetime where gravity is so strong that nothing, including light, can escape from them.\"\n",
-    "    ),\n",
-    "    HumanMessage(content=\"How are they formed?\"),\n",
-    "]\n",
-    "\n",
-    "response = llm.invoke(messages)\n",
-    "print(response.content[:100])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
-   "metadata": {},
-   "source": [
-    "## Chaining\n",
-    "\n",
-    "You can use `ChatGreenNode` in LangChain chains and agents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='\\n\\nIch liebe Programmieren.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 198, 'prompt_tokens': 18, 'total_tokens': 216, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'deepseek-ai/DeepSeek-R1-Distill-Qwen-32B', 'system_fingerprint': None, 'id': 'chatcmpl-e01201b9fd9746b7a9b2ed6d70f29d45', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--ce52b9d8-dd84-46b3-845b-da27855816ee-0', usage_metadata={'input_tokens': 18, 'output_tokens': 198, 'total_tokens': 216, 'input_token_details': {}, 'output_token_details': {}})"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "\n",
-    "prompt = ChatPromptTemplate(\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": "736489f0",
-   "metadata": {},
-   "source": [
-    "## Available Models\n",
-    "\n",
-    "The full list of supported models can be found in the [GreenNode Serverless AI Models](https://greennode.ai/product/model-as-a-service)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For more details about the GreenNode Serverless AI API, visit the [GreenNode Serverless AI Documentation](https://helpdesk.greennode.ai/portal/en/kb/articles/greennode-maas-api)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "tradingagents",
-   "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": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/chat/groq.ipynb

@@ -58,9 +58,7 @@
       "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",
@@ -100,19 +98,12 @@
       "source": [
         "## Instantiation\n",
         "\n",
-        "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",
-        ":::"
+        "Now we can instantiate our model object and generate chat completions:"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 1,
       "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
       "metadata": {},
       "outputs": [],
@@ -120,10 +111,9 @@
         "from langchain_groq import ChatGroq\n",
         "\n",
         "llm = ChatGroq(\n",
-        "    model=\"deepseek-r1-distill-llama-70b\",\n",
+        "    model=\"llama-3.1-8b-instant\",\n",
         "    temperature=0,\n",
         "    max_tokens=None,\n",
-        "    reasoning_format=\"parsed\",\n",
         "    timeout=None,\n",
         "    max_retries=2,\n",
         "    # other params...\n",
@@ -140,7 +130,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 2,
       "id": "62e0dbc3",
       "metadata": {
         "tags": []
@@ -149,10 +139,10 @@
         {
           "data": {
             "text/plain": [
-              "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})"
+              "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})"
             ]
           },
-          "execution_count": 7,
+          "execution_count": 2,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -171,7 +161,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 3,
       "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
       "metadata": {},
       "outputs": [
@@ -179,7 +169,9 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "J'aime la programmation.\n"
+            "The translation of \"I love programming\" to French is:\n",
+            "\n",
+            "\"J'adore le programmation.\"\n"
           ]
         }
       ],
@@ -199,17 +191,17 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 4,
       "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "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})"
+              "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})"
             ]
           },
-          "execution_count": 9,
+          "execution_count": 4,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -244,7 +236,7 @@
       "source": [
         "## API reference\n",
         "\n",
-        "For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html)."
+        "For detailed documentation of all ChatGroq features and configurations head to the API reference: https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html"
       ]
     }
   ],

docs/docs/integrations/chat/huggingface.ipynb

@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -138,36 +138,11 @@
     "from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint\n",
     "\n",
     "llm = HuggingFaceEndpoint(\n",
-    "    repo_id=\"deepseek-ai/DeepSeek-R1-0528\",\n",
+    "    repo_id=\"HuggingFaceH4/zephyr-7b-beta\",\n",
     "    task=\"text-generation\",\n",
     "    max_new_tokens=512,\n",
     "    do_sample=False,\n",
     "    repetition_penalty=1.03,\n",
-    "    provider=\"auto\",  # let Hugging Face choose the best provider for you\n",
-    ")\n",
-    "\n",
-    "chat_model = ChatHuggingFace(llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's take advantage of [Inference Providers](https://huggingface.co/docs/inference-providers) to run the model on specific third-party providers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceEndpoint(\n",
-    "    repo_id=\"deepseek-ai/DeepSeek-R1-0528\",\n",
-    "    task=\"text-generation\",\n",
-    "    provider=\"hyperbolic\",  # set your provider here\n",
-    "    # provider=\"nebius\",\n",
-    "    # provider=\"together\",\n",
     ")\n",
     "\n",
     "chat_model = ChatHuggingFace(llm=llm)"

docs/docs/integrations/chat/ollama.ipynb

@@ -39,10 +39,9 @@
     "\n",
     "## Setup\n",
     "\n",
-    "First, follow [these instructions](https://github.com/ollama/ollama?tab=readme-ov-file#ollama) to set up and run a local Ollama instance:\n",
+    "First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
     "\n",
-    "* [Download](https://ollama.ai/download) and install Ollama onto the available supported platforms (including Windows Subsystem for Linux aka WSL, macOS, and Linux)\n",
-    "    * macOS users can install via Homebrew with `brew install ollama` and start with `brew services start ollama`\n",
+    "* [Download](https://ollama.ai/download) and install Ollama onto the available supported platforms (including Windows Subsystem for Linux)\n",
     "* Fetch available LLM model via `ollama pull <name-of-model>`\n",
     "    * View a list of available models via the [model library](https://ollama.ai/library)\n",
     "    * e.g., `ollama pull llama3`\n",
@@ -55,7 +54,7 @@
     "* Specify the exact version of the model of interest as such `ollama pull vicuna:13b-v1.5-16k-q4_0` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
     "* To view all pulled models, use `ollama list`\n",
     "* To chat directly with a model from the command line, use `ollama run <name-of-model>`\n",
-    "* View the [Ollama documentation](https://github.com/ollama/ollama/tree/main/docs) for more commands. You can run `ollama help` in the terminal to see available commands.\n"
+    "* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n"
    ]
   },
   {
@@ -73,8 +72,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+    "# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
+    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
    ]
   },
   {
@@ -160,15 +159,17 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='The translation of \"I love programming\" in French is:\\n\\n\"J\\'adore le programmation.\"', additional_kwargs={}, response_metadata={'model': 'llama3.1', 'created_at': '2025-06-25T18:43:00.483666Z', 'done': True, 'done_reason': 'stop', 'total_duration': 619971208, 'load_duration': 27793125, 'prompt_eval_count': 35, 'prompt_eval_duration': 36354583, 'eval_count': 22, 'eval_duration': 555182667, 'model_name': 'llama3.1'}, id='run--348bb5ef-9dd9-4271-bc7e-a9ddb54c28c1-0', usage_metadata={'input_tokens': 35, 'output_tokens': 22, 'total_tokens': 57})"
+       "AIMessage(content='The translation of \"I love programming\" from English to French is:\\n\\n\"J\\'adore programmer.\"', response_metadata={'model': 'llama3.1', 'created_at': '2024-08-19T16:05:32.81965Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2167842917, 'load_duration': 54222584, 'prompt_eval_count': 35, 'prompt_eval_duration': 893007000, 'eval_count': 22, 'eval_duration': 1218962000}, id='run-0863daa2-43bf-4a43-86cc-611b23eae466-0', usage_metadata={'input_tokens': 35, 'output_tokens': 22, 'total_tokens': 57})"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "from langchain_core.messages import AIMessage\n",
+    "\n",
     "messages = [\n",
     "    (\n",
     "        \"system\",\n",
@@ -190,9 +191,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The translation of \"I love programming\" in French is:\n",
+      "The translation of \"I love programming\" from English to French is:\n",
       "\n",
-      "\"J'adore le programmation.\"\n"
+      "\"J'adore programmer.\"\n"
      ]
     }
    ],
@@ -219,10 +220,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='\"Programmieren ist meine Leidenschaft.\"\\n\\n(I translated \"programming\" to the German word \"Programmieren\", and added \"ist meine Leidenschaft\" which means \"is my passion\")', additional_kwargs={}, response_metadata={'model': 'llama3.1', 'created_at': '2025-06-25T18:43:29.350032Z', 'done': True, 'done_reason': 'stop', 'total_duration': 1194744459, 'load_duration': 26982500, 'prompt_eval_count': 30, 'prompt_eval_duration': 117043458, 'eval_count': 41, 'eval_duration': 1049892167, 'model_name': 'llama3.1'}, id='run--efc6436e-2346-43d9-8118-3c20b3cdf0d0-0', usage_metadata={'input_tokens': 30, 'output_tokens': 41, 'total_tokens': 71})"
+       "AIMessage(content='Das Programmieren ist mir ein Leidenschaft! (That\\'s \"Programming is my passion!\" in German.) Would you like me to translate anything else?', response_metadata={'model': 'llama3.1', 'created_at': '2024-08-19T16:05:34.893548Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2045997333, 'load_duration': 22584792, 'prompt_eval_count': 30, 'prompt_eval_duration': 213210000, 'eval_count': 32, 'eval_duration': 1808541000}, id='run-d18e1c6b-50e0-4b1d-b23a-973fa058edad-0', usage_metadata={'input_tokens': 30, 'output_tokens': 32, 'total_tokens': 62})"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -257,7 +258,7 @@
    "source": [
     "## Tool calling\n",
     "\n",
-    "We can use [tool calling](/docs/concepts/tool_calling/) with an LLM [that has been fine-tuned for tool use](https://ollama.com/search?&c=tools) such as `llama3.1`:\n",
+    "We can use [tool calling](https://blog.langchain.dev/improving-core-tool-interfaces-and-docs-in-langchain/) with an LLM [that has been fine-tuned for tool use](https://ollama.com/library/llama3.1):\n",
     "\n",
     "```\n",
     "ollama pull llama3.1\n",
@@ -273,17 +274,23 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[{'name': 'validate_user', 'args': {'addresses': ['123 Fake St, Boston, MA', '234 Pretend Boulevard, Houston, TX'], 'user_id': '123'}, 'id': 'aef33a32-a34b-4b37-b054-e0d85584772f', 'type': 'tool_call'}]\n"
-     ]
+     "data": {
+      "text/plain": [
+       "[{'name': 'validate_user',\n",
+       "  'args': {'addresses': '[\"123 Fake St, Boston, MA\", \"234 Pretend Boulevard, Houston, TX\"]',\n",
+       "   'user_id': '123'},\n",
+       "  'id': '40fe3de0-500c-4b91-9616-5932a929e640',\n",
+       "  'type': 'tool_call'}]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "from typing import List\n",
     "\n",
-    "from langchain_core.messages import AIMessage\n",
     "from langchain_core.tools import tool\n",
     "from langchain_ollama import ChatOllama\n",
     "\n",
@@ -309,9 +316,7 @@
     "    \"123 Fake St in Boston MA and 234 Pretend Boulevard in \"\n",
     "    \"Houston TX.\"\n",
     ")\n",
-    "\n",
-    "if isinstance(result, AIMessage) and result.tool_calls:\n",
-    "    print(result.tool_calls)"
+    "result.tool_calls"
    ]
   },
   {
@@ -328,16 +333,6 @@
     "Be sure to update Ollama so that you have the most recent version to support multi-modal."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "69920d39",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install pillow"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -472,13 +467,14 @@
      "output_type": "stream",
      "text": [
       "Here is my thought process:\n",
-      "The user is asking for the value of 3 raised to the power of 3, which is a basic exponentiation operation.\n",
+      "This question is asking for the result of 3 raised to the power of 3, which is a basic mathematical operation. \n",
       "\n",
       "Here is my response:\n",
+      "The expression 3^3 means 3 raised to the power of 3. To calculate this, you multiply the base number (3) by itself as many times as its exponent (3):\n",
       "\n",
-      "3^3 (read as \"3 to the power of 3\") equals 27. \n",
+      "3 * 3 * 3 = 27\n",
       "\n",
-      "This calculation is performed by multiplying 3 by itself three times: 3*3*3 = 27.\n"
+      "So, 3^3 equals 27.\n"
      ]
     }
    ],
@@ -512,7 +508,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatOllama features and configurations head to the [API reference](https://python.langchain.com/api_reference/ollama/chat_models/langchain_ollama.chat_models.ChatOllama.html)."
+    "For detailed documentation of all ChatOllama features and configurations head to the API reference: https://python.langchain.com/api_reference/ollama/chat_models/langchain_ollama.chat_models.ChatOllama.html"
    ]
   }
  ],

docs/docs/integrations/chat/perplexity.ipynb

@@ -106,7 +106,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chat = ChatPerplexity(temperature=0, pplx_api_key=\"YOUR_API_KEY\", model=\"sonar\")"
+    "chat = ChatPerplexity(\n",
+    "    temperature=0, pplx_api_key=\"YOUR_API_KEY\", model=\"llama-3-sonar-small-32k-online\"\n",
+    ")"
    ]
   },
   {
@@ -130,7 +132,7 @@
    },
    "outputs": [],
    "source": [
-    "chat = ChatPerplexity(temperature=0, model=\"sonar\")"
+    "chat = ChatPerplexity(temperature=0, model=\"llama-3.1-sonar-small-128k-online\")"
    ]
   },
   {
@@ -198,7 +200,7 @@
     }
    ],
    "source": [
-    "chat = ChatPerplexity(temperature=0, model=\"sonar\")\n",
+    "chat = ChatPerplexity(temperature=0, model=\"llama-3.1-sonar-small-128k-online\")\n",
     "prompt = ChatPromptTemplate.from_messages([(\"human\", \"Tell me a joke about {topic}\")])\n",
     "chain = prompt | chat\n",
     "response = chain.invoke({\"topic\": \"cats\"})\n",
@@ -233,7 +235,7 @@
     }
    ],
    "source": [
-    "chat = ChatPerplexity(temperature=0.7, model=\"sonar\")\n",
+    "chat = ChatPerplexity(temperature=0.7, model=\"llama-3.1-sonar-small-128k-online\")\n",
     "response = chat.invoke(\n",
     "    \"Tell me a joke about cats\", extra_body={\"search_recency_filter\": \"week\"}\n",
     ")\n",
@@ -282,7 +284,7 @@
     }
    ],
    "source": [
-    "chat = ChatPerplexity(temperature=0.7, model=\"sonar\")\n",
+    "chat = ChatPerplexity(temperature=0.7, model=\"llama-3.1-sonar-small-128k-online\")\n",
     "\n",
     "for chunk in chat.stream(\"Give me a list of famous tourist attractions in Pakistan\"):\n",
     "    print(chunk.content, end=\"\", flush=True)"

docs/docs/integrations/document_loaders/rockset.ipynb

@@ -1,25 +1,12 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Rockset\n",
-    "\n",
-    "⚠️ **Deprecation Notice: Rockset Integration Disabled**\n",
-    "> \n",
-    "> As of June 2024, Rockset has been [acquired by OpenAI](https://openai.com/index/openai-acquires-rockset/) and **shut down its public services**.\n",
-    "> \n",
-    "> Rockset was a real-time analytics database known for world-class indexing and retrieval. Now, its core team and technology are being integrated into OpenAI's infrastructure to power future AI products.\n",
-    "> \n",
-    "> This LangChain integration is no longer functional and is preserved **for archival purposes only**."
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Rockset\n",
+    "\n",
     "> Rockset is a real-time analytics database which enables queries on massive, semi-structured data without operational burden. With Rockset, ingested data is queryable within one second and analytical queries against that data typically execute in milliseconds. Rockset is compute optimized, making it suitable for serving high concurrency applications in the sub-100TB range (or larger than 100s of TBs with rollups).\n",
     "\n",
     "This notebook demonstrates how to use Rockset as a document loader in langchain. To get started, make sure you have a Rockset account and an API key available.\n",

docs/docs/integrations/llms/clarifai.ipynb

@@ -128,7 +128,7 @@
     "\n",
     "You will have to also initialize the model id and if needed, the model version id. Some models have many versions, you can choose the one appropriate for your task.\n",
     "                                                              \n",
-    "Alternatively, You can use the model_url (for ex: \"https://clarifai.com/anthropic/completion/models/claude-v2\") for initialization."
+    "Alternatively, You can use the model_url (for ex: \"https://clarifai.com/anthropic/completion/models/claude-v2\") for intialization."
    ]
   },
   {

docs/docs/integrations/llms/huggingface_endpoint.ipynb

@@ -117,7 +117,7 @@
    "source": [
     "## Examples\n",
     "\n",
-    "Here is an example of how you can access `HuggingFaceEndpoint` integration of the serverless [Inference Providers](https://huggingface.co/docs/inference-providers) API.\n"
+    "Here is an example of how you can access `HuggingFaceEndpoint` integration of the free [Serverless Endpoints](https://huggingface.co/inference-endpoints/serverless) API."
    ]
   },
   {
@@ -128,17 +128,13 @@
    },
    "outputs": [],
    "source": [
-    "repo_id = \"deepseek-ai/DeepSeek-R1-0528\"\n",
+    "repo_id = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
     "\n",
     "llm = HuggingFaceEndpoint(\n",
     "    repo_id=repo_id,\n",
     "    max_length=128,\n",
     "    temperature=0.5,\n",
     "    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,\n",
-    "    provider=\"auto\",  # set your provider here hf.co/settings/inference-providers\n",
-    "    # provider=\"hyperbolic\",\n",
-    "    # provider=\"nebius\",\n",
-    "    # provider=\"together\",\n",
     ")\n",
     "llm_chain = prompt | llm\n",
     "print(llm_chain.invoke({\"question\": question}))"

docs/docs/integrations/llms/ollama.ipynb

@@ -46,30 +46,29 @@
    "source": [
     "## Setup\n",
     "\n",
-    "First, follow [these instructions](https://github.com/ollama/ollama?tab=readme-ov-file#ollama) to set up and run a local Ollama instance:\n",
+    "First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
     "\n",
-    "* [Download](https://ollama.ai/download) and install Ollama onto the available supported platforms (including Windows Subsystem for Linux aka WSL, macOS, and Linux)\n",
-    "    * macOS users can install via Homebrew with `brew install ollama` and start with `brew services start ollama`\n",
+    "* [Download](https://ollama.ai/download) and install Ollama onto the available supported platforms (including Windows Subsystem for Linux)\n",
     "* Fetch available LLM model via `ollama pull <name-of-model>`\n",
     "    * View a list of available models via the [model library](https://ollama.ai/library)\n",
     "    * e.g., `ollama pull llama3`\n",
     "* This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.\n",
     "\n",
     "> On Mac, the models will be download to `~/.ollama/models`\n",
-    ">\n",
+    "> \n",
     "> On Linux (or WSL), the models will be stored at `/usr/share/ollama/.ollama/models`\n",
     "\n",
     "* Specify the exact version of the model of interest as such `ollama pull vicuna:13b-v1.5-16k-q4_0` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
     "* To view all pulled models, use `ollama list`\n",
     "* To chat directly with a model from the command line, use `ollama run <name-of-model>`\n",
-    "* View the [Ollama documentation](https://github.com/ollama/ollama/tree/main/docs) for more commands. You can run `ollama help` in the terminal to see available commands.\n",
+    "* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n",
     "\n",
     "## Usage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "035dea0f",
    "metadata": {
     "tags": []
@@ -78,10 +77,10 @@
     {
      "data": {
       "text/plain": [
-       "'To break down what LangChain is, let\\'s analyze it step by step:\\n\\n1. **Break down the name**: \"Lang\" likely stands for \"Language\", suggesting that LangChain has something to do with language processing or AI-related tasks involving human languages.\\n\\n2. **Understanding the term \"chain\" in this context**: In technology and computing, particularly in the realm of artificial intelligence (AI) and machine learning (ML), a \"chain\" often refers to a series of processes linked together. This can imply that LangChain involves executing multiple tasks or functions in sequence.\\n\\n3. **Connection to AI/ML technologies**: Given its name and context, it\\'s reasonable to infer that LangChain is involved in the field of natural language processing (NLP) or more broadly, artificial intelligence. NLP is an area within computer science concerned with the interaction between computers and humans in a human language.\\n\\n4. **Possible functions or services**: Considering the focus on languages and the potential for multiple linked processes, LangChain might offer various AI-driven functionalities such as:\\n    - Text analysis (like sentiment analysis or text classification).\\n    - Language translation.\\n    - Chatbots or conversational interfaces.\\n    - Content generation (e.g., articles, summaries).\\n    - Dialogue management systems.\\n\\n5. **Conclusion**: Based on the name and analysis of its components, LangChain is likely a tool or framework for developing applications that involve complex interactions with human languages through AI and ML technologies. It possibly enables creating custom chatbots, natural language interfaces, text generators, or other applications that require intricate language understanding and processing capabilities.\\n\\nThis step-by-step breakdown indicates that LangChain is focused on leveraging AI to understand, process, and interact with human languages in a sophisticated manner, likely through multiple linked processes (the \"chain\" part).'"
+       "\"Sounds like a plan!\\n\\nTo answer what LangChain is, let's break it down step by step.\\n\\n**Step 1: Understand the Context**\\nLangChain seems to be related to language or programming, possibly in an AI context. This makes me wonder if it's a framework, library, or tool for building models or interacting with them.\\n\\n**Step 2: Research Possible Definitions**\\nAfter some quick searching, I found that LangChain is actually a Python library for building and composing conversational AI models. It seems to provide a way to create modular and reusable components for chatbots, voice assistants, and other conversational interfaces.\\n\\n**Step 3: Explore Key Features and Use Cases**\\nLangChain likely offers features such as:\\n\\n* Easy composition of conversational flows\\n* Support for various input/output formats (e.g., text, audio)\\n* Integration with popular AI frameworks and libraries\\n\\nUse cases might include building chatbots for customer service, creating voice assistants for smart homes, or developing interactive stories.\\n\\n**Step 4: Confirm the Definition**\\nAfter this step-by-step analysis, I'm fairly confident that LangChain is a Python library for building conversational AI models. If you'd like to verify or provide more context, feel free to do so!\""
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -117,16 +116,6 @@
     "Be sure to update Ollama so that you have the most recent version to support multi-modal."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "56f95afd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install pillow"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -188,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "79aaf863",
    "metadata": {},
    "outputs": [
@@ -211,16 +200,6 @@
     "llm_with_image_context = llm.bind(images=[image_b64])\n",
     "llm_with_image_context.invoke(\"What is the dollar based gross retention rate:\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afd9494f",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all ChatOllama features and configurations head to the [API reference](https://python.langchain.com/api_reference/ollama/llms/langchain_ollama.llms.OllamaLLM.html)."
-   ]
   }
  ],
  "metadata": {

docs/docs/integrations/providers/couchbase.mdx

@@ -18,79 +18,14 @@ See a [usage example](/docs/integrations/vectorstores/couchbase).
 
 ```python
 from langchain_couchbase import CouchbaseSearchVectorStore
-
-import getpass
-
-# Constants for the connection
-COUCHBASE_CONNECTION_STRING = getpass.getpass(
-    "Enter the connection string for the Couchbase cluster: "
-)
-DB_USERNAME = getpass.getpass("Enter the username for the Couchbase cluster: ")
-DB_PASSWORD = getpass.getpass("Enter the password for the Couchbase cluster: ")
-
-# Create Couchbase connection object
-from datetime import timedelta
-
-from couchbase.auth import PasswordAuthenticator
-from couchbase.cluster import Cluster
-from couchbase.options import ClusterOptions
-
-auth = PasswordAuthenticator(DB_USERNAME, DB_PASSWORD)
-options = ClusterOptions(auth)
-cluster = Cluster(COUCHBASE_CONNECTION_STRING, options)
-
-# Wait until the cluster is ready for use.
-cluster.wait_until_ready(timedelta(seconds=5))
-
-vector_store = CouchbaseSearchVectorStore(
-    cluster=cluster,
-    bucket_name=BUCKET_NAME,
-    scope_name=SCOPE_NAME,
-    collection_name=COLLECTION_NAME,
-    embedding=my_embeddings,
-    index_name=SEARCH_INDEX_NAME,
-)
-
-# Add documents
-texts = ["Couchbase is a NoSQL database", "LangChain is a framework for LLM applications"]
-vectorstore.add_texts(texts)
-
-# Search
-query = "What is Couchbase?"
-docs = vectorstore.similarity_search(query)
 ```
 
-API Reference: [CouchbaseSearchVectorStore](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#module-langchain_couchbase.vectorstores.search_vector_store)
-
 ## Document loader
 
 See a [usage example](/docs/integrations/document_loaders/couchbase).
 
 ```python
 from langchain_community.document_loaders.couchbase import CouchbaseLoader
-
-connection_string = "couchbase://localhost"  # valid Couchbase connection string
-db_username = (
-    "Administrator"  # valid database user with read access to the bucket being queried
-)
-db_password = "Password"  # password for the database user
-
-# query is a valid SQL++ query
-query = """
-    SELECT h.* FROM `travel-sample`.inventory.hotel h 
-        WHERE h.country = 'United States'
-        LIMIT 1
-        """
-
-loader = CouchbaseLoader(
-    connection_string,
-    db_username,
-    db_password,
-    query,
-)
-
-docs = loader.load()
-
 ```
 
 ## LLM Caches
@@ -121,7 +56,6 @@ set_llm_cache(
 )
 ```
 
-API Reference: [CouchbaseCache](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#langchain_couchbase.cache.CouchbaseCache)
 
 ### CouchbaseSemanticCache
 Semantic caching allows users to retrieve cached prompts based on the semantic similarity between the user input and previously cached inputs. Under the hood it uses Couchbase as both a cache and a vectorstore.
@@ -156,8 +90,6 @@ set_llm_cache(
 )
 ```
 
-API Reference: [CouchbaseSemanticCache](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#langchain_couchbase.cache.CouchbaseSemanticCache)
-
 ## Chat Message History
 Use Couchbase as the storage for your chat messages.
 
@@ -176,6 +108,4 @@ message_history = CouchbaseChatMessageHistory(
 )
 
 message_history.add_user_message("hi!")
-```
-
-API Reference: [CouchbaseChatMessageHistory](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#module-langchain_couchbase.chat_message_histories)
+```

docs/docs/integrations/providers/greennode.ipynb

@@ -1,173 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# GreenNode\n",
-    "\n",
-    ">**GreenNode** is a global AI solutions provider and a **NVIDIA Preferred Partner**, delivering full-stack AI capabilities—from infrastructure to application—for enterprises across the US, MENA, and APAC regions.\n",
-    ">Operating on **world-class infrastructure** (LEED Gold, TIA‑942, Uptime Tier III), **GreenNode** empowers enterprises, startups, and researchers with a comprehensive suite of AI services:\n",
-    ">- [Powerful AI Infrastructure:](https://greennode.ai/) As one of the first hyperscale AI clusters in APAC, powered by NVIDIA H100 GPUs, GreenNode's infrastructure is optimized for high-throughput machine learning and deep learning workloads.\n",
-    ">- [GreenNode AI Platform:](https://greennode.ai/product/ai-platform) Designed for technical teams, GreenNode’s self-service AI platform enables fast deployment of Jupyter notebook environments, preconfigured with optimized compute instances. From this portal, developers can launch ML training, fine-tuning, hyperparameter optimization, and inference workflows with minimal setup time. The platform includes access to 100+ curated open-source models and supports integrations with common MLOps tools and storage frameworks.\n",
-    ">- [GreenNode Serverless AI:](https://greennode.ai/product/model-as-a-service) GreenNode Serverless AI features a library of pre-trained production-ready models across domains such as text gen, code gen, text to speech, speech to text, embedding and reranking models. This service is ideal for teams looking to prototype or deploy AI solutions without managing model infrastructure.\n",
-    ">- [AI Applications:](https://vngcloud.vn/en/solution) From intelligent data management and document processing (IDP) to smart video analytics—GreenNode supports real-world AI use cases at scale.\n",
-    ">Whether you're building your next LLM workflow, scaling AI research, or deploying enterprise-grade applications, **GreenNode** provides the tools and infrastructure to accelerate your journey.\n",
-    "\n",
-    "## Installation and Setup\n",
-    "\n",
-    "The GreenNode integration can be installed via pip:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "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-greennode"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### API Key\n",
-    "\n",
-    "To use GreenNode Serverless AI, you'll need an API key which you can obtain from [GreenNode Serverless AI](https://aiplatform.console.greennode.ai/api-keys). The API key can be passed as an initialization parameter `api_key` or set as the environment variable `GREENNODE_API_KEY`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"GREENNODE_API_KEY\"):\n",
-    "    os.environ[\"GREENNODE_API_KEY\"] = getpass.getpass(\"Enter your GreenNode API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Chat models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_greennode import ChatGreenNode\n",
-    "\n",
-    "chat = ChatGreenNode(\n",
-    "    model=\"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B\",  # Choose from available models\n",
-    "    temperature=0.6,\n",
-    "    top_p=0.95,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Usage of the GreenNode [Chat Model](https://python.langchain.com/docs/integrations/chat/greennode/)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Embedding models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_greennode import GreenNodeEmbeddings\n",
-    "\n",
-    "# Initialize embeddings\n",
-    "embeddings = GreenNodeEmbeddings(\n",
-    "    model=\"BAAI/bge-m3\"  # Choose from available models\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Usage of the GreenNode [Embedding Model](https://python.langchain.com/docs/integrations/text_embedding/greennode)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Rerank"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_greennode import GreenNodeRerank\n",
-    "\n",
-    "# Initialize reranker\n",
-    "rerank = GreenNodeRerank(\n",
-    "    model=\"BAAI/bge-reranker-v2-m3\",  # Choose from available models\n",
-    "    top_n=-1,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Usage of the GreenNode [Rerank Model](https://python.langchain.com/docs/integrations/retrievers/greennode-reranker)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "tradingagents",
-   "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": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

docs/docs/integrations/providers/huggingface.mdx

@@ -1,11 +1,6 @@
 # Hugging Face
 
-All functionality related to [Hugging Face Hub](https://huggingface.co/) and libraries like [transformers](https://huggingface.co/docs/transformers/index), [sentence transformers](https://sbert.net/), and [datasets](https://huggingface.co/docs/datasets/index).
-
-> [Hugging Face](https://huggingface.co/) is an AI platform with all major open source models, datasets, MCPs, and demos. 
-> It supplies model inference locally and via serverless [Inference Providers](https://huggingface.co/docs/inference-providers).
->
-> You can use [Inference Providers](https://huggingface.co/docs/inference-providers) to run open source models like DeepSeek R1 on scalable serverless infrastructure.
+All functionality related to the [Hugging Face Platform](https://huggingface.co/).
 
 ## Installation
 
@@ -31,7 +26,6 @@ from langchain_huggingface import ChatHuggingFace
 
 ### HuggingFaceEndpoint
 
-We can use the `HuggingFaceEndpoint` class to run open source models via serverless [Inference Providers](https://huggingface.co/docs/inference-providers) or via dedicated [Inference Endpoints](https://huggingface.co/inference-endpoints/dedicated).
 
 See a [usage example](/docs/integrations/llms/huggingface_endpoint).
 
@@ -41,7 +35,7 @@ from langchain_huggingface import HuggingFaceEndpoint
 
 ### HuggingFacePipeline
 
-We can use the `HuggingFacePipeline` class to run open source models locally.
+Hugging Face models can be run locally through the `HuggingFacePipeline` class.
 
 See a [usage example](/docs/integrations/llms/huggingface_pipelines).
 
@@ -53,8 +47,6 @@ from langchain_huggingface import HuggingFacePipeline
 
 ### HuggingFaceEmbeddings
 
-We can use the `HuggingFaceEmbeddings` class to run open source embedding models locally.
-
 See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
 
 ```python
@@ -63,8 +55,6 @@ from langchain_huggingface import HuggingFaceEmbeddings
 
 ### HuggingFaceEndpointEmbeddings
 
-We can use the `HuggingFaceEndpointEmbeddings` class to run open source embedding models via a dedicated [Inference Endpoint](https://huggingface.co/inference-endpoints/dedicated).
-
 See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
 
 ```python
@@ -73,8 +63,6 @@ from langchain_huggingface import HuggingFaceEndpointEmbeddings
 
 ### HuggingFaceInferenceAPIEmbeddings
 
-We can use the `HuggingFaceInferenceAPIEmbeddings` class to run open source embedding models via [Inference Providers](https://huggingface.co/docs/inference-providers).
-
 See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
 
 ```python
@@ -83,8 +71,6 @@ from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
 
 ### HuggingFaceInstructEmbeddings
 
-We can use the `HuggingFaceInstructEmbeddings` class to run open source embedding models locally.
-
 See a [usage example](/docs/integrations/text_embedding/instruct_embeddings).
 
 ```python

docs/docs/integrations/providers/ibm.mdx

@@ -1,9 +1,7 @@
 # IBM
 
-LangChain integrations related to IBM technologies, including the
-[IBM watsonx.ai](https://www.ibm.com/products/watsonx-ai) platform and DB2 database.
+The `LangChain` integrations related to [IBM watsonx.ai](https://www.ibm.com/products/watsonx-ai) platform.
 
-## Watsonx AI
 IBM® watsonx.ai™ AI studio is part of the IBM [watsonx](https://www.ibm.com/watsonx)™ AI and data platform, bringing together new generative 
 AI capabilities powered by [foundation models](https://www.ibm.com/products/watsonx-ai/foundation-models) and traditional machine learning (ML) 
 into a powerful studio spanning the AI lifecycle. Tune and guide models with your enterprise data to meet your needs with easy-to-use tools for 
@@ -16,7 +14,7 @@ Watsonx.ai offers:
 - **Hybrid, multi-cloud deployments:** IBM provides the flexibility to integrate and deploy your AI workloads into your hybrid-cloud stack of choice.
 
 
-### Installation and Setup
+## Installation and Setup
 
 Install the integration package with
 ```bash
@@ -30,9 +28,9 @@ import os
 os.environ["WATSONX_APIKEY"] = "your IBM watsonx.ai api key"
 ```
 
-### Chat Model
+## Chat Model
 
-#### ChatWatsonx
+### ChatWatsonx
 
 See a [usage example](/docs/integrations/chat/ibm_watsonx).
 
@@ -40,9 +38,9 @@ See a [usage example](/docs/integrations/chat/ibm_watsonx).
 from langchain_ibm import ChatWatsonx
 ```
 
-### LLMs
+## LLMs
 
-#### WatsonxLLM
+### WatsonxLLM
 
 See a [usage example](/docs/integrations/llms/ibm_watsonx).
 
@@ -50,9 +48,9 @@ See a [usage example](/docs/integrations/llms/ibm_watsonx).
 from langchain_ibm import WatsonxLLM
 ```
 
-### Embedding Models
+## Embedding Models
 
-#### WatsonxEmbeddings
+### WatsonxEmbeddings
 
 See a [usage example](/docs/integrations/text_embedding/ibm_watsonx).
 
@@ -60,9 +58,9 @@ See a [usage example](/docs/integrations/text_embedding/ibm_watsonx).
 from langchain_ibm import WatsonxEmbeddings
 ```
 
-### Reranker
+## Reranker
 
-#### WatsonxRerank
+### WatsonxRerank
 
 See a [usage example](/docs/integrations/retrievers/ibm_watsonx_ranker).
 
@@ -70,35 +68,12 @@ See a [usage example](/docs/integrations/retrievers/ibm_watsonx_ranker).
 from langchain_ibm import WatsonxRerank
 ```
 
-### Toolkit
+## Toolkit
 
-#### WatsonxToolkit
+### WatsonxToolkit
 
 See a [usage example](/docs/integrations/tools/ibm_watsonx).
 
 ```python
 from langchain_ibm import WatsonxToolkit
 ```
-
-## DB2
-
-### Vector stores
-
-#### IBM DB2 Vector Store and Vector Search
-
-The IBM DB2 relational database v12.1.2 and above offers the abilities of vector store
-and vector search. Installation of `langchain-db2` package will give Langchain users
-the support of DB2 vector store and vector search.
-
-See detailed usage examples in the guide [here](/docs/integrations/vectorstores/db2).
-
-Installation: This is a seperate package for vector store feature only and can be run
-without the `langchain-ibm` package.
-```bash
-pip install -U langchain-db2
-```
-Usage:
-```python
-from langchain_db2 import db2vs
-from langchain_db2.db2vs import DB2VS
-```

docs/docs/integrations/providers/langfuse.mdx

@@ -15,35 +15,19 @@ pip install langfuse
 ```
 
 ```python
-from langfuse import Langfuse, get_client
-from langfuse.langchain import CallbackHandler
-from langchain_openai import ChatOpenAI  # Example LLM
-from langchain_core.prompts import ChatPromptTemplate
- 
-# Initialize Langfuse client with constructor arguments
-Langfuse(
-    public_key="your-public-key",
-    secret_key="your-secret-key",
-    host="https://cloud.langfuse.com"  # Optional: defaults to https://cloud.langfuse.com
+# Initialize Langfuse handler
+from langfuse.callback import CallbackHandler
+langfuse_handler = CallbackHandler(
+    secret_key="sk-lf-...",
+    public_key="pk-lf-...",
+    host="https://cloud.langfuse.com", # 🇪🇺 EU region
+  # host="https://us.cloud.langfuse.com", # 🇺🇸 US region
 )
  
-# Get the configured client instance
-langfuse = get_client()
+# Your Langchain code
  
-# Initialize the Langfuse handler
-langfuse_handler = CallbackHandler()
- 
-# Create your LangChain components
-llm = ChatOpenAI(model_name="gpt-4o")
-prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
-chain = prompt | llm
- 
-# Run your chain with Langfuse tracing
-response = chain.invoke({"topic": "cats"}, config={"callbacks": [langfuse_handler]})
-print(response.content)
- 
-# Flush events to Langfuse in short-lived applications
-langfuse.flush()
+# Add Langfuse handler as callback (classic and LCEL)
+chain.invoke({"input": "<user_input>"}, config={"callbacks": [langfuse_handler]})
 ```
 
 ### Environment variables
@@ -59,7 +43,7 @@ LANGFUSE_HOST="https://cloud.langfuse.com"
 
 ```python
 # Initialize Langfuse handler
-from langfuse.langchain import CallbackHandler
+from langfuse.callback import CallbackHandler
 langfuse_handler = CallbackHandler()
  
 # Your Langchain code
@@ -156,7 +140,7 @@ Now, we will add then [Langfuse callback handler for LangChain](https://langfuse
 
 
 ```python
-from langfuse.langchain import CallbackHandler
+from langfuse.callback import CallbackHandler
 
 # Initialize Langfuse CallbackHandler for Langchain (tracing)
 langfuse_handler = CallbackHandler()

docs/docs/integrations/providers/mongodb_atlas.mdx

@@ -99,3 +99,4 @@ set_llm_cache(MongoDBAtlasSemanticCache(
     database_name=DATABASE_NAME,
 ))
 ```
+``

docs/docs/integrations/providers/ollama.mdx

@@ -23,15 +23,13 @@ Ollama will start as a background service automatically, if this is disabled, ru
 ollama serve
 ```
 
-After starting ollama, run `ollama pull <name-of-model>` to download a model from the [Ollama model library](https://ollama.ai/library):
+After starting ollama, run `ollama pull <model_checkpoint>` to download a model 
+from the [Ollama model library](https://ollama.ai/library).
 
 ```bash
 ollama pull llama3.1
 ```
 
-- This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.
-- To view all pulled (downloaded) models, use `ollama list`
-
 We're now ready to install the `langchain-ollama` partner package and run a model.
 
 ### Ollama LangChain partner package install

docs/docs/integrations/providers/streamlit.mdx

@@ -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).
 
 ## Installation and Setup

docs/docs/integrations/providers/surrealdb.mdx

@@ -1,22 +0,0 @@
-# SurrealDB
-
-[SurrealDB](https://surrealdb.com) is a unified, multi-model database purpose-built for AI systems. It combines structured and unstructured data (including vector search, graph traversal, relational queries, full-text search, document storage, and time-series data) into a single ACID-compliant engine, scaling from a 3 MB edge binary to petabyte-scale clusters in the cloud. By eliminating the need for multiple specialized stores, SurrealDB simplifies architectures, reduces latency, and ensures consistency for AI workloads.
-
-**Why SurrealDB Matters for GenAI Systems**
-- **One engine for storage and memory:** Combine durable storage and fast, agent-friendly memory in a single system, providing all the data your agent needs and removing the need to sync multiple systems.
-- **One-hop memory for agents:** Run vector search, graph traversal, semantic joins, and transactional writes in a single query, giving LLM agents fast, consistent memory access without stitching relational, graph and vector databases together.
-- **In-place inference and real-time updates:** SurrealDB enables agents to run inference next to data and receive millisecond-fresh updates, critical for real-time reasoning and collaboration.
-- **Versioned, durable context:** SurrealDB supports time-travel queries and versioned records, letting agents audit or “replay” past states for consistent, explainable reasoning.
-- **Plug-and-play agent memory:** Expose AI memory as a native concept, making it easy to use SurrealDB as a drop-in backend for AI frameworks.
-
-## Installation and Setup
-
-```bash
-pip install langchain-surrealdb
-```
-
-## Vector Store
-
-[This notebook](/docs/integrations/vectorstores/surrealdb) covers how to get started with the SurrealDB vector store.
-
-Find more [examples](https://github.com/surrealdb/langchain-surrealdb/blob/main/README.md#simple-example) in the repository.

docs/docs/integrations/retrievers/greennode_reranker.ipynb

@@ -1,361 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: GreenNode\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e49f1e0d",
-   "metadata": {},
-   "source": [
-    "# GreenNodeRetriever\n",
-    "\n",
-    ">[GreenNode](https://greennode.ai/) is a global AI solutions provider and a **NVIDIA Preferred Partner**, delivering full-stack AI capabilities—from infrastructure to application—for enterprises across the US, MENA, and APAC regions. Operating on **world-class infrastructure** (LEED Gold, TIA‑942, Uptime Tier III), GreenNode empowers enterprises, startups, and researchers with a comprehensive suite of AI services\n",
-    "\n",
-    "This notebook provides a walkthrough on getting started with the `GreenNodeRerank` retriever. It enables you to perform document search using built-in connectors or by integrating your own data sources, leveraging GreenNode's reranking capabilities for improved relevance.\n",
-    "\n",
-    "### Integration details\n",
-    "\n",
-    "- **Provider**: [GreenNode Serverless AI](https://aiplatform.console.greennode.ai/playground)\n",
-    "- **Model Types**: Reranking models\n",
-    "- **Primary Use Case**: Reranking search results based on semantic relevance\n",
-    "- **Available Models**: Includes [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and other high-performance reranking models\n",
-    "- **Scoring**: Returns relevance scores used to reorder document candidates based on query alignment\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access GreenNode models you'll need to create a GreenNode account, get an API key, and install the `langchain-greennode` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "Head to [this page](https://aiplatform.console.greennode.ai/api-keys) to sign up to GreenNode AI Platform and generate an API key. Once you've done this, set the GREENNODE_API_KEY environment variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a92b5a70",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"GREENNODE_API_KEY\"):\n",
-    "    os.environ[\"GREENNODE_API_KEY\"] = getpass.getpass(\"Enter your GreenNode API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing from individual queries, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# os.environ[\"LANGSMITH_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",
-    "This retriever lives in the `langchain-greennode` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "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-greennode"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "The `GreenNodeRerank` class can be instantiated with optional parameters for the API key and model name:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "70cc8e65-2a02-408a-bbc6-8ef649057d82",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_greennode import GreenNodeRerank\n",
-    "\n",
-    "# Initialize the embeddings model\n",
-    "reranker = GreenNodeRerank(\n",
-    "    # api_key=\"YOUR_API_KEY\",  # You can pass the API key directly\n",
-    "    model=\"BAAI/bge-reranker-v2-m3\",  # The default embedding model\n",
-    "    top_n=3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c5f2839-4020-424e-9fc9-07777eede442",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "\n",
-    "### Reranking Search Results\n",
-    "\n",
-    "Reranking models enhance retrieval-augmented generation (RAG) workflows by refining and reordering initial search results based on semantic relevance. The example below demonstrates how to integrate GreenNodeRerank with a base retriever to improve the quality of retrieved documents."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/bs/g52lln652z11zjp98qf9wcy40000gn/T/ipykernel_96362/2544494776.py:41: LangChainDeprecationWarning: The method `BaseRetriever.get_relevant_documents` was deprecated in langchain-core 0.1.46 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
-      "  results = rerank_retriever.get_relevant_documents(query)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(metadata={'relevance_score': 0.125}, page_content='Central banks use interest rates to control inflation and stabilize the economy'),\n",
-       " Document(metadata={'relevance_score': 0.004913330078125}, page_content='Inflation represents the rate at which the general level of prices for goods and services rises'),\n",
-       " Document(metadata={'relevance_score': 1.6689300537109375e-05}, page_content='Cryptocurrencies like Bitcoin operate on decentralized blockchain networks')]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.retrievers.contextual_compression import ContextualCompressionRetriever\n",
-    "from langchain_community.vectorstores import FAISS\n",
-    "from langchain_core.documents import Document\n",
-    "from langchain_greennode import GreenNodeEmbeddings\n",
-    "\n",
-    "# Initialize the embeddings model\n",
-    "embeddings = GreenNodeEmbeddings(\n",
-    "    # api_key=\"YOUR_API_KEY\",  # You can pass the API key directly\n",
-    "    model=\"BAAI/bge-m3\"  # The default embedding model\n",
-    ")\n",
-    "\n",
-    "# Prepare documents (finance/economics domain)\n",
-    "docs = [\n",
-    "    Document(\n",
-    "        page_content=\"Inflation represents the rate at which the general level of prices for goods and services rises\"\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Central banks use interest rates to control inflation and stabilize the economy\"\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Cryptocurrencies like Bitcoin operate on decentralized blockchain networks\"\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Stock markets are influenced by corporate earnings, investor sentiment, and economic indicators\"\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "# Create a vector store and a base retriever\n",
-    "vector_store = FAISS.from_documents(docs, embeddings)\n",
-    "base_retriever = vector_store.as_retriever(search_kwargs={\"k\": 4})\n",
-    "\n",
-    "\n",
-    "rerank_retriever = ContextualCompressionRetriever(\n",
-    "    base_compressor=reranker, base_retriever=base_retriever\n",
-    ")\n",
-    "\n",
-    "# Perform retrieval with reranking\n",
-    "query = \"How do central banks fight rising prices?\"\n",
-    "results = rerank_retriever.get_relevant_documents(query)\n",
-    "\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7efa742d",
-   "metadata": {},
-   "source": [
-    "### Direct Usage\n",
-    "\n",
-    "The `GreenNodeRerank` class can be used independently to perform reranking of retrieved documents based on relevance scores. This functionality is particularly useful in scenarios where a primary retrieval step (e.g., keyword or vector search) returns a broad set of candidates, and a secondary model is needed to refine the results using more sophisticated semantic understanding. The class accepts a query and a list of candidate documents and returns a reordered list based on predicted relevance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "78d9051e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'index': 1, 'relevance_score': 1.0},\n",
-       " {'index': 0, 'relevance_score': 0.01165771484375},\n",
-       " {'index': 3, 'relevance_score': 0.0012054443359375}]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_documents = [\n",
-    "    Document(\n",
-    "        page_content=\"Carson City is the capital city of the American state of Nevada.\"\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Washington, D.C. (also known as simply Washington or D.C.) is the capital of the United States.\"\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Capital punishment has existed in the United States since beforethe United States was a country.\"\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.\"\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "test_query = \"What is the capital of the United States?\"\n",
-    "results = reranker.rerank(test_documents, test_query)\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e",
-   "metadata": {},
-   "source": [
-    "## Use within a chain\n",
-    "\n",
-    "GreenNodeRerank works seamlessly in LangChain RAG pipelines. Here's an example of creating a simple RAG chain with the GreenNodeRerank:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "25b647a3-f8f2-4541-a289-7a241e43f9df",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\n\\nCentral banks combat rising prices, or inflation, by adjusting interest rates. By raising interest rates, they increase the cost of borrowing, which discourages spending and investment. This reduction in demand helps slow down the rate of price increases, thereby controlling inflation and contributing to economic stability.'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
-    "from langchain_greennode import ChatGreenNode\n",
-    "\n",
-    "# Initialize LLM\n",
-    "llm = ChatGreenNode(model=\"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B\")\n",
-    "\n",
-    "# Create a prompt template\n",
-    "prompt = ChatPromptTemplate.from_template(\n",
-    "    \"\"\"\n",
-    "Answer the question based only on the following context:\n",
-    "\n",
-    "Context:\n",
-    "{context}\n",
-    "\n",
-    "Question: {question}\n",
-    "\"\"\"\n",
-    ")\n",
-    "\n",
-    "\n",
-    "# Format documents function\n",
-    "def format_docs(docs):\n",
-    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
-    "\n",
-    "\n",
-    "# Create RAG chain\n",
-    "rag_chain = (\n",
-    "    {\"context\": rerank_retriever | format_docs, \"question\": RunnablePassthrough()}\n",
-    "    | prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
-    "# Run the chain\n",
-    "answer = rag_chain.invoke(\"How do central banks fight rising prices?\")\n",
-    "answer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For more details about the GreenNode Serverless AI API, visit the [GreenNode Serverless AI Documentation](https://aiplatform.console.greennode.ai/api-docs/maas)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "tradingagents",
-   "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": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/text_embedding/greennode.ipynb

@@ -1,379 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: GreenNode\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a3d6f34",
-   "metadata": {},
-   "source": [
-    "# GreenNodeEmbeddings\n",
-    "\n",
-    ">[GreenNode](https://greennode.ai/) is a global AI solutions provider and a **NVIDIA Preferred Partner**, delivering full-stack AI capabilities—from infrastructure to application—for enterprises across the US, MENA, and APAC regions. Operating on **world-class infrastructure** (LEED Gold, TIA‑942, Uptime Tier III), GreenNode empowers enterprises, startups, and researchers with a comprehensive suite of AI services\n",
-    "\n",
-    "This notebook provides a guide to getting started with `GreenNodeEmbeddings`. It enables you to perform semantic document search using various built-in connectors or your own custom data sources by generating high-quality vector representations of text.\n",
-    "\n",
-    "## Overview\n",
-    "### Integration details\n",
-    "\n",
-    "| Provider | Package |\n",
-    "|:--------:|:-------:|\n",
-    "| [GreenNode](/docs/integrations/providers/greennode/) | [langchain-greennode](https://python.langchain.com/v0.2/api_reference/langchain_greennode/embeddings/langchain_greennode.embeddingsGreenNodeEmbeddings.html) |\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To access GreenNode embedding models you'll need to create a GreenNode account, get an API key, and install the `langchain-greennode` integration package.\n",
-    "\n",
-    "### Credentials\n",
-    "\n",
-    "GreenNode requires an API key for authentication, which can be provided either as the `api_key` parameter during initialization or set as the environment variable `GREENNODE_API_KEY`. You can obtain an API key by registering for an account on [GreenNode Serverless AI](https://aiplatform.console.greennode.ai/playground)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "36521c2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.getenv(\"GREENNODE_API_KEY\"):\n",
-    "    os.environ[\"GREENNODE_API_KEY\"] = getpass.getpass(\"Enter your GreenNode API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c84fb993",
-   "metadata": {},
-   "source": [
-    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "39a4953b",
-   "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": "d9664366",
-   "metadata": {},
-   "source": [
-    "### Installation\n",
-    "\n",
-    "The LangChain GreenNode integration lives in the `langchain-greennode` package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "64853226",
-   "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-greennode"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd1724",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "The `GreenNodeEmbeddings` class can be instantiated with optional parameters for the API key and model name:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "9ea7a09b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_greennode import GreenNodeEmbeddings\n",
-    "\n",
-    "# Initialize the embeddings model\n",
-    "embeddings = GreenNodeEmbeddings(\n",
-    "    # api_key=\"YOUR_API_KEY\",  # You can pass the API key directly\n",
-    "    model=\"BAAI/bge-m3\"  # The default embedding model\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d271b6",
-   "metadata": {},
-   "source": [
-    "## Indexing and Retrieval\n",
-    "\n",
-    "Embedding models play a key role in retrieval-augmented generation (RAG) workflows by enabling both the indexing of content and its efficient retrieval. \n",
-    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "23df9f54",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'LangChain is the framework for building context-aware reasoning applications'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a vector store with a sample text\n",
-    "from langchain_core.vectorstores import InMemoryVectorStore\n",
-    "\n",
-    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
-    "\n",
-    "vectorstore = InMemoryVectorStore.from_texts(\n",
-    "    [text],\n",
-    "    embedding=embeddings,\n",
-    ")\n",
-    "\n",
-    "# Use the vectorstore as a retriever\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "\n",
-    "# Retrieve the most similar text\n",
-    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
-    "\n",
-    "# show the retrieved document's content\n",
-    "retrieved_documents[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e02b9855",
-   "metadata": {},
-   "source": [
-    "## Direct Usage\n",
-    "\n",
-    "The `GreenNodeEmbeddings` class can be used independently to generate text embeddings without the need for a vector store. This is useful for tasks such as similarity scoring, clustering, or custom processing pipelines.\n",
-    "\n",
-    "### Embed single texts\n",
-    "\n",
-    "You can embed single texts or documents with `embed_query`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d2befcd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.01104736328125, -0.0281982421875, 0.0035858154296875, -0.0311279296875, -0.0106201171875, -0.039\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_vector = embeddings.embed_query(text)\n",
-    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b5a7d03",
-   "metadata": {},
-   "source": [
-    "### Embed multiple texts\n",
-    "\n",
-    "You can embed multiple texts with `embed_documents`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2f4d6e97",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.01104736328125, -0.0281982421875, 0.0035858154296875, -0.0311279296875, -0.0106201171875, -0.039\n",
-      "[-0.07177734375, -0.00017452239990234375, -0.002044677734375, -0.0299072265625, -0.0184326171875, -0\n"
-     ]
-    }
-   ],
-   "source": [
-    "text2 = (\n",
-    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
-    ")\n",
-    "two_vectors = embeddings.embed_documents([text, text2])\n",
-    "for vector in two_vectors:\n",
-    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be19dda0",
-   "metadata": {},
-   "source": [
-    "### Async Support\n",
-    "\n",
-    "GreenNodeEmbeddings supports async operations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "d556e655",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Async query embedding dimension: 1024\n",
-      "Async document embeddings count: 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "import asyncio\n",
-    "\n",
-    "\n",
-    "async def generate_embeddings_async():\n",
-    "    # Embed a single query\n",
-    "    query_result = await embeddings.aembed_query(\"What is the capital of France?\")\n",
-    "    print(f\"Async query embedding dimension: {len(query_result)}\")\n",
-    "\n",
-    "    # Embed multiple documents\n",
-    "    docs = [\n",
-    "        \"Paris is the capital of France\",\n",
-    "        \"Berlin is the capital of Germany\",\n",
-    "        \"Rome is the capital of Italy\",\n",
-    "    ]\n",
-    "    docs_result = await embeddings.aembed_documents(docs)\n",
-    "    print(f\"Async document embeddings count: {len(docs_result)}\")\n",
-    "\n",
-    "\n",
-    "await generate_embeddings_async()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "207a7966",
-   "metadata": {},
-   "source": [
-    "### Document Similarity Example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8bdb003b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Document Similarity Matrix:\n",
-      "Document 1: ['1.0000', '0.6005', '0.3542', '0.5788']\n",
-      "Document 2: ['0.6005', '1.0000', '0.4154', '0.6170']\n",
-      "Document 3: ['0.3542', '0.4154', '1.0000', '0.3528']\n",
-      "Document 4: ['0.5788', '0.6170', '0.3528', '1.0000']\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from scipy.spatial.distance import cosine\n",
-    "\n",
-    "# Create some documents\n",
-    "documents = [\n",
-    "    \"Machine learning algorithms build mathematical models based on sample data\",\n",
-    "    \"Deep learning uses neural networks with many layers\",\n",
-    "    \"Climate change is a major global environmental challenge\",\n",
-    "    \"Neural networks are inspired by the human brain's structure\",\n",
-    "]\n",
-    "\n",
-    "# Embed the documents\n",
-    "embeddings_list = embeddings.embed_documents(documents)\n",
-    "\n",
-    "\n",
-    "# Function to calculate similarity\n",
-    "def calculate_similarity(embedding1, embedding2):\n",
-    "    return 1 - cosine(embedding1, embedding2)\n",
-    "\n",
-    "\n",
-    "# Print similarity matrix\n",
-    "print(\"Document Similarity Matrix:\")\n",
-    "for i, emb_i in enumerate(embeddings_list):\n",
-    "    similarities = []\n",
-    "    for j, emb_j in enumerate(embeddings_list):\n",
-    "        similarity = calculate_similarity(emb_i, emb_j)\n",
-    "        similarities.append(f\"{similarity:.4f}\")\n",
-    "    print(f\"Document {i + 1}: {similarities}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "98785c12",
-   "metadata": {},
-   "source": [
-    "## API Reference\n",
-    "\n",
-    "For more details about the GreenNode Serverless AI API, visit the [GreenNode Serverless AI Documentation](https://aiplatform.console.greennode.ai/api-docs/maas).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "tradingagents",
-   "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": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/text_embedding/huggingfacehub.ipynb

@@ -95,36 +95,35 @@
    "id": "92019ef1-5d30-4985-b4e6-c0d98bdfe265",
    "metadata": {},
    "source": [
-    "## Hugging Face Inference Providers\n",
-    "\n",
-    "We can also access embedding models via the [Inference Providers](https://huggingface.co/docs/inference-providers), which let's us use open source models on scalable serverless infrastructure.\n",
-    "\n",
-    "First, we need to get a read-only API key from [Hugging Face](https://huggingface.co/settings/tokens).\n"
+    "## Hugging Face Inference API\n",
+    "We can also access embedding models via the Hugging Face Inference API, which does not require us to install ``sentence_transformers`` and download models locally."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "c5576a6c",
+   "execution_count": 1,
+   "id": "66f5c6ba-1446-43e1-b012-800d17cef300",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter your HF Inference API Key:\n",
+      "\n",
+      " ········\n"
+     ]
+    }
+   ],
    "source": [
-    "from getpass import getpass\n",
+    "import getpass\n",
     "\n",
-    "huggingfacehub_api_token = getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ad10337",
-   "metadata": {},
-   "source": [
-    "Now we can use the `HuggingFaceInferenceAPIEmbeddings` class to run open source embedding models via [Inference Providers](https://huggingface.co/docs/inference-providers)."
+    "inference_api_key = getpass.getpass(\"Enter your HF Inference API Key:\\n\\n\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "d0623c1f-cd82-4862-9bce-3655cb9b66ac",
    "metadata": {},
    "outputs": [
@@ -140,11 +139,10 @@
     }
    ],
    "source": [
-    "from langchain_huggingface import HuggingFaceInferenceAPIEmbeddings\n",
+    "from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings\n",
     "\n",
     "embeddings = HuggingFaceInferenceAPIEmbeddings(\n",
-    "    api_key=huggingfacehub_api_token,\n",
-    "    model_name=\"sentence-transformers/all-MiniLM-l6-v2\",\n",
+    "    api_key=inference_api_key, model_name=\"sentence-transformers/all-MiniLM-l6-v2\"\n",
     ")\n",
     "\n",
     "query_result = embeddings.embed_query(text)\n",

docs/docs/integrations/text_embedding/ollama.ipynb

@@ -28,10 +28,9 @@
         "\n",
         "## Setup\n",
         "\n",
-        "First, follow [these instructions](https://github.com/ollama/ollama?tab=readme-ov-file#ollama) to set up and run a local Ollama instance:\n",
+        "First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
         "\n",
-        "* [Download](https://ollama.ai/download) and install Ollama onto the available supported platforms (including Windows Subsystem for Linux aka WSL, macOS, and Linux)\n",
-        "    * macOS users can install via Homebrew with `brew install ollama` and start with `brew services start ollama`\n",
+        "* [Download](https://ollama.ai/download) and install Ollama onto the available supported platforms (including Windows Subsystem for Linux)\n",
         "* Fetch available LLM model via `ollama pull <name-of-model>`\n",
         "    * View a list of available models via the [model library](https://ollama.ai/library)\n",
         "    * e.g., `ollama pull llama3`\n",
@@ -44,16 +43,19 @@
         "* Specify the exact version of the model of interest as such `ollama pull vicuna:13b-v1.5-16k-q4_0` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
         "* To view all pulled models, use `ollama list`\n",
         "* To chat directly with a model from the command line, use `ollama run <name-of-model>`\n",
-        "* View the [Ollama documentation](https://github.com/ollama/ollama/tree/main/docs) for more commands. You can run `ollama help` in the terminal to see available commands."
+        "* View the [Ollama documentation](https://github.com/jmorganca/ollama) for more commands. Run `ollama help` in the terminal to see available commands too.\n",
+        "\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "There is no built-in auth mechanism for Ollama."
       ]
     },
     {
       "cell_type": "markdown",
       "id": "c84fb993",
       "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",
@@ -106,7 +108,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 3,
       "id": "9ea7a09b",
       "metadata": {},
       "outputs": [],
@@ -125,7 +127,7 @@
       "source": [
         "## Indexing and Retrieval\n",
         "\n",
-        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/rag/).\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
         "\n",
         "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
       ]
@@ -137,11 +139,14 @@
       "metadata": {},
       "outputs": [
         {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "LangChain is the framework for building context-aware reasoning applications\n"
-          ]
+          "data": {
+            "text/plain": [
+              "'LangChain is the framework for building context-aware reasoning applications'"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -161,8 +166,8 @@
         "# Retrieve the most similar text\n",
         "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
         "\n",
-        "# Show the retrieved document's content\n",
-        "print(retrieved_documents[0].page_content)"
+        "# show the retrieved document's content\n",
+        "retrieved_documents[0].page_content"
       ]
     },
     {
@@ -191,7 +196,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "[-0.0039849705, 0.023019705, -0.001768838, -0.0058736936, 0.00040999008, 0.017861595, -0.011274585, \n"
+            "[-0.001288981, 0.006547121, 0.018376578, 0.025603496, 0.009599175, -0.0042578303, -0.023250086, -0.0\n"
           ]
         }
       ],
@@ -220,8 +225,8 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "[-0.0039849705, 0.023019705, -0.001768838, -0.0058736936, 0.00040999008, 0.017861595, -0.011274585, \n",
-            "[-0.0066985516, 0.009878328, 0.008019467, -0.009384944, -0.029560851, 0.025744654, 0.004872892, -0.0\n"
+            "[-0.0013138362, 0.006438795, 0.018304596, 0.025530428, 0.009717592, -0.004225636, -0.023363983, -0.0\n",
+            "[-0.010317663, 0.01632489, 0.0070348927, 0.017076202, 0.008924255, 0.007399284, -0.023064945, -0.003\n"
           ]
         }
       ],
@@ -247,7 +252,7 @@
   ],
   "metadata": {
     "kernelspec": {
-      "display_name": ".venv",
+      "display_name": "Python 3 (ipykernel)",
       "language": "python",
       "name": "python3"
     },
@@ -261,7 +266,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.5"
+      "version": "3.9.6"
     }
   },
   "nbformat": 4,

docs/docs/integrations/tools/google_serper.ipynb

@@ -119,7 +119,7 @@
    "id": "2eb1b45b",
    "metadata": {},
    "source": [
-    "and use the `create_react_agent` functionality to initialize a ReAct agent. You will also need to set up your OPENAI_API_KEY (visit https://platform.openai.com) in order to access OpenAI's chat models."
+    "and use the `create_react_agent` functionality to initialize a ReAct agent. You will also need to set up your OPEN_API_KEY (visit https://platform.openai.com) in order to access OpenAI's chat models."
    ]
   },
   {
@@ -713,7 +713,7 @@
     "`qdr:d3` (past 3 days)\n",
     "`qdr:w2` (past 2 weeks)\n",
     "`qdr:m6` (past 6 months)\n",
-    "`qdr:y2` (past 2 years)\n",
+    "`qdr:m2` (past 2 years)\n",
     "\n",
     "For all supported filters simply go to [Google Search](https://google.com), search for something, click on \"Tools\", add your date filter and check the URL for \"tbs=\".\n"
    ]

docs/docs/integrations/tools/vectara.ipynb

@@ -238,7 +238,7 @@
    "id": "b651396a-5726-4d49-bacf-c9d7a5ddcf7a",
    "metadata": {},
    "source": [
-    "## Vectara as a langchain retriever\n",
+    "## Vectara as a langchain retreiver\n",
     "\n",
     "The `VectaraSearch` tool can be used just as a retriever. \n",
     "\n",

docs/docs/integrations/vectorstores/couchbase.ipynb

@@ -28,7 +28,18 @@
    "execution_count": 1,
    "id": "bec8d532-fec7-4dc7-9be3-020aa7bdb01f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "pip install -qU langchain-couchbase"
    ]
@@ -50,7 +61,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stdin",
      "output_type": "stream",
      "text": [
       "Enter the connection string for the Couchbase cluster:  ········\n",
@@ -267,16 +278,16 @@
     {
      "data": {
       "text/plain": [
-       "['f125b836-f555-4449-98dc-cbda4e77ae3f',\n",
-       " 'a28fccde-fd32-4775-9ca8-6cdb22ca7031',\n",
-       " 'b1037c4b-947f-497f-84db-63a4def5080b',\n",
-       " 'c7082b74-b385-4c4b-bbe5-0740909c01db',\n",
-       " 'a7e31f62-13a5-4109-b881-8631aff7d46c',\n",
-       " '9fcc2894-fdb1-41bd-9a93-8547747650f4',\n",
-       " 'a5b0632d-abaf-4802-99b3-df6b6c99be29',\n",
-       " '0475592e-4b7f-425d-91fd-ac2459d48a36',\n",
-       " '94c6db4e-ba07-43ff-aa96-3a5d577db43a',\n",
-       " 'd21c7feb-ad47-4e7d-84c5-785afb189160']"
+       "['4a6b5252-24ca-4e48-97a9-c33211fc7736',\n",
+       " '594a413d-761a-44f1-8f0c-6418700b198d',\n",
+       " 'fdd8461c-f4e3-4c85-af8e-7782ce4d2311',\n",
+       " '3f6a82b2-7464-4eee-b209-cbca5a236a8a',\n",
+       " 'df8b87ad-464e-4f83-a007-ccf5a8fa4ff5',\n",
+       " 'aa18502e-6fb4-4578-9c63-b9a299259b01',\n",
+       " '8c55a17d-5fa7-4c30-a55d-7ded0d39bf46',\n",
+       " '41b68c5a-ebf5-4d7a-a079-5e32926ca484',\n",
+       " '146ac3e0-474a-422a-b0ac-c9fee718396b',\n",
+       " 'e44941e9-fb3a-4090-88a0-9ffecee3e80e']"
       ]
      },
      "execution_count": 8,
@@ -445,7 +456,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "* [SIM=0.553112] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]\n"
+      "* [SIM=0.553145] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]\n"
      ]
     }
    ],
@@ -455,52 +466,6 @@
     "    print(f\"* [SIM={score:3f}] {res.page_content} [{res.metadata}]\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "faa8ed12-989a-4cd4-90bf-6156f242f008",
-   "metadata": {},
-   "source": [
-    "### Filtering Results\n",
-    "\n",
-    "You can filter the search results by specifying any filter on the text or metadata in the document that is supported by the Couchbase Search service. \n",
-    "\n",
-    "The `filter` can be any valid [SearchQuery](https://docs.couchbase.com/python-sdk/current/howtos/full-text-searching-with-sdk.html#search-queries) supported by the Couchbase Python SDK. These filters are applied before the Vector Search is performed. \n",
-    "\n",
-    "If you want to filter on one of the fields in the metadata, you need to specify it using `.`\n",
-    "\n",
-    "For example, to fetch the `source` field in the metadata, you need to specify `metadata.source`.\n",
-    "\n",
-    "Note that the filter needs to be supported by the Search Index."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "b1c4d3ac-e3d2-4cba-b765-954bf45357aa",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "* The stock market is down 500 points today due to fears of a recession. [{'source': 'news'}] 0.3873019218444824\n",
-      "* Robbers broke into the city bank and stole $1 million in cash. [{'source': 'news'}] 0.20637212693691254\n",
-      "* The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}] 0.10404900461435318\n"
-     ]
-    }
-   ],
-   "source": [
-    "from couchbase import search\n",
-    "\n",
-    "query = \"Are there any concerning financial news?\"\n",
-    "filter_on_source = search.MatchQuery(\"news\", field=\"metadata.source\")\n",
-    "results = vector_store.similarity_search_with_score(\n",
-    "    query, fields=[\"metadata.source\"], filter=filter_on_source, k=5\n",
-    ")\n",
-    "for res, score in results:\n",
-    "    print(f\"* {res.page_content} [{res.metadata}] {score}\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "9983e83d-efd0-4b75-80db-150e0694e822",
@@ -519,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "ffa743dc-4e89-405b-ad71-7390338889e6",
    "metadata": {},
    "outputs": [
@@ -537,44 +502,6 @@
     "print(results[0])"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "2e01eb05-77fc-49f8-a552-8af3c5d4460c",
-   "metadata": {},
-   "source": [
-    "### Query by turning into retriever\n",
-    "\n",
-    "You can also transform the vector store into a retriever for easier usage in your chains. \n",
-    "\n",
-    "Here is how to transform your vector store into a retriever and then invoke the retreiever with a simple query and filter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "3666265a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(id='c7082b74-b385-4c4b-bbe5-0740909c01db', metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "retriever = vector_store.as_retriever(\n",
-    "    search_type=\"similarity\",\n",
-    "    search_kwargs={\"k\": 1, \"score_threshold\": 0.5},\n",
-    ")\n",
-    "filter_on_source = search.MatchQuery(\"news\", field=\"metadata.source\")\n",
-    "retriever.invoke(\"Stealing from the bank is a crime\", filter=filter_on_source)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "a5e45eb2-aa97-45df-bcc5-410e9626e506",
@@ -602,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "id": "7d2e607d-6bbc-4cef-83e3-b6a28bb269ea",
    "metadata": {},
    "outputs": [
@@ -647,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "id": "dc06ba4a-8a6b-4c55-bb69-95cd92db273f",
    "metadata": {},
    "outputs": [
@@ -684,7 +611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "fd4749e6-ef4f-4cb5-95ff-37c4fa8283d8",
    "metadata": {},
    "outputs": [
@@ -721,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "id": "b7b47e7d-c32f-4999-bce9-3c3c3cebffd0",
    "metadata": {},
    "outputs": [
@@ -729,15 +656,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "page_content='And with 75% of adult Americans fully vaccinated and hospitalizations down by 77%, most Americans can remove their masks, return to work, stay in the classroom, and move forward safely. \n",
+      "page_content='We are cutting off Russia’s largest banks from the international financial system.  \n",
       "\n",
-      "We achieved this because we provided free vaccines, treatments, tests, and masks. \n",
+      "Preventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless.   \n",
       "\n",
-      "Of course, continuing this costs money. \n",
+      "We are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come.  \n",
       "\n",
-      "I will soon send Congress a request. \n",
-      "\n",
-      "The vast majority of Americans have used these tools and may want to again, so I expect Congress to pass it quickly.' metadata={'author': 'Jane Doe', 'date': '2017-01-01', 'rating': 3, 'source': '../../how_to/state_of_the_union.txt'}\n"
+      "Tonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more.' metadata={'author': 'Jane Doe', 'date': '2017-01-01', 'rating': 3, 'source': '../../how_to/state_of_the_union.txt'}\n"
      ]
     }
    ],
@@ -769,7 +694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "id": "7e8bf7c5-07d1-4c3f-86d7-1fa3a454dc7f",
    "metadata": {},
    "outputs": [
@@ -777,7 +702,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(Document(id='3a90405c0f5b4c09a6646259678f1f61', metadata={'author': 'John Doe', 'date': '2014-01-01', 'rating': 5, 'source': '../../how_to/state_of_the_union.txt'}, page_content='In this Capitol, generation after generation, Americans have debated great questions amid great strife, and have done great things. \\n\\nWe have fought for freedom, expanded liberty, defeated totalitarianism and terror. \\n\\nAnd built the strongest, freest, and most prosperous nation the world has ever known. \\n\\nNow is the hour. \\n\\nOur moment of responsibility. \\n\\nOur test of resolve and conscience, of history itself.'), 0.3573387440020518)\n"
+      "(Document(id='8616f24425b94a52af3d32d20e6ffb4b', metadata={'author': 'John Doe', 'date': '2014-01-01', 'rating': 5, 'source': '../../how_to/state_of_the_union.txt'}, page_content='In this Capitol, generation after generation, Americans have debated great questions amid great strife, and have done great things. \\n\\nWe have fought for freedom, expanded liberty, defeated totalitarianism and terror. \\n\\nAnd built the strongest, freest, and most prosperous nation the world has ever known. \\n\\nNow is the hour. \\n\\nOur moment of responsibility. \\n\\nOur test of resolve and conscience, of history itself.'), 0.361933544533826)\n"
      ]
     }
    ],
@@ -811,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "id": "dd0fe7f1-aa40-4c6f-889b-99ad5efcd88b",
    "metadata": {},
    "outputs": [
@@ -819,7 +744,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(Document(id='7115a704877a46ad94d661dd9c81cbc3', metadata={'author': 'Jane Doe', 'date': '2017-01-01', 'rating': 3, 'source': '../../how_to/state_of_the_union.txt'}, page_content='And with 75% of adult Americans fully vaccinated and hospitalizations down by 77%, most Americans can remove their masks, return to work, stay in the classroom, and move forward safely. \\n\\nWe achieved this because we provided free vaccines, treatments, tests, and masks. \\n\\nOf course, continuing this costs money. \\n\\nI will soon send Congress a request. \\n\\nThe vast majority of Americans have used these tools and may want to again, so I expect Congress to pass it quickly.'), 0.6898253780130769)\n"
+      "(Document(id='d9b36ef70b8942dda4db63563f51cf0f', metadata={'author': 'Jane Doe', 'date': '2017-01-01', 'rating': 3, 'source': '../../how_to/state_of_the_union.txt'}, page_content='We are cutting off Russia’s largest banks from the international financial system.  \\n\\nPreventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless.   \\n\\nWe are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come.  \\n\\nTonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more.'), 0.7107075545629284)\n"
      ]
     }
    ],
@@ -839,64 +764,6 @@
     "print(results[0])"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "65f9a24e-8c67-42e9-b995-6b4137da8c36",
-   "metadata": {},
-   "source": [
-    "**Note** \n",
-    "\n",
-    "The hybrid search results might contain documents that do not satisfy all the search parameters. This is due to the way the [scoring is calculated](https://docs.couchbase.com/server/current/search/run-searches.html#scoring). \n",
-    "The score is a sum of both the vector search score and the queries in the hybrid search. If the Vector Search score is high, the combined score will be more than the results that match all the queries in the hybrid search. \n",
-    "To avoid such results, please use the `filter` parameter instead of hybrid search."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "334de7ac-8fd1-42b1-856e-834508af8738",
-   "metadata": {},
-   "source": [
-    "### Combining Hybrid Search Query with Filters\n",
-    "Hybrid Search can be combined with filters to get the best of both hybrid search and the filters for results matching the requirements.\n",
-    "\n",
-    "In this example, we are checking for documents with a rating between 3 & 5 and matching the string \"independence\" in the text field."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "a360adba-03d2-4e25-877e-438538d2ea37",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(Document(id='23bb51b4e4d54a94ab0a95e72be8428c', metadata={'author': 'John Doe', 'date': '2012-01-01', 'rating': 3, 'source': '../../how_to/state_of_the_union.txt'}, page_content='And we remain clear-eyed. The Ukrainians are fighting back with pure courage. But the next few days weeks, months, will be hard on them.  \\n\\nPutin has unleashed violence and chaos.  But while he may make gains on the battlefield – he will pay a continuing high price over the long run. \\n\\nAnd a proud Ukrainian people, who have known 30 years  of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards.'), 0.30549919644400614)\n"
-     ]
-    }
-   ],
-   "source": [
-    "filter_text = search.MatchQuery(\"independence\", field=\"text\")\n",
-    "\n",
-    "query = \"Any mention about independence?\"\n",
-    "results = vector_store.similarity_search_with_score(\n",
-    "    query,\n",
-    "    search_options={\n",
-    "        \"query\": {\n",
-    "            \"min\": 3,\n",
-    "            \"max\": 5,\n",
-    "            \"inclusive_min\": True,\n",
-    "            \"inclusive_max\": True,\n",
-    "            \"field\": \"metadata.rating\",\n",
-    "        }\n",
-    "    },\n",
-    "    filter=filter_text,\n",
-    ")\n",
-    "\n",
-    "print(results[0])"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "39258571-3233-45c3-a6ad-5c3c90ea2b1c",
@@ -909,6 +776,43 @@
     "- [Couchbase Server](https://docs.couchbase.com/server/current/search/search-request-params.html#query-object)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "db0a1d74",
+   "metadata": {},
+   "source": [
+    "### Query by turning into retriever\n",
+    "\n",
+    "You can also transform the vector store into a retriever for easier usage in your chains. \n",
+    "\n",
+    "Here is how to transform your vector store into a retriever and then invoke the retreiever with a simple query and filter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3666265a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id='3f6a82b2-7464-4eee-b209-cbca5a236a8a', metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retriever = vector_store.as_retriever(\n",
+    "    search_type=\"similarity\",\n",
+    "    search_kwargs={\"k\": 1, \"score_threshold\": 0.5},\n",
+    ")\n",
+    "retriever.invoke(\"Stealing from the bank is a crime\", filter={\"source\": \"news\"})"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "28ab35ec",
@@ -928,7 +832,7 @@
    "id": "80958c2b-6a67-45e6-b7f0-fd2461d75e0f",
    "metadata": {},
    "source": [
-    "## Frequently Asked Questions"
+    "# Frequently Asked Questions"
    ]
   },
   {
@@ -936,8 +840,8 @@
    "id": "4f7f9838-cc20-44bc-a72d-06f2cb6c3fca",
    "metadata": {},
    "source": [
-    "### Question: Should I create the Search index before creating the CouchbaseSearchVectorStore object?\n",
-    "Yes, currently you need to create the Search index before creating the `CouchbaseSearchVectoreStore` object.\n"
+    "## Question: Should I create the Search index before creating the CouchbaseVectorStore object?\n",
+    "Yes, currently you need to create the Search index before creating the `CouchbaseVectoreStore` object.\n"
    ]
   },
   {
@@ -945,7 +849,7 @@
    "id": "3f0dbc1b-9e82-4ec3-9330-6b54de00661e",
    "metadata": {},
    "source": [
-    "### Question: I am not seeing all the fields that I specified in my search results. \n",
+    "## Question: I am not seeing all the fields that I specified in my search results. \n",
     "\n",
     "In Couchbase, we can only return the fields stored in the Search index. Please ensure that the field that you are trying to access in the search results is part of the Search index.\n",
     "\n",
@@ -961,10 +865,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0449a2e3-59d7-4b25-b09e-a2b062fef01f",
+   "id": "3702977a-2e25-48b6-b662-edd5cb94cdec",
    "metadata": {},
    "source": [
-    "### Question: I am unable to see the metadata object in my search results. \n",
+    "## Question: I am unable to see the metadata object in my search results. \n",
     "This is most likely due to the `metadata` field in the document not being indexed and/or stored by the Couchbase Search index. In order to index the `metadata` field in the document, you need to add it to the index as a child mapping. \n",
     "\n",
     "If you select to map all the fields in the mapping, you will be able to search by all metadata fields. Alternatively, to optimize the index, you can select the specific fields inside `metadata` object to be indexed. You can refer to the [docs](https://docs.couchbase.com/cloud/search/customize-index.html) to learn more about indexing child mappings.\n",
@@ -975,19 +879,6 @@
     "* [Couchbase Server](https://docs.couchbase.com/server/current/search/create-child-mapping.html)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "c9b8632e-9bce-41c8-b6aa-e527b41de9b2",
-   "metadata": {},
-   "source": [
-    "### Question: What is the difference between filter and search_options / hybrid queries? \n",
-    "Filters are [pre-filters](https://docs.couchbase.com/server/current/vector-search/pre-filtering-vector-search.html#about-pre-filtering) that are used to restrict the documents searched in a Search index. It is available in Couchbase Server 7.6.4 & higher.\n",
-    "\n",
-    "Hybrid Queries are additional search queries that can be used to tune the results being returned from the search index. \n",
-    "\n",
-    "Both filters and hybrid search queries have the same capabilites with slightly different syntax. Filters are [SearchQuery](https://docs.couchbase.com/python-sdk/current/howtos/full-text-searching-with-sdk.html#search-queries) objects while the hybrid search queries are [dictionaries](https://docs.couchbase.com/server/current/search/search-request-params.html).\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "d876b769",
@@ -995,7 +886,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `CouchbaseSearchVectorStore` features and configurations head to the [API reference](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#module-langchain_couchbase.vectorstores.search_vector_store)"
+    "For detailed documentation of all `CouchbaseSearchVectorStore` features and configurations head to the API reference: https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html "
    ]
   }
  ],

docs/docs/integrations/vectorstores/db2.ipynb

@@ -1,391 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "dd33e9d5-9dba-4aac-9f7f-4cf9e6686593",
-   "metadata": {},
-   "source": [
-    "# IBM Db2 Vector Store and Vector Search\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "67520bec",
-   "metadata": {},
-   "source": [
-    "LangChain's Db2 integration (langchain-db2) provides vector store and vector search capabilities for working with IBM relational database Db2 version v12.1.2 and above, distributed under the MIT license. Users can use the provided implementations as-is or customize them for specific needs.\n",
-    " Key features include:\n",
-    "\n",
-    " * Vector storage with metadata\n",
-    " * Vector similarity search and max marginal relevance search, with metadata filtering options\n",
-    " * Support for dot production, cosine, and euclidean distance metrics\n",
-    " * Performance optimization by index creation and Approximate nearest neighbors search. (Will be added shortly)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc94b35a",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7bd80054-c803-47e1-a259-c40ed073c37d",
-   "metadata": {},
-   "source": [
-    "### Prerequisites for using Langchain with Db2 Vector Store and Search\n",
-    "\n",
-    "Install package `langchain-db2` which is the integration package for the db2 LangChain Vector Store and Search.\n",
-    "\n",
-    "The installation of the package should also install its dependencies like `langchain-core` and `ibm_db`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2bbb989d-c6fb-4ab9-bafd-a95fd48538d0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pip install -U langchain-db2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0fceaa5a-95da-4ebd-8b8d-5e73bb653172",
-   "metadata": {},
-   "source": [
-    "### Connect to Db2 Vector Store\n",
-    "\n",
-    "The following sample code will show how to connect to Db2 Database. Besides the dependencies above, you will need a Db2 database instance (with version v12.1.2+, which has the vector datatype support) running."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4421e4b7-2c7e-4bcd-82b3-9576595edd0f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import ibm_db\n",
-    "import ibm_db_dbi\n",
-    "\n",
-    "database = \"\"\n",
-    "username = \"\"\n",
-    "password = \"\"\n",
-    "\n",
-    "try:\n",
-    "    connection = ibm_db_dbi.connect(database, username, password)\n",
-    "    print(\"Connection successful!\")\n",
-    "except Exception as e:\n",
-    "    print(\"Connection failed!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b11cf362-01b0-485d-8527-31b0fbb5028e",
-   "metadata": {},
-   "source": [
-    "### Import the required dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "43ea59e3-2910-45a6-b195-5f06094bb7c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
-    "from langchain_community.vectorstores.utils import DistanceStrategy\n",
-    "from langchain_core.documents import Document\n",
-    "from langchain_db2 import db2vs\n",
-    "from langchain_db2.db2vs import DB2VS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66d56383",
-   "metadata": {},
-   "source": [
-    "## Initialization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0aac10dc-a9cc-4fdb-901c-1b7a4bbbe5a7",
-   "metadata": {},
-   "source": [
-    "### Create Documents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "70ac6982-b13a-4e8c-9c47-57c6d136ac60",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define a list of documents\n",
-    "documents_json_list = [\n",
-    "    {\n",
-    "        \"id\": \"doc_1_2_P4\",\n",
-    "        \"text\": \"Db2 handles LOB data differently than other kinds of data. As a result, you sometimes need to take additional actions when you define LOB columns and insert the LOB data.\",\n",
-    "        \"link\": \"https://www.ibm.com/docs/en/db2-for-zos/12?topic=programs-storing-lob-data-in-tables\",\n",
-    "    },\n",
-    "    {\n",
-    "        \"id\": \"doc_11.1.0_P1\",\n",
-    "        \"text\": \"Db2® column-organized tables add columnar capabilities to Db2 databases, which include data that is stored with column organization and vector processing of column data. Using this table format with star schema data marts provides significant improvements to storage, query performance, and ease of use through simplified design and tuning.\",\n",
-    "        \"link\": \"https://www.ibm.com/docs/en/db2/11.1.0?topic=organization-column-organized-tables\",\n",
-    "    },\n",
-    "    {\n",
-    "        \"id\": \"id_22.3.4.3.1_P2\",\n",
-    "        \"text\": \"Data structures are elements that are required to use Db2®. You can access and use these elements to organize your data. Examples of data structures include tables, table spaces, indexes, index spaces, keys, views, and databases.\",\n",
-    "        \"link\": \"https://www.ibm.com/docs/en/zos-basic-skills?topic=concepts-db2-data-structures\",\n",
-    "    },\n",
-    "    {\n",
-    "        \"id\": \"id_3.4.3.1_P3\",\n",
-    "        \"text\": \"Db2® maintains a set of tables that contain information about the data that Db2 controls. These tables are collectively known as the catalog. The catalog tables contain information about Db2 objects such as tables, views, and indexes. When you create, alter, or drop an object, Db2 inserts, updates, or deletes rows of the catalog that describe the object.\",\n",
-    "        \"link\": \"https://www.ibm.com/docs/en/zos-basic-skills?topic=objects-db2-catalog\",\n",
-    "    },\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eaa942d6-5954-4898-8c32-3627b923a3a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Langchain Documents\n",
-    "\n",
-    "documents_langchain = []\n",
-    "\n",
-    "for doc in documents_json_list:\n",
-    "    metadata = {\"id\": doc[\"id\"], \"link\": doc[\"link\"]}\n",
-    "    doc_langchain = Document(page_content=doc[\"text\"], metadata=metadata)\n",
-    "    documents_langchain.append(doc_langchain)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6823f5e6-997c-4f15-927b-bd44c61f105f",
-   "metadata": {},
-   "source": [
-    "### Create Vector Stores with different distance metrics\n",
-    "\n",
-    "First we will create three vector stores each with different distance strategies. \n",
-    "\n",
-    "(You can manually connect to the Db2 Database and will see three tables : \n",
-    "Documents_DOT, Documents_COSINE and Documents_EUCLIDEAN. )\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ed1b253e-5f5c-4a81-983c-74645213a170",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Db2 Vector Stores using different distance strategies\n",
-    "\n",
-    "# When using our API calls, start by initializing your vector store with a subset of your documents\n",
-    "# through from_documents(), then incrementally add more documents using add_texts().\n",
-    "# This approach prevents system overload and ensures efficient document processing.\n",
-    "\n",
-    "model = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")\n",
-    "\n",
-    "vector_store_dot = DB2VS.from_documents(\n",
-    "    documents_langchain,\n",
-    "    model,\n",
-    "    client=connection,\n",
-    "    table_name=\"Documents_DOT\",\n",
-    "    distance_strategy=DistanceStrategy.DOT_PRODUCT,\n",
-    ")\n",
-    "vector_store_max = DB2VS.from_documents(\n",
-    "    documents_langchain,\n",
-    "    model,\n",
-    "    client=connection,\n",
-    "    table_name=\"Documents_COSINE\",\n",
-    "    distance_strategy=DistanceStrategy.COSINE,\n",
-    ")\n",
-    "vector_store_euclidean = DB2VS.from_documents(\n",
-    "    documents_langchain,\n",
-    "    model,\n",
-    "    client=connection,\n",
-    "    table_name=\"Documents_EUCLIDEAN\",\n",
-    "    distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "804b9142",
-   "metadata": {},
-   "source": [
-    "## Manage vector store"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77c29505-8688-4b87-9a99-e648fbb2d425",
-   "metadata": {},
-   "source": [
-    "### Demonstrating add and delete operations for texts, along with basic similarity search\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "306563ae-577b-4bc7-8a92-3dd6a59310f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def manage_texts(vector_stores):\n",
-    "    \"\"\"\n",
-    "    Adds texts to each vector store, demonstrates error handling for duplicate additions,\n",
-    "    and performs deletion of texts. Showcases similarity searches and index creation for each vector store.\n",
-    "\n",
-    "    Args:\n",
-    "    - vector_stores (list): A list of DB2VS instances.\n",
-    "    \"\"\"\n",
-    "    texts = [\"Rohan\", \"Shailendra\"]\n",
-    "    metadata = [\n",
-    "        {\"id\": \"100\", \"link\": \"Document Example Test 1\"},\n",
-    "        {\"id\": \"101\", \"link\": \"Document Example Test 2\"},\n",
-    "    ]\n",
-    "\n",
-    "    for i, vs in enumerate(vector_stores, start=1):\n",
-    "        # Adding texts\n",
-    "        try:\n",
-    "            vs.add_texts(texts, metadata)\n",
-    "            print(f\"\\n\\n\\nAdd texts complete for vector store {i}\\n\\n\\n\")\n",
-    "        except Exception as ex:\n",
-    "            print(f\"\\n\\n\\nExpected error on duplicate add for vector store {i}\\n\\n\\n\")\n",
-    "\n",
-    "        # Deleting texts using the value of 'id'\n",
-    "        vs.delete([metadata[0][\"id\"], metadata[1][\"id\"]])\n",
-    "        print(f\"\\n\\n\\nDelete texts complete for vector store {i}\\n\\n\\n\")\n",
-    "\n",
-    "        # Similarity search\n",
-    "        results = vs.similarity_search(\"How are LOBS stored in Db2 Database\", 2)\n",
-    "        print(f\"\\n\\n\\nSimilarity search results for vector store {i}: {results}\\n\\n\\n\")\n",
-    "\n",
-    "\n",
-    "vector_store_list = [\n",
-    "    vector_store_dot,\n",
-    "    vector_store_max,\n",
-    "    vector_store_euclidean,\n",
-    "]\n",
-    "manage_texts(vector_store_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "37ebcb44",
-   "metadata": {},
-   "source": [
-    "## Query vector store"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7223d048-5c0b-4e91-a91b-a7daa9f86758",
-   "metadata": {},
-   "source": [
-    "### Demonstrate advanced searches on vector stores, with and without attribute filtering \n",
-    "\n",
-    "With filtering, we only select the document id 101 and nothing else"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "37ca2e7d-9803-4260-95e7-62776d4fb820",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Conduct advanced searches\n",
-    "def conduct_advanced_searches(vector_stores):\n",
-    "    query = \"How are LOBS stored in Db2 Database\"\n",
-    "    # Constructing a filter for direct comparison against document metadata\n",
-    "    # This filter aims to include documents whose metadata 'id' is exactly '101'\n",
-    "    filter_criteria = {\"id\": [\"101\"]}  # Direct comparison filter\n",
-    "\n",
-    "    for i, vs in enumerate(vector_stores, start=1):\n",
-    "        print(f\"\\n--- Vector Store {i} Advanced Searches ---\")\n",
-    "        # Similarity search without a filter\n",
-    "        print(\"\\nSimilarity search results without filter:\")\n",
-    "        print(vs.similarity_search(query, 2))\n",
-    "\n",
-    "        # Similarity search with a filter\n",
-    "        print(\"\\nSimilarity search results with filter:\")\n",
-    "        print(vs.similarity_search(query, 2, filter=filter_criteria))\n",
-    "\n",
-    "        # Similarity search with relevance score\n",
-    "        print(\"\\nSimilarity search with relevance score:\")\n",
-    "        print(vs.similarity_search_with_score(query, 2))\n",
-    "\n",
-    "        # Similarity search with relevance score with filter\n",
-    "        print(\"\\nSimilarity search with relevance score with filter:\")\n",
-    "        print(vs.similarity_search_with_score(query, 2, filter=filter_criteria))\n",
-    "\n",
-    "        # Max marginal relevance search\n",
-    "        print(\"\\nMax marginal relevance search results:\")\n",
-    "        print(vs.max_marginal_relevance_search(query, 2, fetch_k=20, lambda_mult=0.5))\n",
-    "\n",
-    "        # Max marginal relevance search with filter\n",
-    "        print(\"\\nMax marginal relevance search results with filter:\")\n",
-    "        print(\n",
-    "            vs.max_marginal_relevance_search(\n",
-    "                query, 2, fetch_k=20, lambda_mult=0.5, filter=filter_criteria\n",
-    "            )\n",
-    "        )\n",
-    "\n",
-    "\n",
-    "conduct_advanced_searches(vector_store_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7db8d14",
-   "metadata": {},
-   "source": [
-    "## Usage for retrieval-augmented generation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eb5e7288",
-   "metadata": {},
-   "source": [
-    "## API reference"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "pythonEnv",
-   "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": "3.9.21"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/vectorstores/surrealdb.ipynb

@@ -2,353 +2,279 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ef1f0986",
+   "id": "a3afefb0-7e99-4912-a222-c6b186da11af",
    "metadata": {},
    "source": [
-    "# SurrealDBVectorStore\n",
+    "# SurrealDB\n",
     "\n",
-    "> [SurrealDB](https://surrealdb.com) is a unified, multi-model database purpose-built for AI systems. It combines structured and unstructured data (including vector search, graph traversal, relational queries, full-text search, document storage, and time-series data) into a single ACID-compliant engine, scaling from a 3 MB edge binary to petabyte-scale clusters in the cloud. By eliminating the need for multiple specialized stores, SurrealDB simplifies architectures, reduces latency, and ensures consistency for AI workloads.\n",
+    ">[SurrealDB](https://surrealdb.com/) is an end-to-end cloud-native database designed for modern applications, including web, mobile, serverless, Jamstack, backend, and traditional applications. With SurrealDB, you can simplify your database and API infrastructure, reduce development time, and build secure, performant apps quickly and cost-effectively.\n",
     ">\n",
-    "> **Why SurrealDB Matters for GenAI Systems**\n",
-    "> - **One engine for storage and memory:** Combine durable storage and fast, agent-friendly memory in a single system, providing all the data your agent needs and removing the need to sync multiple systems.\n",
-    "> - **One-hop memory for agents:** Run vector search, graph traversal, semantic joins, and transactional writes in a single query, giving LLM agents fast, consistent memory access without stitching relational, graph and vector databases together.\n",
-    "> - **In-place inference and real-time updates:** SurrealDB enables agents to run inference next to data and receive millisecond-fresh updates, critical for real-time reasoning and collaboration.\n",
-    "> - **Versioned, durable context:** SurrealDB supports time-travel queries and versioned records, letting agents audit or “replay” past states for consistent, explainable reasoning.\n",
-    "> - **Plug-and-play agent memory:** Expose AI memory as a native concept, making it easy to use SurrealDB as a drop-in backend for AI frameworks.\n",
+    ">**Key features of SurrealDB include:**\n",
+    ">\n",
+    ">* **Reduces development time:** SurrealDB simplifies your database and API stack by removing the need for most server-side components, allowing you to build secure, performant apps faster and cheaper.\n",
+    ">* **Real-time collaborative API backend service:** SurrealDB functions as both a database and an API backend service, enabling real-time collaboration.\n",
+    ">* **Support for multiple querying languages:** SurrealDB supports SQL querying from client devices, GraphQL, ACID transactions, WebSocket connections, structured and unstructured data, graph querying, full-text indexing, and geospatial querying.\n",
+    ">* **Granular access control:** SurrealDB provides row-level permissions-based access control, giving you the ability to manage data access with precision.\n",
+    ">\n",
+    ">View the [features](https://surrealdb.com/features), the latest [releases](https://surrealdb.com/releases), and [documentation](https://surrealdb.com/docs).\n",
     "\n",
-    "This notebook covers how to get started with the SurrealDB vector store."
+    "This notebook shows how to use functionality related to the `SurrealDBStore`."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "255057477211075c",
+   "id": "5031a3ec",
    "metadata": {},
    "source": [
     "## Setup\n",
     "\n",
-    "You can run SurrealDB locally or start with a [free SurrealDB cloud account](https://surrealdb.com/docs/cloud/getting-started).\n",
-    "\n",
-    "For local, two options:\n",
-    "1. [Install SurrealDB](https://surrealdb.com/docs/surrealdb/installation) and [run SurrealDB](https://surrealdb.com/docs/surrealdb/installation/running). Run in-memory with:\n",
-    "\n",
-    "    ```bash\n",
-    "    surreal start -u root -p root\n",
-    "    ```\n",
-    "\n",
-    "2. [Run with Docker](https://surrealdb.com/docs/surrealdb/installation/running/docker).\n",
-    "\n",
-    "    ```bash\n",
-    "    docker run --rm --pull always -p 8000:8000 surrealdb/surrealdb:latest start\n",
-    "    ```\n",
-    "\n",
-    "## Install dependencies\n",
-    "\n",
-    "Install `langchain-surrealdb` and `surrealdb` python packages.\n",
-    "\n",
-    "```shell\n",
-    "# -- Using pip\n",
-    "pip install --upgrade langchain-surrealdb surrealdb\n",
-    "# -- Using poetry\n",
-    "poetry add langchain-surrealdb surrealdb\n",
-    "# -- Using uv\n",
-    "uv add --upgrade langchain-surrealdb surrealdb\n",
-    "```\n",
-    "\n",
-    "To run this notebook, we just need to install the additional dependencies required by this example:\n"
+    "Uncomment the below cells to install surrealdb."
    ]
   },
   {
    "cell_type": "code",
-   "id": "e403ffc28477aee5",
+   "execution_count": null,
+   "id": "7cd7391f-7759-4a21-952a-2ec972d818c6",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-06-18T14:34:13.173597Z",
-     "start_time": "2025-06-18T14:34:11.280299Z"
-    }
+    "tags": []
    },
-   "source": [
-    "!poetry add --quiet --group docs langchain-ollama langchain-surrealdb"
-   ],
    "outputs": [],
-   "execution_count": 3
+   "source": [
+    "# %pip install --upgrade --quiet  surrealdb langchain langchain-community"
+   ]
   },
   {
    "cell_type": "markdown",
-   "id": "93df377e",
-   "metadata": {},
-   "source": "## Initialization\n"
-  },
-  {
-   "cell_type": "code",
-   "id": "dc37144c-208d-4ab3-9f3a-0407a69fe052",
-   "metadata": {
-    "tags": [],
-    "ExecuteTime": {
-     "end_time": "2025-06-18T14:34:13.432705Z",
-     "start_time": "2025-06-18T14:34:13.179649Z"
-    }
-   },
-   "source": [
-    "from langchain_ollama import OllamaEmbeddings\n",
-    "from langchain_surrealdb.vectorstores import SurrealDBVectorStore\n",
-    "from surrealdb import Surreal\n",
-    "\n",
-    "conn = Surreal(\"ws://localhost:8000/rpc\")\n",
-    "conn.signin({\"username\": \"root\", \"password\": \"root\"})\n",
-    "conn.use(\"langchain\", \"demo\")\n",
-    "vector_store = SurrealDBVectorStore(OllamaEmbeddings(model=\"llama3.2\"), conn)"
-   ],
-   "outputs": [],
-   "execution_count": 4
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac6071d4",
+   "id": "6e57a389-f637-4b8f-9ab2-759ae7485f78",
    "metadata": {},
    "source": [
-    "## Manage vector store\n",
-    "\n",
-    "### Add items to vector store\n"
+    "## Using SurrealDBStore"
    ]
   },
   {
    "cell_type": "code",
-   "id": "8f03bdd3ffc7d75c",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-06-18T14:34:13.622354Z",
-     "start_time": "2025-06-18T14:34:13.438965Z"
-    }
-   },
+   "execution_count": 1,
+   "id": "1c2d942d-5d90-4f9f-af96-dff976e4510f",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "from langchain_core.documents import Document\n",
+    "# add this import for running in jupyter notebook\n",
+    "import nest_asyncio\n",
     "\n",
-    "_url = \"https://surrealdb.com\"\n",
-    "d1 = Document(page_content=\"foo\", metadata={\"source\": _url})\n",
-    "d2 = Document(page_content=\"SurrealDB\", metadata={\"source\": _url})\n",
-    "d3 = Document(page_content=\"bar\", metadata={\"source\": _url})\n",
-    "d4 = Document(page_content=\"this is surreal\", metadata={\"source\": _url})\n",
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e49be085-ddf1-4028-8c0c-97836ce4a873",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import TextLoader\n",
+    "from langchain_community.vectorstores import SurrealDBStore\n",
+    "from langchain_huggingface import HuggingFaceEmbeddings\n",
+    "from langchain_text_splitters import CharacterTextSplitter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "38222aee-adc5-44c2-913c-97977b394cf5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "documents = TextLoader(\"../../how_to/state_of_the_union.txt\").load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
     "\n",
-    "vector_store.add_documents(documents=[d1, d2, d3, d4], ids=[\"1\", \"2\", \"3\", \"4\"])"
-   ],
+    "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
+    "embeddings = HuggingFaceEmbeddings(model_name=model_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e240306-803c-4c1a-b036-b9fc69eb6cba",
+   "metadata": {},
+   "source": [
+    "### Creating a SurrealDBStore object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ff9d0304-1e11-4db2-9454-1350db7907e6",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['1', '2', '3', '4']"
+       "['documents:38hz49bv1p58f5lrvrdc',\n",
+       " 'documents:niayw63vzwm2vcbh6w2s',\n",
+       " 'documents:it1fa3ktplbuye43n0ch',\n",
+       " 'documents:il8f7vgbbp9tywmsn98c',\n",
+       " 'documents:vza4c6cqje0avqd58gal']"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 5
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91a19092754d6723",
-   "metadata": {},
-   "source": "### Update items in vector store"
-  },
-  {
-   "cell_type": "code",
-   "id": "9e6d3ff68383d6da",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-06-18T14:34:13.699211Z",
-     "start_time": "2025-06-18T14:34:13.628105Z"
-    }
-   },
    "source": [
-    "updated_document = Document(\n",
-    "    page_content=\"zar\", metadata={\"source\": \"https://example.com\"}\n",
+    "db = SurrealDBStore(\n",
+    "    dburl=\"ws://localhost:8000/rpc\",  # url for the hosted SurrealDB database\n",
+    "    embedding_function=embeddings,\n",
+    "    db_user=\"root\",  # SurrealDB credentials if needed: db username\n",
+    "    db_pass=\"root\",  # SurrealDB credentials if needed: db password\n",
+    "    # ns=\"langchain\", # namespace to use for vectorstore\n",
+    "    # db=\"database\",  # database to use for vectorstore\n",
+    "    # collection=\"documents\", #collection to use for vectorstore\n",
     ")\n",
     "\n",
-    "vector_store.add_documents(documents=[updated_document], ids=[\"3\"])"
-   ],
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['3']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 6
+    "# this is needed to initialize the underlying async library for SurrealDB\n",
+    "await db.initialize()\n",
+    "\n",
+    "# delete all existing documents from the vectorstore collection\n",
+    "await db.adelete()\n",
+    "\n",
+    "# add documents to the vectorstore\n",
+    "ids = await db.aadd_documents(docs)\n",
+    "\n",
+    "# document ids of the added documents\n",
+    "ids[:5]"
+   ]
   },
   {
    "cell_type": "markdown",
-   "id": "d645a4f864f0b374",
-   "metadata": {},
-   "source": "### Delete items from vector store\n"
-  },
-  {
-   "cell_type": "code",
-   "id": "9f31cc27bf61959e",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-06-18T14:34:13.723069Z",
-     "start_time": "2025-06-18T14:34:13.716396Z"
-    }
-   },
-   "source": [
-    "vector_store.delete(ids=[\"3\"])"
-   ],
-   "outputs": [],
-   "execution_count": 7
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3620501",
+   "id": "94a742a9-9507-4076-9cc3-616a4ed6866f",
    "metadata": {},
    "source": [
-    "## Query vector store\n",
-    "\n",
-    "Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent. \n",
-    "\n",
-    "### Query directly\n",
-    "\n",
-    "Performing a simple similarity search can be done as follows:\n"
+    "### (alternatively) Create a SurrealDBStore object and add documents"
    ]
   },
   {
    "cell_type": "code",
-   "id": "b14e63173710a63f",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-06-18T14:34:13.808318Z",
-     "start_time": "2025-06-18T14:34:13.734463Z"
-    }
-   },
+   "execution_count": 5,
+   "id": "73d66563-4e1f-4edf-9e95-5fc9adcfa2cb",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "results = vector_store.similarity_search(\n",
-    "    query=\"surreal\", k=1, custom_filter={\"source\": \"https://surrealdb.com\"}\n",
-    ")\n",
-    "for doc in results:\n",
-    "    print(f\"{doc.page_content} [{doc.metadata}]\")  # noqa: T201"
-   ],
+    "await db.adelete()\n",
+    "\n",
+    "db = await SurrealDBStore.afrom_documents(\n",
+    "    dburl=\"ws://localhost:8000/rpc\",  # url for the hosted SurrealDB database\n",
+    "    embedding=embeddings,\n",
+    "    documents=docs,\n",
+    "    db_user=\"root\",  # SurrealDB credentials if needed: db username\n",
+    "    db_pass=\"root\",  # SurrealDB credentials if needed: db password\n",
+    "    # ns=\"langchain\", # namespace to use for vectorstore\n",
+    "    # db=\"database\",  # database to use for vectorstore\n",
+    "    # collection=\"documents\", #collection to use for vectorstore\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efbb6684-3846-4332-a624-ddd4d75844c1",
+   "metadata": {},
+   "source": [
+    "### Similarity search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aa28a7f8-41d0-4299-84eb-91d1576e8a63",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs = await db.asimilarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1eb16d2a-b466-456a-b412-5e74bb8523dd",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "this is surreal [{'source': 'https://surrealdb.com'}]\n"
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
      ]
     }
    ],
-   "execution_count": 8
+   "source": [
+    "print(docs[0].page_content)"
+   ]
   },
   {
    "cell_type": "markdown",
-   "id": "3ed9d733",
+   "id": "43896697-f99e-47b6-9117-47a25e9afa9c",
    "metadata": {},
-   "source": "If you want to execute a similarity search and receive the corresponding scores you can run:\n"
-  },
-  {
-   "cell_type": "code",
-   "id": "20b694cd6fc9529c",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-06-18T14:34:13.880648Z",
-     "start_time": "2025-06-18T14:34:13.812341Z"
-    }
-   },
    "source": [
-    "results = vector_store.similarity_search_with_score(\n",
-    "    query=\"thud\", k=1, custom_filter={\"source\": \"https://surrealdb.com\"}\n",
-    ")\n",
-    "for doc, score in results:\n",
-    "    print(f\"[similarity={score:.0%}] {doc.page_content}\")  # noqa: T201"
-   ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[similarity=57%] this is surreal\n"
-     ]
-    }
-   ],
-   "execution_count": 9
+    "### Similarity search with score"
+   ]
   },
   {
    "cell_type": "markdown",
-   "id": "b1e75fd5932f4c6a",
+   "id": "414a9bc9",
    "metadata": {},
    "source": [
-    "### Query by turning into retriever\n",
-    "\n",
-    "You can also transform the vector store into a retriever for easier usage in your chains. \n"
+    "The returned distance score is cosine distance. Therefore, a lower score is better."
    ]
   },
   {
    "cell_type": "code",
-   "id": "73b330f83225256b",
+   "execution_count": 8,
+   "id": "8e9eef05-1516-469a-ad36-880c69aef7a9",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-06-18T14:34:13.970711Z",
-     "start_time": "2025-06-18T14:34:13.884730Z"
-    }
+    "tags": []
    },
+   "outputs": [],
    "source": [
-    "retriever = vector_store.as_retriever(\n",
-    "    search_type=\"mmr\", search_kwargs={\"k\": 1, \"lambda_mult\": 0.5}\n",
-    ")\n",
-    "retriever.invoke(\"surreal\")"
-   ],
+    "docs = await db.asimilarity_search_with_score(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bd5fb0e4-2a94-4bb4-af8a-27327ecb1a7f",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(id='4', metadata={'source': 'https://surrealdb.com'}, page_content='this is surreal')]"
+       "(Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'id': 'documents:slgdlhjkfknhqo15xz0w', 'source': '../../how_to/state_of_the_union.txt'}),\n",
+       " 0.39839531721941895)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 10
-  },
-  {
-   "cell_type": "markdown",
-   "id": "901c75dc",
-   "metadata": {},
    "source": [
-    "## Usage for retrieval-augmented generation\n",
-    "\n",
-    "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
-    "\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval/)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8a27244f",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For detailed documentation of all SurrealDBVectorStore features and configurations head to the API reference: https://python.langchain.com/api_reference/surrealdb/index.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "85901cdb62057fe5",
-   "metadata": {},
-   "source": [
-    "## Next steps\n",
-    "\n",
-    "- look at the [basic example](https://github.com/surrealdb/langchain-surrealdb/tree/main/examples/basic). Use the Dockerfile to try it out!\n",
-    "- look at the [graph example](https://github.com/surrealdb/langchain-surrealdb/tree/main/examples/graph)\n",
-    "- try the [jupyther notebook](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/vectorstores/surrealdb.ipynb)\n",
-    "- [Awesome SurrealDB](https://github.com/surrealdb/awesome-surreal), A curated list of SurrealDB resources, tools, utilities, and applications\n"
+    "docs[0]"
    ]
   }
  ],

docs/docs/integrations/vectorstores/timescalevector.ipynb

@@ -821,7 +821,7 @@
       "--------------------------------------------------------------------------------\n",
       "Score:  0.21698051691055298\n",
       "Date:  2023-08-2 20:24:14+0140\n",
-      "{\"commit\": \" 3af0d282ea71d9a8f27159a6171e9516e62ec9cb\", \"author\": \"Lakshmi Narayanan Sreethar<lakshmi@timescale.com>\", \"date\": \"Wed Aug 2 20:24:14 2023 +0100\", \"change summary\": \"PG16: ExecInsertIndexTuples requires additional parameter\", \"change details\": \"PG16 adds a new boolean parameter to the ExecInsertIndexTuples function to denote if the index is a BRIN index, which is then used to determine if the index update can be skipped. The fix also removes the INDEX_ATTR_BITMAP_ALL enum value.  Adapt these changes by updating the compat function to accommodate the new parameter added to the ExecInsertIndexTuples function and using an alternative for the removed INDEX_ATTR_BITMAP_ALL enum value.  postgres/postgres@19d8e23 \"}\n",
+      "{\"commit\": \" 3af0d282ea71d9a8f27159a6171e9516e62ec9cb\", \"author\": \"Lakshmi Narayanan Sreethar<lakshmi@timescale.com>\", \"date\": \"Wed Aug 2 20:24:14 2023 +0100\", \"change summary\": \"PG16: ExecInsertIndexTuples requires additional parameter\", \"change details\": \"PG16 adds a new boolean parameter to the ExecInsertIndexTuples function to denote if the index is a BRIN index, which is then used to determine if the index update can be skipped. The fix also removes the INDEX_ATTR_BITMAP_ALL enum value.  Adapt these changes by updating the compat function to accomodate the new parameter added to the ExecInsertIndexTuples function and using an alternative for the removed INDEX_ATTR_BITMAP_ALL enum value.  postgres/postgres@19d8e23 \"}\n",
       "--------------------------------------------------------------------------------\n"
      ]
     }

docs/docs/integrations/vectorstores/vectara.ipynb

@@ -274,7 +274,7 @@
    "id": "b651396a-5726-4d49-bacf-c9d7a5ddcf7a",
    "metadata": {},
    "source": [
-    "## Vectara as a langchain retriever\n",
+    "## Vectara as a langchain retreiver\n",
     "\n",
     "The Vectara component can also be used just as a retriever. \n",
     "\n",

docs/docs/tutorials/agents.ipynb

@@ -21,7 +21,9 @@
    "source": [
     "# Build an Agent\n",
     "\n",
-    "LangChain supports the creation of [agents](/docs/concepts/agents), or systems that use [LLMs](/docs/concepts/chat_models) as reasoning engines to determine which actions to take and the inputs necessary to perform the action.\n",
+    "By themselves, language models can't take actions - they just output text.\n",
+    "A big use case for LangChain is creating **agents**.\n",
+    "[Agents](/docs/concepts/agents) are systems that use [LLMs](/docs/concepts/chat_models) as reasoning engines to determine which actions to take and the inputs necessary to perform the action.\n",
     "After executing actions, the results can be fed back into the LLM to determine whether more actions are needed, or whether it is okay to finish. This is often achieved via [tool-calling](/docs/concepts/tool_calling).\n",
     "\n",
     "In this tutorial we will build an agent that can interact with a search engine. You will be able to ask this agent questions, watch it call the search tool, and have conversations with it.\n",
@@ -41,15 +43,16 @@
    "outputs": [],
    "source": [
     "# Import relevant functionality\n",
-    "from langchain.chat_models import init_chat_model\n",
-    "from langchain_tavily import TavilySearch\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.messages import HumanMessage\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
     "# Create the agent\n",
     "memory = MemorySaver()\n",
-    "model = init_chat_model(\"anthropic:claude-3-5-sonnet-latest\")\n",
-    "search = TavilySearch(max_results=2)\n",
+    "model = ChatAnthropic(model_name=\"claude-3-sonnet-20240229\")\n",
+    "search = TavilySearchResults(max_results=2)\n",
     "tools = [search]\n",
     "agent_executor = create_react_agent(model, tools, checkpointer=memory)"
    ]
@@ -66,23 +69,20 @@
      "text": [
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
-      "Hi, I'm Bob and I life in SF.\n",
+      "hi im bob! and i live in sf\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "Hello Bob! I notice you've introduced yourself and mentioned you live in SF (San Francisco), but you haven't asked a specific question or made a request that requires the use of any tools. Is there something specific you'd like to know about San Francisco or any other topic? I'd be happy to help you find information using the available search tools.\n"
+      "Hello Bob! Since you didn't ask a specific question, I don't need to use any tools right now. I'm an AI assistant created by Anthropic to be helpful, honest, and harmless. Feel free to ask me anything and I'll do my best to provide a useful response or look up information using my capabilities.\n"
      ]
     }
    ],
    "source": [
     "# Use the agent\n",
     "config = {\"configurable\": {\"thread_id\": \"abc123\"}}\n",
-    "\n",
-    "input_message = {\n",
-    "    \"role\": \"user\",\n",
-    "    \"content\": \"Hi, I'm Bob and I life in SF.\",\n",
-    "}\n",
     "for step in agent_executor.stream(\n",
-    "    {\"messages\": [input_message]}, config, stream_mode=\"values\"\n",
+    "    {\"messages\": [HumanMessage(content=\"hi im bob! and i live in sf\")]},\n",
+    "    config,\n",
+    "    stream_mode=\"values\",\n",
     "):\n",
     "    step[\"messages\"][-1].pretty_print()"
    ]
@@ -99,40 +99,32 @@
      "text": [
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
-      "What's the weather where I live?\n",
+      "whats the weather where I live?\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "[{'text': 'Let me search for current weather information in San Francisco.', 'type': 'text'}, {'id': 'toolu_011kSdheoJp8THURoLmeLtZo', 'input': {'query': 'current weather San Francisco CA'}, 'name': 'tavily_search', 'type': 'tool_use'}]\n",
+      "[{'text': 'To get the current weather for your location in San Francisco, I can use the tavily_search_results_json tool:', 'type': 'text'}, {'id': 'toolu_01AKa2MErG1CU3zRiGsvpBud', 'input': {'query': 'san francisco weather'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  tavily_search (toolu_011kSdheoJp8THURoLmeLtZo)\n",
-      " Call ID: toolu_011kSdheoJp8THURoLmeLtZo\n",
+      "  tavily_search_results_json (toolu_01AKa2MErG1CU3zRiGsvpBud)\n",
+      " Call ID: toolu_01AKa2MErG1CU3zRiGsvpBud\n",
       "  Args:\n",
-      "    query: current weather San Francisco CA\n",
+      "    query: san francisco weather\n",
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: tavily_search\n",
+      "Name: tavily_search_results_json\n",
       "\n",
-      "{\"query\": \"current weather San Francisco CA\", \"follow_up_questions\": null, \"answer\": null, \"images\": [], \"results\": [{\"title\": \"Weather in San Francisco, CA\", \"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1750168606, 'localtime': '2025-06-17 06:56'}, 'current': {'last_updated_epoch': 1750167900, 'last_updated': '2025-06-17 06:45', 'temp_c': 11.7, 'temp_f': 53.1, 'is_day': 1, 'condition': {'text': 'Fog', 'icon': '//cdn.weatherapi.com/weather/64x64/day/248.png', 'code': 1135}, 'wind_mph': 4.0, 'wind_kph': 6.5, 'wind_degree': 215, 'wind_dir': 'SW', 'pressure_mb': 1017.0, 'pressure_in': 30.02, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 86, 'cloud': 0, 'feelslike_c': 11.3, 'feelslike_f': 52.4, 'windchill_c': 8.7, 'windchill_f': 47.7, 'heatindex_c': 9.8, 'heatindex_f': 49.7, 'dewpoint_c': 9.6, 'dewpoint_f': 49.2, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 0.0, 'gust_mph': 6.3, 'gust_kph': 10.2}}\", \"score\": 0.944705, \"raw_content\": null}, {\"title\": \"Weather in San Francisco in June 2025\", \"url\": \"https://world-weather.info/forecast/usa/san_francisco/june-2025/\", \"content\": \"Detailed ⚡ San Francisco Weather Forecast for June 2025 - day/night 🌡️ temperatures, precipitations - World-Weather.info. Add the current city. Search. Weather; Archive; Weather Widget °F. World; United States; California; Weather in San Francisco; ... 17 +64° +54° 18 +61° +54° 19\", \"score\": 0.86441374, \"raw_content\": null}], \"response_time\": 2.34}\n",
+      "[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1739994486, 'localtime': '2025-02-19 11:48'}, 'current': {'last_updated_epoch': 1739994300, 'last_updated': '2025-02-19 11:45', 'temp_c': 13.3, 'temp_f': 55.9, 'is_day': 1, 'condition': {'text': 'Light rain', 'icon': '//cdn.weatherapi.com/weather/64x64/day/296.png', 'code': 1183}, 'wind_mph': 5.8, 'wind_kph': 9.4, 'wind_degree': 195, 'wind_dir': 'SSW', 'pressure_mb': 1023.0, 'pressure_in': 30.2, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 87, 'cloud': 100, 'feelslike_c': 12.7, 'feelslike_f': 54.8, 'windchill_c': 9.1, 'windchill_f': 48.4, 'heatindex_c': 10.2, 'heatindex_f': 50.3, 'dewpoint_c': 9.8, 'dewpoint_f': 49.7, 'vis_km': 4.0, 'vis_miles': 2.0, 'uv': 1.4, 'gust_mph': 8.9, 'gust_kph': 14.4}}\"}, {\"url\": \"https://world-weather.info/forecast/usa/san_francisco/february-2025/\", \"content\": \"Weather in San Francisco in February 2025 (California) - Detailed Weather Forecast for a Month Weather World Weather in San Francisco Weather in San Francisco in February 2025 San Francisco Weather Forecast for February 2025, is based on previous years' statistical data. +59°+50° +59°+52° +59°+50° +61°+52° +59°+50° +61°+50° +61°+52° +63°+52° +61°+52° +61°+50° +61°+50° +61°+50° +59°+50° +59°+50° +61°+50° +61°+52° +59°+50° +59°+48° +57°+48° +59°+50° +59°+48° +59°+50° +57°+46° +61°+50° +61°+50° +59°+50° +59°+48° +59°+50° Extended weather forecast in San Francisco HourlyWeek10-Day14-Day30-DayYear Weather in large and nearby cities Weather in Washington, D.C.+41° Sacramento+55° Pleasanton+55° Redwood City+55° San Leandro+55° San Mateo+54° San Rafael+52° San Ramon+52° South San Francisco+54° Vallejo+50° Palo Alto+55° Pacifica+55° Berkeley+54° Castro Valley+55° Concord+52° Daly City+54° Noverd+52° Sign Hill+54° world's temperature today day day Temperature units\"}]\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "Based on the search results, here's the current weather in San Francisco:\n",
-      "- Temperature: 53.1°F (11.7°C)\n",
-      "- Condition: Foggy\n",
-      "- Wind: 4.0 mph from the Southwest\n",
-      "- Humidity: 86%\n",
-      "- Visibility: 9 miles\n",
+      "The search results provide the current weather conditions and forecast for San Francisco. According to the data from WeatherAPI, the current temperature in San Francisco is around 55°F (13°C) with light rain and winds around 6 mph. The extended forecast shows temperatures ranging from the upper 40s to low 60s Fahrenheit over the next few weeks.\n",
       "\n",
-      "This is quite typical weather for San Francisco, with the characteristic fog that the city is known for. Would you like to know anything else about the weather or San Francisco in general?\n"
+      "So in summary, it's a cool, rainy day currently in San Francisco where you live, Bob. Let me know if you need any other details about the weather there!\n"
      ]
     }
    ],
    "source": [
-    "input_message = {\n",
-    "    \"role\": \"user\",\n",
-    "    \"content\": \"What's the weather where I live?\",\n",
-    "}\n",
-    "\n",
     "for step in agent_executor.stream(\n",
-    "    {\"messages\": [input_message]}, config, stream_mode=\"values\"\n",
+    "    {\"messages\": [HumanMessage(content=\"whats the weather where I live?\")]},\n",
+    "    config,\n",
+    "    stream_mode=\"values\",\n",
     "):\n",
     "    step[\"messages\"][-1].pretty_print()"
    ]
@@ -162,7 +154,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -U langgraph langchain-tavily langgraph-checkpoint-sqlite"
+    "%pip install -U langchain-community langgraph langchain-anthropic tavily-python langgraph-checkpoint-sqlite"
    ]
   },
   {
@@ -221,48 +213,34 @@
    "source": [
     "## Define tools\n",
     "\n",
-    "We first need to create the tools we want to use. Our main tool of choice will be [Tavily](/docs/integrations/tools/tavily_search) - a search engine. We can use the dedicated [langchain-tavily](https://pypi.org/project/langchain-tavily/) [integration package](/docs/concepts/architecture/#integration-packages) to easily use Tavily search engine as tool with LangChain.\n"
+    "We first need to create the tools we want to use. Our main tool of choice will be [Tavily](/docs/integrations/tools/tavily_search) - a search engine. We have a built-in tool in LangChain to easily use Tavily search engine as tool.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "76a02d36-6ea2-4e62-88b4-6c480dd9c04f",
+   "execution_count": 4,
+   "id": "002e23b1-fdf9-46e9-82d9-f467abdd3f35",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'query': 'What is the weather in SF', 'follow_up_questions': None, 'answer': None, 'images': [], 'results': [{'title': 'Weather in San Francisco, CA', 'url': 'https://www.weatherapi.com/', 'content': \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1750168606, 'localtime': '2025-06-17 06:56'}, 'current': {'last_updated_epoch': 1750167900, 'last_updated': '2025-06-17 06:45', 'temp_c': 11.7, 'temp_f': 53.1, 'is_day': 1, 'condition': {'text': 'Fog', 'icon': '//cdn.weatherapi.com/weather/64x64/day/248.png', 'code': 1135}, 'wind_mph': 4.0, 'wind_kph': 6.5, 'wind_degree': 215, 'wind_dir': 'SW', 'pressure_mb': 1017.0, 'pressure_in': 30.02, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 86, 'cloud': 0, 'feelslike_c': 11.3, 'feelslike_f': 52.4, 'windchill_c': 8.7, 'windchill_f': 47.7, 'heatindex_c': 9.8, 'heatindex_f': 49.7, 'dewpoint_c': 9.6, 'dewpoint_f': 49.2, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 0.0, 'gust_mph': 6.3, 'gust_kph': 10.2}}\", 'score': 0.9185379, 'raw_content': None}, {'title': 'Weather in San Francisco in June 2025', 'url': 'https://world-weather.info/forecast/usa/san_francisco/june-2025/', 'content': \"Weather in San Francisco in June 2025 (California) - Detailed Weather Forecast for a Month *   Weather in San Francisco Weather in San Francisco in June 2025 *   1 +63° +55° *   2 +66° +54° *   3 +66° +55° *   4 +66° +54° *   5 +66° +55° *   6 +66° +57° *   7 +64° +55° *   8 +63° +55° *   9 +63° +54° *   10 +59° +54° *   11 +59° +54° *   12 +61° +54° Weather in Washington, D.C.**+68°** Sacramento**+81°** Pleasanton**+72°** Redwood City**+68°** San Leandro**+61°** San Mateo**+64°** San Rafael**+70°** San Ramon**+64°** South San Francisco**+61°** Daly City**+59°** Wilder**+66°** Woodacre**+70°** world's temperature today Colchani day+50°F night+16°F Az Zubayr day+124°F night+93°F Weather forecast on your site Install _San Francisco_ +61° Temperature units\", 'score': 0.7978881, 'raw_content': None}], 'response_time': 2.62}\n"
+      "[{'url': 'https://www.weatherapi.com/', 'content': \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1739993250, 'localtime': '2025-02-19 11:27'}, 'current': {'last_updated_epoch': 1739992500, 'last_updated': '2025-02-19 11:15', 'temp_c': 13.3, 'temp_f': 55.9, 'is_day': 1, 'condition': {'text': 'Light rain', 'icon': '//cdn.weatherapi.com/weather/64x64/day/296.png', 'code': 1183}, 'wind_mph': 5.8, 'wind_kph': 9.4, 'wind_degree': 195, 'wind_dir': 'SSW', 'pressure_mb': 1023.0, 'pressure_in': 30.2, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 87, 'cloud': 100, 'feelslike_c': 12.7, 'feelslike_f': 54.8, 'windchill_c': 9.1, 'windchill_f': 48.4, 'heatindex_c': 10.2, 'heatindex_f': 50.3, 'dewpoint_c': 9.8, 'dewpoint_f': 49.7, 'vis_km': 4.0, 'vis_miles': 2.0, 'uv': 1.4, 'gust_mph': 8.9, 'gust_kph': 14.4}}\"}, {'url': 'https://weathershogun.com/weather/usa/ca/san-francisco/480/february/2025-02-19', 'content': 'San Francisco, California Weather: Wednesday, February 19, 2025. Cloudy weather, overcast skies with clouds. Day 61°. Night 43°.'}]\n"
      ]
     }
    ],
    "source": [
-    "from langchain_tavily import TavilySearch\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
     "\n",
-    "search = TavilySearch(max_results=2)\n",
-    "search_results = search.invoke(\"What is the weather in SF\")\n",
+    "search = TavilySearchResults(max_results=2)\n",
+    "search_results = search.invoke(\"what is the weather in SF\")\n",
     "print(search_results)\n",
     "# If we want, we can create other tools.\n",
     "# Once we have all the tools we want, we can put them in a list that we will reference later.\n",
     "tools = [search]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ecbc86d8",
-   "metadata": {},
-   "source": [
-    ":::tip\n",
-    "\n",
-    "In many applications, you may want to define custom tools. LangChain supports custom\n",
-    "tool creation via Python functions and other means. Refer to the\n",
-    "[How to create tools](/docs/how_to/custom_tools/) guide for details.\n",
-    "\n",
-    ":::"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "e00068b0",
@@ -274,12 +252,12 @@
     "\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
-    "<ChatModelTabs overrideParams={{openai: {model: \"gpt-4.1\"}}} />\n"
+    "<ChatModelTabs overrideParams={{openai: {model: \"gpt-4\"}}} />\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "69185491",
    "metadata": {},
    "outputs": [],
@@ -289,7 +267,7 @@
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "\n",
-    "model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")"
+    "model = ChatAnthropic(model=\"claude-3-sonnet-20240229\")"
    ]
   },
   {
@@ -302,25 +280,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "c96c960b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Hello! How can I help you today?'"
+       "'Hi there!'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "query = \"Hi!\"\n",
-    "response = model.invoke([{\"role\": \"user\", \"content\": query}])\n",
-    "response.text()"
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "response = model.invoke([HumanMessage(content=\"hi!\")])\n",
+    "response.content"
    ]
   },
   {
@@ -333,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "ba692a74",
    "metadata": {},
    "outputs": [],
@@ -351,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "b6a7e925",
    "metadata": {},
    "outputs": [
@@ -359,20 +338,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Message content: Hello! I'm here to help you. I have access to a powerful search tool that can help answer questions and find information about various topics. What would you like to know about?\n",
-      "\n",
-      "Feel free to ask any question or request information, and I'll do my best to assist you using the available tools.\n",
-      "\n",
-      "Tool calls: []\n"
+      "ContentString: Hello!\n",
+      "ToolCalls: []\n"
      ]
     }
    ],
    "source": [
-    "query = \"Hi!\"\n",
-    "response = model_with_tools.invoke([{\"role\": \"user\", \"content\": query}])\n",
+    "response = model_with_tools.invoke([HumanMessage(content=\"Hi!\")])\n",
     "\n",
-    "print(f\"Message content: {response.text()}\\n\")\n",
-    "print(f\"Tool calls: {response.tool_calls}\")"
+    "print(f\"ContentString: {response.content}\")\n",
+    "print(f\"ToolCalls: {response.tool_calls}\")"
    ]
   },
   {
@@ -385,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "id": "688b465d",
    "metadata": {},
    "outputs": [
@@ -393,18 +368,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Message content: I'll help you search for information about the weather in San Francisco.\n",
-      "\n",
-      "Tool calls: [{'name': 'tavily_search', 'args': {'query': 'current weather San Francisco'}, 'id': 'toolu_015gdPn1jbB2Z21DmN2RAnti', 'type': 'tool_call'}]\n"
+      "ContentString: \n",
+      "ToolCalls: [{'name': 'tavily_search_results_json', 'args': {'query': 'weather san francisco'}, 'id': 'toolu_01VTP7DUvSfgtYxsq9x4EwMp'}]\n"
      ]
     }
    ],
    "source": [
-    "query = \"Search for the weather in SF\"\n",
-    "response = model_with_tools.invoke([{\"role\": \"user\", \"content\": query}])\n",
+    "response = model_with_tools.invoke([HumanMessage(content=\"What's the weather in SF?\")])\n",
     "\n",
-    "print(f\"Message content: {response.text()}\\n\")\n",
-    "print(f\"Tool calls: {response.tool_calls}\")"
+    "print(f\"ContentString: {response.content}\")\n",
+    "print(f\"ToolCalls: {response.tool_calls}\")"
    ]
   },
   {
@@ -440,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "id": "89cf72b4-6046-4b47-8f27-5522d8cb8036",
    "metadata": {},
    "outputs": [],
@@ -464,29 +437,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "id": "114ba50d",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "Hi!\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "Hello! I'm here to help you with your questions using the available search tools. Please feel free to ask any question, and I'll do my best to find relevant and accurate information for you.\n"
-     ]
+     "data": {
+      "text/plain": [
+       "[HumanMessage(content='hi!', id='a820fcc5-9b87-457a-9af0-f21768143ee3'),\n",
+       " AIMessage(content='Hello!', response_metadata={'id': 'msg_01VbC493X1VEDyusgttiEr1z', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 264, 'output_tokens': 5}}, id='run-0e0ddae8-a85b-4bd6-947c-c36c857a4698-0', usage_metadata={'input_tokens': 264, 'output_tokens': 5, 'total_tokens': 269})]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "input_message = {\"role\": \"user\", \"content\": \"Hi!\"}\n",
-    "response = agent_executor.invoke({\"messages\": [input_message]})\n",
+    "response = agent_executor.invoke({\"messages\": [HumanMessage(content=\"hi!\")]})\n",
     "\n",
-    "for message in response[\"messages\"]:\n",
-    "    message.pretty_print()"
+    "response[\"messages\"]"
    ]
   },
   {
@@ -501,48 +471,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "id": "77c2f769",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "Search for the weather in SF\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "[{'text': \"I'll help you search for weather information in San Francisco. Let me use the search engine to find current weather conditions.\", 'type': 'text'}, {'id': 'toolu_01WWcXGnArosybujpKzdmARZ', 'input': {'query': 'current weather San Francisco SF'}, 'name': 'tavily_search', 'type': 'tool_use'}]\n",
-      "Tool Calls:\n",
-      "  tavily_search (toolu_01WWcXGnArosybujpKzdmARZ)\n",
-      " Call ID: toolu_01WWcXGnArosybujpKzdmARZ\n",
-      "  Args:\n",
-      "    query: current weather San Francisco SF\n",
-      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: tavily_search\n",
-      "\n",
-      "{\"query\": \"current weather San Francisco SF\", \"follow_up_questions\": null, \"answer\": null, \"images\": [], \"results\": [{\"title\": \"Weather in San Francisco, CA\", \"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1750168606, 'localtime': '2025-06-17 06:56'}, 'current': {'last_updated_epoch': 1750167900, 'last_updated': '2025-06-17 06:45', 'temp_c': 11.7, 'temp_f': 53.1, 'is_day': 1, 'condition': {'text': 'Fog', 'icon': '//cdn.weatherapi.com/weather/64x64/day/248.png', 'code': 1135}, 'wind_mph': 4.0, 'wind_kph': 6.5, 'wind_degree': 215, 'wind_dir': 'SW', 'pressure_mb': 1017.0, 'pressure_in': 30.02, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 86, 'cloud': 0, 'feelslike_c': 11.3, 'feelslike_f': 52.4, 'windchill_c': 8.7, 'windchill_f': 47.7, 'heatindex_c': 9.8, 'heatindex_f': 49.7, 'dewpoint_c': 9.6, 'dewpoint_f': 49.2, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 0.0, 'gust_mph': 6.3, 'gust_kph': 10.2}}\", \"score\": 0.885373, \"raw_content\": null}, {\"title\": \"Weather in San Francisco in June 2025\", \"url\": \"https://world-weather.info/forecast/usa/san_francisco/june-2025/\", \"content\": \"Detailed ⚡ San Francisco Weather Forecast for June 2025 - day/night 🌡️ temperatures, precipitations - World-Weather.info. Add the current city. Search. Weather; Archive; Weather Widget °F. World; United States; California; Weather in San Francisco; ... 17 +64° +54° 18 +61° +54° 19\", \"score\": 0.8830044, \"raw_content\": null}], \"response_time\": 2.6}\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "Based on the search results, here's the current weather in San Francisco:\n",
-      "- Temperature: 53.1°F (11.7°C)\n",
-      "- Conditions: Foggy\n",
-      "- Wind: 4.0 mph from the SW\n",
-      "- Humidity: 86%\n",
-      "- Visibility: 9.0 miles\n",
-      "\n",
-      "The weather appears to be typical for San Francisco, with morning fog and mild temperatures. The \"feels like\" temperature is 52.4°F (11.3°C).\n"
-     ]
+     "data": {
+      "text/plain": [
+       "[HumanMessage(content='whats the weather in sf?', id='1d6c96bb-4ddb-415c-a579-a07d5264de0d'),\n",
+       " AIMessage(content=[{'id': 'toolu_01Y5EK4bw2LqsQXeaUv8iueF', 'input': {'query': 'weather in san francisco'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], response_metadata={'id': 'msg_0132wQUcEduJ8UKVVVqwJzM4', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 269, 'output_tokens': 61}}, id='run-26d5e5e8-d4fd-46d2-a197-87b95b10e823-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in san francisco'}, 'id': 'toolu_01Y5EK4bw2LqsQXeaUv8iueF'}], usage_metadata={'input_tokens': 269, 'output_tokens': 61, 'total_tokens': 330}),\n",
+       " ToolMessage(content='[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{\\'location\\': {\\'name\\': \\'San Francisco\\', \\'region\\': \\'California\\', \\'country\\': \\'United States of America\\', \\'lat\\': 37.78, \\'lon\\': -122.42, \\'tz_id\\': \\'America/Los_Angeles\\', \\'localtime_epoch\\': 1717238703, \\'localtime\\': \\'2024-06-01 3:45\\'}, \\'current\\': {\\'last_updated_epoch\\': 1717237800, \\'last_updated\\': \\'2024-06-01 03:30\\', \\'temp_c\\': 12.0, \\'temp_f\\': 53.6, \\'is_day\\': 0, \\'condition\\': {\\'text\\': \\'Mist\\', \\'icon\\': \\'//cdn.weatherapi.com/weather/64x64/night/143.png\\', \\'code\\': 1030}, \\'wind_mph\\': 5.6, \\'wind_kph\\': 9.0, \\'wind_degree\\': 310, \\'wind_dir\\': \\'NW\\', \\'pressure_mb\\': 1013.0, \\'pressure_in\\': 29.92, \\'precip_mm\\': 0.0, \\'precip_in\\': 0.0, \\'humidity\\': 88, \\'cloud\\': 100, \\'feelslike_c\\': 10.5, \\'feelslike_f\\': 50.8, \\'windchill_c\\': 9.3, \\'windchill_f\\': 48.7, \\'heatindex_c\\': 11.1, \\'heatindex_f\\': 51.9, \\'dewpoint_c\\': 8.8, \\'dewpoint_f\\': 47.8, \\'vis_km\\': 6.4, \\'vis_miles\\': 3.0, \\'uv\\': 1.0, \\'gust_mph\\': 12.5, \\'gust_kph\\': 20.1}}\"}, {\"url\": \"https://www.timeanddate.com/weather/usa/san-francisco/hourly\", \"content\": \"Sun & Moon. Weather Today Weather Hourly 14 Day Forecast Yesterday/Past Weather Climate (Averages) Currently: 59 \\\\u00b0F. Passing clouds. (Weather station: San Francisco International Airport, USA). See more current weather.\"}]', name='tavily_search_results_json', id='37aa1fd9-b232-4a02-bd22-bc5b9b44a22c', tool_call_id='toolu_01Y5EK4bw2LqsQXeaUv8iueF'),\n",
+       " AIMessage(content='Based on the search results, here is a summary of the current weather in San Francisco:\\n\\nThe weather in San Francisco is currently misty with a temperature of around 53°F (12°C). There is complete cloud cover and moderate winds from the northwest around 5-9 mph (9-14 km/h). Humidity is high at 88%. Visibility is around 3 miles (6.4 km). \\n\\nThe results provide an hourly forecast as well as current conditions from a couple different weather sources. Let me know if you need any additional details about the San Francisco weather!', response_metadata={'id': 'msg_01BRX9mrT19nBDdHYtR7wJ92', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 920, 'output_tokens': 132}}, id='run-d0325583-3ddc-4432-b2b2-d023eb97660f-0', usage_metadata={'input_tokens': 920, 'output_tokens': 132, 'total_tokens': 1052})]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "input_message = {\"role\": \"user\", \"content\": \"Search for the weather in SF\"}\n",
-    "response = agent_executor.invoke({\"messages\": [input_message]})\n",
-    "\n",
-    "for message in response[\"messages\"]:\n",
-    "    message.pretty_print()"
+    "response = agent_executor.invoke(\n",
+    "    {\"messages\": [HumanMessage(content=\"whats the weather in sf?\")]}\n",
+    ")\n",
+    "response[\"messages\"]"
    ]
   },
   {
@@ -565,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "id": "bd93812b-2350-4d7f-9643-34c753503754",
    "metadata": {},
    "outputs": [
@@ -575,35 +526,36 @@
      "text": [
       "================================\u001b[1m Human Message \u001b[0m=================================\n",
       "\n",
-      "Search for the weather in SF\n",
+      "whats the weather in sf?\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "[{'text': \"I'll help you search for information about the weather in San Francisco.\", 'type': 'text'}, {'id': 'toolu_01DCPnJES53Fcr7YWnZ47kDG', 'input': {'query': 'current weather San Francisco'}, 'name': 'tavily_search', 'type': 'tool_use'}]\n",
+      "[{'text': 'Okay, let me look up the current weather for San Francisco using a search engine:', 'type': 'text'}, {'id': 'toolu_01H1brh5EZpZqtqHBxkosPtN', 'input': {'query': 'san francisco weather'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  tavily_search (toolu_01DCPnJES53Fcr7YWnZ47kDG)\n",
-      " Call ID: toolu_01DCPnJES53Fcr7YWnZ47kDG\n",
+      "  tavily_search_results_json (toolu_01H1brh5EZpZqtqHBxkosPtN)\n",
+      " Call ID: toolu_01H1brh5EZpZqtqHBxkosPtN\n",
       "  Args:\n",
-      "    query: current weather San Francisco\n",
+      "    query: san francisco weather\n",
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: tavily_search\n",
+      "Name: tavily_search_results_json\n",
       "\n",
-      "{\"query\": \"current weather San Francisco\", \"follow_up_questions\": null, \"answer\": null, \"images\": [], \"results\": [{\"title\": \"Weather in San Francisco\", \"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1750168506, 'localtime': '2025-06-17 06:55'}, 'current': {'last_updated_epoch': 1750167900, 'last_updated': '2025-06-17 06:45', 'temp_c': 11.7, 'temp_f': 53.1, 'is_day': 1, 'condition': {'text': 'Fog', 'icon': '//cdn.weatherapi.com/weather/64x64/day/248.png', 'code': 1135}, 'wind_mph': 4.0, 'wind_kph': 6.5, 'wind_degree': 215, 'wind_dir': 'SW', 'pressure_mb': 1017.0, 'pressure_in': 30.02, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 86, 'cloud': 0, 'feelslike_c': 11.3, 'feelslike_f': 52.4, 'windchill_c': 8.7, 'windchill_f': 47.7, 'heatindex_c': 9.8, 'heatindex_f': 49.7, 'dewpoint_c': 9.6, 'dewpoint_f': 49.2, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 0.0, 'gust_mph': 6.3, 'gust_kph': 10.2}}\", \"score\": 0.9542825, \"raw_content\": null}, {\"title\": \"Weather in San Francisco in June 2025\", \"url\": \"https://world-weather.info/forecast/usa/san_francisco/june-2025/\", \"content\": \"Detailed ⚡ San Francisco Weather Forecast for June 2025 - day/night 🌡️ temperatures, precipitations - World-Weather.info. Add the current city. Search. Weather; Archive; Weather Widget °F. World; United States; California; Weather in San Francisco; ... 17 +64° +54° 18 +61° +54° 19\", \"score\": 0.8638634, \"raw_content\": null}], \"response_time\": 2.57}\n",
+      "[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1739994486, 'localtime': '2025-02-19 11:48'}, 'current': {'last_updated_epoch': 1739994300, 'last_updated': '2025-02-19 11:45', 'temp_c': 13.3, 'temp_f': 55.9, 'is_day': 1, 'condition': {'text': 'Light rain', 'icon': '//cdn.weatherapi.com/weather/64x64/day/296.png', 'code': 1183}, 'wind_mph': 5.8, 'wind_kph': 9.4, 'wind_degree': 195, 'wind_dir': 'SSW', 'pressure_mb': 1023.0, 'pressure_in': 30.2, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 87, 'cloud': 100, 'feelslike_c': 12.7, 'feelslike_f': 54.8, 'windchill_c': 9.1, 'windchill_f': 48.4, 'heatindex_c': 10.2, 'heatindex_f': 50.3, 'dewpoint_c': 9.8, 'dewpoint_f': 49.7, 'vis_km': 4.0, 'vis_miles': 2.0, 'uv': 1.4, 'gust_mph': 8.9, 'gust_kph': 14.4}}\"}, {\"url\": \"https://world-weather.info/forecast/usa/san_francisco/february-2025/\", \"content\": \"Weather in San Francisco in February 2025 (California) - Detailed Weather Forecast for a Month Weather World Weather in San Francisco Weather in San Francisco in February 2025 San Francisco Weather Forecast for February 2025, is based on previous years' statistical data. +59°+50° +59°+52° +59°+50° +61°+52° +59°+50° +61°+50° +61°+52° +63°+52° +61°+52° +61°+50° +61°+50° +61°+50° +59°+50° +59°+50° +61°+50° +61°+52° +59°+50° +59°+48° +57°+48° +59°+50° +59°+48° +59°+50° +57°+46° +61°+50° +61°+50° +59°+50° +59°+48° +59°+50° Extended weather forecast in San Francisco HourlyWeek10-Day14-Day30-DayYear Weather in large and nearby cities Weather in Washington, D.C.+41° Sacramento+55° Pleasanton+55° Redwood City+55° San Leandro+55° San Mateo+54° San Rafael+52° San Ramon+52° South San Francisco+54° Vallejo+50° Palo Alto+55° Pacifica+55° Berkeley+54° Castro Valley+55° Concord+52° Daly City+54° Noverd+52° Sign Hill+54° world's temperature today day day Temperature units\"}]\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "Based on the search results, here's the current weather in San Francisco:\n",
-      "- Temperature: 53.1°F (11.7°C)\n",
-      "- Condition: Foggy\n",
-      "- Wind: 4.0 mph from the Southwest\n",
-      "- Humidity: 86%\n",
-      "- Visibility: 9.0 miles\n",
-      "- Feels like: 52.4°F (11.3°C)\n",
+      "The search results provide details on the current weather conditions and forecast for San Francisco. Some key details:\n",
       "\n",
-      "This is quite typical weather for San Francisco, which is known for its fog, especially during the morning hours. The city's proximity to the ocean and unique geographical features often result in mild temperatures and foggy conditions.\n"
+      "- It is lightly raining in San Francisco right now, with a temperature around 55°F/13°C. \n",
+      "- The forecast for the rest of February 2025 shows daytime highs mostly in the upper 50s to low 60s F, with night lows in the upper 40s to low 50s F. \n",
+      "- Typical weather includes some rain, clouds, cool temperatures and breezy conditions.\n",
+      "\n",
+      "So in summary, as is common for San Francisco in late winter, it is currently cool with light rain showers, and similar mild, unsettled weather is expected over the next couple weeks. Layers and a light jacket would be advisable for being outdoors. Let me know if you need any other details!\n"
      ]
     }
    ],
    "source": [
-    "for step in agent_executor.stream({\"messages\": [input_message]}, stream_mode=\"values\"):\n",
+    "for step in agent_executor.stream(\n",
+    "    {\"messages\": [HumanMessage(content=\"whats the weather in sf?\")]},\n",
+    "    stream_mode=\"values\",\n",
+    "):\n",
     "    step[\"messages\"][-1].pretty_print()"
    ]
   },
@@ -627,7 +579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "id": "63198158-380e-43a3-a2ad-d4288949c1d4",
    "metadata": {},
    "outputs": [
@@ -635,21 +587,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "I|'ll help you search for information| about the weather in San Francisco.|Base|d on the search results, here|'s the current weather in| San Francisco:\n",
-      "-| Temperature: 53.1°F (|11.7°C)\n",
-      "-| Condition: Foggy\n",
-      "- Wind:| 4.0 mph from| the Southwest\n",
-      "- Humidity|: 86%|\n",
-      "- Visibility: 9|.0 miles\n",
-      "- Pressure: |30.02 in|Hg\n",
       "\n",
-      "The weather| is characteristic of San Francisco, with| foggy conditions and mild temperatures|. The \"feels like\" temperature is slightly| lower at 52.4|°F (11.|3°C)| due to the wind chill effect|.|"
+      "\n",
+      "Base|d on the weather| search| results, here| are the key details| about the weather in| San Francisco:|\n",
+      "\n",
+      "- The current temperature| in| San Francisco is aroun|d 55|-|56|°F (13|°|C).| Light| rain is occurring with| |100|% clou|d cover. |\n",
+      "\n",
+      "-| Winds| are aroun|d 5-9| mph from| the south|-southwest.|\n",
+      "\n",
+      "- The| forecast| for| the rest| of February| 2025 |shows da|ytime highs mostly| in the upper| 50s to| low| 60s°|F,| with overnight lows| in| the upper| 40s to| low| 50s°|F.|\n",
+      "\n",
+      "-| Overall|, typical| cool| an|d show|ery late| winter weather is| expected in San Francisco| for the remainder| of February,| with a| mix| of rain| and dry| periods|.| Temperatures will be| season|able| for| this| time of year.|\n",
+      "\n",
+      "So| in summary, San| Francisco is| experiencing light| rain an|d cool| temperatures currently, but| the late| winter forecast| shows typical mil|d and show|ery conditions| pers|isting through the en|d of the| month.| Let| me know if you| need any other| details about| the weather in the| city!|"
      ]
     }
    ],
    "source": [
     "for step, metadata in agent_executor.stream(\n",
-    "    {\"messages\": [input_message]}, stream_mode=\"messages\"\n",
+    "    {\"messages\": [HumanMessage(content=\"whats the weather in sf?\")]},\n",
+    "    stream_mode=\"messages\",\n",
     "):\n",
     "    if metadata[\"langgraph_node\"] == \"agent\" and (text := step.text()):\n",
     "        print(text, end=\"|\")"
@@ -667,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "c4073e35",
    "metadata": {},
    "outputs": [],
@@ -679,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "id": "e64a944e-f9ac-43cf-903c-d3d28d765377",
    "metadata": {},
    "outputs": [],
@@ -691,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "id": "a13462d0-2d02-4474-921e-15a1ba1fa274",
    "metadata": {},
    "outputs": [
@@ -699,26 +656,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "Hi, I'm Bob!\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "Hello Bob! I'm an AI assistant who can help you search for information using specialized search tools. Is there anything specific you'd like to know about or search for? I'm happy to help you find accurate and up-to-date information on various topics.\n"
+      "{'agent': {'messages': [AIMessage(content=\"Hello Bob! It's nice to meet you again.\", response_metadata={'id': 'msg_013C1z2ZySagEFwmU1EsysR2', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 1162, 'output_tokens': 14}}, id='run-f878acfd-d195-44e8-9166-e2796317e3f8-0', usage_metadata={'input_tokens': 1162, 'output_tokens': 14, 'total_tokens': 1176})]}}\n",
+      "----\n"
      ]
     }
    ],
    "source": [
-    "input_message = {\"role\": \"user\", \"content\": \"Hi, I'm Bob!\"}\n",
-    "for step in agent_executor.stream(\n",
-    "    {\"messages\": [input_message]}, config, stream_mode=\"values\"\n",
+    "for chunk in agent_executor.stream(\n",
+    "    {\"messages\": [HumanMessage(content=\"hi im bob!\")]}, config\n",
     "):\n",
-    "    step[\"messages\"][-1].pretty_print()"
+    "    print(chunk)\n",
+    "    print(\"----\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 14,
    "id": "56d8028b-5dbc-40b2-86f5-ed60631d86a3",
    "metadata": {},
    "outputs": [
@@ -726,21 +679,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "What's my name?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "Your name is Bob, as you introduced yourself earlier. I can remember information shared within our conversation without needing to search for it.\n"
+      "{'agent': {'messages': [AIMessage(content='You mentioned your name is Bob when you introduced yourself earlier. So your name is Bob.', response_metadata={'id': 'msg_01WNwnRNGwGDRw6vRdivt6i1', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 1184, 'output_tokens': 21}}, id='run-f5c0b957-8878-405a-9d4b-a7cd38efe81f-0', usage_metadata={'input_tokens': 1184, 'output_tokens': 21, 'total_tokens': 1205})]}}\n",
+      "----\n"
      ]
     }
    ],
    "source": [
-    "input_message = {\"role\": \"user\", \"content\": \"What's my name?\"}\n",
-    "for step in agent_executor.stream(\n",
-    "    {\"messages\": [input_message]}, config, stream_mode=\"values\"\n",
+    "for chunk in agent_executor.stream(\n",
+    "    {\"messages\": [HumanMessage(content=\"whats my name?\")]}, config\n",
     "):\n",
-    "    step[\"messages\"][-1].pretty_print()"
+    "    print(chunk)\n",
+    "    print(\"----\")"
    ]
   },
   {
@@ -761,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "id": "24460239",
    "metadata": {},
    "outputs": [
@@ -769,24 +718,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "What's my name?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "I apologize, but I don't have access to any tools that would tell me your name. I can only assist you with searching for publicly available information using the tavily_search function. I don't have access to personal information about users. If you'd like to tell me your name, I'll be happy to address you by it.\n"
+      "{'agent': {'messages': [AIMessage(content=\"I'm afraid I don't actually know your name. As an AI assistant without personal information about you, I don't have a specific name associated with our conversation.\", response_metadata={'id': 'msg_01NoaXNNYZKSoBncPcLkdcbo', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 267, 'output_tokens': 36}}, id='run-c9f7df3d-525a-4d8f-bbcf-a5b4a5d2e4b0-0', usage_metadata={'input_tokens': 267, 'output_tokens': 36, 'total_tokens': 303})]}}\n",
+      "----\n"
      ]
     }
    ],
    "source": [
-    "# highlight-next-line\n",
     "config = {\"configurable\": {\"thread_id\": \"xyz123\"}}\n",
-    "\n",
-    "input_message = {\"role\": \"user\", \"content\": \"What's my name?\"}\n",
-    "for step in agent_executor.stream(\n",
-    "    {\"messages\": [input_message]}, config, stream_mode=\"values\"\n",
+    "for chunk in agent_executor.stream(\n",
+    "    {\"messages\": [HumanMessage(content=\"whats my name?\")]}, config\n",
     "):\n",
-    "    step[\"messages\"][-1].pretty_print()"
+    "    print(chunk)\n",
+    "    print(\"----\")"
    ]
   },
   {

docs/docs/versions/migrating_memory/long_term_memory_agent.ipynb

@@ -42,7 +42,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -U --quiet langgraph langchain-openai langchain-tavily tiktoken"
+    "%pip install -U --quiet langgraph langchain-openai langchain-community tiktoken"
    ]
   },
   {
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "dab4e96a-8a90-4df9-8818-5a6edf5805d7",
    "metadata": {},
    "outputs": [],
@@ -85,6 +85,7 @@
     "from typing import List, Literal, Optional\n",
     "\n",
     "import tiktoken\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
     "from langchain_core.documents import Document\n",
     "from langchain_core.embeddings import Embeddings\n",
     "from langchain_core.messages import get_buffer_string\n",
@@ -94,7 +95,6 @@
     "from langchain_core.vectorstores import InMemoryVectorStore\n",
     "from langchain_openai import ChatOpenAI\n",
     "from langchain_openai.embeddings import OpenAIEmbeddings\n",
-    "from langchain_tavily import TavilySearch\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
     "from langgraph.graph import END, START, MessagesState, StateGraph\n",
     "from langgraph.prebuilt import ToolNode"
@@ -200,7 +200,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "search = TavilySearch(max_results=1)\n",
+    "search = TavilySearchResults(max_results=1)\n",
     "tools = [save_recall_memory, search_recall_memories, search]"
    ]
   },
@@ -1074,7 +1074,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

docs/docusaurus.config.js

@@ -139,7 +139,7 @@ const config = {
       },
       announcementBar: {
         content:
-          '<strong>Our <a href="https://academy.langchain.com/courses/ambient-agents/?utm_medium=internal&utm_source=docs&utm_campaign=q2-2025_ambient-agents_co" target="_blank">Building Ambient Agents with LangGraph</a> course is now available on LangChain Academy!</strong>',
+          '<strong>We are growing and hiring for multiple roles for LangChain, LangGraph and LangSmith. <a href="https://www.langchain.com/careers" target="_blank" rel="noopener noreferrer"> Join our team!</a></strong>',
           backgroundColor: '#d0c9fe'
       },
       prism: {
@@ -253,10 +253,6 @@ const config = {
           {
             title: "Community",
             items: [
-              {
-                label: "LangChain Forum",
-                href: "https://forum.langchain.com/",
-              },
               {
                 label: "Twitter",
                 href: "https://twitter.com/LangChainAI",

docs/package.json

@@ -26,6 +26,7 @@
     "@docusaurus/preset-classic": "3.5.2",
     "@docusaurus/remark-plugin-npm2yarn": "^3.5.2",
     "@docusaurus/theme-mermaid": "3.5.2",
+    "@giscus/react": "^3.0.0",
     "@mdx-js/react": "^3",
     "@supabase/supabase-js": "^2.39.7",
     "clsx": "^1.2.1",