Use run tree post/patch

.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

SECURITY.md

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

cookbook/LLaMA2_sql_chat.ipynb

@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 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/Multi_modal_RAG_google.ipynb

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

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -204,14 +204,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 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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

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

@@ -0,0 +1 @@
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

docs/docs/concepts/retrievers.mdx

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

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

docs/docs/concepts/tool_calling.mdx

@@ -21,10 +21,10 @@ You will sometimes hear the term `function calling`. We use this term interchang
 
 ## Key concepts 
 
-1. **Tool Creation:** Use the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator to create a [tool](/docs/concepts/tools). A tool is an association between a function and its schema.<br/>
-2. **Tool Binding:** The tool needs to be connected to a model that supports tool calling. This gives the model awareness of the tool and the associated input schema required by the tool.<br/>
-3. **Tool Calling:** When appropriate, the model can decide to call a tool and ensure its response conforms to the tool's input schema.<br/>
-4. **Tool Execution:** The tool can be executed using the arguments provided by the model.
+**(1) Tool Creation:** Use the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator to create a [tool](/docs/concepts/tools). A tool is an association between a function and its schema.
+**(2) Tool Binding:** The tool needs to be connected to a model that supports tool calling. This gives the model awareness of the tool and the associated input schema required by the tool.
+**(3) Tool Calling:** When appropriate, the model can decide to call a tool and ensure its response conforms to the tool's input schema.
+**(4) Tool Execution:** The tool can be executed using the arguments provided by the model.
 
 ![Conceptual parts of tool calling](/img/tool_calling_components.png)
 
@@ -114,12 +114,12 @@ result = llm_with_tools.invoke("What is 2 multiplied by 3?")
 ```
 
 As before, the output `result` will be an `AIMessage`. 
-But, if the tool was called, `result` will have a `tool_calls` [attribute](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/custom_tools.ipynb

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

docs/docs/how_to/multimodal_inputs.ipynb

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

docs/docs/how_to/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/google_vertex_ai_palm.ipynb

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

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/nebius.ipynb

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

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/exa_search.ipynb

@@ -1,107 +1,117 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Exa\n",
-        "\n",
-        ">[Exa](https://exa.ai/) is a knowledge API for AI and developers.\n",
-        ">\n",
-        "\n",
-        "## Installation and Setup\n",
-        "\n",
-        "`Exa` integration exists in its own [partner package](https://pypi.org/project/langchain-exa/). You can install it with:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install -qU langchain-exa"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In order to use the package, you will also need to set the `EXA_API_KEY` environment variable to your Exa API key.\n",
-        "\n",
-        "## Retriever\n",
-        "\n",
-        "You can use the [`ExaSearchRetriever`](/docs/integrations/tools/exa_search#using-exasearchretriever) in a standard retrieval pipeline. You can import it as follows.\n",
-        "\n",
-        "See a [usage example](/docs/integrations/tools/exa_search).\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "y8ku6X96sebl"
-      },
-      "outputs": [],
-      "source": [
-        "from langchain_exa import ExaSearchRetriever"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": "## Tools\n\nYou can use Exa as an agent tool as described in the [Exa tool calling docs](/docs/integrations/tools/exa_search#use-within-an-agent).\n\nSee a [usage example](/docs/integrations/tools/exa_search).\n\n### ExaFindSimilarResults\n\nA tool that queries the Metaphor Search API and gets back JSON."
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain_exa.tools import ExaFindSimilarResults"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### ExaSearchResults\n",
-        "\n",
-        "Exa Search tool."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain_exa.tools import ExaSearchResults"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.12"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exa\n",
+    "\n",
+    ">[Exa](https://exa.ai/) is a knowledge API for AI and developers.\n",
+    ">\n",
+    "\n",
+    "## Installation and Setup\n",
+    "\n",
+    "`Exa` integration exists in its own [partner package](https://pypi.org/project/langchain-exa/). You can install it with:"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-exa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to use the package, you will also need to set the `EXA_API_KEY` environment variable to your Exa API key.\n",
+    "\n",
+    "## Retriever\n",
+    "\n",
+    "You can use the [`ExaSearchRetriever`](/docs/integrations/tools/exa_search#using-exasearchretriever) in a standard retrieval pipeline. You can import it as follows.\n",
+    "\n",
+    "See a [usage example](/docs/integrations/tools/exa_search).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "y8ku6X96sebl"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_exa import ExaSearchRetriever"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tools\n",
+    "\n",
+    "You can use Exa as an agent tool as described in the [Exa tool calling docs](/docs/integrations/tools/exa_search#using-the-exa-sdk-as-langchain-agent-tools).\n",
+    "\n",
+    "See a [usage example](/docs/integrations/tools/exa_search).\n",
+    "\n",
+    "### ExaFindSimilarResults\n",
+    "\n",
+    "A tool that queries the Metaphor Search API and gets back JSON."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_exa.tools import ExaFindSimilarResults"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ExaSearchResults\n",
+    "\n",
+    "Exa Search tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_exa.tools import ExaSearchResults"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }

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/nebius.mdx

@@ -1,118 +0,0 @@
-# Nebius
-
-All functionality related to Nebius AI Studio
-
->[Nebius AI Studio](https://studio.nebius.ai/) provides API access to a wide range of state-of-the-art large language models and embedding models for various use cases.
-
-## Installation and Setup
-
-The Nebius integration can be installed via pip:
-
-```bash
-pip install langchain-nebius
-```
-
-To use Nebius AI Studio, you'll need an API key which you can obtain from [Nebius AI Studio](https://studio.nebius.ai/). The API key can be passed as an initialization parameter `api_key` or set as the environment variable `NEBIUS_API_KEY`.
-
-```python
-import os
-os.environ["NEBIUS_API_KEY"] = "YOUR-NEBIUS-API-KEY"
-```
-
-### Available Models
-
-The full list of supported models can be found in the [Nebius AI Studio Documentation](https://studio.nebius.com/).
-
-
-## Chat models
-
-### ChatNebius
-
-The `ChatNebius` class allows you to interact with Nebius AI Studio's chat models.
-
-See a [usage example](/docs/integrations/chat/nebius).
-
-```python
-from langchain_nebius import ChatNebius
-
-# Initialize the chat model
-chat = ChatNebius(
-    model="Qwen/Qwen3-30B-A3B-fast",  # Choose from available models
-    temperature=0.6,
-    top_p=0.95
-)
-```
-
-## Embedding models
-
-### NebiusEmbeddings
-
-The `NebiusEmbeddings` class allows you to generate vector embeddings using Nebius AI Studio's embedding models.
-
-See a [usage example](/docs/integrations/text_embedding/nebius).
-
-```python
-from langchain_nebius import NebiusEmbeddings
-
-# Initialize embeddings
-embeddings = NebiusEmbeddings(
-    model="BAAI/bge-en-icl"  # Default embedding model
-)
-```
-
-## Retrievers
-
-### NebiusRetriever
-
-The `NebiusRetriever` enables efficient similarity search using embeddings from Nebius AI Studio. It leverages high-quality embedding models to enable semantic search over documents.
-
-See a [usage example](/docs/integrations/retrievers/nebius).
-
-```python
-from langchain_core.documents import Document
-from langchain_nebius import NebiusEmbeddings, NebiusRetriever
-
-# Create sample documents
-docs = [
-    Document(page_content="Paris is the capital of France"),
-    Document(page_content="Berlin is the capital of Germany"),
-]
-
-# Initialize embeddings
-embeddings = NebiusEmbeddings()
-
-# Create retriever
-retriever = NebiusRetriever(
-    embeddings=embeddings,
-    docs=docs,
-    k=2  # Number of documents to return
-)
-```
-
-## Tools
-
-### NebiusRetrievalTool
-
-The `NebiusRetrievalTool` allows you to create a tool for agents based on the NebiusRetriever.
-
-```python
-from langchain_nebius import NebiusEmbeddings, NebiusRetriever, NebiusRetrievalTool
-from langchain_core.documents import Document
-
-# Create sample documents
-docs = [
-    Document(page_content="Paris is the capital of France and has the Eiffel Tower"),
-    Document(page_content="Berlin is the capital of Germany and has the Brandenburg Gate"),
-]
-
-# Create embeddings and retriever
-embeddings = NebiusEmbeddings()
-retriever = NebiusRetriever(embeddings=embeddings, docs=docs)
-
-# Create retrieval tool
-tool = NebiusRetrievalTool(
-    retriever=retriever,
-    name="nebius_search",
-    description="Search for information about European capitals"
-)
-``` 

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/graph_rag.mdx

@@ -31,20 +31,20 @@ retrieval over an **existing** vector store.
 
 ## Benefits
 
-* [**Link based on existing metadata:**](https://datastax.github.io/graph-rag/guide/get-started/)
+* [**Link based on existing metadata:**](https://datastax.github.io/graph-rag/get-started/)
   Use existing metadata fields without additional processing. Retrieve more from an
   existing vector store!
 
-* [**Change links on demand:**](https://datastax.github.io/graph-rag/guide/edges/)
+* [**Change links on demand:**](https://datastax.github.io/graph-rag/get-started/edges/)
   Edges can be specified on-the-fly, allowing different relationships to be traversed
   based on the question.
 
 
-* [**Pluggable Traversal Strategies:**](https://datastax.github.io/graph-rag/guide/strategies/)
+* [**Pluggable Traversal Strategies:**](https://datastax.github.io/graph-rag/get-started/strategies/)
   Use built-in traversal strategies like Eager or MMR, or define custom logic to select
   which nodes to explore.
 
-* [**Broad compatibility:**](https://datastax.github.io/graph-rag/guide/adapters/)
+* [**Broad compatibility:**](https://datastax.github.io/graph-rag/get-started/adapters/)
   Adapters are available for a variety of vector stores with support for additional
   stores easily added.
 

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/retrievers/nebius.ipynb

@@ -1,514 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "afaf8039",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_label: Nebius\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2970dd75-8ebf-4b51-8282-9b454b8f356d",
-   "metadata": {},
-   "source": [
-    "# Nebius Retriever\n",
-    "\n",
-    "The `NebiusRetriever` enables efficient similarity search using embeddings from [Nebius AI Studio](https://studio.nebius.ai/). It leverages high-quality embedding models to enable semantic search over documents.\n",
-    "\n",
-    "This retriever is optimized for scenarios where you need to perform similarity search over a collection of documents, but don't need to persist the vectors to a vector database. It performs vector similarity search in-memory using matrix operations, making it efficient for medium-sized document collections."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c47fc36",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "### Installation\n",
-    "\n",
-    "The Nebius integration can be installed via pip:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1ecdb29d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade langchain-nebius"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89883202",
-   "metadata": {},
-   "source": [
-    "### Credentials\n",
-    "\n",
-    "Nebius requires an API key that can be passed as an initialization parameter `api_key` or set as the environment variable `NEBIUS_API_KEY`. You can obtain an API key by creating an account on [Nebius AI Studio](https://studio.nebius.ai/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "637bb53f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "# Make sure you've set your API key as an environment variable\n",
-    "if \"NEBIUS_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"NEBIUS_API_KEY\"] = getpass.getpass(\"Enter your Nebius API key: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8304b4d9",
-   "metadata": {},
-   "source": [
-    "## Instantiation\n",
-    "\n",
-    "The `NebiusRetriever` requires a `NebiusEmbeddings` instance and a list of documents. Here's how to initialize it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "37e9dc05",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.documents import Document\n",
-    "from langchain_nebius import NebiusEmbeddings, NebiusRetriever\n",
-    "\n",
-    "# Create sample documents\n",
-    "docs = [\n",
-    "    Document(\n",
-    "        page_content=\"Paris is the capital of France\", metadata={\"country\": \"France\"}\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Berlin is the capital of Germany\", metadata={\"country\": \"Germany\"}\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Rome is the capital of Italy\", metadata={\"country\": \"Italy\"}\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Madrid is the capital of Spain\", metadata={\"country\": \"Spain\"}\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"London is the capital of the United Kingdom\",\n",
-    "        metadata={\"country\": \"UK\"},\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Moscow is the capital of Russia\", metadata={\"country\": \"Russia\"}\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Washington DC is the capital of the United States\",\n",
-    "        metadata={\"country\": \"USA\"},\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Tokyo is the capital of Japan\", metadata={\"country\": \"Japan\"}\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Beijing is the capital of China\", metadata={\"country\": \"China\"}\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Canberra is the capital of Australia\",\n",
-    "        metadata={\"country\": \"Australia\"},\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "# Initialize embeddings\n",
-    "embeddings = NebiusEmbeddings()\n",
-    "\n",
-    "# Create retriever\n",
-    "retriever = NebiusRetriever(\n",
-    "    embeddings=embeddings,\n",
-    "    docs=docs,\n",
-    "    k=3,  # Number of documents to return\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5a731d2",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "\n",
-    "### Retrieve Relevant Documents\n",
-    "\n",
-    "You can use the retriever to find documents related to a query:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "3ed26f78",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Query: What are some capitals in Europe?\n",
-      "Top 3 results:\n",
-      "1. Paris is the capital of France (Country: France)\n",
-      "2. Berlin is the capital of Germany (Country: Germany)\n",
-      "3. Rome is the capital of Italy (Country: Italy)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Query for European capitals\n",
-    "query = \"What are some capitals in Europe?\"\n",
-    "results = retriever.invoke(query)\n",
-    "\n",
-    "print(f\"Query: {query}\")\n",
-    "print(f\"Top {len(results)} results:\")\n",
-    "for i, doc in enumerate(results):\n",
-    "    print(f\"{i+1}. {doc.page_content} (Country: {doc.metadata['country']})\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72f31d5a",
-   "metadata": {},
-   "source": [
-    "### Using get_relevant_documents\n",
-    "\n",
-    "You can also use the `get_relevant_documents` method directly (though `invoke` is the preferred interface):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "e7b7170d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Query: What are the capitals in Asia?\n",
-      "Top 3 results:\n",
-      "1. Beijing is the capital of China (Country: China)\n",
-      "2. Tokyo is the capital of Japan (Country: Japan)\n",
-      "3. Canberra is the capital of Australia (Country: Australia)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Query for Asian countries\n",
-    "query = \"What are the capitals in Asia?\"\n",
-    "results = retriever.get_relevant_documents(query)\n",
-    "\n",
-    "print(f\"Query: {query}\")\n",
-    "print(f\"Top {len(results)} results:\")\n",
-    "for i, doc in enumerate(results):\n",
-    "    print(f\"{i+1}. {doc.page_content} (Country: {doc.metadata['country']})\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8d6a31c2",
-   "metadata": {},
-   "source": [
-    "### Customizing Number of Results\n",
-    "\n",
-    "You can adjust the number of results at query time by passing `k` as a parameter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "5d81af33",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Query: Where is France?\n",
-      "Top 1 result:\n",
-      "1. Paris is the capital of France (Country: France)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Query for a specific country, with custom k\n",
-    "query = \"Where is France?\"\n",
-    "results = retriever.invoke(query, k=1)  # Override default k\n",
-    "\n",
-    "print(f\"Query: {query}\")\n",
-    "print(f\"Top {len(results)} result:\")\n",
-    "for i, doc in enumerate(results):\n",
-    "    print(f\"{i+1}. {doc.page_content} (Country: {doc.metadata['country']})\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e8a40f1",
-   "metadata": {},
-   "source": [
-    "### Async Support\n",
-    "\n",
-    "NebiusRetriever supports async operations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "8fc36122",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Async query: What are some capital cities?\n",
-      "Top 3 results:\n",
-      "1. Washington DC is the capital of the United States (Country: USA)\n",
-      "2. Canberra is the capital of Australia (Country: Australia)\n",
-      "3. Paris is the capital of France (Country: France)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import asyncio\n",
-    "\n",
-    "\n",
-    "async def retrieve_async():\n",
-    "    query = \"What are some capital cities?\"\n",
-    "    results = await retriever.ainvoke(query)\n",
-    "\n",
-    "    print(f\"Async query: {query}\")\n",
-    "    print(f\"Top {len(results)} results:\")\n",
-    "    for i, doc in enumerate(results):\n",
-    "        print(f\"{i+1}. {doc.page_content} (Country: {doc.metadata['country']})\")\n",
-    "\n",
-    "\n",
-    "await retrieve_async()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d5bc71e5",
-   "metadata": {},
-   "source": [
-    "### Handling Empty Documents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "123da4fb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of results: 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create a retriever with empty documents\n",
-    "empty_retriever = NebiusRetriever(\n",
-    "    embeddings=embeddings,\n",
-    "    docs=[],\n",
-    "    k=2,  # Empty document list\n",
-    ")\n",
-    "\n",
-    "# Test the retriever with empty docs\n",
-    "results = empty_retriever.invoke(\"What are the capitals of European countries?\")\n",
-    "print(f\"Number of results: {len(results)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9db2f342",
-   "metadata": {},
-   "source": [
-    "## Use within a chain\n",
-    "\n",
-    "NebiusRetriever works seamlessly in LangChain RAG pipelines. Here's an example of creating a simple RAG chain with the NebiusRetriever:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "e1e8c9f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Based on the context provided, three European capitals are:\n",
-      "\n",
-      "1. Paris\n",
-      "2. Berlin\n",
-      "3. Rome\n"
-     ]
-    }
-   ],
-   "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_nebius import ChatNebius\n",
-    "\n",
-    "# Initialize LLM\n",
-    "llm = ChatNebius(model=\"meta-llama/Llama-3.3-70B-Instruct-fast\")\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\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
-    "    | prompt\n",
-    "    | llm\n",
-    "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
-    "# Run the chain\n",
-    "answer = rag_chain.invoke(\"What are three European capitals?\")\n",
-    "print(answer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b3a6f2c4",
-   "metadata": {},
-   "source": [
-    "### Creating a Search Tool\n",
-    "\n",
-    "You can use the `NebiusRetrievalTool` to create a tool for agents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "784d53c4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tool results:\n",
-      "Document 1:\n",
-      "Paris is the capital of France\n",
-      "\n",
-      "Document 2:\n",
-      "Berlin is the capital of Germany\n",
-      "\n",
-      "Document 3:\n",
-      "Rome is the capital of Italy\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_nebius import NebiusRetrievalTool\n",
-    "\n",
-    "# Create a retrieval tool\n",
-    "tool = NebiusRetrievalTool(\n",
-    "    retriever=retriever,\n",
-    "    name=\"capital_search\",\n",
-    "    description=\"Search for information about capital cities around the world\",\n",
-    ")\n",
-    "\n",
-    "# Use the tool\n",
-    "result = tool.invoke({\"query\": \"capitals in Europe\", \"k\": 3})\n",
-    "print(\"Tool results:\")\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a4a3453",
-   "metadata": {},
-   "source": [
-    "## How It Works\n",
-    "\n",
-    "The NebiusRetriever works by:\n",
-    "\n",
-    "1. During initialization:\n",
-    "   - It stores the provided documents\n",
-    "   - It uses the provided NebiusEmbeddings to compute embeddings for all documents\n",
-    "   - These embeddings are stored in memory for quick retrieval\n",
-    "\n",
-    "2. During retrieval (`invoke` or `get_relevant_documents`):\n",
-    "   - It embeds the query using the same embedding model\n",
-    "   - It computes similarity scores between the query embedding and all document embeddings\n",
-    "   - It returns the top-k documents sorted by similarity\n",
-    "\n",
-    "This approach is efficient for medium-sized document collections, as it avoids the need for a separate vector database while still providing high-quality semantic search."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7a35f40",
-   "metadata": {},
-   "source": [
-    "## API reference\n",
-    "\n",
-    "For more details about the Nebius AI Studio API, visit the [Nebius AI Studio Documentation](https://studio.nebius.com/api-reference)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96439983",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

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/ibm_watsonx.ipynb

@@ -135,14 +135,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_ibm import WatsonxEmbeddings\n",
     "\n",
     "watsonx_embedding = WatsonxEmbeddings(\n",
-    "    model_id=\"ibm/granite-embedding-107m-multilingual\",\n",
+    "    model_id=\"ibm/slate-125m-english-rtrvr\",\n",
     "    url=\"https://us-south.ml.cloud.ibm.com\",\n",
     "    project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
     "    params=embed_params,\n",
@@ -163,7 +163,7 @@
    "outputs": [],
    "source": [
     "watsonx_embedding = WatsonxEmbeddings(\n",
-    "    model_id=\"ibm/granite-embedding-107m-multilingual\",\n",
+    "    model_id=\"ibm/slate-125m-english-rtrvr\",\n",
     "    url=\"PASTE YOUR URL HERE\",\n",
     "    username=\"PASTE YOUR USERNAME HERE\",\n",
     "    password=\"PASTE YOUR PASSWORD HERE\",\n",
@@ -192,7 +192,7 @@
     "api_client = APIClient(...)\n",
     "\n",
     "watsonx_embedding = WatsonxEmbeddings(\n",
-    "    model_id=\"ibm/granite-embedding-107m-multilingual\",\n",
+    "    model_id=\"ibm/slate-125m-english-rtrvr\",\n",
     "    watsonx_client=api_client,\n",
     ")"
    ]
@@ -341,7 +341,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.12"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,