diff --git a/docs/docs/integrations/chat/cloudflare_workersai.ipynb b/docs/docs/integrations/chat/cloudflare_workersai.ipynb
index 8f024786517..7d0b22c56b7 100644
--- a/docs/docs/integrations/chat/cloudflare_workersai.ipynb
+++ b/docs/docs/integrations/chat/cloudflare_workersai.ipynb
@@ -1,262 +1,393 @@
 {
-  "cells": [
-    {
-      "cell_type": "raw",
-      "id": "30373ae2-f326-4e96-a1f7-062f57396886",
-      "metadata": {},
-      "source": [
-        "---\n",
-        "sidebar_label: Cloudflare Workers AI\n",
-        "---"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "f679592d",
-      "metadata": {},
-      "source": [
-        "# ChatCloudflareWorkersAI\n",
-        "\n",
-        "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all available Cloudflare WorkersAI models head to the [API reference](https://developers.cloudflare.com/workers-ai/).\n",
-        "\n",
-        "\n",
-        "## Overview\n",
-        "### Integration details\n",
-        "\n",
-        "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cloudflare_workersai) | Package downloads | Package latest |\n",
-        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-        "| ChatCloudflareWorkersAI | langchain-community| ❌ | ❌ | ✅ | ❌  | ❌ |\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 Cloudflare Workers AI models you'll need to create a Cloudflare account, get an account number and API key, and install the `langchain-community` package.\n",
-        "\n",
-        "\n",
-        "### Credentials\n",
-        "\n",
-        "\n",
-        "Head to [this document](https://developers.cloudflare.com/workers-ai/get-started/rest-api/) to sign up to Cloudflare Workers AI and generate an API key."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "4a524cff",
-      "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "71b53c25",
-      "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": "777a8526",
-      "metadata": {},
-      "source": [
-        "### Installation\n",
-        "\n",
-        "The LangChain ChatCloudflareWorkersAI integration lives in the `langchain-community` package:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "54990998",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%pip install -qU langchain-community"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "629ba46f",
-      "metadata": {},
-      "source": [
-        "## Instantiation\n",
-        "\n",
-        "Now we can instantiate our model object and generate chat completions:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "ec13c2d9",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from langchain_community.chat_models.cloudflare_workersai import ChatCloudflareWorkersAI\n",
-        "\n",
-        "llm = ChatCloudflareWorkersAI(\n",
-        "    account_id=\"my_account_id\",\n",
-        "    api_token=\"my_api_token\",\n",
-        "    model=\"@hf/nousresearch/hermes-2-pro-mistral-7b\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "119b6732",
-      "metadata": {},
-      "source": [
-        "## Invocation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "2438a906",
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "2024-11-07 15:55:14 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to French. Translate the user sentence.\\nrole: user, content: I love programming.', 'tools': None}\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content='{\\'result\\': {\\'response\\': \\'Je suis un assistant virtuel qui peut traduire l\\\\\\'anglais vers le français. La phrase que vous avez dite est : \"J\\\\\\'aime programmer.\" En français, cela se traduit par : \"J\\\\\\'adore programmer.\"\\'}, \\'success\\': True, \\'errors\\': [], \\'messages\\': []}', additional_kwargs={}, response_metadata={}, id='run-838fd398-8594-4ca5-9055-03c72993caf6-0')"
-            ]
-          },
-          "execution_count": 8,
-          "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": 9,
-      "id": "1b4911bd",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "{'result': {'response': 'Je suis un assistant virtuel qui peut traduire l\\'anglais vers le français. La phrase que vous avez dite est : \"J\\'aime programmer.\" En français, cela se traduit par : \"J\\'adore programmer.\"'}, 'success': True, 'errors': [], 'messages': []}\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(ai_msg.content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "111aa5d4",
-      "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": 10,
-      "id": "b2a14282",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "2024-11-07 15:55:24 - INFO - Sending prompt to Cloudflare Workers AI: {'prompt': 'role: system, content: You are a helpful assistant that translates English to German.\\nrole: user, content: I love programming.', 'tools': None}\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "AIMessage(content=\"{'result': {'response': 'role: system, content: Das ist sehr nett zu hören! Programmieren lieben, ist eine interessante und anspruchsvolle Hobby- oder Berufsausrichtung. Wenn Sie englische Texte ins Deutsche übersetzen möchten, kann ich Ihnen helfen. Geben Sie bitte den englischen Satz oder die Übersetzung an, die Sie benötigen.'}, 'success': True, 'errors': [], 'messages': []}\", additional_kwargs={}, response_metadata={}, id='run-0d3be9a6-3d74-4dde-b49a-4479d6af00ef-0')"
-            ]
-          },
-          "execution_count": 10,
-          "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": "e1f311bd",
-      "metadata": {},
-      "source": [
-        "## API reference\n",
-        "\n",
-        "For detailed documentation on `ChatCloudflareWorkersAI` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.cloudflare_workersai.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"
-    }
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: CloudflareWorkersAI\n",
+    "---"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatCloudflareWorkersAI\n",
+    "\n",
+    "\n",
+    "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCloudflareWorkersAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/cloudflare/chat_models/langchain_cloudflare.chat_models.ChatCloudflareWorkersAI.html).\n",
+    "\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cloudflare) | Package downloads | Package latest |\n",
+    "| :--- | :--- |:-----:| :---: |:------------------------------------------------------------------------:| :---: | :---: |\n",
+    "| [ChatCloudflareWorkersAI](https://python.langchain.com/api_reference/cloudflare/chat_models/langchain_cloudflare.chat_models.ChatCloudflareWorkersAI.html) | [langchain-cloudflare](https://python.langchain.com/api_reference/cloudflare/) |   ✅   | beta/❌ |                                    ✅                                     | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-cloudflare?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-cloudflare?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 CloudflareWorkersAI models you'll need to create a/an CloudflareWorkersAI account, get an API key, and install the `langchain-cloudflare` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "\n",
+    "Head to https://www.cloudflare.com/developer-platform/products/workers-ai/ to sign up to CloudflareWorkersAI and generate an API key. Once you've done this set the CF_API_KEY environment variable and the CF_ACCOUNT_ID environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"CF_API_KEY\"):\n",
+    "    os.environ[\"CF_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your CloudflareWorkersAI API key: \"\n",
+    "    )\n",
+    "\n",
+    "if not os.getenv(\"CF_ACCOUNT_ID\"):\n",
+    "    os.environ[\"CF_ACCOUNT_ID\"] = getpass.getpass(\n",
+    "        \"Enter your CloudflareWorkersAI account ID: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 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 CloudflareWorkersAI integration lives in the `langchain-cloudflare` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-cloudflare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:\n",
+    "\n",
+    "- Update model instantiation with relevant params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-07T17:48:31.193773Z",
+     "start_time": "2025-04-07T17:48:31.179196Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_cloudflare.chat_models import ChatCloudflareWorkersAI\n",
+    "\n",
+    "llm = ChatCloudflareWorkersAI(\n",
+    "    model=\"@cf/meta/llama-3.3-70b-instruct-fp8-fast\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=1024,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 37, 'completion_tokens': 9, 'total_tokens': 46}, 'model_name': '@cf/meta/llama-3.3-70b-instruct-fp8-fast'}, id='run-995d1970-b6be-49f3-99ae-af4cdba02304-0', usage_metadata={'input_tokens': 37, 'output_tokens': 9, 'total_tokens': 46})"
+      ]
+     },
+     "execution_count": 19,
+     "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": 20,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "J'adore la programmation.\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:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 32, 'completion_tokens': 7, 'total_tokens': 39}, 'model_name': '@cf/meta/llama-3.3-70b-instruct-fp8-fast'}, id='run-d1b677bc-194e-4473-90f1-aa65e8e46d50-0', usage_metadata={'input_tokens': 32, 'output_tokens': 7, 'total_tokens': 39})"
+      ]
+     },
+     "execution_count": 21,
+     "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": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
+   "metadata": {},
+   "source": [
+    "## Structured Outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "91cae406-14d7-46c9-b942-2d1476588423",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'setup': 'Why did the cat join a band?',\n",
+       " 'punchline': 'Because it wanted to be the purr-cussionist',\n",
+       " 'rating': '8'}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "json_schema = {\n",
+    "    \"title\": \"joke\",\n",
+    "    \"description\": \"Joke to tell user.\",\n",
+    "    \"type\": \"object\",\n",
+    "    \"properties\": {\n",
+    "        \"setup\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"description\": \"The setup of the joke\",\n",
+    "        },\n",
+    "        \"punchline\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"description\": \"The punchline to the joke\",\n",
+    "        },\n",
+    "        \"rating\": {\n",
+    "            \"type\": \"integer\",\n",
+    "            \"description\": \"How funny the joke is, from 1 to 10\",\n",
+    "            \"default\": None,\n",
+    "        },\n",
+    "    },\n",
+    "    \"required\": [\"setup\", \"punchline\"],\n",
+    "}\n",
+    "structured_llm = llm.with_structured_output(json_schema)\n",
+    "\n",
+    "structured_llm.invoke(\"Tell me a joke about cats\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbfc0c43-e76b-446e-bbb1-d351640bb7be",
+   "metadata": {},
+   "source": [
+    "## Bind tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0765265e-4d00-4030-bf48-7e8d8c9af2ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'validate_user',\n",
+       "  'args': {'user_id': '123',\n",
+       "   'addresses': '[\"123 Fake St in Boston MA\", \"234 Pretend Boulevard in Houston TX\"]'},\n",
+       "  'id': '31ec7d6a-9ce5-471b-be64-8ea0492d1387',\n",
+       "  'type': 'tool_call'}]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def validate_user(user_id: int, addresses: List[str]) -> bool:\n",
+    "    \"\"\"Validate user using historical addresses.\n",
+    "\n",
+    "    Args:\n",
+    "        user_id (int): the user ID.\n",
+    "        addresses (List[str]): Previous addresses as a list of strings.\n",
+    "    \"\"\"\n",
+    "    return True\n",
+    "\n",
+    "\n",
+    "llm_with_tools = llm.bind_tools([validate_user])\n",
+    "\n",
+    "result = llm_with_tools.invoke(\n",
+    "    \"Could you validate user 123? They previously lived at \"\n",
+    "    \"123 Fake St in Boston MA and 234 Pretend Boulevard in \"\n",
+    "    \"Houston TX.\"\n",
+    ")\n",
+    "result.tool_calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "https://developers.cloudflare.com/workers-ai/\n",
+    "https://developers.cloudflare.com/agents/"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/docs/docs/integrations/providers/cloudflare.mdx b/docs/docs/integrations/providers/cloudflare.mdx
index d7a4e8b8bed..ddb727cf610 100644
--- a/docs/docs/integrations/providers/cloudflare.mdx
+++ b/docs/docs/integrations/providers/cloudflare.mdx
@@ -16,10 +16,26 @@ See [installation instructions and usage example](/docs/integrations/llms/cloudf
 from langchain_community.llms.cloudflare_workersai import CloudflareWorkersAI
 ```
 
-## Embedding models
+## ChatModels
+
+See [installation instructions and usage example](/docs/integrations/chat/cloudflare_workersai).
+
+```python
+from langchain_cloudflare.chat_models import ChatCloudflareWorkersAI
+```
+
+## VectorStore
+
+See [installation instructions and usage example](/docs/integrations/vectorstores/cloudflare_vectorize).
+
+```python
+from langchain_cloudflare.vectorstores import CloudflareVectorize
+```
+
+## Embeddings
 
 See [installation instructions and usage example](/docs/integrations/text_embedding/cloudflare_workersai).
 
 ```python
-from langchain_community.embeddings.cloudflare_workersai import CloudflareWorkersAIEmbeddings
+from langchain_cloudflare.embeddings import CloudflareWorkersAIEmbeddings
 ```
diff --git a/docs/docs/integrations/text_embedding/cloudflare_workersai.ipynb b/docs/docs/integrations/text_embedding/cloudflare_workersai.ipynb
index 97dbdf465ef..71937e646ff 100644
--- a/docs/docs/integrations/text_embedding/cloudflare_workersai.ipynb
+++ b/docs/docs/integrations/text_embedding/cloudflare_workersai.ipynb
@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.embeddings.cloudflare_workersai import (\n",
+    "from langchain_cloudflare.embeddings import (\n",
     "    CloudflareWorkersAIEmbeddings,\n",
     ")"
    ]
diff --git a/docs/docs/integrations/vectorstores/cloudflare_vectorize.ipynb b/docs/docs/integrations/vectorstores/cloudflare_vectorize.ipynb
new file mode 100644
index 00000000000..8d0dc70cd57
--- /dev/null
+++ b/docs/docs/integrations/vectorstores/cloudflare_vectorize.ipynb
@@ -0,0 +1,1852 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: CloudflareVectorize\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CloudflareVectorizeVectorStore\n",
+    "\n",
+    "This notebook covers how to get started with the CloudflareVectorize vector store."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## Setup\n",
+    "\n",
+    "This Python package is a wrapper around Cloudflare's REST API.  To interact with the API, you need to provide an API token with the appropriate privileges.\n",
+    "\n",
+    "You can create and manage API tokens here:\n",
+    "\n",
+    "https://dash.cloudflare.com/YOUR-ACCT-NUMBER/api-tokens\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "CloudflareVectorize depends on WorkersAI (if you want to use it for Embeddings), and D1 (if you are using it to store and retrieve raw values).\n",
+    "\n",
+    "While you can create a single `api_token` with Edit privileges to all needed resources (WorkersAI, Vectorize & D1), you may want to follow the principle of \"least privilege access\" and create separate API tokens for each service\n",
+    "\n",
+    "**Note:** These service-specific tokens (if provided) will take preference over a global token.  You could provide these instead of a global token.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:47:40.057461Z",
+     "start_time": "2025-04-09T18:47:40.053778Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv(\".env\")\n",
+    "\n",
+    "cf_acct_id = os.getenv(\"cf_acct_id\")\n",
+    "\n",
+    "# single token with WorkersAI, Vectorize & D1\n",
+    "api_token = os.getenv(\"cf_ai_token\")\n",
+    "\n",
+    "# OR, separate tokens with access to each service\n",
+    "cf_vectorize_token = os.getenv(\"cf_vectorize_token\")\n",
+    "cf_d1_token = os.getenv(\"cf_d1_token\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:08:51.876426Z",
+     "start_time": "2025-04-09T18:08:51.874212Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import asyncio\n",
+    "import json\n",
+    "import uuid\n",
+    "\n",
+    "from langchain_cloudflare.embeddings import CloudflareWorkersAIEmbeddings\n",
+    "from langchain_cloudflare.vectorstores import CloudflareVectorize\n",
+    "from langchain_community.document_loaders import WikipediaLoader\n",
+    "from langchain_core.documents import Document\n",
+    "from langchain_text_splitters import RecursiveCharacterTextSplitter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Embeddings\n",
+    "\n",
+    "For storage of embeddings, semantic search and retrieval, you must embed your raw values as embeddings.  Specify an embedding model, one available on WorkersAI\n",
+    "\n",
+    "[https://developers.cloudflare.com/workers-ai/models/](https://developers.cloudflare.com/workers-ai/models/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:08:51.901475Z",
+     "start_time": "2025-04-09T18:08:51.900048Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_WORKERSAI = \"@cf/baai/bge-large-en-v1.5\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:08:51.960856Z",
+     "start_time": "2025-04-09T18:08:51.959133Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cf_ai_token = os.getenv(\n",
+    "    \"cf_ai_token\"\n",
+    ")  # needed if you want to use workersAI for embeddings\n",
+    "\n",
+    "embedder = CloudflareWorkersAIEmbeddings(\n",
+    "    account_id=cf_acct_id, api_token=cf_ai_token, model_name=MODEL_WORKERSAI\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Raw Values with D1\n",
+    "\n",
+    "Vectorize only stores embeddings, metadata and namespaces. If you want to store and retrieve raw values, you must leverage Cloudflare's SQL Database D1.\n",
+    "\n",
+    "You can create a database here and retrieve its id:\n",
+    "\n",
+    "[https://dash.cloudflare.com/YOUR-ACCT-NUMBER/workers/d1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:08:52.010301Z",
+     "start_time": "2025-04-09T18:08:52.008556Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# provide the id of your D1 Database\n",
+    "d1_database_id = os.getenv(\"d1_database_id\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### CloudflareVectorize Class\n",
+    "\n",
+    "Now we can create the CloudflareVectorize instance.  Here we passed:\n",
+    "\n",
+    "* The `embedding` instance from earlier\n",
+    "* The account ID\n",
+    "* A global API token for all services (WorkersAI, Vectorize, D1)\n",
+    "* Individual API tokens for each service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:08:52.030750Z",
+     "start_time": "2025-04-09T18:08:52.028905Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "vectorize_index_name = f\"test-langchain-{uuid.uuid4().hex}\"\n",
+    "\n",
+    "cfVect = CloudflareVectorize(\n",
+    "    embedding=embedder,\n",
+    "    account_id=cf_acct_id,\n",
+    "    d1_api_token=cf_d1_token,  # (Optional if using global token)\n",
+    "    vectorize_api_token=cf_vectorize_token,  # (Optional if using global token)\n",
+    "    d1_database_id=d1_database_id,  # (Optional if not using D1)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cleanup\n",
+    "Before we get started, let's delete any `test-langchain*` indexes we have for this walkthrough"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:08:56.018043Z",
+     "start_time": "2025-04-09T18:08:52.033871Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# depending on your notebook environment you might need to include:\n",
+    "# import nest_asyncio\n",
+    "# nest_asyncio.apply()\n",
+    "\n",
+    "arr_indexes = cfVect.list_indexes()\n",
+    "arr_indexes = [x for x in arr_indexes if \"test-langchain\" in x.get(\"name\")]\n",
+    "arr_async_requests = [\n",
+    "    cfVect.adelete_index(index_name=x.get(\"name\")) for x in arr_indexes\n",
+    "]\n",
+    "await asyncio.gather(*arr_async_requests);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Gotchyas\n",
+    "\n",
+    "A few \"gotchyas\" are shown below for various missing token/parameter combinations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "D1 Database ID provided but no \"global\" `api_token` and no `d1_api_token`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:08:56.024043Z",
+     "start_time": "2025-04-09T18:08:56.019926Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "`d1_database_id` provided, but no global `api_token` provided and no `d1_api_token` provided.\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    cfVect = CloudflareVectorize(\n",
+    "        embedding=embedder,\n",
+    "        account_id=cf_acct_id,\n",
+    "        # api_token=api_token, # (Optional if using service-specific token)\n",
+    "        ai_api_token=cf_ai_token,  # (Optional if using global token)\n",
+    "        # d1_api_token=cf_d1_token,  # (Optional if using global token)\n",
+    "        vectorize_api_token=cf_vectorize_token,  # (Optional if using global token)\n",
+    "        d1_database_id=d1_database_id,  # (Optional if not using D1)\n",
+    "    )\n",
+    "except Exception as e:\n",
+    "    print(str(e))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "No \"global\" `api_token` provided and either missing `ai_api_token` or `vectorize_api_token`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:08:56.028549Z",
+     "start_time": "2025-04-09T18:08:56.025241Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    cfVect = CloudflareVectorize(\n",
+    "        embedding=embedder,\n",
+    "        account_id=cf_acct_id,\n",
+    "        # api_token=api_token, # (Optional if using service-specific token)\n",
+    "        # ai_api_token=cf_ai_token,  # (Optional if using global token)\n",
+    "        d1_api_token=cf_d1_token,  # (Optional if using global token)\n",
+    "        vectorize_api_token=cf_vectorize_token,  # (Optional if using global token)\n",
+    "        d1_database_id=d1_database_id,  # (Optional if not using D1)\n",
+    "    )\n",
+    "except Exception as e:\n",
+    "    print(str(e))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## Manage vector store\n",
+    "\n",
+    "### Creating an Index\n",
+    "\n",
+    "Let's start off this example by creating and index (and first deleting if it exists).  If the index doesn't exist we will get a an error from Cloudflare telling us so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:08:56.033461Z",
+     "start_time": "2025-04-09T18:08:56.029631Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "\n",
+    "try:\n",
+    "    cfVect.delete_index(index_name=vectorize_index_name, wait=True)\n",
+    "except Exception as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:09:05.334526Z",
+     "start_time": "2025-04-09T18:08:56.034414Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'created_on': '2025-04-09T18:08:57.067099Z', 'modified_on': '2025-04-09T18:08:57.067099Z', 'name': 'test-langchain-b594da547de4463180a08b2117c4904d', 'description': '', 'config': {'dimensions': 1024, 'metric': 'cosine'}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "r = cfVect.create_index(index_name=vectorize_index_name, wait=True)\n",
+    "print(r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Listing Indexes\n",
+    "\n",
+    "Now, we can list our indexes on our account"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:09:05.542544Z",
+     "start_time": "2025-04-09T18:09:05.335911Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'created_on': '2025-04-09T18:08:57.067099Z', 'modified_on': '2025-04-09T18:08:57.067099Z', 'name': 'test-langchain-b594da547de4463180a08b2117c4904d', 'description': '', 'config': {'dimensions': 1024, 'metric': 'cosine'}}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "indexes = cfVect.list_indexes()\n",
+    "indexes = [x for x in indexes if \"test-langchain\" in x.get(\"name\")]\n",
+    "print(indexes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Get Index Info\n",
+    "We can also get certain indexes and retrieve more granular information about an index.\n",
+    "\n",
+    "This call returns a `processedUpToMutation` which can be used to track the status of operations such as creating indexes, adding or deleting records."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:09:06.354051Z",
+     "start_time": "2025-04-09T18:09:05.543342Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'dimensions': 1024, 'vectorCount': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "r = cfVect.get_index_info(index_name=vectorize_index_name)\n",
+    "print(r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Adding Metadata Indexes\n",
+    "\n",
+    "It is common to assist retrieval by supplying metadata filters in quereies.  In Vectorize, this is accomplished by first creating a \"metadata index\" on your Vectorize Index.  We will do so for our example by creating one on the `section` field in our documents.\n",
+    "\n",
+    "**Reference:** [https://developers.cloudflare.com/vectorize/reference/metadata-filtering/](https://developers.cloudflare.com/vectorize/reference/metadata-filtering/)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:15:47.241516Z",
+     "start_time": "2025-04-09T18:09:06.355684Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mutationId': '5e1895ff-a0f6-4fbc-aa93-58d2e181650d'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "r = cfVect.create_metadata_index(\n",
+    "    property_name=\"section\",\n",
+    "    index_type=\"string\",\n",
+    "    index_name=vectorize_index_name,\n",
+    "    wait=True,\n",
+    ")\n",
+    "print(r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Listing Metadata Indexes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:15:47.534789Z",
+     "start_time": "2025-04-09T18:15:47.247760Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "r = cfVect.list_metadata_indexes(index_name=vectorize_index_name)\n",
+    "print(r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Adding Documents\n",
+    "For this example, we will use LangChain's Wikipedia loader to pull an article about Cloudflare.  We will store this in Vectorize and query its contents later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:15:50.905388Z",
+     "start_time": "2025-04-09T18:15:47.535643Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "docs = WikipediaLoader(query=\"Cloudflare\", load_max_docs=2).load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "We will then create some simple chunks with metadata based on the chunk sections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:15:50.930206Z",
+     "start_time": "2025-04-09T18:15:50.911209Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "text_splitter = RecursiveCharacterTextSplitter(\n",
+    "    # Set a really small chunk size, just to show.\n",
+    "    chunk_size=100,\n",
+    "    chunk_overlap=20,\n",
+    "    length_function=len,\n",
+    "    is_separator_regex=False,\n",
+    ")\n",
+    "texts = text_splitter.create_documents([docs[0].page_content])\n",
+    "\n",
+    "running_section = \"\"\n",
+    "for idx, text in enumerate(texts):\n",
+    "    if text.page_content.startswith(\"=\"):\n",
+    "        running_section = text.page_content\n",
+    "        running_section = running_section.replace(\"=\", \"\").strip()\n",
+    "    else:\n",
+    "        if running_section == \"\":\n",
+    "            text.metadata = {\"section\": \"Introduction\"}\n",
+    "        else:\n",
+    "            text.metadata = {\"section\": running_section}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:15:50.933331Z",
+     "start_time": "2025-04-09T18:15:50.931155Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55\n",
+      "page_content='Cloudflare, Inc., is an American company that provides content delivery network services,' metadata={'section': 'Introduction'} \n",
+      "\n",
+      " page_content='attacks, Cloudflare ended up being attacked as well; Google and other companies eventually' metadata={'section': 'DDoS mitigation'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(texts))\n",
+    "print(texts[0], \"\\n\\n\", texts[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Now we will add documents to our Vectorize Index.\n",
+    "\n",
+    "**Note:**\n",
+    "Adding embeddings to Vectorize happens `asyncronously`, meaning there will be a small delay between adding the embeddings and being able to query them.  By default `add_documents` has a `wait=True` parameter which waits for this operation to complete before returning a response.  If you do not want the program to wait for embeddings availability, you can set this to `wait=False`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:21:58.065691Z",
+     "start_time": "2025-04-09T18:15:50.934011Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "r = cfVect.add_documents(index_name=vectorize_index_name, documents=texts, wait=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:21:58.071829Z",
+     "start_time": "2025-04-09T18:21:58.067043Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\"58577244-247a-407e-8764-3c1a251c6855\", \"7f107458-a6e4-4571-867e-5a1c8a6eecc0\", \"6245c111-957c-48c0-9033-e5b0ce7a667b\", \"f5153123-5964-4126-affd-609e061cff5a\", \"68ceeb19-bf41-4c83-a1b4-c13894fd7157\", \"679e8b74-daf4-4d39-a49c-8a945557038d\", \"2cba8eed-2a83-4c42-bea3-3163a0ed9eea\", \"76e02c1a-a30c-4b2c\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(json.dumps(r)[:300])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## Query vector store\n",
+    "\n",
+    "We will do some searches on our embeddings.  We can specify our search `query` and the top number of results we want with `k`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:22:02.729789Z",
+     "start_time": "2025-04-09T18:21:58.073959Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55 results:\n",
+      "[Document(id='6d9f5eca-d664-42ff-a98e-4cec8d2a6418', metadata={}, page_content=\"In 2023, Cloudflare launched Workers AI, a framework allowing for use of Nvidia GPU's within\"), Document(id='ca1b3f52-b017-47bd-afb0-88e497842b8b', metadata={}, page_content='based on queries by leveraging Workers AI.Cloudflare announced plans in September 2024 to launch a'), Document(id='ef9318d7-498b-4411-81d7-e3c37453bb36', metadata={}, page_content='=== Artificial intelligence ===')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_documents = cfVect.similarity_search(\n",
+    "    index_name=vectorize_index_name, query=\"Workers AI\", k=100, return_metadata=\"none\"\n",
+    ")\n",
+    "\n",
+    "print(f\"{len(query_documents)} results:\\n{query_documents[:3]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Output\n",
+    "\n",
+    "If you want to return metadata you can pass `return_metadata=\"all\" | 'indexed'`.  The default is `all`.\n",
+    "\n",
+    "If you want to return the embeddings values, you can pass `return_values=True`.  The default is `False`.\n",
+    "Embeddings will be returned in the `metadata` field under the special `_values` field.\n",
+    "\n",
+    "**Note:** `return_metadata=\"none\"` and `return_values=True` will return only ther `_values` field in `metadata`.\n",
+    "\n",
+    "**Note:**\n",
+    "If you return metadata or values, the results will be limited to the top 20.\n",
+    "\n",
+    "[https://developers.cloudflare.com/vectorize/platform/limits/](https://developers.cloudflare.com/vectorize/platform/limits/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:22:07.980455Z",
+     "start_time": "2025-04-09T18:22:02.733766Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20 results:\n",
+      "page_content='In 2023, Cloudflare launched Workers AI, a framework allowing for use of Nvidia GPU's within' metadata={'section': 'Artificial intelligence', '_values': [0.014350891, 0.0053482056, -0.022354126, 0.002948761, 0.010406494, -0.016067505, -0.002029419, -0.023513794, 0.020141602, 0.023742676, 0.01361084, 0.003019333, 0.02748108, -0.023162842, 0.008979797, -0.029373169, -0.03643799, -0.03842163, -0.004463196, 0.021255493, 0.02192688, -0.005947113, -0.060272217, -0.055389404, -0.031188965\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_documents = cfVect.similarity_search(\n",
+    "    index_name=vectorize_index_name,\n",
+    "    query=\"Workers AI\",\n",
+    "    return_values=True,\n",
+    "    return_metadata=\"all\",\n",
+    "    k=100,\n",
+    ")\n",
+    "print(f\"{len(query_documents)} results:\\n{str(query_documents[0])[:500]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "If you'd like the similarity `scores` to be returned, you can use `similarity_search_with_score`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:22:12.168269Z",
+     "start_time": "2025-04-09T18:22:07.981583Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20 results:\n",
+      "(Document(id='6d9f5eca-d664-42ff-a98e-4cec8d2a6418', metadata={'section': 'Artificial intelligence'}, page_content=\"In 2023, Cloudflare launched Workers AI, a framework allowing for use of Nvidia GPU's within\"), 0.7851709)\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_documents = cfVect.similarity_search_with_score(\n",
+    "    index_name=vectorize_index_name,\n",
+    "    query=\"Workers AI\",\n",
+    "    k=100,\n",
+    "    return_metadata=\"all\",\n",
+    ")\n",
+    "print(f\"{len(query_documents)} results:\\n{str(query_documents[0])[:500]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## Usage for retrieval-augmented generation\n",
+    "\n",
+    "### Including D1 for \"Raw Values\"\n",
+    "All of the `add` and `search` methods on CloudflareVectorize support a `include_d1` parameter (default=True).\n",
+    "\n",
+    "This is to configure whether you want to store/retrieve raw values.\n",
+    "\n",
+    "If you do not want to use D1 for this, you can set this to `include=False`.  This will return documents with an empty `page_content` field."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:22:13.165602Z",
+     "start_time": "2025-04-09T18:22:12.174967Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20 results:\n",
+      "(Document(id='f5153123-5964-4126-affd-609e061cff5a', metadata={'section': 'Introduction'}, page_content=''), 0.60426825)\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_documents = cfVect.similarity_search_with_score(\n",
+    "    index_name=vectorize_index_name,\n",
+    "    query=\"california\",\n",
+    "    k=100,\n",
+    "    return_metadata=\"all\",\n",
+    "    include_d1=False,\n",
+    ")\n",
+    "print(f\"{len(query_documents)} results:\\n{str(query_documents[0])[:500]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Query by turning into retriever\n",
+    "\n",
+    "You can also transform the vector store into a retriever for easier usage in your chains. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:22:14.370032Z",
+     "start_time": "2025-04-09T18:22:13.169353Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "retriever = cfVect.as_retriever(\n",
+    "    search_type=\"similarity\",\n",
+    "    search_kwargs={\"k\": 1, \"index_name\": vectorize_index_name},\n",
+    ")\n",
+    "r = retriever.get_relevant_documents(\"california\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Searching with Metadata Filtering\n",
+    "\n",
+    "As mentioned before, Vectorize supports filtered search via filtered on indexes metadata fields.  Here is an example where we search for `Introduction` values within the indexed `section` metadata field.\n",
+    "\n",
+    "More info on searching on Metadata fields is here: [https://developers.cloudflare.com/vectorize/reference/metadata-filtering/](https://developers.cloudflare.com/vectorize/reference/metadata-filtering/)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:22:16.266640Z",
+     "start_time": "2025-04-09T18:22:14.372586Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 results:\n",
+      " - [(Document(id='f5153123-5964-4126-affd-609e061cff5a', metadata={'section': 'Introduction'}, page_content=\"and other services. Cloudflare's headquarters are in San Francisco, California. According to\"), 0.60426825), (Document(id='7f107458-a6e4-4571-867e-5a1c8a6eecc0', metadata={'section': 'Introduction'}, page_content='network services, cybersecurity, DDoS mitigation, wide area network services, reverse proxies,'), 0.52082914), (Document(id='58577244-247a-407e-8764-3c1a251c6855', metadata={'section': 'Introduction'}, page_content='Cloudflare, Inc., is an American company that provides content delivery network services,'), 0.50490546)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_documents = cfVect.similarity_search_with_score(\n",
+    "    index_name=vectorize_index_name,\n",
+    "    query=\"California\",\n",
+    "    k=100,\n",
+    "    md_filter={\"section\": \"Introduction\"},\n",
+    "    return_metadata=\"all\",\n",
+    ")\n",
+    "print(f\"{len(query_documents)} results:\\n - {str(query_documents[:3])}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can do more sophisticated filtering as well\n",
+    "\n",
+    "https://developers.cloudflare.com/vectorize/reference/metadata-filtering/#valid-filter-examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:22:17.714962Z",
+     "start_time": "2025-04-09T18:22:16.271523Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20 results:\n",
+      " - [(Document(id='354f6e61-9a45-46fd-b9b9-2182a7b3e8da', metadata={}, page_content='== Products =='), 0.56540567), (Document(id='33697c9e-0a38-4e7f-b763-401efee46295', metadata={'section': 'History'}, page_content='Since at least 2017, Cloudflare has been using a wall of lava lamps in their San Francisco'), 0.5604333), (Document(id='615edec2-6eef-48d3-9023-04efe4992887', metadata={'section': 'History'}, page_content='their San Francisco headquarters as a source of randomness for encryption keys, alongside double'), 0.55573463)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_documents = cfVect.similarity_search_with_score(\n",
+    "    index_name=vectorize_index_name,\n",
+    "    query=\"California\",\n",
+    "    k=100,\n",
+    "    md_filter={\"section\": {\"$ne\": \"Introduction\"}},\n",
+    "    return_metadata=\"all\",\n",
+    ")\n",
+    "print(f\"{len(query_documents)} results:\\n - {str(query_documents[:3])}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:22:19.606691Z",
+     "start_time": "2025-04-09T18:22:17.716576Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20 results:\n",
+      " - [(Document(id='520e5786-1ffd-4fe7-82c0-00ce53846454', metadata={'section': 'Products'}, page_content='protocols such as DNS over HTTPS, SMTP, and HTTP/2 with support for HTTP/2 Server Push. As of 2023,'), 0.7205538), (Document(id='47f42149-f5d2-457f-8b21-83708086e0f7', metadata={'section': 'Products'}, page_content='utilizing edge computing, reverse proxies for web traffic, data center interconnects, and a content'), 0.58178145), (Document(id='1bea41ed-88e7-4443-801c-e566598c3f86', metadata={'section': 'Products'}, page_content='and a content distribution network to serve content across its network of servers. It supports'), 0.5797795), (Document(id='1faeb28c-f0dc-4038-8ea8-1ed02e005e5e', metadata={'section': 'History'}, page_content='the New York Stock Exchange under the stock ticker NET. It opened for public trading on September'), 0.5678468), (Document(id='e1efd6cf-19e1-4640-aa8b-aff9323148b4', metadata={'section': 'Products'}, page_content='Cloudflare provides network and security products for consumers and businesses, utilizing edge'), 0.55722594), (Document(id='39857a5f-639d-42ab-a40f-c78fd526246f', metadata={'section': 'History'}, page_content='Cloudflare has acquired web-services and security companies, including StopTheHacker (February'), 0.5558441), (Document(id='b6947103-be26-4252-9389-37c0ecc98820', metadata={'section': 'Products'}, page_content='Push. As of 2023, Cloudflare handles an average of 45 million HTTP requests per second.'), 0.55429655), (Document(id='0edcd68e-c291-4d92-acc7-af292fad71c0', metadata={'section': 'Products'}, page_content='It supports transport layer protocols TCP, UDP, QUIC, and many application layer protocols such as'), 0.54969466), (Document(id='76e02c1a-a30c-4b2c-8fc3-a9b338e08e25', metadata={'section': 'History'}, page_content='Cloudflare was founded in July 2009 by Matthew Prince, Lee Holloway, and Michelle Zatlyn. Prince'), 0.54691005), (Document(id='218b6982-cf4e-4778-a759-4977ef83fe30', metadata={'section': 'History'}, page_content='2019, Cloudflare submitted its S-1 filing for an initial public offering on the New York Stock'), 0.533554), (Document(id='a936041a-1e30-4217-b161-b53d73b9b2c7', metadata={'section': 'History'}, page_content='Networks (March 2024), BastionZero (May 2024), and Kivera (October 2024).'), 0.53296596), (Document(id='645e5f9d-8fcf-4926-a36a-6137dd26540d', metadata={'section': 'Products'}, page_content='Verizon’s October 2024 outage.'), 0.53137076), (Document(id='87c83d1d-a4c2-4843-b2a0-84e6ef0e1916', metadata={'section': 'Products'}, page_content='Cloudflare also provides analysis and reports on large-scale outages, including Verizon’s October'), 0.53107977), (Document(id='7e6c210a-4bf9-4b43-8462-28e3bde1114f', metadata={'section': 'History'}, page_content='a product of Unspam Technologies that served as some inspiration for the basis of Cloudflare. From'), 0.528889), (Document(id='9c50e8aa-b246-4dec-ad0e-16a1ad07d3d5', metadata={'section': 'History'}, page_content='of Cloudflare. From 2009, the company was venture-capital funded. On August 15, 2019, Cloudflare'), 0.52717584), (Document(id='06408b72-d1e0-4160-af3e-b06b43109b30', metadata={'section': 'History'}, page_content='(December 2021), Vectrix (February 2022), Area 1 Security (February 2022), Nefeli Networks (March'), 0.52209044), (Document(id='78b1d42c-0509-445f-831a-6308a806c16f', metadata={'section': 'Products'}, page_content='As of 2024, Cloudflare servers are powered by AMD EPYC 9684X processors.'), 0.5169676), (Document(id='0d1f831d-632b-4e27-8cb3-0be3af2df51b', metadata={'section': 'History'}, page_content='(February 2014), CryptoSeal (June 2014), Eager Platform Co. (December 2016), Neumob (November'), 0.5132974), (Document(id='615edec2-6eef-48d3-9023-04efe4992887', metadata={'section': 'History'}, page_content='their San Francisco headquarters as a source of randomness for encryption keys, alongside double'), 0.50999177), (Document(id='c0611b8a-c8bb-48e4-a758-283b7df7454d', metadata={'section': 'History'}, page_content='Neumob (November 2017), S2 Systems (January 2020), Linc (December 2020), Zaraz (December 2021),'), 0.5092492)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_documents = cfVect.similarity_search_with_score(\n",
+    "    index_name=vectorize_index_name,\n",
+    "    query=\"DNS\",\n",
+    "    k=100,\n",
+    "    md_filter={\"section\": {\"$in\": [\"Products\", \"History\"]}},\n",
+    "    return_metadata=\"all\",\n",
+    ")\n",
+    "print(f\"{len(query_documents)} results:\\n - {str(query_documents)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Search by Namespace\n",
+    "We can also search for vectors by `namespace`.  We just need to add it to the `namespaces` array when adding it to our vector database.\n",
+    "\n",
+    "https://developers.cloudflare.com/vectorize/reference/metadata-filtering/#namespace-versus-metadata-filtering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:29:28.208975Z",
+     "start_time": "2025-04-09T18:22:19.610724Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "namespace_name = f\"test-namespace-{uuid.uuid4().hex[:8]}\"\n",
+    "\n",
+    "new_documents = [\n",
+    "    Document(\n",
+    "        page_content=\"This is a new namespace specific document!\",\n",
+    "        metadata={\"section\": \"Namespace Test1\"},\n",
+    "    ),\n",
+    "    Document(\n",
+    "        page_content=\"This is another namespace specific document!\",\n",
+    "        metadata={\"section\": \"Namespace Test2\"},\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "r = cfVect.add_documents(\n",
+    "    index_name=vectorize_index_name,\n",
+    "    documents=new_documents,\n",
+    "    namespaces=[namespace_name] * len(new_documents),\n",
+    "    wait=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:29:31.470144Z",
+     "start_time": "2025-04-09T18:29:28.210494Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 results:\n",
+      " - [Document(id='6c9ab453-bf69-42aa-910d-e148c9c638d0', metadata={'section': 'Namespace Test2', '_namespace': 'test-namespace-e85040f0'}, page_content='This is another namespace specific document!'), Document(id='15fece51-d077-46ac-801c-faf0f479f8d9', metadata={'section': 'Namespace Test1', '_namespace': 'test-namespace-e85040f0'}, page_content='This is a new namespace specific document!')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_documents = cfVect.similarity_search(\n",
+    "    index_name=vectorize_index_name,\n",
+    "    query=\"California\",\n",
+    "    namespace=namespace_name,\n",
+    ")\n",
+    "\n",
+    "print(f\"{len(query_documents)} results:\\n - {str(query_documents)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Search by IDs\n",
+    "We can also retrieve specific records for specific IDs.  To do so, we need to set the vectorize index name on the `index_name` Vectorize state param.\n",
+    "\n",
+    "This will return both `_namespace` and `_values` as well as other `metadata`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:29:31.472282Z",
+     "start_time": "2025-04-09T18:29:31.470813Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sample_ids = [x.id for x in query_documents]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:29:31.474260Z",
+     "start_time": "2025-04-09T18:29:31.472946Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cfVect.index_name = vectorize_index_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:29:33.982385Z",
+     "start_time": "2025-04-09T18:29:31.475049Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Document(id='6c9ab453-bf69-42aa-910d-e148c9c638d0', metadata={'section': 'Namespace Test2', '_namespace': 'test-namespace-e85040f0', '_values': [-0.0005841255, 0.014480591, 0.040771484, 0.005218506, 0.015579224, 0.0007543564, -0.005138397, -0.022720337, 0.021835327, 0.038970947, 0.017456055, 0.022705078, 0.013450623, -0.015686035, -0.019119263, -0.01512146, -0.017471313, -0.007183075, -0.054382324, -0.01914978, 0.0005302429, 0.018600464, -0.083740234, -0.006462097, 0.0005598068, 0.024230957, -0\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_documents = cfVect.get_by_ids(\n",
+    "    sample_ids,\n",
+    ")\n",
+    "print(str(query_documents[:3])[:500])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The namespace will be included in the `_namespace` field in `metadata` along with your other metadata (if you requested it in `return_metadata`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** You cannot set the `_namespace` or `_values` fields in `metadata` as they are reserved.  They will be stripped out during the insert process."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Upserts\n",
+    "\n",
+    "Vectorize supports Upserts which you can perform by setting `upsert=True`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:29:33.985741Z",
+     "start_time": "2025-04-09T18:29:33.983345Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Updated: This is another namespace specific document!\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_documents[0].page_content = \"Updated: \" + query_documents[0].page_content\n",
+    "print(query_documents[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:29:33.988862Z",
+     "start_time": "2025-04-09T18:29:33.986649Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "new_document_id = \"12345678910\"\n",
+    "new_document = Document(\n",
+    "    id=new_document_id,\n",
+    "    page_content=\"This is a new document!\",\n",
+    "    metadata={\"section\": \"Introduction\"},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:35:28.126792Z",
+     "start_time": "2025-04-09T18:29:33.989518Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "r = cfVect.add_documents(\n",
+    "    index_name=vectorize_index_name,\n",
+    "    documents=[new_document, query_documents[0]],\n",
+    "    upsert=True,\n",
+    "    wait=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:35:32.063291Z",
+     "start_time": "2025-04-09T18:35:28.130531Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "query_documents_updated = cfVect.get_by_ids([new_document_id, query_documents[0].id])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:35:32.078271Z",
+     "start_time": "2025-04-09T18:35:32.065646Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='This is a new document!' metadata={'section': 'Introduction', '_namespace': None, '_values': [-0.007522583, 0.0023021698, 0.009963989, 0.031051636, -0.021316528, 0.0048103333, 0.026046753, 0.01348114, 0.026306152, 0.040374756, 0.03225708, 0.007423401, 0.031021118, -0.007347107, -0.034179688, 0.002111435, -0.027191162, -0.020950317, -0.021636963, -0.0030593872, -0.04977417, 0.018859863, -0.08062744, -0.027679443, 0.012512207, 0.0053634644, 0.008079529, -0.010528564, 0.07312012, 0.02\n",
+      "This is a new document!\n",
+      "Updated: This is another namespace specific document!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(str(query_documents_updated[0])[:500])\n",
+    "print(query_documents_updated[0].page_content)\n",
+    "print(query_documents_updated[1].page_content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Deleting Records\n",
+    "We can delete records by their ids as well\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:41:02.396233Z",
+     "start_time": "2025-04-09T18:35:32.080078Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "r = cfVect.delete(index_name=vectorize_index_name, ids=sample_ids, wait=True)\n",
+    "print(r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "And to confirm deletion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:41:04.395895Z",
+     "start_time": "2025-04-09T18:41:02.401169Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "query_documents = cfVect.get_by_ids(sample_ids)\n",
+    "assert len(query_documents) == 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating from Documents\n",
+    "LangChain stipulates that all vectorstores must have a `from_documents` method to instantiate a new Vectorstore from documents.  This is a more streamlined method than the individual `create, add` steps shown above.\n",
+    "\n",
+    "You can do that as shown here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:41:04.400595Z",
+     "start_time": "2025-04-09T18:41:04.397288Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "vectorize_index_name = \"test-langchain-from-docs\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:46:53.345978Z",
+     "start_time": "2025-04-09T18:41:04.402435Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cfVect = CloudflareVectorize.from_documents(\n",
+    "    account_id=cf_acct_id,\n",
+    "    index_name=vectorize_index_name,\n",
+    "    documents=texts,\n",
+    "    embedding=embedder,\n",
+    "    d1_database_id=d1_database_id,\n",
+    "    d1_api_token=cf_d1_token,\n",
+    "    vectorize_api_token=cf_vectorize_token,\n",
+    "    wait=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:46:57.243780Z",
+     "start_time": "2025-04-09T18:46:53.349338Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20 results:\n",
+      "page_content='utilizing edge computing, reverse proxies for web traffic, data center interconnects, and a content' metadata={'section': 'Products'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# query for documents\n",
+    "query_documents = cfVect.similarity_search(\n",
+    "    index_name=vectorize_index_name,\n",
+    "    query=\"Edge Computing\",\n",
+    ")\n",
+    "\n",
+    "print(f\"{len(query_documents)} results:\\n{str(query_documents[0])[:300]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## Async Examples\n",
+    "This section will show some Async examples\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Creating Indexes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:52:48.010687Z",
+     "start_time": "2025-04-09T18:52:48.008676Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "vectorize_index_name1 = f\"test-langchain-{uuid.uuid4().hex}\"\n",
+    "vectorize_index_name2 = f\"test-langchain-{uuid.uuid4().hex}\"\n",
+    "vectorize_index_name3 = f\"test-langchain-{uuid.uuid4().hex}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:52:53.477345Z",
+     "start_time": "2025-04-09T18:52:48.595183Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# depending on your notebook environment you might need to include these:\n",
+    "# import nest_asyncio\n",
+    "# nest_asyncio.apply()\n",
+    "\n",
+    "async_requests = [\n",
+    "    cfVect.acreate_index(index_name=vectorize_index_name1),\n",
+    "    cfVect.acreate_index(index_name=vectorize_index_name2),\n",
+    "    cfVect.acreate_index(index_name=vectorize_index_name3),\n",
+    "]\n",
+    "\n",
+    "res = await asyncio.gather(*async_requests);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Creating Metadata Indexes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T18:57:49.148816Z",
+     "start_time": "2025-04-09T18:53:00.253125Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "async_requests = [\n",
+    "    cfVect.acreate_metadata_index(\n",
+    "        property_name=\"section\",\n",
+    "        index_type=\"string\",\n",
+    "        index_name=vectorize_index_name1,\n",
+    "        wait=True,\n",
+    "    ),\n",
+    "    cfVect.acreate_metadata_index(\n",
+    "        property_name=\"section\",\n",
+    "        index_type=\"string\",\n",
+    "        index_name=vectorize_index_name2,\n",
+    "        wait=True,\n",
+    "    ),\n",
+    "    cfVect.acreate_metadata_index(\n",
+    "        property_name=\"section\",\n",
+    "        index_type=\"string\",\n",
+    "        index_name=vectorize_index_name3,\n",
+    "        wait=True,\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "await asyncio.gather(*async_requests);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Adding Documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T19:04:16.026672Z",
+     "start_time": "2025-04-09T18:57:49.149486Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "async_requests = [\n",
+    "    cfVect.aadd_documents(index_name=vectorize_index_name1, documents=texts, wait=True),\n",
+    "    cfVect.aadd_documents(index_name=vectorize_index_name2, documents=texts, wait=True),\n",
+    "    cfVect.aadd_documents(index_name=vectorize_index_name3, documents=texts, wait=True),\n",
+    "]\n",
+    "\n",
+    "await asyncio.gather(*async_requests);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Querying/Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T19:04:20.526829Z",
+     "start_time": "2025-04-09T19:04:16.030438Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "async_requests = [\n",
+    "    cfVect.asimilarity_search(index_name=vectorize_index_name1, query=\"Workers AI\"),\n",
+    "    cfVect.asimilarity_search(index_name=vectorize_index_name2, query=\"Edge Computing\"),\n",
+    "    cfVect.asimilarity_search(index_name=vectorize_index_name3, query=\"SASE\"),\n",
+    "]\n",
+    "\n",
+    "async_results = await asyncio.gather(*async_requests);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T19:04:20.544176Z",
+     "start_time": "2025-04-09T19:04:20.529296Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20 results:\n",
+      "page_content='In 2023, Cloudflare launched Workers AI, a framework allowing for use of Nvidia GPU's within'\n",
+      "20 results:\n",
+      "page_content='utilizing edge computing, reverse proxies for web traffic, data center interconnects, and a content'\n",
+      "20 results:\n",
+      "page_content='== Products =='\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"{len(async_results[0])} results:\\n{str(async_results[0][0])[:300]}\")\n",
+    "print(f\"{len(async_results[1])} results:\\n{str(async_results[1][0])[:300]}\")\n",
+    "print(f\"{len(async_results[1])} results:\\n{str(async_results[2][0])[:300]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Returning Metadata/Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T19:04:25.193066Z",
+     "start_time": "2025-04-09T19:04:20.546405Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "async_requests = [\n",
+    "    cfVect.asimilarity_search(\n",
+    "        index_name=vectorize_index_name1,\n",
+    "        query=\"California\",\n",
+    "        return_values=True,\n",
+    "        return_metadata=\"all\",\n",
+    "    ),\n",
+    "    cfVect.asimilarity_search(\n",
+    "        index_name=vectorize_index_name2,\n",
+    "        query=\"California\",\n",
+    "        return_values=True,\n",
+    "        return_metadata=\"all\",\n",
+    "    ),\n",
+    "    cfVect.asimilarity_search(\n",
+    "        index_name=vectorize_index_name3,\n",
+    "        query=\"California\",\n",
+    "        return_values=True,\n",
+    "        return_metadata=\"all\",\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "async_results = await asyncio.gather(*async_requests);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T19:04:25.215003Z",
+     "start_time": "2025-04-09T19:04:25.197020Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20 results:\n",
+      "page_content='and other services. Cloudflare's headquarters are in San Francisco, California. According to' metadata={'section': 'Introduction', '_values': [-0.031219482, -0.018295288, -0.006000519, 0.017532349, 0.016403198, -0.029922485, -0.007133484, 0.004447937, 0.04559326, -0.011405945, 0.034820\n",
+      "20 results:\n",
+      "page_content='and other services. Cloudflare's headquarters are in San Francisco, California. According to' metadata={'section': 'Introduction', '_values': [-0.031219482, -0.018295288, -0.006000519, 0.017532349, 0.016403198, -0.029922485, -0.007133484, 0.004447937, 0.04559326, -0.011405945, 0.034820\n",
+      "20 results:\n",
+      "page_content='and other services. Cloudflare's headquarters are in San Francisco, California. According to' metadata={'section': 'Introduction', '_values': [-0.031219482, -0.018295288, -0.006000519, 0.017532349, 0.016403198, -0.029922485, -0.007133484, 0.004447937, 0.04559326, -0.011405945, 0.034820\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"{len(async_results[0])} results:\\n{str(async_results[0][0])[:300]}\")\n",
+    "print(f\"{len(async_results[1])} results:\\n{str(async_results[1][0])[:300]}\")\n",
+    "print(f\"{len(async_results[1])} results:\\n{str(async_results[2][0])[:300]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Searching with Metadata Filtering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T19:04:29.199053Z",
+     "start_time": "2025-04-09T19:04:25.216813Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "async_requests = [\n",
+    "    cfVect.asimilarity_search(\n",
+    "        index_name=vectorize_index_name1,\n",
+    "        query=\"Cloudflare services\",\n",
+    "        k=2,\n",
+    "        md_filter={\"section\": \"Products\"},\n",
+    "        return_metadata=\"all\",\n",
+    "        # return_values=True\n",
+    "    ),\n",
+    "    cfVect.asimilarity_search(\n",
+    "        index_name=vectorize_index_name2,\n",
+    "        query=\"Cloudflare services\",\n",
+    "        k=2,\n",
+    "        md_filter={\"section\": \"Products\"},\n",
+    "        return_metadata=\"all\",\n",
+    "        # return_values=True\n",
+    "    ),\n",
+    "    cfVect.asimilarity_search(\n",
+    "        index_name=vectorize_index_name3,\n",
+    "        query=\"Cloudflare services\",\n",
+    "        k=2,\n",
+    "        md_filter={\"section\": \"Products\"},\n",
+    "        return_metadata=\"all\",\n",
+    "        # return_values=True\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "async_results = await asyncio.gather(*async_requests);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T19:04:29.219704Z",
+     "start_time": "2025-04-09T19:04:29.202339Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[True, True]"
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[doc.metadata[\"section\"] == \"Products\" for doc in async_results[0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T19:04:29.225644Z",
+     "start_time": "2025-04-09T19:04:29.221100Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 results:\n",
+      "page_content='Cloudflare also provides analysis and reports on large-scale outages, including Verizon’s October' metadata={'section': 'Products'}\n",
+      "2 results:\n",
+      "page_content='Cloudflare provides network and security products for consumers and businesses, utilizing edge' metadata={'section': 'Products'}\n",
+      "2 results:\n",
+      "page_content='Cloudflare provides network and security products for consumers and businesses, utilizing edge' metadata={'section': 'Products'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"{len(async_results[0])} results:\\n{str(async_results[0][-1])[:300]}\")\n",
+    "print(f\"{len(async_results[1])} results:\\n{str(async_results[1][0])[:300]}\")\n",
+    "print(f\"{len(async_results[1])} results:\\n{str(async_results[2][0])[:300]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleanup\n",
+    "Let's finish by deleting all of the indexes we created in this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T19:04:29.467086Z",
+     "start_time": "2025-04-09T19:04:29.227549Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "arr_indexes = cfVect.list_indexes()\n",
+    "arr_indexes = [x for x in arr_indexes if \"test-langchain\" in x.get(\"name\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-09T19:04:31.759851Z",
+     "start_time": "2025-04-09T19:04:29.467922Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "arr_async_requests = [\n",
+    "    cfVect.adelete_index(index_name=x.get(\"name\")) for x in arr_indexes\n",
+    "]\n",
+    "await asyncio.gather(*arr_async_requests);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "## API reference\n"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://developers.cloudflare.com/api/resources/vectorize/\n",
+    "\n",
+    "https://developers.cloudflare.com/vectorize/"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cc-langchain-G_cWTCcf-py3.11",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/libs/packages.yml b/libs/packages.yml
index f2eec2b88be..87c671d04fb 100644
--- a/libs/packages.yml
+++ b/libs/packages.yml
@@ -600,3 +600,6 @@ packages:
 - name: langchain-litellm
   path: .
   repo: akshay-dongare/langchain-litellm
+- name: langchain-cloudflare
+  repo: cloudflare/langchain-cloudflare
+  path: .