> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Cloudflare vectorize integration

> Integrate with the Cloudflare vectorize vector store using LangChain JavaScript.

If you're deploying your project in a Cloudflare worker, you can use [Cloudflare Vectorize](https://developers.cloudflare.com/vectorize/) with LangChain.js.
It's a powerful and convenient option that's built directly into Cloudflare.

## Setup

<Tip>
  **Compatibility**

  Cloudflare Vectorize is currently in open beta, and requires a Cloudflare account on a paid plan to use.
</Tip>

After [setting up your project](https://developers.cloudflare.com/vectorize/get-started/intro/#prerequisites),
create an index by running the following Wrangler command:

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
$ npx wrangler vectorize create <index_name> --preset @cf/baai/bge-small-en-v1.5
```

You can see a full list of options for the `vectorize` command [in the official documentation](https://developers.cloudflare.com/workers/wrangler/commands/#vectorize).

You'll then need to update your `wrangler.toml` file to include an entry for `[[vectorize]]`:

```toml theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[[vectorize]]
binding = "VECTORIZE_INDEX"
index_name = "<index_name>"
```

Finally, you'll need to install the LangChain Cloudflare integration package:

<Tip>
  See [this section for general instructions on installing LangChain packages](/oss/javascript/langchain/install).
</Tip>

<CodeGroup>
  ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  npm install @langchain/cloudflare @langchain/core
  ```

  ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  yarn add @langchain/cloudflare @langchain/core
  ```

  ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pnpm add @langchain/cloudflare @langchain/core
  ```
</CodeGroup>

## Usage

Below is an example worker that adds documents to a vectorstore, queries it, or clears it depending on the path used. It also uses [Cloudflare Workers AI Embeddings](/oss/javascript/integrations/embeddings/cloudflare_ai).

<Note>
  If running locally, be sure to run wrangler as `npx wrangler dev --remote`!
</Note>

```toml theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
name = "langchain-test"
main = "worker.ts"
compatibility_date = "2024-01-10"

[[vectorize]]
binding = "VECTORIZE_INDEX"
index_name = "langchain-test"

[ai]
binding = "AI"
```

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// @ts-nocheck

import type {
  VectorizeIndex,
  Fetcher,
  Request,
} from "@cloudflare/workers-types";

import {
  CloudflareVectorizeStore,
  CloudflareWorkersAIEmbeddings,
} from "@langchain/cloudflare";

export interface Env {
  VECTORIZE_INDEX: VectorizeIndex;
  AI: Fetcher;
}

export default {
  async fetch(request: Request, env: Env) {
    const { pathname } = new URL(request.url);
    const embeddings = new CloudflareWorkersAIEmbeddings({
      binding: env.AI,
      model: "@cf/baai/bge-small-en-v1.5",
    });
    const store = new CloudflareVectorizeStore(embeddings, {
      index: env.VECTORIZE_INDEX,
    });
    if (pathname === "/") {
      const results = await store.similaritySearch("hello", 5);
      return Response.json(results);
    } else if (pathname === "/load") {
      // Upsertion by id is supported
      await store.addDocuments(
        [
          {
            pageContent: "hello",
            metadata: {},
          },
          {
            pageContent: "world",
            metadata: {},
          },
          {
            pageContent: "hi",
            metadata: {},
          },
        ],
        { ids: ["id1", "id2", "id3"] }
      );

      return Response.json({ success: true });
    } else if (pathname === "/clear") {
      await store.delete({ ids: ["id1", "id2", "id3"] });
      return Response.json({ success: true });
    }

    return Response.json({ error: "Not Found" }, { status: 404 });
  },
};
```

You can also pass a `filter` parameter to filter by previously loaded metadata.
See [the official documentation](https://developers.cloudflare.com/vectorize/learning/metadata-filtering/)
for information on the required format.

## Related

* Vector store [conceptual guide](/oss/javascript/integrations/vectorstores)
* Vector store [how-to guides](/oss/javascript/integrations/vectorstores)

***

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