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*   [Providers](/v0.1/docs/integrations/platforms/)
    
    *   [Providers](/v0.1/docs/integrations/platforms/)
    *   [Anthropic](/v0.1/docs/integrations/platforms/anthropic/)
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*   [Components](/v0.1/docs/integrations/components/)
    
    *   [LLMs](/v0.1/docs/integrations/llms/)
        
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    *   [Document transformers](/v0.1/docs/integrations/document_transformers/)
        
    *   [Document compressors](/v0.1/docs/integrations/document_compressors/)
        
    *   [Text embedding models](/v0.1/docs/integrations/text_embedding/)
        
    *   [Vector stores](/v0.1/docs/integrations/vectorstores/)
        
        *   [Memory](/v0.1/docs/integrations/vectorstores/memory/)
        *   [AnalyticDB](/v0.1/docs/integrations/vectorstores/analyticdb/)
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        *   [Chroma](/v0.1/docs/integrations/vectorstores/chroma/)
        *   [ClickHouse](/v0.1/docs/integrations/vectorstores/clickhouse/)
        *   [CloseVector](/v0.1/docs/integrations/vectorstores/closevector/)
        *   [Cloudflare Vectorize](/v0.1/docs/integrations/vectorstores/cloudflare_vectorize/)
        *   [Convex](/v0.1/docs/integrations/vectorstores/convex/)
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        *   [Momento Vector Index (MVI)](/v0.1/docs/integrations/vectorstores/momento_vector_index/)
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        *   [Redis](/v0.1/docs/integrations/vectorstores/redis/)
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        *   [SingleStore](/v0.1/docs/integrations/vectorstores/singlestore/)
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        *   [TypeORM](/v0.1/docs/integrations/vectorstores/typeorm/)
        *   [Typesense](/v0.1/docs/integrations/vectorstores/typesense/)
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        *   [USearch](/v0.1/docs/integrations/vectorstores/usearch/)
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        *   [Vercel Postgres](/v0.1/docs/integrations/vectorstores/vercel_postgres/)
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        *   [Weaviate](/v0.1/docs/integrations/vectorstores/weaviate/)
        *   [Xata](/v0.1/docs/integrations/vectorstores/xata/)
        *   [Zep](/v0.1/docs/integrations/vectorstores/zep/)
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    *   [Stores](/v0.1/docs/integrations/stores/)
        

*   [](/v0.1/)
*   [Components](/v0.1/docs/integrations/components/)
*   [Vector stores](/v0.1/docs/integrations/vectorstores/)
*   Cloudflare Vectorize

Cloudflare Vectorize
====================

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[​](#setup "Direct link to Setup")
---------------------------------------

Compatibility

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

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

    $ 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]]`:

    [[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 integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/cloudflare

    yarn add @langchain/cloudflare

    pnpm add @langchain/cloudflare

Usage[​](#usage "Direct link to 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](/v0.1/docs/integrations/text_embedding/cloudflare_ai/).

note

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

    name = "langchain-test"main = "worker.ts"compatibility_date = "2024-01-10"[[vectorize]]binding = "VECTORIZE_INDEX"index_name = "langchain-test"[ai]binding = "AI"

    // @ts-nocheckimport 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 });  },};

#### API Reference:

*   [CloudflareVectorizeStore](https://api.js.langchain.com/classes/langchain_cloudflare.CloudflareVectorizeStore.html) from `@langchain/cloudflare`
*   [CloudflareWorkersAIEmbeddings](https://api.js.langchain.com/classes/langchain_cloudflare.CloudflareWorkersAIEmbeddings.html) from `@langchain/cloudflare`

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.

* * *

#### Help us out by providing feedback on this documentation page:

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