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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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        *   [Alibaba Tongyi](/v0.1/docs/integrations/text_embedding/alibaba_tongyi/)
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*   [](/v0.1/)
*   [Components](/v0.1/docs/integrations/components/)
*   [Text embedding models](/v0.1/docs/integrations/text_embedding/)
*   Cloudflare Workers AI

Cloudflare Workers AI
=====================

If you're deploying your project in a Cloudflare worker, you can use Cloudflare's [built-in Workers AI embeddings](https://developers.cloudflare.com/workers-ai/) with LangChain.js.

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

First, [follow the official docs](https://developers.cloudflare.com/workers-ai/get-started/workers-wrangler/) to set up your worker.

You'll also 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 uses Workers AI embeddings with a [Cloudflare Vectorize](/v0.1/docs/integrations/vectorstores/cloudflare_vectorize/) vectorstore.

note

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

    name = "langchain-test"main = "worker.js"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`

* * *

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