This will help you get started with Cloudflare Workers AI embedding models using LangChain. For detailed documentation on CloudflareWorkersAIEmbeddings features and configuration options, please refer to the API reference.

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Overview

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Integration details

ClassPackageLocalPy supportPackage downloadsPackage latest
CloudflareWorkersAIEmbeddings@langchain/cloudflare❌❌NPM - DownloadsNPM - Version

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Setup

To access Cloudflare embedding models you’ll need to create a Cloudflare account and install the @langchain/cloudflare integration package. This integration is made to run in a Cloudflare worker and accept a binding. Follow the official docs to set up your worker. Your wrangler.toml file should look similar to this: 
name = "langchain-test"
main = "worker.js"
compatibility_date = "2024-01-10"

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

[ai]
binding = "AI"

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Credentials

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

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Installation

The LangChain CloudflareWorkersAIEmbeddings integration lives in the @langchain/cloudflare package: 
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/cloudflare @langchain/core
</Npm2Yarn>

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Usage

Below is an example worker that uses Workers AI embeddings with a Cloudflare Vectorize vectorstore. 
// @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 });
  },
};

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API reference

For detailed documentation of all CloudflareWorkersAIEmbeddings features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_cloudflare.CloudflareWorkersAIEmbeddings.html