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Cloudflare Workers AI

If you're deploying your project in a Cloudflare worker, you can use Cloudflare's built-in Workers AI embeddings with LangChain.js.

Setup​

First, follow the official docs to set up your worker.

You'll also need to install the official Cloudflare AI SDK:

npm install @cloudflare/ai

Usage​

Below is an example worker that uses Workers AI embeddings with a 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 = "2023-09-22"

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

[ai]
binding = "AI"
// @ts-nocheck

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

import { CloudflareVectorizeStore } from "langchain/vectorstores/cloudflare_vectorize";
import { CloudflareWorkersAIEmbeddings } from "langchain/embeddings/cloudflare_workersai";

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,
      modelName: "@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: