This will help you get started with Cloudflare Workers AI text completion models (LLMs) using LangChain. For detailed documentation on CloudflareWorkersAI features and configuration options, please refer to the API reference.

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Overview

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Integration details

ClassPackageLocalSerializablePY supportPackage downloadsPackage latest
CloudflareWorkersAI@langchain/cloudflare❌✅❌NPM - DownloadsNPM - Version

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Setup

To access Cloudflare Workers AI models you’ll need to create a Cloudflare account, get an API key, and install the @langchain/cloudflare integration package.

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Credentials

Head to this page to sign up to Cloudflare and generate an API key. Once you’ve done this, note your CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN.

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Installation

The LangChain Cloudflare 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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Instantiation

Now we can instantiate our model object and generate chat completions: 
// @lc-docs-hide-cell

// @ts-expect-error Deno is not recognized
const CLOUDFLARE_ACCOUNT_ID = Deno.env.get("CLOUDFLARE_ACCOUNT_ID");
// @ts-expect-error Deno is not recognized
const CLOUDFLARE_API_TOKEN = Deno.env.get("CLOUDFLARE_API_TOKEN");

import { CloudflareWorkersAI } from "@langchain/cloudflare";

const llm = new CloudflareWorkersAI({
  model: "@cf/meta/llama-3.1-8b-instruct", // Default value
  cloudflareAccountId: CLOUDFLARE_ACCOUNT_ID,
  cloudflareApiToken: CLOUDFLARE_API_TOKEN,
  // Pass a custom base URL to use Cloudflare AI Gateway
  // baseUrl: `https://gateway.ai.cloudflare.com/v1/{YOUR_ACCOUNT_ID}/{GATEWAY_NAME}/workers-ai/`,
});

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Invocation

const inputText = "Cloudflare is an AI company that "

const completion = await llm.invoke(inputText);
completion

"Cloudflare is not an AI company, but rather a content delivery network (CDN) and security company. T"... 876 more characters

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Chaining

We can chain our completion model with a prompt template like so: 
import { PromptTemplate } from "@langchain/core/prompts"

const prompt = PromptTemplate.fromTemplate("How to say {input} in {output_language}:\n")

const chain = prompt.pipe(llm);
await chain.invoke(
  {
    output_language: "German",
    input: "I love programming.",
  }
)

"That's a simple but sweet statement! \n" +
  "\n" +
  'To say "I love programming" in German, you can say: "ICH LIEB'... 366 more characters

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API reference

For detailed documentation of all CloudflareWorkersAI features and configurations head to the API reference.