ChatCloudflareWorkersAI

Workers AI allows you to run machine learning models, on the Cloudflare network, from your own code.

This will help you getting started with Cloudflare Workers AI chat models. For detailed documentation of all ChatCloudflareWorkersAI features and configurations head to the API reference.

Overview

Integration details

Class	Package	Local	Serializable	PY support	Package downloads	Package latest
ChatCloudflareWorkersAI	@langchain/cloudflare	❌	✅	❌

Model features

See the links in the table headers below for guides on how to use specific features.

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Token usage	Logprobs
❌	❌	❌	✅	❌	❌	✅	❌	❌

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.

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.

Passing a binding within a Cloudflare Worker is not yet supported.

Installation

The LangChain ChatCloudflareWorkersAI integration lives in the @langchain/cloudflare package:

tip

See this section for general instructions on installing integration packages.

npm

npm i @langchain/cloudflare

yarn

yarn add @langchain/cloudflare

pnpm

pnpm add @langchain/cloudflare

Instantiation

Now we can instantiate our model object and generate chat completions:

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

const llm = new ChatCloudflareWorkersAI({
  model: "@cf/meta/llama-2-7b-chat-int8", // 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/`,
});

Invocation

const aiMsg = await llm.invoke([
  [
    "system",
    "You are a helpful assistant that translates English to French. Translate the user sentence.",
  ],
  ["human", "I love programming."],
]);
aiMsg;

AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: 'I can help with that! The translation of "I love programming" in French is:\n' +
      "\n" +
      `"J'adore le programmati`... 4 more characters,
    tool_calls: [],
    invalid_tool_calls: [],
    additional_kwargs: {},
    response_metadata: {}
  },
  lc_namespace: [ "langchain_core", "messages" ],
  content: 'I can help with that! The translation of "I love programming" in French is:\n' +
    "\n" +
    `"J'adore le programmati`... 4 more characters,
  name: undefined,
  additional_kwargs: {},
  response_metadata: {},
  tool_calls: [],
  invalid_tool_calls: []
}

console.log(aiMsg.content);

I can help with that! The translation of "I love programming" in French is:

"J'adore le programmation."

Chaining

We can chain our model with a prompt template like so:

import { ChatPromptTemplate } from "@langchain/core/prompts";

const prompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You are a helpful assistant that translates {input_language} to {output_language}.",
  ],
  ["human", "{input}"],
]);

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

AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: "Das Programmieren ist für mich sehr Valent sein!",
    tool_calls: [],
    invalid_tool_calls: [],
    additional_kwargs: {},
    response_metadata: {}
  },
  lc_namespace: [ "langchain_core", "messages" ],
  content: "Das Programmieren ist für mich sehr Valent sein!",
  name: undefined,
  additional_kwargs: {},
  response_metadata: {},
  tool_calls: [],
  invalid_tool_calls: []
}

API reference

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

Related

• Chat model conceptual guide
• Chat model how-to guides