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ChatCloudflareWorkersAI

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

Usage​

You'll first need to install the LangChain Cloudflare integration package:

tip

See this section for general instructions on installing integration packages.

npm install @langchain/cloudflare
tip

We're unifying model params across all packages. We now suggest using model instead of modelName, and apiKey for API keys.

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

const model = new ChatCloudflareWorkersAI({
  model: "@cf/meta/llama-2-7b-chat-int8", // Default value
  cloudflareAccountId: process.env.CLOUDFLARE_ACCOUNT_ID,
  cloudflareApiToken: process.env.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/`,
});

const response = await model.invoke([
  ["system", "You are a helpful assistant that translates English to German."],
  ["human", `Translate "I love programming".`],
]);

console.log(response);

/*
AIMessage {
  content: `Sure! Here's the translation of "I love programming" into German:\n` +
    '\n' +
    '"Ich liebe Programmieren."\n' +
    '\n' +
    'In this sentence, "Ich" means "I," "liebe" means "love," and "Programmieren" means "programming."',
  additional_kwargs: {}
}
*/

const stream = await model.stream([
  ["system", "You are a helpful assistant that translates English to German."],
  ["human", `Translate "I love programming".`],
]);

for await (const chunk of stream) {
  console.log(chunk);
}

/*
  AIMessageChunk {
    content: 'S',
    additional_kwargs: {}
  }
  AIMessageChunk {
    content: 'ure',
    additional_kwargs: {}
  }
  AIMessageChunk {
    content: '!',
    additional_kwargs: {}
  }
  AIMessageChunk {
    content: ' Here',
    additional_kwargs: {}
  }
  ...
*/

API Reference:

Tool calling​

note

Tool calling is only available in @langchain/cloudflare version 0.0.7 and above.

Cloudflare's API now supports tool calling! The below example demonstrates how to invoke and stream tool calls.

import { ChatCloudflareWorkersAI } from "@langchain/cloudflare";
import {
  AIMessageChunk,
  HumanMessage,
  SystemMessage,
} from "@langchain/core/messages";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const model = new ChatCloudflareWorkersAI({
  model: "@hf/nousresearch/hermes-2-pro-mistral-7b",
  cloudflareAccountId: process.env.CLOUDFLARE_ACCOUNT_ID,
  cloudflareApiToken: process.env.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/`,
});

const weatherSchema = z.object({
  location: z.string().describe("The location to get the weather for"),
});
const weatherTool = tool<typeof weatherSchema>(
  (input) => {
    return `The weather in ${input.location} is sunny.`;
  },
  {
    name: "get_weather",
    description: "Get the weather",
  }
);

const modelWithTools = model.bindTools([weatherTool]);

const inputMessages = [
  new SystemMessage("You are a helpful assistant."),
  new HumanMessage("What's the weather like in the North Pole?"),
];

const response = await modelWithTools.invoke(inputMessages);

console.log(response.tool_calls);

/*
[ { name: 'get_weather', args: { input: 'North Pole' } } ]
*/

const stream = await modelWithTools.stream(inputMessages);

let finalChunk: AIMessageChunk | undefined;
for await (const chunk of stream) {
  if (!finalChunk) {
    finalChunk = chunk;
  } else {
    finalChunk = finalChunk.concat(chunk);
  }
}

console.log(finalChunk?.tool_calls);

/*
[
  { name: 'get_weather', args: { input: 'North Pole' }, id: undefined }
]
*/

API Reference:

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