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Cloudflare Workers AI

content/providers/03-community-providers/12-cloudflare-workers-ai.mdx

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Cloudflare Workers AI

workers-ai-provider is a community provider that allows you to use Cloudflare's Workers AI models with the AI SDK.

Setup

The Cloudflare Workers AI provider is available in the workers-ai-provider module. You can install it with:

<Tabs items={['pnpm', 'npm', 'yarn', 'bun']}> <Tab> <Snippet text="pnpm add workers-ai-provider" /> </Tab> <Tab> <Snippet text="npm install workers-ai-provider" /> </Tab> <Tab> <Snippet text="yarn add workers-ai-provider" /> </Tab>

<Tab> <Snippet text="bun add workers-ai-provider" dark /> </Tab> </Tabs>

Then, setup an AI binding in your Cloudflare Workers project wrangler.toml file:

bash
[ai]
binding = "AI"

Provider Instance

To create a workersai provider instance, use the createWorkersAI function, passing in the AI binding as an option:

typescript
import { createWorkersAI } from 'workers-ai-provider';

const workersai = createWorkersAI({ binding: env.AI });

Language Models

To create a model instance, call the provider instance and specify the model you would like to use as the first argument. You can also pass additional settings in the second argument:

typescript
import { createWorkersAI } from 'workers-ai-provider';

const workersai = createWorkersAI({ binding: env.AI });
const model = workersai('@cf/meta/llama-3.1-8b-instruct', {
  // additional settings
  safePrompt: true,
});

You can use the following optional settings to customize:

  • safePrompt boolean

    Whether to inject a safety prompt before all conversations. Defaults to false

Examples

You can use Cloudflare Workers AI language models to generate text with the generateText or streamText function:

generateText

typescript
import { createWorkersAI } from 'workers-ai-provider';
import { generateText } from 'ai';

type Env = {
  AI: Ai;
};

export default {
  async fetch(_: Request, env: Env) {
    const workersai = createWorkersAI({ binding: env.AI });
    const result = await generateText({
      model: workersai('@cf/meta/llama-2-7b-chat-int8'),
      prompt: 'Write a 50-word essay about hello world.',
    });

    return new Response(result.text);
  },
};

streamText

typescript
import { createWorkersAI } from 'workers-ai-provider';
import { streamText } from 'ai';

type Env = {
  AI: Ai;
};

export default {
  async fetch(_: Request, env: Env) {
    const workersai = createWorkersAI({ binding: env.AI });
    const result = streamText({
      model: workersai('@cf/meta/llama-2-7b-chat-int8'),
      prompt: 'Write a 50-word essay about hello world.',
    });

    return result.toTextStreamResponse({
      headers: {
        // add these headers to ensure that the
        // response is chunked and streamed
        'Content-Type': 'text/x-unknown',
        'content-encoding': 'identity',
        'transfer-encoding': 'chunked',
      },
    });
  },
};

Structured Output

Some Cloudflare Workers AI language models also support structured data generation with Output:

typescript
import { createWorkersAI } from 'workers-ai-provider';
import { generateText, Output } from 'ai';
import { z } from 'zod';

type Env = {
  AI: Ai;
};

export default {
  async fetch(_: Request, env: Env) {
    const workersai = createWorkersAI({ binding: env.AI });
    const result = await generateText({
      model: workersai('@cf/meta/llama-3.1-8b-instruct'),
      prompt: 'Generate a Lasagna recipe',
      output: Output.object({
        schema: z.object({
          recipe: z.object({
            ingredients: z.array(z.string()),
            description: z.string(),
          }),
        }),
      }),
    });

    return Response.json(result.output);
  },
};