content/providers/03-community-providers/12-cloudflare-workers-ai.mdx
workers-ai-provider is a community provider that allows you to use Cloudflare's Workers AI models with the AI SDK.
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:
[ai]
binding = "AI"
To create a workersai provider instance, use the createWorkersAI function, passing in the AI binding as an option:
import { createWorkersAI } from 'workers-ai-provider';
const workersai = createWorkersAI({ binding: env.AI });
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:
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
You can use Cloudflare Workers AI language models to generate text with the generateText or streamText function:
generateTextimport { 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);
},
};
streamTextimport { 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',
},
});
},
};
Some Cloudflare Workers AI language models also support structured data generation with Output:
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);
},
};