Anthropic
Vercel AI SDK provides a set of utilities to make it easy to use Anthropic's API. In this guide, we'll walk through how to use the utilities to create a chat bot and a text completion app.
Guide: Chat Bot
Create a Next.js app
Create a Next.js application and install ai:
pnpm dlx create-next-app my-ai-app
cd my-ai-app
pnpm install aiAdd your Anthropic API Key to .env
Create a .env file in your project root and add your Anthropic API Key:
ANTHROPIC_API_KEY=xxxxxxxCreate a Route Handler
Create a Next.js Route Handler that uses the Edge Runtime to generate a response to a series of messages via Anthropic's TypeScript SDK, and returns the response as a streaming text response.
For this example, we'll create a route handler at app/api/chat/route.ts that accepts a POST request with a messages array of strings:
import Anthropic from '@anthropic-ai/sdk';
import { AnthropicStream, StreamingTextResponse } from 'ai';
import { experimental_buildAnthropicPrompt } from 'ai/prompts';
// Create an Anthropic API client (that's edge friendly??)
const anthropic = new Anthropic({
apiKey: process.env.ANTHROPIC_API_KEY || '',
});
// IMPORTANT! Set the runtime to edge
export const runtime = 'edge';
export async function POST(req: Request) {
// Extract the `prompt` from the body of the request
const { messages } = await req.json();
// Ask Claude for a streaming chat completion given the prompt
const response = await anthropic.completions.create({
prompt: experimental_buildAnthropicPrompt(messages),
model: 'claude-2',
stream: true,
max_tokens_to_sample: 300,
});
// Convert the response into a friendly text-stream
const stream = AnthropicStream(response);
// Respond with the stream
return new StreamingTextResponse(stream);
}Vercel AI SDK provides 2 utility helpers to make the above seamless: First, we
pass the streaming response we receive from Anthropic's TypeScript SDK to
AnthropicStream. This utility class
decodes/extracts the text tokens in the response and then re-encodes them
properly for simple consumption. We can then pass that new stream directly to
StreamingTextResponse. This
is another utility class that extends the normal Node/Edge Runtime Response
class with the default headers you probably want (hint: 'Content-Type': 'text/plain; charset=utf-8' is already set for you).
Wire up the UI
Create a Client component with a form that we'll use to gather the prompt from the user and then stream back the completion from.
By default, the useChat hook will use the POST Route Handler we created above (it defaults to /api/chat). You can override this by passing a api prop to useChat({ api: '...'}).
'use client';
import { useChat } from 'ai/react';
export default function Chat() {
const { messages, input, handleInputChange, handleSubmit } = useChat();
return (
<div>
{messages.map(m => (
<div key={m.id}>
{m.role === 'user' ? 'User: ' : 'AI: '}
{m.content}
</div>
))}
<form onSubmit={handleSubmit}>
<input
value={input}
placeholder="Say something..."
onChange={handleInputChange}
/>
</form>
</div>
);
}Guide: Text Completion
Use the Completion API
Similar to the Chat Bot example above, we'll create a Next.js Route Handler that generates a text completion via the same Anthropic SDK that we'll then stream back to our Next.js. It accepts a POST request with a prompt string:
import Anthropic from '@anthropic-ai/sdk';
import { AnthropicStream, StreamingTextResponse } from 'ai';
import { experimental_buildAnthropicPrompt } from 'ai/prompts';
// Create an Anthropic API client (that's edge friendly??)
const anthropic = new Anthropic({
apiKey: process.env.ANTHROPIC_API_KEY || '',
});
// IMPORTANT! Set the runtime to edge
export const runtime = 'edge';
export async function POST(req: Request) {
// Extract the `prompt` from the body of the request
const { prompt } = await req.json();
// Ask Claude for a streaming chat completion given the prompt
const response = await anthropic.completions.create({
prompt: `Human: ${prompt}\n\nAssistant:`,
model: 'claude-2',
stream: true,
max_tokens_to_sample: 300,
});
// Convert the response into a friendly text-stream
const stream = AnthropicStream(response);
// Respond with the stream
return new StreamingTextResponse(stream);
}Wire up the UI
We can use the useCompletion hook to make it easy to wire up the UI. By default, the useCompletion hook will use the POST Route Handler we created above (it defaults to /api/completion). You can override this by passing a api prop to useCompletion({ api: '...'}).
'use client';
import { useCompletion } from 'ai/react';
export default function Completion() {
const {
completion,
input,
stop,
isLoading,
handleInputChange,
handleSubmit,
} = useCompletion();
return (
<div className="mx-auto w-full max-w-md py-24 flex flex-col stretch">
<form onSubmit={handleSubmit}>
<label>
Say something...
<input
className="fixed w-full max-w-md bottom-0 border border-gray-300 rounded mb-8 shadow-xl p-2"
value={input}
onChange={handleInputChange}
/>
</label>
<output>Completion result: {completion}</output>
<button type="button" onClick={stop}>
Stop
</button>
<button disabled={isLoading} type="submit">
Send
</button>
</form>
</div>
);
}Guide: Save to Database After Completion
It’s common to want to save the result of a completion to a database after streaming it back to the user. The AnthropicStream adapter accepts a couple of optional callbacks that can be used to do this.
export async function POST(req: Request) {
// ...
// Convert the response into a friendly text-stream
const stream = AnthropicStream(response, {
onStart: async () => {
// This callback is called when the stream starts
// You can use this to save the prompt to your database
await savePromptToDatabase(prompt);
},
onToken: async (token: string) => {
// This callback is called for each token in the stream
// You can use this to debug the stream or save the tokens to your database
console.log(token);
},
onCompletion: async (completion: string) => {
// This callback is called when the completion is ready
// You can use this to save the final completion to your database
await saveCompletionToDatabase(completion);
},
});
// Respond with the stream
return new StreamingTextResponse(response);
}