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Stream Text with Chat Prompt

content/cookbook/01-next/21-stream-text-with-chat-prompt.mdx

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Stream Text with Chat Prompt

Chat completion can sometimes take a long time to finish, especially when the response is big. In such cases, it is useful to stream the chat completion to the client in real-time. This allows the client to display the new message as it is being generated by the model, rather than have users wait for it to finish.

<Browser> <ChatGeneration stream history={[ { role: 'User', content: 'How is it going?' }, { role: 'Assistant', content: 'All good, how may I help you?' }, ]} inputMessage={{ role: 'User', content: 'Why is the sky blue?' }} outputMessage={{ role: 'Assistant', content: 'The sky is blue because of rayleigh scattering.', }} /> </Browser>

Client

Let's create a React component that imports the useChat hook from the @ai-sdk/react module. The useChat hook will call the /api/chat endpoint when the user sends a message. The endpoint will generate the assistant's response based on the conversation history and stream it to the client.

tsx
'use client';

import { useChat } from '@ai-sdk/react';
import { DefaultChatTransport } from 'ai';
import { useState } from 'react';

export default function Page() {
  const [input, setInput] = useState('');

  const { messages, sendMessage } = useChat({
    transport: new DefaultChatTransport({
      api: '/api/chat',
    }),
  });

  return (
    <div>
      <input
        value={input}
        onChange={event => {
          setInput(event.target.value);
        }}
        onKeyDown={async event => {
          if (event.key === 'Enter') {
            sendMessage({
              parts: [{ type: 'text', text: input }],
            });
          }
        }}
      />

      {messages.map((message, index) => (
        <div key={index}>
          {message.parts.map(part => {
            if (part.type === 'text') {
              return <div key={`${message.id}-text`}>{part.text}</div>;
            }
          })}
        </div>
      ))}
    </div>
  );
}

Server

Next, let's create the /api/chat endpoint that generates the assistant's response based on the conversation history.

typescript
import { convertToModelMessages, streamText, type UIMessage } from 'ai';

export async function POST(req: Request) {
  const { messages }: { messages: UIMessage[] } = await req.json();

  const result = streamText({
    model: 'openai/gpt-4o',
    system: 'You are a helpful assistant.',
    messages: await convertToModelMessages(messages),
  });

  return result.toUIMessageStreamResponse();
}

<GithubLink link="https://github.com/vercel/ai/blob/main/examples/next-openai-pages/pages/chat/stream-chat/index.tsx" />