---
title: Stream Text with Chat Prompt
description: Learn how to generate text using the AI SDK and Next.js
url: "https://ai-sdk.dev/cookbook/next/stream-text-with-chat-prompt"
docs_index: /llms.txt
tags:
  - next
  - streaming
  - chat
---

> For an index of all documentation, see [/llms.txt](/llms.txt).

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.

## 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 title='app/page.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 title='app/api/chat/route.ts'
import {
  convertToModelMessages,
  createUIMessageStreamResponse,
  streamText,
  toUIMessageStream,
  type UIMessage,
} from 'ai';

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

  const result = streamText({
    model: 'openai/gpt-6-astra',
    instructions: 'You are a helpful assistant.',
    messages: await convertToModelMessages(messages),
  });

  return createUIMessageStreamResponse({
    stream: toUIMessageStream({ stream: result.stream }),
  });
}
```

***

---

For a semantic overview of all documentation, see [/sitemap.md](/sitemap.md)

For an index of all available documentation, see [/llms.txt](/llms.txt)

For agent-facing discovery, including API and MCP surfaces, see [/agents.md](/agents.md)