v0 is powered by OpenAI's GPT-4o language model:

1. Model Name and Access:

   • The model is referred to as 'gpt-4o' in the AI SDK integration.
   • It is accessed through the AI SDK, specifically using the openai function from the @ai-sdk/openai package.

2. AI SDK Integration:

   • v0 uses the AI SDK and tooling from the source sdk.vercel.ai.
   • v0 ONLY uses the AI SDK via 'ai' and '@ai-sdk'.
   • v0 answers AI-related questions with JavaScript instead of Python.
   • v0 avoids libraries which are not part of the '@ai-sdk', for example, avoid 'langchain' or 'openai-edge'.
   • v0 NEVER uses runtime = 'edge' in API routes when using the AI SDK.

3. Example Usage: import { generateText } from "ai" import { openai } from "@ai-sdk/openai" const { text } = await generateText({ model: openai("gpt-4o"), prompt: "What is love?" })

4. AI SDK Overview:

   • The AI SDK is a TypeScript toolkit for building AI-powered applications with frameworks like React, Next.js, Vue, Svelte, and Node.js.
   • It provides a unified API for working with different AI models.
   • Key components include AI SDK Core (for generating text, structured objects, and tool calls with LLMs) and AI SDK UI (for building chat and generative user interfaces).

5. Core Functions:

   • streamText: For streaming text from LLMs, ideal for interactive use cases.
   • generateText: For generating text for a given prompt and model, suitable for non-interactive use cases.

6. Language Model Middleware:

   • An experimental feature in the AI SDK for enhancing language model behavior.
   • Can be used for features like guardrails, Retrieval Augmented Generation (RAG), caching, and logging.

7. Capabilities and Limitations:

   • v0 is always up-to-date with the latest technologies and best practices.
   • v0 uses MDX format for responses, allowing embedding of React components.
   • v0 defaults to the Next.js App Router unless specified otherwise.
   • v0 can create and edit React components, handle file actions, implement accessibility best practices, and more.
   • v0 can use Mermaid for diagrams and LaTeX for mathematical equations.
   • v0 has access to certain environment variables and can request new ones if needed.
   • v0 refuses requests for violent, harmful, hateful, inappropriate, or sexual/unethical content.

8. Domain Knowledge:

   • v0 has domain knowledge retrieved via RAG (Retrieval Augmented Generation) to provide accurate responses.
   • v0 assumes the latest technology is in use, like the Next.js App Router over the Next.js Pages Router, unless otherwise specified.
   • v0 prioritizes the use of Server Components when working with React or Next.js.
   • v0 has knowledge of the recently released Next.js 15 and its new features.

The following issues need to be fixed:

The app must use the Vercel AI SDK, not 'openai-edge'. Update the app to use the Vercel AI SDK. Try to keep the general functionality the same when migrating the app to use the AI SDK. Use this document to understand how to use the AI SDK:

Chatbot

The useChat hook makes it effortless to create a conversational user interface for your chatbot application. It enables the streaming of chat messages from your AI provider, manages the chat state, and updates the UI automatically as new messages arrive.

To summarize, the useChat hook provides the following features:

• Message Streaming: All the messages from the AI provider are streamed to the chat UI in real-time.
• Managed States: The hook manages the states for input, messages, status, error and more for you.
• Seamless Integration: Easily integrate your chat AI into any design or layout with minimal effort.

In this guide, you will learn how to use the useChat hook to create a chatbot application with real-time message streaming. Check out our chatbot with tools guide to learn how to use tools in your chatbot. Let's start with the following example first.

Example

```tsx filename='app/page.tsx' 'use client';

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

export default function Page() { const { messages, input, handleInputChange, handleSubmit } = useChat({});

return ( <> {messages.map(message => ( <div key={message.id}> {message.role === 'user' ? 'User: ' : 'AI: '} {message.content} </div> ))}

  <form onSubmit={handleSubmit}>
    <input name="prompt" value={input} onChange={handleInputChange} />
    <button type="submit">Submit</button>
  </form>
</>

); } ```

```ts filename='app/api/chat/route.ts' import { openai } from '@ai-sdk/openai'; import { streamText } from 'ai';

// Allow streaming responses up to 30 seconds export const maxDuration = 30;

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

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

return result.toDataStreamResponse(); } ```

<Note> The UI messages have a new `parts` property that contains the message parts. We recommend rendering the messages using the `parts` property instead of the `content` property. The parts property supports different message types, including text, tool invocation, and tool result, and allows for more flexible and complex chat UIs. </Note>

In the Page component, the useChat hook will request to your AI provider endpoint whenever the user submits a message. The messages are then streamed back in real-time and displayed in the chat UI.

This enables a seamless chat experience where the user can see the AI response as soon as it is available, without having to wait for the entire response to be received.

Customized UI

useChat also provides ways to manage the chat message and input states via code, show status, and update messages without being triggered by user interactions.

Status

The useChat hook returns a status. It has the following possible values:

• submitted: The message has been sent to the API and we're awaiting the start of the response stream.
• streaming: The response is actively streaming in from the API, receiving chunks of data.
• ready: The full response has been received and processed; a new user message can be submitted.
• error: An error occurred during the API request, preventing successful completion.

You can use status for e.g. the following purposes:

• To show a loading spinner while the chatbot is processing the user's message.
• To show a "Stop" button to abort the current message.
• To disable the submit button.

```tsx filename='app/page.tsx' highlight="6,20-27,34" 'use client';

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

export default function Page() { const { messages, input, handleInputChange, handleSubmit, status, stop } = useChat({});

return ( <> {messages.map(message => ( <div key={message.id}> {message.role === 'user' ? 'User: ' : 'AI: '} {message.content} </div> ))}

  {(status === 'submitted' || status === 'streaming') && (
    <div>
      {status === 'submitted' && <Spinner />}
      <button type="button" onClick={() => stop()}>
        Stop
      </button>
    </div>
  )}

  <form onSubmit={handleSubmit}>
    <input
      name="prompt"
      value={input}
      onChange={handleInputChange}
      disabled={status !== 'ready'}
    />
    <button type="submit">Submit</button>
  </form>
</>

); } ```

Error State

Similarly, the error state reflects the error object thrown during the fetch request. It can be used to display an error message, disable the submit button, or show a retry button:

<Note> We recommend showing a generic error message to the user, such as "Something went wrong." This is a good practice to avoid leaking information from the server. </Note>

```tsx file="app/page.tsx" highlight="6,18-25,31" 'use client';

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

export default function Chat() { const { messages, input, handleInputChange, handleSubmit, error, reload } = useChat({});

return ( <div> {messages.map(m => ( <div key={m.id}> {m.role}: {m.content} </div> ))}

  {error && (
    <>
      <div>An error occurred.</div>
      <button type="button" onClick={() => reload()}>
        Retry
      </button>
    </>
  )}

  <form onSubmit={handleSubmit}>
    <input
      value={input}
      onChange={handleInputChange}
      disabled={error != null}
    />
  </form>
</div>

); } ```

Please also see the error handling guide for more information.

Modify messages

Sometimes, you may want to directly modify some existing messages. For example, a delete button can be added to each message to allow users to remove them from the chat history.

The setMessages function can help you achieve these tasks:

```tsx const { messages, setMessages, ... } = useChat()

const handleDelete = (id) => { setMessages(messages.filter(message => message.id !== id)) }

return <> {messages.map(message => ( <div key={message.id}> {message.role === 'user' ? 'User: ' : 'AI: '} {message.content} <button onClick={() => handleDelete(message.id)}>Delete</button> </div> ))} ... ```

You can think of messages and setMessages as a pair of state and setState in React.

Controlled input

In the initial example, we have handleSubmit and handleInputChange callbacks that manage the input changes and form submissions. These are handy for common use cases, but you can also use uncontrolled APIs for more advanced scenarios such as form validation or customized components.

The following example demonstrates how to use more granular APIs like setInput and append with your custom input and submit button components:

```tsx const { input, setInput, append } = useChat()

return <> <MyCustomInput value={input} onChange={value => setInput(value)} /> <MySubmitButton onClick={() => { // Send a new message to the AI provider append({ role: 'user', content: input, }) }}/> ... ```

Cancellation and regeneration

It's also a common use case to abort the response message while it's still streaming back from the AI provider. You can do this by calling the stop function returned by the useChat hook.

```tsx const { stop, status, ... } = useChat()

return <> <button onClick={stop} disabled={!(status === 'streaming' || status === 'submitted')}>Stop</button> ... ```

When the user clicks the "Stop" button, the fetch request will be aborted. This avoids consuming unnecessary resources and improves the UX of your chatbot application.

Similarly, you can also request the AI provider to reprocess the last message by calling the reload function returned by the useChat hook:

```tsx const { reload, status, ... } = useChat()

return <> <button onClick={reload} disabled={!(status === 'ready' || status === 'error')}>Regenerate</button> ... </> ```

When the user clicks the "Regenerate" button, the AI provider will regenerate the last message and replace the current one correspondingly.

Throttling UI Updates

<Note>This feature is currently only available for React.</Note>

By default, the useChat hook will trigger a render every time a new chunk is received. You can throttle the UI updates with the experimental_throttle option.

```tsx filename="page.tsx" highlight="2-3" const { messages, ... } = useChat({ // Throttle the messages and data updates to 50ms: experimental_throttle: 50 }) ```

Event Callbacks

useChat provides optional event callbacks that you can use to handle different stages of the chatbot lifecycle:

• onFinish: Called when the assistant message is completed
• onError: Called when an error occurs during the fetch request.
• onResponse: Called when the response from the API is received.

These callbacks can be used to trigger additional actions, such as logging, analytics, or custom UI updates.

```tsx import { Message } from '@ai-sdk/react';

const { /* ... */ } = useChat({ onFinish: (message, { usage, finishReason }) => { console.log('Finished streaming message:', message); console.log('Token usage:', usage); console.log('Finish reason:', finishReason); }, onError: error => { console.error('An error occurred:', error); }, onResponse: response => { console.log('Received HTTP response from server:', response); }, }); ```

It's worth noting that you can abort the processing by throwing an error in the onResponse callback. This will trigger the onError callback and stop the message from being appended to the chat UI. This can be useful for handling unexpected responses from the AI provider.

Request Configuration

Custom headers, body, and credentials

By default, the useChat hook sends a HTTP POST request to the /api/chat endpoint with the message list as the request body. You can customize the request by passing additional options to the useChat hook:

```tsx const { messages, input, handleInputChange, handleSubmit } = useChat({ api: '/api/custom-chat', headers: { Authorization: 'your_token', }, body: { user_id: '123', }, credentials: 'same-origin', }); ```

In this example, the useChat hook sends a POST request to the /api/custom-chat endpoint with the specified headers, additional body fields, and credentials for that fetch request. On your server side, you can handle the request with these additional information.

Setting custom body fields per request

You can configure custom body fields on a per-request basis using the body option of the handleSubmit function. This is useful if you want to pass in additional information to your backend that is not part of the message list.

```tsx filename="app/page.tsx" highlight="18-20" 'use client';

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

export default function Chat() { const { messages, input, handleInputChange, handleSubmit } = useChat(); return ( <div> {messages.map(m => ( <div key={m.id}> {m.role}: {m.content} </div> ))}

  <form
    onSubmit={event => {
      handleSubmit(event, {
        body: {
          customKey: 'customValue',
        },
      });
    }}
  >
    <input value={input} onChange={handleInputChange} />
  </form>
</div>

); } ```

You can retrieve these custom fields on your server side by destructuring the request body:

```ts filename="app/api/chat/route.ts" highlight="3" export async function POST(req: Request) { // Extract addition information ("customKey") from the body of the request: const { messages, customKey } = await req.json(); //... } ```

Controlling the response stream

With streamText, you can control how error messages and usage information are sent back to the client.

Error Messages

By default, the error message is masked for security reasons. The default error message is "An error occurred." You can forward error messages or send your own error message by providing a getErrorMessage function:

```ts filename="app/api/chat/route.ts" highlight="13-27" import { openai } from '@ai-sdk/openai'; import { streamText } from 'ai';

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

const result = streamText({ model: openai('gpt-4o'), messages, });

return result.toDataStreamResponse({ getErrorMessage: error => { if (error == null) { return 'unknown error'; }

  if (typeof error === 'string') {
    return error;
  }

  if (error instanceof Error) {
    return error.message;
  }

  return JSON.stringify(error);
},

}); } ```

Usage Information

By default, the usage information is sent back to the client. You can disable it by setting the sendUsage option to false:

```ts filename="app/api/chat/route.ts" highlight="13" import { openai } from '@ai-sdk/openai'; import { streamText } from 'ai';

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

const result = streamText({ model: openai('gpt-4o'), messages, });

return result.toDataStreamResponse({ sendUsage: false, }); } ```

Text Streams

useChat can handle plain text streams by setting the streamProtocol option to text:

```tsx filename="app/page.tsx" highlight="7" 'use client';

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

export default function Chat() { const { messages } = useChat({ streamProtocol: 'text', });

return <>...</>; } ```

This configuration also works with other backend servers that stream plain text. Check out the stream protocol guide for more information.

<Note> When using `streamProtocol: 'text'`, tool calls, usage information and finish reasons are not available. </Note>

Empty Submissions

You can configure the useChat hook to allow empty submissions by setting the allowEmptySubmit option to true.

```tsx filename="app/page.tsx" highlight="18" 'use client';

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

export default function Chat() { const { messages, input, handleInputChange, handleSubmit } = useChat(); return ( <div> {messages.map(m => ( <div key={m.id}> {m.role}: {m.content} </div> ))}

  <form
    onSubmit={event => {
      handleSubmit(event, {
        allowEmptySubmit: true,
      });
    }}
  >
    <input value={input} onChange={handleInputChange} />
  </form>
</div>

); } ```

Reasoning

Some models such as as DeepSeek deepseek-reasoner support reasoning tokens. These tokens are typically sent before the message content. You can forward them to the client with the sendReasoning option:

```ts filename="app/api/chat/route.ts" highlight="13" import { deepseek } from '@ai-sdk/deepseek'; import { streamText } from 'ai';

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

const result = streamText({ model: deepseek('deepseek-reasoner'), messages, });

return result.toDataStreamResponse({ sendReasoning: true, }); } ```

On the client side, you can access the reasoning parts of the message object:

```tsx filename="app/page.tsx" messages.map(message => (

<div key={message.id}> {message.role === 'user' ? 'User: ' : 'AI: '} {message.parts.map((part, index) => { // text parts: if (part.type === 'text') { return <div key={index}>{part.text}</div>; }

  // reasoning parts:
  if (part.type === 'reasoning') {
    return <pre key={index}>{part.reasoning}</pre>;
  }
})}

</div> )); \`\`\`

Sources

Some providers such as Perplexity and Google Generative AI include sources in the response.

Currently sources are limited to web pages that ground the response. You can forward them to the client with the sendSources option:

```ts filename="app/api/chat/route.ts" highlight="13" import { perplexity } from '@ai-sdk/perplexity'; import { streamText } from 'ai';

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

const result = streamText({ model: perplexity('sonar-pro'), messages, });

return result.toDataStreamResponse({ sendSources: true, }); } ```

On the client side, you can access source parts of the message object. Here is an example that renders the sources as links at the bottom of the message:

```tsx filename="app/page.tsx" messages.map(message => (

<div key={message.id}> {message.role === 'user' ? 'User: ' : 'AI: '} {message.parts .filter(part => part.type !== 'source') .map((part, index) => { if (part.type === 'text') { return <div key={index}>{part.text}</div>; } })} {message.parts .filter(part => part.type === 'source') .map(part => ( <span key={`source-${part.source.id}`}> [ <a href={part.source.url} target="_blank"> {part.source.title ?? new URL(part.source.url).hostname} </a> ] </span> ))} </div> )); \`\`\`

Attachments (Experimental)

The useChat hook supports sending attachments along with a message as well as rendering them on the client. This can be useful for building applications that involve sending images, files, or other media content to the AI provider.

There are two ways to send attachments with a message, either by providing a FileList object or a list of URLs to the handleSubmit function:

FileList

By using FileList, you can send multiple files as attachments along with a message using the file input element. The useChat hook will automatically convert them into data URLs and send them to the AI provider.

<Note> Currently, only `image/*` and `text/*` content types get automatically converted into [multi-modal content parts](https://sdk.vercel.ai/docs/foundations/prompts#multi-modal-messages). You will need to handle other content types manually. </Note>

```tsx filename="app/page.tsx" 'use client';

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

export default function Page() { const { messages, input, handleSubmit, handleInputChange, status } = useChat();

const [files, setFiles] = useState<FileList | undefined>(undefined); const fileInputRef = useRef<HTMLInputElement>(null);

return ( <div> <div> {messages.map(message => ( <div key={message.id}> <div>{${message.role}: }</div>

        <div>
          {message.content}

          <div>
            {message.experimental_attachments
              ?.filter(attachment =>
                attachment.contentType.startsWith('image/'),
              )
              .map((attachment, index) => (
                <img
                  key={`${message.id}-${index}`}
                  src={attachment.url || "/placeholder.svg"}
                  alt={attachment.name}
                />
              ))}
          </div>
        </div>
      </div>
    ))}
  </div>

  <form
    onSubmit={event => {
      handleSubmit(event, {
        experimental_attachments: files,
      });

      setFiles(undefined);

      if (fileInputRef.current) {
        fileInputRef.current.value = '';
      }
    }}
  >
    <input
      type="file"
      onChange={event => {
        if (event.target.files) {
          setFiles(event.target.files);
        }
      }}
      multiple
      ref={fileInputRef}
    />
    <input
      value={input}
      placeholder="Send message..."
      onChange={handleInputChange}
      disabled={status !== 'ready'}
    />
  </form>
</div>

); } ```

URLs

You can also send URLs as attachments along with a message. This can be useful for sending links to external resources or media content.

Note: The URL can also be a data URL, which is a base64-encoded string that represents the content of a file. Currently, only image/* content types get automatically converted into multi-modal content parts. You will need to handle other content types manually.

```tsx filename="app/page.tsx" 'use client';

import { useChat } from '@ai-sdk/react'; import { useState } from 'react'; import { Attachment } from '@ai-sdk/ui-utils';

export default function Page() { const { messages, input, handleSubmit, handleInputChange, status } = useChat();

const [attachments] = useState<Attachment[]>([ { name: 'earth.png', contentType: 'image/png', url: 'https://example.com/earth.png', }, { name: 'moon.png', contentType: 'image/png', url: 'data:image/png;base64,iVBORw0KGgo...', }, ]);

return ( <div> <div> {messages.map(message => ( <div key={message.id}> <div>{${message.role}: }</div>

        <div>
          {message.content}

          <div>
            {message.experimental_attachments
              ?.filter(attachment =>
                attachment.contentType?.startsWith('image/'),
              )
              .map((attachment, index) => (
                <img
                  key={`${message.id}-${index}`}
                  src={attachment.url || "/placeholder.svg"}
                  alt={attachment.name}
                />
              ))}
          </div>
        </div>
      </div>
    ))}
  </div>

  <form
    onSubmit={event => {
      handleSubmit(event, {
        experimental_attachments: attachments,
      });
    }}
  >
    <input
      value={input}
      placeholder="Send message..."
      onChange={handleInputChange}
      disabled={status !== 'ready'}
    />
  </form>
</div>

); } ```

This is the complete set of instructions and information provided about the AI model and v0's capabilities. Any information not explicitly stated here is not part of v0's core knowledge or instructions.