import { GeistMono } from 'geist/font/mono'; import Link from 'next/link'; import type { ReactNode } from 'react'; const Code = ({ children }: { children: ReactNode }) => { return (


      {children}

); }; export const Card = ({ type }: { type: string }) => { return type === 'chat-text' ? (

Stream Chat Completions

The useChat hook can be integrated with a Python FastAPI backend to stream chat completions in real-time. The most basic setup involves streaming plain text chunks by setting the{' '} streamProtocol to text.

To make your responses streamable, you will have to use the{' '} StreamingResponse class provided by FastAPI.

) : type === 'chat-data' ? (

Stream Chat Completions with Tools

The useChat hook can be integrated with a Python FastAPI backend to stream chat completions in real-time. However, the most basic setup that involves streaming plain text chunks by setting the streamProtocol to text is limited.

As a result, setting the streamProtocol to data allows you to stream chunks that include information about tool calls and results.

To make your responses streamable, you will have to use the{' '} StreamingResponse class provided by FastAPI. You will also have to ensure that your chunks follow the{' '} data stream protocol {' '} and that the response has x-vercel-ai-data-stream{' '} header set to v1.

) : type === 'chat-attachments' ? (

Stream Chat Completions with Attachments

The useChat hook can be integrated with a Python FastAPI backend to stream chat completions in real-time. To make your responses streamable, you will have to use the{' '} StreamingResponse class provided by FastAPI.

Furthermore, you can send files along with your messages by setting{' '} experimental_attachments to true in{' '} handleSubmit. This will allow you to use process these attachments in your FastAPI backend.

) : null; };