Stream OpenAI API with FastAPI & Next.js: SSE Guide (2026)

Integrating OpenAI's API with modern web applications requires an efficient way to handle real-time data. Streaming responses in chunks can enhance the user experience, especially in AI chat applications. This tutorial will guide you through setting up a FastAPI backend to stream OpenAI responses to a Next.js 14 frontend using Server-Sent Events (SSE).

Key Takeaways

• Learn to set up FastAPI to stream OpenAI responses.
• Implement Server-Sent Events (SSE) in Next.js 14.
• Understand how to handle real-time data streams effectively.
• Troubleshoot common streaming issues.

By the end of this tutorial, you'll know how to configure your FastAPI backend to send data in chunks to a Next.js frontend using SSE. This approach ensures a smooth, real-time chat experience essential for modern AI applications. Let's dive into why this setup matters and how you can achieve it.

Prerequisites

• Basic knowledge of Python and JavaScript.
• Experience with FastAPI and Next.js frameworks.
• OpenAI API key and account access.
• Node.js and Python installed on your machine.

Step 1: Set Up FastAPI with Streaming Capability

First, you'll need to configure your FastAPI application to handle streaming responses. This is done by using the StreamingResponse object in FastAPI, which allows you to send data in chunks. Here's how you can do it:

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from openai import AsyncOpenAI

app = FastAPI()
client = AsyncOpenAI(api_key="your-api-key")

@app.post("/chat")
async def chat(prompt: str):
    async def generate():
        response = await client.Completion.create(prompt=prompt, stream=True)
        for chunk in response:
            yield f"data: {chunk['choices'][0]['text']}\n\n"
    return StreamingResponse(generate(), media_type="text/event-stream")

In this code, the generate function asynchronously yields each chunk of the response, which is then sent to the client as part of an SSE stream.

Step 2: Create a Next.js 14 App with SSE Client

Next.js 14 offers a powerful way to create real-time applications. To set up a frontend that handles SSE, follow these steps:

import { useEffect, useState } from 'react';

export default function Chat() {
    const [messages, setMessages] = useState([]);

    useEffect(() => {
        const eventSource = new EventSource('/api/chat');
        eventSource.onmessage = function(event) {
            setMessages(prevMessages => [...prevMessages, event.data]);
        };

        return () => {
            eventSource.close();
        };
    }, []);

    return (
        
            AI Chat
            
                {messages.map((msg, index) => (
                    {msg}
                ))}
            
        
    );
}

This React component uses the EventSource API to listen for messages from the FastAPI backend. Each message is appended to the state, updating the UI in real-time.

Step 3: Deploy and Test Your Application

To ensure everything works correctly, deploy your FastAPI server and Next.js application. You can use platforms like Vercel for Next.js and Heroku for FastAPI. Once deployed, test the chat functionality to verify that messages stream progressively.

Common Errors/Troubleshooting

If you encounter issues where streams stop after the first chunk or deliver all at once, consider these solutions:

• Ensure your FastAPI route and streaming logic are correctly set up.
• Verify that your frontend is connecting to the correct API endpoint.
• Check network logs for any interruptions or errors in the data stream.

Conclusion

By following this guide, you've learned how to set up a real-time streaming chat application using FastAPI and Next.js. This setup provides a robust foundation for any AI-driven application requiring real-time interaction.

Frequently Asked Questions

What are Server-Sent Events (SSE)?

SSE is a server push technology enabling servers to send data to web applications over a single HTTP connection.

Why use SSE with FastAPI and Next.js?

SSE provides a simple way to implement real-time features in applications, ensuring data is pushed to clients as it becomes available.

How can I troubleshoot streaming issues?

Check your network logs for connection issues, ensure correct API endpoints are used, and verify backend logic for streaming data.