Building AI Applications with FastAPI

1. Why FastAPI for AI Backends

Serving Large Language Model requests requires handling asynchronous operations efficiently. LLM generation takes time, and blocking the main server thread halts incoming requests. FastAPI built-in async support handles long-lived network connections, making it suitable for streaming responses.

2. Async Streaming Setup

To stream tokens as they are generated by the model, we use FastAPI StreamingResponse. The client receives characters incrementally, which reduces perceived latency.

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
import asyncio

app = FastAPI()

async def token_generator():
    tokens = ["Connecting", " to", " agent...", "\nReady."]
    for token in tokens:
        yield token
        await asyncio.sleep(0.15)

@app.get("/stream")
async def stream_tokens():
    return StreamingResponse(token_generator(), media_type="text/event-stream")

3. Best Practices for Production

• Implement timeouts: AI APIs can hang, so set read and write timeouts on HTTP clients.
• Offload blocking code: If doing local tokenizing or CPU-bound data parsing, use loops with run_in_executor.
• Validate inputs: Use Pydantic schemas to sanitize incoming JSON payloads.