Building FastAPI project architecture with FastAPI and Py...
This guide provides a structured approach to building scalable FastAPI applications with async database integration, LLM streaming, and production deployment. Focuses on architecture decisions and common pitfalls for real-world implementation.
Project Structure for Scalability
Create a modular structure with separate modules for routers, models, services, and dependencies. Use FastAPI's dependency injection system to decouple components.
app/main.py
from fastapi import FastAPI
from app.routers import items
app = FastAPI()
app.include_router(items.router)
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)Async Database Configuration
Set up SQLAlchemy with async drivers (asyncpg) and connection pooling. Use async sessionmaker for database operations.
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker
engine = create_async_engine("postgresql+asyncpg://user:password@localhost/dbname")
AsyncSessionLocal = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)Deployment with Docker and Reverse Proxy
Create a Dockerfile with Uvicorn and Gunicorn for production. Configure Nginx or Traefik as reverse proxy with WebSocket support.
FROM python:3.10
WORKDIR /app
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]LLM Streaming Endpoint Implementation
Implement a streaming route using Server-Sent Events (SSE) with proper backpressure handling. Use Redis for request tracking if needed.
from fastapi import APIRouter
from fastapi.responses import StreamingResponse
import asyncio
router = APIRouter()
@router.get("/stream")
def stream_response():
async def generate():
for i in range(10):
yield f"Chunk {i}\n"
await asyncio.sleep(0.1)
return StreamingResponse(generate(), media_type="text/event-stream")JWT-Based Authentication Setup
Implement JWT authentication with Pydantic models for credentials. Create a dependency to verify tokens and extract user information.
from fastapi import Depends, HTTPException, status
from pydantic import BaseModel
import jwt
class TokenData(BaseModel):
username: str
def get_current_user(token: str = Depends(oauth2_scheme)):
try:
payload = jwt.decode(token, "secret_key", algorithms=["HS256"])
user = get_user(username=payload["sub"])
if not user:
raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED)
return user
except jwt.PyJWTError:
raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED)What you built
This implementation guide addresses core challenges in FastAPI development through concrete architecture decisions. Validate each component with pytest and monitor performance in production using metrics collection.
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