These features make FastAPI an ideal choice for building robust, high-traffic web applications, microservices, and even for serving machine learning models.
from fastapi import FastAPI, HTTPException app = FastAPI() # Database simulation user_db = 1: "name": "Alice", "role": "Admin", 2: "name": "Bob", "role": "User", @app.get("/users/user_id") def get_user(user_id: int, details: bool = False): """ - **user_id**: Must be an integer (Path Parameter) - **details**: Optional boolean flag (Query Parameter) """ if user_id not in user_db: raise HTTPException(status_code=404, detail="User not found") user = user_db[user_id] if details: return **user, "status": "active", "accessed_via": "tutorial" return user Use code with caution. How Validation Works fastapi tutorial pdf
Use :
FastAPI works seamlessly with any Database Management System (DBMS). Here is how to hook up using SQLAlchemy . Structure Setup Create a package structure for database management: Share public link These features make FastAPI an ideal choice for
app = FastAPI()
from pydantic import BaseModel, EmailStr class UserBase(BaseModel): email: EmailStr class UserCreate(UserBase): password: str class UserResponse(UserBase): id: int is_active: bool class Config: from_attributes = True Use code with caution. main.py Here is how to hook up using SQLAlchemy