/fastapi-development

name: fastapi-development
description: >
  Build high-performance FastAPI applications with async routes, validation,
  dependency injection, security, and automatic API documentation. Use when
  developing modern Python APIs with async support, automatic OpenAPI
  documentation, and high performance requirements.

FastAPI Development

Table of Contents

• [Overview](#overview)
• [When to Use](#when-to-use)
• [Quick Start](#quick-start)
• [Reference Guides](#reference-guides)
• [Best Practices](#best-practices)

Overview

Create fast, modern Python APIs using FastAPI with async/await support, automatic API documentation, type validation using Pydantic, dependency injection, JWT authentication, and SQLAlchemy ORM integration.

When to Use

• Building high-performance Python REST APIs
• Creating async API endpoints
• Implementing automatic OpenAPI/Swagger documentation
• Leveraging Python type hints for validation
• Building microservices with async support
• Integrating Pydantic for data validation

Quick Start

Minimal working example:

# main.py
from fastapi import FastAPI, HTTPException, status
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager
import logging

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Create FastAPI instance
app = FastAPI(
    title="API Service",
    description="A modern FastAPI application",
    version="1.0.0",
    docs_url="/api/docs",
    openapi_url="/api/openapi.json"
)

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["http://localhost:3000"],
    allow_credentials=True,
    allow_methods=["*"],
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the `references/` directory:

| Guide | Contents | |---|---| | [FastAPI Application Setup](references/fastapi-application-setup.md) | FastAPI Application Setup | | [Pydantic Models for Validation](references/pydantic-models-for-validation.md) | Pydantic Models for Validation | | [Async Database Models and Queries](references/async-database-models-and-queries.md) | Async Database Models and Queries | | [Security and JWT Authentication](references/security-and-jwt-authentication.md) | Security and JWT Authentication | | [Service Layer for Business Logic](references/service-layer-for-business-logic.md) | Service Layer for Business Logic | | [API Routes with Async Endpoints](references/api-routes-with-async-endpoints.md) | API Routes with Async Endpoints |

Best Practices

✅ DO

• Use async/await for I/O operations
• Leverage Pydantic for validation
• Use dependency injection for services
• Implement proper error handling with HTTPException
• Use type hints for automatic OpenAPI documentation
• Create service layers for business logic
• Implement authentication on protected routes
• Use environment variables for configuration
• Return appropriate HTTP status codes
• Document endpoints with docstrings and tags

❌ DON'T

• Use synchronous database operations
• Trust user input without validation
• Store secrets in code
• Ignore type hints
• Return database models in responses
• Implement authentication in route handlers
• Use mutable default arguments
• Forget to validate query parameters
• Expose stack traces in production