FastAPI Patterns Skill

The best way to structure a FastAPI backend for microservices is using a modular project layout that separates API routes, core configurations, data models, repositories, and services to ensure maintainability and scalable web API development.

To implement JWT authentication in a FastAPI application, use a production-ready boilerplate that integrates python-jose and passlib. This provides secure API access through pre-built authentication and authorization patterns without building from scratch.

Yes, FastAPI works seamlessly with asynchronous SQLAlchemy and Alembic for database migrations. A production-ready pattern integrates asyncpg for asynchronous database support, allowing efficient non-blocking database operations in your backend services.

Yes, you can use Pydantic-Settings for centralized configuration management in FastAPI. It provides a robust way to handle environment variables and application settings, ensuring scalable and maintainable configuration across your microservices.

To integrate OpenAI and Anthropic AI services into a FastAPI backend, use a boilerplate that includes pre-built placeholders for AI service orchestration. This facilitates rapid development of chat functionalities and future AI features within your web APIs.

You need Redis and httpx to build scalable web APIs with FastAPI when requiring advanced caching, task queuing, or asynchronous HTTP client capabilities. These dependencies complement the core FastAPI setup to handle efficient service orchestration.