fastapi-async-patterns

Structure an async FastAPI project by separating business logic into a service layer and data access into a repository layer, wiring endpoints via dependency injection to maximize testability and maintainability. This enforces a clean architecture for scalable APIs.

Handle async database pagination and filtering by implementing a repository pattern with async SQLAlchemy 2.0. This isolates data access logic, efficiently manages large datasets, and keeps database queries asynchronous for non-blocking API performance.

Integrate Gemini content generation into async FastAPI services by adding it to the service layer with optional configurations. The architecture supports graceful fallbacks to maintain operational resilience if the external Gemini API requests fail or timeout.

Yes, Pydantic models are required for data validation and serialization. Combining Pydantic models with async SQLAlchemy 2.0 and FastAPI dependency injection ensures clear layering, consistent error handling, and strict separation between routing, services, and repositories.

Use a service layer architecture to encapsulate business logic separately from routing and data access. This makes async FastAPI endpoints easier to test, reuse, and scale while enforcing asynchronous programming best practices and consistent error handling across the API.