python-api-development

FastAPI with Pydantic enables you to define request schemas as models, automatically validate incoming data against type hints, and return structured error responses. Define a Pydantic model for your request body, declare it in your endpoint signature, and FastAPI handles validation and documentation automatically.

Dependency injection decouples components by passing dependencies explicitly rather than creating them inside functions. FastAPI's DI system makes endpoints testable and modular—you declare what you need as function parameters, and the framework resolves and injects them, simplifying mocking and unit tests.

Structured error handling in FastAPI returns consistent JSON responses with proper HTTP status codes. Use HTTPException to raise errors with status codes and detail messages, or define exception handlers to transform errors into uniform response formats across all endpoints.

Yes. Pydantic models serve dual purposes: they validate incoming requests and automatically generate OpenAPI documentation. Type hints and field definitions create searchable, interactive API docs that clients can use to understand request and response schemas without separate documentation.

FastAPI middleware and dependency injection work together for authentication. Create a dependency that extracts and validates tokens from request headers, then include it in endpoint signatures or apply middleware globally to enforce authentication across routes with structured error responses.

FastAPI encourages explicit status code declaration. Use the `status_code` parameter in route decorators and raise HTTPException with appropriate codes—404 for not found, 201 for created, 400 for validation errors—ensuring clients receive correct semantics and proper error handling responses.