Pydantic: The Modern Standard for Runtime Data Validation in Python
Patch: use Pydantic BaseModel for runtime validation and coercion.
Define your request schemas with Pydantic BaseModel to enforce validation.
Summary
Python's type hints are static and ignored at runtime, so developers historically wrote boilerplate validation code. Pydantic, powering FastAPI, bridges this gap by using runtime introspection of __annotations__, a metaclass to intercept construction, and a Rust‑powered core (pydantic‑core) for high‑performance parsing. It automatically coerces types (e.g., "42" → 42), enforces strict validation, and aggregates all errors into a structured ValidationError.
Custom field validators via @field_validator allow business logic such as password strength checks. Pydantic V2's Rust core delivers up to 50× faster validation than the pure‑Python predecessor, making it suitable for high‑throughput APIs.
Key changes
- Runtime introspection of __annotations__ drives schema definition
- Metaclass intercepts construction to apply validation and coercion
- Rust‑powered pydantic‑core achieves 50× faster parsing
- Automatic type coercion ("42" → 42) and strict validation raise ValidationError
- Custom @field_validator decorators enable business‑logic checks
- ValidationError aggregates all field errors for detailed reporting