pydantic

Validate API data using Pydantic BaseModel with Field constraints and custom validators. Define models for request/response bodies, apply business rules via validators, and Pydantic automatically checks incoming payloads—raising ValidationError with detailed messages for invalid data.

Use Pydantic's BaseSettings (via pydantic-settings) to load and validate environment variables with type safety. Define a settings model with Field defaults and validators, and Pydantic reads .env files and OS variables, enforcing types and constraints automatically.

Pydantic v2 supports field validators and model validators using decorators. Write custom validation functions that check individual fields or entire models, return modified values or raise validation errors, enabling domain-specific rules alongside type checking.

Yes. Pydantic v2 provides model_dump() for Python dicts and model_dump_json() for JSON strings. Both support filtering, excluding fields, and custom serialization, making it simple to format response payloads for FastAPI or other frameworks.

Pydantic integrates email-validator for email field validation. Declare email fields using EmailStr type, and Pydantic automatically validates format and deliverability alongside other Field constraints, raising ValidationError if invalid.

Pydantic v2 offers improved performance, ConfigDict-based configuration, refined validator syntax, and better serialization. Use v2 for new projects and APIs requiring strict validation, cleaner config patterns, and faster runtime performance.