pydantic-models

Use Pydantic BaseModel to define data models with type hints and Field constraints like min/max length or regex patterns. Pydantic automatically validates incoming data against these rules, rejecting invalid input before it reaches your application logic.

Pydantic provides field_validator for single-field logic and model_validator for cross-field checks. Model validators let you implement complex business rules—like password confirmation matching—that depend on multiple fields simultaneously.

Yes. Pydantic models generate JSON schemas automatically via json_schema, with support for examples and detailed field documentation. This schema serves as a contract for API clients and enables automatic documentation generation.

Compose Pydantic models by nesting BaseModel classes and using generic types for reusable response templates. This approach scales to complex hierarchical data while maintaining type safety and validation throughout the structure.

Use model_config to enforce strict validation, field_serializer for custom JSON output formatting, and computed_field to derive data from other fields. These tools ensure your models produce exactly the serialized format your API requires.

No. Pydantic handles serialization natively through field_serializer and model configuration. You can export models directly to JSON or Python dicts without external dependencies beyond Pydantic itself.