pydantic

Use BaseModel definitions with model_config and ConfigDict to control validation and serialization rules. This pattern enforces strict type validation for Python data models and API schemas while reducing runtime errors.

The best way to add field validators in Pydantic v2 is using the built-in field validator decorators. This ensures complex data integrity by validating inputs before serialization without writing boilerplate validation logic.

Configure serialization and validation behavior in Pydantic v2 by defining a ConfigDict within your model_config. This allows you to customize ORM integration, control serialization options, and apply performance-focused patterns like TypeAdapter.

Yes, you can use computed fields in Python data models to dynamically calculate values during serialization. Pydantic v2 supports computed fields alongside model-level validators to handle complex data transformation and integrity checks cleanly.

Pydantic v2 works seamlessly with ORM integrations to map database models directly to API schemas. You can use ConfigDict to control this mapping and ensure strict type validation across your data boundary.