pydantic-patterns

Validate nested data structures in Pydantic v2 by defining BaseModel schemas with field_validator and model_validator. This enforces data quality, cross-field constraints, and consistent contracts for API request payloads.

Pydantic v2 ORM mode integrates with SQLAlchemy by mapping ORM instances to BaseModel schemas via model_config. This enables seamless serialization and validation of database records without manual boilerplate.

Handle discriminated unions in Pydantic v2 by defining a common discriminator field across multiple BaseModel variants. This routes incoming API payloads to the correct model for flexible and robust data validation.

Add computed fields in Pydantic v2 using the computed_field decorator on BaseModel properties, and customize serialization via model_dump arguments. This dynamically derives response values and controls output structure.

Use Pydantic v2 for configuration settings management by defining BaseSettings models. This validates environment variables and application configuration, ensuring type safety and robust settings enforcement across services.

Use Pydantic v2 for data transformation pipelines to enforce strict validation and serialization contracts between services. model_validate parses raw inputs accurately, reducing errors and ensuring consistent data quality across transformations.