pydantic-models

Use Pydantic BaseModel classes with Field constraints and validators to validate API payloads and configuration data. This approach parses external JSON or environment variables, ensuring data structures are type-safe and reducing runtime errors from invalid inputs.

The best way to manage environment variables and settings in Python is using Pydantic Settings management classes. This allows you to load and validate environment variables directly into type-safe data models, ensuring your application configuration is strictly validated at startup.

You can add custom validation rules to a Python data model by applying Pydantic field and model validators. These validators execute during parsing to enforce specific constraints and complex business logic, ensuring invalid data is rejected before processing.

Yes, Pydantic v2 supports discriminated unions and computed fields. These advanced patterns allow you to build scalable APIs by dynamically calculating derived fields and routing data to the correct typed structure during validation and API serialization.

You can use Pydantic models for parsing YAML files and external data by loading the raw data and passing it through BaseModel validation. This process automatically coerces and validates the parsed structures into your defined domain models.

You configure API serialization with Pydantic v2 by using ConfigDict and model_config. These settings manage how your data models serialize to JSON, controlling field exclusion, alias generation, and strict data output formatting for API responses.