pydantic/pydantic

Features

• Data Validation Libraries - Enforces schema constraints and performs type conversion on complex data structures using standard language type hints.
• Type-Hint-Based Schema Definitions - Uses standard Python type annotations to define data structures and validation rules without custom domain-specific languages.
• Schema-Driven Modeling Frameworks - Creates structured data models that automatically enforce business rules and constraints through type annotations.
• Type-Safe Configuration Managers - Parses and validates application settings from environment variables or files into structured, type-checked objects.
• Data Sanitization Utilities - Ensures incoming data from external sources matches expected formats and types before processing.
• Type-Safe API Schemas - Defines strict data schemas for request and response payloads to ensure consistent communication.
• Data Quality and Validation - Data validation using Python type annotations.
• Data Validation - Data validation using type hints.
• Developer Tools - Data validation using Python type hints.
• Data Serialization Frameworks - Transforms complex objects into serialized formats while ensuring strict adherence to defined data schemas.
• Configuration Management Libraries - Loads and validates application settings from environment variables or configuration files to ensure valid parameters.
• Metadata-Driven Frameworks - Extracts constraints and validation logic from field-level metadata to decouple schema definitions from the validation engine.
• Code Generation Utilities - Transforms high-level model definitions into optimized code during initialization to reduce runtime overhead.
• Recursive Validation Engines - Traverses nested data structures by dynamically dispatching validation logic to child models based on the type hierarchy.

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