FastAPI + Pydantic: Best Practices for Data Model Definition and Serialization

Combining FastAPI with Pydantic is an efficient combination for data processing in modern web development, where Pydantic focuses on data validation and serialization, and FastAPI provides high performance, automatic documentation, and asynchronous support. Base models are defined by inheriting from `BaseModel`, with field types specified via Python annotations. Fields without default values are required, while optional types are indicated using `| None` or `Optional`. Pydantic automatically validates types and formats, throwing detailed error messages for input mistakes. It also supports `Field` for custom constraints (e.g., length, range, regex). Models can be bidirectionally converted with dictionaries/JSON. In FastAPI, they can be directly used as request/response bodies, automatically validating request data and returning structured responses. Best practices include: using consistent naming styles for field aliases, handling complex structures with nested models, reusing code via model inheritance, and `extra="ignore"` to disregard unknown fields. Mastering these techniques enables robust data processing, reduces repetitive code, and enhances API reliability, making it suitable for quickly building efficient, type-safe web services.