Detailed Explanation of FastAPI Request Bodies: Defining Complex Data Structures with Pydantic

This article introduces Pydantic, a core tool in FastAPI for handling complex request bodies. Request bodies are used to pass complex data (e.g., JSON) in POST/PUT requests and are more suitable for structured data compared to query parameters. As the recommended data validation and parsing library by FastAPI, Pydantic allows defining data structures and automatically validates types and formats, reducing the need for manual parsing code. For basic models, such as a `User` class with `name` and `age` fields, FastAPI automatically parses the request body into an object. Nested models are implemented through sub-models (e.g., a user with an address). List types are supported using `List` and nested lists (e.g., an order containing multiple products). Pydantic automatically intercepts invalid data and returns a 422 error when there is a type mismatch. In summary, mastering Pydantic standardizes API development. It enables handling complex structures through nested models and list support, and combining with automatic validation enhances robustness. It is a key skill for FastAPI to process request bodies.