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.
Read MoreBeginners Guide: How to Use Pydantic for Data Validation in FastAPI
This article introduces the core content of using Pydantic for data validation in FastAPI. Data validation is crucial in web development, and FastAPI leverages the built-in Pydantic library to achieve efficient validation. Pydantic enables automatic checking of input validity by defining data models based on type hints (inheriting from BaseModel), supporting both basic and complex types (e.g., list, dict), and distinguishing between required fields (without default values) and optional fields (with default values). In FastAPI, Pydantic models are mainly used to handle request data (such as POST request bodies). FastAPI automatically parses and validates the data, returning a 422 error with detailed information if validation fails. Response data can also be validated using a Pydantic model via the response_model parameter to ensure the correct return format. Additionally, Pydantic supports custom validation, such as setting field constraints (e.g., length, range) through Field or using custom functions (e.g., email format verification). The advantages of Pydantic lie in automatic validation, user-friendly error messages, type safety, and flexible extension. It prevents program crashes or security vulnerabilities caused by invalid data, making it a core tool for building secure and robust APIs with FastAPI.
Read MoreFlask Form Handling: Complete Process from User Input to Data Display
This article introduces the complete process of implementing form handling using Flask and Flask-WTF, suitable for web development scenarios requiring user information collection. First, install the Flask and Flask-WTF extensions, then create form classes by inheriting the `FlaskForm` class, defining fields (e.g., username, password) and validation rules (required, length, email format, etc.). In Flask applications, view functions must handle GET (rendering the form) and POST (validating submitted data) requests. Use `form.validate_on_submit()` to check the request type and validate data. If validation fails, error messages are stored in `form.<field>.errors`, and templates display errors through loops. Templates must include `form.hidden_tag()` to enable CSRF protection and avoid form submission failures. Key details include: setting `SECRET_KEY` to ensure CSRF security, using redirects to prevent duplicate submissions, and encrypting stored data (e.g., passwords with bcrypt). The complete workflow is: user fills out form → frontend validation → backend validation → data processing → result display. Advanced features can extend to custom validators, multi-form handling, or file uploads. This article helps quickly master core skills of Flask form implementation from definition to data processing.
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