How Data Validation Works
In FastAPI, you define the structure of your data by creating Python classes that inherit from Pydantic’s BaseModel. By adding type hints to these classes, you create a blueprint for incoming requests. FastAPI parses the incoming JSON, checks it against your model, and converts the data into the appropriate Python types automatically.
Automatic Error Handling
One of the primary benefits of this system is automatic error reporting. If a client sends data that does not match your defined schema—such as a string where an integer is expected—FastAPI intercepts the request and returns a detailed 422 Unprocessable Entity response. This provides immediate feedback to the client without requiring you to write manual validation logic or try-except blocks.
Scopes of Validation
Validation is not limited to just the request body. Pydantic models and type hints can be applied across your entire API surface, ensuring consistency for:
- Request Bodies: Validating JSON payloads.
- Query Parameters: Ensuring URL parameters meet specific types or constraints.
- Path Variables: Validating dynamic segments of your URL.
- Headers and Cookies: Enforcing structure on metadata sent by the client.