How to implement requested data validation and cleaning in FastAPI

Title: How to implement requested data validation and cleaning in FastAPI

FastAPI is a high-performance, easy-to-use web framework that provides powerful data validation and cleaning functions to help us write robust API. This article will introduce how to implement requested data verification and cleaning in FastAPI, and attach corresponding code examples.

1. Install and create FastAPI application
First, we need to install FastAPI and its dependencies. You can use pip to install:

$ pip install fastapi

Next, create a new Python file app.py and import the necessary modules:

from fastapi import FastAPI
from pydantic import BaseModel

Then, create An example of a FastAPI application:

app = FastAPI()

2. Create a model class for data verification
In FastAPI, we can use the pydantic library to create a model class for request data verification and cleaning. Model classes are created by inheriting BaseModel. We can define the fields to be validated and their types in the model class.

The following is an example that demonstrates how to create a model class for validating user requests:

class UserRequest(BaseModel):
    username: str
    age: int
    email: str

In the above example, we define a UserRequest model class with three fields: username , age and email, and specify their types as string, integer and string.

3. Use model classes for data verification and cleaning
In order to use model classes for data verification and cleaning in FastAPI, we only need to use the model class as an annotation of the parameter and use the model class in the function Examples are enough.

The following is an example that demonstrates how to use model classes for data validation and cleaning in FastAPI:

@app.post("/user")
def create_user(user: UserRequest):
    """
    创建用户
    """
    # 进行业务逻辑处理
    # ...
    return {"message": "用户创建成功"}

In the above example, we defined a create_user function, using the UserRequest model class Perform data validation and cleaning. When we send a POST request to the /user path, FastAPI will automatically verify whether the request data conforms to the definition of the UserRequest model class.

If the request data does not meet the definition of the model class, FastAPI will return a 400 Bad Request response. If the request data is verified successfully, FastAPI will automatically convert the request data into an instance of the UserRequest model class for us to use in the function.

4. Custom verification function and error handling
Sometimes, we need to perform some complex business logic verification. At this time, we can use the verification decorator in pydantic to write a custom verification function.

Here is an example that demonstrates how to use custom validation functions and error handling in FastAPI:

from pydantic import validator

class UserRequest(BaseModel):
    username: str
    age: int
    email: str
    
    @validator('age')
    def validate_age(cls, age):
        if age < 0 or age > 120:
            raise ValueError('年龄应在0到120之间')
        return age

In the above example, we define a validate_age function and use the validator decorator Apply it to the age field. In the function, we have some custom validation logic that will throw a value error if the age is not between 0 and 120.

After using a custom verification function, FastAPI will automatically apply it and return a 400 Bad Request response when the verification fails.

Summary
In this article, we learned how to use model classes in FastAPI to verify and clean request data. We created a model class and implemented data validation and cleaning in functions that use this class. We also learned how to write custom validation functions and error handling to meet complex business needs.

FastAPI provides powerful data verification and cleaning functions, which can greatly simplify our work of writing APIs and improve the reliability and security of APIs. I hope this article will help you understand and apply the data validation and cleaning functions of FastAPI.

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