引言

在构建现代Web应用程序时，数据校验是一个至关重要的环节。它确保了传入的数据符合预期的格式和类型，从而提高了应用程序的稳定性和安全性。FastAPI是一个流行的Python Web框架，它提供了强大的数据校验功能。本文将深入探讨FastAPI结合Pydantic库进行数据校验的技巧，并通过实际操作指南帮助读者轻松掌握。

FastAPI简介

FastAPI是一个现代、快速（高性能）的Web框架，用于构建APIs，由Python 3.6+编写。它具有以下特点：

• 基于标准Python类型注解
• 自动生成交互式API文档
• 高性能

Pydantic简介

Pydantic是一个数据校验和设置管理的库，它允许开发者定义数据结构，并自动进行数据验证。Pydantic与FastAPI无缝集成，可以极大地简化数据校验过程。

快速开始

安装FastAPI和Pydantic

首先，确保你的环境中安装了FastAPI和Pydantic：

pip install fastapi pydantic 

创建FastAPI应用

创建一个基本的FastAPI应用：

from fastapi import FastAPI app = FastAPI() @app.get("/") async def root(): return {"message": "Hello World"} 

定义数据模型

使用Pydantic定义数据模型，并进行校验：

from pydantic import BaseModel class Item(BaseModel): name: str description: str = None price: float tax: float = None 

使用数据模型

在路由处理函数中使用Pydantic模型：

@app.get("/items/{item_id}") async def read_item(item_id: int, item: Item): return item 

在这个例子中，read_item 函数接收一个整数 item_id 和一个 Item 对象。如果传入的数据不符合 Item 模型的定义，FastAPI会自动返回一个错误响应。

高级数据校验技巧

必选和可选字段

在Pydantic模型中，可以通过设置字段为 None 来指定该字段为可选：

class Item(BaseModel): name: str description: str = None price: float tax: float = None 

验证器

Pydantic提供了丰富的验证器，例如 min_length, max_length, regex 等：

”`python class Item(BaseModel):

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