Pydantic 2：使用模型校验器处理含逗号的浮点数字符串

from pydantic import BaseModel

class User(BaseModel):
    name: str
    balance: float
    weight: float

from pydantic import BaseModel, model_validator
from typing import Dict, Any

class User(BaseModel):
    name: str
    balance: float
    weight: float
    # ... 其他可能的浮点数字段

    @model_validator(mode='before')
    @classmethod
    def fix_float_strings_with_commas(cls, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        在模型验证前，将所有浮点数字段中包含逗号的字符串值替换为点号。
        """
        # 遍历输入数据的所有键值对
        for k, v in data.items():
            # 检查字段k是否存在于模型的注解中，并且其类型是float
            # 同时确保当前值v是字符串类型
            if k in cls.__annotations__ and issubclass(float, cls.__annotations__[k]) and isinstance(v, str):
                # 执行替换操作：将逗号替换为点号
                data[k] = v.replace(',', '.')
        return data

# 示例使用
if __name__ == "__main__":
    # 包含逗号的原始数据
    bad_data = {
        "name": "Alice",
        "balance": "13,7",
        "weight": "65,2",
        "other_str_field": "some,text" # 非浮点数字段不受影响
    }

    try:
        user_instance = User(**bad_data)
        print("成功解析用户数据:")
        print(f"姓名: {user_instance.name}")
        print(f"余额: {user_instance.balance} (类型: {type(user_instance.balance)})")
        print(f"体重: {user_instance.weight} (类型: {type(user_instance.weight)})")
        # 验证其他字段是否被意外修改
        # print(f"其他字符串字段: {user_instance.other_str_field}") # Pydantic 会忽略未定义的字段
    except Exception as e:
        print(f"解析失败: {e}")

    # 包含标准点号的数据
    good_data = {
        "name": "Bob",
        "balance": "100.5",
        "weight": "70.0"
    }
    try:
        user_instance_good = User(**good_data)
        print("\n成功解析标准数据:")
        print(f"姓名: {user_instance_good.name}")
        print(f"余额: {user_instance_good.balance} (类型: {type(user_instance_good.balance)})")
        print(f"体重: {user_instance_good.weight} (类型: {type(user_instance_good.weight)})")
    except Exception as e:
        print(f"解析失败: {e}")