pydantic如何使用pydantic设置十六进制字符串？

Question

有时，任何数据都会返回像“ BBB”这样的十六进制字符串。我想用“ bbb”来估算。 所以我写了代码案例1 ..但这是一个错误。

如何使用basemodel将十六进制字符串转换为int值？ Pydantic是否还具有Setter Getter函数？ 我知道像case2这样的方法，但是我想直接使用JSON数据，因为某些密钥是空的...！

案例1

from pydantic import BaseModel
from typing import Optional

data = {
    "aaa" : 4,
    "bbb" : "0x3",
}

class DataTemp(BaseModel):
    aaa : int
    bbb : Optional[int]
    
def main():
    _data = DataTemp(**data)
    print(_data)
    

if __name__ == "__main__":
    main()

案例2

_data2 = DataTemp(aaa= data["aaa"], bbb=int(data["bbb"],16))

Answer 1

_{根据他们的迁移指南的这一部分 此先前的修订版。。}

---

pydantic确实允许通过现场验证器。创建一个时，您需要注意处理传入的数据并根据您的要求处理。在您的示例中，它应允许无，标准int s以及代表十六进制字符串的任何str。因此，以下实施可以满足上述约束：

from typing import Optional
from pydantic import BaseModel, ValidationInfo, field_validator

class DataTemp(BaseModel):
    aaa: int
    # to allow the assignment of str along with int, even though the
    # value will always be casted back to an int via the field validator,
    # and set the default value to None to permit omitted key in V2
    bbb: Optional[int | str] = None

    @field_validator('bbb')
    @classmethod
    def validate_bbb(cls, v: Optional[int | str], info: ValidationInfo):
        return int(v, 16) if isinstance(v, str) else v

用法：

>>> DataTemp(aaa=4, bbb=33)
DataTemp(aaa=4, bbb=33)
>>> DataTemp(aaa=4, bbb=None)
DataTemp(aaa=4, bbb=None)
>>> DataTemp(aaa=4)  # bbb omitted, defaults to None as specified
DataTemp(aaa=4, bbb=None)
>>> DataTemp(aaa=4, bbb='0x333')
DataTemp(aaa=4, bbb=819)
>>> DataTemp(aaa=4, bbb='32')
DataTemp(aaa=4, bbb=50)
>>> DataTemp(aaa=4, bbb='asdf')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "pydantic/main.py", line 164, in __init__
    __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)
pydantic_core._pydantic_core.ValidationError: 1 validation error for DataTemp
bbb
  Value error, invalid literal for int() with base 16: 'asdf' [type=value_error, input_value='asdf', input_type=str]
    For further information visit https://errors.pydantic.dev/2.5/v/value_error