Python Pydantic中将JSON编码器声明在类本身上

在我看来，这实际上是一个比平庸模型更深层次的问题.我找到了this ongoing discussion个关于是否应该在Python中引入带有__json__或__serialize__方法的标准协议的问题.

问题在于，在定制类型的编码/序列化逻辑与类本身分离的情况下，PYDANIC受到标准库json模块的相同限制.

无论引入这样一个协议的更广泛的 idea 是否有意义，我们都可以稍微借鉴一下它来定义一个定制的json.dumps版本，该版本判断是否存在__serialize__方法，并将其用作default函数来序列化对象.(有关default参数的说明，请参阅json.dump文档.)

然后，我们可以设置一个自定义基本型号，并将Config.json_dumps选项设置为该功能.这样，所有子模型将自动回退到用于序列化的子模型(例如，除非被BaseModel.json方法的encoder参数覆盖).

下面是一个例子:

base.py个

from collections.abc import Callable
from json import dumps as json_dumps
from typing import Any

from pydantic import BaseModel as PydanticBaseModel


def json_dumps_extended(obj: object, **kwargs: Any) -> str:
    default: Callable[[object], object] = kwargs.pop("default", lambda x: x)

    def custom_default(to_encode: object) -> object:
        serialize_method = getattr(to_encode, "__serialize__", None)
        if serialize_method is None:
            return default(to_encode)
        return serialize_method()  # <-- already bound to `to_encode`

    return json_dumps(obj, default=custom_default, **kwargs)


class BaseModel(PydanticBaseModel):
    class Config:
        json_dumps = json_dumps_extended

application.py个

from __future__ import annotations
from collections.abc import Callable, Iterator

from .base import BaseModel


class Thing:
    def __init__(self, x: str) -> None:
        self.x = x

    def __str__(self) -> str:
        return self.x

    def __serialize__(self) -> str:  # <-- this is the magic method
        return self.x

    @classmethod
    def __get_validators__(cls) -> Iterator[Callable[..., Thing]]:
        yield cls.validate

    @classmethod
    def validate(cls, v: str) -> Thing:
        return cls(v)


class Model(BaseModel):
    thing: Thing
    num: float = 3.14


instance = Model(thing=Thing("foo"))
print(instance.json(indent=4))

输出:

{
    "thing": "foo",
    "num": 3.14
}

给Python<3.9用户的注意事项:从typing而不是collections.abc导入Callable和Iterator类型.

PS

如果您希望能够在除基本模型之外的更多地方重新使用这种方法进行序列化，那么在类型上多花点功夫可能是个好主意.我们的__serialize__方法的runtime_checkable个定制协议可能会很有用.

此外，我们还可以通过使用functools.partial使json_dumps_extended方法不那么笨重.

Here is a slightly more sophisticated version of the suggested base.py个:

from collections.abc import Callable
from functools import partial
from json import dumps as json_dumps
from typing import Any, Optional, Protocol, TypeVar, overload, runtime_checkable

from pydantic import BaseModel as PydanticBaseModel

T = TypeVar("T")
T_co = TypeVar("T_co", covariant=True)
Func1Arg = Callable[[object], T]


@runtime_checkable
class Serializable(Protocol[T_co]):
    def __serialize__(self) -> T_co: ...


@overload
def serialize(obj: Serializable[T_co]) -> T_co: ...


@overload
def serialize(obj: Any, fallback: Func1Arg[T]) -> T: ...


def serialize(obj: Any, fallback: Optional[Func1Arg[Any]] = None) -> Any:
    if isinstance(obj, Serializable):
        return obj.__serialize__()
    if fallback is None:
        raise TypeError(f"Object not serializable: {obj}")
    return fallback(obj)


def _id(x: T) -> T: return x


def json_dumps_extended(obj: object, **kwargs: Any) -> str:
    custom_default = partial(serialize, fallback=kwargs.pop("default", _id))
    return json_dumps(obj, default=custom_default, **kwargs)


class BaseModel(PydanticBaseModel):
    class Config:
        json_dumps = json_dumps_extended

另一种 Select 可能是直接修补JSONEncoder.default个猴子.但是，在没有进一步配置的情况下，Pydtic似乎仍然自己执行类型判断，甚至在调用该方法之前就阻止了序列化.

我不认为我们有更好的 Select ，除非引入一些标准的序列化协议(至少对于JSON).