类型

在可能的情况下，Pydantic 使用标准库类型来定义字段，从而平滑学习曲线。然而，对于许多有用的应用程序，不存在标准库类型，因此 Pydantic 实现了许多常用类型。

也可以在 Pydantic Extra Types 包中找到更复杂的类型。

如果没有现有的类型适合您的目的，您也可以使用自定义属性和验证实现自己的与 Pydantic 兼容的类型。

以下各节描述了 Pydantic 支持的类型。

• 标准库类型——来自 Python 标准库的类型。

• 严格类型——能够防止从兼容类型进行强制转换的类型。

• 自定义数据类型——创建自己的自定义数据类型。

• 字段类型转换——不同字段类型之间的严格和宽松转换。

Type conversion

在验证期间，Pydantic 可以将数据强制转换为预期的类型。

有两种强制模式：严格和宽松。有关 Pydantic 在严格和宽松模式下如何转换数据的更多详细信息，请参见转换表。

请参阅严格模式和严格类型，以获取有关启用严格强制的详细信息。

Strict Types

Pydantic 提供了以下严格的类型：

• StrictBool

• StrictBytes

• StrictFloat

• StrictInt

• StrictStr

这些类型仅当经过验证的值为相应类型或该类型的子类型时才会通过验证。

Constrained types

这种行为也通过受约束类型的 strict 字段暴露出来，并且可以与多种复杂的验证规则结合使用。请参阅各个类型签名以获取支持的参数。

• conbytes()
• condate()
• condecimal()
• confloat()
• confrozenset()
• conint()
• conlist()
• conset()
• constr()

以下注意事项适用：

• StrictBytes （以及 strict 的 conbytes() 选项）将同时接受 bytes 和 bytearray 类型。

• StrictInt （以及 strict 的 conint() 选项）将不接受 bool 类型，即使 bool 是 Python 中的 int 的子类也是如此。其他子类将起作用。

• StrictFloat （以及 strict 的 confloat() 选项）将不接受 int 。

除此之外，你还可以有一个 FiniteFloat 类型，它只接受有限的值（即不是 inf 、 -inf 或 nan ）。

Custom Types

你还可以定义自己的自定义数据类型。有几种方法可以实现它。

Composing types via Annotated

PEP 593 引入了 Annotated 作为一种向类型添加运行时元数据的方法，而不会改变类型检查器对它们的解释方式。Pydantic 利用这一点允许你创建与原始类型在类型检查器方面完全相同的类型，但添加验证、以不同的方式序列化等。

例如，要创建一个表示正整数的类型：

# or `from typing import Annotated` for Python 3.9+
from typing_extensions import Annotated

from pydantic import Field, TypeAdapter, ValidationError

PositiveInt = Annotated[int, Field(gt=0)]

ta = TypeAdapter(PositiveInt)

print(ta.validate_python(1))
#> 1

try:
    ta.validate_python(-1)
except ValidationError as exc:
    print(exc)
    """
    1 validation error for constrained-int
      Input should be greater than 0 [type=greater_than, input_value=-1, input_type=int]
    """

请注意，您还可以使用已注释类型的约束来使此 Pydantic 不可知：

from annotated_types import Gt
from typing_extensions import Annotated

from pydantic import TypeAdapter, ValidationError

PositiveInt = Annotated[int, Gt(0)]

ta = TypeAdapter(PositiveInt)

print(ta.validate_python(1))
#> 1

try:
    ta.validate_python(-1)
except ValidationError as exc:
    print(exc)
    """
    1 validation error for constrained-int
      Input should be greater than 0 [type=greater_than, input_value=-1, input_type=int]
    """

添加验证和序列化

你可以使用 Pydantic 导出的标记向任意类型添加或覆盖验证、序列化和 JSON 模式：

from typing_extensions import Annotated

from pydantic import (
    AfterValidator,
    PlainSerializer,
    TypeAdapter,
    WithJsonSchema,
)

TruncatedFloat = Annotated[
    float,
    AfterValidator(lambda x: round(x, 1)),
    PlainSerializer(lambda x: f'{x:.1e}', return_type=str),
    WithJsonSchema({'type': 'string'}, mode='serialization'),
]


ta = TypeAdapter(TruncatedFloat)

input = 1.02345
assert input != 1.0

assert ta.validate_python(input) == 1.0

assert ta.dump_json(input) == b'"1.0e+00"'

assert ta.json_schema(mode='validation') == {'type': 'number'}
assert ta.json_schema(mode='serialization') == {'type': 'string'}

Generics

你可以在 Annotated 中使用类型变量来对类型进行可重用的修改：

from typing import Any, List, Sequence, TypeVar

from annotated_types import Gt, Len
from typing_extensions import Annotated

from pydantic import ValidationError
from pydantic.type_adapter import TypeAdapter

SequenceType = TypeVar('SequenceType', bound=Sequence[Any])


ShortSequence = Annotated[SequenceType, Len(max_length=10)]


ta = TypeAdapter(ShortSequence[List[int]])

v = ta.validate_python([1, 2, 3, 4, 5])
assert v == [1, 2, 3, 4, 5]

try:
    ta.validate_python([1] * 100)
except ValidationError as exc:
    print(exc)
    """
    1 validation error for list[int]
      List should have at most 10 items after validation, not 100 [type=too_long, input_value=[1, 1, 1, 1, 1, 1, 1, 1, ... 1, 1, 1, 1, 1, 1, 1, 1], input_type=list]
    """


T = TypeVar('T')  # or a bound=SupportGt

PositiveList = List[Annotated[T, Gt(0)]]

ta = TypeAdapter(PositiveList[float])

v = ta.validate_python([1])
assert type(v[0]) is float


try:
    ta.validate_python([-1])
except ValidationError as exc:
    print(exc)
    """
    1 validation error for list[constrained-float]
    0
      Input should be greater than 0 [type=greater_than, input_value=-1, input_type=int]
    """

命名类型别名

上述示例使用了隐式类型别名。这意味着它们将无法在 JSON 模式中拥有 title ，并且它们的模式将在字段之间复制。你可以使用 PEP 695 的 TypeAliasType 通过其 typing-extensions 后端来创建命名别名，从而允许你在不创建子类的情况下定义新类型。这个新类型可以像一个名称一样简单，也可以附加复杂的验证逻辑：

from typing import List

from annotated_types import Gt
from typing_extensions import Annotated, TypeAliasType

from pydantic import BaseModel

ImplicitAliasPositiveIntList = List[Annotated[int, Gt(0)]]


class Model1(BaseModel):
    x: ImplicitAliasPositiveIntList
    y: ImplicitAliasPositiveIntList


print(Model1.model_json_schema())
"""
{
    'properties': {
        'x': {
            'items': {'exclusiveMinimum': 0, 'type': 'integer'},
            'title': 'X',
            'type': 'array',
        },
        'y': {
            'items': {'exclusiveMinimum': 0, 'type': 'integer'},
            'title': 'Y',
            'type': 'array',
        },
    },
    'required': ['x', 'y'],
    'title': 'Model1',
    'type': 'object',
}
"""

PositiveIntList = TypeAliasType('PositiveIntList', List[Annotated[int, Gt(0)]])


class Model2(BaseModel):
    x: PositiveIntList
    y: PositiveIntList


print(Model2.model_json_schema())
"""
{
    '$defs': {
        'PositiveIntList': {
            'items': {'exclusiveMinimum': 0, 'type': 'integer'},
            'type': 'array',
        }
    },
    'properties': {
        'x': {'$ref': '#/$defs/PositiveIntList'},
        'y': {'$ref': '#/$defs/PositiveIntList'},
    },
    'required': ['x', 'y'],
    'title': 'Model2',
    'type': 'object',
}
"""

这些命名的类型别名也可以是泛型的：

from typing import Generic, List, TypeVar

from annotated_types import Gt
from typing_extensions import Annotated, TypeAliasType

from pydantic import BaseModel, ValidationError

T = TypeVar('T')  # or a `bound=SupportGt`

PositiveList = TypeAliasType(
    'PositiveList', List[Annotated[T, Gt(0)]], type_params=(T,)
)


class Model(BaseModel, Generic[T]):
    x: PositiveList[T]


assert Model[int].model_validate_json('{"x": ["1"]}').x == [1]

try:
    Model[int](x=[-1])
except ValidationError as exc:
    print(exc)
    """
    1 validation error for Model[int]
    x.0
      Input should be greater than 0 [type=greater_than, input_value=-1, input_type=int]
    """

命名递归类型

也可以使用 TypeAliasType 创建递归类型：

from typing import Any, Dict, List, Union

from pydantic_core import PydanticCustomError
from typing_extensions import Annotated, TypeAliasType

from pydantic import (
    TypeAdapter,
    ValidationError,
    ValidationInfo,
    ValidatorFunctionWrapHandler,
    WrapValidator,
)


def json_custom_error_validator(
    value: Any, handler: ValidatorFunctionWrapHandler, _info: ValidationInfo
) -> Any:
    """Simplify the error message to avoid a gross error stemming
    from exhaustive checking of all union options.
    """
    try:
        return handler(value)
    except ValidationError:
        raise PydanticCustomError(
            'invalid_json',
            'Input is not valid json',
        )


Json = TypeAliasType(
    'Json',
    Annotated[
        Union[Dict[str, 'Json'], List['Json'], str, int, float, bool, None],
        WrapValidator(json_custom_error_validator),
    ],
)


ta = TypeAdapter(Json)

v = ta.validate_python({'x': [1], 'y': {'z': True}})
assert v == {'x': [1], 'y': {'z': True}}

try:
    ta.validate_python({'x': object()})
except ValidationError as exc:
    print(exc)
    """
    1 validation error for function-wrap[json_custom_error_validator()]
      Input is not valid json [type=invalid_json, input_value={'x': <object object at 0x0123456789ab>}, input_type=dict]
    """

使用 __get_pydantic_core_schema__ 进行自定义验证

要对 Pydantic 处理自定义类的方式进行更广泛的定制，特别是当您可以访问该类或可以对其进行子类化时，可以实现一个特殊的 __get_pydantic_core_schema__ 来告诉 Pydantic 如何生成 pydantic-core 模式。

虽然 pydantic 在内部使用 pydantic-core 来处理验证和序列化，但它是 Pydantic V2 的新 API，因此它是未来最有可能被调整的领域之一，你应该尽量坚持使用内置的构造，如 annotated-types 、 pydantic.Field 、 BeforeValidator 等。

你可以在自定义类型和旨在放入 Annotated 的元数据上实现 __get_pydantic_core_schema__ 。在这两种情况下，API 都类似于中间件，类似于“wrap”验证器：你得到一个 source_type （不一定与类相同，特别是对于泛型）和一个 handler ，你可以用一个类型调用它，要么调用 Annotated 中的下一个元数据，要么调用 Pydantic 的内部模式生成。

最简单的空操作实现是使用给定的类型调用处理程序，然后将结果返回。您还可以选择在调用处理程序之前修改类型、修改处理程序返回的核心模式或根本不调用处理程序。

作为自定义类型上的方法

以下是一个使用 __get_pydantic_core_schema__ 自定义其验证方式的类型的示例。这相当于在 Pydantic V1 中实现 __get_validators__ 。

from typing import Any

from pydantic_core import CoreSchema, core_schema

from pydantic import GetCoreSchemaHandler, TypeAdapter


class Username(str):
    @classmethod
    def __get_pydantic_core_schema__(
        cls, source_type: Any, handler: GetCoreSchemaHandler
    ) -> CoreSchema:
        return core_schema.no_info_after_validator_function(cls, handler(str))


ta = TypeAdapter(Username)
res = ta.validate_python('abc')
assert isinstance(res, Username)
assert res == 'abc'

请参阅 JSON Schema 以获取有关如何为自定义类型自定义 JSON 模式的更多详细信息。

作为注释

通常，您需要通过不仅仅是泛型类型参数来对自定义类型进行参数化（您可以通过类型系统来实现，后面将讨论）。或者，您可能实际上并不关心（或不想）创建子类的实例；您实际上想要原始类型，只是进行了一些额外的验证。

例如，如果你要自己实现 pydantic.AfterValidator （请参阅添加验证和序列化），你可以做类似于以下的事情：

from dataclasses import dataclass
from typing import Any, Callable

from pydantic_core import CoreSchema, core_schema
from typing_extensions import Annotated

from pydantic import BaseModel, GetCoreSchemaHandler


@dataclass(frozen=True)  # (1)!
class MyAfterValidator:
    func: Callable[[Any], Any]

    def __get_pydantic_core_schema__(
        self, source_type: Any, handler: GetCoreSchemaHandler
    ) -> CoreSchema:
        return core_schema.no_info_after_validator_function(
            self.func, handler(source_type)
        )


Username = Annotated[str, MyAfterValidator(str.lower)]


class Model(BaseModel):
    name: Username


assert Model(name='ABC').name == 'abc'  # (2)!

1. frozen=True 规范使 MyAfterValidator 可哈希。如果没有这个，像 Username | None 这样的联合将引发错误。

2. 请注意，类型检查器不会像上一个示例那样对将 'ABC' 分配给 Username 提出抱怨，因为它们不认为 Username 与 str 是不同的类型。

处理第三方类型

上一节模式的另一个用例是处理第三方类型。

from typing import Any

from pydantic_core import core_schema
from typing_extensions import Annotated

from pydantic import (
    BaseModel,
    GetCoreSchemaHandler,
    GetJsonSchemaHandler,
    ValidationError,
)
from pydantic.json_schema import JsonSchemaValue


class ThirdPartyType:
    """
    This is meant to represent a type from a third-party library that wasn't designed with Pydantic
    integration in mind, and so doesn't have a `pydantic_core.CoreSchema` or anything.
    """

    x: int

    def __init__(self):
        self.x = 0


class _ThirdPartyTypePydanticAnnotation:
    @classmethod
    def __get_pydantic_core_schema__(
        cls,
        _source_type: Any,
        _handler: GetCoreSchemaHandler,
    ) -> core_schema.CoreSchema:
        """
        We return a pydantic_core.CoreSchema that behaves in the following ways:

        * ints will be parsed as `ThirdPartyType` instances with the int as the x attribute
        * `ThirdPartyType` instances will be parsed as `ThirdPartyType` instances without any changes
        * Nothing else will pass validation
        * Serialization will always return just an int
        """

        def validate_from_int(value: int) -> ThirdPartyType:
            result = ThirdPartyType()
            result.x = value
            return result

        from_int_schema = core_schema.chain_schema(
            [
                core_schema.int_schema(),
                core_schema.no_info_plain_validator_function(validate_from_int),
            ]
        )

        return core_schema.json_or_python_schema(
            json_schema=from_int_schema,
            python_schema=core_schema.union_schema(
                [
                    # check if it's an instance first before doing any further work
                    core_schema.is_instance_schema(ThirdPartyType),
                    from_int_schema,
                ]
            ),
            serialization=core_schema.plain_serializer_function_ser_schema(
                lambda instance: instance.x
            ),
        )

    @classmethod
    def __get_pydantic_json_schema__(
        cls, _core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
    ) -> JsonSchemaValue:
        # Use the same schema that would be used for `int`
        return handler(core_schema.int_schema())


# We now create an `Annotated` wrapper that we'll use as the annotation for fields on `BaseModel`s, etc.
PydanticThirdPartyType = Annotated[
    ThirdPartyType, _ThirdPartyTypePydanticAnnotation
]


# Create a model class that uses this annotation as a field
class Model(BaseModel):
    third_party_type: PydanticThirdPartyType


# Demonstrate that this field is handled correctly, that ints are parsed into `ThirdPartyType`, and that
# these instances are also "dumped" directly into ints as expected.
m_int = Model(third_party_type=1)
assert isinstance(m_int.third_party_type, ThirdPartyType)
assert m_int.third_party_type.x == 1
assert m_int.model_dump() == {'third_party_type': 1}

# Do the same thing where an instance of ThirdPartyType is passed in
instance = ThirdPartyType()
assert instance.x == 0
instance.x = 10

m_instance = Model(third_party_type=instance)
assert isinstance(m_instance.third_party_type, ThirdPartyType)
assert m_instance.third_party_type.x == 10
assert m_instance.model_dump() == {'third_party_type': 10}

# Demonstrate that validation errors are raised as expected for invalid inputs
try:
    Model(third_party_type='a')
except ValidationError as e:
    print(e)
    """
    2 validation errors for Model
    third_party_type.is-instance[ThirdPartyType]
      Input should be an instance of ThirdPartyType [type=is_instance_of, input_value='a', input_type=str]
    third_party_type.chain[int,function-plain[validate_from_int()]]
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
    """


assert Model.model_json_schema() == {
    'properties': {
        'third_party_type': {'title': 'Third Party Type', 'type': 'integer'}
    },
    'required': ['third_party_type'],
    'title': 'Model',
    'type': 'object',
}

例如，你可以使用这种方法来定义 Pandas 或 Numpy 类型的行为。

Using GetPydanticSchema to reduce boilerplate

API Documentation

pydantic.types.GetPydanticSchema

使用 GetPydanticSchema 减少样板代码

API 文档

pydantic.types.GetPydanticSchema

你可能会注意到，上面我们创建标记类的示例需要大量的样板代码。对于许多简单情况，你可以通过使用 pydantic.GetPydanticSchema 极大地减少这种情况：

from pydantic_core import core_schema
from typing_extensions import Annotated

from pydantic import BaseModel, GetPydanticSchema


class Model(BaseModel):
    y: Annotated[
        str,
        GetPydanticSchema(
            lambda tp, handler: core_schema.no_info_after_validator_function(
                lambda x: x * 2, handler(tp)
            )
        ),
    ]


assert Model(y='ab').y == 'abab'

摘要

让我们回顾一下：

1. Pydantic 通过 Annotated 提供了高级钩子来通过 AfterValidator 和 Field 等方式自定义类型。尽可能使用这些。

2. 在引擎盖下，这些使用 pydantic-core 来定制验证，您可以直接使用 GetPydanticSchema 或带有 __get_pydantic_core_schema__ 的标记类挂钩到该验证。

3. 如果你真的想要一个自定义类型，你可以在该类型本身实现 __get_pydantic_core_schema__ 。

Handling custom generic classes

警告

这是一项高级技术，您可能一开始并不需要。在大多数情况下，您可能使用标准的 Pydantic 模型就可以了。

你可以将泛型类用作字段类型，并使用 __get_pydantic_core_schema__ 根据“类型参数”（或子类型）进行自定义验证。

如果您正在使用的作为子类型的泛型类有一个类方法 __get_pydantic_core_schema__ ，则无需使用 arbitrary_types_allowed 使其正常工作。

因为 source_type 参数与 cls 参数不同，所以可以使用 typing.get_args （或 typing_extensions.get_args ）提取泛型参数。然后可以使用 handler 通过调用 handler.generate_schema 为它们生成模式。请注意，我们不会执行类似 handler(get_args(source_type)[0]) 的操作，因为我们希望为该泛型参数生成一个不相关的模式，而不是受 Annotated 元数据当前上下文等影响的模式。这对于自定义类型不太重要，但对于修改模式构建的注释元数据至关重要。

from dataclasses import dataclass
from typing import Any, Generic, TypeVar

from pydantic_core import CoreSchema, core_schema
from typing_extensions import get_args, get_origin

from pydantic import (
    BaseModel,
    GetCoreSchemaHandler,
    ValidationError,
    ValidatorFunctionWrapHandler,
)

ItemType = TypeVar('ItemType')


# This is not a pydantic model, it's an arbitrary generic class
@dataclass
class Owner(Generic[ItemType]):
    name: str
    item: ItemType

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source_type: Any, handler: GetCoreSchemaHandler
    ) -> CoreSchema:
        origin = get_origin(source_type)
        if origin is None:  # used as `x: Owner` without params
            origin = source_type
            item_tp = Any
        else:
            item_tp = get_args(source_type)[0]
        # both calling handler(...) and handler.generate_schema(...)
        # would work, but prefer the latter for conceptual and consistency reasons
        item_schema = handler.generate_schema(item_tp)

        def val_item(
            v: Owner[Any], handler: ValidatorFunctionWrapHandler
        ) -> Owner[Any]:
            v.item = handler(v.item)
            return v

        python_schema = core_schema.chain_schema(
            # `chain_schema` means do the following steps in order:
            [
                # Ensure the value is an instance of Owner
                core_schema.is_instance_schema(cls),
                # Use the item_schema to validate `items`
                core_schema.no_info_wrap_validator_function(
                    val_item, item_schema
                ),
            ]
        )

        return core_schema.json_or_python_schema(
            # for JSON accept an object with name and item keys
            json_schema=core_schema.chain_schema(
                [
                    core_schema.typed_dict_schema(
                        {
                            'name': core_schema.typed_dict_field(
                                core_schema.str_schema()
                            ),
                            'item': core_schema.typed_dict_field(item_schema),
                        }
                    ),
                    # after validating the json data convert it to python
                    core_schema.no_info_before_validator_function(
                        lambda data: Owner(
                            name=data['name'], item=data['item']
                        ),
                        # note that we re-use the same schema here as below
                        python_schema,
                    ),
                ]
            ),
            python_schema=python_schema,
        )


class Car(BaseModel):
    color: str


class House(BaseModel):
    rooms: int


class Model(BaseModel):
    car_owner: Owner[Car]
    home_owner: Owner[House]


model = Model(
    car_owner=Owner(name='John', item=Car(color='black')),
    home_owner=Owner(name='James', item=House(rooms=3)),
)
print(model)
"""
car_owner=Owner(name='John', item=Car(color='black')) home_owner=Owner(name='James', item=House(rooms=3))
"""

try:
    # If the values of the sub-types are invalid, we get an error
    Model(
        car_owner=Owner(name='John', item=House(rooms=3)),
        home_owner=Owner(name='James', item=Car(color='black')),
    )
except ValidationError as e:
    print(e)
    """
    2 validation errors for Model
    wine
      Input should be a valid number, unable to parse string as a number [type=float_parsing, input_value='Kinda good', input_type=str]
    cheese
      Input should be a valid boolean, unable to interpret input [type=bool_parsing, input_value='yeah', input_type=str]
    """

# Similarly with JSON
model = Model.model_validate_json(
    '{"car_owner":{"name":"John","item":{"color":"black"}},"home_owner":{"name":"James","item":{"rooms":3}}}'
)
print(model)
"""
car_owner=Owner(name='John', item=Car(color='black')) home_owner=Owner(name='James', item=House(rooms=3))
"""

try:
    Model.model_validate_json(
        '{"car_owner":{"name":"John","item":{"rooms":3}},"home_owner":{"name":"James","item":{"color":"black"}}}'
    )
except ValidationError as e:
    print(e)
    """
    2 validation errors for Model
    car_owner.item.color
      Field required [type=missing, input_value={'rooms': 3}, input_type=dict]
    home_owner.item.rooms
      Field required [type=missing, input_value={'color': 'black'}, input_type=dict]
    """

Generic containers

同样的思路也可以应用于创建通用容器类型，比如自定义的 Sequence 类型：

from typing import Any, Sequence, TypeVar

from pydantic_core import ValidationError, core_schema
from typing_extensions import get_args

from pydantic import BaseModel, GetCoreSchemaHandler

T = TypeVar('T')


class MySequence(Sequence[T]):
    def __init__(self, v: Sequence[T]):
        self.v = v

    def __getitem__(self, i):
        return self.v[i]

    def __len__(self):
        return len(self.v)

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: Any, handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        instance_schema = core_schema.is_instance_schema(cls)

        args = get_args(source)
        if args:
            # replace the type and rely on Pydantic to generate the right schema
            # for `Sequence`
            sequence_t_schema = handler.generate_schema(Sequence[args[0]])
        else:
            sequence_t_schema = handler.generate_schema(Sequence)

        non_instance_schema = core_schema.no_info_after_validator_function(
            MySequence, sequence_t_schema
        )
        return core_schema.union_schema([instance_schema, non_instance_schema])


class M(BaseModel):
    model_config = dict(validate_default=True)

    s1: MySequence = [3]


m = M()
print(m)
#> s1=<__main__.MySequence object at 0x0123456789ab>
print(m.s1.v)
#> [3]


class M(BaseModel):
    s1: MySequence[int]


M(s1=[1])
try:
    M(s1=['a'])
except ValidationError as exc:
    print(exc)
    """
    2 validation errors for M
    s1.is-instance[MySequence]
      Input should be an instance of MySequence [type=is_instance_of, input_value=['a'], input_type=list]
    s1.function-after[MySequence(), json-or-python[json=list[int],python=chain[is-instance[Sequence],function-wrap[sequence_validator()]]]].0
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
    """

字段名称的访问权限

!!!note 这在 Pydantic V2 到 V2.3 中是不可能的，在 Pydantic V2.4 中重新添加了。

截至 Pydantic V2.4，你可以通过 handler.field_name 访问 __get_pydantic_core_schema__ 中的字段名称，从而设置字段名称，该字段名称将从 info.field_name 可用。

from typing import Any

from pydantic_core import core_schema

from pydantic import BaseModel, GetCoreSchemaHandler, ValidationInfo


class CustomType:
    """Custom type that stores the field it was used in."""

    def __init__(self, value: int, field_name: str):
        self.value = value
        self.field_name = field_name

    def __repr__(self):
        return f'CustomType<{self.value} {self.field_name!r}>'

    @classmethod
    def validate(cls, value: int, info: ValidationInfo):
        return cls(value, info.field_name)

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source_type: Any, handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        return core_schema.with_info_after_validator_function(
            cls.validate, handler(int), field_name=handler.field_name
        )


class MyModel(BaseModel):
    my_field: CustomType


m = MyModel(my_field=1)
print(m.my_field)
#> CustomType<1 'my_field'>

你还可以从与 Annotated 使用的标记中访问 field_name ，例如 AfterValidator 。

from typing_extensions import Annotated

from pydantic import AfterValidator, BaseModel, ValidationInfo


def my_validators(value: int, info: ValidationInfo):
    return f'<{value} {info.field_name!r}>'


class MyModel(BaseModel):
    my_field: Annotated[int, AfterValidator(my_validators)]


m = MyModel(my_field=1)
print(m.my_field)
#> <1 'my_field'>

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