类型

Pydantic 使用类型来定义如何执行验证和序列化。内置和标准库类型（例如 int、str、date）可以按原样使用。严格模式可以控制，并且可以对其应用约束。

在此之上，Pydantic 提供了额外的类型，它们要么直接在库中（例如SecretStr），要么在 pydantic-extra-types 外部库中。这些都是使用自定义类型部分中描述的模式实现的。严格模式和约束**不能**应用于它们。

内置和标准库类型文档介绍了支持的类型：允许的值、可能的验证约束以及是否可以配置严格模式。

另请参阅转换表，了解每种类型的允许值的摘要。

本页将介绍如何定义自己的自定义类型。

自定义类型¶

有几种方法可以定义您的自定义类型。

使用注解模式¶

注解模式可以用于使类型在您的代码库中可重用。例如，要创建一个表示正整数的类型

from typing import Annotated

from pydantic import Field, TypeAdapter, ValidationError

PositiveInt = Annotated[int, Field(gt=0)]  # (1)!

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 annotated-types 库中的约束，使其与 Pydantic 无关
```
from annotated_types import Gt

PositiveInt = Annotated[int, Gt(0)]
```

添加验证和序列化¶

您可以使用 Pydantic 导出的标记来添加或覆盖任意类型的验证、序列化和 JSON Schema

from typing 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'}

泛型¶

类型变量可以在 Annotated 类型中使用

from typing import Annotated, TypeVar

from annotated_types import Gt, Len

from pydantic import TypeAdapter, ValidationError

T = TypeVar('T')


ShortList = Annotated[list[T], Len(max_length=4)]


ta = TypeAdapter(ShortList[int])

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

try:
    ta.validate_python([1, 2, 3, 4, 5])
except ValidationError as exc:
    print(exc)
    """
    1 validation error for list[int]
      List should have at most 4 items after validation, not 5 [type=too_long, input_value=[1, 2, 3, 4, 5], input_type=list]
    """


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

ta = TypeAdapter(PositiveList[float])

v = ta.validate_python([1.0])
assert type(v[0]) is float


try:
    ta.validate_python([-1.0])
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=-1.0, input_type=float]
    """

命名类型别名¶

上面的示例使用了赋值给变量的*隐式*类型别名。在运行时，Pydantic 无法知道它被赋值到的变量的名称，这可能会导致两个问题

别名的JSON Schema不会转换为定义。这主要在模型定义中多次使用别名时很有用。
在大多数情况下，递归类型别名将不起作用。

通过利用新的 type 语句（在 PEP 695 中引入），您可以按如下方式定义别名

Python 3.9 及以上版本Python 3.12 及更高版本（新语法）

from typing import Annotated

from annotated_types import Gt
from typing_extensions import TypeAliasType

from pydantic import BaseModel

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


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


print(Model.model_json_schema())  # (1)!
"""
{
    '$defs': {
        'PositiveIntList': {
            'items': {'exclusiveMinimum': 0, 'type': 'integer'},
            'type': 'array',
        }
    },
    'properties': {
        'x': {'$ref': '#/$defs/PositiveIntList'},
        'y': {'$ref': '#/$defs/PositiveIntList'},
    },
    'required': ['x', 'y'],
    'title': 'Model',
    'type': 'object',
}
"""

如果 PositiveIntList 被定义为隐式类型别名，则其定义将在 'x' 和 'y' 中重复。

from typing import Annotated

from annotated_types import Gt

from pydantic import BaseModel

type PositiveIntList = list[Annotated[int, Gt(0)]]


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


print(Model.model_json_schema())  # (1)!
"""
{
    '$defs': {
        'PositiveIntList': {
            'items': {'exclusiveMinimum': 0, 'type': 'integer'},
            'type': 'array',
        }
    },
    'properties': {
        'x': {'$ref': '#/$defs/PositiveIntList'},
        'y': {'$ref': '#/$defs/PositiveIntList'},
    },
    'required': ['x', 'y'],
    'title': 'Model',
    'type': 'object',
}
"""

如果 PositiveIntList 被定义为隐式类型别名，则其定义将在 'x' 和 'y' 中重复。

何时使用命名类型别名

虽然（命名）PEP 695 和隐式类型别名对于静态类型检查器来说是等效的，但 Pydantic **不会**理解命名别名内部的字段特定元数据。也就是说，`alias`、`default`、`deprecated` 等元数据**不能**使用

Python 3.9 及以上版本Python 3.12 及更高版本（新语法）

from typing import Annotated

from typing_extensions import TypeAliasType

from pydantic import BaseModel, Field

MyAlias = TypeAliasType('MyAlias', Annotated[int, Field(default=1)])


class Model(BaseModel):
    x: MyAlias  # This is not allowed

from typing import Annotated

from pydantic import BaseModel, Field

type MyAlias = Annotated[int, Field(default=1)]


class Model(BaseModel):
    x: MyAlias  # This is not allowed

只允许应用于注解类型本身的元数据（例如验证约束和 JSON 元数据）。尝试支持字段特定元数据将需要急切地检查类型别名的__value__，因此 Pydantic 将无法将别名存储为 JSON Schema 定义。

注意

与隐式类型别名一样，类型变量也可以在泛型别名中使用

Python 3.9 及以上版本Python 3.12 及更高版本（新语法）

from typing import Annotated, TypeVar

from annotated_types import Len
from typing_extensions import TypeAliasType

T = TypeVar('T')

ShortList = TypeAliasType(
    'ShortList', Annotated[list[T], Len(max_length=4)], type_params=(T,)
)

from typing import Annotated, TypeVar

from annotated_types import Len

type ShortList[T] = Annotated[list[T], Len(max_length=4)]

命名递归类型¶

当您需要定义递归类型别名时，应使用命名类型别名 (1)。

由于多种原因，Pydantic 无法支持隐式递归别名。例如，它将无法解析跨模块的前向注解。

例如，这是一个 JSON 类型的定义示例

Python 3.9 及以上版本Python 3.12 及更高版本（新语法）

from typing import Union

from typing_extensions import TypeAliasType

from pydantic import TypeAdapter

Json = TypeAliasType(
    'Json',
    'Union[dict[str, Json], list[Json], str, int, float, bool, None]',  # (1)!
)

ta = TypeAdapter(Json)
print(ta.json_schema())
"""
{
    '$defs': {
        'Json': {
            'anyOf': [
                {
                    'additionalProperties': {'$ref': '#/$defs/Json'},
                    'type': 'object',
                },
                {'items': {'$ref': '#/$defs/Json'}, 'type': 'array'},
                {'type': 'string'},
                {'type': 'integer'},
                {'type': 'number'},
                {'type': 'boolean'},
                {'type': 'null'},
            ]
        }
    },
    '$ref': '#/$defs/Json',
}
"""

将注解用引号括起来是必要的，因为它是急切评估的（并且 Json 尚未定义）。

from pydantic import TypeAdapter

type Json = dict[str, Json] | list[Json] | str | int | float | bool | None  # (1)!

ta = TypeAdapter(Json)
print(ta.json_schema())
"""
{
    '$defs': {
        'Json': {
            'anyOf': [
                {
                    'additionalProperties': {'$ref': '#/$defs/Json'},
                    'type': 'object',
                },
                {'items': {'$ref': '#/$defs/Json'}, 'type': 'array'},
                {'type': 'string'},
                {'type': 'integer'},
                {'type': 'number'},
                {'type': 'boolean'},
                {'type': 'null'},
            ]
        }
    },
    '$ref': '#/$defs/Json',
}
"""

命名类型别名的值是延迟评估的，因此无需使用前向注解。

提示

Pydantic 定义了一个JsonValue类型以方便使用。

使用 `__get_pydantic_core_schema__` 定制验证 ¶

要更广泛地定制 Pydantic 处理自定义类的方式，特别是当您拥有该类或可以继承它时，您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 内部使用 pydantic-core 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，您应该尽量坚持使用 annotated-types、pydantic.Field 或 BeforeValidator 等提供的内置构造。

您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：您会得到一个 source_type（不一定与类相同，特别是对于泛型）和一个 handler，您可以使用该类型调用它，以调用 Annotated 中的下一个元数据或调用 Pydantic 的内部模式生成。

最简单的无操作实现是用您给定的类型调用处理程序，然后将其作为结果返回。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，或者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__ 定制验证方式的类型示例。这等同于在 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'

有关如何为自定义类型定制 JSON Schema 的更多详细信息，请参阅JSON Schema。

作为注解¶

通常，您希望通过不仅仅是泛型类型参数（您可以通过类型系统实现，稍后将讨论）来参数化您的自定义类型。或者您可能并不关心（或不想）创建子类的实例；您实际上想要原始类型，只是做了一些额外的验证。

例如，如果您要自己实现 pydantic.AfterValidator（参见添加验证和序列化），您会做类似以下的事情

Python 3.9 及以上版本Python 3.10 及以上版本

from dataclasses import dataclass
from typing import Annotated, Any, Callable

from pydantic_core import CoreSchema, core_schema

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)!

frozen=True 规范使 MyAfterValidator 可哈希。没有它，像 Username | None 这样的联合将引发错误。
请注意，类型检查器不会抱怨将 'ABC' 赋值给 Username，就像它们在前面的示例中那样，因为它们不认为 Username 与 str 是不同的类型。

from dataclasses import dataclass
from typing import Annotated, Any
from collections.abc import Callable

from pydantic_core import CoreSchema, core_schema

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)!

frozen=True 规范使 MyAfterValidator 可哈希。没有它，像 Username | None 这样的联合将引发错误。
请注意，类型检查器不会抱怨将 'ABC' 赋值给 Username，就像它们在前面的示例中那样，因为它们不认为 Username 与 str 是不同的类型。

处理第三方类型¶

上一节中的模式的另一个用例是处理第三方类型。

from typing import Annotated, Any

from pydantic_core import core_schema

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',
}

您可以使用这种方法来定义 Pandas 或 Numpy 类型的行为。

使用 `GetPydanticSchema` 减少样板代码¶

API 文档

pydantic.types.GetPydanticSchema

您可能会注意到，上面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，您可以通过使用 pydantic.GetPydanticSchema 来大大减少它

from typing import Annotated

from pydantic_core import core_schema

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'

总结¶

让我们回顾一下

Pydantic 提供了高级挂钩，可以通过 Annotated（例如 AfterValidator 和 Field）自定义类型。尽可能使用这些挂钩。
在底层，这些挂钩使用 pydantic-core 来自定义验证，您可以使用 GetPydanticSchema 或带有 __get_pydantic_core_schema__ 的标记类直接挂接到该机制。
如果您确实想要一个自定义类型，您可以在类型本身上实现 __get_pydantic_core_schema__。

处理自定义泛型类¶

警告

这是一种高级技术，您一开始可能不需要。在大多数情况下，使用标准 Pydantic 模型可能就足够了。

您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）执行自定义验证。

如果用作子类型的泛型类具有类方法 __get_pydantic_core_schema__，则无需使用 arbitrary_types_allowed 即可使其工作。

因为 source_type 参数与 cls 参数不同，所以您可以使用 typing.get_args（或 typing_extensions.get_args）来提取泛型参数。然后，您可以调用 handler.generate_schema 来为它们生成模式。请注意，我们没有做类似 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 元数据等当前上下文影响的模式。这对于自定义类型来说不那么重要，但对于修改模式构建的注解元数据来说至关重要。

Python 3.9 及以上版本Python 3.10 及以上版本

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 reuse 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]
    """

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

from pydantic_core import CoreSchema, core_schema
from typing 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 reuse 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]
    """

泛型容器¶

同样的想法也可以应用于创建泛型容器类型，例如自定义的 Sequence 类型

Python 3.9 及以上版本Python 3.10 及以上版本

from collections.abc import Sequence
from typing import Any, 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]
    """

from collections.abc import Sequence
from typing import Any, TypeVar

from pydantic_core import ValidationError, core_schema
from typing 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]
    """

访问字段名¶

注意

Pydantic V2 到 V2.3 不可能实现此功能，Pydantic V2.4 重新添加了此功能。

从 Pydantic V2.4 开始，您可以通过 __get_pydantic_core_schema__ 中的 handler.field_name 访问字段名，从而设置可从 info.field_name 获得的字段名。

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)
        )


class MyModel(BaseModel):
    my_field: CustomType


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

您还可以从与 Annotated 一起使用的标记中访问 field_name，例如 AfterValidator。

from typing 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'>

类型

自定义类型¶

使用注解模式¶

添加验证和序列化¶

泛型¶

命名类型别名¶

命名递归类型¶

使用 __get_pydantic_core_schema__ 定制验证 ¶

作为自定义类型上的方法¶

作为注解¶

处理第三方类型¶

使用 GetPydanticSchema 减少样板代码¶

总结¶

处理自定义泛型类¶

泛型容器¶

访问字段名¶

使用 `__get_pydantic_core_schema__` 定制验证 ¶

使用 `GetPydanticSchema` 减少样板代码¶