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Pydantic 类型

pydantic.types

types 模块包含 pydantic 使用的自定义类型。

StrictBool module-attribute 

StrictBool = Annotated[bool, Strict()]

一个布尔值，必须是 True 或 False。

PositiveInt module-attribute 

PositiveInt = Annotated[int, Gt(0)]

一个必须大于零的整数。

from pydantic import BaseModel, PositiveInt, ValidationError

class Model(BaseModel):
    positive_int: PositiveInt

m = Model(positive_int=1)
print(repr(m))
#> Model(positive_int=1)

try:
    Model(positive_int=-1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('positive_int',),
            'msg': 'Input should be greater than 0',
            'input': -1,
            'ctx': {'gt': 0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''

NegativeInt module-attribute 

NegativeInt = Annotated[int, Lt(0)]

一个必须小于零的整数。

from pydantic import BaseModel, NegativeInt, ValidationError

class Model(BaseModel):
    negative_int: NegativeInt

m = Model(negative_int=-1)
print(repr(m))
#> Model(negative_int=-1)

try:
    Model(negative_int=1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than',
            'loc': ('negative_int',),
            'msg': 'Input should be less than 0',
            'input': 1,
            'ctx': {'lt': 0},
            'url': 'https://errors.pydantic.dev/2/v/less_than',
        }
    ]
    '''

NonPositiveInt module-attribute 

NonPositiveInt = Annotated[int, Le(0)]

一个必须小于或等于零的整数。

from pydantic import BaseModel, NonPositiveInt, ValidationError

class Model(BaseModel):
    non_positive_int: NonPositiveInt

m = Model(non_positive_int=0)
print(repr(m))
#> Model(non_positive_int=0)

try:
    Model(non_positive_int=1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than_equal',
            'loc': ('non_positive_int',),
            'msg': 'Input should be less than or equal to 0',
            'input': 1,
            'ctx': {'le': 0},
            'url': 'https://errors.pydantic.dev/2/v/less_than_equal',
        }
    ]
    '''

NonNegativeInt module-attribute 

NonNegativeInt = Annotated[int, Ge(0)]

一个必须大于或等于零的整数。

from pydantic import BaseModel, NonNegativeInt, ValidationError

class Model(BaseModel):
    non_negative_int: NonNegativeInt

m = Model(non_negative_int=0)
print(repr(m))
#> Model(non_negative_int=0)

try:
    Model(non_negative_int=-1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than_equal',
            'loc': ('non_negative_int',),
            'msg': 'Input should be greater than or equal to 0',
            'input': -1,
            'ctx': {'ge': 0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than_equal',
        }
    ]
    '''

StrictInt module-attribute 

StrictInt = Annotated[int, Strict()]

一个必须在严格模式下验证的整数。

from pydantic import BaseModel, StrictInt, ValidationError

class StrictIntModel(BaseModel):
    strict_int: StrictInt

try:
    StrictIntModel(strict_int=3.14159)
except ValidationError as e:
    print(e)
    '''
    1 validation error for StrictIntModel
    strict_int
      Input should be a valid integer [type=int_type, input_value=3.14159, input_type=float]
    '''

PositiveFloat module-attribute 

PositiveFloat = Annotated[float, Gt(0)]

一个必须大于零的浮点数。

from pydantic import BaseModel, PositiveFloat, ValidationError

class Model(BaseModel):
    positive_float: PositiveFloat

m = Model(positive_float=1.0)
print(repr(m))
#> Model(positive_float=1.0)

try:
    Model(positive_float=-1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('positive_float',),
            'msg': 'Input should be greater than 0',
            'input': -1.0,
            'ctx': {'gt': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''

NegativeFloat module-attribute 

NegativeFloat = Annotated[float, Lt(0)]

一个必须小于零的浮点数。

from pydantic import BaseModel, NegativeFloat, ValidationError

class Model(BaseModel):
    negative_float: NegativeFloat

m = Model(negative_float=-1.0)
print(repr(m))
#> Model(negative_float=-1.0)

try:
    Model(negative_float=1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than',
            'loc': ('negative_float',),
            'msg': 'Input should be less than 0',
            'input': 1.0,
            'ctx': {'lt': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/less_than',
        }
    ]
    '''

NonPositiveFloat module-attribute 

NonPositiveFloat = Annotated[float, Le(0)]

一个必须小于或等于零的浮点数。

from pydantic import BaseModel, NonPositiveFloat, ValidationError

class Model(BaseModel):
    non_positive_float: NonPositiveFloat

m = Model(non_positive_float=0.0)
print(repr(m))
#> Model(non_positive_float=0.0)

try:
    Model(non_positive_float=1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than_equal',
            'loc': ('non_positive_float',),
            'msg': 'Input should be less than or equal to 0',
            'input': 1.0,
            'ctx': {'le': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/less_than_equal',
        }
    ]
    '''

NonNegativeFloat module-attribute 

NonNegativeFloat = Annotated[float, Ge(0)]

一个必须大于或等于零的浮点数。

from pydantic import BaseModel, NonNegativeFloat, ValidationError

class Model(BaseModel):
    non_negative_float: NonNegativeFloat

m = Model(non_negative_float=0.0)
print(repr(m))
#> Model(non_negative_float=0.0)

try:
    Model(non_negative_float=-1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than_equal',
            'loc': ('non_negative_float',),
            'msg': 'Input should be greater than or equal to 0',
            'input': -1.0,
            'ctx': {'ge': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than_equal',
        }
    ]
    '''

StrictFloat module-attribute 

StrictFloat = Annotated[float, Strict(True)]

一个必须在严格模式下验证的浮点数。

from pydantic import BaseModel, StrictFloat, ValidationError

class StrictFloatModel(BaseModel):
    strict_float: StrictFloat

try:
    StrictFloatModel(strict_float='1.0')
except ValidationError as e:
    print(e)
    '''
    1 validation error for StrictFloatModel
    strict_float
      Input should be a valid number [type=float_type, input_value='1.0', input_type=str]
    '''

FiniteFloat module-attribute 

FiniteFloat = Annotated[float, AllowInfNan(False)]

一个必须是有限的浮点数 (不能是 -inf, inf, 或 nan)。

from pydantic import BaseModel, FiniteFloat

class Model(BaseModel):
    finite: FiniteFloat

m = Model(finite=1.0)
print(m)
#> finite=1.0

StrictBytes module-attribute 

StrictBytes = Annotated[bytes, Strict()]

一个必须在严格模式下验证的字节串。

StrictStr module-attribute 

StrictStr = Annotated[str, Strict()]

一个必须在严格模式下验证的字符串。

UUID1 module-attribute 

UUID1 = Annotated[UUID, UuidVersion(1)]

一个 UUID，其版本必须为 1。

import uuid

from pydantic import UUID1, BaseModel

class Model(BaseModel):
    uuid1: UUID1

Model(uuid1=uuid.uuid1())

UUID3 module-attribute 

UUID3 = Annotated[UUID, UuidVersion(3)]

一个 UUID，其版本必须为 3。

import uuid

from pydantic import UUID3, BaseModel

class Model(BaseModel):
    uuid3: UUID3

Model(uuid3=uuid.uuid3(uuid.NAMESPACE_DNS, 'pydantic.org'))

UUID4 module-attribute 

UUID4 = Annotated[UUID, UuidVersion(4)]

一个 UUID，其版本必须为 4。

import uuid

from pydantic import UUID4, BaseModel

class Model(BaseModel):
    uuid4: UUID4

Model(uuid4=uuid.uuid4())

UUID5 module-attribute 

UUID5 = Annotated[UUID, UuidVersion(5)]

一个 UUID，其版本必须为 5。

import uuid

from pydantic import UUID5, BaseModel

class Model(BaseModel):
    uuid5: UUID5

Model(uuid5=uuid.uuid5(uuid.NAMESPACE_DNS, 'pydantic.org'))

UUID6 module-attribute 

UUID6 = Annotated[UUID, UuidVersion(6)]

一个 UUID，其版本必须为 6。

import uuid

from pydantic import UUID6, BaseModel

class Model(BaseModel):
    uuid6: UUID6

Model(uuid6=uuid.UUID('1efea953-c2d6-6790-aa0a-69db8c87df97'))

UUID7 module-attribute 

UUID7 = Annotated[UUID, UuidVersion(7)]

一个 UUID，其版本必须为 7。

import uuid

from pydantic import UUID7, BaseModel

class Model(BaseModel):
    uuid7: UUID7

Model(uuid7=uuid.UUID('0194fdcb-1c47-7a09-b52c-561154de0b4a'))

UUID8 module-attribute 

UUID8 = Annotated[UUID, UuidVersion(8)]

一个 UUID，其版本必须为 8。

import uuid

from pydantic import UUID8, BaseModel

class Model(BaseModel):
    uuid8: UUID8

Model(uuid8=uuid.UUID('81a0b92e-6078-8551-9c81-8ccb666bdab8'))

FilePath module-attribute 

FilePath = Annotated[Path, PathType('file')]

一个必须指向文件的路径。

from pathlib import Path

from pydantic import BaseModel, FilePath, ValidationError

class Model(BaseModel):
    f: FilePath

path = Path('text.txt')
path.touch()
m = Model(f='text.txt')
print(m.model_dump())
#> {'f': PosixPath('text.txt')}
path.unlink()

path = Path('directory')
path.mkdir(exist_ok=True)
try:
    Model(f='directory')  # directory
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a file [type=path_not_file, input_value='directory', input_type=str]
    '''
path.rmdir()

try:
    Model(f='not-exists-file')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a file [type=path_not_file, input_value='not-exists-file', input_type=str]
    '''

DirectoryPath module-attribute 

DirectoryPath = Annotated[Path, PathType('dir')]

一个必须指向目录的路径。

from pathlib import Path

from pydantic import BaseModel, DirectoryPath, ValidationError

class Model(BaseModel):
    f: DirectoryPath

path = Path('directory/')
path.mkdir()
m = Model(f='directory/')
print(m.model_dump())
#> {'f': PosixPath('directory')}
path.rmdir()

path = Path('file.txt')
path.touch()
try:
    Model(f='file.txt')  # file
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a directory [type=path_not_directory, input_value='file.txt', input_type=str]
    '''
path.unlink()

try:
    Model(f='not-exists-directory')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a directory [type=path_not_directory, input_value='not-exists-directory', input_type=str]
    '''

NewPath module-attribute 

NewPath = Annotated[Path, PathType('new')]

一个用于新文件或目录的路径，该路径必须尚不存在。父目录必须已存在。

SocketPath module-attribute 

SocketPath = Annotated[Path, PathType('socket')]

一个指向现有套接字文件的路径

Base64Bytes module-attribute 

Base64Bytes = Annotated[
    bytes, EncodedBytes(encoder=Base64Encoder)
]

一种字节串类型，使用标准（非 URL 安全）base64 编码器进行编码和解码。

注意

在底层，Base64Bytes 使用标准库 base64.b64encode 和 base64.b64decode 函数。

因此，尝试使用 Base64Bytes 类型解码 URL 安全的 base64 数据可能会失败或产生不正确的解码。

警告

在 Pydantic v2.10 之前的版本中，Base64Bytes 使用 base64.encodebytes 和 base64.decodebytes 函数。根据 base64 文档，这些方法被认为是过时的实现，因此，Pydantic v2.10+ 现在使用现代的 base64.b64encode 和 base64.b64decode 函数。

如果您仍然想使用这些过时的编码器/解码器，您可以通过创建一个自定义的注解类型来实现，如下所示

import base64
from typing import Annotated, Literal

from pydantic_core import PydanticCustomError

from pydantic import EncodedBytes, EncoderProtocol

class LegacyBase64Encoder(EncoderProtocol):
    @classmethod
    def decode(cls, data: bytes) -> bytes:
        try:
            return base64.decodebytes(data)
        except ValueError as e:
            raise PydanticCustomError(
                'base64_decode',
                "Base64 decoding error: '{error}'",
                {'error': str(e)},
            )

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        return base64.encodebytes(value)

    @classmethod
    def get_json_format(cls) -> Literal['base64']:
        return 'base64'

LegacyBase64Bytes = Annotated[bytes, EncodedBytes(encoder=LegacyBase64Encoder)]

from pydantic import Base64Bytes, BaseModel, ValidationError

class Model(BaseModel):
    base64_bytes: Base64Bytes

# Initialize the model with base64 data
m = Model(base64_bytes=b'VGhpcyBpcyB0aGUgd2F5')

# Access decoded value
print(m.base64_bytes)
#> b'This is the way'

# Serialize into the base64 form
print(m.model_dump())
#> {'base64_bytes': b'VGhpcyBpcyB0aGUgd2F5'}

# Validate base64 data
try:
    print(Model(base64_bytes=b'undecodable').base64_bytes)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    base64_bytes
      Base64 decoding error: 'Incorrect padding' [type=base64_decode, input_value=b'undecodable', input_type=bytes]
    '''

Base64Str module-attribute 

Base64Str = Annotated[
    str, EncodedStr(encoder=Base64Encoder)
]

一种字符串类型，使用标准（非 URL 安全）base64 编码器进行编码和解码。

注意

在底层，Base64Str 使用标准库 base64.b64encode 和 base64.b64decode 函数。

因此，尝试使用 Base64Str 类型解码 URL 安全的 base64 数据可能会失败或产生不正确的解码。

警告

在 Pydantic v2.10 之前的版本中，Base64Str 使用 base64.encodebytes 和 base64.decodebytes 函数。根据 base64 文档，这些方法被认为是过时的实现，因此，Pydantic v2.10+ 现在使用现代的 base64.b64encode 和 base64.b64decode 函数。

有关如何使用过时的编码器/解码器复制旧行为的更多信息，请参阅 Base64Bytes 类型。

from pydantic import Base64Str, BaseModel, ValidationError

class Model(BaseModel):
    base64_str: Base64Str

# Initialize the model with base64 data
m = Model(base64_str='VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y')

# Access decoded value
print(m.base64_str)
#> These aren't the droids you're looking for

# Serialize into the base64 form
print(m.model_dump())
#> {'base64_str': 'VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y'}

# Validate base64 data
try:
    print(Model(base64_str='undecodable').base64_str)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    base64_str
      Base64 decoding error: 'Incorrect padding' [type=base64_decode, input_value='undecodable', input_type=str]
    '''

Base64UrlBytes module-attribute 

Base64UrlBytes = Annotated[
    bytes, EncodedBytes(encoder=Base64UrlEncoder)
]

一种字节串类型，使用 URL 安全的 base64 编码器进行编码和解码。

注意

在底层，Base64UrlBytes 使用标准库 base64.urlsafe_b64encode 和 base64.urlsafe_b64decode 函数。

因此，Base64UrlBytes 类型可用于忠实地解码“原始”base64 数据（使用 '+' 和 '/'）。

from pydantic import Base64UrlBytes, BaseModel

class Model(BaseModel):
    base64url_bytes: Base64UrlBytes

# Initialize the model with base64 data
m = Model(base64url_bytes=b'SHc_dHc-TXc==')
print(m)
#> base64url_bytes=b'Hw?tw>Mw'

Base64UrlStr module-attribute 

Base64UrlStr = Annotated[
    str, EncodedStr(encoder=Base64UrlEncoder)
]

一种字符串类型，使用 URL 安全的 base64 编码器进行编码和解码。

注意

在底层，Base64UrlStr 使用标准库 base64.urlsafe_b64encode 和 base64.urlsafe_b64decode 函数。

因此，Base64UrlStr 类型可用于忠实地解码“原始”base64 数据（使用 '+' 和 '/'）。

from pydantic import Base64UrlStr, BaseModel

class Model(BaseModel):
    base64url_str: Base64UrlStr

# Initialize the model with base64 data
m = Model(base64url_str='SHc_dHc-TXc==')
print(m)
#> base64url_str='Hw?tw>Mw'

JsonValue module-attribute 

JsonValue: TypeAlias = Union[
    list["JsonValue"],
    dict[str, "JsonValue"],
    str,
    bool,
    int,
    float,
    None,
]

JsonValue 用于表示可以序列化为 JSON 的值。

它可以是以下类型之一

  • list['JsonValue']
  • dict[str, 'JsonValue']
  • str
  • bool
  • int
  • float
  • None

以下示例演示了如何使用 JsonValue 验证 JSON 数据，以及当输入数据不可 JSON 序列化时会发生什么样的错误。

import json

from pydantic import BaseModel, JsonValue, ValidationError

class Model(BaseModel):
    j: JsonValue

valid_json_data = {'j': {'a': {'b': {'c': 1, 'd': [2, None]}}}}
invalid_json_data = {'j': {'a': {'b': ...}}}

print(repr(Model.model_validate(valid_json_data)))
#> Model(j={'a': {'b': {'c': 1, 'd': [2, None]}}})
print(repr(Model.model_validate_json(json.dumps(valid_json_data))))
#> Model(j={'a': {'b': {'c': 1, 'd': [2, None]}}})

try:
    Model.model_validate(invalid_json_data)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    j.dict.a.dict.b
      input was not a valid JSON value [type=invalid-json-value, input_value=Ellipsis, input_type=ellipsis]
    '''

OnErrorOmit module-attribute 

OnErrorOmit = Annotated[T, _OnErrorOmit]

当用作列表中的项、字典中的键类型、TypedDict 的可选值等时，如果验证项时出现任何错误，此注解将从迭代中省略该项。也就是说，不是将 ValidationError 向上传播并丢弃整个可迭代对象，而是丢弃任何无效项并返回有效项。

Strict dataclass

基类： PydanticMetadata, BaseMetadata

使用文档

使用 Annotated Strict 的严格模式

一个字段元数据类，用于指示应以严格模式验证字段。使用此类作为通过 Annotated 的注解，如下所示。

属性

名称 类型 描述
strict bool

是否以严格模式验证字段。
示例 
from typing import Annotated

from pydantic.types import Strict

StrictBool = Annotated[bool, Strict()]
源代码位于 pydantic/types.py 中 
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@_dataclasses.dataclass
class Strict(_fields.PydanticMetadata, BaseMetadata):
    """!!! abstract "Usage Documentation"
        [Strict Mode with `Annotated` `Strict`](../concepts/strict_mode.md#strict-mode-with-annotated-strict)

    A field metadata class to indicate that a field should be validated in strict mode.
    Use this class as an annotation via [`Annotated`](https://docs.pythonlang.cn/3/library/typing.html#typing.Annotated), as seen below.

    Attributes:
        strict: Whether to validate the field in strict mode.

    Example:
        ```python
        from typing import Annotated

        from pydantic.types import Strict

        StrictBool = Annotated[bool, Strict()]
        ```
    """

    strict: bool = True

    def __hash__(self) -> int:
        return hash(self.strict)

AllowInfNan dataclass

基类： PydanticMetadata

一个字段元数据类，用于指示字段应允许 -inf、inf 和 nan。

使用此类作为通过 Annotated 的注解，如下所示。

属性

名称 类型 描述
allow_inf_nan bool

是否允许 -inf、inf 和 nan。默认为 True。
示例 
from typing import Annotated

from pydantic.types import AllowInfNan

LaxFloat = Annotated[float, AllowInfNan()]
源代码位于 pydantic/types.py 中 
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@_dataclasses.dataclass
class AllowInfNan(_fields.PydanticMetadata):
    """A field metadata class to indicate that a field should allow `-inf`, `inf`, and `nan`.

    Use this class as an annotation via [`Annotated`](https://docs.pythonlang.cn/3/library/typing.html#typing.Annotated), as seen below.

    Attributes:
        allow_inf_nan: Whether to allow `-inf`, `inf`, and `nan`. Defaults to `True`.

    Example:
        ```python
        from typing import Annotated

        from pydantic.types import AllowInfNan

        LaxFloat = Annotated[float, AllowInfNan()]
        ```
    """

    allow_inf_nan: bool = True

    def __hash__(self) -> int:
        return hash(self.allow_inf_nan)

StringConstraints dataclass

基类： GroupedMetadata

使用文档

StringConstraints

一个字段元数据类，用于将约束应用于 str 类型。使用此类作为通过 Annotated 的注解，如下所示。

属性

名称 类型 描述
strip_whitespace bool | None

是否删除前导和尾随空格。
to_upper bool | None

是否将字符串转换为大写。
to_lower bool | None

是否将字符串转换为小写。
strict bool | None

是否以严格模式验证字符串。
min_length int | None

字符串的最小长度。
max_length int | None

字符串的最大长度。
pattern str | Pattern[str] | None

字符串必须匹配的正则表达式模式。
示例 
from typing import Annotated

from pydantic.types import StringConstraints

ConstrainedStr = Annotated[str, StringConstraints(min_length=1, max_length=10)]
源代码位于 pydantic/types.py 中 
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@_dataclasses.dataclass(frozen=True)
class StringConstraints(annotated_types.GroupedMetadata):
    """!!! abstract "Usage Documentation"
        [`StringConstraints`](../concepts/fields.md#string-constraints)

    A field metadata class to apply constraints to `str` types.
    Use this class as an annotation via [`Annotated`](https://docs.pythonlang.cn/3/library/typing.html#typing.Annotated), as seen below.

    Attributes:
        strip_whitespace: Whether to remove leading and trailing whitespace.
        to_upper: Whether to convert the string to uppercase.
        to_lower: Whether to convert the string to lowercase.
        strict: Whether to validate the string in strict mode.
        min_length: The minimum length of the string.
        max_length: The maximum length of the string.
        pattern: A regex pattern that the string must match.

    Example:
        ```python
        from typing import Annotated

        from pydantic.types import StringConstraints

        ConstrainedStr = Annotated[str, StringConstraints(min_length=1, max_length=10)]
        ```
    """

    strip_whitespace: bool | None = None
    to_upper: bool | None = None
    to_lower: bool | None = None
    strict: bool | None = None
    min_length: int | None = None
    max_length: int | None = None
    pattern: str | Pattern[str] | None = None

    def __iter__(self) -> Iterator[BaseMetadata]:
        if self.min_length is not None:
            yield MinLen(self.min_length)
        if self.max_length is not None:
            yield MaxLen(self.max_length)
        if self.strict is not None:
            yield Strict(self.strict)
        if (
            self.strip_whitespace is not None
            or self.pattern is not None
            or self.to_lower is not None
            or self.to_upper is not None
        ):
            yield _fields.pydantic_general_metadata(
                strip_whitespace=self.strip_whitespace,
                to_upper=self.to_upper,
                to_lower=self.to_lower,
                pattern=self.pattern,
            )

ImportString

一种可用于从字符串导入 Python 对象的类型。

ImportString 期望一个字符串，并加载在该点路径上可导入的 Python 对象。模块的属性可以用 : 或 . 与模块分隔，例如，如果提供 'math:cos'，则生成的字段值将是函数 cos。如果使用 . 并且在同一路径下同时存在属性和子模块，则模块将被优先选择。

在模型实例化时，指针将被评估和导入。这种行为有一些细微之处，在下面的示例中进行了演示。

import math

from pydantic import BaseModel, Field, ImportString, ValidationError

class ImportThings(BaseModel):
    obj: ImportString

# A string value will cause an automatic import
my_cos = ImportThings(obj='math.cos')

# You can use the imported function as you would expect
cos_of_0 = my_cos.obj(0)
assert cos_of_0 == 1

# A string whose value cannot be imported will raise an error
try:
    ImportThings(obj='foo.bar')
except ValidationError as e:
    print(e)
    '''
    1 validation error for ImportThings
    obj
      Invalid python path: No module named 'foo.bar' [type=import_error, input_value='foo.bar', input_type=str]
    '''

# Actual python objects can be assigned as well
my_cos = ImportThings(obj=math.cos)
my_cos_2 = ImportThings(obj='math.cos')
my_cos_3 = ImportThings(obj='math:cos')
assert my_cos == my_cos_2 == my_cos_3

# You can set default field value either as Python object:
class ImportThingsDefaultPyObj(BaseModel):
    obj: ImportString = math.cos

# or as a string value (but only if used with `validate_default=True`)
class ImportThingsDefaultString(BaseModel):
    obj: ImportString = Field(default='math.cos', validate_default=True)

my_cos_default1 = ImportThingsDefaultPyObj()
my_cos_default2 = ImportThingsDefaultString()
assert my_cos_default1.obj == my_cos_default2.obj == math.cos

# note: this will not work!
class ImportThingsMissingValidateDefault(BaseModel):
    obj: ImportString = 'math.cos'

my_cos_default3 = ImportThingsMissingValidateDefault()
assert my_cos_default3.obj == 'math.cos'  # just string, not evaluated

将 ImportString 类型序列化为 json 也是可能的。

from pydantic import BaseModel, ImportString

class ImportThings(BaseModel):
    obj: ImportString

# Create an instance
m = ImportThings(obj='math.cos')
print(m)
#> obj=<built-in function cos>
print(m.model_dump_json())
#> {"obj":"math.cos"}
源代码位于 pydantic/types.py 中 
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class ImportString:
    """A type that can be used to import a Python object from a string.

    `ImportString` expects a string and loads the Python object importable at that dotted path.
    Attributes of modules may be separated from the module by `:` or `.`, e.g. if `'math:cos'` is provided,
    the resulting field value would be the function `cos`. If a `.` is used and both an attribute and submodule
    are present at the same path, the module will be preferred.

    On model instantiation, pointers will be evaluated and imported. There is
    some nuance to this behavior, demonstrated in the examples below.

    ```python
    import math

    from pydantic import BaseModel, Field, ImportString, ValidationError

    class ImportThings(BaseModel):
        obj: ImportString

    # A string value will cause an automatic import
    my_cos = ImportThings(obj='math.cos')

    # You can use the imported function as you would expect
    cos_of_0 = my_cos.obj(0)
    assert cos_of_0 == 1

    # A string whose value cannot be imported will raise an error
    try:
        ImportThings(obj='foo.bar')
    except ValidationError as e:
        print(e)
        '''
        1 validation error for ImportThings
        obj
          Invalid python path: No module named 'foo.bar' [type=import_error, input_value='foo.bar', input_type=str]
        '''

    # Actual python objects can be assigned as well
    my_cos = ImportThings(obj=math.cos)
    my_cos_2 = ImportThings(obj='math.cos')
    my_cos_3 = ImportThings(obj='math:cos')
    assert my_cos == my_cos_2 == my_cos_3

    # You can set default field value either as Python object:
    class ImportThingsDefaultPyObj(BaseModel):
        obj: ImportString = math.cos

    # or as a string value (but only if used with `validate_default=True`)
    class ImportThingsDefaultString(BaseModel):
        obj: ImportString = Field(default='math.cos', validate_default=True)

    my_cos_default1 = ImportThingsDefaultPyObj()
    my_cos_default2 = ImportThingsDefaultString()
    assert my_cos_default1.obj == my_cos_default2.obj == math.cos

    # note: this will not work!
    class ImportThingsMissingValidateDefault(BaseModel):
        obj: ImportString = 'math.cos'

    my_cos_default3 = ImportThingsMissingValidateDefault()
    assert my_cos_default3.obj == 'math.cos'  # just string, not evaluated
    ```

    Serializing an `ImportString` type to json is also possible.

    ```python
    from pydantic import BaseModel, ImportString

    class ImportThings(BaseModel):
        obj: ImportString

    # Create an instance
    m = ImportThings(obj='math.cos')
    print(m)
    #> obj=<built-in function cos>
    print(m.model_dump_json())
    #> {"obj":"math.cos"}
    ```
    """

    @classmethod
    def __class_getitem__(cls, item: AnyType) -> AnyType:
        return Annotated[item, cls()]

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        serializer = core_schema.plain_serializer_function_ser_schema(cls._serialize, when_used='json')
        if cls is source:
            # Treat bare usage of ImportString (`schema is None`) as the same as ImportString[Any]
            return core_schema.no_info_plain_validator_function(
                function=_validators.import_string, serialization=serializer
            )
        else:
            return core_schema.no_info_before_validator_function(
                function=_validators.import_string, schema=handler(source), serialization=serializer
            )

    @classmethod
    def __get_pydantic_json_schema__(cls, cs: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
        return handler(core_schema.str_schema())

    @staticmethod
    def _serialize(v: Any) -> str:
        if isinstance(v, ModuleType):
            return v.__name__
        elif hasattr(v, '__module__') and hasattr(v, '__name__'):
            return f'{v.__module__}.{v.__name__}'
        # Handle special cases for sys.XXX streams
        # if we see more of these, we should consider a more general solution
        elif hasattr(v, 'name'):
            if v.name == '<stdout>':
                return 'sys.stdout'
            elif v.name == '<stdin>':
                return 'sys.stdin'
            elif v.name == '<stderr>':
                return 'sys.stderr'
        else:
            return v

    def __repr__(self) -> str:
        return 'ImportString'

UuidVersion dataclass

一个字段元数据类，用于指示 UUID 版本。

使用此类作为通过 Annotated 的注解，如下所示。

属性

名称 类型 描述
uuid_version Literal[1, 3, 4, 5, 6, 7, 8]

UUID 的版本。必须是 1、3、4、5 或 7 之一。
示例 
from typing import Annotated
from uuid import UUID

from pydantic.types import UuidVersion

UUID1 = Annotated[UUID, UuidVersion(1)]
源代码位于 pydantic/types.py 中 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true)
class UuidVersion:
    """A field metadata class to indicate a [UUID](https://docs.pythonlang.cn/3/library/uuid.html) version.

    Use this class as an annotation via [`Annotated`](https://docs.pythonlang.cn/3/library/typing.html#typing.Annotated), as seen below.

    Attributes:
        uuid_version: The version of the UUID. Must be one of 1, 3, 4, 5, or 7.

    Example:
        ```python
        from typing import Annotated
        from uuid import UUID

        from pydantic.types import UuidVersion

        UUID1 = Annotated[UUID, UuidVersion(1)]
        ```
    """

    uuid_version: Literal[1, 3, 4, 5, 6, 7, 8]

    def __get_pydantic_json_schema__(
        self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
    ) -> JsonSchemaValue:
        field_schema = handler(core_schema)
        field_schema.pop('anyOf', None)  # remove the bytes/str union
        field_schema.update(type='string', format=f'uuid{self.uuid_version}')
        return field_schema

    def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        if isinstance(self, source):
            # used directly as a type
            return core_schema.uuid_schema(version=self.uuid_version)
        else:
            # update existing schema with self.uuid_version
            schema = handler(source)
            _check_annotated_type(schema['type'], 'uuid', self.__class__.__name__)
            schema['version'] = self.uuid_version  # type: ignore
            return schema

    def __hash__(self) -> int:
        return hash(type(self.uuid_version))

Json

一个特殊的类型包装器，在解析之前加载 JSON。

您可以使用 Json 数据类型，使 Pydantic 首先加载原始 JSON 字符串，然后再将加载的数据验证为参数化类型

from typing import Any

from pydantic import BaseModel, Json, ValidationError

class AnyJsonModel(BaseModel):
    json_obj: Json[Any]

class ConstrainedJsonModel(BaseModel):
    json_obj: Json[list[int]]

print(AnyJsonModel(json_obj='{"b": 1}'))
#> json_obj={'b': 1}
print(ConstrainedJsonModel(json_obj='[1, 2, 3]'))
#> json_obj=[1, 2, 3]

try:
    ConstrainedJsonModel(json_obj=12)
except ValidationError as e:
    print(e)
    '''
    1 validation error for ConstrainedJsonModel
    json_obj
      JSON input should be string, bytes or bytearray [type=json_type, input_value=12, input_type=int]
    '''

try:
    ConstrainedJsonModel(json_obj='[a, b]')
except ValidationError as e:
    print(e)
    '''
    1 validation error for ConstrainedJsonModel
    json_obj
      Invalid JSON: expected value at line 1 column 2 [type=json_invalid, input_value='[a, b]', input_type=str]
    '''

try:
    ConstrainedJsonModel(json_obj='["a", "b"]')
except ValidationError as e:
    print(e)
    '''
    2 validation errors for ConstrainedJsonModel
    json_obj.0
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
    json_obj.1
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='b', input_type=str]
    '''

当您使用 model_dump 或 model_dump_json 转储模型时，转储的值将是验证的结果，而不是原始 JSON 字符串。但是，您可以使用参数 round_trip=True 来取回原始 JSON 字符串

from pydantic import BaseModel, Json

class ConstrainedJsonModel(BaseModel):
    json_obj: Json[list[int]]

print(ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json())
#> {"json_obj":[1,2,3]}
print(
    ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json(round_trip=True)
)
#> {"json_obj":"[1,2,3]"}
源代码位于 pydantic/types.py 中 
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class Json:
    """A special type wrapper which loads JSON before parsing.

    You can use the `Json` data type to make Pydantic first load a raw JSON string before
    validating the loaded data into the parametrized type:

    ```python
    from typing import Any

    from pydantic import BaseModel, Json, ValidationError

    class AnyJsonModel(BaseModel):
        json_obj: Json[Any]

    class ConstrainedJsonModel(BaseModel):
        json_obj: Json[list[int]]

    print(AnyJsonModel(json_obj='{"b": 1}'))
    #> json_obj={'b': 1}
    print(ConstrainedJsonModel(json_obj='[1, 2, 3]'))
    #> json_obj=[1, 2, 3]

    try:
        ConstrainedJsonModel(json_obj=12)
    except ValidationError as e:
        print(e)
        '''
        1 validation error for ConstrainedJsonModel
        json_obj
          JSON input should be string, bytes or bytearray [type=json_type, input_value=12, input_type=int]
        '''

    try:
        ConstrainedJsonModel(json_obj='[a, b]')
    except ValidationError as e:
        print(e)
        '''
        1 validation error for ConstrainedJsonModel
        json_obj
          Invalid JSON: expected value at line 1 column 2 [type=json_invalid, input_value='[a, b]', input_type=str]
        '''

    try:
        ConstrainedJsonModel(json_obj='["a", "b"]')
    except ValidationError as e:
        print(e)
        '''
        2 validation errors for ConstrainedJsonModel
        json_obj.0
          Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
        json_obj.1
          Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='b', input_type=str]
        '''
    ```

    When you dump the model using `model_dump` or `model_dump_json`, the dumped value will be the result of validation,
    not the original JSON string. However, you can use the argument `round_trip=True` to get the original JSON string back:

    ```python
    from pydantic import BaseModel, Json

    class ConstrainedJsonModel(BaseModel):
        json_obj: Json[list[int]]

    print(ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json())
    #> {"json_obj":[1,2,3]}
    print(
        ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json(round_trip=True)
    )
    #> {"json_obj":"[1,2,3]"}
    ```
    """

    @classmethod
    def __class_getitem__(cls, item: AnyType) -> AnyType:
        return Annotated[item, cls()]

    @classmethod
    def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        if cls is source:
            return core_schema.json_schema(None)
        else:
            return core_schema.json_schema(handler(source))

    def __repr__(self) -> str:
        return 'Json'

    def __hash__(self) -> int:
        return hash(type(self))

    def __eq__(self, other: Any) -> bool:
        return type(other) is type(self)

Secret

基类： _SecretBase[SecretType]

一个泛型基类，用于定义包含敏感信息的字段，您不希望这些信息在日志记录或回溯中可见。

您可以直接使用类型参数化 Secret，或者从使用参数化类型的 Secret 中子类化。子类化的好处是您可以定义自定义的 _display 方法，该方法将用于 repr() 和 str() 方法。下面的示例演示了使用 Secret 创建新 secret 类型的两种方法。

  1. 直接使用类型参数化 Secret
from pydantic import BaseModel, Secret

SecretBool = Secret[bool]

class Model(BaseModel):
    secret_bool: SecretBool

m = Model(secret_bool=True)
print(m.model_dump())
#> {'secret_bool': Secret('**********')}

print(m.model_dump_json())
#> {"secret_bool":"**********"}

print(m.secret_bool.get_secret_value())
#> True
  1. 从参数化 Secret 子类化
from datetime import date

from pydantic import BaseModel, Secret

class SecretDate(Secret[date]):
    def _display(self) -> str:
        return '****/**/**'

class Model(BaseModel):
    secret_date: SecretDate

m = Model(secret_date=date(2022, 1, 1))
print(m.model_dump())
#> {'secret_date': SecretDate('****/**/**')}

print(m.model_dump_json())
#> {"secret_date":"****/**/**"}

print(m.secret_date.get_secret_value())
#> 2022-01-01

_display 方法返回的值将用于 repr() 和 str()。

您可以通过注解对底层类型强制执行约束：例如

from typing import Annotated

from pydantic import BaseModel, Field, Secret, ValidationError

SecretPosInt = Secret[Annotated[int, Field(gt=0, strict=True)]]

class Model(BaseModel):
    sensitive_int: SecretPosInt

m = Model(sensitive_int=42)
print(m.model_dump())
#> {'sensitive_int': Secret('**********')}

try:
    m = Model(sensitive_int=-42)  # (1)!
except ValidationError as exc_info:
    print(exc_info.errors(include_url=False, include_input=False))
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('sensitive_int',),
            'msg': 'Input should be greater than 0',
            'ctx': {'gt': 0},
        }
    ]
    '''

try:
    m = Model(sensitive_int='42')  # (2)!
except ValidationError as exc_info:
    print(exc_info.errors(include_url=False, include_input=False))
    '''
    [
        {
            'type': 'int_type',
            'loc': ('sensitive_int',),
            'msg': 'Input should be a valid integer',
        }
    ]
    '''
  1. 输入值不大于 0，因此会引发验证错误。
  2. 输入值不是整数，因此会引发验证错误，因为 SecretPosInt 类型启用了严格模式。
源代码位于 pydantic/types.py 中 
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class Secret(_SecretBase[SecretType]):
    """A generic base class used for defining a field with sensitive information that you do not want to be visible in logging or tracebacks.

    You may either directly parametrize `Secret` with a type, or subclass from `Secret` with a parametrized type. The benefit of subclassing
    is that you can define a custom `_display` method, which will be used for `repr()` and `str()` methods. The examples below demonstrate both
    ways of using `Secret` to create a new secret type.

    1. Directly parametrizing `Secret` with a type:

    ```python
    from pydantic import BaseModel, Secret

    SecretBool = Secret[bool]

    class Model(BaseModel):
        secret_bool: SecretBool

    m = Model(secret_bool=True)
    print(m.model_dump())
    #> {'secret_bool': Secret('**********')}

    print(m.model_dump_json())
    #> {"secret_bool":"**********"}

    print(m.secret_bool.get_secret_value())
    #> True
    ```

    2. Subclassing from parametrized `Secret`:

    ```python
    from datetime import date

    from pydantic import BaseModel, Secret

    class SecretDate(Secret[date]):
        def _display(self) -> str:
            return '****/**/**'

    class Model(BaseModel):
        secret_date: SecretDate

    m = Model(secret_date=date(2022, 1, 1))
    print(m.model_dump())
    #> {'secret_date': SecretDate('****/**/**')}

    print(m.model_dump_json())
    #> {"secret_date":"****/**/**"}

    print(m.secret_date.get_secret_value())
    #> 2022-01-01
    ```

    The value returned by the `_display` method will be used for `repr()` and `str()`.

    You can enforce constraints on the underlying type through annotations:
    For example:

    ```python
    from typing import Annotated

    from pydantic import BaseModel, Field, Secret, ValidationError

    SecretPosInt = Secret[Annotated[int, Field(gt=0, strict=True)]]

    class Model(BaseModel):
        sensitive_int: SecretPosInt

    m = Model(sensitive_int=42)
    print(m.model_dump())
    #> {'sensitive_int': Secret('**********')}

    try:
        m = Model(sensitive_int=-42)  # (1)!
    except ValidationError as exc_info:
        print(exc_info.errors(include_url=False, include_input=False))
        '''
        [
            {
                'type': 'greater_than',
                'loc': ('sensitive_int',),
                'msg': 'Input should be greater than 0',
                'ctx': {'gt': 0},
            }
        ]
        '''

    try:
        m = Model(sensitive_int='42')  # (2)!
    except ValidationError as exc_info:
        print(exc_info.errors(include_url=False, include_input=False))
        '''
        [
            {
                'type': 'int_type',
                'loc': ('sensitive_int',),
                'msg': 'Input should be a valid integer',
            }
        ]
        '''
    ```

    1. The input value is not greater than 0, so it raises a validation error.
    2. The input value is not an integer, so it raises a validation error because the `SecretPosInt` type has strict mode enabled.
    """

    def _display(self) -> str | bytes:
        return '**********' if self.get_secret_value() else ''

    @classmethod
    def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        inner_type = None
        # if origin_type is Secret, then cls is a GenericAlias, and we can extract the inner type directly
        origin_type = get_origin(source)
        if origin_type is not None:
            inner_type = get_args(source)[0]
        # otherwise, we need to get the inner type from the base class
        else:
            bases = getattr(cls, '__orig_bases__', getattr(cls, '__bases__', []))
            for base in bases:
                if get_origin(base) is Secret:
                    inner_type = get_args(base)[0]
            if bases == [] or inner_type is None:
                raise TypeError(
                    f"Can't get secret type from {cls.__name__}. "
                    'Please use Secret[<type>], or subclass from Secret[<type>] instead.'
                )

        inner_schema = handler.generate_schema(inner_type)  # type: ignore

        def validate_secret_value(value, handler) -> Secret[SecretType]:
            if isinstance(value, Secret):
                value = value.get_secret_value()
            validated_inner = handler(value)
            return cls(validated_inner)

        return core_schema.json_or_python_schema(
            python_schema=core_schema.no_info_wrap_validator_function(
                validate_secret_value,
                inner_schema,
            ),
            json_schema=core_schema.no_info_after_validator_function(lambda x: cls(x), inner_schema),
            serialization=core_schema.plain_serializer_function_ser_schema(
                _serialize_secret,
                info_arg=True,
                when_used='always',
            ),
        )

    __pydantic_serializer__ = SchemaSerializer(
        core_schema.any_schema(
            serialization=core_schema.plain_serializer_function_ser_schema(
                _serialize_secret,
                info_arg=True,
                when_used='always',
            )
        )
    )

SecretStr

基类： _SecretField[str]

一个用于存储敏感信息的字符串，您不希望这些信息在日志记录或回溯中可见。

当 secret 值非空时，在调用 repr() 和 str() 时，它会显示为 '**********' 而不是底层值。如果该值为空，则显示为 ''。

from pydantic import BaseModel, SecretStr

class User(BaseModel):
    username: str
    password: SecretStr

user = User(username='scolvin', password='password1')

print(user)
#> username='scolvin' password=SecretStr('**********')
print(user.password.get_secret_value())
#> password1
print((SecretStr('password'), SecretStr('')))
#> (SecretStr('**********'), SecretStr(''))

如上所示，默认情况下，SecretStr（和 SecretBytes）将被序列化为 ********** 当序列化为 json 时。

您可以使用 field_serializer 在序列化为 json 时将 secret 转储为纯文本。

from pydantic import BaseModel, SecretBytes, SecretStr, field_serializer

class Model(BaseModel):
    password: SecretStr
    password_bytes: SecretBytes

    @field_serializer('password', 'password_bytes', when_used='json')
    def dump_secret(self, v):
        return v.get_secret_value()

model = Model(password='IAmSensitive', password_bytes=b'IAmSensitiveBytes')
print(model)
#> password=SecretStr('**********') password_bytes=SecretBytes(b'**********')
print(model.password)
#> **********
print(model.model_dump())
'''
{
    'password': SecretStr('**********'),
    'password_bytes': SecretBytes(b'**********'),
}
'''
print(model.model_dump_json())
#> {"password":"IAmSensitive","password_bytes":"IAmSensitiveBytes"}
源代码位于 pydantic/types.py 中 
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class SecretStr(_SecretField[str]):
    """A string used for storing sensitive information that you do not want to be visible in logging or tracebacks.

    When the secret value is nonempty, it is displayed as `'**********'` instead of the underlying value in
    calls to `repr()` and `str()`. If the value _is_ empty, it is displayed as `''`.

    ```python
    from pydantic import BaseModel, SecretStr

    class User(BaseModel):
        username: str
        password: SecretStr

    user = User(username='scolvin', password='password1')

    print(user)
    #> username='scolvin' password=SecretStr('**********')
    print(user.password.get_secret_value())
    #> password1
    print((SecretStr('password'), SecretStr('')))
    #> (SecretStr('**********'), SecretStr(''))
    ```

    As seen above, by default, [`SecretStr`][pydantic.types.SecretStr] (and [`SecretBytes`][pydantic.types.SecretBytes])
    will be serialized as `**********` when serializing to json.

    You can use the [`field_serializer`][pydantic.functional_serializers.field_serializer] to dump the
    secret as plain-text when serializing to json.

    ```python
    from pydantic import BaseModel, SecretBytes, SecretStr, field_serializer

    class Model(BaseModel):
        password: SecretStr
        password_bytes: SecretBytes

        @field_serializer('password', 'password_bytes', when_used='json')
        def dump_secret(self, v):
            return v.get_secret_value()

    model = Model(password='IAmSensitive', password_bytes=b'IAmSensitiveBytes')
    print(model)
    #> password=SecretStr('**********') password_bytes=SecretBytes(b'**********')
    print(model.password)
    #> **********
    print(model.model_dump())
    '''
    {
        'password': SecretStr('**********'),
        'password_bytes': SecretBytes(b'**********'),
    }
    '''
    print(model.model_dump_json())
    #> {"password":"IAmSensitive","password_bytes":"IAmSensitiveBytes"}
    ```
    """

    _inner_schema: ClassVar[CoreSchema] = core_schema.str_schema()
    _error_kind: ClassVar[str] = 'string_type'

    def __len__(self) -> int:
        return len(self._secret_value)

    def _display(self) -> str:
        return _secret_display(self._secret_value)

SecretBytes

基类： _SecretField[bytes]

一个用于存储敏感信息的字节串，您不希望这些信息在日志记录或回溯中可见。

在 repr() 和 str() 调用时，它会显示 b'**********' 而不是字符串值。当 secret 值非空时，在调用 repr() 和 str() 时，它会显示为 b'**********' 而不是底层值。如果该值为空，则显示为 b''。

from pydantic import BaseModel, SecretBytes

class User(BaseModel):
    username: str
    password: SecretBytes

user = User(username='scolvin', password=b'password1')
#> username='scolvin' password=SecretBytes(b'**********')
print(user.password.get_secret_value())
#> b'password1'
print((SecretBytes(b'password'), SecretBytes(b'')))
#> (SecretBytes(b'**********'), SecretBytes(b''))
源代码位于 pydantic/types.py 中 
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class SecretBytes(_SecretField[bytes]):
    """A bytes used for storing sensitive information that you do not want to be visible in logging or tracebacks.

    It displays `b'**********'` instead of the string value on `repr()` and `str()` calls.
    When the secret value is nonempty, it is displayed as `b'**********'` instead of the underlying value in
    calls to `repr()` and `str()`. If the value _is_ empty, it is displayed as `b''`.

    ```python
    from pydantic import BaseModel, SecretBytes

    class User(BaseModel):
        username: str
        password: SecretBytes

    user = User(username='scolvin', password=b'password1')
    #> username='scolvin' password=SecretBytes(b'**********')
    print(user.password.get_secret_value())
    #> b'password1'
    print((SecretBytes(b'password'), SecretBytes(b'')))
    #> (SecretBytes(b'**********'), SecretBytes(b''))
    ```
    """

    _inner_schema: ClassVar[CoreSchema] = core_schema.bytes_schema()
    _error_kind: ClassVar[str] = 'bytes_type'

    def __len__(self) -> int:
        return len(self._secret_value)

    def _display(self) -> bytes:
        return _secret_display(self._secret_value).encode()

PaymentCardNumber

基类： str

基于：https://en.wikipedia.org/wiki/Payment_card_number。

源代码位于 pydantic/types.py 中 
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@deprecated(
    'The `PaymentCardNumber` class is deprecated, use `pydantic_extra_types` instead. '
    'See https://docs.pydantic.org.cn/latest/api/pydantic_extra_types_payment/#pydantic_extra_types.payment.PaymentCardNumber.',
    category=PydanticDeprecatedSince20,
)
class PaymentCardNumber(str):
    """Based on: https://en.wikipedia.org/wiki/Payment_card_number."""

    strip_whitespace: ClassVar[bool] = True
    min_length: ClassVar[int] = 12
    max_length: ClassVar[int] = 19
    bin: str
    last4: str
    brand: PaymentCardBrand

    def __init__(self, card_number: str):
        self.validate_digits(card_number)

        card_number = self.validate_luhn_check_digit(card_number)

        self.bin = card_number[:6]
        self.last4 = card_number[-4:]
        self.brand = self.validate_brand(card_number)

    @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,
            core_schema.str_schema(
                min_length=cls.min_length, max_length=cls.max_length, strip_whitespace=cls.strip_whitespace
            ),
        )

    @classmethod
    def validate(cls, input_value: str, /, _: core_schema.ValidationInfo) -> PaymentCardNumber:
        """Validate the card number and return a `PaymentCardNumber` instance."""
        return cls(input_value)

    @property
    def masked(self) -> str:
        """Mask all but the last 4 digits of the card number.

        Returns:
            A masked card number string.
        """
        num_masked = len(self) - 10  # len(bin) + len(last4) == 10
        return f'{self.bin}{"*" * num_masked}{self.last4}'

    @classmethod
    def validate_digits(cls, card_number: str) -> None:
        """Validate that the card number is all digits."""
        if not card_number.isdigit():
            raise PydanticCustomError('payment_card_number_digits', 'Card number is not all digits')

    @classmethod
    def validate_luhn_check_digit(cls, card_number: str) -> str:
        """Based on: https://en.wikipedia.org/wiki/Luhn_algorithm."""
        sum_ = int(card_number[-1])
        length = len(card_number)
        parity = length % 2
        for i in range(length - 1):
            digit = int(card_number[i])
            if i % 2 == parity:
                digit *= 2
            if digit > 9:
                digit -= 9
            sum_ += digit
        valid = sum_ % 10 == 0
        if not valid:
            raise PydanticCustomError('payment_card_number_luhn', 'Card number is not luhn valid')
        return card_number

    @staticmethod
    def validate_brand(card_number: str) -> PaymentCardBrand:
        """Validate length based on BIN for major brands:
        https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN).
        """
        if card_number[0] == '4':
            brand = PaymentCardBrand.visa
        elif 51 <= int(card_number[:2]) <= 55:
            brand = PaymentCardBrand.mastercard
        elif card_number[:2] in {'34', '37'}:
            brand = PaymentCardBrand.amex
        else:
            brand = PaymentCardBrand.other

        required_length: None | int | str = None
        if brand in PaymentCardBrand.mastercard:
            required_length = 16
            valid = len(card_number) == required_length
        elif brand == PaymentCardBrand.visa:
            required_length = '13, 16 or 19'
            valid = len(card_number) in {13, 16, 19}
        elif brand == PaymentCardBrand.amex:
            required_length = 15
            valid = len(card_number) == required_length
        else:
            valid = True

        if not valid:
            raise PydanticCustomError(
                'payment_card_number_brand',
                'Length for a {brand} card must be {required_length}',
                {'brand': brand, 'required_length': required_length},
            )
        return brand

masked property 

masked: str

屏蔽卡号除最后 4 位数字外的所有数字。

返回

类型 描述
str

一个屏蔽的卡号字符串。

validate classmethod 

validate(
    input_value: str, /, _: ValidationInfo
) -> PaymentCardNumber

验证卡号并返回一个 PaymentCardNumber 实例。

源代码位于 pydantic/types.py 中 
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@classmethod
def validate(cls, input_value: str, /, _: core_schema.ValidationInfo) -> PaymentCardNumber:
    """Validate the card number and return a `PaymentCardNumber` instance."""
    return cls(input_value)

validate_digits classmethod 

validate_digits(card_number: str) -> None

验证卡号是否全部为数字。

源代码位于 pydantic/types.py 中 
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@classmethod
def validate_digits(cls, card_number: str) -> None:
    """Validate that the card number is all digits."""
    if not card_number.isdigit():
        raise PydanticCustomError('payment_card_number_digits', 'Card number is not all digits')

validate_luhn_check_digit classmethod 

validate_luhn_check_digit(card_number: str) -> str

基于：https://en.wikipedia.org/wiki/Luhn_algorithm。

源代码位于 pydantic/types.py 中 
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@classmethod
def validate_luhn_check_digit(cls, card_number: str) -> str:
    """Based on: https://en.wikipedia.org/wiki/Luhn_algorithm."""
    sum_ = int(card_number[-1])
    length = len(card_number)
    parity = length % 2
    for i in range(length - 1):
        digit = int(card_number[i])
        if i % 2 == parity:
            digit *= 2
        if digit > 9:
            digit -= 9
        sum_ += digit
    valid = sum_ % 10 == 0
    if not valid:
        raise PydanticCustomError('payment_card_number_luhn', 'Card number is not luhn valid')
    return card_number

validate_brand staticmethod 

validate_brand(card_number: str) -> PaymentCardBrand

基于主要品牌的 BIN 验证长度：https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN)。

源代码位于 pydantic/types.py 中 
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@staticmethod
def validate_brand(card_number: str) -> PaymentCardBrand:
    """Validate length based on BIN for major brands:
    https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN).
    """
    if card_number[0] == '4':
        brand = PaymentCardBrand.visa
    elif 51 <= int(card_number[:2]) <= 55:
        brand = PaymentCardBrand.mastercard
    elif card_number[:2] in {'34', '37'}:
        brand = PaymentCardBrand.amex
    else:
        brand = PaymentCardBrand.other

    required_length: None | int | str = None
    if brand in PaymentCardBrand.mastercard:
        required_length = 16
        valid = len(card_number) == required_length
    elif brand == PaymentCardBrand.visa:
        required_length = '13, 16 or 19'
        valid = len(card_number) in {13, 16, 19}
    elif brand == PaymentCardBrand.amex:
        required_length = 15
        valid = len(card_number) == required_length
    else:
        valid = True

    if not valid:
        raise PydanticCustomError(
            'payment_card_number_brand',
            'Length for a {brand} card must be {required_length}',
            {'brand': brand, 'required_length': required_length},
        )
    return brand

ByteSize

基类： int

将表示字节数和单位的字符串（例如 '1KB' 或 '11.5MiB'）转换为整数。

您可以使用 ByteSize 数据类型（不区分大小写）将字节数的字符串表示形式转换为整数，还可以打印输出表示字节数的人类可读字符串。

根据 IEC 80000-13 标准，我们将 '1KB' 解释为 1000 字节，将 '1KiB' 解释为 1024 字节。一般来说，包含中间的 'i' 将导致单位被解释为 2 的幂，而不是 10 的幂（因此，例如，'1 MB' 被视为 1_000_000 字节，而 '1 MiB' 被视为 1_048_576 字节）。

信息

请注意，1b 将被解析为 “1 字节” 而不是 “1 比特”。

from pydantic import BaseModel, ByteSize

class MyModel(BaseModel):
    size: ByteSize

print(MyModel(size=52000).size)
#> 52000
print(MyModel(size='3000 KiB').size)
#> 3072000

m = MyModel(size='50 PB')
print(m.size.human_readable())
#> 44.4PiB
print(m.size.human_readable(decimal=True))
#> 50.0PB
print(m.size.human_readable(separator=' '))
#> 44.4 PiB

print(m.size.to('TiB'))
#> 45474.73508864641
源代码位于 pydantic/types.py 中 
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class ByteSize(int):
    """Converts a string representing a number of bytes with units (such as `'1KB'` or `'11.5MiB'`) into an integer.

    You can use the `ByteSize` data type to (case-insensitively) convert a string representation of a number of bytes into
    an integer, and also to print out human-readable strings representing a number of bytes.

    In conformance with [IEC 80000-13 Standard](https://en.wikipedia.org/wiki/ISO/IEC_80000) we interpret `'1KB'` to mean 1000 bytes,
    and `'1KiB'` to mean 1024 bytes. In general, including a middle `'i'` will cause the unit to be interpreted as a power of 2,
    rather than a power of 10 (so, for example, `'1 MB'` is treated as `1_000_000` bytes, whereas `'1 MiB'` is treated as `1_048_576` bytes).

    !!! info
        Note that `1b` will be parsed as "1 byte" and not "1 bit".

    ```python
    from pydantic import BaseModel, ByteSize

    class MyModel(BaseModel):
        size: ByteSize

    print(MyModel(size=52000).size)
    #> 52000
    print(MyModel(size='3000 KiB').size)
    #> 3072000

    m = MyModel(size='50 PB')
    print(m.size.human_readable())
    #> 44.4PiB
    print(m.size.human_readable(decimal=True))
    #> 50.0PB
    print(m.size.human_readable(separator=' '))
    #> 44.4 PiB

    print(m.size.to('TiB'))
    #> 45474.73508864641
    ```
    """

    byte_sizes = {
        'b': 1,
        'kb': 10**3,
        'mb': 10**6,
        'gb': 10**9,
        'tb': 10**12,
        'pb': 10**15,
        'eb': 10**18,
        'kib': 2**10,
        'mib': 2**20,
        'gib': 2**30,
        'tib': 2**40,
        'pib': 2**50,
        'eib': 2**60,
        'bit': 1 / 8,
        'kbit': 10**3 / 8,
        'mbit': 10**6 / 8,
        'gbit': 10**9 / 8,
        'tbit': 10**12 / 8,
        'pbit': 10**15 / 8,
        'ebit': 10**18 / 8,
        'kibit': 2**10 / 8,
        'mibit': 2**20 / 8,
        'gibit': 2**30 / 8,
        'tibit': 2**40 / 8,
        'pibit': 2**50 / 8,
        'eibit': 2**60 / 8,
    }
    byte_sizes.update({k.lower()[0]: v for k, v in byte_sizes.items() if 'i' not in k})

    byte_string_pattern = r'^\s*(\d*\.?\d+)\s*(\w+)?'
    byte_string_re = re.compile(byte_string_pattern, re.IGNORECASE)

    @classmethod
    def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        return core_schema.with_info_after_validator_function(
            function=cls._validate,
            schema=core_schema.union_schema(
                [
                    core_schema.str_schema(pattern=cls.byte_string_pattern),
                    core_schema.int_schema(ge=0),
                ],
                custom_error_type='byte_size',
                custom_error_message='could not parse value and unit from byte string',
            ),
            serialization=core_schema.plain_serializer_function_ser_schema(
                int, return_schema=core_schema.int_schema(ge=0)
            ),
        )

    @classmethod
    def _validate(cls, input_value: Any, /, _: core_schema.ValidationInfo) -> ByteSize:
        try:
            return cls(int(input_value))
        except ValueError:
            pass

        str_match = cls.byte_string_re.match(str(input_value))
        if str_match is None:
            raise PydanticCustomError('byte_size', 'could not parse value and unit from byte string')

        scalar, unit = str_match.groups()
        if unit is None:
            unit = 'b'

        try:
            unit_mult = cls.byte_sizes[unit.lower()]
        except KeyError:
            raise PydanticCustomError('byte_size_unit', 'could not interpret byte unit: {unit}', {'unit': unit})

        return cls(int(float(scalar) * unit_mult))

    def human_readable(self, decimal: bool = False, separator: str = '') -> str:
        """Converts a byte size to a human readable string.

        Args:
            decimal: If True, use decimal units (e.g. 1000 bytes per KB). If False, use binary units
                (e.g. 1024 bytes per KiB).
            separator: A string used to split the value and unit. Defaults to an empty string ('').

        Returns:
            A human readable string representation of the byte size.
        """
        if decimal:
            divisor = 1000
            units = 'B', 'KB', 'MB', 'GB', 'TB', 'PB'
            final_unit = 'EB'
        else:
            divisor = 1024
            units = 'B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB'
            final_unit = 'EiB'

        num = float(self)
        for unit in units:
            if abs(num) < divisor:
                if unit == 'B':
                    return f'{num:0.0f}{separator}{unit}'
                else:
                    return f'{num:0.1f}{separator}{unit}'
            num /= divisor

        return f'{num:0.1f}{separator}{final_unit}'

    def to(self, unit: str) -> float:
        """Converts a byte size to another unit, including both byte and bit units.

        Args:
            unit: The unit to convert to. Must be one of the following: B, KB, MB, GB, TB, PB, EB,
                KiB, MiB, GiB, TiB, PiB, EiB (byte units) and
                bit, kbit, mbit, gbit, tbit, pbit, ebit,
                kibit, mibit, gibit, tibit, pibit, eibit (bit units).

        Returns:
            The byte size in the new unit.
        """
        try:
            unit_div = self.byte_sizes[unit.lower()]
        except KeyError:
            raise PydanticCustomError('byte_size_unit', 'Could not interpret byte unit: {unit}', {'unit': unit})

        return self / unit_div

human_readable 

human_readable(
    decimal: bool = False, separator: str = ""
) -> str

将字节大小转换为人类可读的字符串。

参数

名称 类型 描述 默认值
decimal bool

如果为 True，则使用十进制单位（例如，每 KB 1000 字节）。如果为 False，则使用二进制单位（例如，每 KiB 1024 字节）。

 False
separator str

用于分隔值和单位的字符串。默认为空字符串 (``)。

 ''

返回

类型 描述
str

字节大小的人类可读字符串表示形式。
源代码位于 pydantic/types.py 中 
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def human_readable(self, decimal: bool = False, separator: str = '') -> str:
    """Converts a byte size to a human readable string.

    Args:
        decimal: If True, use decimal units (e.g. 1000 bytes per KB). If False, use binary units
            (e.g. 1024 bytes per KiB).
        separator: A string used to split the value and unit. Defaults to an empty string ('').

    Returns:
        A human readable string representation of the byte size.
    """
    if decimal:
        divisor = 1000
        units = 'B', 'KB', 'MB', 'GB', 'TB', 'PB'
        final_unit = 'EB'
    else:
        divisor = 1024
        units = 'B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB'
        final_unit = 'EiB'

    num = float(self)
    for unit in units:
        if abs(num) < divisor:
            if unit == 'B':
                return f'{num:0.0f}{separator}{unit}'
            else:
                return f'{num:0.1f}{separator}{unit}'
        num /= divisor

    return f'{num:0.1f}{separator}{final_unit}'

to 

to(unit: str) -> float

将字节大小转换为另一个单位，包括字节和比特单位。

参数

名称 类型 描述 默认值
unit str

要转换成的单位。必须是以下之一：B、KB、MB、GB、TB、PB、EB、KiB、MiB、GiB、TiB、PiB、EiB（字节单位）和 bit、kbit、mbit、gbit、tbit、pbit、ebit、kibit、mibit、gibit、tibit、pibit、eibit（比特单位）。

 required

返回

类型 描述
float

新单位的字节大小。
源代码位于 pydantic/types.py 中 
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def to(self, unit: str) -> float:
    """Converts a byte size to another unit, including both byte and bit units.

    Args:
        unit: The unit to convert to. Must be one of the following: B, KB, MB, GB, TB, PB, EB,
            KiB, MiB, GiB, TiB, PiB, EiB (byte units) and
            bit, kbit, mbit, gbit, tbit, pbit, ebit,
            kibit, mibit, gibit, tibit, pibit, eibit (bit units).

    Returns:
        The byte size in the new unit.
    """
    try:
        unit_div = self.byte_sizes[unit.lower()]
    except KeyError:
        raise PydanticCustomError('byte_size_unit', 'Could not interpret byte unit: {unit}', {'unit': unit})

    return self / unit_div

PastDate

过去的日期。

源代码位于 pydantic/types.py 中 
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class PastDate:
    """A date in the past."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.date_schema(now_op='past')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'date', cls.__name__)
            schema['now_op'] = 'past'
            return schema

    def __repr__(self) -> str:
        return 'PastDate'

FutureDate

将来的日期。

源代码位于 pydantic/types.py 中 
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class FutureDate:
    """A date in the future."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.date_schema(now_op='future')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'date', cls.__name__)
            schema['now_op'] = 'future'
            return schema

    def __repr__(self) -> str:
        return 'FutureDate'

AwareDatetime

需要时区信息的日期时间。

源代码位于 pydantic/types.py 中 
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class AwareDatetime:
    """A datetime that requires timezone info."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.datetime_schema(tz_constraint='aware')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'datetime', cls.__name__)
            schema['tz_constraint'] = 'aware'
            return schema

    def __repr__(self) -> str:
        return 'AwareDatetime'

NaiveDatetime

不需要时区信息的日期时间。

源代码位于 pydantic/types.py 中 
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class NaiveDatetime:
    """A datetime that doesn't require timezone info."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.datetime_schema(tz_constraint='naive')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'datetime', cls.__name__)
            schema['tz_constraint'] = 'naive'
            return schema

    def __repr__(self) -> str:
        return 'NaiveDatetime'

PastDatetime

必须是过去的日期时间。

源代码位于 pydantic/types.py 中 
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class PastDatetime:
    """A datetime that must be in the past."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.datetime_schema(now_op='past')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'datetime', cls.__name__)
            schema['now_op'] = 'past'
            return schema

    def __repr__(self) -> str:
        return 'PastDatetime'

FutureDatetime

必须是未来的日期时间。

源代码位于 pydantic/types.py 中 
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class FutureDatetime:
    """A datetime that must be in the future."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.datetime_schema(now_op='future')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'datetime', cls.__name__)
            schema['now_op'] = 'future'
            return schema

    def __repr__(self) -> str:
        return 'FutureDatetime'

EncoderProtocol

基类: Protocol

用于编码和解码数据为字节的协议。

源代码位于 pydantic/types.py 中 
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class EncoderProtocol(Protocol):
    """Protocol for encoding and decoding data to and from bytes."""

    @classmethod
    def decode(cls, data: bytes) -> bytes:
        """Decode the data using the encoder.

        Args:
            data: The data to decode.

        Returns:
            The decoded data.
        """
        ...

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        """Encode the data using the encoder.

        Args:
            value: The data to encode.

        Returns:
            The encoded data.
        """
        ...

    @classmethod
    def get_json_format(cls) -> str:
        """Get the JSON format for the encoded data.

        Returns:
            The JSON format for the encoded data.
        """
        ...

decode classmethod 

decode(data: bytes) -> bytes

使用编码器解码数据。

参数

名称 类型 描述 默认值
data bytes

要解码的数据。

 required

返回

类型 描述
bytes

解码后的数据。
源代码位于 pydantic/types.py 中 
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@classmethod
def decode(cls, data: bytes) -> bytes:
    """Decode the data using the encoder.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    ...

encode classmethod 

encode(value: bytes) -> bytes

使用编码器编码数据。

参数

名称 类型 描述 默认值
value bytes

要编码的数据。

 required

返回

类型 描述
bytes

编码后的数据。
源代码位于 pydantic/types.py 中 
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@classmethod
def encode(cls, value: bytes) -> bytes:
    """Encode the data using the encoder.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    ...

get_json_format classmethod 

get_json_format() -> str

获取编码数据的 JSON 格式。

返回

类型 描述
str

编码数据的 JSON 格式。
源代码位于 pydantic/types.py 中 
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@classmethod
def get_json_format(cls) -> str:
    """Get the JSON format for the encoded data.

    Returns:
        The JSON format for the encoded data.
    """
    ...

Base64Encoder

基类: EncoderProtocol

标准（非 URL 安全）Base64 编码器。

源代码位于 pydantic/types.py 中 
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class Base64Encoder(EncoderProtocol):
    """Standard (non-URL-safe) Base64 encoder."""

    @classmethod
    def decode(cls, data: bytes) -> bytes:
        """Decode the data from base64 encoded bytes to original bytes data.

        Args:
            data: The data to decode.

        Returns:
            The decoded data.
        """
        try:
            return base64.b64decode(data)
        except ValueError as e:
            raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        """Encode the data from bytes to a base64 encoded bytes.

        Args:
            value: The data to encode.

        Returns:
            The encoded data.
        """
        return base64.b64encode(value)

    @classmethod
    def get_json_format(cls) -> Literal['base64']:
        """Get the JSON format for the encoded data.

        Returns:
            The JSON format for the encoded data.
        """
        return 'base64'

decode classmethod 

decode(data: bytes) -> bytes

将 base64 编码的字节数据解码为原始字节数据。

参数

名称 类型 描述 默认值
data bytes

要解码的数据。

 required

返回

类型 描述
bytes

解码后的数据。
源代码位于 pydantic/types.py 中 
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@classmethod
def decode(cls, data: bytes) -> bytes:
    """Decode the data from base64 encoded bytes to original bytes data.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    try:
        return base64.b64decode(data)
    except ValueError as e:
        raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})

encode classmethod 

encode(value: bytes) -> bytes

将字节数据编码为 base64 编码的字节数据。

参数

名称 类型 描述 默认值
value bytes

要编码的数据。

 required

返回

类型 描述
bytes

编码后的数据。
源代码位于 pydantic/types.py 中 
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@classmethod
def encode(cls, value: bytes) -> bytes:
    """Encode the data from bytes to a base64 encoded bytes.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return base64.b64encode(value)

get_json_format classmethod 

get_json_format() -> Literal['base64']

获取编码数据的 JSON 格式。

返回

类型 描述
Literal['base64']

编码数据的 JSON 格式。
源代码位于 pydantic/types.py 中 
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@classmethod
def get_json_format(cls) -> Literal['base64']:
    """Get the JSON format for the encoded data.

    Returns:
        The JSON format for the encoded data.
    """
    return 'base64'

Base64UrlEncoder

基类: EncoderProtocol

URL 安全的 Base64 编码器。

源代码位于 pydantic/types.py 中 
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class Base64UrlEncoder(EncoderProtocol):
    """URL-safe Base64 encoder."""

    @classmethod
    def decode(cls, data: bytes) -> bytes:
        """Decode the data from base64 encoded bytes to original bytes data.

        Args:
            data: The data to decode.

        Returns:
            The decoded data.
        """
        try:
            return base64.urlsafe_b64decode(data)
        except ValueError as e:
            raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        """Encode the data from bytes to a base64 encoded bytes.

        Args:
            value: The data to encode.

        Returns:
            The encoded data.
        """
        return base64.urlsafe_b64encode(value)

    @classmethod
    def get_json_format(cls) -> Literal['base64url']:
        """Get the JSON format for the encoded data.

        Returns:
            The JSON format for the encoded data.
        """
        return 'base64url'

decode classmethod 

decode(data: bytes) -> bytes

将 base64 编码的字节数据解码为原始字节数据。

参数

名称 类型 描述 默认值
data bytes

要解码的数据。

 required

返回

类型 描述
bytes

解码后的数据。
源代码位于 pydantic/types.py 中 
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@classmethod
def decode(cls, data: bytes) -> bytes:
    """Decode the data from base64 encoded bytes to original bytes data.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    try:
        return base64.urlsafe_b64decode(data)
    except ValueError as e:
        raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})

encode classmethod 

encode(value: bytes) -> bytes

将字节数据编码为 base64 编码的字节数据。

参数

名称 类型 描述 默认值
value bytes

要编码的数据。

 required

返回

类型 描述
bytes

编码后的数据。
源代码位于 pydantic/types.py 中 
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@classmethod
def encode(cls, value: bytes) -> bytes:
    """Encode the data from bytes to a base64 encoded bytes.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return base64.urlsafe_b64encode(value)

get_json_format classmethod 

get_json_format() -> Literal['base64url']

获取编码数据的 JSON 格式。

返回

类型 描述
Literal['base64url']

编码数据的 JSON 格式。
源代码位于 pydantic/types.py 中 
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@classmethod
def get_json_format(cls) -> Literal['base64url']:
    """Get the JSON format for the encoded data.

    Returns:
        The JSON format for the encoded data.
    """
    return 'base64url'

EncodedBytes dataclass

一种使用指定编码器进行编码和解码的字节类型。

EncodedBytes 需要一个实现 EncoderProtocol 的编码器才能运行。

from typing import Annotated

from pydantic import BaseModel, EncodedBytes, EncoderProtocol, ValidationError

class MyEncoder(EncoderProtocol):
    @classmethod
    def decode(cls, data: bytes) -> bytes:
        if data == b'**undecodable**':
            raise ValueError('Cannot decode data')
        return data[13:]

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        return b'**encoded**: ' + value

    @classmethod
    def get_json_format(cls) -> str:
        return 'my-encoder'

MyEncodedBytes = Annotated[bytes, EncodedBytes(encoder=MyEncoder)]

class Model(BaseModel):
    my_encoded_bytes: MyEncodedBytes

# Initialize the model with encoded data
m = Model(my_encoded_bytes=b'**encoded**: some bytes')

# Access decoded value
print(m.my_encoded_bytes)
#> b'some bytes'

# Serialize into the encoded form
print(m.model_dump())
#> {'my_encoded_bytes': b'**encoded**: some bytes'}

# Validate encoded data
try:
    Model(my_encoded_bytes=b'**undecodable**')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    my_encoded_bytes
      Value error, Cannot decode data [type=value_error, input_value=b'**undecodable**', input_type=bytes]
    '''
源代码位于 pydantic/types.py 中 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true)
class EncodedBytes:
    """A bytes type that is encoded and decoded using the specified encoder.

    `EncodedBytes` needs an encoder that implements `EncoderProtocol` to operate.

    ```python
    from typing import Annotated

    from pydantic import BaseModel, EncodedBytes, EncoderProtocol, ValidationError

    class MyEncoder(EncoderProtocol):
        @classmethod
        def decode(cls, data: bytes) -> bytes:
            if data == b'**undecodable**':
                raise ValueError('Cannot decode data')
            return data[13:]

        @classmethod
        def encode(cls, value: bytes) -> bytes:
            return b'**encoded**: ' + value

        @classmethod
        def get_json_format(cls) -> str:
            return 'my-encoder'

    MyEncodedBytes = Annotated[bytes, EncodedBytes(encoder=MyEncoder)]

    class Model(BaseModel):
        my_encoded_bytes: MyEncodedBytes

    # Initialize the model with encoded data
    m = Model(my_encoded_bytes=b'**encoded**: some bytes')

    # Access decoded value
    print(m.my_encoded_bytes)
    #> b'some bytes'

    # Serialize into the encoded form
    print(m.model_dump())
    #> {'my_encoded_bytes': b'**encoded**: some bytes'}

    # Validate encoded data
    try:
        Model(my_encoded_bytes=b'**undecodable**')
    except ValidationError as e:
        print(e)
        '''
        1 validation error for Model
        my_encoded_bytes
          Value error, Cannot decode data [type=value_error, input_value=b'**undecodable**', input_type=bytes]
        '''
    ```
    """

    encoder: type[EncoderProtocol]

    def __get_pydantic_json_schema__(
        self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
    ) -> JsonSchemaValue:
        field_schema = handler(core_schema)
        field_schema.update(type='string', format=self.encoder.get_json_format())
        return field_schema

    def __get_pydantic_core_schema__(self, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        schema = handler(source)
        _check_annotated_type(schema['type'], 'bytes', self.__class__.__name__)
        return core_schema.with_info_after_validator_function(
            function=self.decode,
            schema=schema,
            serialization=core_schema.plain_serializer_function_ser_schema(function=self.encode),
        )

    def decode(self, data: bytes, _: core_schema.ValidationInfo) -> bytes:
        """Decode the data using the specified encoder.

        Args:
            data: The data to decode.

        Returns:
            The decoded data.
        """
        return self.encoder.decode(data)

    def encode(self, value: bytes) -> bytes:
        """Encode the data using the specified encoder.

        Args:
            value: The data to encode.

        Returns:
            The encoded data.
        """
        return self.encoder.encode(value)

    def __hash__(self) -> int:
        return hash(self.encoder)

decode 

decode(data: bytes, _: ValidationInfo) -> bytes

使用指定的编码器解码数据。

参数

名称 类型 描述 默认值
data bytes

要解码的数据。

 required

返回

类型 描述
bytes

解码后的数据。
源代码位于 pydantic/types.py 中 
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def decode(self, data: bytes, _: core_schema.ValidationInfo) -> bytes:
    """Decode the data using the specified encoder.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    return self.encoder.decode(data)

encode 

encode(value: bytes) -> bytes

使用指定的编码器编码数据。

参数

名称 类型 描述 默认值
value bytes

要编码的数据。

 required

返回

类型 描述
bytes

编码后的数据。
源代码位于 pydantic/types.py 中 
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def encode(self, value: bytes) -> bytes:
    """Encode the data using the specified encoder.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return self.encoder.encode(value)

EncodedStr dataclass

一种使用指定编码器进行编码和解码的字符串类型。

EncodedStr 需要一个实现 EncoderProtocol 的编码器才能运行。

from typing import Annotated

from pydantic import BaseModel, EncodedStr, EncoderProtocol, ValidationError

class MyEncoder(EncoderProtocol):
    @classmethod
    def decode(cls, data: bytes) -> bytes:
        if data == b'**undecodable**':
            raise ValueError('Cannot decode data')
        return data[13:]

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        return b'**encoded**: ' + value

    @classmethod
    def get_json_format(cls) -> str:
        return 'my-encoder'

MyEncodedStr = Annotated[str, EncodedStr(encoder=MyEncoder)]

class Model(BaseModel):
    my_encoded_str: MyEncodedStr

# Initialize the model with encoded data
m = Model(my_encoded_str='**encoded**: some str')

# Access decoded value
print(m.my_encoded_str)
#> some str

# Serialize into the encoded form
print(m.model_dump())
#> {'my_encoded_str': '**encoded**: some str'}

# Validate encoded data
try:
    Model(my_encoded_str='**undecodable**')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    my_encoded_str
      Value error, Cannot decode data [type=value_error, input_value='**undecodable**', input_type=str]
    '''
源代码位于 pydantic/types.py 中 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true)
class EncodedStr:
    """A str type that is encoded and decoded using the specified encoder.

    `EncodedStr` needs an encoder that implements `EncoderProtocol` to operate.

    ```python
    from typing import Annotated

    from pydantic import BaseModel, EncodedStr, EncoderProtocol, ValidationError

    class MyEncoder(EncoderProtocol):
        @classmethod
        def decode(cls, data: bytes) -> bytes:
            if data == b'**undecodable**':
                raise ValueError('Cannot decode data')
            return data[13:]

        @classmethod
        def encode(cls, value: bytes) -> bytes:
            return b'**encoded**: ' + value

        @classmethod
        def get_json_format(cls) -> str:
            return 'my-encoder'

    MyEncodedStr = Annotated[str, EncodedStr(encoder=MyEncoder)]

    class Model(BaseModel):
        my_encoded_str: MyEncodedStr

    # Initialize the model with encoded data
    m = Model(my_encoded_str='**encoded**: some str')

    # Access decoded value
    print(m.my_encoded_str)
    #> some str

    # Serialize into the encoded form
    print(m.model_dump())
    #> {'my_encoded_str': '**encoded**: some str'}

    # Validate encoded data
    try:
        Model(my_encoded_str='**undecodable**')
    except ValidationError as e:
        print(e)
        '''
        1 validation error for Model
        my_encoded_str
          Value error, Cannot decode data [type=value_error, input_value='**undecodable**', input_type=str]
        '''
    ```
    """

    encoder: type[EncoderProtocol]

    def __get_pydantic_json_schema__(
        self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
    ) -> JsonSchemaValue:
        field_schema = handler(core_schema)
        field_schema.update(type='string', format=self.encoder.get_json_format())
        return field_schema

    def __get_pydantic_core_schema__(self, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        schema = handler(source)
        _check_annotated_type(schema['type'], 'str', self.__class__.__name__)
        return core_schema.with_info_after_validator_function(
            function=self.decode_str,
            schema=schema,
            serialization=core_schema.plain_serializer_function_ser_schema(function=self.encode_str),
        )

    def decode_str(self, data: str, _: core_schema.ValidationInfo) -> str:
        """Decode the data using the specified encoder.

        Args:
            data: The data to decode.

        Returns:
            The decoded data.
        """
        return self.encoder.decode(data.encode()).decode()

    def encode_str(self, value: str) -> str:
        """Encode the data using the specified encoder.

        Args:
            value: The data to encode.

        Returns:
            The encoded data.
        """
        return self.encoder.encode(value.encode()).decode()  # noqa: UP008

    def __hash__(self) -> int:
        return hash(self.encoder)

decode_str 

decode_str(data: str, _: ValidationInfo) -> str

使用指定的编码器解码数据。

参数

名称 类型 描述 默认值
data str

要解码的数据。

 required

返回

类型 描述
str

解码后的数据。
源代码位于 pydantic/types.py 中 
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def decode_str(self, data: str, _: core_schema.ValidationInfo) -> str:
    """Decode the data using the specified encoder.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    return self.encoder.decode(data.encode()).decode()

encode_str 

encode_str(value: str) -> str

使用指定的编码器编码数据。

参数

名称 类型 描述 默认值
value str

要编码的数据。

 required

返回

类型 描述
str

编码后的数据。
源代码位于 pydantic/types.py 中 
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def encode_str(self, value: str) -> str:
    """Encode the data using the specified encoder.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return self.encoder.encode(value.encode()).decode()  # noqa: UP008

GetPydanticSchema dataclass

使用文档

使用 GetPydanticSchema 减少样板代码

一个方便的类，用于创建提供 pydantic 自定义类型钩子的注解。

这个类旨在消除创建自定义“标记”的需求，该标记定义了 __get_pydantic_core_schema__ 和 __get_pydantic_json_schema__ 自定义钩子方法。

例如，要使字段在类型检查器中被视为 int，但在 pydantic 中被视为 Any，您可以这样做

from typing import Annotated, Any

from pydantic import BaseModel, GetPydanticSchema

HandleAsAny = GetPydanticSchema(lambda _s, h: h(Any))

class Model(BaseModel):
    x: Annotated[int, HandleAsAny]  # pydantic sees `x: Any`

print(repr(Model(x='abc').x))
#> 'abc'

源代码位于 pydantic/types.py 中 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true)
class GetPydanticSchema:
    """!!! abstract "Usage Documentation"
        [Using `GetPydanticSchema` to Reduce Boilerplate](../concepts/types.md#using-getpydanticschema-to-reduce-boilerplate)

    A convenience class for creating an annotation that provides pydantic custom type hooks.

    This class is intended to eliminate the need to create a custom "marker" which defines the
     `__get_pydantic_core_schema__` and `__get_pydantic_json_schema__` custom hook methods.

    For example, to have a field treated by type checkers as `int`, but by pydantic as `Any`, you can do:
    ```python
    from typing import Annotated, Any

    from pydantic import BaseModel, GetPydanticSchema

    HandleAsAny = GetPydanticSchema(lambda _s, h: h(Any))

    class Model(BaseModel):
        x: Annotated[int, HandleAsAny]  # pydantic sees `x: Any`

    print(repr(Model(x='abc').x))
    #> 'abc'
    ```
    """

    get_pydantic_core_schema: Callable[[Any, GetCoreSchemaHandler], CoreSchema] | None = None
    get_pydantic_json_schema: Callable[[Any, GetJsonSchemaHandler], JsonSchemaValue] | None = None

    # Note: we may want to consider adding a convenience staticmethod `def for_type(type_: Any) -> GetPydanticSchema:`
    #   which returns `GetPydanticSchema(lambda _s, h: h(type_))`

    if not TYPE_CHECKING:
        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access

        def __getattr__(self, item: str) -> Any:
            """Use this rather than defining `__get_pydantic_core_schema__` etc. to reduce the number of nested calls."""
            if item == '__get_pydantic_core_schema__' and self.get_pydantic_core_schema:
                return self.get_pydantic_core_schema
            elif item == '__get_pydantic_json_schema__' and self.get_pydantic_json_schema:
                return self.get_pydantic_json_schema
            else:
                return object.__getattribute__(self, item)

    __hash__ = object.__hash__

Tag dataclass

提供一种方法来指定用于（可调用）可辨别联合的案例的预期标签。

还提供一种在错误消息中标记联合案例的方法。

当使用可调用的 Discriminator 时，将 Tag 附加到 Union 中的每个案例，以指定应用于标识该案例的标签。例如，在下面的示例中，Tag 用于指定如果 get_discriminator_value 返回 'apple'，则输入应验证为 ApplePie，如果返回 'pumpkin'，则输入应验证为 PumpkinPie。

Tag 的主要作用是将可调用 Discriminator 函数的返回值映射到相关 Union 的适当成员。

from typing import Annotated, Any, Literal, Union

from pydantic import BaseModel, Discriminator, Tag

class Pie(BaseModel):
    time_to_cook: int
    num_ingredients: int

class ApplePie(Pie):
    fruit: Literal['apple'] = 'apple'

class PumpkinPie(Pie):
    filling: Literal['pumpkin'] = 'pumpkin'

def get_discriminator_value(v: Any) -> str:
    if isinstance(v, dict):
        return v.get('fruit', v.get('filling'))
    return getattr(v, 'fruit', getattr(v, 'filling', None))

class ThanksgivingDinner(BaseModel):
    dessert: Annotated[
        Union[
            Annotated[ApplePie, Tag('apple')],
            Annotated[PumpkinPie, Tag('pumpkin')],
        ],
        Discriminator(get_discriminator_value),
    ]

apple_variation = ThanksgivingDinner.model_validate(
    {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
)
print(repr(apple_variation))
'''
ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
'''

pumpkin_variation = ThanksgivingDinner.model_validate(
    {
        'dessert': {
            'filling': 'pumpkin',
            'time_to_cook': 40,
            'num_ingredients': 6,
        }
    }
)
print(repr(pumpkin_variation))
'''
ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
'''

注意

您必须为与可调用 Discriminator 关联的 Tag 中的每个案例指定 Tag。 否则将导致 PydanticUserError，错误代码为 callable-discriminator-no-tag。

有关如何使用 Tag 的更多详细信息，请参阅 可辨别联合 概念文档。

源代码位于 pydantic/types.py 中 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
class Tag:
    """Provides a way to specify the expected tag to use for a case of a (callable) discriminated union.

    Also provides a way to label a union case in error messages.

    When using a callable `Discriminator`, attach a `Tag` to each case in the `Union` to specify the tag that
    should be used to identify that case. For example, in the below example, the `Tag` is used to specify that
    if `get_discriminator_value` returns `'apple'`, the input should be validated as an `ApplePie`, and if it
    returns `'pumpkin'`, the input should be validated as a `PumpkinPie`.

    The primary role of the `Tag` here is to map the return value from the callable `Discriminator` function to
    the appropriate member of the `Union` in question.

    ```python
    from typing import Annotated, Any, Literal, Union

    from pydantic import BaseModel, Discriminator, Tag

    class Pie(BaseModel):
        time_to_cook: int
        num_ingredients: int

    class ApplePie(Pie):
        fruit: Literal['apple'] = 'apple'

    class PumpkinPie(Pie):
        filling: Literal['pumpkin'] = 'pumpkin'

    def get_discriminator_value(v: Any) -> str:
        if isinstance(v, dict):
            return v.get('fruit', v.get('filling'))
        return getattr(v, 'fruit', getattr(v, 'filling', None))

    class ThanksgivingDinner(BaseModel):
        dessert: Annotated[
            Union[
                Annotated[ApplePie, Tag('apple')],
                Annotated[PumpkinPie, Tag('pumpkin')],
            ],
            Discriminator(get_discriminator_value),
        ]

    apple_variation = ThanksgivingDinner.model_validate(
        {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
    )
    print(repr(apple_variation))
    '''
    ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
    '''

    pumpkin_variation = ThanksgivingDinner.model_validate(
        {
            'dessert': {
                'filling': 'pumpkin',
                'time_to_cook': 40,
                'num_ingredients': 6,
            }
        }
    )
    print(repr(pumpkin_variation))
    '''
    ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
    '''
    ```

    !!! note
        You must specify a `Tag` for every case in a `Tag` that is associated with a
        callable `Discriminator`. Failing to do so will result in a `PydanticUserError` with code
        [`callable-discriminator-no-tag`](../errors/usage_errors.md#callable-discriminator-no-tag).

    See the [Discriminated Unions] concepts docs for more details on how to use `Tag`s.

    [Discriminated Unions]: ../concepts/unions.md#discriminated-unions
    """

    tag: str

    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema:
        schema = handler(source_type)
        metadata = cast('CoreMetadata', schema.setdefault('metadata', {}))
        metadata['pydantic_internal_union_tag_key'] = self.tag
        return schema

Discriminator dataclass

使用文档

具有 Callable Discriminator 的可辨别联合

提供一种使用自定义可调用对象作为提取联合辨别器值的方法。

这使您可以获得类似于 Field(discriminator=<field_name>) 的验证行为，而无需在所有联合选择中拥有单个共享字段。 这也使得处理模型和原始类型的联合以及可辨别联合风格的验证错误成为可能。 最后，这允许您使用自定义可调用对象作为识别值属于哪个联合成员的方法，同时仍然看到可辨别联合的所有性能优势。

考虑这个例子，与普通的 Union 相比，使用 Discriminator 和 TaggedUnion 的性能更高。

from typing import Annotated, Any, Literal, Union

from pydantic import BaseModel, Discriminator, Tag

class Pie(BaseModel):
    time_to_cook: int
    num_ingredients: int

class ApplePie(Pie):
    fruit: Literal['apple'] = 'apple'

class PumpkinPie(Pie):
    filling: Literal['pumpkin'] = 'pumpkin'

def get_discriminator_value(v: Any) -> str:
    if isinstance(v, dict):
        return v.get('fruit', v.get('filling'))
    return getattr(v, 'fruit', getattr(v, 'filling', None))

class ThanksgivingDinner(BaseModel):
    dessert: Annotated[
        Union[
            Annotated[ApplePie, Tag('apple')],
            Annotated[PumpkinPie, Tag('pumpkin')],
        ],
        Discriminator(get_discriminator_value),
    ]

apple_variation = ThanksgivingDinner.model_validate(
    {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
)
print(repr(apple_variation))
'''
ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
'''

pumpkin_variation = ThanksgivingDinner.model_validate(
    {
        'dessert': {
            'filling': 'pumpkin',
            'time_to_cook': 40,
            'num_ingredients': 6,
        }
    }
)
print(repr(pumpkin_variation))
'''
ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
'''

有关如何使用 Discriminator 的更多详细信息，请参阅 可辨别联合 概念文档。

源代码位于 pydantic/types.py 中 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
class Discriminator:
    """!!! abstract "Usage Documentation"
        [Discriminated Unions with `Callable` `Discriminator`](../concepts/unions.md#discriminated-unions-with-callable-discriminator)

    Provides a way to use a custom callable as the way to extract the value of a union discriminator.

    This allows you to get validation behavior like you'd get from `Field(discriminator=<field_name>)`,
    but without needing to have a single shared field across all the union choices. This also makes it
    possible to handle unions of models and primitive types with discriminated-union-style validation errors.
    Finally, this allows you to use a custom callable as the way to identify which member of a union a value
    belongs to, while still seeing all the performance benefits of a discriminated union.

    Consider this example, which is much more performant with the use of `Discriminator` and thus a `TaggedUnion`
    than it would be as a normal `Union`.

    ```python
    from typing import Annotated, Any, Literal, Union

    from pydantic import BaseModel, Discriminator, Tag

    class Pie(BaseModel):
        time_to_cook: int
        num_ingredients: int

    class ApplePie(Pie):
        fruit: Literal['apple'] = 'apple'

    class PumpkinPie(Pie):
        filling: Literal['pumpkin'] = 'pumpkin'

    def get_discriminator_value(v: Any) -> str:
        if isinstance(v, dict):
            return v.get('fruit', v.get('filling'))
        return getattr(v, 'fruit', getattr(v, 'filling', None))

    class ThanksgivingDinner(BaseModel):
        dessert: Annotated[
            Union[
                Annotated[ApplePie, Tag('apple')],
                Annotated[PumpkinPie, Tag('pumpkin')],
            ],
            Discriminator(get_discriminator_value),
        ]

    apple_variation = ThanksgivingDinner.model_validate(
        {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
    )
    print(repr(apple_variation))
    '''
    ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
    '''

    pumpkin_variation = ThanksgivingDinner.model_validate(
        {
            'dessert': {
                'filling': 'pumpkin',
                'time_to_cook': 40,
                'num_ingredients': 6,
            }
        }
    )
    print(repr(pumpkin_variation))
    '''
    ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
    '''
    ```

    See the [Discriminated Unions] concepts docs for more details on how to use `Discriminator`s.

    [Discriminated Unions]: ../concepts/unions.md#discriminated-unions
    """

    discriminator: str | Callable[[Any], Hashable]
    """The callable or field name for discriminating the type in a tagged union.

    A `Callable` discriminator must extract the value of the discriminator from the input.
    A `str` discriminator must be the name of a field to discriminate against.
    """
    custom_error_type: str | None = None
    """Type to use in [custom errors](../errors/errors.md) replacing the standard discriminated union
    validation errors.
    """
    custom_error_message: str | None = None
    """Message to use in custom errors."""
    custom_error_context: dict[str, int | str | float] | None = None
    """Context to use in custom errors."""

    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema:
        if not is_union_origin(get_origin(source_type)):
            raise TypeError(f'{type(self).__name__} must be used with a Union type, not {source_type}')

        if isinstance(self.discriminator, str):
            from pydantic import Field

            return handler(Annotated[source_type, Field(discriminator=self.discriminator)])
        else:
            original_schema = handler(source_type)
            return self._convert_schema(original_schema)

    def _convert_schema(self, original_schema: core_schema.CoreSchema) -> core_schema.TaggedUnionSchema:
        if original_schema['type'] != 'union':
            # This likely indicates that the schema was a single-item union that was simplified.
            # In this case, we do the same thing we do in
            # `pydantic._internal._discriminated_union._ApplyInferredDiscriminator._apply_to_root`, namely,
            # package the generated schema back into a single-item union.
            original_schema = core_schema.union_schema([original_schema])

        tagged_union_choices = {}
        for choice in original_schema['choices']:
            tag = None
            if isinstance(choice, tuple):
                choice, tag = choice
            metadata = cast('CoreMetadata | None', choice.get('metadata'))
            if metadata is not None:
                tag = metadata.get('pydantic_internal_union_tag_key') or tag
            if tag is None:
                raise PydanticUserError(
                    f'`Tag` not provided for choice {choice} used with `Discriminator`',
                    code='callable-discriminator-no-tag',
                )
            tagged_union_choices[tag] = choice

        # Have to do these verbose checks to ensure falsy values ('' and {}) don't get ignored
        custom_error_type = self.custom_error_type
        if custom_error_type is None:
            custom_error_type = original_schema.get('custom_error_type')

        custom_error_message = self.custom_error_message
        if custom_error_message is None:
            custom_error_message = original_schema.get('custom_error_message')

        custom_error_context = self.custom_error_context
        if custom_error_context is None:
            custom_error_context = original_schema.get('custom_error_context')

        custom_error_type = original_schema.get('custom_error_type') if custom_error_type is None else custom_error_type
        return core_schema.tagged_union_schema(
            tagged_union_choices,
            self.discriminator,
            custom_error_type=custom_error_type,
            custom_error_message=custom_error_message,
            custom_error_context=custom_error_context,
            strict=original_schema.get('strict'),
            ref=original_schema.get('ref'),
            metadata=original_schema.get('metadata'),
            serialization=original_schema.get('serialization'),
        )

discriminator instance-attribute 

discriminator: str | Callable[[Any], Hashable]

用于辨别标记联合中类型的可调用对象或字段名称。

Callable 辨别器必须从输入中提取辨别器的值。 str 辨别器必须是要辨别的字段的名称。

custom_error_type class-attribute instance-attribute 

custom_error_type: str | None = None

在 自定义错误 中使用的类型，用于替换标准的可辨别联合验证错误。

custom_error_message class-attribute instance-attribute 

custom_error_message: str | None = None

在自定义错误中使用的消息。

custom_error_context class-attribute instance-attribute 

custom_error_context: (
    dict[str, int | str | float] | None
) = None

在自定义错误中使用的上下文。

FailFast dataclass

基类： PydanticMetadata, BaseMetadata

FailFast 注解可用于指定验证应在第一个错误处停止。

当您想要验证大量数据并且只需要知道它是否有效时，这可能很有用。

如果您想更快地验证数据，您可能需要启用此设置（基本上，如果您使用此设置，验证性能会更高，但缺点是您获得的信息较少）。

from typing import Annotated

from pydantic import BaseModel, FailFast, ValidationError

class Model(BaseModel):
    x: Annotated[list[int], FailFast()]

# This will raise a single error for the first invalid value and stop validation
try:
    obj = Model(x=[1, 2, 'a', 4, 5, 'b', 7, 8, 9, 'c'])
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    x.2
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
    '''
源代码位于 pydantic/types.py 中 
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@_dataclasses.dataclass
class FailFast(_fields.PydanticMetadata, BaseMetadata):
    """A `FailFast` annotation can be used to specify that validation should stop at the first error.

    This can be useful when you want to validate a large amount of data and you only need to know if it's valid or not.

    You might want to enable this setting if you want to validate your data faster (basically, if you use this,
    validation will be more performant with the caveat that you get less information).

    ```python
    from typing import Annotated

    from pydantic import BaseModel, FailFast, ValidationError

    class Model(BaseModel):
        x: Annotated[list[int], FailFast()]

    # This will raise a single error for the first invalid value and stop validation
    try:
        obj = Model(x=[1, 2, 'a', 4, 5, 'b', 7, 8, 9, 'c'])
    except ValidationError as e:
        print(e)
        '''
        1 validation error for Model
        x.2
          Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
        '''
    ```
    """

    fail_fast: bool = True

conint 

conint(
    *,
    strict: bool | None = None,
    gt: int | None = None,
    ge: int | None = None,
    lt: int | None = None,
    le: int | None = None,
    multiple_of: int | None = None
) -> type[int]

不推荐使用

不推荐使用此函数，建议使用带有 Field 的 Annotated 代替。

此函数将在 Pydantic 3.0 中弃用。

原因是 conint 返回一个类型，这与静态分析工具不太兼容。

from pydantic import BaseModel, conint

class Foo(BaseModel):
    bar: conint(strict=True, gt=0)

from typing import Annotated

from pydantic import BaseModel, Field

class Foo(BaseModel):
    bar: Annotated[int, Field(strict=True, gt=0)]

int 的包装器，允许额外的约束。

参数

名称 类型 描述 默认值
strict bool | None

是否以严格模式验证整数。 默认为 None。

 None
gt int | None

值必须大于此值。

 None
ge int | None

值必须大于或等于此值。

 None
lt int | None

值必须小于此值。

 None
le int | None

值必须小于或等于此值。

 None
multiple_of int | None

值必须是此值的倍数。

 None

返回

类型 描述
type[int]

包装的整数类型。

 
from pydantic import BaseModel, ValidationError, conint

class ConstrainedExample(BaseModel):
    constrained_int: conint(gt=1)

m = ConstrainedExample(constrained_int=2)
print(repr(m))
#> ConstrainedExample(constrained_int=2)

try:
    ConstrainedExample(constrained_int=0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('constrained_int',),
            'msg': 'Input should be greater than 1',
            'input': 0,
            'ctx': {'gt': 1},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''
源代码位于 pydantic/types.py 中 
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def conint(
    *,
    strict: bool | None = None,
    gt: int | None = None,
    ge: int | None = None,
    lt: int | None = None,
    le: int | None = None,
    multiple_of: int | None = None,
) -> type[int]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.pythonlang.cn/3/library/typing.html#typing.Annotated) with
        [`Field`][pydantic.fields.Field] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `conint` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```python
            from pydantic import BaseModel, conint

            class Foo(BaseModel):
                bar: conint(strict=True, gt=0)
            ```

        === ":white_check_mark: Do this"
            ```python
            from typing import Annotated

            from pydantic import BaseModel, Field

            class Foo(BaseModel):
                bar: Annotated[int, Field(strict=True, gt=0)]
            ```

    A wrapper around `int` that allows for additional constraints.

    Args:
        strict: Whether to validate the integer in strict mode. Defaults to `None`.
        gt: The value must be greater than this.
        ge: The value must be greater than or equal to this.
        lt: The value must be less than this.
        le: The value must be less than or equal to this.
        multiple_of: The value must be a multiple of this.

    Returns:
        The wrapped integer type.

    ```python
    from pydantic import BaseModel, ValidationError, conint

    class ConstrainedExample(BaseModel):
        constrained_int: conint(gt=1)

    m = ConstrainedExample(constrained_int=2)
    print(repr(m))
    #> ConstrainedExample(constrained_int=2)

    try:
        ConstrainedExample(constrained_int=0)
    except ValidationError as e:
        print(e.errors())
        '''
        [
            {
                'type': 'greater_than',
                'loc': ('constrained_int',),
                'msg': 'Input should be greater than 1',
                'input': 0,
                'ctx': {'gt': 1},
                'url': 'https://errors.pydantic.dev/2/v/greater_than',
            }
        ]
        '''
    ```

    """  # noqa: D212
    return Annotated[  # pyright: ignore[reportReturnType]
        int,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
    ]

confloat 

confloat(
    *,
    strict: bool | None = None,
    gt: float | None = None,
    ge: float | None = None,
    lt: float | None = None,
    le: float | None = None,
    multiple_of: float | None = None,
    allow_inf_nan: bool | None = None
) -> type[float]

不推荐使用

不推荐使用此函数，建议使用带有 Field 的 Annotated 代替。

此函数将在 Pydantic 3.0 中弃用。

原因是 confloat 返回一个类型，这与静态分析工具不太兼容。

from pydantic import BaseModel, confloat

class Foo(BaseModel):
    bar: confloat(strict=True, gt=0)

from typing import Annotated

from pydantic import BaseModel, Field

class Foo(BaseModel):
    bar: Annotated[float, Field(strict=True, gt=0)]

float 的包装器，允许额外的约束。

参数

名称 类型 描述 默认值
strict bool | None

是否以严格模式验证浮点数。

 None
gt float | None

值必须大于此值。

 None
ge float | None

值必须大于或等于此值。

 None
lt float | None

值必须小于此值。

 None
le float | None

值必须小于或等于此值。

 None
multiple_of float | None

值必须是此值的倍数。

 None
allow_inf_nan bool | None

是否允许 -inf、inf 和 nan。

 None

返回

类型 描述
type[float]

包装的浮点数类型。

 
from pydantic import BaseModel, ValidationError, confloat

class ConstrainedExample(BaseModel):
    constrained_float: confloat(gt=1.0)

m = ConstrainedExample(constrained_float=1.1)
print(repr(m))
#> ConstrainedExample(constrained_float=1.1)

try:
    ConstrainedExample(constrained_float=0.9)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('constrained_float',),
            'msg': 'Input should be greater than 1',
            'input': 0.9,
            'ctx': {'gt': 1.0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''
源代码位于 pydantic/types.py 中 
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def confloat(
    *,
    strict: bool | None = None,
    gt: float | None = None,
    ge: float | None = None,
    lt: float | None = None,
    le: float | None = None,
    multiple_of: float | None = None,
    allow_inf_nan: bool | None = None,
) -> type[float]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.pythonlang.cn/3/library/typing.html#typing.Annotated) with
        [`Field`][pydantic.fields.Field] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `confloat` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```python
            from pydantic import BaseModel, confloat

            class Foo(BaseModel):
                bar: confloat(strict=True, gt=0)
            ```

        === ":white_check_mark: Do this"
            ```python
            from typing import Annotated

            from pydantic import BaseModel, Field

            class Foo(BaseModel):
                bar: Annotated[float, Field(strict=True, gt=0)]
            ```

    A wrapper around `float` that allows for additional constraints.

    Args:
        strict: Whether to validate the float in strict mode.
        gt: The value must be greater than this.
        ge: The value must be greater than or equal to this.
        lt: The value must be less than this.
        le: The value must be less than or equal to this.
        multiple_of: The value must be a multiple of this.
        allow_inf_nan: Whether to allow `-inf`, `inf`, and `nan`.

    Returns:
        The wrapped float type.

    ```python
    from pydantic import BaseModel, ValidationError, confloat

    class ConstrainedExample(BaseModel):
        constrained_float: confloat(gt=1.0)

    m = ConstrainedExample(constrained_float=1.1)
    print(repr(m))
    #> ConstrainedExample(constrained_float=1.1)

    try:
        ConstrainedExample(constrained_float=0.9)
    except ValidationError as e:
        print(e.errors())
        '''
        [
            {
                'type': 'greater_than',
                'loc': ('constrained_float',),
                'msg': 'Input should be greater than 1',
                'input': 0.9,
                'ctx': {'gt': 1.0},
                'url': 'https://errors.pydantic.dev/2/v/greater_than',
            }
        ]
        '''
    ```
    """  # noqa: D212
    return Annotated[  # pyright: ignore[reportReturnType]
        float,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
        AllowInfNan(allow_inf_nan) if allow_inf_nan is not None else None,
    ]

conbytes 

conbytes(
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None
) -> type[bytes]

bytes 的包装器，允许额外的约束。

参数

名称 类型 描述 默认值
min_length int | None

字节的最小长度。

 None
max_length int | None

字节的最大长度。

 None
strict bool | None

是否以严格模式验证字节。

 None

返回

类型 描述
type[bytes]

包装的字节类型。
源代码位于 pydantic/types.py 中 
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def conbytes(
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
) -> type[bytes]:
    """A wrapper around `bytes` that allows for additional constraints.

    Args:
        min_length: The minimum length of the bytes.
        max_length: The maximum length of the bytes.
        strict: Whether to validate the bytes in strict mode.

    Returns:
        The wrapped bytes type.
    """
    return Annotated[  # pyright: ignore[reportReturnType]
        bytes,
        Strict(strict) if strict is not None else None,
        annotated_types.Len(min_length or 0, max_length),
    ]

constr 

constr(
    *,
    strip_whitespace: bool | None = None,
    to_upper: bool | None = None,
    to_lower: bool | None = None,
    strict: bool | None = None,
    min_length: int | None = None,
    max_length: int | None = None,
    pattern: str | Pattern[str] | None = None
) -> type[str]

不推荐使用

不推荐使用此函数，建议使用带有 StringConstraints 的 Annotated 代替。

此函数将在 Pydantic 3.0 中弃用。

原因是 constr 返回一个类型，这与静态分析工具不太兼容。

from pydantic import BaseModel, constr

class Foo(BaseModel):
    bar: constr(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')

from typing import Annotated

from pydantic import BaseModel, StringConstraints

class Foo(BaseModel):
    bar: Annotated[
        str,
        StringConstraints(
            strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$'
        ),
    ]

str 的包装器，允许额外的约束。

from pydantic import BaseModel, constr

class Foo(BaseModel):
    bar: constr(strip_whitespace=True, to_upper=True)

foo = Foo(bar='  hello  ')
print(foo)
#> bar='HELLO'

参数

名称 类型 描述 默认值
strip_whitespace bool | None

是否删除前导和尾随空格。

 None
to_upper bool | None

是否将所有字符转换为大写。

 None
to_lower bool | None

是否将所有字符转换为小写。

 None
strict bool | None

是否以严格模式验证字符串。

 None
min_length int | None

字符串的最小长度。

 None
max_length int | None

字符串的最大长度。

 None
pattern str | Pattern[str] | None

用于验证字符串的正则表达式模式。

 None

返回

类型 描述
type[str]

包装的字符串类型。
源代码位于 pydantic/types.py 中 
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def constr(
    *,
    strip_whitespace: bool | None = None,
    to_upper: bool | None = None,
    to_lower: bool | None = None,
    strict: bool | None = None,
    min_length: int | None = None,
    max_length: int | None = None,
    pattern: str | Pattern[str] | None = None,
) -> type[str]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.pythonlang.cn/3/library/typing.html#typing.Annotated) with
        [`StringConstraints`][pydantic.types.StringConstraints] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `constr` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```python
            from pydantic import BaseModel, constr

            class Foo(BaseModel):
                bar: constr(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')
            ```

        === ":white_check_mark: Do this"
            ```python
            from typing import Annotated

            from pydantic import BaseModel, StringConstraints

            class Foo(BaseModel):
                bar: Annotated[
                    str,
                    StringConstraints(
                        strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$'
                    ),
                ]
            ```

    A wrapper around `str` that allows for additional constraints.

    ```python
    from pydantic import BaseModel, constr

    class Foo(BaseModel):
        bar: constr(strip_whitespace=True, to_upper=True)

    foo = Foo(bar='  hello  ')
    print(foo)
    #> bar='HELLO'
    ```

    Args:
        strip_whitespace: Whether to remove leading and trailing whitespace.
        to_upper: Whether to turn all characters to uppercase.
        to_lower: Whether to turn all characters to lowercase.
        strict: Whether to validate the string in strict mode.
        min_length: The minimum length of the string.
        max_length: The maximum length of the string.
        pattern: A regex pattern to validate the string against.

    Returns:
        The wrapped string type.
    """  # noqa: D212
    return Annotated[  # pyright: ignore[reportReturnType]
        str,
        StringConstraints(
            strip_whitespace=strip_whitespace,
            to_upper=to_upper,
            to_lower=to_lower,
            strict=strict,
            min_length=min_length,
            max_length=max_length,
            pattern=pattern,
        ),
    ]

conset 

conset(
    item_type: type[HashableItemType],
    *,
    min_length: int | None = None,
    max_length: int | None = None
) -> type[set[HashableItemType]]

typing.Set 的包装器，允许额外的约束。

参数

名称 类型 描述 默认值
item_type type[HashableItemType]

集合中项目的类型。

 required
min_length int | None

集合的最小长度。

 None
max_length int | None

集合的最大长度。

 None

返回

类型 描述
type[set[HashableItemType]]

包装的集合类型。
源代码位于 pydantic/types.py 中 
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def conset(
    item_type: type[HashableItemType], *, min_length: int | None = None, max_length: int | None = None
) -> type[set[HashableItemType]]:
    """A wrapper around `typing.Set` that allows for additional constraints.

    Args:
        item_type: The type of the items in the set.
        min_length: The minimum length of the set.
        max_length: The maximum length of the set.

    Returns:
        The wrapped set type.
    """
    return Annotated[set[item_type], annotated_types.Len(min_length or 0, max_length)]  # pyright: ignore[reportReturnType]

confrozenset 

confrozenset(
    item_type: type[HashableItemType],
    *,
    min_length: int | None = None,
    max_length: int | None = None
) -> type[frozenset[HashableItemType]]

typing.FrozenSet 的包装器，允许额外的约束。

参数

名称 类型 描述 默认值
item_type type[HashableItemType]

frozenset 中项目的类型。

 required
min_length int | None

frozenset 的最小长度。

 None
max_length int | None

frozenset 的最大长度。

 None

返回

类型 描述
type[frozenset[HashableItemType]]

包装的 frozenset 类型。
源代码位于 pydantic/types.py 中 
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def confrozenset(
    item_type: type[HashableItemType], *, min_length: int | None = None, max_length: int | None = None
) -> type[frozenset[HashableItemType]]:
    """A wrapper around `typing.FrozenSet` that allows for additional constraints.

    Args:
        item_type: The type of the items in the frozenset.
        min_length: The minimum length of the frozenset.
        max_length: The maximum length of the frozenset.

    Returns:
        The wrapped frozenset type.
    """
    return Annotated[frozenset[item_type], annotated_types.Len(min_length or 0, max_length)]  # pyright: ignore[reportReturnType]

conlist 

conlist(
    item_type: type[AnyItemType],
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    unique_items: bool | None = None
) -> type[list[AnyItemType]]

list 的包装器，添加了验证功能。

参数

名称 类型 描述 默认值
item_type type[AnyItemType]

列表中项目的类型。

 required
min_length int | None

列表的最小长度。 默认为 None。

 None
max_length int | None

列表的最大长度。 默认为 None。

 None
unique_items bool | None

列表中的项目是否必须唯一。 默认为 None。

警告

unique_items 参数已弃用，请改用 Set。 有关更多详细信息，请参阅 此问题。

 None

返回

类型 描述
type[list[AnyItemType]]

包装的列表类型。
源代码位于 pydantic/types.py 中 
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def conlist(
    item_type: type[AnyItemType],
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    unique_items: bool | None = None,
) -> type[list[AnyItemType]]:
    """A wrapper around [`list`][] that adds validation.

    Args:
        item_type: The type of the items in the list.
        min_length: The minimum length of the list. Defaults to None.
        max_length: The maximum length of the list. Defaults to None.
        unique_items: Whether the items in the list must be unique. Defaults to None.
            !!! warning Deprecated
                The `unique_items` parameter is deprecated, use `Set` instead.
                See [this issue](https://github.com/pydantic/pydantic-core/issues/296) for more details.

    Returns:
        The wrapped list type.
    """
    if unique_items is not None:
        raise PydanticUserError(
            (
                '`unique_items` is removed, use `Set` instead'
                '(this feature is discussed in https://github.com/pydantic/pydantic-core/issues/296)'
            ),
            code='removed-kwargs',
        )
    return Annotated[list[item_type], annotated_types.Len(min_length or 0, max_length)]  # pyright: ignore[reportReturnType]

condecimal 

condecimal(
    *,
    strict: bool | None = None,
    gt: int | Decimal | None = None,
    ge: int | Decimal | None = None,
    lt: int | Decimal | None = None,
    le: int | Decimal | None = None,
    multiple_of: int | Decimal | None = None,
    max_digits: int | None = None,
    decimal_places: int | None = None,
    allow_inf_nan: bool | None = None
) -> type[Decimal]

不推荐使用

不推荐使用此函数，建议使用带有 Field 的 Annotated 代替。

此函数将在 Pydantic 3.0 中弃用。

原因是 condecimal 返回一个类型，这与静态分析工具不太兼容。

from pydantic import BaseModel, condecimal

class Foo(BaseModel):
    bar: condecimal(strict=True, allow_inf_nan=True)

from decimal import Decimal
from typing import Annotated

from pydantic import BaseModel, Field

class Foo(BaseModel):
    bar: Annotated[Decimal, Field(strict=True, allow_inf_nan=True)]

Decimal 的包装器，添加了验证功能。

参数

名称 类型 描述 默认值
strict bool | None

是否以严格模式验证值。 默认为 None。

 None
gt int | Decimal | None

值必须大于此值。 默认为 None。

 None
ge int | Decimal | None

值必须大于或等于此值。 默认为 None。

 None
lt int | Decimal | None

值必须小于此值。 默认为 None。

 None
le int | Decimal | None

值必须小于或等于此值。 默认为 None。

 None
multiple_of int | Decimal | None

值必须是此值的倍数。 默认为 None。

 None
max_digits int | None

最大位数。 默认为 None。

 None
decimal_places int | None

小数位数。 默认为 None。

 None
allow_inf_nan bool | None

是否允许无穷大和 NaN。 默认为 None。

 None 
from decimal import Decimal

from pydantic import BaseModel, ValidationError, condecimal

class ConstrainedExample(BaseModel):
    constrained_decimal: condecimal(gt=Decimal('1.0'))

m = ConstrainedExample(constrained_decimal=Decimal('1.1'))
print(repr(m))
#> ConstrainedExample(constrained_decimal=Decimal('1.1'))

try:
    ConstrainedExample(constrained_decimal=Decimal('0.9'))
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('constrained_decimal',),
            'msg': 'Input should be greater than 1.0',
            'input': Decimal('0.9'),
            'ctx': {'gt': Decimal('1.0')},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''
源代码位于 pydantic/types.py 中 
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def condecimal(
    *,
    strict: bool | None = None,
    gt: int | Decimal | None = None,
    ge: int | Decimal | None = None,
    lt: int | Decimal | None = None,
    le: int | Decimal | None = None,
    multiple_of: int | Decimal | None = None,
    max_digits: int | None = None,
    decimal_places: int | None = None,
    allow_inf_nan: bool | None = None,
) -> type[Decimal]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.pythonlang.cn/3/library/typing.html#typing.Annotated) with
        [`Field`][pydantic.fields.Field] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `condecimal` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```python
            from pydantic import BaseModel, condecimal

            class Foo(BaseModel):
                bar: condecimal(strict=True, allow_inf_nan=True)
            ```

        === ":white_check_mark: Do this"
            ```python
            from decimal import Decimal
            from typing import Annotated

            from pydantic import BaseModel, Field

            class Foo(BaseModel):
                bar: Annotated[Decimal, Field(strict=True, allow_inf_nan=True)]
            ```

    A wrapper around Decimal that adds validation.

    Args:
        strict: Whether to validate the value in strict mode. Defaults to `None`.
        gt: The value must be greater than this. Defaults to `None`.
        ge: The value must be greater than or equal to this. Defaults to `None`.
        lt: The value must be less than this. Defaults to `None`.
        le: The value must be less than or equal to this. Defaults to `None`.
        multiple_of: The value must be a multiple of this. Defaults to `None`.
        max_digits: The maximum number of digits. Defaults to `None`.
        decimal_places: The number of decimal places. Defaults to `None`.
        allow_inf_nan: Whether to allow infinity and NaN. Defaults to `None`.

    ```python
    from decimal import Decimal

    from pydantic import BaseModel, ValidationError, condecimal

    class ConstrainedExample(BaseModel):
        constrained_decimal: condecimal(gt=Decimal('1.0'))

    m = ConstrainedExample(constrained_decimal=Decimal('1.1'))
    print(repr(m))
    #> ConstrainedExample(constrained_decimal=Decimal('1.1'))

    try:
        ConstrainedExample(constrained_decimal=Decimal('0.9'))
    except ValidationError as e:
        print(e.errors())
        '''
        [
            {
                'type': 'greater_than',
                'loc': ('constrained_decimal',),
                'msg': 'Input should be greater than 1.0',
                'input': Decimal('0.9'),
                'ctx': {'gt': Decimal('1.0')},
                'url': 'https://errors.pydantic.dev/2/v/greater_than',
            }
        ]
        '''
    ```
    """  # noqa: D212
    return Annotated[  # pyright: ignore[reportReturnType]
        Decimal,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
        _fields.pydantic_general_metadata(max_digits=max_digits, decimal_places=decimal_places),
        AllowInfNan(allow_inf_nan) if allow_inf_nan is not None else None,
    ]

condate 

condate(
    *,
    strict: bool | None = None,
    gt: date | None = None,
    ge: date | None = None,
    lt: date | None = None,
    le: date | None = None
) -> type[date]

date 的包装器，添加了约束。

参数

名称 类型 描述 默认值
strict bool | None

是否以严格模式验证日期值。 默认为 None。

 None
gt date | None

值必须大于此值。 默认为 None。

 None
ge date | None

值必须大于或等于此值。 默认为 None。

 None
lt date | None

值必须小于此值。 默认为 None。

 None
le date | None

值必须小于或等于此值。 默认为 None。

 None

返回

类型 描述
type[date]

具有指定约束的日期类型。
源代码位于 pydantic/types.py 中 
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def condate(
    *,
    strict: bool | None = None,
    gt: date | None = None,
    ge: date | None = None,
    lt: date | None = None,
    le: date | None = None,
) -> type[date]:
    """A wrapper for date that adds constraints.

    Args:
        strict: Whether to validate the date value in strict mode. Defaults to `None`.
        gt: The value must be greater than this. Defaults to `None`.
        ge: The value must be greater than or equal to this. Defaults to `None`.
        lt: The value must be less than this. Defaults to `None`.
        le: The value must be less than or equal to this. Defaults to `None`.

    Returns:
        A date type with the specified constraints.
    """
    return Annotated[  # pyright: ignore[reportReturnType]
        date,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
    ]