Skip to content

Pydantic Types

pydantic.types

The types module contains custom types used by pydantic.

StrictBool module-attribute

StrictBool = Annotated[bool, Strict()]

A boolean that must be either True or False.

PositiveInt module-attribute

PositiveInt = Annotated[int, Gt(0)]

An integer that must be greater than zero.

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)]

An integer that must be less than zero.

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)]

An integer that must be less than or equal to zero.

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)]

An integer that must be greater than or equal to zero.

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()]

An integer that must be validated in strict mode.

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)]

A float that must be greater than zero.

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)]

A float that must be less than zero.

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)]

A float that must be less than or equal to zero.

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)]

A float that must be greater than or equal to zero.

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)]

A float that must be validated in strict mode.

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)]

A float that must be finite (not -inf, inf, or 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()]

A bytes that must be validated in strict mode.

StrictStr module-attribute

StrictStr = Annotated[str, Strict()]

A string that must be validated in strict mode.

UUID1 module-attribute

UUID1 = Annotated[UUID, UuidVersion(1)]

A UUID that must be version 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)]

A UUID that must be version 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)]

A UUID that must be version 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)]

A UUID that must be version 5.

import uuid

from pydantic import UUID5, BaseModel

class Model(BaseModel):
    uuid5: UUID5

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

FilePath module-attribute

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

A path that must point to a file.

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')]

A path that must point to a directory.

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')]

A path for a new file or directory that must not already exist.

Base64Bytes module-attribute

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

A bytes type that is encoded and decoded using the standard (non-URL-safe) base64 encoder.

Note

Under the hood, Base64Bytes use standard library base64.encodebytes and base64.decodebytes functions.

As a result, attempting to decode url-safe base64 data using the Base64Bytes type may fail or produce an incorrect decoding.

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)
]

A str type that is encoded and decoded using the standard (non-URL-safe) base64 encoder.

Note

Under the hood, Base64Bytes use standard library base64.encodebytes and base64.decodebytes functions.

As a result, attempting to decode url-safe base64 data using the Base64Str type may fail or produce an incorrect decoding.

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)
]

A bytes type that is encoded and decoded using the URL-safe base64 encoder.

Note

Under the hood, Base64UrlBytes use standard library base64.urlsafe_b64encode and base64.urlsafe_b64decode functions.

As a result, the Base64UrlBytes type can be used to faithfully decode "vanilla" base64 data (using '+' and '/').

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)
]

A str type that is encoded and decoded using the URL-safe base64 encoder.

Note

Under the hood, Base64UrlStr use standard library base64.urlsafe_b64encode and base64.urlsafe_b64decode functions.

As a result, the Base64UrlStr type can be used to faithfully decode "vanilla" base64 data (using '+' and '/').

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,
]

A JsonValue is used to represent a value that can be serialized to JSON.

It may be one of:

  • List['JsonValue']
  • Dict[str, 'JsonValue']
  • str
  • bool
  • int
  • float
  • None

The following example demonstrates how to use JsonValue to validate JSON data, and what kind of errors to expect when input data is not json serializable.

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]

When used as an item in a list, the key type in a dict, optional values of a TypedDict, etc. this annotation omits the item from the iteration if there is any error validating it. That is, instead of a ValidationError being propagated up and the entire iterable being discarded any invalid items are discarded and the valid ones are returned.

Strict dataclass

Bases: PydanticMetadata, BaseMetadata

A field metadata class to indicate that a field should be validated in strict mode.

Attributes:

Name Type Description
strict bool

Whether to validate the field in strict mode.

Example
from typing_extensions import Annotated

from pydantic.types import Strict

StrictBool = Annotated[bool, Strict()]
Source code in pydantic/types.py
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
@_dataclasses.dataclass
class Strict(_fields.PydanticMetadata, BaseMetadata):
    """Usage docs: https://docs.pydantic.dev/2.7/concepts/strict_mode/#strict-mode-with-annotated-strict

    A field metadata class to indicate that a field should be validated in strict mode.

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

    Example:
        ```python
        from typing_extensions import Annotated

        from pydantic.types import Strict

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

    strict: bool = True

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

AllowInfNan dataclass

Bases: PydanticMetadata

A field metadata class to indicate that a field should allow -inf, inf, and nan.

Source code in pydantic/types.py
385
386
387
388
389
390
391
392
@_dataclasses.dataclass
class AllowInfNan(_fields.PydanticMetadata):
    """A field metadata class to indicate that a field should allow ``-inf``, ``inf``, and ``nan``."""

    allow_inf_nan: bool = True

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

StringConstraints dataclass

Bases: GroupedMetadata

Usage Documentation

String Constraints

Apply constraints to str types.

Attributes:

Name Type Description
strip_whitespace bool | None

Whether to strip whitespace from the string.

to_upper bool | None

Whether to convert the string to uppercase.

to_lower bool | None

Whether to convert the string to lowercase.

strict bool | None

Whether to validate the string in strict mode.

min_length int | None

The minimum length of the string.

max_length int | None

The maximum length of the string.

pattern str | None

A regex pattern that the string must match.

Source code in pydantic/types.py
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
@_dataclasses.dataclass(frozen=True)
class StringConstraints(annotated_types.GroupedMetadata):
    """Usage docs: https://docs.pydantic.dev/2.7/concepts/fields/#string-constraints

    Apply constraints to `str` types.

    Attributes:
        strip_whitespace: Whether to strip whitespace from the string.
        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.
    """

    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 | 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()
        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

A type that can be used to import a type 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' was provided, the resulting field value would be the functioncos. 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.

Good behavior:

from math import cos

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=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.

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"}
Source code in pydantic/types.py
 882
 883
 884
 885
 886
 887
 888
 889
 890
 891
 892
 893
 894
 895
 896
 897
 898
 899
 900
 901
 902
 903
 904
 905
 906
 907
 908
 909
 910
 911
 912
 913
 914
 915
 916
 917
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
class ImportString:
    """A type that can be used to import a type 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'` was 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.

    **Good behavior:**
    ```py
    from math import cos

    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=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.

    ```py
    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
            )

    @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__}'
        else:
            return v

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

UuidVersion dataclass

A field metadata class to indicate a UUID version.

Source code in pydantic/types.py
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
@_dataclasses.dataclass(**_internal_dataclass.slots_true)
class UuidVersion:
    """A field metadata class to indicate a [UUID](https://docs.python.org/3/library/uuid.html) version."""

    uuid_version: Literal[1, 3, 4, 5]

    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

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:

from typing import Any, List

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:

from typing import List

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]"}
Source code in pydantic/types.py
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
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:

    ```py
    from typing import Any, List

    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:

    ```py
    from typing import List

    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) == type(self)

Secret

Bases: _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:
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. Subclassing from parametrized Secret:
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().

Source code in pydantic/types.py
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
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:

    ```py
    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`:

    ```py
    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()`.
    """

    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,
                serialization=core_schema.plain_serializer_function_ser_schema(lambda x: x),
            ),
            json_schema=core_schema.no_info_after_validator_function(
                lambda x: cls(x), inner_schema, serialization=core_schema.to_string_ser_schema(when_used='json')
            ),
        )

SecretStr

Bases: _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 ''.

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(''))
Source code in pydantic/types.py
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
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 `''`.

    ```py
    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(''))
    ```
    """

    _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

Bases: _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''.

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''))
Source code in pydantic/types.py
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
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''`.

    ```py
    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

Bases: str

Based on: https://en.wikipedia.org/wiki/Payment_card_number.

Source code in pydantic/types.py
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
@deprecated(
    'The `PaymentCardNumber` class is deprecated, use `pydantic_extra_types` instead. '
    'See https://docs.pydantic.dev/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

Mask all but the last 4 digits of the card number.

Returns:

Type Description
str

A masked card number string.

validate classmethod

validate(
    __input_value: str, _: ValidationInfo
) -> PaymentCardNumber

Validate the card number and return a PaymentCardNumber instance.

Source code in pydantic/types.py
1749
1750
1751
1752
@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

Validate that the card number is all digits.

Source code in pydantic/types.py
1764
1765
1766
1767
1768
@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

Based on: https://en.wikipedia.org/wiki/Luhn_algorithm.

Source code in pydantic/types.py
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
@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

Validate length based on BIN for major brands: https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN).

Source code in pydantic/types.py
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
@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

Bases: 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 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".

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
Source code in pydantic/types.py
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
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".

    ```py
    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

Converts a byte size to a human readable string.

Parameters:

Name Type Description Default
decimal bool

If True, use decimal units (e.g. 1000 bytes per KB). If False, use binary units (e.g. 1024 bytes per KiB).

False
separator str

A string used to split the value and unit. Defaults to an empty string ('').

''

Returns:

Type Description
str

A human readable string representation of the byte size.

Source code in pydantic/types.py
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
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

Converts a byte size to another unit, including both byte and bit units.

Parameters:

Name Type Description Default
unit str

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).

required

Returns:

Type Description
float

The byte size in the new unit.

Source code in pydantic/types.py
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
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

A date in the past.

Source code in pydantic/types.py
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
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

A date in the future.

Source code in pydantic/types.py
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
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

A datetime that requires timezone info.

Source code in pydantic/types.py
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
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

A datetime that doesn't require timezone info.

Source code in pydantic/types.py
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
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

A datetime that must be in the past.

Source code in pydantic/types.py
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
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

A datetime that must be in the future.

Source code in pydantic/types.py
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
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

Bases: Protocol

Protocol for encoding and decoding data to and from bytes.

Source code in pydantic/types.py
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
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

Decode the data using the encoder.

Parameters:

Name Type Description Default
data bytes

The data to decode.

required

Returns:

Type Description
bytes

The decoded data.

Source code in pydantic/types.py
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
@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

Encode the data using the encoder.

Parameters:

Name Type Description Default
value bytes

The data to encode.

required

Returns:

Type Description
bytes

The encoded data.

Source code in pydantic/types.py
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
@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

Get the JSON format for the encoded data.

Returns:

Type Description
str

The JSON format for the encoded data.

Source code in pydantic/types.py
2183
2184
2185
2186
2187
2188
2189
2190
@classmethod
def get_json_format(cls) -> str:
    """Get the JSON format for the encoded data.

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

Base64Encoder

Bases: EncoderProtocol

Standard (non-URL-safe) Base64 encoder.

Source code in pydantic/types.py
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
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.decodebytes(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.encodebytes(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

Decode the data from base64 encoded bytes to original bytes data.

Parameters:

Name Type Description Default
data bytes

The data to decode.

required

Returns:

Type Description
bytes

The decoded data.

Source code in pydantic/types.py
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
@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.decodebytes(data)
    except ValueError as e:
        raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})

encode classmethod

encode(value: bytes) -> bytes

Encode the data from bytes to a base64 encoded bytes.

Parameters:

Name Type Description Default
value bytes

The data to encode.

required

Returns:

Type Description
bytes

The encoded data.

Source code in pydantic/types.py
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
@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.encodebytes(value)

get_json_format classmethod

get_json_format() -> Literal['base64']

Get the JSON format for the encoded data.

Returns:

Type Description
Literal['base64']

The JSON format for the encoded data.

Source code in pydantic/types.py
2223
2224
2225
2226
2227
2228
2229
2230
@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

Bases: EncoderProtocol

URL-safe Base64 encoder.

Source code in pydantic/types.py
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
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

Decode the data from base64 encoded bytes to original bytes data.

Parameters:

Name Type Description Default
data bytes

The data to decode.

required

Returns:

Type Description
bytes

The decoded data.

Source code in pydantic/types.py
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
@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

Encode the data from bytes to a base64 encoded bytes.

Parameters:

Name Type Description Default
value bytes

The data to encode.

required

Returns:

Type Description
bytes

The encoded data.

Source code in pydantic/types.py
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
@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']

Get the JSON format for the encoded data.

Returns:

Type Description
Literal['base64url']

The JSON format for the encoded data.

Source code in pydantic/types.py
2263
2264
2265
2266
2267
2268
2269
2270
@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

A bytes type that is encoded and decoded using the specified encoder.

EncodedBytes needs an encoder that implements EncoderProtocol to operate.

from typing_extensions 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]
    '''
Source code in pydantic/types.py
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
@_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.

    ```py
    from typing_extensions 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:
        return core_schema.with_info_after_validator_function(
            function=self.decode,
            schema=core_schema.bytes_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

Decode the data using the specified encoder.

Parameters:

Name Type Description Default
data bytes

The data to decode.

required

Returns:

Type Description
bytes

The decoded data.

Source code in pydantic/types.py
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
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

Encode the data using the specified encoder.

Parameters:

Name Type Description Default
value bytes

The data to encode.

required

Returns:

Type Description
bytes

The encoded data.

Source code in pydantic/types.py
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
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

Bases: EncodedBytes

A str type that is encoded and decoded using the specified encoder.

EncodedStr needs an encoder that implements EncoderProtocol to operate.

from typing_extensions 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]
    '''
Source code in pydantic/types.py
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
@_dataclasses.dataclass(**_internal_dataclass.slots_true)
class EncodedStr(EncodedBytes):
    """A str type that is encoded and decoded using the specified encoder.

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

    ```py
    from typing_extensions 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]
        '''
    ```
    """

    def __get_pydantic_core_schema__(self, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        return core_schema.with_info_after_validator_function(
            function=self.decode_str,
            schema=super(EncodedStr, self).__get_pydantic_core_schema__(source=source, handler=handler),  # noqa: UP008
            serialization=core_schema.plain_serializer_function_ser_schema(function=self.encode_str),
        )

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

        Args:
            data: The data to decode.

        Returns:
            The decoded data.
        """
        return data.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 super(EncodedStr, self).encode(value=value.encode()).decode()  # noqa: UP008

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

decode_str

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

Decode the data using the specified encoder.

Parameters:

Name Type Description Default
data bytes

The data to decode.

required

Returns:

Type Description
str

The decoded data.

Source code in pydantic/types.py
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
def decode_str(self, data: bytes, _: core_schema.ValidationInfo) -> str:
    """Decode the data using the specified encoder.

    Args:
        data: The data to decode.

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

encode_str

encode_str(value: str) -> str

Encode the data using the specified encoder.

Parameters:

Name Type Description Default
value str

The data to encode.

required

Returns:

Type Description
str

The encoded data.

Source code in pydantic/types.py
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
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 super(EncodedStr, self).encode(value=value.encode()).decode()  # noqa: UP008

GetPydanticSchema dataclass

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:

from typing import Any

from typing_extensions import Annotated

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'

Source code in pydantic/types.py
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
@_dataclasses.dataclass(**_internal_dataclass.slots_true)
class GetPydanticSchema:
    """Usage docs: https://docs.pydantic.dev/2.7/concepts/types/#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 Any

    from typing_extensions import Annotated

    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

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.

from typing import Any, Union

from typing_extensions import Annotated, Literal

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.

See the Discriminated Unions concepts docs for more details on how to use Tags.

Source code in pydantic/types.py
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
@_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.

    ```py
    from typing import Any, Union

    from typing_extensions import Annotated, Literal

    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 = schema.setdefault('metadata', {})
        assert isinstance(metadata, dict)
        metadata[_core_utils.TAGGED_UNION_TAG_KEY] = self.tag
        return schema

Discriminator dataclass

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.

from typing import Any, Union

from typing_extensions import Annotated, Literal

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 Discriminators.

Source code in pydantic/types.py
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
@_dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
class Discriminator:
    """Usage docs: https://docs.pydantic.dev/2.7/concepts/unions/#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`.

    ```py
    from typing import Any, Union

    from typing_extensions import Annotated, Literal

    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#custom-errors) 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:
        origin = _typing_extra.get_origin(source_type)
        if not origin or not _typing_extra.origin_is_union(origin):
            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 i, choice in enumerate(original_schema['choices']):
            tag = None
            if isinstance(choice, tuple):
                choice, tag = choice
            metadata = choice.get('metadata')
            if metadata is not None:
                metadata_tag = metadata.get(_core_utils.TAGGED_UNION_TAG_KEY)
                if metadata_tag is not None:
                    tag = metadata_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]

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 class-attribute instance-attribute

custom_error_type: str | None = None

Type to use in custom errors replacing the standard discriminated union validation errors.

custom_error_message class-attribute instance-attribute

custom_error_message: str | None = None

Message to use in custom errors.

custom_error_context class-attribute instance-attribute

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

Context to use in custom errors.

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]

Discouraged

This function is discouraged in favor of using Annotated with 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.

from pydantic import BaseModel, conint

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

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the integer in strict mode. Defaults to None.

None
gt int | None

The value must be greater than this.

None
ge int | None

The value must be greater than or equal to this.

None
lt int | None

The value must be less than this.

None
le int | None

The value must be less than or equal to this.

None
multiple_of int | None

The value must be a multiple of this.

None

Returns:

Type Description
type[int]

The wrapped integer type.

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',
        }
    ]
    '''
Source code in pydantic/types.py
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
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.python.org/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"
            ```py
            from pydantic import BaseModel, conint

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

        === ":white_check_mark: Do this"
            ```py
            from typing_extensions 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.

    ```py
    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[
        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]

Discouraged

This function is discouraged in favor of using Annotated with 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.

from pydantic import BaseModel, confloat

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

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the float in strict mode.

None
gt float | None

The value must be greater than this.

None
ge float | None

The value must be greater than or equal to this.

None
lt float | None

The value must be less than this.

None
le float | None

The value must be less than or equal to this.

None
multiple_of float | None

The value must be a multiple of this.

None
allow_inf_nan bool | None

Whether to allow -inf, inf, and nan.

None

Returns:

Type Description
type[float]

The wrapped float type.

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',
        }
    ]
    '''
Source code in pydantic/types.py
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
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.python.org/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"
            ```py
            from pydantic import BaseModel, confloat

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

        === ":white_check_mark: Do this"
            ```py
            from typing_extensions 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.

    ```py
    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[
        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]

A wrapper around bytes that allows for additional constraints.

Parameters:

Name Type Description Default
min_length int | None

The minimum length of the bytes.

None
max_length int | None

The maximum length of the bytes.

None
strict bool | None

Whether to validate the bytes in strict mode.

None

Returns:

Type Description
type[bytes]

The wrapped bytes type.

Source code in pydantic/types.py
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
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[
        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 | None = None
) -> type[str]

Discouraged

This function is discouraged in favor of using Annotated with 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.

from pydantic import BaseModel, constr

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

from pydantic import BaseModel, constr

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


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

Parameters:

Name Type Description Default
strip_whitespace bool | None

Whether to remove leading and trailing whitespace.

None
to_upper bool | None

Whether to turn all characters to uppercase.

None
to_lower bool | None

Whether to turn all characters to lowercase.

None
strict bool | None

Whether to validate the string in strict mode.

None
min_length int | None

The minimum length of the string.

None
max_length int | None

The maximum length of the string.

None
pattern str | None

A regex pattern to validate the string against.

None

Returns:

Type Description
type[str]

The wrapped string type.

Source code in pydantic/types.py
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
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 | None = None,
) -> type[str]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.python.org/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"
            ```py
            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"
            ```py
            from typing_extensions 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.

    ```py
    from pydantic import BaseModel, constr

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


    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[
        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]]

A wrapper around typing.Set that allows for additional constraints.

Parameters:

Name Type Description Default
item_type type[HashableItemType]

The type of the items in the set.

required
min_length int | None

The minimum length of the set.

None
max_length int | None

The maximum length of the set.

None

Returns:

Type Description
type[set[HashableItemType]]

The wrapped set type.

Source code in pydantic/types.py
808
809
810
811
812
813
814
815
816
817
818
819
820
821
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)]

confrozenset

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.

Parameters:

Name Type Description Default
item_type type[HashableItemType]

The type of the items in the frozenset.

required
min_length int | None

The minimum length of the frozenset.

None
max_length int | None

The maximum length of the frozenset.

None

Returns:

Type Description
type[frozenset[HashableItemType]]

The wrapped frozenset type.

Source code in pydantic/types.py
824
825
826
827
828
829
830
831
832
833
834
835
836
837
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)]

conlist

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 typing.List that adds validation.

Parameters:

Name Type Description Default
item_type type[AnyItemType]

The type of the items in the list.

required
min_length int | None

The minimum length of the list. Defaults to None.

None
max_length int | None

The maximum length of the list. Defaults to None.

None
unique_items bool | None

Whether the items in the list must be unique. Defaults to None.

Warning

The unique_items parameter is deprecated, use Set instead. See this issue for more details.

None

Returns:

Type Description
type[list[AnyItemType]]

The wrapped list type.

Source code in pydantic/types.py
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
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 typing.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)]

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]

Discouraged

This function is discouraged in favor of using Annotated with 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.

from pydantic import BaseModel, condecimal

class Foo(BaseModel):
    bar: condecimal(strict=True, allow_inf_nan=True)
from decimal import Decimal

from typing_extensions 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.

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the value in strict mode. Defaults to None.

None
gt int | Decimal | None

The value must be greater than this. Defaults to None.

None
ge int | Decimal | None

The value must be greater than or equal to this. Defaults to None.

None
lt int | Decimal | None

The value must be less than this. Defaults to None.

None
le int | Decimal | None

The value must be less than or equal to this. Defaults to None.

None
multiple_of int | Decimal | None

The value must be a multiple of this. Defaults to None.

None
max_digits int | None

The maximum number of digits. Defaults to None.

None
decimal_places int | None

The number of decimal places. Defaults to None.

None
allow_inf_nan bool | None

Whether to allow infinity and NaN. Defaults to 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',
        }
    ]
    '''
Source code in pydantic/types.py
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
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.python.org/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"
            ```py
            from pydantic import BaseModel, condecimal

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

        === ":white_check_mark: Do this"
            ```py
            from decimal import Decimal

            from typing_extensions 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`.

    ```py
    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[
        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]

A wrapper for date that adds constraints.

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the date value in strict mode. Defaults to None.

None
gt date | None

The value must be greater than this. Defaults to None.

None
ge date | None

The value must be greater than or equal to this. Defaults to None.

None
lt date | None

The value must be less than this. Defaults to None.

None
le date | None

The value must be less than or equal to this. Defaults to None.

None

Returns:

Type Description
type[date]

A date type with the specified constraints.

Source code in pydantic/types.py
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
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[
        date,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
    ]