Functional Validators

This module contains related classes and functions for validation.

ModelAfterValidatorWithoutInfo `module-attribute` ¶

ModelAfterValidatorWithoutInfo = Callable[
    [_ModelType], _ModelType
]

A @model_validator decorated function signature. This is used when mode='after' and the function does not have info argument.

ModelAfterValidator `module-attribute` ¶

ModelAfterValidator = Callable[
    [_ModelType, ValidationInfo], _ModelType
]

A @model_validator decorated function signature. This is used when mode='after'.

AfterValidator `dataclass` ¶

AfterValidator(
    func: (
        NoInfoValidatorFunction | WithInfoValidatorFunction
    ),
)

Usage Documentation

Annotated Validators

A metadata class that indicates that a validation should be applied after the inner validation logic.

Attributes:

Name	Type	Description
`func`	`NoInfoValidatorFunction \| WithInfoValidatorFunction`	The validator function.

Example

from typing_extensions import Annotated

from pydantic import AfterValidator, BaseModel, ValidationError

MyInt = Annotated[int, AfterValidator(lambda v: v + 1)]

class Model(BaseModel):
    a: MyInt

print(Model(a=1).a)
#> 2

try:
    Model(a='a')
except ValidationError as e:
    print(e.json(indent=2))
    '''
    [
      {
        "type": "int_parsing",
        "loc": [
          "a"
        ],
        "msg": "Input should be a valid integer, unable to parse string as an integer",
        "input": "a",
        "url": "https://errors.pydantic.dev/2/v/int_parsing"
      }
    ]
    '''

BeforeValidator `dataclass` ¶

BeforeValidator(
    func: (
        NoInfoValidatorFunction | WithInfoValidatorFunction
    ),
    json_schema_input_type: Any = PydanticUndefined,
)

Usage Documentation

Annotated Validators

A metadata class that indicates that a validation should be applied before the inner validation logic.

Attributes:

Name	Type	Description
`func`	`NoInfoValidatorFunction \| WithInfoValidatorFunction`	The validator function.
`json_schema_input_type`	`Any`	The input type of the function. This is only used to generate the appropriate JSON Schema (in validation mode).

Example

from typing_extensions import Annotated

from pydantic import BaseModel, BeforeValidator

MyInt = Annotated[int, BeforeValidator(lambda v: v + 1)]

class Model(BaseModel):
    a: MyInt

print(Model(a=1).a)
#> 2

try:
    Model(a='a')
except TypeError as e:
    print(e)
    #> can only concatenate str (not "int") to str

PlainValidator `dataclass` ¶

PlainValidator(
    func: (
        NoInfoValidatorFunction | WithInfoValidatorFunction
    ),
    json_schema_input_type: Any = Any,
)

Usage Documentation

Annotated Validators

A metadata class that indicates that a validation should be applied instead of the inner validation logic.

!!! note: Before v2.9, PlainValidator wasn't always compatible with JSON Schema generation for mode='validation'. You can now use the json_schema_input_type argument to specify the input type of the function to be used in the JSON schema when mode='validation' (the default). See the example below for more details.

Attributes:

Name	Type	Description
`func`	`NoInfoValidatorFunction \| WithInfoValidatorFunction`	The validator function.
`json_schema_input_type`	`Any`	The input type of the function. This is only used to generate the appropriate JSON Schema (in validation mode). If not provided, will default to `Any`.

Example

from typing import Union
from typing_extensions import Annotated

from pydantic import BaseModel, PlainValidator

MyInt = Annotated[
    int,
    PlainValidator(
        lambda v: int(v) + 1, json_schema_input_type=Union[str, int]  # (1)!
    ),
]

class Model(BaseModel):
    a: MyInt

print(Model(a='1').a)
#> 2

print(Model(a=1).a)
#> 2

In this example, we've specified the json_schema_input_type as Union[str, int] which indicates to the JSON schema generator that in validation mode, the input type for the a field can be either a str or an int.

WrapValidator `dataclass` ¶

WrapValidator(
    func: (
        NoInfoWrapValidatorFunction
        | WithInfoWrapValidatorFunction
    ),
    json_schema_input_type: Any = PydanticUndefined,
)

Usage Documentation

Annotated Validators

A metadata class that indicates that a validation should be applied around the inner validation logic.

Attributes:

Name	Type	Description
`func`	`NoInfoWrapValidatorFunction \| WithInfoWrapValidatorFunction`	The validator function.
`json_schema_input_type`	`Any`	The input type of the function. This is only used to generate the appropriate JSON Schema (in validation mode).

from datetime import datetime

from typing_extensions import Annotated

from pydantic import BaseModel, ValidationError, WrapValidator

def validate_timestamp(v, handler):
    if v == 'now':
        # we don't want to bother with further validation, just return the new value
        return datetime.now()
    try:
        return handler(v)
    except ValidationError:
        # validation failed, in this case we want to return a default value
        return datetime(2000, 1, 1)

MyTimestamp = Annotated[datetime, WrapValidator(validate_timestamp)]

class Model(BaseModel):
    a: MyTimestamp

print(Model(a='now').a)
#> 2032-01-02 03:04:05.000006
print(Model(a='invalid').a)
#> 2000-01-01 00:00:00

ModelWrapValidatorHandler ¶

Bases: ValidatorFunctionWrapHandler, Protocol[_ModelTypeCo]

@model_validator decorated function handler argument type. This is used when mode='wrap'.

ModelWrapValidatorWithoutInfo ¶

Bases: Protocol[_ModelType]

A @model_validator decorated function signature. This is used when mode='wrap' and the function does not have info argument.

ModelWrapValidator ¶

Bases: Protocol[_ModelType]

A @model_validator decorated function signature. This is used when mode='wrap'.

FreeModelBeforeValidatorWithoutInfo ¶

Bases: Protocol

A @model_validator decorated function signature. This is used when mode='before' and the function does not have info argument.

ModelBeforeValidatorWithoutInfo ¶

Bases: Protocol

A @model_validator decorated function signature. This is used when mode='before' and the function does not have info argument.

FreeModelBeforeValidator ¶

Bases: Protocol

A @model_validator decorated function signature. This is used when mode='before'.

ModelBeforeValidator ¶

Bases: Protocol

A @model_validator decorated function signature. This is used when mode='before'.

InstanceOf `dataclass` ¶

InstanceOf()

Generic type for annotating a type that is an instance of a given class.

Example

from pydantic import BaseModel, InstanceOf

class Foo:
    ...

class Bar(BaseModel):
    foo: InstanceOf[Foo]

Bar(foo=Foo())
try:
    Bar(foo=42)
except ValidationError as e:
    print(e)
    """
    [
    │   {
    │   │   'type': 'is_instance_of',
    │   │   'loc': ('foo',),
    │   │   'msg': 'Input should be an instance of Foo',
    │   │   'input': 42,
    │   │   'ctx': {'class': 'Foo'},
    │   │   'url': 'https://errors.pydantic.dev/0.38.0/v/is_instance_of'
    │   }
    ]
    """

SkipValidation `dataclass` ¶

SkipValidation()

If this is applied as an annotation (e.g., via x: Annotated[int, SkipValidation]), validation will be skipped. You can also use SkipValidation[int] as a shorthand for Annotated[int, SkipValidation].

This can be useful if you want to use a type annotation for documentation/IDE/type-checking purposes, and know that it is safe to skip validation for one or more of the fields.

Because this converts the validation schema to any_schema, subsequent annotation-applied transformations may not have the expected effects. Therefore, when used, this annotation should generally be the final annotation applied to a type.

field_validator ¶

field_validator(
    field: str,
    /,
    *fields: str,
    mode: FieldValidatorModes = "after",
    check_fields: bool | None = None,
    json_schema_input_type: Any = PydanticUndefined,
) -> Callable[[Any], Any]

Usage Documentation

Field validators

Decorate methods on the class indicating that they should be used to validate fields.

Example usage:

from typing import Any

from pydantic import (
    BaseModel,
    ValidationError,
    field_validator,
)

class Model(BaseModel):
    a: str

    @field_validator('a')
    @classmethod
    def ensure_foobar(cls, v: Any):
        if 'foobar' not in v:
            raise ValueError('"foobar" not found in a')
        return v

print(repr(Model(a='this is foobar good')))
#> Model(a='this is foobar good')

try:
    Model(a='snap')
except ValidationError as exc_info:
    print(exc_info)
    '''
    1 validation error for Model
    a
      Value error, "foobar" not found in a [type=value_error, input_value='snap', input_type=str]
    '''

For more in depth examples, see Field Validators.

Parameters:

Name	Type	Description	Default
`field`	`str`	The first field the `field_validator` should be called on; this is separate from `fields` to ensure an error is raised if you don't pass at least one.	required
`*fields`	`str`	Additional field(s) the `field_validator` should be called on.	`()`
`mode`	`FieldValidatorModes`	Specifies whether to validate the fields before or after validation.	`'after'`
`check_fields`	`bool \| None`	Whether to check that the fields actually exist on the model.	`None`
`json_schema_input_type`	`Any`	The input type of the function. This is only used to generate the appropriate JSON Schema (in validation mode) and can only specified when `mode` is either `'before'`, `'plain'` or `'wrap'`.	`PydanticUndefined`

Returns:

Type	Description
`Callable[[Any], Any]`	A decorator that can be used to decorate a function to be used as a field_validator.

Raises:

Type	Description
`PydanticUserError`	If `@field_validator` is used bare (with no fields). If the args passed to `@field_validator` as fields are not strings. If `@field_validator` applied to instance methods.

Source code in pydantic/functional_validators.py

def field_validator(
    field: str,
    /,
    *fields: str,
    mode: FieldValidatorModes = 'after',
    check_fields: bool | None = None,
    json_schema_input_type: Any = PydanticUndefined,
) -> Callable[[Any], Any]:
    """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#field-validators

    Decorate methods on the class indicating that they should be used to validate fields.

    Example usage:
    ```py
    from typing import Any

    from pydantic import (
        BaseModel,
        ValidationError,
        field_validator,
    )

    class Model(BaseModel):
        a: str

        @field_validator('a')
        @classmethod
        def ensure_foobar(cls, v: Any):
            if 'foobar' not in v:
                raise ValueError('"foobar" not found in a')
            return v

    print(repr(Model(a='this is foobar good')))
    #> Model(a='this is foobar good')

    try:
        Model(a='snap')
    except ValidationError as exc_info:
        print(exc_info)
        '''
        1 validation error for Model
        a
          Value error, "foobar" not found in a [type=value_error, input_value='snap', input_type=str]
        '''
    ```

    For more in depth examples, see [Field Validators](../concepts/validators.md#field-validators).

    Args:
        field: The first field the `field_validator` should be called on; this is separate
            from `fields` to ensure an error is raised if you don't pass at least one.
        *fields: Additional field(s) the `field_validator` should be called on.
        mode: Specifies whether to validate the fields before or after validation.
        check_fields: Whether to check that the fields actually exist on the model.
        json_schema_input_type: The input type of the function. This is only used to generate
            the appropriate JSON Schema (in validation mode) and can only specified
            when `mode` is either `'before'`, `'plain'` or `'wrap'`.

    Returns:
        A decorator that can be used to decorate a function to be used as a field_validator.

    Raises:
        PydanticUserError:
            - If `@field_validator` is used bare (with no fields).
            - If the args passed to `@field_validator` as fields are not strings.
            - If `@field_validator` applied to instance methods.
    """
    if isinstance(field, FunctionType):
        raise PydanticUserError(
            '`@field_validator` should be used with fields and keyword arguments, not bare. '
            "E.g. usage should be `@validator('<field_name>', ...)`",
            code='validator-no-fields',
        )

    if mode not in ('before', 'plain', 'wrap') and json_schema_input_type is not PydanticUndefined:
        raise PydanticUserError(
            f"`json_schema_input_type` can't be used when mode is set to {mode!r}",
            code='validator-input-type',
        )

    if json_schema_input_type is PydanticUndefined and mode == 'plain':
        json_schema_input_type = Any

    fields = field, *fields
    if not all(isinstance(field, str) for field in fields):
        raise PydanticUserError(
            '`@field_validator` fields should be passed as separate string args. '
            "E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`",
            code='validator-invalid-fields',
        )

    def dec(
        f: Callable[..., Any] | staticmethod[Any, Any] | classmethod[Any, Any, Any],
    ) -> _decorators.PydanticDescriptorProxy[Any]:
        if _decorators.is_instance_method_from_sig(f):
            raise PydanticUserError(
                '`@field_validator` cannot be applied to instance methods', code='validator-instance-method'
            )

        # auto apply the @classmethod decorator
        f = _decorators.ensure_classmethod_based_on_signature(f)

        dec_info = _decorators.FieldValidatorDecoratorInfo(
            fields=fields, mode=mode, check_fields=check_fields, json_schema_input_type=json_schema_input_type
        )
        return _decorators.PydanticDescriptorProxy(f, dec_info)

    return dec

model_validator ¶

model_validator(
    *, mode: Literal["wrap", "before", "after"]
) -> Any

Usage Documentation

Model validators

Decorate model methods for validation purposes.

Example usage:

from typing_extensions import Self

from pydantic import BaseModel, ValidationError, model_validator

class Square(BaseModel):
    width: float
    height: float

    @model_validator(mode='after')
    def verify_square(self) -> Self:
        if self.width != self.height:
            raise ValueError('width and height do not match')
        return self

s = Square(width=1, height=1)
print(repr(s))
#> Square(width=1.0, height=1.0)

try:
    Square(width=1, height=2)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Square
      Value error, width and height do not match [type=value_error, input_value={'width': 1, 'height': 2}, input_type=dict]
    '''

For more in depth examples, see Model Validators.

Parameters:

Name	Type	Description	Default
`mode`	`Literal['wrap', 'before', 'after']`	A required string literal that specifies the validation mode. It can be one of the following: 'wrap', 'before', or 'after'.	required

Returns:

Type	Description
`Any`	A decorator that can be used to decorate a function to be used as a model validator.

Source code in pydantic/functional_validators.py

def model_validator(
    *,
    mode: Literal['wrap', 'before', 'after'],
) -> Any:
    """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#model-validators

    Decorate model methods for validation purposes.

    Example usage:
    ```py
    from typing_extensions import Self

    from pydantic import BaseModel, ValidationError, model_validator

    class Square(BaseModel):
        width: float
        height: float

        @model_validator(mode='after')
        def verify_square(self) -> Self:
            if self.width != self.height:
                raise ValueError('width and height do not match')
            return self

    s = Square(width=1, height=1)
    print(repr(s))
    #> Square(width=1.0, height=1.0)

    try:
        Square(width=1, height=2)
    except ValidationError as e:
        print(e)
        '''
        1 validation error for Square
          Value error, width and height do not match [type=value_error, input_value={'width': 1, 'height': 2}, input_type=dict]
        '''
    ```

    For more in depth examples, see [Model Validators](../concepts/validators.md#model-validators).

    Args:
        mode: A required string literal that specifies the validation mode.
            It can be one of the following: 'wrap', 'before', or 'after'.

    Returns:
        A decorator that can be used to decorate a function to be used as a model validator.
    """

    def dec(f: Any) -> _decorators.PydanticDescriptorProxy[Any]:
        # auto apply the @classmethod decorator
        f = _decorators.ensure_classmethod_based_on_signature(f)
        dec_info = _decorators.ModelValidatorDecoratorInfo(mode=mode)
        return _decorators.PydanticDescriptorProxy(f, dec_info)

    return dec

Functional Validators

ModelAfterValidatorWithoutInfo module-attribute ¶

ModelAfterValidator module-attribute ¶

AfterValidator dataclass ¶

BeforeValidator dataclass ¶

PlainValidator dataclass ¶

WrapValidator dataclass ¶

ModelWrapValidatorHandler ¶

ModelWrapValidatorWithoutInfo ¶

ModelWrapValidator ¶

FreeModelBeforeValidatorWithoutInfo ¶

ModelBeforeValidatorWithoutInfo ¶

FreeModelBeforeValidator ¶

ModelBeforeValidator ¶

InstanceOf dataclass ¶

SkipValidation dataclass ¶

field_validator ¶

model_validator ¶

ModelAfterValidatorWithoutInfo `module-attribute` ¶

ModelAfterValidator `module-attribute` ¶

AfterValidator `dataclass` ¶

BeforeValidator `dataclass` ¶

PlainValidator `dataclass` ¶

WrapValidator `dataclass` ¶

InstanceOf `dataclass` ¶

SkipValidation `dataclass` ¶