# Functional Validators

This module contains related classes and functions for validation.

## AfterValidator

:::callout{intent="note" title="Usage Documentation"}
[field _after_ validators](/guides/concepts-validators#field-after-validator)
:::

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

### Attributes

#### func

The validator function.

**Type:** `core_schema.NoInfoValidatorFunction` | `core_schema.WithInfoValidatorFunction`

## BeforeValidator

:::callout{intent="note" title="Usage Documentation"}
[field _before_ validators](/guides/concepts-validators#field-before-validator)
:::

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

### Attributes

#### func

The validator function.

**Type:** `core_schema.NoInfoValidatorFunction` | `core_schema.WithInfoValidatorFunction`

#### json\_schema\_input\_type

The input type used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`.

**Type:** [`Any`](https://docs.python.org/3/library/typing.html#typing.Any)

## PlainValidator

:::callout{intent="note" title="Usage Documentation"}
[field _plain_ validators](/guides/concepts-validators#field-plain-validator)
:::

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

:::callout{intent="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

#### func

The validator function.

**Type:** `core_schema.NoInfoValidatorFunction` | `core_schema.WithInfoValidatorFunction`

#### json\_schema\_input\_type

The input type used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`.

**Type:** [`Any`](https://docs.python.org/3/library/typing.html#typing.Any)

## WrapValidator

:::callout{intent="note" title="Usage Documentation"}
[field _wrap_ validators](/guides/concepts-validators#field-wrap-validator)
:::

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

### Attributes

#### func

The validator function.

**Type:** `core_schema.NoInfoWrapValidatorFunction` | `core_schema.WithInfoWrapValidatorFunction`

#### json\_schema\_input\_type

The input type used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`.

**Type:** [`Any`](https://docs.python.org/3/library/typing.html#typing.Any)

```python
from datetime import datetime
from typing 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`](https://docs.python.org/3/library/typing.html#typing.Protocol)

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

## ModelBeforeValidatorWithoutInfo

**Bases:** [`Protocol`](https://docs.python.org/3/library/typing.html#typing.Protocol)

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

## FreeModelBeforeValidator

**Bases:** [`Protocol`](https://docs.python.org/3/library/typing.html#typing.Protocol)

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

## ModelBeforeValidator

**Bases:** [`Protocol`](https://docs.python.org/3/library/typing.html#typing.Protocol)

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

## InstanceOf

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

## 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.

## ValidateAs

A helper class to validate a custom type from a type that is natively supported by Pydantic.

### Constructor Parameters

**`from_type`** : [`type`](https://docs.python.org/3/glossary.html#term-type)\[`_FromTypeT`]

The type natively supported by Pydantic to use to perform validation.

**`instantiation_hook`** : [`Callable`](https://docs.python.org/3/library/typing.html#typing.Callable)\[\[`_FromTypeT`], [`Any`](https://docs.python.org/3/library/typing.html#typing.Any)]

A callable taking the validated type as an argument, and returning the populated custom type.

## field\_validator

```python
def field_validator(
    field: str,
    /,
    *fields: str,
    mode: Literal['wrap'],
    check_fields: bool | None = ...,
    json_schema_input_type: Any = ...,
) -> Callable[[_V2WrapValidatorType], _V2WrapValidatorType]
def field_validator(
    field: str,
    /,
    *fields: str,
    mode: Literal['before', 'plain'],
    check_fields: bool | None = ...,
    json_schema_input_type: Any = ...,
) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]
def field_validator(
    field: str,
    /,
    *fields: str,
    mode: Literal['after'] = ...,
    check_fields: bool | None = ...,
) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]
```

:::callout{intent="note" title="Usage Documentation"}
[field validators](/guides/concepts-validators#field-validators)
:::

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

Example usage:

```python
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](/guides/concepts-validators#field-validators).

Errors raised in a field validator become part of the model’s [`ValidationError`](/guides/pydantic-core-pydantic-core#validationerror). In a running application, [Logfire](/guides/integrations-logfire) can record the input each failed validation rejected — see [Troubleshooting validation errors](/guides/error-messages-troubleshooting).

### Returns

[`Callable`](https://docs.python.org/3/library/typing.html#typing.Callable)\[\[[`Any`](https://docs.python.org/3/library/typing.html#typing.Any)], [`Any`](https://docs.python.org/3/library/typing.html#typing.Any)]

### Parameters

**`*fields`** : [`str`](https://docs.python.org/3/builtins/stdtypes.html#str) _Default:_ `()`

The field names the validator should apply to.

**`mode`** : `FieldValidatorModes` _Default:_ `'after'`

Specifies whether to validate the fields before or after validation.

**`check_fields`** : [`bool`](https://docs.python.org/3/builtins/functions.html#bool) | [`None`](https://docs.python.org/3/builtins/constants.html#None) _Default:_ `None`

Whether to check that the fields actually exist on the model.

**`json_schema_input_type`** : [`Any`](https://docs.python.org/3/library/typing.html#typing.Any) _Default:_ `PydanticUndefined`

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'`.

### Raises

- `PydanticUserError` —
- If the decorator is used without any arguments (at least one field name must be provided).
- If the provided field names are not strings.
- If `json_schema_input_type` is provided with an unsupported `mode`.
- If the decorator is applied to an instance method.

## model\_validator

```python
def model_validator(
    *,
    mode: Literal['wrap'],
) -> Callable[[_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]
def model_validator(
    *,
    mode: Literal['before'],
) -> Callable[[_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]
def model_validator(
    *,
    mode: Literal['after'],
) -> Callable[[_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]
```

:::callout{intent="note" title="Usage Documentation"}
[Model Validators](/guides/concepts-validators#model-validators)
:::

Decorate model methods for validation purposes.

Example usage:

```python
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](/guides/concepts-validators#model-validators).

Cross-field rules like the one above tend to fail on combinations of values you didn’t anticipate. To see the combination that failed in a running application, record validations with [Logfire](/guides/integrations-logfire) (see [Troubleshooting validation errors](/guides/error-messages-troubleshooting)).

### Returns

[`Any`](https://docs.python.org/3/library/typing.html#typing.Any) — A decorator that can be used to decorate a function to be used as a model validator.

### Parameters

**`mode`** : [`Literal`](https://docs.python.org/3/library/typing.html#typing.Literal)\[‘wrap’, ‘before’, ‘after’]

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

## ModelAfterValidatorWithoutInfo

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

**Default:** `Callable[[_ModelType], _ModelType]`

## ModelAfterValidator

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

**Default:** `Callable[[_ModelType, core_schema.ValidationInfo[Any]], _ModelType]`

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## Related pages

- [API Documentation](./api-documentation-index.md)
- [Concepts](./concepts-index.md)
- [Dev Tools](./dev-tools-index.md)
- [Error Messages](./error-messages-index.md)
- [Examples](./examples-index.md)
- [Integrations](./integrations-index.md)
- [Internals](./internals-index.md)
- [Production Tools](./production-tools-index.md)
- [Pydantic](./pydantic-index.md)
- [Pydantic Core](./pydantic-core-index.md)

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