This page provides example snippets for creating more complex, custom validators in Pydantic.
Many of these examples are adapted from Pydantic issues and discussions, and are intended to showcase
the flexibility and power of Pydantic's validation system.

## Custom `datetime` Validator via [`Annotated`][typing.Annotated] Metadata

In this example, we'll construct a custom validator, attached to an [`Annotated`][typing.Annotated] type,
that ensures a [`datetime`][datetime.datetime] object adheres to a given timezone constraint.

The custom validator supports string specification of the timezone, and will raise an error if the [`datetime`][datetime.datetime] object does not have the correct timezone.

We use `__get_pydantic_core_schema__` in the validator to customize the schema of the annotated type (in this case, [`datetime`][datetime.datetime]), which allows us to add custom validation logic. Notably, we use a `wrap` validator function so that we can perform operations both before and after the default `pydantic` validation of a [`datetime`][datetime.datetime].

```python
import datetime as dt
from dataclasses import dataclass
from pprint import pprint
from typing import Annotated, Any, Callable, Optional

import pytz
from pydantic_core import CoreSchema, core_schema

from pydantic import (
    GetCoreSchemaHandler,
    PydanticUserError,
    TypeAdapter,
    ValidationError,
)


@dataclass(frozen=True)
class MyDatetimeValidator:
    tz_constraint: Optional[str] = None

    def tz_constraint_validator(
        self,
        value: dt.datetime,
        handler: Callable,  # (1)!
    ):
        """Validate tz_constraint and tz_info."""
        # handle naive datetimes
        if self.tz_constraint is None:
            assert (
                value.tzinfo is None
            ), 'tz_constraint is None, but provided value is tz-aware.'
            return handler(value)

        # validate tz_constraint and tz-aware tzinfo
        if self.tz_constraint not in pytz.all_timezones:
            raise PydanticUserError(
                f'Invalid tz_constraint: {self.tz_constraint}',
                code='unevaluable-type-annotation',
            )
        result = handler(value)  # (2)!
        assert self.tz_constraint == str(
            result.tzinfo
        ), f'Invalid tzinfo: {str(result.tzinfo)}, expected: {self.tz_constraint}'

        return result

    def __get_pydantic_core_schema__(
        self,
        source_type: Any,
        handler: GetCoreSchemaHandler,
    ) -> CoreSchema:
        return core_schema.no_info_wrap_validator_function(
            self.tz_constraint_validator,
            handler(source_type),
        )


LA = 'America/Los_Angeles'
ta = TypeAdapter(Annotated[dt.datetime, MyDatetimeValidator(LA)])
print(
    ta.validate_python(dt.datetime(2023, 1, 1, 0, 0, tzinfo=pytz.timezone(LA)))
)
#> 2023-01-01 00:00:00-07:53

LONDON = 'Europe/London'
try:
    ta.validate_python(
        dt.datetime(2023, 1, 1, 0, 0, tzinfo=pytz.timezone(LONDON))
    )
except ValidationError as ve:
    pprint(ve.errors(), width=100)
    """
    [{'ctx': {'error': AssertionError('Invalid tzinfo: Europe/London, expected: America/Los_Angeles')},
    'input': datetime.datetime(2023, 1, 1, 0, 0, tzinfo=<DstTzInfo 'Europe/London' LMT-1 day, 23:59:00 STD>),
    'loc': (),
    'msg': 'Assertion failed, Invalid tzinfo: Europe/London, expected: America/Los_Angeles',
    'type': 'assertion_error',
    'url': 'https://errors.pydantic.dev/2.8/v/assertion_error'}]
    """
```

1. The `handler` function is what we call to validate the input with standard `pydantic` validation
2. We call the `handler` function to validate the input with standard `pydantic` validation in this wrap validator

We can also enforce UTC offset constraints in a similar way.  Assuming we have a `lower_bound` and an `upper_bound`, we can create a custom validator to ensure our `datetime` has a UTC offset that is inclusive within the boundary we define:

```python
import datetime as dt
from dataclasses import dataclass
from pprint import pprint
from typing import Annotated, Any, Callable

import pytz
from pydantic_core import CoreSchema, core_schema

from pydantic import GetCoreSchemaHandler, TypeAdapter, ValidationError


@dataclass(frozen=True)
class MyDatetimeValidator:
    lower_bound: int
    upper_bound: int

    def validate_tz_bounds(self, value: dt.datetime, handler: Callable):
        """Validate and test bounds"""
        assert value.utcoffset() is not None, 'UTC offset must exist'
        assert self.lower_bound <= self.upper_bound, 'Invalid bounds'

        result = handler(value)

        hours_offset = value.utcoffset().total_seconds() / 3600
        assert (
            self.lower_bound <= hours_offset <= self.upper_bound
        ), 'Value out of bounds'

        return result

    def __get_pydantic_core_schema__(
        self,
        source_type: Any,
        handler: GetCoreSchemaHandler,
    ) -> CoreSchema:
        return core_schema.no_info_wrap_validator_function(
            self.validate_tz_bounds,
            handler(source_type),
        )


LA = 'America/Los_Angeles'  # UTC-7 or UTC-8
ta = TypeAdapter(Annotated[dt.datetime, MyDatetimeValidator(-10, -5)])
print(
    ta.validate_python(dt.datetime(2023, 1, 1, 0, 0, tzinfo=pytz.timezone(LA)))
)
#> 2023-01-01 00:00:00-07:53

LONDON = 'Europe/London'
try:
    print(
        ta.validate_python(
            dt.datetime(2023, 1, 1, 0, 0, tzinfo=pytz.timezone(LONDON))
        )
    )
except ValidationError as e:
    pprint(e.errors(), width=100)
    """
    [{'ctx': {'error': AssertionError('Value out of bounds')},
    'input': datetime.datetime(2023, 1, 1, 0, 0, tzinfo=<DstTzInfo 'Europe/London' LMT-1 day, 23:59:00 STD>),
    'loc': (),
    'msg': 'Assertion failed, Value out of bounds',
    'type': 'assertion_error',
    'url': 'https://errors.pydantic.dev/2.8/v/assertion_error'}]
    """
```

## Validating Nested Model Fields

Here, we demonstrate two ways to validate a field of a nested model, where the validator utilizes data from the parent model.

In this example, we construct a validator that checks that each user's password is not in a list of forbidden passwords specified by the parent model.

One way to do this is to place a custom validator on the outer model:

```python
from typing_extensions import Self

from pydantic import BaseModel, ValidationError, model_validator


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


class Organization(BaseModel):
    forbidden_passwords: list[str]
    users: list[User]

    @model_validator(mode='after')
    def validate_user_passwords(self) -> Self:
        """Check that user password is not in forbidden list. Raise a validation error if a forbidden password is encountered."""
        for user in self.users:
            current_pw = user.password
            if current_pw in self.forbidden_passwords:
                raise ValueError(
                    f'Password {current_pw} is forbidden. Please choose another password for user {user.username}.'
                )
        return self


data = {
    'forbidden_passwords': ['123'],
    'users': [
        {'username': 'Spartacat', 'password': '123'},
        {'username': 'Iceburgh', 'password': '87'},
    ],
}
try:
    org = Organization(**data)
except ValidationError as e:
    print(e)
    """
    1 validation error for Organization
      Value error, Password 123 is forbidden. Please choose another password for user Spartacat. [type=value_error, input_value={'forbidden_passwords': [...gh', 'password': '87'}]}, input_type=dict]
    """
```

Alternatively, a custom validator can be used in the nested model class (`User`), with the forbidden passwords data from the parent model being passed in via validation context.

!!! warning
    The ability to mutate the context within a validator adds a lot of power to nested validation, but can also lead to confusing or hard-to-debug code. Use this approach at your own risk!

```python
from pydantic import BaseModel, ValidationError, ValidationInfo, field_validator


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

    @field_validator('password', mode='after')
    @classmethod
    def validate_user_passwords(
        cls, password: str, info: ValidationInfo
    ) -> str:
        """Check that user password is not in forbidden list."""
        forbidden_passwords = (
            info.context.get('forbidden_passwords', []) if info.context else []
        )
        if password in forbidden_passwords:
            raise ValueError(f'Password {password} is forbidden.')
        return password


class Organization(BaseModel):
    forbidden_passwords: list[str]
    users: list[User]

    @field_validator('forbidden_passwords', mode='after')
    @classmethod
    def add_context(cls, v: list[str], info: ValidationInfo) -> list[str]:
        if info.context is not None:
            info.context.update({'forbidden_passwords': v})
        return v


data = {
    'forbidden_passwords': ['123'],
    'users': [
        {'username': 'Spartacat', 'password': '123'},
        {'username': 'Iceburgh', 'password': '87'},
    ],
}

try:
    org = Organization.model_validate(data, context={})
except ValidationError as e:
    print(e)
    """
    1 validation error for Organization
    users.0.password
      Value error, Password 123 is forbidden. [type=value_error, input_value='123', input_type=str]
    """
```

Note that if the context property is not included in `model_validate`, then `info.context` will be `None` and the forbidden passwords list will not get added to the context in the above implementation. As such, `validate_user_passwords` would not carry out the desired password validation.

More details about validation context can be found in the [validators documentation](../concepts/validators.md#validation-context).