Functional Serializers

This module contains related classes and functions for serialization.

FieldPlainSerializer module-attribute

FieldPlainSerializer: TypeAlias = (
    "core_schema.SerializerFunction | _Partial"
)

A field serializer method or function in plain mode.

FieldWrapSerializer module-attribute

FieldWrapSerializer: TypeAlias = (
    "core_schema.WrapSerializerFunction | _Partial"
)

A field serializer method or function in wrap mode.

FieldSerializer module-attribute

FieldSerializer: TypeAlias = (
    "FieldPlainSerializer | FieldWrapSerializer"
)

A field serializer method or function.

ModelPlainSerializerWithInfo module-attribute

ModelPlainSerializerWithInfo: TypeAlias = Callable[
    [Any, SerializationInfo], Any
]

A model serializer method with the info argument, in plain mode.

ModelPlainSerializerWithoutInfo module-attribute

ModelPlainSerializerWithoutInfo: TypeAlias = Callable[
    [Any], Any
]

A model serializer method without the info argument, in plain mode.

ModelPlainSerializer module-attribute

ModelPlainSerializer: TypeAlias = (
    "ModelPlainSerializerWithInfo | ModelPlainSerializerWithoutInfo"
)

A model serializer method in plain mode.

ModelWrapSerializerWithInfo module-attribute

ModelWrapSerializerWithInfo: TypeAlias = Callable[
    [Any, SerializerFunctionWrapHandler, SerializationInfo],
    Any,
]

A model serializer method with the info argument, in wrap mode.

ModelWrapSerializerWithoutInfo module-attribute

ModelWrapSerializerWithoutInfo: TypeAlias = Callable[
    [Any, SerializerFunctionWrapHandler], Any
]

A model serializer method without the info argument, in wrap mode.

ModelWrapSerializer module-attribute

ModelWrapSerializer: TypeAlias = (
    "ModelWrapSerializerWithInfo | ModelWrapSerializerWithoutInfo"
)

A model serializer method in wrap mode.

PlainSerializer dataclass

PlainSerializer(
    func: SerializerFunction,
    return_type: Any = PydanticUndefined,
    when_used: WhenUsed = "always",
)

Plain serializers use a function to modify the output of serialization.

This is particularly helpful when you want to customize the serialization for annotated types. Consider an input of list, which will be serialized into a space-delimited string.

from typing import List

from typing_extensions import Annotated

from pydantic import BaseModel, PlainSerializer

CustomStr = Annotated[
    List, PlainSerializer(lambda x: ' '.join(x), return_type=str)
]

class StudentModel(BaseModel):
    courses: CustomStr

student = StudentModel(courses=['Math', 'Chemistry', 'English'])
print(student.model_dump())
#> {'courses': 'Math Chemistry English'}

Attributes:

Name	Type	Description
func	SerializerFunction	The serializer function.
return_type	Any	The return type for the function. If omitted it will be inferred from the type annotation.
when_used	WhenUsed	Determines when this serializer should be used. Accepts a string with values 'always', 'unless-none', 'json', and 'json-unless-none'. Defaults to 'always'.

WrapSerializer dataclass

WrapSerializer(
    func: WrapSerializerFunction,
    return_type: Any = PydanticUndefined,
    when_used: WhenUsed = "always",
)

Wrap serializers receive the raw inputs along with a handler function that applies the standard serialization logic, and can modify the resulting value before returning it as the final output of serialization.

For example, here's a scenario in which a wrap serializer transforms timezones to UTC and utilizes the existing datetime serialization logic.

from datetime import datetime, timezone
from typing import Any, Dict

from typing_extensions import Annotated

from pydantic import BaseModel, WrapSerializer

class EventDatetime(BaseModel):
    start: datetime
    end: datetime

def convert_to_utc(value: Any, handler, info) -> Dict[str, datetime]:
    # Note that `handler` can actually help serialize the `value` for
    # further custom serialization in case it's a subclass.
    partial_result = handler(value, info)
    if info.mode == 'json':
        return {
            k: datetime.fromisoformat(v).astimezone(timezone.utc)
            for k, v in partial_result.items()
        }
    return {k: v.astimezone(timezone.utc) for k, v in partial_result.items()}

UTCEventDatetime = Annotated[EventDatetime, WrapSerializer(convert_to_utc)]

class EventModel(BaseModel):
    event_datetime: UTCEventDatetime

dt = EventDatetime(
    start='2024-01-01T07:00:00-08:00', end='2024-01-03T20:00:00+06:00'
)
event = EventModel(event_datetime=dt)
print(event.model_dump())
'''
{
    'event_datetime': {
        'start': datetime.datetime(
            2024, 1, 1, 15, 0, tzinfo=datetime.timezone.utc
        ),
        'end': datetime.datetime(
            2024, 1, 3, 14, 0, tzinfo=datetime.timezone.utc
        ),
    }
}
'''

print(event.model_dump_json())
'''
{"event_datetime":{"start":"2024-01-01T15:00:00Z","end":"2024-01-03T14:00:00Z"}}
'''

Attributes:

Name	Type	Description
func	WrapSerializerFunction	The serializer function to be wrapped.
return_type	Any	The return type for the function. If omitted it will be inferred from the type annotation.
when_used	WhenUsed	Determines when this serializer should be used. Accepts a string with values 'always', 'unless-none', 'json', and 'json-unless-none'. Defaults to 'always'.

field_serializer

field_serializer(
    *fields: str,
    mode: Literal["plain", "wrap"] = "plain",
    return_type: Any = PydanticUndefined,
    when_used: WhenUsed = "always",
    check_fields: bool | None = None
) -> (
    Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]
    | Callable[
        [_FieldPlainSerializerT], _FieldPlainSerializerT
    ]
)

Decorator that enables custom field serialization.

In the below example, a field of type set is used to mitigate duplication. A field_serializer is used to serialize the data as a sorted list.

from typing import Set

from pydantic import BaseModel, field_serializer

class StudentModel(BaseModel):
    name: str = 'Jane'
    courses: Set[str]

    @field_serializer('courses', when_used='json')
    def serialize_courses_in_order(self, courses: Set[str]):
        return sorted(courses)

student = StudentModel(courses={'Math', 'Chemistry', 'English'})
print(student.model_dump_json())
#> {"name":"Jane","courses":["Chemistry","English","Math"]}

See Custom serializers for more information.

Four signatures are supported:

• (self, value: Any, info: FieldSerializationInfo)
• (self, value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo)
• (value: Any, info: SerializationInfo)
• (value: Any, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)

Parameters:

Name	Type	Description	Default
fields	str	Which field(s) the method should be called on.	()
mode	Literal['plain', 'wrap']	The serialization mode. plain means the function will be called instead of the default serialization logic, wrap means the function will be called with an argument to optionally call the default serialization logic.	'plain'
return_type	Any	Optional return type for the function, if omitted it will be inferred from the type annotation.	PydanticUndefined
when_used	WhenUsed	Determines the serializer will be used for serialization.	'always'
check_fields	bool | None	Whether to check that the fields actually exist on the model.	None

Returns:

Type	Description
Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT] | Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]	The decorator function.

Source code in pydantic/functional_serializers.py

def field_serializer(
    *fields: str,
    mode: Literal['plain', 'wrap'] = 'plain',
    return_type: Any = PydanticUndefined,
    when_used: WhenUsed = 'always',
    check_fields: bool | None = None,
) -> (
    Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]
    | Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]
):
    """Decorator that enables custom field serialization.

    In the below example, a field of type `set` is used to mitigate duplication. A `field_serializer` is used to serialize the data as a sorted list.

    ```python
    from typing import Set

    from pydantic import BaseModel, field_serializer

    class StudentModel(BaseModel):
        name: str = 'Jane'
        courses: Set[str]

        @field_serializer('courses', when_used='json')
        def serialize_courses_in_order(self, courses: Set[str]):
            return sorted(courses)

    student = StudentModel(courses={'Math', 'Chemistry', 'English'})
    print(student.model_dump_json())
    #> {"name":"Jane","courses":["Chemistry","English","Math"]}
    ```

    See [Custom serializers](../concepts/serialization.md#custom-serializers) for more information.

    Four signatures are supported:

    - `(self, value: Any, info: FieldSerializationInfo)`
    - `(self, value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo)`
    - `(value: Any, info: SerializationInfo)`
    - `(value: Any, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)`

    Args:
        fields: Which field(s) the method should be called on.
        mode: The serialization mode.

            - `plain` means the function will be called instead of the default serialization logic,
            - `wrap` means the function will be called with an argument to optionally call the
               default serialization logic.
        return_type: Optional return type for the function, if omitted it will be inferred from the type annotation.
        when_used: Determines the serializer will be used for serialization.
        check_fields: Whether to check that the fields actually exist on the model.

    Returns:
        The decorator function.
    """

    def dec(f: FieldSerializer) -> _decorators.PydanticDescriptorProxy[Any]:
        dec_info = _decorators.FieldSerializerDecoratorInfo(
            fields=fields,
            mode=mode,
            return_type=return_type,
            when_used=when_used,
            check_fields=check_fields,
        )
        return _decorators.PydanticDescriptorProxy(f, dec_info)  # pyright: ignore[reportArgumentType]

    return dec  # pyright: ignore[reportReturnType]

model_serializer

model_serializer(
    f: (
        _ModelPlainSerializerT
        | _ModelWrapSerializerT
        | None
    ) = None,
    /,
    *,
    mode: Literal["plain", "wrap"] = "plain",
    when_used: WhenUsed = "always",
    return_type: Any = PydanticUndefined,
) -> (
    _ModelPlainSerializerT
    | Callable[
        [_ModelWrapSerializerT], _ModelWrapSerializerT
    ]
    | Callable[
        [_ModelPlainSerializerT], _ModelPlainSerializerT
    ]
)

Decorator that enables custom model serialization.

This is useful when a model need to be serialized in a customized manner, allowing for flexibility beyond just specific fields.

An example would be to serialize temperature to the same temperature scale, such as degrees Celsius.

from typing import Literal

from pydantic import BaseModel, model_serializer

class TemperatureModel(BaseModel):
    unit: Literal['C', 'F']
    value: int

    @model_serializer()
    def serialize_model(self):
        if self.unit == 'F':
            return {'unit': 'C', 'value': int((self.value - 32) / 1.8)}
        return {'unit': self.unit, 'value': self.value}

temperature = TemperatureModel(unit='F', value=212)
print(temperature.model_dump())
#> {'unit': 'C', 'value': 100}

Two signatures are supported for mode='plain', which is the default:

• (self)
• (self, info: SerializationInfo)

And two other signatures for mode='wrap':

• (self, nxt: SerializerFunctionWrapHandler)

• (self, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)

  See Custom serializers for more information.

Parameters:

Name	Type	Description	Default
f	_ModelPlainSerializerT | _ModelWrapSerializerT | None	The function to be decorated.	None
mode	Literal['plain', 'wrap']	The serialization mode. 'plain' means the function will be called instead of the default serialization logic 'wrap' means the function will be called with an argument to optionally call the default serialization logic.	'plain'
when_used	WhenUsed	Determines when this serializer should be used.	'always'
return_type	Any	The return type for the function. If omitted it will be inferred from the type annotation.	PydanticUndefined

Returns:

Type	Description
_ModelPlainSerializerT | Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT] | Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT]	The decorator function.

Source code in pydantic/functional_serializers.py

def model_serializer(
    f: _ModelPlainSerializerT | _ModelWrapSerializerT | None = None,
    /,
    *,
    mode: Literal['plain', 'wrap'] = 'plain',
    when_used: WhenUsed = 'always',
    return_type: Any = PydanticUndefined,
) -> (
    _ModelPlainSerializerT
    | Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT]
    | Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT]
):
    """Decorator that enables custom model serialization.

    This is useful when a model need to be serialized in a customized manner, allowing for flexibility beyond just specific fields.

    An example would be to serialize temperature to the same temperature scale, such as degrees Celsius.

    ```python
    from typing import Literal

    from pydantic import BaseModel, model_serializer

    class TemperatureModel(BaseModel):
        unit: Literal['C', 'F']
        value: int

        @model_serializer()
        def serialize_model(self):
            if self.unit == 'F':
                return {'unit': 'C', 'value': int((self.value - 32) / 1.8)}
            return {'unit': self.unit, 'value': self.value}

    temperature = TemperatureModel(unit='F', value=212)
    print(temperature.model_dump())
    #> {'unit': 'C', 'value': 100}
    ```

    Two signatures are supported for `mode='plain'`, which is the default:

    - `(self)`
    - `(self, info: SerializationInfo)`

    And two other signatures for `mode='wrap'`:

    - `(self, nxt: SerializerFunctionWrapHandler)`
    - `(self, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)`

        See [Custom serializers](../concepts/serialization.md#custom-serializers) for more information.

    Args:
        f: The function to be decorated.
        mode: The serialization mode.

            - `'plain'` means the function will be called instead of the default serialization logic
            - `'wrap'` means the function will be called with an argument to optionally call the default
                serialization logic.
        when_used: Determines when this serializer should be used.
        return_type: The return type for the function. If omitted it will be inferred from the type annotation.

    Returns:
        The decorator function.
    """

    def dec(f: ModelSerializer) -> _decorators.PydanticDescriptorProxy[Any]:
        dec_info = _decorators.ModelSerializerDecoratorInfo(mode=mode, return_type=return_type, when_used=when_used)
        return _decorators.PydanticDescriptorProxy(f, dec_info)

    if f is None:
        return dec  # pyright: ignore[reportReturnType]
    else:
        return dec(f)  # pyright: ignore[reportReturnType]