Fields

Defining fields on models.

Field

Field(
    default=PydanticUndefined,
    *,
    default_factory=_Unset,
    alias=_Unset,
    alias_priority=_Unset,
    validation_alias=_Unset,
    serialization_alias=_Unset,
    title=_Unset,
    description=_Unset,
    examples=_Unset,
    exclude=_Unset,
    discriminator=_Unset,
    json_schema_extra=_Unset,
    frozen=_Unset,
    validate_default=_Unset,
    repr=_Unset,
    init=_Unset,
    init_var=_Unset,
    kw_only=_Unset,
    pattern=_Unset,
    strict=_Unset,
    gt=_Unset,
    ge=_Unset,
    lt=_Unset,
    le=_Unset,
    multiple_of=_Unset,
    allow_inf_nan=_Unset,
    max_digits=_Unset,
    decimal_places=_Unset,
    min_length=_Unset,
    max_length=_Unset,
    union_mode=_Unset,
    **extra
)

Usage Documentation

Fields

Create a field for objects that can be configured.

Used to provide extra information about a field, either for the model schema or complex validation. Some arguments apply only to number fields (int, float, Decimal) and some apply only to str.

Note

• Any _Unset objects will be replaced by the corresponding value defined in the _DefaultValues dictionary. If a key for the _Unset object is not found in the _DefaultValues dictionary, it will default to None

Parameters:

Name	Type	Description	Default
default	Any	Default value if the field is not set.	PydanticUndefined
default_factory	Callable[[], Any] | None	A callable to generate the default value, such as :func:~datetime.utcnow.	_Unset
alias	str | None	The name to use for the attribute when validating or serializing by alias. This is often used for things like converting between snake and camel case.	_Unset
alias_priority	int | None	Priority of the alias. This affects whether an alias generator is used.	_Unset
validation_alias	str | AliasPath | AliasChoices | None	Like alias, but only affects validation, not serialization.	_Unset
serialization_alias	str | None	Like alias, but only affects serialization, not validation.	_Unset
title	str | None	Human-readable title.	_Unset
description	str | None	Human-readable description.	_Unset
examples	list[Any] | None	Example values for this field.	_Unset
exclude	bool | None	Whether to exclude the field from the model serialization.	_Unset
discriminator	str | Discriminator | None	Field name or Discriminator for discriminating the type in a tagged union.	_Unset
json_schema_extra	JsonDict | Callable[[JsonDict], None] | None	A dict or callable to provide extra JSON schema properties.	_Unset
frozen	bool | None	Whether the field is frozen. If true, attempts to change the value on an instance will raise an error.	_Unset
validate_default	bool | None	If True, apply validation to the default value every time you create an instance. Otherwise, for performance reasons, the default value of the field is trusted and not validated.	_Unset
repr	bool	A boolean indicating whether to include the field in the __repr__ output.	_Unset
init	bool | None	Whether the field should be included in the constructor of the dataclass. (Only applies to dataclasses.)	_Unset
init_var	bool | None	Whether the field should only be included in the constructor of the dataclass. (Only applies to dataclasses.)	_Unset
kw_only	bool | None	Whether the field should be a keyword-only argument in the constructor of the dataclass. (Only applies to dataclasses.)	_Unset
strict	bool | None	If True, strict validation is applied to the field. See Strict Mode for details.	_Unset
gt	float | None	Greater than. If set, value must be greater than this. Only applicable to numbers.	_Unset
ge	float | None	Greater than or equal. If set, value must be greater than or equal to this. Only applicable to numbers.	_Unset
lt	float | None	Less than. If set, value must be less than this. Only applicable to numbers.	_Unset
le	float | None	Less than or equal. If set, value must be less than or equal to this. Only applicable to numbers.	_Unset
multiple_of	float | None	Value must be a multiple of this. Only applicable to numbers.	_Unset
min_length	int | None	Minimum length for strings.	_Unset
max_length	int | None	Maximum length for strings.	_Unset
pattern	str | None	Pattern for strings (a regular expression).	_Unset
allow_inf_nan	bool | None	Allow inf, -inf, nan. Only applicable to numbers.	_Unset
max_digits	int | None	Maximum number of allow digits for strings.	_Unset
decimal_places	int | None	Maximum number of decimal places allowed for numbers.	_Unset
union_mode	Literal['smart', 'left_to_right']	The strategy to apply when validating a union. Can be smart (the default), or left_to_right. See Union Mode for details.	_Unset
extra	Unpack[_EmptyKwargs]	(Deprecated) Extra fields that will be included in the JSON schema. Warning The extra kwargs is deprecated. Use json_schema_extra instead.	{}

Returns:

Type	Description
Any	A new FieldInfo. The return annotation is Any so Field can be used on type-annotated fields without causing a type error.

Source code in pydantic/fields.py

def Field(  # noqa: C901
    default: Any = PydanticUndefined,
    *,
    default_factory: typing.Callable[[], Any] | None = _Unset,
    alias: str | None = _Unset,
    alias_priority: int | None = _Unset,
    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
    serialization_alias: str | None = _Unset,
    title: str | None = _Unset,
    description: str | None = _Unset,
    examples: list[Any] | None = _Unset,
    exclude: bool | None = _Unset,
    discriminator: str | types.Discriminator | None = _Unset,
    json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None = _Unset,
    frozen: bool | None = _Unset,
    validate_default: bool | None = _Unset,
    repr: bool = _Unset,
    init: bool | None = _Unset,
    init_var: bool | None = _Unset,
    kw_only: bool | None = _Unset,
    pattern: str | None = _Unset,
    strict: bool | None = _Unset,
    gt: float | None = _Unset,
    ge: float | None = _Unset,
    lt: float | None = _Unset,
    le: float | None = _Unset,
    multiple_of: float | None = _Unset,
    allow_inf_nan: bool | None = _Unset,
    max_digits: int | None = _Unset,
    decimal_places: int | None = _Unset,
    min_length: int | None = _Unset,
    max_length: int | None = _Unset,
    union_mode: Literal['smart', 'left_to_right'] = _Unset,
    **extra: Unpack[_EmptyKwargs],
) -> Any:
    """Usage docs: https://docs.pydantic.dev/2.6/concepts/fields

    Create a field for objects that can be configured.

    Used to provide extra information about a field, either for the model schema or complex validation. Some arguments
    apply only to number fields (`int`, `float`, `Decimal`) and some apply only to `str`.

    Note:
        - Any `_Unset` objects will be replaced by the corresponding value defined in the `_DefaultValues` dictionary. If a key for the `_Unset` object is not found in the `_DefaultValues` dictionary, it will default to `None`

    Args:
        default: Default value if the field is not set.
        default_factory: A callable to generate the default value, such as :func:`~datetime.utcnow`.
        alias: The name to use for the attribute when validating or serializing by alias.
            This is often used for things like converting between snake and camel case.
        alias_priority: Priority of the alias. This affects whether an alias generator is used.
        validation_alias: Like `alias`, but only affects validation, not serialization.
        serialization_alias: Like `alias`, but only affects serialization, not validation.
        title: Human-readable title.
        description: Human-readable description.
        examples: Example values for this field.
        exclude: Whether to exclude the field from the model serialization.
        discriminator: Field name or Discriminator for discriminating the type in a tagged union.
        json_schema_extra: A dict or callable to provide extra JSON schema properties.
        frozen: Whether the field is frozen. If true, attempts to change the value on an instance will raise an error.
        validate_default: If `True`, apply validation to the default value every time you create an instance.
            Otherwise, for performance reasons, the default value of the field is trusted and not validated.
        repr: A boolean indicating whether to include the field in the `__repr__` output.
        init: Whether the field should be included in the constructor of the dataclass.
            (Only applies to dataclasses.)
        init_var: Whether the field should _only_ be included in the constructor of the dataclass.
            (Only applies to dataclasses.)
        kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass.
            (Only applies to dataclasses.)
        strict: If `True`, strict validation is applied to the field.
            See [Strict Mode](../concepts/strict_mode.md) for details.
        gt: Greater than. If set, value must be greater than this. Only applicable to numbers.
        ge: Greater than or equal. If set, value must be greater than or equal to this. Only applicable to numbers.
        lt: Less than. If set, value must be less than this. Only applicable to numbers.
        le: Less than or equal. If set, value must be less than or equal to this. Only applicable to numbers.
        multiple_of: Value must be a multiple of this. Only applicable to numbers.
        min_length: Minimum length for strings.
        max_length: Maximum length for strings.
        pattern: Pattern for strings (a regular expression).
        allow_inf_nan: Allow `inf`, `-inf`, `nan`. Only applicable to numbers.
        max_digits: Maximum number of allow digits for strings.
        decimal_places: Maximum number of decimal places allowed for numbers.
        union_mode: The strategy to apply when validating a union. Can be `smart` (the default), or `left_to_right`.
            See [Union Mode](standard_library_types.md#union-mode) for details.
        extra: (Deprecated) Extra fields that will be included in the JSON schema.

            !!! warning Deprecated
                The `extra` kwargs is deprecated. Use `json_schema_extra` instead.

    Returns:
        A new [`FieldInfo`][pydantic.fields.FieldInfo]. The return annotation is `Any` so `Field` can be used on
            type-annotated fields without causing a type error.
    """
    # Check deprecated and removed params from V1. This logic should eventually be removed.
    const = extra.pop('const', None)  # type: ignore
    if const is not None:
        raise PydanticUserError('`const` is removed, use `Literal` instead', code='removed-kwargs')

    min_items = extra.pop('min_items', None)  # type: ignore
    if min_items is not None:
        warn('`min_items` is deprecated and will be removed, use `min_length` instead', DeprecationWarning)
        if min_length in (None, _Unset):
            min_length = min_items  # type: ignore

    max_items = extra.pop('max_items', None)  # type: ignore
    if max_items is not None:
        warn('`max_items` is deprecated and will be removed, use `max_length` instead', DeprecationWarning)
        if max_length in (None, _Unset):
            max_length = max_items  # type: ignore

    unique_items = extra.pop('unique_items', None)  # type: ignore
    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',
        )

    allow_mutation = extra.pop('allow_mutation', None)  # type: ignore
    if allow_mutation is not None:
        warn('`allow_mutation` is deprecated and will be removed. use `frozen` instead', DeprecationWarning)
        if allow_mutation is False:
            frozen = True

    regex = extra.pop('regex', None)  # type: ignore
    if regex is not None:
        raise PydanticUserError('`regex` is removed. use `pattern` instead', code='removed-kwargs')

    if extra:
        warn(
            'Using extra keyword arguments on `Field` is deprecated and will be removed.'
            ' Use `json_schema_extra` instead.'
            f' (Extra keys: {", ".join(k.__repr__() for k in extra.keys())})',
            DeprecationWarning,
        )
        if not json_schema_extra or json_schema_extra is _Unset:
            json_schema_extra = extra  # type: ignore

    if (
        validation_alias
        and validation_alias is not _Unset
        and not isinstance(validation_alias, (str, AliasChoices, AliasPath))
    ):
        raise TypeError('Invalid `validation_alias` type. it should be `str`, `AliasChoices`, or `AliasPath`')

    if serialization_alias in (_Unset, None) and isinstance(alias, str):
        serialization_alias = alias

    if validation_alias in (_Unset, None):
        validation_alias = alias

    include = extra.pop('include', None)  # type: ignore
    if include is not None:
        warn('`include` is deprecated and does nothing. It will be removed, use `exclude` instead', DeprecationWarning)

    return FieldInfo.from_field(
        default,
        default_factory=default_factory,
        alias=alias,
        alias_priority=alias_priority,
        validation_alias=validation_alias,
        serialization_alias=serialization_alias,
        title=title,
        description=description,
        examples=examples,
        exclude=exclude,
        discriminator=discriminator,
        json_schema_extra=json_schema_extra,
        frozen=frozen,
        pattern=pattern,
        validate_default=validate_default,
        repr=repr,
        init=init,
        init_var=init_var,
        kw_only=kw_only,
        strict=strict,
        gt=gt,
        ge=ge,
        lt=lt,
        le=le,
        multiple_of=multiple_of,
        min_length=min_length,
        max_length=max_length,
        allow_inf_nan=allow_inf_nan,
        max_digits=max_digits,
        decimal_places=decimal_places,
        union_mode=union_mode,
    )

FieldInfo

FieldInfo(**kwargs)

Bases: Representation

This class holds information about a field.

FieldInfo is used for any field definition regardless of whether the Field() function is explicitly used.

Warning

You generally shouldn't be creating FieldInfo directly, you'll only need to use it when accessing BaseModel .model_fields internals.

Attributes:

Name	Type	Description
annotation	type[Any] | None	The type annotation of the field.
default	Any	The default value of the field.
default_factory	Callable[[], Any] | None	The factory function used to construct the default for the field.
alias	str | None	The alias name of the field.
alias_priority	int | None	The priority of the field's alias.
validation_alias	str | AliasPath | AliasChoices | None	The validation alias of the field.
serialization_alias	str | None	The serialization alias of the field.
title	str | None	The title of the field.
description	str | None	The description of the field.
examples	list[Any] | None	List of examples of the field.
exclude	bool | None	Whether to exclude the field from the model serialization.
discriminator	str | Discriminator | None	Field name or Discriminator for discriminating the type in a tagged union.
json_schema_extra	JsonDict | Callable[[JsonDict], None] | None	A dict or callable to provide extra JSON schema properties.
frozen	bool | None	Whether the field is frozen.
validate_default	bool | None	Whether to validate the default value of the field.
repr	bool	Whether to include the field in representation of the model.
init	bool | None	Whether the field should be included in the constructor of the dataclass.
init_var	bool | None	Whether the field should only be included in the constructor of the dataclass, and not stored.
kw_only	bool | None	Whether the field should be a keyword-only argument in the constructor of the dataclass.
metadata	list[Any]	List of metadata constraints.

See the signature of pydantic.fields.Field for more details about the expected arguments.

Source code in pydantic/fields.py

def __init__(self, **kwargs: Unpack[_FieldInfoInputs]) -> None:
    """This class should generally not be initialized directly; instead, use the `pydantic.fields.Field` function
    or one of the constructor classmethods.

    See the signature of `pydantic.fields.Field` for more details about the expected arguments.
    """
    self._attributes_set = {k: v for k, v in kwargs.items() if v is not _Unset}
    kwargs = {k: _DefaultValues.get(k) if v is _Unset else v for k, v in kwargs.items()}  # type: ignore
    self.annotation, annotation_metadata = self._extract_metadata(kwargs.get('annotation'))

    default = kwargs.pop('default', PydanticUndefined)
    if default is Ellipsis:
        self.default = PydanticUndefined
    else:
        self.default = default

    self.default_factory = kwargs.pop('default_factory', None)

    if self.default is not PydanticUndefined and self.default_factory is not None:
        raise TypeError('cannot specify both default and default_factory')

    self.title = kwargs.pop('title', None)
    self.alias = kwargs.pop('alias', None)
    self.validation_alias = kwargs.pop('validation_alias', None)
    self.serialization_alias = kwargs.pop('serialization_alias', None)
    alias_is_set = any(alias is not None for alias in (self.alias, self.validation_alias, self.serialization_alias))
    self.alias_priority = kwargs.pop('alias_priority', None) or 2 if alias_is_set else None
    self.description = kwargs.pop('description', None)
    self.examples = kwargs.pop('examples', None)
    self.exclude = kwargs.pop('exclude', None)
    self.discriminator = kwargs.pop('discriminator', None)
    self.repr = kwargs.pop('repr', True)
    self.json_schema_extra = kwargs.pop('json_schema_extra', None)
    self.validate_default = kwargs.pop('validate_default', None)
    self.frozen = kwargs.pop('frozen', None)
    # currently only used on dataclasses
    self.init = kwargs.pop('init', None)
    self.init_var = kwargs.pop('init_var', None)
    self.kw_only = kwargs.pop('kw_only', None)

    self.metadata = self._collect_metadata(kwargs) + annotation_metadata  # type: ignore

from_field staticmethod

from_field(default=PydanticUndefined, **kwargs)

Create a new FieldInfo object with the Field function.

Parameters:

Name	Type	Description	Default
default	Any	The default value for the field. Defaults to Undefined.	PydanticUndefined
**kwargs	Unpack[_FromFieldInfoInputs]	Additional arguments dictionary.	{}

Raises:

Type	Description
TypeError	If 'annotation' is passed as a keyword argument.

Returns:

Type	Description
FieldInfo	A new FieldInfo object with the given parameters.

Example

This is how you can create a field with default value like this:

import pydantic

class MyModel(pydantic.BaseModel):
    foo: int = pydantic.Field(4)

Source code in pydantic/fields.py

@staticmethod
def from_field(default: Any = PydanticUndefined, **kwargs: Unpack[_FromFieldInfoInputs]) -> FieldInfo:
    """Create a new `FieldInfo` object with the `Field` function.

    Args:
        default: The default value for the field. Defaults to Undefined.
        **kwargs: Additional arguments dictionary.

    Raises:
        TypeError: If 'annotation' is passed as a keyword argument.

    Returns:
        A new FieldInfo object with the given parameters.

    Example:
        This is how you can create a field with default value like this:

        ```python
        import pydantic

        class MyModel(pydantic.BaseModel):
            foo: int = pydantic.Field(4)
        ```
    """
    if 'annotation' in kwargs:
        raise TypeError('"annotation" is not permitted as a Field keyword argument')
    return FieldInfo(default=default, **kwargs)

from_annotation staticmethod

from_annotation(annotation)

Creates a FieldInfo instance from a bare annotation.

This function is used internally to create a FieldInfo from a bare annotation like this:

import pydantic

class MyModel(pydantic.BaseModel):
    foo: int  # <-- like this

We also account for the case where the annotation can be an instance of Annotated and where one of the (not first) arguments in Annotated is an instance of FieldInfo, e.g.:

import annotated_types
from typing_extensions import Annotated

import pydantic

class MyModel(pydantic.BaseModel):
    foo: Annotated[int, annotated_types.Gt(42)]
    bar: Annotated[int, pydantic.Field(gt=42)]

Parameters:

Name	Type	Description	Default
annotation	type[Any]	An annotation object.	required

Returns:

Type	Description
FieldInfo	An instance of the field metadata.

Source code in pydantic/fields.py

@staticmethod
def from_annotation(annotation: type[Any]) -> FieldInfo:
    """Creates a `FieldInfo` instance from a bare annotation.

    This function is used internally to create a `FieldInfo` from a bare annotation like this:

    ```python
    import pydantic

    class MyModel(pydantic.BaseModel):
        foo: int  # <-- like this
    ```

    We also account for the case where the annotation can be an instance of `Annotated` and where
    one of the (not first) arguments in `Annotated` is an instance of `FieldInfo`, e.g.:

    ```python
    import annotated_types
    from typing_extensions import Annotated

    import pydantic

    class MyModel(pydantic.BaseModel):
        foo: Annotated[int, annotated_types.Gt(42)]
        bar: Annotated[int, pydantic.Field(gt=42)]
    ```

    Args:
        annotation: An annotation object.

    Returns:
        An instance of the field metadata.
    """
    final = False
    if _typing_extra.is_finalvar(annotation):
        final = True
        if annotation is not typing_extensions.Final:
            annotation = typing_extensions.get_args(annotation)[0]

    if _typing_extra.is_annotated(annotation):
        first_arg, *extra_args = typing_extensions.get_args(annotation)
        if _typing_extra.is_finalvar(first_arg):
            final = True
        field_info_annotations = [a for a in extra_args if isinstance(a, FieldInfo)]
        field_info = FieldInfo.merge_field_infos(*field_info_annotations, annotation=first_arg)
        if field_info:
            new_field_info = copy(field_info)
            new_field_info.annotation = first_arg
            new_field_info.frozen = final or field_info.frozen
            metadata: list[Any] = []
            for a in extra_args:
                if not isinstance(a, FieldInfo):
                    metadata.append(a)
                else:
                    metadata.extend(a.metadata)
            new_field_info.metadata = metadata
            return new_field_info

    return FieldInfo(annotation=annotation, frozen=final or None)

from_annotated_attribute staticmethod

from_annotated_attribute(annotation, default)

Create FieldInfo from an annotation with a default value.

This is used in cases like the following:

import annotated_types
from typing_extensions import Annotated

import pydantic

class MyModel(pydantic.BaseModel):
    foo: int = 4  # <-- like this
    bar: Annotated[int, annotated_types.Gt(4)] = 4  # <-- or this
    spam: Annotated[int, pydantic.Field(gt=4)] = 4  # <-- or this

Parameters:

Name	Type	Description	Default
annotation	type[Any]	The type annotation of the field.	required
default	Any	The default value of the field.	required

Returns:

Type	Description
FieldInfo	A field object with the passed values.

Source code in pydantic/fields.py

@staticmethod
def from_annotated_attribute(annotation: type[Any], default: Any) -> FieldInfo:
    """Create `FieldInfo` from an annotation with a default value.

    This is used in cases like the following:

    ```python
    import annotated_types
    from typing_extensions import Annotated

    import pydantic

    class MyModel(pydantic.BaseModel):
        foo: int = 4  # <-- like this
        bar: Annotated[int, annotated_types.Gt(4)] = 4  # <-- or this
        spam: Annotated[int, pydantic.Field(gt=4)] = 4  # <-- or this
    ```

    Args:
        annotation: The type annotation of the field.
        default: The default value of the field.

    Returns:
        A field object with the passed values.
    """
    if annotation is default:
        raise PydanticUserError(
            'Error when building FieldInfo from annotated attribute. '
            "Make sure you don't have any field name clashing with a type annotation ",
            code='unevaluable-type-annotation',
        )

    final = False
    if _typing_extra.is_finalvar(annotation):
        final = True
        if annotation is not typing_extensions.Final:
            annotation = typing_extensions.get_args(annotation)[0]

    if isinstance(default, FieldInfo):
        default.annotation, annotation_metadata = FieldInfo._extract_metadata(annotation)
        default.metadata += annotation_metadata
        default = default.merge_field_infos(
            *[x for x in annotation_metadata if isinstance(x, FieldInfo)], default, annotation=default.annotation
        )
        default.frozen = final or default.frozen
        return default
    elif isinstance(default, dataclasses.Field):
        init_var = False
        if annotation is dataclasses.InitVar:
            init_var = True
            annotation = Any
        elif isinstance(annotation, dataclasses.InitVar):
            init_var = True
            annotation = annotation.type
        pydantic_field = FieldInfo._from_dataclass_field(default)
        pydantic_field.annotation, annotation_metadata = FieldInfo._extract_metadata(annotation)
        pydantic_field.metadata += annotation_metadata
        pydantic_field = pydantic_field.merge_field_infos(
            *[x for x in annotation_metadata if isinstance(x, FieldInfo)],
            pydantic_field,
            annotation=pydantic_field.annotation,
        )
        pydantic_field.frozen = final or pydantic_field.frozen
        pydantic_field.init_var = init_var
        pydantic_field.init = getattr(default, 'init', None)
        pydantic_field.kw_only = getattr(default, 'kw_only', None)
        return pydantic_field
    else:
        if _typing_extra.is_annotated(annotation):
            first_arg, *extra_args = typing_extensions.get_args(annotation)
            field_infos = [a for a in extra_args if isinstance(a, FieldInfo)]
            field_info = FieldInfo.merge_field_infos(*field_infos, annotation=first_arg, default=default)
            metadata: list[Any] = []
            for a in extra_args:
                if not isinstance(a, FieldInfo):
                    metadata.append(a)
                else:
                    metadata.extend(a.metadata)
            field_info.metadata = metadata
            return field_info

        return FieldInfo(annotation=annotation, default=default, frozen=final or None)

merge_field_infos staticmethod

merge_field_infos(*field_infos, **overrides)

Merge FieldInfo instances keeping only explicitly set attributes.

Later FieldInfo instances override earlier ones.

Returns:

Name	Type	Description
FieldInfo	FieldInfo	A merged FieldInfo instance.

Source code in pydantic/fields.py

@staticmethod
def merge_field_infos(*field_infos: FieldInfo, **overrides: Any) -> FieldInfo:
    """Merge `FieldInfo` instances keeping only explicitly set attributes.

    Later `FieldInfo` instances override earlier ones.

    Returns:
        FieldInfo: A merged FieldInfo instance.
    """
    flattened_field_infos: list[FieldInfo] = []
    for field_info in field_infos:
        flattened_field_infos.extend(x for x in field_info.metadata if isinstance(x, FieldInfo))
        flattened_field_infos.append(field_info)
    field_infos = tuple(flattened_field_infos)
    if len(field_infos) == 1:
        # No merging necessary, but we still need to make a copy and apply the overrides
        field_info = copy(field_infos[0])
        field_info._attributes_set.update(overrides)
        for k, v in overrides.items():
            setattr(field_info, k, v)
        return field_info  # type: ignore

    new_kwargs: dict[str, Any] = {}
    metadata = {}
    for field_info in field_infos:
        new_kwargs.update(field_info._attributes_set)
        for x in field_info.metadata:
            if not isinstance(x, FieldInfo):
                metadata[type(x)] = x
    new_kwargs.update(overrides)
    field_info = FieldInfo(**new_kwargs)
    field_info.metadata = list(metadata.values())
    return field_info

get_default

get_default(*, call_default_factory=False)

Get the default value.

We expose an option for whether to call the default_factory (if present), as calling it may result in side effects that we want to avoid. However, there are times when it really should be called (namely, when instantiating a model via model_construct).

Parameters:

Name	Type	Description	Default
call_default_factory	bool	Whether to call the default_factory or not. Defaults to False.	False

Returns:

Type	Description
Any	The default value, calling the default factory if requested or None if not set.

Source code in pydantic/fields.py

def get_default(self, *, call_default_factory: bool = False) -> Any:
    """Get the default value.

    We expose an option for whether to call the default_factory (if present), as calling it may
    result in side effects that we want to avoid. However, there are times when it really should
    be called (namely, when instantiating a model via `model_construct`).

    Args:
        call_default_factory: Whether to call the default_factory or not. Defaults to `False`.

    Returns:
        The default value, calling the default factory if requested or `None` if not set.
    """
    if self.default_factory is None:
        return _utils.smart_deepcopy(self.default)
    elif call_default_factory:
        return self.default_factory()
    else:
        return None

is_required

is_required()

Check if the field is required (i.e., does not have a default value or factory).

Returns:

Type	Description
bool	True if the field is required, False otherwise.

Source code in pydantic/fields.py

def is_required(self) -> bool:
    """Check if the field is required (i.e., does not have a default value or factory).

    Returns:
        `True` if the field is required, `False` otherwise.
    """
    return self.default is PydanticUndefined and self.default_factory is None

rebuild_annotation

rebuild_annotation()

Attempts to rebuild the original annotation for use in function signatures.

If metadata is present, it adds it to the original annotation using Annotated. Otherwise, it returns the original annotation as-is.

Note that because the metadata has been flattened, the original annotation may not be reconstructed exactly as originally provided, e.g. if the original type had unrecognized annotations, or was annotated with a call to pydantic.Field.

Returns:

Type	Description
Any	The rebuilt annotation.

Source code in pydantic/fields.py

def rebuild_annotation(self) -> Any:
    """Attempts to rebuild the original annotation for use in function signatures.

    If metadata is present, it adds it to the original annotation using
    `Annotated`. Otherwise, it returns the original annotation as-is.

    Note that because the metadata has been flattened, the original annotation
    may not be reconstructed exactly as originally provided, e.g. if the original
    type had unrecognized annotations, or was annotated with a call to `pydantic.Field`.

    Returns:
        The rebuilt annotation.
    """
    if not self.metadata:
        return self.annotation
    else:
        # Annotated arguments must be a tuple
        return typing_extensions.Annotated[(self.annotation, *self.metadata)]  # type: ignore

apply_typevars_map

apply_typevars_map(typevars_map, types_namespace)

Apply a typevars_map to the annotation.

This method is used when analyzing parametrized generic types to replace typevars with their concrete types.

This method applies the typevars_map to the annotation in place.

Parameters:

Name	Type	Description	Default
typevars_map	dict[Any, Any] | None	A dictionary mapping type variables to their concrete types.	required
types_namespace	dict | None	A dictionary containing related types to the annotated type.	required

See Also

pydantic._internal._generics.replace_types is used for replacing the typevars with their concrete types.

Source code in pydantic/fields.py

def apply_typevars_map(self, typevars_map: dict[Any, Any] | None, types_namespace: dict[str, Any] | None) -> None:
    """Apply a `typevars_map` to the annotation.

    This method is used when analyzing parametrized generic types to replace typevars with their concrete types.

    This method applies the `typevars_map` to the annotation in place.

    Args:
        typevars_map: A dictionary mapping type variables to their concrete types.
        types_namespace (dict | None): A dictionary containing related types to the annotated type.

    See Also:
        pydantic._internal._generics.replace_types is used for replacing the typevars with
            their concrete types.
    """
    annotation = _typing_extra.eval_type_lenient(self.annotation, types_namespace)
    self.annotation = _generics.replace_types(annotation, typevars_map)

PrivateAttr

PrivateAttr(
    default=PydanticUndefined, *, default_factory=None
)

Usage Documentation

Private model attributes

Indicates that an attribute is intended for private use and not handled during normal validation/serialization.

Private attributes are not validated by Pydantic, so it's up to you to ensure they are used in a type-safe manner.

Private attributes are stored in __private_attributes__ on the model.

Parameters:

Name	Type	Description	Default
default	Any	The attribute's default value. Defaults to Undefined.	PydanticUndefined
default_factory	Callable[[], Any] | None	Callable that will be called when a default value is needed for this attribute. If both default and default_factory are set, an error will be raised.	None

Returns:

Type	Description
Any	An instance of ModelPrivateAttr class.

Raises:

Type	Description
ValueError	If both default and default_factory are set.

Source code in pydantic/fields.py

def PrivateAttr(
    default: Any = PydanticUndefined,
    *,
    default_factory: typing.Callable[[], Any] | None = None,
) -> Any:
    """Usage docs: https://docs.pydantic.dev/2.6/concepts/models/#private-model-attributes

    Indicates that an attribute is intended for private use and not handled during normal validation/serialization.

    Private attributes are not validated by Pydantic, so it's up to you to ensure they are used in a type-safe manner.

    Private attributes are stored in `__private_attributes__` on the model.

    Args:
        default: The attribute's default value. Defaults to Undefined.
        default_factory: Callable that will be
            called when a default value is needed for this attribute.
            If both `default` and `default_factory` are set, an error will be raised.

    Returns:
        An instance of [`ModelPrivateAttr`][pydantic.fields.ModelPrivateAttr] class.

    Raises:
        ValueError: If both `default` and `default_factory` are set.
    """
    if default is not PydanticUndefined and default_factory is not None:
        raise TypeError('cannot specify both default and default_factory')

    return ModelPrivateAttr(
        default,
        default_factory=default_factory,
    )

ModelPrivateAttr

ModelPrivateAttr(
    default=PydanticUndefined, *, default_factory=None
)

Bases: Representation

A descriptor for private attributes in class models.

Warning

You generally shouldn't be creating ModelPrivateAttr instances directly, instead use pydantic.fields.PrivateAttr. (This is similar to FieldInfo vs. Field.)

Attributes:

Name	Type	Description
default		The default value of the attribute if not provided.
default_factory		A callable function that generates the default value of the attribute if not provided.

Source code in pydantic/fields.py

def __init__(
    self, default: Any = PydanticUndefined, *, default_factory: typing.Callable[[], Any] | None = None
) -> None:
    self.default = default
    self.default_factory = default_factory

get_default

get_default()

Retrieve the default value of the object.

If self.default_factory is None, the method will return a deep copy of the self.default object.

If self.default_factory is not None, it will call self.default_factory and return the value returned.

Returns:

Type	Description
Any	The default value of the object.

Source code in pydantic/fields.py

def get_default(self) -> Any:
    """Retrieve the default value of the object.

    If `self.default_factory` is `None`, the method will return a deep copy of the `self.default` object.

    If `self.default_factory` is not `None`, it will call `self.default_factory` and return the value returned.

    Returns:
        The default value of the object.
    """
    return _utils.smart_deepcopy(self.default) if self.default_factory is None else self.default_factory()

computed_field

computed_field(
    __f=None,
    *,
    alias=None,
    alias_priority=None,
    title=None,
    description=None,
    examples=None,
    json_schema_extra=None,
    repr=None,
    return_type=PydanticUndefined
)

Usage Documentation

The computed_field decorator

Decorator to include property and cached_property when serializing models or dataclasses.

This is useful for fields that are computed from other fields, or for fields that are expensive to compute and should be cached.

from pydantic import BaseModel, computed_field

class Rectangle(BaseModel):
    width: int
    length: int

    @computed_field
    @property
    def area(self) -> int:
        return self.width * self.length

print(Rectangle(width=3, length=2).model_dump())
#> {'width': 3, 'length': 2, 'area': 6}

If applied to functions not yet decorated with @property or @cached_property, the function is automatically wrapped with property. Although this is more concise, you will lose IntelliSense in your IDE, and confuse static type checkers, thus explicit use of @property is recommended.

Mypy Warning

Even with the @property or @cached_property applied to your function before @computed_field, mypy may throw a Decorated property not supported error. See mypy issue #1362, for more information. To avoid this error message, add # type: ignore[misc] to the @computed_field line.

pyright supports @computed_field without error.

import random

from pydantic import BaseModel, computed_field

class Square(BaseModel):
    width: float

    @computed_field
    def area(self) -> float:  # converted to a `property` by `computed_field`
        return round(self.width**2, 2)

    @area.setter
    def area(self, new_area: float) -> None:
        self.width = new_area**0.5

    @computed_field(alias='the magic number', repr=False)
    def random_number(self) -> int:
        return random.randint(0, 1_000)

square = Square(width=1.3)

# `random_number` does not appear in representation
print(repr(square))
#> Square(width=1.3, area=1.69)

print(square.random_number)
#> 3

square.area = 4

print(square.model_dump_json(by_alias=True))
#> {"width":2.0,"area":4.0,"the magic number":3}

Overriding with computed_field

You can't override a field from a parent class with a computed_field in the child class. mypy complains about this behavior if allowed, and dataclasses doesn't allow this pattern either. See the example below:

from pydantic import BaseModel, computed_field

class Parent(BaseModel):
    a: str

try:

    class Child(Parent):
        @computed_field
        @property
        def a(self) -> str:
            return 'new a'

except ValueError as e:
    print(repr(e))
    #> ValueError("you can't override a field with a computed field")

Private properties decorated with @computed_field have repr=False by default.

from functools import cached_property

from pydantic import BaseModel, computed_field

class Model(BaseModel):
    foo: int

    @computed_field
    @cached_property
    def _private_cached_property(self) -> int:
        return -self.foo

    @computed_field
    @property
    def _private_property(self) -> int:
        return -self.foo

m = Model(foo=1)
print(repr(m))
#> M(foo=1)

Parameters:

Name	Type	Description	Default
__f	PropertyT | None	the function to wrap.	None
alias	str | None	alias to use when serializing this computed field, only used when by_alias=True	None
alias_priority	int | None	priority of the alias. This affects whether an alias generator is used	None
title	str | None	Title to use when including this computed field in JSON Schema	None
description	str | None	Description to use when including this computed field in JSON Schema, defaults to the function's docstring	None
examples	list[Any] | None	Example values to use when including this computed field in JSON Schema	None
json_schema_extra	JsonDict | Callable[[JsonDict], None] | None	A dict or callable to provide extra JSON schema properties.	None
repr	bool | None	whether to include this computed field in model repr. Default is False for private properties and True for public properties.	None
return_type	Any	optional return for serialization logic to expect when serializing to JSON, if included this must be correct, otherwise a TypeError is raised. If you don't include a return type Any is used, which does runtime introspection to handle arbitrary objects.	PydanticUndefined

Returns:

Type	Description
PropertyT | Callable[[PropertyT], PropertyT]	A proxy wrapper for the property.

Source code in pydantic/fields.py

def computed_field(
    __f: PropertyT | None = None,
    *,
    alias: str | None = None,
    alias_priority: int | None = None,
    title: str | None = None,
    description: str | None = None,
    examples: list[Any] | None = None,
    json_schema_extra: JsonDict | typing.Callable[[JsonDict], None] | None = None,
    repr: bool | None = None,
    return_type: Any = PydanticUndefined,
) -> PropertyT | typing.Callable[[PropertyT], PropertyT]:
    """Usage docs: https://docs.pydantic.dev/2.6/concepts/fields#the-computed_field-decorator

    Decorator to include `property` and `cached_property` when serializing models or dataclasses.

    This is useful for fields that are computed from other fields, or for fields that are expensive to compute and should be cached.

    ```py
    from pydantic import BaseModel, computed_field

    class Rectangle(BaseModel):
        width: int
        length: int

        @computed_field
        @property
        def area(self) -> int:
            return self.width * self.length

    print(Rectangle(width=3, length=2).model_dump())
    #> {'width': 3, 'length': 2, 'area': 6}
    ```

    If applied to functions not yet decorated with `@property` or `@cached_property`, the function is
    automatically wrapped with `property`. Although this is more concise, you will lose IntelliSense in your IDE,
    and confuse static type checkers, thus explicit use of `@property` is recommended.

    !!! warning "Mypy Warning"
        Even with the `@property` or `@cached_property` applied to your function before `@computed_field`,
        mypy may throw a `Decorated property not supported` error.
        See [mypy issue #1362](https://github.com/python/mypy/issues/1362), for more information.
        To avoid this error message, add `# type: ignore[misc]` to the `@computed_field` line.

        [pyright](https://github.com/microsoft/pyright) supports `@computed_field` without error.

    ```py
    import random

    from pydantic import BaseModel, computed_field

    class Square(BaseModel):
        width: float

        @computed_field
        def area(self) -> float:  # converted to a `property` by `computed_field`
            return round(self.width**2, 2)

        @area.setter
        def area(self, new_area: float) -> None:
            self.width = new_area**0.5

        @computed_field(alias='the magic number', repr=False)
        def random_number(self) -> int:
            return random.randint(0, 1_000)

    square = Square(width=1.3)

    # `random_number` does not appear in representation
    print(repr(square))
    #> Square(width=1.3, area=1.69)

    print(square.random_number)
    #> 3

    square.area = 4

    print(square.model_dump_json(by_alias=True))
    #> {"width":2.0,"area":4.0,"the magic number":3}
    ```

    !!! warning "Overriding with `computed_field`"
        You can't override a field from a parent class with a `computed_field` in the child class.
        `mypy` complains about this behavior if allowed, and `dataclasses` doesn't allow this pattern either.
        See the example below:

    ```py
    from pydantic import BaseModel, computed_field

    class Parent(BaseModel):
        a: str

    try:

        class Child(Parent):
            @computed_field
            @property
            def a(self) -> str:
                return 'new a'

    except ValueError as e:
        print(repr(e))
        #> ValueError("you can't override a field with a computed field")
    ```

    Private properties decorated with `@computed_field` have `repr=False` by default.

    ```py
    from functools import cached_property

    from pydantic import BaseModel, computed_field

    class Model(BaseModel):
        foo: int

        @computed_field
        @cached_property
        def _private_cached_property(self) -> int:
            return -self.foo

        @computed_field
        @property
        def _private_property(self) -> int:
            return -self.foo

    m = Model(foo=1)
    print(repr(m))
    #> M(foo=1)
    ```

    Args:
        __f: the function to wrap.
        alias: alias to use when serializing this computed field, only used when `by_alias=True`
        alias_priority: priority of the alias. This affects whether an alias generator is used
        title: Title to use when including this computed field in JSON Schema
        description: Description to use when including this computed field in JSON Schema, defaults to the function's
            docstring
        examples: Example values to use when including this computed field in JSON Schema
        json_schema_extra: A dict or callable to provide extra JSON schema properties.
        repr: whether to include this computed field in model repr.
            Default is `False` for private properties and `True` for public properties.
        return_type: optional return for serialization logic to expect when serializing to JSON, if included
            this must be correct, otherwise a `TypeError` is raised.
            If you don't include a return type Any is used, which does runtime introspection to handle arbitrary
            objects.

    Returns:
        A proxy wrapper for the property.
    """

    def dec(f: Any) -> Any:
        nonlocal description, return_type, alias_priority
        unwrapped = _decorators.unwrap_wrapped_function(f)
        if description is None and unwrapped.__doc__:
            description = inspect.cleandoc(unwrapped.__doc__)

        # if the function isn't already decorated with `@property` (or another descriptor), then we wrap it now
        f = _decorators.ensure_property(f)
        alias_priority = (alias_priority or 2) if alias is not None else None

        if repr is None:
            repr_: bool = False if _wrapped_property_is_private(property_=f) else True
        else:
            repr_ = repr

        dec_info = ComputedFieldInfo(
            f, return_type, alias, alias_priority, title, description, examples, json_schema_extra, repr_
        )
        return _decorators.PydanticDescriptorProxy(f, dec_info)

    if __f is None:
        return dec
    else:
        return dec(__f)

ComputedFieldInfo dataclass

A container for data from @computed_field so that we can access it while building the pydantic-core schema.

Attributes:

Name	Type	Description
decorator_repr	str	A class variable representing the decorator string, '@computed_field'.
wrapped_property	property	The wrapped computed field property.
return_type	Any	The type of the computed field property's return value.
alias	str | None	The alias of the property to be used during serialization.
alias_priority	int | None	The priority of the alias. This affects whether an alias generator is used.
title	str | None	Title of the computed field to include in the serialization JSON schema.
description	str | None	Description of the computed field to include in the serialization JSON schema.
examples	list[Any] | None	Example values of the computed field to include in the serialization JSON schema.
json_schema_extra	JsonDict | Callable[[JsonDict], None] | None	A dict or callable to provide extra JSON schema properties.
repr	bool	A boolean indicating whether to include the field in the repr output.