Pydantic Dataclasses

Provide an enhanced dataclass that performs validation.

dataclass

dataclass(
    _cls: type[_T] | None = None,
    *,
    init: Literal[False] = False,
    repr: bool = True,
    eq: bool = True,
    order: bool = False,
    unsafe_hash: bool = False,
    frozen: bool = False,
    config: ConfigDict | type[object] | None = None,
    validate_on_init: bool | None = None,
    kw_only: bool = False,
    slots: bool = False
) -> (
    Callable[[type[_T]], type[PydanticDataclass]]
    | type[PydanticDataclass]
)

Usage Documentation

Dataclasses

A decorator used to create a Pydantic-enhanced dataclass, similar to the standard Python dataclass, but with added validation.

This function should be used similarly to dataclasses.dataclass.

Parameters:

Name	Type	Description	Default
_cls	type[_T] | None	The target dataclass.	None
init	Literal[False]	Included for signature compatibility with dataclasses.dataclass, and is passed through to dataclasses.dataclass when appropriate. If specified, must be set to False, as pydantic inserts its own __init__ function.	False
repr	bool	A boolean indicating whether to include the field in the __repr__ output.	True
eq	bool	Determines if a __eq__ method should be generated for the class.	True
order	bool	Determines if comparison magic methods should be generated, such as __lt__, but not __eq__.	False
unsafe_hash	bool	Determines if a __hash__ method should be included in the class, as in dataclasses.dataclass.	False
frozen	bool	Determines if the generated class should be a 'frozen' dataclass, which does not allow its attributes to be modified after it has been initialized.	False
config	ConfigDict | type[object] | None	The Pydantic config to use for the dataclass.	None
validate_on_init	bool | None	A deprecated parameter included for backwards compatibility; in V2, all Pydantic dataclasses are validated on init.	None
kw_only	bool	Determines if __init__ method parameters must be specified by keyword only. Defaults to False.	False
slots	bool	Determines if the generated class should be a 'slots' dataclass, which does not allow the addition of new attributes after instantiation.	False

Returns:

Type	Description
Callable[[type[_T]], type[PydanticDataclass]] | type[PydanticDataclass]	A decorator that accepts a class as its argument and returns a Pydantic dataclass.

Raises:

Type	Description
AssertionError	Raised if init is not False or validate_on_init is False.

Source code in pydantic/dataclasses.py

@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
def dataclass(  # noqa: C901
    _cls: type[_T] | None = None,
    *,
    init: Literal[False] = False,
    repr: bool = True,
    eq: bool = True,
    order: bool = False,
    unsafe_hash: bool = False,
    frozen: bool = False,
    config: ConfigDict | type[object] | None = None,
    validate_on_init: bool | None = None,
    kw_only: bool = False,
    slots: bool = False,
) -> Callable[[type[_T]], type[PydanticDataclass]] | type[PydanticDataclass]:
    """Usage docs: https://docs.pydantic.dev/2.8/concepts/dataclasses/

    A decorator used to create a Pydantic-enhanced dataclass, similar to the standard Python `dataclass`,
    but with added validation.

    This function should be used similarly to `dataclasses.dataclass`.

    Args:
        _cls: The target `dataclass`.
        init: Included for signature compatibility with `dataclasses.dataclass`, and is passed through to
            `dataclasses.dataclass` when appropriate. If specified, must be set to `False`, as pydantic inserts its
            own  `__init__` function.
        repr: A boolean indicating whether to include the field in the `__repr__` output.
        eq: Determines if a `__eq__` method should be generated for the class.
        order: Determines if comparison magic methods should be generated, such as `__lt__`, but not `__eq__`.
        unsafe_hash: Determines if a `__hash__` method should be included in the class, as in `dataclasses.dataclass`.
        frozen: Determines if the generated class should be a 'frozen' `dataclass`, which does not allow its
            attributes to be modified after it has been initialized.
        config: The Pydantic config to use for the `dataclass`.
        validate_on_init: A deprecated parameter included for backwards compatibility; in V2, all Pydantic dataclasses
            are validated on init.
        kw_only: Determines if `__init__` method parameters must be specified by keyword only. Defaults to `False`.
        slots: Determines if the generated class should be a 'slots' `dataclass`, which does not allow the addition of
            new attributes after instantiation.

    Returns:
        A decorator that accepts a class as its argument and returns a Pydantic `dataclass`.

    Raises:
        AssertionError: Raised if `init` is not `False` or `validate_on_init` is `False`.
    """
    assert init is False, 'pydantic.dataclasses.dataclass only supports init=False'
    assert validate_on_init is not False, 'validate_on_init=False is no longer supported'

    if sys.version_info >= (3, 10):
        kwargs = dict(kw_only=kw_only, slots=slots)
    else:
        kwargs = {}

    def make_pydantic_fields_compatible(cls: type[Any]) -> None:
        """Make sure that stdlib `dataclasses` understands `Field` kwargs like `kw_only`
        To do that, we simply change
          `x: int = pydantic.Field(..., kw_only=True)`
        into
          `x: int = dataclasses.field(default=pydantic.Field(..., kw_only=True), kw_only=True)`
        """
        for annotation_cls in cls.__mro__:
            # In Python < 3.9, `__annotations__` might not be present if there are no fields.
            # we therefore need to use `getattr` to avoid an `AttributeError`.
            annotations = getattr(annotation_cls, '__annotations__', [])
            for field_name in annotations:
                field_value = getattr(cls, field_name, None)
                # Process only if this is an instance of `FieldInfo`.
                if not isinstance(field_value, FieldInfo):
                    continue

                # Initialize arguments for the standard `dataclasses.field`.
                field_args: dict = {'default': field_value}

                # Handle `kw_only` for Python 3.10+
                if sys.version_info >= (3, 10) and field_value.kw_only:
                    field_args['kw_only'] = True

                # Set `repr` attribute if it's explicitly specified to be not `True`.
                if field_value.repr is not True:
                    field_args['repr'] = field_value.repr

                setattr(cls, field_name, dataclasses.field(**field_args))
                # In Python 3.8, dataclasses checks cls.__dict__['__annotations__'] for annotations,
                # so we must make sure it's initialized before we add to it.
                if cls.__dict__.get('__annotations__') is None:
                    cls.__annotations__ = {}
                cls.__annotations__[field_name] = annotations[field_name]

    def create_dataclass(cls: type[Any]) -> type[PydanticDataclass]:
        """Create a Pydantic dataclass from a regular dataclass.

        Args:
            cls: The class to create the Pydantic dataclass from.

        Returns:
            A Pydantic dataclass.
        """
        from ._internal._utils import is_model_class

        if is_model_class(cls):
            raise PydanticUserError(
                f'Cannot create a Pydantic dataclass from {cls.__name__} as it is already a Pydantic model',
                code='dataclass-on-model',
            )

        original_cls = cls

        config_dict = config
        if config_dict is None:
            # if not explicitly provided, read from the type
            cls_config = getattr(cls, '__pydantic_config__', None)
            if cls_config is not None:
                config_dict = cls_config
        config_wrapper = _config.ConfigWrapper(config_dict)
        decorators = _decorators.DecoratorInfos.build(cls)

        # Keep track of the original __doc__ so that we can restore it after applying the dataclasses decorator
        # Otherwise, classes with no __doc__ will have their signature added into the JSON schema description,
        # since dataclasses.dataclass will set this as the __doc__
        original_doc = cls.__doc__

        if _pydantic_dataclasses.is_builtin_dataclass(cls):
            # Don't preserve the docstring for vanilla dataclasses, as it may include the signature
            # This matches v1 behavior, and there was an explicit test for it
            original_doc = None

            # We don't want to add validation to the existing std lib dataclass, so we will subclass it
            #   If the class is generic, we need to make sure the subclass also inherits from Generic
            #   with all the same parameters.
            bases = (cls,)
            if issubclass(cls, Generic):
                generic_base = Generic[cls.__parameters__]  # type: ignore
                bases = bases + (generic_base,)
            cls = types.new_class(cls.__name__, bases)

        make_pydantic_fields_compatible(cls)

        cls = dataclasses.dataclass(  # type: ignore[call-overload]
            cls,
            # the value of init here doesn't affect anything except that it makes it easier to generate a signature
            init=True,
            repr=repr,
            eq=eq,
            order=order,
            unsafe_hash=unsafe_hash,
            frozen=frozen,
            **kwargs,
        )

        cls.__pydantic_decorators__ = decorators  # type: ignore
        cls.__doc__ = original_doc
        cls.__module__ = original_cls.__module__
        cls.__qualname__ = original_cls.__qualname__
        pydantic_complete = _pydantic_dataclasses.complete_dataclass(
            cls, config_wrapper, raise_errors=False, types_namespace=None
        )
        cls.__pydantic_complete__ = pydantic_complete  # type: ignore
        return cls

    if _cls is None:
        return create_dataclass

    return create_dataclass(_cls)

rebuild_dataclass

rebuild_dataclass(
    cls: type[PydanticDataclass],
    *,
    force: bool = False,
    raise_errors: bool = True,
    _parent_namespace_depth: int = 2,
    _types_namespace: dict[str, Any] | None = None
) -> bool | None

Try to rebuild the pydantic-core schema for the dataclass.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

This is analogous to BaseModel.model_rebuild.

Parameters:

Name	Type	Description	Default
cls	type[PydanticDataclass]	The class to rebuild the pydantic-core schema for.	required
force	bool	Whether to force the rebuilding of the schema, defaults to False.	False
raise_errors	bool	Whether to raise errors, defaults to True.	True
_parent_namespace_depth	int	The depth level of the parent namespace, defaults to 2.	2
_types_namespace	dict[str, Any] | None	The types namespace, defaults to None.	None

Returns:

Type	Description
bool | None	Returns None if the schema is already "complete" and rebuilding was not required.
bool | None	If rebuilding was required, returns True if rebuilding was successful, otherwise False.

Source code in pydantic/dataclasses.py

def rebuild_dataclass(
    cls: type[PydanticDataclass],
    *,
    force: bool = False,
    raise_errors: bool = True,
    _parent_namespace_depth: int = 2,
    _types_namespace: dict[str, Any] | None = None,
) -> bool | None:
    """Try to rebuild the pydantic-core schema for the dataclass.

    This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
    the initial attempt to build the schema, and automatic rebuilding fails.

    This is analogous to `BaseModel.model_rebuild`.

    Args:
        cls: The class to rebuild the pydantic-core schema for.
        force: Whether to force the rebuilding of the schema, defaults to `False`.
        raise_errors: Whether to raise errors, defaults to `True`.
        _parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
        _types_namespace: The types namespace, defaults to `None`.

    Returns:
        Returns `None` if the schema is already "complete" and rebuilding was not required.
        If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
    """
    if not force and cls.__pydantic_complete__:
        return None
    else:
        if _types_namespace is not None:
            types_namespace: dict[str, Any] | None = _types_namespace.copy()
        else:
            if _parent_namespace_depth > 0:
                frame_parent_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth) or {}
                # Note: we may need to add something similar to cls.__pydantic_parent_namespace__ from BaseModel
                #   here when implementing handling of recursive generics. See BaseModel.model_rebuild for reference.
                types_namespace = frame_parent_ns
            else:
                types_namespace = {}

            types_namespace = _typing_extra.get_cls_types_namespace(cls, types_namespace)
        return _pydantic_dataclasses.complete_dataclass(
            cls,
            _config.ConfigWrapper(cls.__pydantic_config__, check=False),
            raise_errors=raise_errors,
            types_namespace=types_namespace,
        )

is_pydantic_dataclass

is_pydantic_dataclass(
    class_: type[Any],
) -> TypeGuard[type[PydanticDataclass]]

Whether a class is a pydantic dataclass.

Parameters:

Name	Type	Description	Default
class_	type[Any]	The class.	required

Returns:

Type	Description
TypeGuard[type[PydanticDataclass]]	True if the class is a pydantic dataclass, False otherwise.

Source code in pydantic/dataclasses.py

def is_pydantic_dataclass(class_: type[Any], /) -> TypeGuard[type[PydanticDataclass]]:
    """Whether a class is a pydantic dataclass.

    Args:
        class_: The class.

    Returns:
        `True` if the class is a pydantic dataclass, `False` otherwise.
    """
    try:
        return '__pydantic_validator__' in class_.__dict__ and dataclasses.is_dataclass(class_)
    except AttributeError:
        return False