Skip to content

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
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
@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
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
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
326
327
328
329
330
331
332
333
334
335
336
337
338
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