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Pydantic Dataclasses

Provide an enhanced dataclass that performs validation.

ConfigDict

Bases: TypedDict

A TypedDict for configuring Pydantic behaviour.

Attributes

title

The title for the generated JSON schema, defaults to the model’s name

Type: str | None

str_to_lower

Whether to convert all characters to lowercase for str types. Defaults to False.

Type: bool

str_to_upper

Whether to convert all characters to uppercase for str types. Defaults to False.

Type: bool

str_strip_whitespace

Whether to strip leading and trailing whitespace for str types.

Type: bool

str_min_length

The minimum length for str types. Defaults to None.

Type: int

str_max_length

The maximum length for str types. Defaults to None.

Type: int | None

extra

Whether to ignore, allow, or forbid extra attributes during model initialization. Defaults to 'ignore'.

You can configure how pydantic handles the attributes that are not defined in the model:

  • allow - Allow any extra attributes.
  • forbid - Forbid any extra attributes.
  • ignore - Ignore any extra attributes.
from pydantic import BaseModel, ConfigDict


class User(BaseModel):
  model_config = ConfigDict(extra='ignore')  # (1)

  name: str


user = User(name='John Doe', age=20)  # (2)
print(user)
#> name='John Doe'

This is the default behaviour.

The age argument is ignored.

Instead, with extra='allow', the age argument is included:

from pydantic import BaseModel, ConfigDict


class User(BaseModel):
  model_config = ConfigDict(extra='allow')

  name: str


user = User(name='John Doe', age=20)  # (1)
print(user)
#> name='John Doe' age=20

The age argument is included.

With extra='forbid', an error is raised:

from pydantic import BaseModel, ConfigDict, ValidationError


class User(BaseModel):
    model_config = ConfigDict(extra='forbid')

    name: str


try:
    User(name='John Doe', age=20)
except ValidationError as e:
    print(e)
    '''
    1 validation error for User
    age
    Extra inputs are not permitted [type=extra_forbidden, input_value=20, input_type=int]
    '''

Type: ExtraValues | None

frozen

Whether or not models are faux-immutable, i.e. whether __setattr__ is allowed, and also generates a __hash__() method for the model. This makes instances of the model potentially hashable if all the attributes are hashable. Defaults to False.

Type: bool

populate_by_name

Whether an aliased field may be populated by its name as given by the model attribute, as well as the alias. Defaults to False.

from pydantic import BaseModel, ConfigDict, Field


class User(BaseModel):
  model_config = ConfigDict(populate_by_name=True)

  name: str = Field(alias='full_name')  # (1)
  age: int


user = User(full_name='John Doe', age=20)  # (2)
print(user)
#> name='John Doe' age=20
user = User(name='John Doe', age=20)  # (3)
print(user)
#> name='John Doe' age=20

The field 'name' has an alias 'full_name'.

The model is populated by the alias 'full_name'.

The model is populated by the field name 'name'.

Type: bool

use_enum_values

Whether to populate models with the value property of enums, rather than the raw enum. This may be useful if you want to serialize model.model_dump() later. Defaults to False.

Type: bool

validate_assignment

Whether to validate the data when the model is changed. Defaults to False.

The default behavior of Pydantic is to validate the data when the model is created.

In case the user changes the data after the model is created, the model is not revalidated.

from pydantic import BaseModel

class User(BaseModel):
  name: str

user = User(name='John Doe')  # (1)
print(user)
#> name='John Doe'
user.name = 123  # (1)
print(user)
#> name=123

The validation happens only when the model is created.

The validation does not happen when the data is changed.

In case you want to revalidate the model when the data is changed, you can use validate_assignment=True:

from pydantic import BaseModel, ValidationError

class User(BaseModel, validate_assignment=True):  # (1)
  name: str

user = User(name='John Doe')  # (2)
print(user)
#> name='John Doe'
try:
  user.name = 123  # (3)
except ValidationError as e:
  print(e)
  '''
  1 validation error for User
  name
    Input should be a valid string [type=string_type, input_value=123, input_type=int]
  '''

You can either use class keyword arguments, or model_config to set validate_assignment=True.

The validation happens when the model is created.

The validation also happens when the data is changed.

Type: bool

arbitrary_types_allowed

Whether arbitrary types are allowed for field types. Defaults to False.

from pydantic import BaseModel, ConfigDict, ValidationError

# This is not a pydantic model, it's an arbitrary class
class Pet:
    def __init__(self, name: str):
        self.name = name

class Model(BaseModel):
    model_config = ConfigDict(arbitrary_types_allowed=True)

    pet: Pet
    owner: str

pet = Pet(name='Hedwig')
# A simple check of instance type is used to validate the data
model = Model(owner='Harry', pet=pet)
print(model)
#> pet=<__main__.Pet object at 0x0123456789ab> owner='Harry'
print(model.pet)
#> <__main__.Pet object at 0x0123456789ab>
print(model.pet.name)
#> Hedwig
print(type(model.pet))
#> <class '__main__.Pet'>
try:
    # If the value is not an instance of the type, it's invalid
    Model(owner='Harry', pet='Hedwig')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    pet
      Input should be an instance of Pet [type=is_instance_of, input_value='Hedwig', input_type=str]
    '''

# Nothing in the instance of the arbitrary type is checked
# Here name probably should have been a str, but it's not validated
pet2 = Pet(name=42)
model2 = Model(owner='Harry', pet=pet2)
print(model2)
#> pet=<__main__.Pet object at 0x0123456789ab> owner='Harry'
print(model2.pet)
#> <__main__.Pet object at 0x0123456789ab>
print(model2.pet.name)
#> 42
print(type(model2.pet))
#> <class '__main__.Pet'>

Type: bool

from_attributes

Whether to build models and look up discriminators of tagged unions using python object attributes.

Type: bool

loc_by_alias

Whether to use the actual key provided in the data (e.g. alias) for error locs rather than the field’s name. Defaults to True.

Type: bool

alias_generator

A callable that takes a field name and returns an alias for it.

If data source field names do not match your code style (e. g. CamelCase fields), you can automatically generate aliases using alias_generator:

from pydantic import BaseModel, ConfigDict
from pydantic.alias_generators import to_pascal

class Voice(BaseModel):
    model_config = ConfigDict(alias_generator=to_pascal)

    name: str
    language_code: str

voice = Voice(Name='Filiz', LanguageCode='tr-TR')
print(voice.language_code)
#> tr-TR
print(voice.model_dump(by_alias=True))
#> {'Name': 'Filiz', 'LanguageCode': 'tr-TR'}

Type: Callable[[str], str] | None

ignored_types

A tuple of types that may occur as values of class attributes without annotations. This is typically used for custom descriptors (classes that behave like property). If an attribute is set on a class without an annotation and has a type that is not in this tuple (or otherwise recognized by pydantic), an error will be raised. Defaults to ().

Type: tuple[type, ...]

allow_inf_nan

Whether to allow infinity (+inf an -inf) and NaN values to float fields. Defaults to True.

Type: bool

json_schema_extra

A dict or callable to provide extra JSON schema properties. Defaults to None.

Type: dict[str, object] | JsonSchemaExtraCallable | None

json_encoders

A dict of custom JSON encoders for specific types. Defaults to None.

Type: dict[type[object], JsonEncoder] | None

strict

(new in V2) If True, strict validation is applied to all fields on the model.

By default, Pydantic attempts to coerce values to the correct type, when possible.

There are situations in which you may want to disable this behavior, and instead raise an error if a value’s type does not match the field’s type annotation.

To configure strict mode for all fields on a model, you can set strict=True on the model.

from pydantic import BaseModel, ConfigDict

class Model(BaseModel):
    model_config = ConfigDict(strict=True)

    name: str
    age: int

See Strict Mode for more details.

See the Conversion Table for more details on how Pydantic converts data in both strict and lax modes.

Type: bool

revalidate_instances

When and how to revalidate models and dataclasses during validation. Accepts the string values of 'never', 'always' and 'subclass-instances'. Defaults to 'never'.

  • 'never' will not revalidate models and dataclasses during validation
  • 'always' will revalidate models and dataclasses during validation
  • 'subclass-instances' will revalidate models and dataclasses during validation if the instance is a subclass of the model or dataclass

By default, model and dataclass instances are not revalidated during validation.

from typing import List

from pydantic import BaseModel

class User(BaseModel, revalidate_instances='never'):  # (1)
  hobbies: List[str]

class SubUser(User):
  sins: List[str]

class Transaction(BaseModel):
  user: User

my_user = User(hobbies=['reading'])
t = Transaction(user=my_user)
print(t)
#> user=User(hobbies=['reading'])

my_user.hobbies = [1]  # (2)
t = Transaction(user=my_user)  # (3)
print(t)
#> user=User(hobbies=[1])

my_sub_user = SubUser(hobbies=['scuba diving'], sins=['lying'])
t = Transaction(user=my_sub_user)
print(t)
#> user=SubUser(hobbies=['scuba diving'], sins=['lying'])

revalidate_instances is set to 'never' by **default.

The assignment is not validated, unless you set validate_assignment to True in the model's config.

Since revalidate_instances is set to never, this is not revalidated.

If you want to revalidate instances during validation, you can set revalidate_instances to 'always' in the model’s config.

from typing import List

from pydantic import BaseModel, ValidationError

class User(BaseModel, revalidate_instances='always'):  # (1)
  hobbies: List[str]

class SubUser(User):
  sins: List[str]

class Transaction(BaseModel):
  user: User

my_user = User(hobbies=['reading'])
t = Transaction(user=my_user)
print(t)
#> user=User(hobbies=['reading'])

my_user.hobbies = [1]
try:
  t = Transaction(user=my_user)  # (2)
except ValidationError as e:
  print(e)
  '''
  1 validation error for Transaction
  user.hobbies.0
    Input should be a valid string [type=string_type, input_value=1, input_type=int]
  '''

my_sub_user = SubUser(hobbies=['scuba diving'], sins=['lying'])
t = Transaction(user=my_sub_user)
print(t)  # (3)
#> user=User(hobbies=['scuba diving'])

revalidate_instances is set to 'always'.

The model is revalidated, since revalidate_instances is set to 'always'.

Using 'never' we would have gotten user=SubUser(hobbies=['scuba diving'], sins=['lying']).

It’s also possible to set revalidate_instances to 'subclass-instances' to only revalidate instances of subclasses of the model.

from typing import List

from pydantic import BaseModel

class User(BaseModel, revalidate_instances='subclass-instances'):  # (1)
  hobbies: List[str]

class SubUser(User):
  sins: List[str]

class Transaction(BaseModel):
  user: User

my_user = User(hobbies=['reading'])
t = Transaction(user=my_user)
print(t)
#> user=User(hobbies=['reading'])

my_user.hobbies = [1]
t = Transaction(user=my_user)  # (2)
print(t)
#> user=User(hobbies=[1])

my_sub_user = SubUser(hobbies=['scuba diving'], sins=['lying'])
t = Transaction(user=my_sub_user)
print(t)  # (3)
#> user=User(hobbies=['scuba diving'])

revalidate_instances is set to 'subclass-instances'.

This is not revalidated, since my_user is not a subclass of User.

Using 'never' we would have gotten user=SubUser(hobbies=['scuba diving'], sins=['lying']).

Type: Literal['always', 'never', 'subclass-instances']

ser_json_timedelta

The format of JSON serialized timedeltas. Accepts the string values of 'iso8601' and 'float'. Defaults to 'iso8601'.

  • 'iso8601' will serialize timedeltas to ISO 8601 durations.
  • 'float' will serialize timedeltas to the total number of seconds.

Type: Literal['iso8601', 'float']

ser_json_bytes

The encoding of JSON serialized bytes. Accepts the string values of 'utf8' and 'base64'. Defaults to 'utf8'.

  • 'utf8' will serialize bytes to UTF-8 strings.
  • 'base64' will serialize bytes to URL safe base64 strings.

Type: Literal['utf8', 'base64']

validate_default

Whether to validate default values during validation. Defaults to False.

Type: bool

validate_return

whether to validate the return value from call validators. Defaults to False.

Type: bool

protected_namespaces

A tuple of strings that prevent model to have field which conflict with them. Defaults to ('model_', )).

Pydantic prevents collisions between model attributes and BaseModel’s own methods by namespacing them with the prefix model_.

import warnings

from pydantic import BaseModel

warnings.filterwarnings('error')  # Raise warnings as errors

try:

    class Model(BaseModel):
        model_prefixed_field: str

except UserWarning as e:
    print(e)
    '''
    Field "model_prefixed_field" has conflict with protected namespace "model_".

    You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.
    '''

You can customize this behavior using the protected_namespaces setting:

import warnings

from pydantic import BaseModel, ConfigDict

warnings.filterwarnings('error')  # Raise warnings as errors

try:

    class Model(BaseModel):
        model_prefixed_field: str
        also_protect_field: str

        model_config = ConfigDict(
            protected_namespaces=('protect_me_', 'also_protect_')
        )

except UserWarning as e:
    print(e)
    '''
    Field "also_protect_field" has conflict with protected namespace "also_protect_".

    You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ('protect_me_',)`.
    '''

While Pydantic will only emit a warning when an item is in a protected namespace but does not actually have a collision, an error is raised if there is an actual collision with an existing attribute:

from pydantic import BaseModel

try:

    class Model(BaseModel):
        model_validate: str

except NameError as e:
    print(e)
    '''
    Field "model_validate" conflicts with member <bound method BaseModel.model_validate of <class 'pydantic.main.BaseModel'>> of protected namespace "model_".
    '''

Type: tuple[str, ...]

hide_input_in_errors

Whether to hide inputs when printing errors. Defaults to False.

Pydantic shows the input value and type when it raises ValidationError during the validation.

from pydantic import BaseModel, ValidationError

class Model(BaseModel):
    a: str

try:
    Model(a=123)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    a
      Input should be a valid string [type=string_type, input_value=123, input_type=int]
    '''

You can hide the input value and type by setting the hide_input_in_errors config to True.

from pydantic import BaseModel, ConfigDict, ValidationError

class Model(BaseModel):
    a: str
    model_config = ConfigDict(hide_input_in_errors=True)

try:
    Model(a=123)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    a
      Input should be a valid string [type=string_type]
    '''

Type: bool

defer_build

Whether to defer model validator and serializer construction until the first model validation.

This can be useful to avoid the overhead of building models which are only used nested within other models, or when you want to manually define type namespace via Model.model_rebuild(_types_namespace=...). Defaults to False.

Type: bool

plugin_settings

A dict of settings for plugins. Defaults to None.

See Pydantic Plugins for details.

Type: dict[str, object] | None

schema_generator

A custom core schema generator class to use when generating JSON schemas. Useful if you want to change the way types are validated across an entire model/schema. Defaults to None.

The GenerateSchema interface is subject to change, currently only the string_schema method is public.

See #6737 for details.

Type: type[_GenerateSchema] | None

json_schema_serialization_defaults_required

Whether fields with default values should be marked as required in the serialization schema. Defaults to False.

This ensures that the serialization schema will reflect the fact a field with a default will always be present when serializing the model, even though it is not required for validation.

However, there are scenarios where this may be undesirable — in particular, if you want to share the schema between validation and serialization, and don’t mind fields with defaults being marked as not required during serialization. See #7209 for more details.

from pydantic import BaseModel, ConfigDict

class Model(BaseModel):
    a: str = 'a'

    model_config = ConfigDict(json_schema_serialization_defaults_required=True)

print(Model.model_json_schema(mode='validation'))
'''
{
    'properties': {'a': {'default': 'a', 'title': 'A', 'type': 'string'}},
    'title': 'Model',
    'type': 'object',
}
'''
print(Model.model_json_schema(mode='serialization'))
'''
{
    'properties': {'a': {'default': 'a', 'title': 'A', 'type': 'string'}},
    'required': ['a'],
    'title': 'Model',
    'type': 'object',
}
'''

Type: bool

json_schema_mode_override

If not None, the specified mode will be used to generate the JSON schema regardless of what mode was passed to the function call. Defaults to None.

This provides a way to force the JSON schema generation to reflect a specific mode, e.g., to always use the validation schema.

It can be useful when using frameworks (such as FastAPI) that may generate different schemas for validation and serialization that must both be referenced from the same schema; when this happens, we automatically append -Input to the definition reference for the validation schema and -Output to the definition reference for the serialization schema. By specifying a json_schema_mode_override though, this prevents the conflict between the validation and serialization schemas (since both will use the specified schema), and so prevents the suffixes from being added to the definition references.

from pydantic import BaseModel, ConfigDict, Json

class Model(BaseModel):
    a: Json[int]  # requires a string to validate, but will dump an int

print(Model.model_json_schema(mode='serialization'))
'''
{
    'properties': {'a': {'title': 'A', 'type': 'integer'}},
    'required': ['a'],
    'title': 'Model',
    'type': 'object',
}
'''

class ForceInputModel(Model):
    # the following ensures that even with mode='serialization', we
    # will get the schema that would be generated for validation.
    model_config = ConfigDict(json_schema_mode_override='validation')

print(ForceInputModel.model_json_schema(mode='serialization'))
'''
{
    'properties': {
        'a': {
            'contentMediaType': 'application/json',
            'contentSchema': {'type': 'integer'},
            'title': 'A',
            'type': 'string',
        }
    },
    'required': ['a'],
    'title': 'ForceInputModel',
    'type': 'object',
}
'''

Type: Literal['validation', 'serialization', None]

coerce_numbers_to_str

If True, enables automatic coercion of any Number type to str in “lax” (non-strict) mode. Defaults to False.

Pydantic doesn’t allow number types (int, float, Decimal) to be coerced as type str by default.

from decimal import Decimal

from pydantic import BaseModel, ConfigDict, ValidationError

class Model(BaseModel):
    value: str

try:
    print(Model(value=42))
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    value
      Input should be a valid string [type=string_type, input_value=42, input_type=int]
    '''

class Model(BaseModel):
    model_config = ConfigDict(coerce_numbers_to_str=True)

    value: str

repr(Model(value=42).value)
#> "42"
repr(Model(value=42.13).value)
#> "42.13"
repr(Model(value=Decimal('42.13')).value)
#> "42.13"

Type: bool

PydanticDataclass

Bases: StandardDataclass, Protocol

A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass.

getattr_migration 

def getattr_migration(module: str) -> Callable[[str], Any]

Implement PEP 562 for objects that were either moved or removed on the migration to V2.

Returns

Callable[[str], Any] — A callable that will raise an error if the object is not found.

Parameters

module : str

The module name.

Field 

def Field(
    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 | None = _Unset,
    json_schema_extra: dict[str, Any] | typing.Callable[[dict[str, Any]], None] | None = _Unset,
    frozen: bool | None = _Unset,
    validate_default: bool | None = _Unset,
    repr: bool = _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/2.4/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.

Returns

Any — A new FieldInfo, the return annotation is Any so Field can be used on type annotated fields without causing a typing error.

Parameters

default : Any Default: PydanticUndefined

Default value if the field is not set.

default_factory : typing.Callable[[], Any] | None Default: _Unset

A callable to generate the default value, such as :func:~datetime.utcnow.

alias : str | None Default: _Unset

An alternative name for the attribute.

alias_priority : int | None Default: _Unset

Priority of the alias. This affects whether an alias generator is used.

validation_alias : str | AliasPath | AliasChoices | None Default: _Unset

‘Whitelist’ validation step. The field will be the single one allowed by the alias or set of aliases defined.

serialization_alias : str | None Default: _Unset

‘Blacklist’ validation step. The vanilla field will be the single one of the alias’ or set of aliases’ fields and all the other fields will be ignored at serialization time.

title : str | None Default: _Unset

Human-readable title.

description : str | None Default: _Unset

Human-readable description.

examples : list[Any] | None Default: _Unset

Example values for this field.

exclude : bool | None Default: _Unset

Whether to exclude the field from the model serialization.

discriminator : str | None Default: _Unset

Field name for discriminating the type in a tagged union.

json_schema_extra : dict[str, Any] | typing.Callable[[dict[str, Any]], None] | None Default: _Unset

Any additional JSON schema data for the schema property.

frozen : bool | None Default: _Unset

Whether the field is frozen.

validate_default : bool | None Default: _Unset

Run validation that isn’t only checking existence of defaults. This can be set to True or False. If not set, it defaults to None.

repr : bool Default: _Unset

A boolean indicating whether to include the field in the __repr__ output.

init_var : bool | None Default: _Unset

Whether the field should be included in the constructor of the dataclass.

kw_only : bool | None Default: _Unset

Whether the field should be a keyword-only argument in the constructor of the dataclass.

strict : bool | None Default: _Unset

If True, strict validation is applied to the field. See Strict Mode for details.

gt : float | None Default: _Unset

Greater than. If set, value must be greater than this. Only applicable to numbers.

ge : float | None Default: _Unset

Greater than or equal. If set, value must be greater than or equal to this. Only applicable to numbers.

lt : float | None Default: _Unset

Less than. If set, value must be less than this. Only applicable to numbers.

le : float | None Default: _Unset

Less than or equal. If set, value must be less than or equal to this. Only applicable to numbers.

multiple_of : float | None Default: _Unset

Value must be a multiple of this. Only applicable to numbers.

min_length : int | None Default: _Unset

Minimum length for strings.

max_length : int | None Default: _Unset

Maximum length for strings.

pattern : str | None Default: _Unset

Pattern for strings.

allow_inf_nan : bool | None Default: _Unset

Allow inf, -inf, nan. Only applicable to numbers.

max_digits : int | None Default: _Unset

Maximum number of allow digits for strings.

decimal_places : int | None Default: _Unset

Maximum number of decimal places allowed for numbers.

union_mode : Literal['smart', 'left_to_right'] Default: _Unset

The strategy to apply when validating a union. Can be smart (the default), or left_to_right. See Union Mode for details.

extra : Unpack[_EmptyKwargs] Default: \{\}

Include extra fields used by the JSON schema.

dataclass 

def dataclass(
    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 = ...,
    slots: bool = ...,
) -> Callable[[type[_T]], type[PydanticDataclass]]
def dataclass(
    _cls: type[_T],
    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 = ...,
    slots: bool = ...,
) -> type[PydanticDataclass]
def dataclass(
    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,
) -> Callable[[type[_T]], type[PydanticDataclass]]
def dataclass(
    _cls: type[_T],
    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,
) -> type[PydanticDataclass]

Usage docs: https://docs.pydantic.dev/2.4/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.

Returns

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

Parameters

_cls : type[_T] | None Default: None

The target dataclass.

init : Literal[False] Default: 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.

repr : bool Default: True

A boolean indicating whether or not to include the field in the __repr__ output.

eq : bool Default: True

Determines if a __eq__ should be generated for the class.

order : bool Default: False

Determines if comparison magic methods should be generated, such as __lt__, but not __eq__.

unsafe_hash : bool Default: False

Determines if an unsafe hashing function should be included in the class.

frozen : bool Default: False

Determines if the generated class should be a ‘frozen’ dataclass, which does not allow its attributes to be modified from its constructor.

config : ConfigDict | type[object] | None Default: None

A configuration for the dataclass generation.

validate_on_init : bool | None Default: None

A deprecated parameter included for backwards compatibility; in V2, all Pydantic dataclasses are validated on init.

kw_only : bool Default: False

Determines if __init__ method parameters must be specified by keyword only. Defaults to False.

slots : bool Default: False

Determines if the generated class should be a ‘slots’ dataclass, which does not allow the addition of new attributes after instantiation.

Raises

  • AssertionError — Raised if init is not False or validate_on_init is False.

rebuild_dataclass 

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.

Returns

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.

Parameters

cls : type[PydanticDataclass]

The class to build the dataclass core schema for.

force : bool Default: False

Whether to force the rebuilding of the model schema, defaults to False.

raise_errors : bool Default: True

Whether to raise errors, defaults to True.

_parent_namespace_depth : int Default: 2

The depth level of the parent namespace, defaults to 2.

_types_namespace : dict[str, Any] | None Default: None

The types namespace, defaults to None.

is_pydantic_dataclass 

def is_pydantic_dataclass(__cls: type[Any]) -> TypeGuard[type[PydanticDataclass]]

Whether a class is a pydantic dataclass.

Returns

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

Parameters

__cls : type[Any]

The class.