RootModel
RootModel class and type definitions.
Bases: PydanticErrorMixin, TypeError
An error raised due to incorrect use of Pydantic.
Usage docs: https://docs.pydantic.dev/2.10/concepts/models/
A base class for creating Pydantic models.
Configuration for the model, should be a dictionary conforming to ConfigDict.
Type: ConfigDict Default: ConfigDict()
The names of the class variables defined on the model.
Type: set[str]
Metadata about the private attributes of the model.
Type: Dict[str, ModelPrivateAttr]
The synthesized __init__ Signature of the model.
Type: Signature
Whether model building is completed, or if there are still undefined fields.
Type: bool Default: False
The core schema of the model.
Type: CoreSchema
Whether the model has a custom __init__ method.
Type: bool
Metadata containing the decorators defined on the model.
This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
Type: _decorators.DecoratorInfos Default: _decorators.DecoratorInfos()
Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.
Type: _generics.PydanticGenericMetadata
Parent namespace of the model, used for automatic rebuilding of models.
Type: Dict[str, Any] | None Default: None
The name of the post-init method for the model, if defined.
Type: None | Literal['model_post_init']
Whether the model is a RootModel.
Type: bool Default: False
The pydantic-core SchemaSerializer used to dump instances of the model.
Type: SchemaSerializer
The pydantic-core SchemaValidator used to validate instances of the model.
Type: SchemaValidator | PluggableSchemaValidator
A dictionary of field names and their corresponding FieldInfo objects.
This replaces Model.__fields__ from Pydantic V1.
Type: Dict[str, FieldInfo]
A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
Type: Dict[str, ComputedFieldInfo]
A dictionary containing extra values, if extra is set to 'allow'.
Type: dict[str, Any] | None Default: _model_construction.NoInitField(init=False)
The names of fields explicitly set during instantiation.
Type: set[str] Default: _model_construction.NoInitField(init=False)
Values of private attributes set on the model instance.
Type: dict[str, Any] | None Default: _model_construction.NoInitField(init=False)
Get metadata about the fields defined on the model.
Deprecation warning: you should be getting this information from the model class, not from an instance.
In V3, this property will be removed from the BaseModel class.
Type: dict[str, FieldInfo]
Get metadata about the computed fields defined on the model.
Deprecation warning: you should be getting this information from the model class, not from an instance.
In V3, this property will be removed from the BaseModel class.
Type: dict[str, ComputedFieldInfo]
Get extra fields set during validation.
Type: dict[str, Any] | None
Returns the set of fields that have been explicitly set on this model instance.
Type: set[str]
def __init__(data: Any = {}) -> None
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be
validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
None
@classmethod
def model_construct(cls, _fields_set: set[str] | None = None, values: Any = {}) -> Self
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Self — A new instance of the Model class with validated data.
A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the model_fields_set attribute.
Otherwise, the field names from the values argument will be used.
Trusted or pre-validated data dictionary.
def model_copy(update: Mapping[str, Any] | None = None, deep: bool = False) -> Self
Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy
Returns a copy of the model.
Self — New model instance.
Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
Set to True to make a deep copy of the model.
def model_dump(
mode: Literal['json', 'python'] | str = 'python',
include: IncEx | None = None,
exclude: IncEx | None = None,
context: Any | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool | Literal['none', 'warn', 'error'] = True,
serialize_as_any: bool = False,
) -> dict[str, Any]
Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
dict[str, Any] — A dictionary representation of the model.
The mode in which to_python should run.
If mode is ‘json’, the output will only contain JSON serializable types.
If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
A set of fields to include in the output.
A set of fields to exclude from the output.
Additional context to pass to the serializer.
Whether to use the field’s alias in the dictionary key if defined.
Whether to exclude fields that have not been explicitly set.
Whether to exclude fields that are set to their default value.
Whether to exclude fields that have a value of None.
If True, dumped values should be valid as input for non-idempotent types such as Json[T].
How to handle serialization errors. False/“none” ignores them, True/“warn” logs errors,
“error” raises a PydanticSerializationError.
Whether to serialize fields with duck-typing serialization behavior.
def model_dump_json(
indent: int | None = None,
include: IncEx | None = None,
exclude: IncEx | None = None,
context: Any | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool | Literal['none', 'warn', 'error'] = True,
serialize_as_any: bool = False,
) -> str
Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s to_json method.
str — A JSON string representation of the model.
Indentation to use in the JSON output. If None is passed, the output will be compact.
Field(s) to include in the JSON output.
Field(s) to exclude from the JSON output.
Additional context to pass to the serializer.
Whether to serialize using field aliases.
Whether to exclude fields that have not been explicitly set.
Whether to exclude fields that are set to their default value.
Whether to exclude fields that have a value of None.
If True, dumped values should be valid as input for non-idempotent types such as Json[T].
How to handle serialization errors. False/“none” ignores them, True/“warn” logs errors,
“error” raises a PydanticSerializationError.
Whether to serialize fields with duck-typing serialization behavior.
@classmethod
def model_json_schema(
cls,
by_alias: bool = True,
ref_template: str = DEFAULT_REF_TEMPLATE,
schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
mode: JsonSchemaMode = 'validation',
) -> dict[str, Any]
Generates a JSON schema for a model class.
dict[str, Any] — The JSON schema for the given model class.
Whether to use attribute aliases or not.
The reference template.
To override the logic used to generate the JSON schema, as a subclass of
GenerateJsonSchema with your desired modifications
The mode in which to generate the schema.
@classmethod
def model_parametrized_name(cls, params: tuple[type[Any], ...]) -> str
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
str — String representing the new class where params are passed to cls as type variables.
Tuple of types of the class. Given a generic class
Model with 2 type variables and a concrete model Model[str, int],
the value (str, int) would be passed to params.
TypeError— Raised when trying to generate concrete names for non-generic models.
def model_post_init(__context: Any) -> None
Override this method to perform additional initialization after __init__ and model_construct.
This is useful if you want to do some validation that requires the entire model to be initialized.
None
@classmethod
def model_rebuild(
cls,
force: bool = False,
raise_errors: bool = True,
_parent_namespace_depth: int = 2,
_types_namespace: MappingNamespace | None = None,
) -> bool | None
Try to rebuild the pydantic-core schema for the model.
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.
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.
Whether to force the rebuilding of the model schema, defaults to False.
Whether to raise errors, defaults to True.
The depth level of the parent namespace, defaults to 2.
The types namespace, defaults to None.
@classmethod
def model_validate(
cls,
obj: Any,
strict: bool | None = None,
from_attributes: bool | None = None,
context: Any | None = None,
) -> Self
Validate a pydantic model instance.
Self — The validated model instance.
The object to validate.
Whether to enforce types strictly.
Whether to extract data from object attributes.
Additional context to pass to the validator.
ValidationError— If the object could not be validated.
@classmethod
def model_validate_json(
cls,
json_data: str | bytes | bytearray,
strict: bool | None = None,
context: Any | None = None,
) -> Self
Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
Self — The validated Pydantic model.
The JSON data to validate.
Whether to enforce types strictly.
Extra variables to pass to the validator.
ValidationError— Ifjson_datais not a JSON string or the object could not be validated.
@classmethod
def model_validate_strings(
cls,
obj: Any,
strict: bool | None = None,
context: Any | None = None,
) -> Self
Validate the given object with string data against the Pydantic model.
Self — The validated Pydantic model.
The object containing string data to validate.
Whether to enforce types strictly.
Extra variables to pass to the validator.
@classmethod
def __get_pydantic_core_schema__(
cls,
source: type[BaseModel],
handler: GetCoreSchemaHandler,
) -> CoreSchema
Hook into generating the model’s CoreSchema.
CoreSchema — A pydantic-core CoreSchema.
The class we are generating a schema for.
This will generally be the same as the cls argument if this is a classmethod.
A callable that calls into Pydantic’s internal CoreSchema generation logic.
@classmethod
def __get_pydantic_json_schema__(
cls,
core_schema: CoreSchema,
handler: GetJsonSchemaHandler,
) -> JsonSchemaValue
Hook into generating the model’s JSON schema.
JsonSchemaValue — A JSON schema, as a Python object.
A pydantic-core CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (\{'type': 'nullable', 'schema': current_schema\}),
or just call the handler with the original schema.
Call into Pydantic’s internal JSON schema generation.
This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema
generation fails.
Since this gets called by BaseModel.model_json_schema you can override the
schema_generator argument to that function to change JSON schema generation globally
for a type.
@classmethod
def __pydantic_init_subclass__(cls, kwargs: Any = {}) -> None
This is intended to behave just like __init_subclass__, but is called by ModelMetaclass
only after the class is actually fully initialized. In particular, attributes like model_fields will
be present when this is called.
This is necessary because __init_subclass__ will always be called by type.__new__,
and it would require a prohibitively large refactor to the ModelMetaclass to ensure that
type.__new__ was called in such a manner that the class would already be sufficiently initialized.
This will receive the same kwargs that would be passed to the standard __init_subclass__, namely,
any kwargs passed to the class definition that aren’t used internally by pydantic.
None
Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
def __copy__() -> Self
Returns a shallow copy of the model.
Self
def __deepcopy__(memo: dict[int, Any] | None = None) -> Self
Returns a deep copy of the model.
Self
def __init_subclass__(cls, kwargs: Unpack[ConfigDict] = {})
This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs.
from pydantic import BaseModel
class MyModel(BaseModel, extra='allow'): ...
However, this may be deceiving, since the actual calls to __init_subclass__ will not receive any
of the config arguments, and will only receive any keyword arguments passed during class initialization
that are not expected keys in ConfigDict. (This is due to the way ModelMetaclass.__new__ works.)
Keyword arguments passed to the class definition, which set model_config
def __iter__() -> TupleGenerator
So dict(model) works.
TupleGenerator
def dict(
include: IncEx | None = None,
exclude: IncEx | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
) -> Dict[str, Any]
Dict[str, Any]
def json(
include: IncEx | None = None,
exclude: IncEx | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
encoder: Callable[[Any], Any] | None = PydanticUndefined,
models_as_dict: bool = PydanticUndefined,
dumps_kwargs: Any = {},
) -> str
str
@classmethod
def parse_obj(cls, obj: Any) -> Self
Self
@classmethod
def parse_raw(
cls,
b: str | bytes,
content_type: str | None = None,
encoding: str = 'utf8',
proto: DeprecatedParseProtocol | None = None,
allow_pickle: bool = False,
) -> Self
Self
@classmethod
def parse_file(
cls,
path: str | Path,
content_type: str | None = None,
encoding: str = 'utf8',
proto: DeprecatedParseProtocol | None = None,
allow_pickle: bool = False,
) -> Self
Self
@classmethod
def from_orm(cls, obj: Any) -> Self
Self
@classmethod
def construct(cls, _fields_set: set[str] | None = None, values: Any = {}) -> Self
Self
def copy(
include: AbstractSetIntStr | MappingIntStrAny | None = None,
exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
update: Dict[str, Any] | None = None,
deep: bool = False,
) -> Self
Returns a copy of the model.
If you need include or exclude, use:
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
Self — A copy of the model with included, excluded and updated fields as specified.
Optional set or mapping specifying which fields to include in the copied model.
Optional set or mapping specifying which fields to exclude in the copied model.
Optional dictionary of field-value pairs to override field values in the copied model.
If True, the values of fields that are Pydantic models will be deep-copied.
@classmethod
def schema(
cls,
by_alias: bool = True,
ref_template: str = DEFAULT_REF_TEMPLATE,
) -> Dict[str, Any]
Dict[str, Any]
@classmethod
def schema_json(
cls,
by_alias: bool = True,
ref_template: str = DEFAULT_REF_TEMPLATE,
dumps_kwargs: Any = {},
) -> str
str
@classmethod
def validate(cls, value: Any) -> Self
Self
@classmethod
def update_forward_refs(cls, localns: Any = {}) -> None
None
Bases: BaseModel, Generic[RootModelRootType]
Usage docs: https://docs.pydantic.dev/2.10/concepts/models/#rootmodel-and-custom-root-types
A Pydantic BaseModel for the root object of the model.
Type: RootModelRootType
@classmethod
def model_construct(
cls,
root: RootModelRootType,
_fields_set: set[str] | None = None,
) -> Self
Create a new model using the provided root object and update fields set.
Self — The new model.
The root object of the model.
The set of fields to be updated.
NotImplemented— If the model is not a subclass ofRootModel.
def __copy__() -> Self
Returns a shallow copy of the model.
Self
def __deepcopy__(memo: dict[int, Any] | None = None) -> Self
Returns a deep copy of the model.
Self
def model_dump(
mode: Literal['json', 'python'] | str = 'python',
include: Any = None,
exclude: Any = None,
context: dict[str, Any] | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool | Literal['none', 'warn', 'error'] = True,
serialize_as_any: bool = False,
) -> Any
This method is included just to get a more accurate return type for type checkers.
It is included in this if TYPE_CHECKING: block since no override is actually necessary.
See the documentation of BaseModel.model_dump for more details about the arguments.
Generally, this method will have a return type of RootModelRootType, assuming that RootModelRootType is
not a BaseModel subclass. If RootModelRootType is a BaseModel subclass, then the return
type will likely be dict[str, Any], as model_dump calls are recursive. The return type could
even be something different, in the case of a custom serializer.
Thus, Any is used here to catch all of these cases.
Any
def PydanticModelField(
default: ellipsis,
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,
field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
description: str | None = _Unset,
examples: list[Any] | None = _Unset,
exclude: bool | None = _Unset,
discriminator: str | types.Discriminator | None = _Unset,
deprecated: Deprecated | str | bool | None = _Unset,
json_schema_extra: JsonDict | 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 | typing.Pattern[str] | None = _Unset,
strict: bool | None = _Unset,
coerce_numbers_to_str: bool | None = _Unset,
gt: annotated_types.SupportsGt | None = _Unset,
ge: annotated_types.SupportsGe | None = _Unset,
lt: annotated_types.SupportsLt | None = _Unset,
le: annotated_types.SupportsLe | 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,
fail_fast: bool | None = _Unset,
extra: Unpack[_EmptyKwargs] = {},
) -> Any
def PydanticModelField(
default: _T,
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,
field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
description: str | None = _Unset,
examples: list[Any] | None = _Unset,
exclude: bool | None = _Unset,
discriminator: str | types.Discriminator | None = _Unset,
deprecated: Deprecated | str | bool | None = _Unset,
json_schema_extra: JsonDict | 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 | typing.Pattern[str] | None = _Unset,
strict: bool | None = _Unset,
coerce_numbers_to_str: bool | None = _Unset,
gt: annotated_types.SupportsGt | None = _Unset,
ge: annotated_types.SupportsGe | None = _Unset,
lt: annotated_types.SupportsLt | None = _Unset,
le: annotated_types.SupportsLe | 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,
fail_fast: bool | None = _Unset,
extra: Unpack[_EmptyKwargs] = {},
) -> _T
def PydanticModelField(
default_factory: Callable[[], _T] | Callable[[dict[str, Any]], _T],
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,
field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
description: str | None = _Unset,
examples: list[Any] | None = _Unset,
exclude: bool | None = _Unset,
discriminator: str | types.Discriminator | None = _Unset,
deprecated: Deprecated | str | bool | None = _Unset,
json_schema_extra: JsonDict | 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 | typing.Pattern[str] | None = _Unset,
strict: bool | None = _Unset,
coerce_numbers_to_str: bool | None = _Unset,
gt: annotated_types.SupportsGt | None = _Unset,
ge: annotated_types.SupportsGe | None = _Unset,
lt: annotated_types.SupportsLt | None = _Unset,
le: annotated_types.SupportsLe | 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,
fail_fast: bool | None = _Unset,
extra: Unpack[_EmptyKwargs] = {},
) -> _T
def PydanticModelField(
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,
field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
description: str | None = _Unset,
examples: list[Any] | None = _Unset,
exclude: bool | None = _Unset,
discriminator: str | types.Discriminator | None = _Unset,
deprecated: Deprecated | str | bool | None = _Unset,
json_schema_extra: JsonDict | 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 | typing.Pattern[str] | None = _Unset,
strict: bool | None = _Unset,
coerce_numbers_to_str: bool | None = _Unset,
gt: annotated_types.SupportsGt | None = _Unset,
ge: annotated_types.SupportsGe | None = _Unset,
lt: annotated_types.SupportsLt | None = _Unset,
le: annotated_types.SupportsLe | 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,
fail_fast: bool | None = _Unset,
extra: Unpack[_EmptyKwargs] = {},
) -> Any
Usage docs: https://docs.pydantic.dev/2.10/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.
Any — A new FieldInfo. The return annotation is Any so Field can be used on
type-annotated fields without causing a type error.
Default value if the field is not set.
A callable to generate the default value. The callable can either take 0 arguments (in which case it is called as is) or a single argument containing the already validated data.
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.
Priority of the alias. This affects whether an alias generator is used.
Like alias, but only affects validation, not serialization.
Like alias, but only affects serialization, not validation.
Human-readable title.
A callable that takes a field name and returns title for it.
Human-readable description.
Example values for this field.
Whether to exclude the field from the model serialization.
Field name or Discriminator for discriminating the type in a tagged union.
A deprecation message, an instance of warnings.deprecated or the typing_extensions.deprecated backport,
or a boolean. If True, a default deprecation message will be emitted when accessing the field.
A dict or callable to provide extra JSON schema properties.
Whether the field is frozen. If true, attempts to change the value on an instance will raise an error.
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.
A boolean indicating whether to include the field in the __repr__ output.
Whether the field should be included in the constructor of the dataclass. (Only applies to dataclasses.)
Whether the field should only be included in the constructor of the dataclass. (Only applies to dataclasses.)
Whether the field should be a keyword-only argument in the constructor of the dataclass. (Only applies to dataclasses.)
Whether to enable coercion of any Number type to str (not applicable in strict mode).
If True, strict validation is applied to the field.
See Strict Mode for details.
Greater than. If set, value must be greater than this. Only applicable to numbers.
Greater than or equal. If set, value must be greater than or equal to this. Only applicable to numbers.
Less than. If set, value must be less than this. Only applicable to numbers.
Less than or equal. If set, value must be less than or equal to this. Only applicable to numbers.
Value must be a multiple of this. Only applicable to numbers.
Minimum length for iterables.
Maximum length for iterables.
Pattern for strings (a regular expression).
Allow inf, -inf, nan. Only applicable to numbers.
Maximum number of allow digits for strings.
Maximum number of decimal places allowed for numbers.
The strategy to apply when validating a union. Can be smart (the default), or left_to_right.
See Union Mode for details.
If True, validation will stop on the first error. If False, all validation errors will be collected.
This option can be applied only to iterable types (list, tuple, set, and frozenset).
(Deprecated) Extra fields that will be included in the JSON schema.
def PydanticModelPrivateAttr(default: _T, init: Literal[False] = False) -> _T
def PydanticModelPrivateAttr(
default_factory: Callable[[], _T],
init: Literal[False] = False,
) -> _T
def PydanticModelPrivateAttr(init: Literal[False] = False) -> Any
Usage docs: https://docs.pydantic.dev/2.10/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.
Any — An instance of ModelPrivateAttr class.
The attribute’s default value. Defaults to Undefined.
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.
Whether the attribute should be included in the constructor of the dataclass. Always False.
ValueError— If bothdefaultanddefault_factoryare set.
Default: typing.TypeVar('RootModelRootType')