BaseModel
Pydantic models are simply classes which inherit from BaseModel and define fields as annotated attributes.
A base class for creating Pydantic models.
The names of the class variables defined on the model.
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
The core schema of the model.
Type: CoreSchema
Whether the model has a custom __init__ function.
Type: bool
Metadata containing the decorators defined on the model.
This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
Type: _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.
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
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.
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'.
The names of fields explicitly set during instantiation.
Values of private attributes set on the model instance.
def __init__(data: Any = {}) -> None
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.
@classmethod
def model_fields(cls) -> dict[str, FieldInfo]
A mapping of field names to their respective FieldInfo instances.
@classmethod
def model_computed_fields(cls) -> dict[str, ComputedFieldInfo]
A mapping of computed field names to their respective ComputedFieldInfo instances.
@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.
values : Any Default: \{\}
Trusted or pre-validated data dictionary.
def model_copy(update: Mapping[str, Any] | None = None, deep: bool = False) -> Self
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.
deep : bool Default: False
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 | None = None,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
exclude_computed_fields: bool = False,
round_trip: bool = False,
warnings: bool | Literal['none', 'warn', 'error'] = True,
fallback: Callable[[Any], Any] | None = None,
serialize_as_any: bool = False,
) -> dict[str, Any]
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.
include : IncEx | None Default: None
A set of fields to include in the output.
exclude : IncEx | None Default: None
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.
exclude_unset : bool Default: False
Whether to exclude fields that have not been explicitly set.
exclude_defaults : bool Default: False
Whether to exclude fields that are set to their default value.
exclude_none : bool Default: False
Whether to exclude fields that have a value of None.
exclude_computed_fields : bool Default: False
Whether to exclude computed fields.
While this can be useful for round-tripping, it is usually recommended to use the dedicated
round_trip parameter instead.
round_trip : bool Default: False
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.
A function to call when an unknown value is encountered. If not provided,
a PydanticSerializationError error is raised.
serialize_as_any : bool Default: False
Whether to serialize fields with duck-typing serialization behavior.
def model_dump_json(
indent: int | None = None,
ensure_ascii: bool = False,
include: IncEx | None = None,
exclude: IncEx | None = None,
context: Any | None = None,
by_alias: bool | None = None,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
exclude_computed_fields: bool = False,
round_trip: bool = False,
warnings: bool | Literal['none', 'warn', 'error'] = True,
fallback: Callable[[Any], Any] | None = None,
serialize_as_any: bool = False,
) -> str
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.
ensure_ascii : bool Default: False
If True, the output is guaranteed to have all incoming non-ASCII characters escaped.
If False (the default), these characters will be output as-is.
include : IncEx | None Default: None
Field(s) to include in the JSON output.
exclude : IncEx | None Default: None
Field(s) to exclude from the JSON output.
Additional context to pass to the serializer.
Whether to serialize using field aliases.
exclude_unset : bool Default: False
Whether to exclude fields that have not been explicitly set.
exclude_defaults : bool Default: False
Whether to exclude fields that are set to their default value.
exclude_none : bool Default: False
Whether to exclude fields that have a value of None.
exclude_computed_fields : bool Default: False
Whether to exclude computed fields.
While this can be useful for round-tripping, it is usually recommended to use the dedicated
round_trip parameter instead.
round_trip : bool Default: False
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.
A function to call when an unknown value is encountered. If not provided,
a PydanticSerializationError error is raised.
serialize_as_any : bool Default: False
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',
union_format: Literal['any_of', 'primitive_type_array'] = 'any_of',
) -> dict[str, Any]
Generates a JSON schema for a model class.
dict[str, Any] — The JSON schema for the given model class.
by_alias : bool Default: True
Whether to use attribute aliases or not.
ref_template : str Default: DEFAULT_REF_TEMPLATE
The reference template.
union_format : Literal[‘any_of’, ‘primitive_type_array’] Default: 'any_of'
The format to use when combining schemas from unions together. Can be one of:
'any_of': Use theanyOfkeyword to combine schemas (the default).'primitive_type_array': Use thetypekeyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string,boolean,null,integerornumber) or contains constraints/metadata, falls back toany_of.
schema_generator : type[GenerateJsonSchema] Default: GenerateJsonSchema
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.
@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.
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 : MappingNamespace | None Default: None
The types namespace, defaults to None.
@classmethod
def model_validate(
cls,
obj: Any,
strict: bool | None = None,
extra: ExtraValues | None = None,
from_attributes: bool | None = None,
context: Any | None = None,
by_alias: bool | None = None,
by_name: bool | None = None,
) -> Self
Validate a pydantic model instance.
Self — The validated model instance.
obj : Any
The object to validate.
Whether to enforce types strictly.
extra : ExtraValues | None Default: None
Whether to ignore, allow, or forbid extra data during model validation.
See the extra configuration value for details.
Whether to extract data from object attributes.
Additional context to pass to the validator.
Whether to use the field’s alias when validating against the provided input data.
Whether to use the field’s name when validating against the provided input data.
ValidationError— If the object could not be validated.
@classmethod
def model_validate_json(
cls,
json_data: str | bytes | bytearray,
strict: bool | None = None,
extra: ExtraValues | None = None,
context: Any | None = None,
by_alias: bool | None = None,
by_name: bool | None = None,
) -> Self
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 : ExtraValues | None Default: None
Whether to ignore, allow, or forbid extra data during model validation.
See the extra configuration value for details.
Extra variables to pass to the validator.
Whether to use the field’s alias when validating against the provided input data.
Whether to use the field’s name when validating against the provided input data.
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,
extra: ExtraValues | None = None,
context: Any | None = None,
by_alias: bool | None = None,
by_name: bool | None = None,
) -> Self
Validate the given object with string data against the Pydantic model.
Self — The validated Pydantic model.
obj : Any
The object containing string data to validate.
Whether to enforce types strictly.
extra : ExtraValues | None Default: None
Whether to ignore, allow, or forbid extra data during model validation.
See the extra configuration value for details.
Extra variables to pass to the validator.
Whether to use the field’s alias when validating against the provided input data.
Whether to use the field’s name when validating against the provided input data.
def create_model(
model_name: str,
__config__: ConfigDict | None = None,
__doc__: str | None = None,
__base__: None = None,
__module__: str = __name__,
__validators__: dict[str, Callable[..., Any]] | None = None,
__cls_kwargs__: dict[str, Any] | None = None,
__qualname__: str | None = None,
field_definitions: Any | tuple[str, Any] = {},
) -> type[BaseModel]
def create_model(
model_name: str,
__config__: ConfigDict | None = None,
__doc__: str | None = None,
__base__: type[ModelT] | tuple[type[ModelT], ...],
__module__: str = __name__,
__validators__: dict[str, Callable[..., Any]] | None = None,
__cls_kwargs__: dict[str, Any] | None = None,
__qualname__: str | None = None,
field_definitions: Any | tuple[str, Any] = {},
) -> type[ModelT]
Dynamically creates and returns a new Pydantic model, in other words, create_model dynamically creates a
subclass of BaseModel.
model_name : str
The name of the newly created model.
__config__ : ConfigDict | None Default: None
The configuration of the new model.
The docstring of the new model.
The base class or classes for the new model.
The name of the module that the model belongs to;
if None, the value is taken from sys._getframe(1)
A dictionary of methods that validate fields. The keys are the names of the validation methods to be added to the model, and the values are the validation methods themselves. You can read more about functional validators here.
A dictionary of keyword arguments for class creation, such as metaclass.
The qualified name of the newly created model.
Field definitions of the new model. Either:
- a single element, representing the type annotation of the field.
- a two-tuple, the first element being the type and the second element the assigned value
(either a default or the
Field()function).
PydanticUserError— If__base__and__config__are both passed.