BaseModel

A base class for creating Pydantic models.

Attributes

Attribute	Type	Description
model_config	ClassVar[[ConfigDict](../v1/config/configdict.md?sid=pydantic_v1_config_configdict)] = ConfigDict()	Configuration for the model, should be a dictionary conforming to ConfigDict.

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Constructor

Signature

def BaseModel(
    data: Any
) - > None

Parameters

Name	Type	Description
data	Any	Keyword arguments representing the model data to be validated.

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Signature

def BaseModel(
    data: Any
) - > None

Parameters

Name	Type	Description
data	Any	The keyword arguments representing model fields to be validated and assigned

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Methods

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model_fields()

@classmethod
def model_fields() - > dict[str, [FieldInfo](../v1/fields/fieldinfo.md?sid=pydantic_v1_fields_fieldinfo)]

A mapping of field names to their respective FieldInfo instances.

Returns

Type	Description
dict[str, [FieldInfo](../v1/fields/fieldinfo.md?sid=pydantic_v1_fields_fieldinfo)]	A dictionary mapping field names to their metadata objects

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model_computed_fields()

@classmethod
def model_computed_fields() - > dict[str, [ComputedFieldInfo](../fields/computedfieldinfo.md?sid=pydantic_fields_computedfieldinfo)]

A mapping of computed field names to their respective ComputedFieldInfo instances.

Returns

Type	Description
dict[str, [ComputedFieldInfo](../fields/computedfieldinfo.md?sid=pydantic_fields_computedfieldinfo)]	A dictionary mapping computed field names to their metadata objects

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model_extra()

@classmethod
def model_extra() - > dict[str, Any]| None

Get extra fields set during validation.

Returns

Type	Description
`dict[str, Any]	None`

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model_fields_set()

@classmethod
def model_fields_set() - > set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns

Type	Description
set[str]	A set of strings representing the fields that have been set, i.e. that were not filled from defaults

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model_construct()

@classmethod
def model_construct(
    _fields_set: set[str]| None = None,
    values: Any
) - > Self

Creates a new instance of the Model class with validated data.

Parameters

Name	Type	Description
_fields_set	`set[str]	None` = None
values	Any	Trusted or pre-validated data dictionary

Returns

Type	Description
Self	A new instance of the Model class with validated data

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model_copy()

@classmethod
def model_copy(
    update: Mapping[str, Any]| None = None,
    deep: bool = False
) - > Self

Returns a copy of the model.

Parameters

Name	Type	Description
update	`Mapping[str, Any]	None` = None
deep	bool = False	Whether to perform a deep copy of the model

Returns

Type	Description
Self	New model instance

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model_dump()

@classmethod
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,
    polymorphic_serialization: bool | None = None
) - > dict[str, Any]

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters

Name	Type	Description
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	Whether to exclude fields that have not been explicitly set
exclude_defaults	bool = False	Whether to exclude fields that are set to their default value
exclude_none	bool = False	Whether to exclude fields that have a value of None
exclude_computed_fields	bool = False	Whether to exclude computed fields from the output
round_trip	bool = False	Whether dumped values should be valid as input for non-idempotent types
warnings	`bool	Literal['none', 'warn', 'error']` = True
fallback	`Callable[[Any], Any]	None` = None
serialize_as_any	bool = False	Whether to serialize fields with duck-typing serialization behavior
polymorphic_serialization	`bool	None` = None

Returns

Type	Description
dict[str, Any]	A dictionary representation of the model

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model_dump_json()

@classmethod
def model_dump_json(
    indent: int | None = None,
    ensure_ascii: bool = False
) - > str

Generates a JSON representation of the model using Pydantic's to_json method.

Parameters

Name	Type	Description
indent	`int	None` = None
ensure_ascii	bool = False	Whether to escape all incoming non-ASCII characters

Returns

Type	Description
str	A JSON string representation of the model

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model_json_schema()

@classmethod
def model_json_schema(
    by_alias: bool = True,
    ref_template: str = DEFAULT_REF_TEMPLATE,
    schema_generator: type[[GenerateJsonSchema](../json/schema/generatejsonschema.md?sid=pydantic_json_schema_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.

Parameters

Name	Type	Description
by_alias	bool = True	Whether to use attribute aliases in the schema
ref_template	str = DEFAULT_REF_TEMPLATE	The reference template for JSON pointers
schema_generator	type[[GenerateJsonSchema](../json/schema/generatejsonschema.md?sid=pydantic_json_schema_generatejsonschema)] = GenerateJsonSchema	The class used to generate the JSON schema
mode	JsonSchemaMode = 'validation'	The mode in which to generate the schema
union_format	Literal['any_of', 'primitive_type_array'] = 'any_of'	The format to use when combining schemas from unions

Returns

Type	Description
dict[str, Any]	The JSON schema for the given model class

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model_parametrized_name()

@classmethod
def model_parametrized_name(
    params: tuple[type[Any], ...]
) - > str

Compute the class name for parametrizations of generic classes.

Parameters

Name	Type	Description
params	tuple[type[Any], ...]	Tuple of types used to parametrize the generic class

Returns

Type	Description
str	String representing the new class where params are passed to cls as type variables

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model_post_init()

@classmethod
def model_post_init(
    context: Any
) - > None

Override this method to perform additional initialization after init and model_construct.

Parameters

Name	Type	Description
context	Any	The validation context passed during initialization

Returns

Type	Description
None

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model_rebuild()

@classmethod
def model_rebuild(
    force: bool = False,
    raise_errors: bool = True
) - > bool | None

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

Parameters

Name	Type	Description
force	bool = False	Whether to force the rebuilding of the model schema
raise_errors	bool = True	Whether to raise errors if rebuilding fails

Returns

Type	Description
`bool	None`

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model_validate()

@classmethod
def model_validate(
    obj: Any,
    strict: bool | None = None,
    from_attributes: bool | None = None
) - > Self

Validate a pydantic model instance.

Parameters

Name	Type	Description
obj	Any	The object to validate against the model schema
strict	`bool	None` = None
from_attributes	`bool	None` = None

Returns

Type	Description
Self	The validated model instance

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model_validate_json()

@classmethod
def model_validate_json(
    json_data: str | bytes | bytearray
) - > Self

Validate the given JSON data against the Pydantic model.

Parameters

Name	Type	Description
json_data	`str	bytes

Returns

Type	Description
Self	The validated Pydantic model

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model_validate_strings()

@classmethod
def model_validate_strings(
    obj: Any
) - > Self

Validate the given object with string data against the Pydantic model.

Parameters

Name	Type	Description
obj	Any	The object containing string data to validate

Returns

Type	Description
Self	The validated Pydantic model

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