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.

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

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

A dictionary containing metadata about generic Pydantic models. The origin and args items map to the __origin__ and __args__ attributes of generic aliases, and the parameter item maps to the __parameter__ attribute of generic classes.

Type: _generics.PydanticGenericMetadata

Parent namespace of the model, used for automatic rebuilding of models.

Type: Dict[str, Any] | 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

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.

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

The names of fields explicitly set during instantiation.

Type: set[str]

Values of private attributes set on the model instance.

Type: Dict[str, Any] | None

Configuration for the model, should be a dictionary conforming to ConfigDict.

Type: ConfigDict Default: ConfigDict()

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

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_fields(cls) -> dict[str, FieldInfo]

A mapping of field names to their respective FieldInfo instances.

dict[str, FieldInfo]

@classmethod

def model_computed_fields(cls) -> dict[str, ComputedFieldInfo]

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

dict[str, ComputedFieldInfo]

@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.

_fields_set : set[str] | None Default: None

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.

update : Mapping[str, Any] | None Default: None

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,
    polymorphic_serialization: bool | None = None,
) -> 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.

mode : Literal[‘json’, ‘python’] | str Default: 'python'

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.

context : Any | None Default: None

Additional context to pass to the serializer.

by_alias : bool | None Default: None

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].

warnings : bool | Literal[‘none’, ‘warn’, ‘error’] Default: True

How to handle serialization errors. False/“none” ignores them, True/“warn” logs errors, “error” raises a PydanticSerializationError.

fallback : Callable[[Any], Any] | None Default: None

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.

polymorphic_serialization : bool | None Default: None

Whether to use model and dataclass polymorphic serialization for this call.

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,
    polymorphic_serialization: bool | None = None,
) -> str

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

str — A JSON string representation of the model.

indent : int | None Default: None

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.

context : Any | None Default: None

Additional context to pass to the serializer.

by_alias : bool | None Default: None

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].

warnings : bool | Literal[‘none’, ‘warn’, ‘error’] Default: True

How to handle serialization errors. False/“none” ignores them, True/“warn” logs errors, “error” raises a PydanticSerializationError.

fallback : Callable[[Any], Any] | None Default: None

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.

polymorphic_serialization : bool | None Default: None

Whether to use model and dataclass polymorphic serialization for this call.

@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 the anyOf keyword to combine schemas (the default).
• 'primitive_type_array': Use the type keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_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

mode : JsonSchemaMode Default: 'validation'

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.

params : tuple[type[Any], …]

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.

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.

strict : bool | None Default: None

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.

from_attributes : bool | None Default: None

Whether to extract data from object attributes.

context : Any | None Default: None

Additional context to pass to the validator.

by_alias : bool | None Default: None

Whether to use the field’s alias when validating against the provided input data.

by_name : bool | None Default: None

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.

A ValidationError raised here names the failing fields, but not the JSON document they came from. Recording validations with Logfire keeps the offending input alongside the error — see Troubleshooting validation errors.

Self — The validated Pydantic model.

json_data : str | bytes | bytearray

The JSON data to validate.

strict : bool | None Default: None

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.

context : Any | None Default: None

Extra variables to pass to the validator.

by_alias : bool | None Default: None

Whether to use the field’s alias when validating against the provided input data.

by_name : bool | None Default: None

Whether to use the field’s name when validating against the provided input data.

• ValidationError — If json_data is 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.

strict : bool | None Default: None

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.

context : Any | None Default: None

Extra variables to pass to the validator.

by_alias : bool | None Default: None

Whether to use the field’s alias when validating against the provided input data.

by_name : bool | None Default: None

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[Any, 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[Any, Any],
) -> type[ModelT]

Dynamically creates and returns a new Pydantic model, in other words, create_model dynamically creates a subclass of BaseModel.

type[ModelT] — The new model.

model_name : str

The name of the newly created model.

__config__ : ConfigDict | None Default: None

The configuration of the new model.

__doc__ : str | None Default: None

The docstring of the new model.

__base__ : type[ModelT] | tuple[type[ModelT], …] | None Default: None

The base class or classes for the new model.

__module__ : str | None Default: None

The name of the module that the model belongs to; if None, the value is taken from sys._getframe(1)

__validators__ : dict[str, Callable[…, Any]] | None Default: None

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.

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

A dictionary of keyword arguments for class creation, such as metaclass.

__qualname__ : str | None Default: None

The qualified name of the newly created model.

**field_definitions : Any | tuple[Any, Any] Default: {}

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.

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