validate_call
Decorator for validating function calls.
Bases: TypedDict
Usage docs: https://docs.pydantic.dev/2.2/usage/model_config/
A TypedDict for configuring Pydantic behaviour.
The title for the generated JSON schema, defaults to the model’s name
Type: str | None
Whether to convert all characters to lowercase for str types. Defaults to False.
Type: bool
Whether to convert all characters to uppercase for str types. Defaults to False.
Type: bool
Whether to strip leading and trailing whitespace for str types.
Type: bool
The minimum length for str types. Defaults to None.
Type: int
The maximum length for str types. Defaults to None.
Type: int | None
Whether to ignore, allow, or forbid extra attributes during model initialization.
The value must be a ExtraValues string. Defaults to 'ignore'.
See Extra Attributes for details.
Type: ExtraValues | None
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
Whether an aliased field may be populated by its name as given by the model
attribute, as well as the alias. Defaults to False.
Type: bool
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
Type: bool
Type: bool
Whether to build models and look up discriminators of tagged unions using python object attributes.
Type: bool
Whether to use the alias for error locs rather than the field’s name. Defaults to True.
Type: bool
A callable that takes a field name and returns an alias for it.
See Alias Generator for details.
Type: Callable[[str], str] | None
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, ...]
Whether to allow infinity (+inf an -inf) and NaN values to float fields. Defaults to True.
Type: bool
A dict or callable to provide extra JSON schema properties. Defaults to None.
Type: dict[str, object] | JsonSchemaExtraCallable | None
A dict of custom JSON encoders for specific types. Defaults to None.
Type: dict[type[object], JsonEncoder] | None
(new in V2) If True, strict validation is applied to all fields on the model.
See Strict Mode for details.
Type: bool
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
See Revalidate Instances for details.
Type: Literal['always', 'never', 'subclass-instances']
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']
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']
Whether to validate default values during validation. Defaults to False.
Type: bool
whether to validate the return value from call validators.
Type: bool
A tuple of strings that prevent model to have field which conflict with them.
Defaults to ('model_', )).
See Protected Namespaces for details.
Type: tuple[str, ...]
Whether to hide inputs when printing errors. Defaults to False.
See Hide Input in Errors.
Type: bool
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
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.
The GenerateSchema interface is subject to change, currently only the string_schema method is public.
See #6737 for details.
Defaults to None.
Type: type[_GenerateSchema] | None
def validate_call(
config: ConfigDict | None = None,
validate_return: bool = False,
) -> Callable[[AnyCallableT], AnyCallableT]
def validate_call(__func: AnyCallableT) -> AnyCallableT
Usage docs: https://docs.pydantic.dev/2.2/usage/validation_decorator/
Returns a decorated wrapper around the function that validates the arguments and, optionally, the return value.
Usage may be either as a plain decorator @validate_call or with arguments @validate_call(...).
AnyCallableT | Callable[[AnyCallableT], AnyCallableT] — The decorated function.
The function to be decorated.
The configuration dictionary.
Whether to validate the return value.
Default: TypeVar('AnyCallableT', bound=(Callable[..., Any]))