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prefect.utilities.pydantic

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Functions

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add_cloudpickle_reduction

add_cloudpickle_reduction(__model_cls: Optional[type[M]] = None, **kwargs: Any) -> Union[type[M], Callable[[type[M]], type[M]]]

Adds a __reducer__ to the given class that ensures it is cloudpickle compatible.

Workaround for issues with cloudpickle when using cythonized pydantic which throws exceptions when attempting to pickle the class which has “compiled” validator methods dynamically attached to it.

We cannot define this utility in the model class itself because the class is the type that contains unserializable methods.

Any model using some features of Pydantic (e.g. Path validation) with a Cython compiled Pydantic installation may encounter pickling issues.

See related issue at https://github.com/cloudpipe/cloudpickle/issues/408

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get_class_fields_only

get_class_fields_only(model: type[BaseModel]) -> set[str]

Gets all the field names defined on the model class but not any parent classes. Any fields that are on the parent but redefined on the subclass are included.

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add_type_dispatch

add_type_dispatch(model_cls: type[M]) -> type[M]

Extend a Pydantic model to add a ‘type’ field that is used as a discriminator field to dynamically determine the subtype that when deserializing models.

This allows automatic resolution to subtypes of the decorated model.

If a type field already exists, it should be a string literal field that has a constant value for each subclass. The default value of this field will be used as the dispatch key.

If a type field does not exist, one will be added. In this case, the value of the field will be set to the value of the __dispatch_key__. The base class should define a __dispatch_key__ class method that is used to determine the unique key for each subclass. Alternatively, each subclass can define the __dispatch_key__ as a string literal.

The base class must not define a ‘type’ field. If it is not desirable to add a field to the model and the dispatch key can be tracked separately, the lower level utilities in prefect.utilities.dispatch should be used directly.

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custom_pydantic_encoder

custom_pydantic_encoder(type_encoders: dict[Any, Callable[[type[Any]], Any]], obj: Any) -> Any

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parse_obj_as

parse_obj_as(type_: type[T], data: Any, mode: Literal['python', 'json', 'strings'] = 'python') -> T

Parse a given data structure as a Pydantic model via TypeAdapter.

Read more about TypeAdapter here.

Args:

• type_: The type to parse the data as.
• data: The data to be parsed.
• mode: The mode to use for parsing, either python, json, or strings. Defaults to python, where data should be a Python object (e.g. dict).

Returns:

• The parsed data as the given type_.

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handle_secret_render

handle_secret_render(value: object, context: dict[str, Any]) -> object

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Classes

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PartialModel

A utility for creating a Pydantic model in several steps.

Fields may be set at initialization, via attribute assignment, or at finalization when the concrete model is returned.

Pydantic validation does not occur until finalization.

Each field can only be set once and a ValueError will be raised on assignment if a field already has a value.

Methods:

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finalize

finalize(self, **kwargs: Any) -> M

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raise_if_already_set

raise_if_already_set(self, name: str) -> None

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raise_if_not_in_model

raise_if_not_in_model(self, name: str) -> None