> ## Documentation Index > Fetch the complete documentation index at: https://docs.prefect.io/llms.txt > Use this file to discover all available pages before exploring further. # pydantic # `prefect.utilities.pydantic` ## Functions ### `add_cloudpickle_reduction` ```python theme={null} 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](https://github.com/cloudpipe/cloudpickle/issues/408) ### `get_class_fields_only` ```python theme={null} 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. ### `add_type_dispatch` ```python theme={null} 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. ### `custom_pydantic_encoder` ```python theme={null} custom_pydantic_encoder(type_encoders: dict[Any, Callable[[type[Any]], Any]], obj: Any) -> Any ``` ### `parse_obj_as` ```python theme={null} 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](https://docs.pydantic.dev/latest/concepts/type_adapter/). **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_`. ### `handle_secret_render` ```python theme={null} handle_secret_render(value: object, context: dict[str, Any]) -> object ``` ## Classes ### `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:** #### `finalize` ```python theme={null} finalize(self, **kwargs: Any) -> M ``` #### `raise_if_already_set` ```python theme={null} raise_if_already_set(self, name: str) -> None ``` #### `raise_if_not_in_model` ```python theme={null} raise_if_not_in_model(self, name: str) -> None ``` Built with [Mintlify](https://mintlify.com).