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Task

Bases: ABC

Abstract class used to define the methods required to create a Task, to be used within an LLM.

Parameters:

Name Type Description Default
system_prompt str

the system prompt to be used for generation.

 required
task_description Union[str, None]

the description of the task. Defaults to None.

 required

Raises:

Type Description
ValueError

if the __jinja2_template__ attribute is not provided.
Source code in src/distilabel/tasks/base.py 
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class Task(ABC):
    """Abstract class used to define the methods required to create a `Task`, to be used
    within an `LLM`.

    Args:
        system_prompt (str): the system prompt to be used for generation.
        task_description (Union[str, None], optional): the description of the task. Defaults to `None`.

    Raises:
        ValueError: if the `__jinja2_template__` attribute is not provided.
    """

    system_prompt: str
    task_description: Union[str, None] = None

    __jinja2_template__: Union[str, None] = None

    def __rich_repr__(self) -> Generator[Any, None, None]:
        yield "system_prompt", self.system_prompt
        yield "task_description", self.task_description
        yield "input_args_names", self.input_args_names
        yield "output_args_names", self.output_args_names

    @property
    def template(self) -> "Template":
        if self.__jinja2_template__ is None:
            raise ValueError(
                "You must provide a `__jinja2_template__` attribute to your Task subclass."
            )

        return Template(open(self.__jinja2_template__).read())

    @abstractmethod
    def generate_prompt(self, **kwargs: Any) -> Union[Prompt, Any]:
        pass

    @abstractmethod
    def parse_output(self, output: str) -> Any:
        pass

    @property
    @abstractmethod
    def input_args_names(self) -> List[str]:
        pass

    @property
    @abstractmethod
    def output_args_names(self) -> List[str]:
        pass

    def validate_dataset(self, columns_in_dataset: List[str]) -> None:
        """Validates that the dataset contains the required columns for the task.

        Args:
            columns_in_dataset (List[str]): the columns in the dataset.

        Raises:
            KeyError: if the dataset does not contain the required columns.
        """
        for input_arg_name in self.input_args_names:
            if input_arg_name not in columns_in_dataset:
                raise KeyError(
                    f"LLM expects a column named '{input_arg_name}' in the provided"
                    " dataset, but it was not found."
                )

    def to_argilla_dataset(
        self, dataset_row: Dict[str, Any], *args: Any, **kwargs: Any
    ) -> "FeedbackDataset":
        raise NotImplementedError(
            "`to_argilla_dataset` is not implemented, if you want to export your dataset as an Argilla"
            " `FeedbackDataset` you will need to implement this method first."
        )

    def to_argilla_record(
        self, dataset_row: Dict[str, Any], *args: Any, **kwargs: Any
    ) -> "FeedbackRecord":
        raise NotImplementedError(
            "`to_argilla_record` is not implemented, if you want to export your dataset as an Argilla"
            " `FeedbackDataset` you will need to implement this method first."
        )

validate_dataset(columns_in_dataset)

Validates that the dataset contains the required columns for the task.

Parameters:

Name Type Description Default
columns_in_dataset List[str]

the columns in the dataset.

 required

Raises:

Type Description
KeyError

if the dataset does not contain the required columns.
Source code in src/distilabel/tasks/base.py 
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def validate_dataset(self, columns_in_dataset: List[str]) -> None:
    """Validates that the dataset contains the required columns for the task.

    Args:
        columns_in_dataset (List[str]): the columns in the dataset.

    Raises:
        KeyError: if the dataset does not contain the required columns.
    """
    for input_arg_name in self.input_args_names:
        if input_arg_name not in columns_in_dataset:
            raise KeyError(
                f"LLM expects a column named '{input_arg_name}' in the provided"
                " dataset, but it was not found."
            )