Base

GeneratorTask

Bases: _Task, GeneratorStep

GeneratorTask is a class that implements the _Task abstract class and adds the GeneratorStep interface to be used as a step in the pipeline.

Attributes:

Name	Type	Description
llm		the LLM to be used to generate the outputs of the task.
group_generations		whether to group the num_generations generated per input in a list or create a row per generation. Defaults to False.
num_generations		The number of generations to be produced per input.

Source code in src/distilabel/steps/tasks/base.py

class GeneratorTask(_Task, GeneratorStep):
    """GeneratorTask is a class that implements the `_Task` abstract class and adds the
    `GeneratorStep` interface to be used as a step in the pipeline.

    Attributes:
        llm: the `LLM` to be used to generate the outputs of the task.
        group_generations: whether to group the `num_generations` generated per input in
            a list or create a row per generation. Defaults to `False`.
        num_generations: The number of generations to be produced per input.
    """

    pass

Task

Bases: _Task, Step

Task is a class that implements the _Task abstract class and adds the Step interface to be used as a step in the pipeline.

Attributes:

Name	Type	Description
llm		the LLM to be used to generate the outputs of the task.
group_generations		whether to group the num_generations generated per input in a list or create a row per generation. Defaults to False.
num_generations		The number of generations to be produced per input.

Source code in src/distilabel/steps/tasks/base.py

class Task(_Task, Step):
    """Task is a class that implements the `_Task` abstract class and adds the `Step`
    interface to be used as a step in the pipeline.

    Attributes:
        llm: the `LLM` to be used to generate the outputs of the task.
        group_generations: whether to group the `num_generations` generated per input in
            a list or create a row per generation. Defaults to `False`.
        num_generations: The number of generations to be produced per input.
    """

    @abstractmethod
    def format_input(self, input: Dict[str, Any]) -> "ChatType":
        """Abstract method to format the inputs of the task. It needs to receive an input
        as a Python dictionary, and generates an OpenAI chat-like list of dicts."""
        pass

    def _format_inputs(self, inputs: List[Dict[str, Any]]) -> List["ChatType"]:
        """Formats the inputs of the task using the `format_input` method.

        Args:
            inputs: A list of Python dictionaries with the inputs of the task.

        Returns:
            A list containing the formatted inputs, which are `ChatType`-like following
            the OpenAI formatting.
        """
        return [self.format_input(input) for input in inputs]

    def process(self, inputs: StepInput) -> "StepOutput":  # type: ignore
        """Processes the inputs of the task and generates the outputs using the LLM.

        Args:
            inputs: A list of Python dictionaries with the inputs of the task.

        Yields:
            A list of Python dictionaries with the outputs of the task.
        """

        formatted_inputs = self._format_inputs(inputs)
        outputs = self.llm.generate(
            inputs=formatted_inputs,
            num_generations=self.num_generations,  # type: ignore
            **self.llm.generation_kwargs,  # type: ignore
        )

        task_outputs = []
        for input, input_outputs in zip(inputs, outputs):
            formatted_outputs = self._format_outputs(input_outputs, inputs)

            if self.group_generations:
                combined = combine_dicts(*formatted_outputs)
                task_outputs.append(
                    {**input, "model_name": self.llm.model_name, **combined}
                )
                continue

            # Create a row per generation
            for formatted_output in formatted_outputs:
                task_outputs.append(
                    {**input, "model_name": self.llm.model_name, **formatted_output}
                )

        yield task_outputs

format_input(input) abstractmethod

Abstract method to format the inputs of the task. It needs to receive an input as a Python dictionary, and generates an OpenAI chat-like list of dicts.

Source code in src/distilabel/steps/tasks/base.py

@abstractmethod
def format_input(self, input: Dict[str, Any]) -> "ChatType":
    """Abstract method to format the inputs of the task. It needs to receive an input
    as a Python dictionary, and generates an OpenAI chat-like list of dicts."""
    pass

process(inputs)

Processes the inputs of the task and generates the outputs using the LLM.

Parameters:

Name	Type	Description	Default
inputs	StepInput	A list of Python dictionaries with the inputs of the task.	required

Yields:

Type	Description
StepOutput	A list of Python dictionaries with the outputs of the task.

Source code in src/distilabel/steps/tasks/base.py

def process(self, inputs: StepInput) -> "StepOutput":  # type: ignore
    """Processes the inputs of the task and generates the outputs using the LLM.

    Args:
        inputs: A list of Python dictionaries with the inputs of the task.

    Yields:
        A list of Python dictionaries with the outputs of the task.
    """

    formatted_inputs = self._format_inputs(inputs)
    outputs = self.llm.generate(
        inputs=formatted_inputs,
        num_generations=self.num_generations,  # type: ignore
        **self.llm.generation_kwargs,  # type: ignore
    )

    task_outputs = []
    for input, input_outputs in zip(inputs, outputs):
        formatted_outputs = self._format_outputs(input_outputs, inputs)

        if self.group_generations:
            combined = combine_dicts(*formatted_outputs)
            task_outputs.append(
                {**input, "model_name": self.llm.model_name, **combined}
            )
            continue

        # Create a row per generation
        for formatted_output in formatted_outputs:
            task_outputs.append(
                {**input, "model_name": self.llm.model_name, **formatted_output}
            )

    yield task_outputs