def for_direct_preference_optimization(
        cls: Type["FeedbackDataset"],
        number_of_responses: int = 2,
        context: bool = False,
        use_markdown: bool = False,
        guidelines: Optional[str] = None,
        metadata_properties: List[AllowedMetadataPropertyTypes] = None,
        vectors_settings: List[VectorSettings] = None,
    ) -> "FeedbackDataset":
        """
        You can use this method to create a basic dataset for direct preference optimization tasks.

        Args:
            number_of_responses: Set this parameter to the number of responses you want to add to your dataset
            context: Set this parameter to True if you want to add context to your dataset
            use_markdown: Set this parameter to True if you want to use markdown in your dataset
            metadata_properties: Contains the metadata properties that will be indexed and could be used to filter the dataset. Defaults to `None`.
            vectors_settings: Define the configuration of the vectors associated to the records that will be used to perform the vector search. Defaults to `None`.

        Returns:
            A `FeedbackDataset` object for direct preference optimization containing "prompt", "response1", "response2" with the optional "context" fields and a RatingQuestion named "preference"
        """
        default_guidelines = "This is a direct preference optimization dataset that contains contexts and options. Please rank the options that you would prefer in the given context."
        return cls.for_preference_modeling(
            number_of_responses=number_of_responses,
            context=context,
            use_markdown=use_markdown,
            guidelines=default_guidelines if guidelines is None else guidelines,
            metadata_properties=metadata_properties,
            vectors_settings=vectors_settings,
        )

        You can use this method to create a basic dataset for direct preference optimization tasks.

        Args:
            number_of_responses: Set this parameter to the number of responses you want to add to your dataset
            context: Set this parameter to True if you want to add context to your dataset
            use_markdown: Set this parameter to True if you want to use markdown in your dataset
            metadata_properties: Contains the metadata properties that will be indexed and could be used to filter the dataset. Defaults to `None`.
            vectors_settings: Define the configuration of the vectors associated to the records that will be used to perform the vector search. Defaults to `None`.

        Returns:
            A `FeedbackDataset` object for direct preference optimization containing "prompt", "response1", "response2" with the optional "context" fields and a RatingQuestion named "preference"
        
argilla-v1/src/argilla_v1/client/feedback/dataset/local/mixins.py