def for_supervised_fine_tuning(
        cls: Type["FeedbackDataset"],
        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 supervised fine-tuning tasks.

        Args:
            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
            guidelines: Contains the guidelines for the 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 supervised fine-tuning containing "instruction" and optional "context" field and a TextQuestion named "response"
        """
        default_guidelines = "This is a supervised fine-tuning dataset that contains instructions. Please write the response to the instruction in the response field."
        fields = [
            TextField(name="prompt", use_markdown=use_markdown),
        ]
        if context:
            fields.append(TextField(name="context", use_markdown=use_markdown, required=False))
        return cls(
            fields=fields,
            questions=[
                TextQuestion(
                    name="response", description="Write the response to the instruction.", use_markdown=use_markdown
                )
            ],
            guidelines=guidelines
            if guidelines is not None
            else default_guidelines + " Take the context into account when writing the response."
            if context
            else default_guidelines,
            metadata_properties=metadata_properties,
            vectors_settings=vectors_settings,
        )

        You can use this method to create a basic dataset for supervised fine-tuning tasks.

        Args:
            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
            guidelines: Contains the guidelines for the 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 supervised fine-tuning containing "instruction" and optional "context" field and a TextQuestion named "response"
        
argilla-v1/src/argilla_v1/client/feedback/dataset/local/mixins.py