def for_question_answering(
        cls: Type["FeedbackDataset"],
        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 question answering tasks.

        Args:
            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 question answering containing "context" and "question" fields and a TextQuestion named "answer"
        """
        default_guidelines = "This is a question answering dataset that contains questions and contexts. Please answer the question by using the context."
        return cls(
            fields=[
                TextField(name="question", use_markdown=use_markdown),
                TextField(name="context", use_markdown=use_markdown),
            ],
            questions=[
                TextQuestion(
                    name="answer",
                    description="Answer the question. Note that the answer must exactly be in the context.",
                    use_markdown=use_markdown,
                    required=True,
                )
            ],
            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 question answering tasks.

        Args:
            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 question answering containing "context" and "question" fields and a TextQuestion named "answer"
        
argilla-v1/src/argilla_v1/client/feedback/dataset/local/mixins.py