def for_retrieval_augmented_generation(
        cls: Type["FeedbackDataset"],
        number_of_retrievals: int = 1,
        rating_scale: int = 7,
        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 retrieval augmented generation tasks.

        Args:
            number_of_retrievals: Set this parameter to the number of documents you want to add to your dataset
            rating_scale: Set this parameter to the number of relevancy scale you want to add 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 retrieval augmented generation containing "query" and "retrieved_document" fields and a TextQuestion named "response"
        """
        default_guidelines = "This is a retrieval augmented generation dataset that contains queries and retrieved documents. Please rate the relevancy of retrieved document and write the response to the query in the response field."
        document_fields = [
            TextField(
                name="retrieved_document_" + str(doc + 1),
                title="Retrieved Document " + str(doc + 1),
                use_markdown=use_markdown,
                required=True if doc == 0 else False,
            )
            for doc in range(number_of_retrievals)
        ]

        rating_questions = [
            RatingQuestion(
                name="rating_retrieved_document_" + str(doc + 1),
                title="Rate the relevance of the Retrieved Document " + str(doc + 1) + " for the query",
                values=list(range(1, rating_scale + 1)),
                description="Rate the relevance of the retrieved document.",
                required=True if doc == 0 else False,
            )
            for doc in range(number_of_retrievals)
        ]

        total_questions = rating_questions + [
            TextQuestion(
                name="response",
                title="Write a helpful, harmless, accurate response to the query.",
                description="Write the response to the query.",
                use_markdown=use_markdown,
                required=False,
            )
        ]

        return cls(
            fields=[TextField(name="query", use_markdown=use_markdown, required=True)] + document_fields,
            questions=total_questions,
            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 retrieval augmented generation tasks.

        Args:
            number_of_retrievals: Set this parameter to the number of documents you want to add to your dataset
            rating_scale: Set this parameter to the number of relevancy scale you want to add 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 retrieval augmented generation containing "query" and "retrieved_document" fields and a TextQuestion named "response"
        
argilla-v1/src/argilla_v1/client/feedback/dataset/local/mixins.py