def run(
        self,
        index: BaseIndex,
        benchmark_df: pd.DataFrame,
        rag_dataset: "LabelledRagDataset",
        name: str,
        description: str,
        baseline_name: str,
        source_urls: Optional[List[str]] = None,
        code_url: Optional[str] = None,
    ):
        """
        Main usage for a llamapack. This will build the card.json and README.md
        and save them to local disk.

        Args:
            index (BaseIndex): the index from which query_engine is derived and
                used in the rag evaluation.
            benchmark_df (pd.DataFrame): the benchmark dataframe after using
                RagEvaluatorPack
            rag_dataset (LabelledRagDataset): the LabelledRagDataset used for
                evaluations
            name (str): The name of the new dataset e.g., "Paul Graham Essay Dataset"
            baseline_name (str): The name of the baseline e.g., "llamaindex"
            description (str): The description of the new dataset.
            source_urls (Optional[List[str]], optional): _description_. Defaults to None.
            code_url (Optional[str], optional): _description_. Defaults to None.

        """
        readme_obj = Readme(name=name)
        card_obj = DatasetCard.from_rag_evaluation(
            index=index,
            benchmark_df=benchmark_df,
            rag_dataset=rag_dataset,
            name=name,
            description=description,
            baseline_name=baseline_name,
            source_urls=source_urls,
            code_url=code_url,
        )

        # save card.json
        with open("card.json", "w") as f:
            json.dump(card_obj.dict(by_alias=True), f)

        # save README.md
        with open("README.md", "w") as f:
            f.write(readme_obj.create_readme())

        Main usage for a llamapack. This will build the card.json and README.md
        and save them to local disk.

        Args:
            index (BaseIndex): the index from which query_engine is derived and
                used in the rag evaluation.
            benchmark_df (pd.DataFrame): the benchmark dataframe after using
                RagEvaluatorPack
            rag_dataset (LabelledRagDataset): the LabelledRagDataset used for
                evaluations
            name (str): The name of the new dataset e.g., "Paul Graham Essay Dataset"
            baseline_name (str): The name of the baseline e.g., "llamaindex"
            description (str): The description of the new dataset.
            source_urls (Optional[List[str]], optional): _description_. Defaults to None.
            code_url (Optional[str], optional): _description_. Defaults to None.

        
llama-index-packs/llama-index-packs-llama-dataset-metadata/llama_index/packs/llama_dataset_metadata/base.py