"automated_readability_index",
                "dale_chall_readability_score",
                "difficult_words",
                "linsear_write_formula",
                "gunning_fog",
                "text_standard",
                "fernandez_huerta",
                "szigriszt_pazos",
                "gutierrez_polini",
                "crawford",
                "gulpease_index",
                "osman",
            ]
        llm_outputs_df = (
            on_llm_end_records_df[
                [
                    "step",
                    "text",
                    "token_usage_total_tokens",
                    "token_usage_prompt_tokens",
                    "token_usage_completion_tokens",
                ]
                + complexity_metrics_columns
                + visualizations_columns
            ]
            .dropna(axis=1)
            .rename({"step": "output_step", "text": "output"}, axis=1)
        )
        session_analysis_df = pd.concat([llm_input_prompts_df, llm_outputs_df], axis=1)
        # session_analysis_df["chat_html"] = session_analysis_df[
        #     ["prompts", "output"]
        # ].apply(
        #     lambda row: construct_html_from_prompt_and_generation(
        #         row["prompts"], row["output"]
        #     ),
        #     axis=1,
        # )
        return session_analysis_df
[docs]    def flush_tracker(
        self,
        name: Optional[str] = None,
        langchain_asset: Any = None,
        finish: bool = False,
    ) -> None:
        """Flush the tracker and setup the session.
        Everything after this will be a new table.
        Args: