pd = import_pandas()
        self.mlflg.table("action_records", pd.DataFrame(self.records["action_records"]))
        session_analysis_df = self._create_session_analysis_df()
        chat_html = session_analysis_df.pop("chat_html")
        chat_html = chat_html.replace("\n", "", regex=True)
        self.mlflg.table("session_analysis", pd.DataFrame(session_analysis_df))
        self.mlflg.html("".join(chat_html.tolist()), "chat_html")
        if langchain_asset:
            # To avoid circular import error
            # mlflow only supports LLMChain asset
            if "langchain.chains.llm.LLMChain" in str(type(langchain_asset)):
                self.mlflg.langchain_artifact(langchain_asset)
            else:
                langchain_asset_path = str(Path(self.temp_dir.name, "model.json"))
                try:
                    langchain_asset.save(langchain_asset_path)
                    self.mlflg.artifact(langchain_asset_path)
                except ValueError:
                    try:
                        langchain_asset.save_agent(langchain_asset_path)
                        self.mlflg.artifact(langchain_asset_path)
                    except AttributeError:
                        print("Could not save model.")
                        traceback.print_exc()
                        pass
                    except NotImplementedError:
                        print("Could not save model.")
                        traceback.print_exc()
                        pass
                except NotImplementedError:
                    print("Could not save model.")
                    traceback.print_exc()
                    pass
        if finish:
            self.mlflg.finish_run()
            self._reset()