[Feature Request] A feature to view comprehensive statistics and optimal settings in AutoRAG

[e7217]

Is your feature request related to a problem? Please describe.
I have been using AutoRAG and performing the parse, chunk, and evaluate steps separately, and then reviewing the data stored in the benchmark one by one. However, it is time-consuming to click through each step and difficult to compare the results. Is there a feature that would allow me to view the optimal settings and detailed statistics considering all the configuration values collectively?

    for parsed_raw in parsed_raw_files:
        for chunk in chunked_file_list:
            parsed_raw_index = parsed_raw.split("/")[-1].split(".")[0]
            chunk_index = str(chunk).split("/")[-1].split(".")[0]
            
            if not parsed_raw_index == chunk.parent.name:
                continue
            initial_raw = Raw(pd.read_parquet(parsed_raw, engine="pyarrow"))
            initial_corpus = Corpus(pd.read_parquet(chunk, engine="pyarrow"), initial_raw)
            qa = initial_corpus.sample(random_single_hop, n=len(initial_corpus.data), random_state=random.randint(1,100)).map(
                    lambda df: df.reset_index(drop=True),
                ).make_retrieval_gt_contents().batch_apply(
                    multiple_queries_gen,  # query generation
                    llm=llm,
                    lang="ko",
                    n=10,
                ).batch_apply(
                    make_basic_gen_gt,  # answer generation (basic)
                    llm=llm,
                    lang="ko",
                ).batch_apply(
                    make_concise_gen_gt,  # answer generation (concise)
                    llm=llm,
                    lang="ko",
                ).filter(
                    dontknow_filter_rule_based,  # filter unanswerable questions
                    lang="ko",
                )
                
            qa_dir_name = "qa"
            
            if not os.path.exists(os.path.join(current_dir, qa_dir_name)):
                os.makedirs(os.path.join(current_dir, qa_dir_name))

            output_path = os.path.join(current_dir, qa_dir_name, f"parsed_{parsed_raw_index}_chunk_{chunk_index}_qa.parquet")
            corpus_output_path = os.path.join(current_dir, qa_dir_name, f"parsed_{parsed_raw_index}_chunk_{chunk_index}_corpus.parquet")
            qa.to_parquet(output_path, corpus_output_path)

    for i in range(10):
        for j in range(4):
            opt["config"] = os.path.join(current_dir, "config", "evaluate_config.yaml")
            opt["qa_data_path"] = os.path.join(current_dir, "qa","parsed_{}_chunk_{}_qa.parquet".format(i,j))
            opt["corpus_data_path"] = os.path.join(current_dir, "qa","parsed_{}_chunk_{}_corpus.parquet".format(i,j))
            opt["project_dir"] = os.path.join(current_dir, "benchmark")
            evaluate(**opt)

[vkehfdl1]

@e7217 That is a really good idea!
We have dashboard, but it doesn't show the parsing and chunking views.