task_lite_clean_en/128/metadata.json

{
  "absolute_id": 128,
  "persona": "Researcher",
  "task": "In my `ST-Raptor` project folder, there are five `.py` files named after their functions. I may run them multiple times with different parameters, so please provide command-line templates for running them, list all parameters completely, summarize everything into documentation, and output `ST-Raptor_run_commands_and_parameter_guide.md`.",
  "task_diff": "hard",
  "output_files": [
    "ST-Raptor_run_commands_and_parameter_guide.md"
  ],
  "rubrics": [
    "Does the output file `ST-Raptor_run_commands_and_parameter_guide.md` include explanations for the five `.py` files `gradio_app.py`, `table_preprocess.py`, `embedding.py`, `clean_cache.py`, and `ai_evaluate.py`?",
    "Does the output file provide a clear terminal command template for each `.py` file?",
    "Does the description of `gradio_app.py` include the parameter information that `server_name` defaults to `0.0.0.0`, `server_port` defaults to `7860`, and `share` defaults to `False`?",
    "Does the description of `gradio_app.py` specify that the default access address is `http://localhost:7860` and that the cache is automatically cleaned up on exit?",
    "For the `excel2tree` function in `table_preprocess.py`, are all 9 parameters listed with explanations: `file`, `pkl_dir`, `convert_pkl`, `json_dir`, `convert_json`, `str_dir`, `convert_str`, `embedding_dir`, and `convert_embedding`?",
    "Does the description of `table_preprocess.py` point out that the `dataset_dir` variable in the source `main()` function needs to be modified to specify the dataset path?",
    "Does `table_preprocess.py` explain that processing each `.xlsx` file outputs four files: `pkl`, `json`, `txt`, and `embedding.json`?",
    "Does `embedding.py` explain the parameter requirements of the main methods of the `EmbeddingModel` class: `one_to_many_semilarity`, `topk_match`, and `top1_match`?",
    "Does `embedding.py` point out that either `value_list` or `embedding_cache_file` must be specified?",
    "Does `clean_cache.py` explain that it clears `CACHE_DIR` by default and that other cache directories can be cleaned by uncommenting the corresponding code?",
    "Does the description of the `evaluate` function in `ai_evaluate.py` explain that `input_file` is in JSONL format and `output_dir` is the output directory?",
    "Does `ai_evaluate.py` explain that it outputs five evaluation metrics: Accuracy, BLEU, ROUGE-1/2/L, and METEOR?",
    "Does `ai_evaluate.py` explain that it supports resume-style evaluation, where already evaluated questions are skipped instead of being processed again?",
    "Does the output document include a recommended general execution workflow, giving the full sequence of clearing cache, preprocessing tables, and then launching interactive question answering?",
    "Is the output file in standard Markdown format, including headings, a table of contents, and code blocks?",
    "Does the explanation for each file in the document include the four parts of functional description, command template, parameter list, and usage example?",
    "Does the document correctly state that all scripts need to be run from the ST-Raptor project root directory?"
  ],
  "rubric_types": [
    "Basic Evaluation",
    "Basic Evaluation",
    "Outcome Evaluation",
    "Outcome Evaluation",
    "Outcome Evaluation",
    "Outcome Evaluation",
    "Outcome Evaluation",
    "Outcome Evaluation",
    "Outcome Evaluation",
    "Outcome Evaluation",
    "Outcome Evaluation",
    "Outcome Evaluation",
    "Process Evaluation",
    "Outcome Evaluation",
    "Basic Evaluation",
    "Basic Evaluation",
    "Basic Evaluation"
  ],
  "file_dep_graph": [
    {
      "from": "clean_cache.py",
      "to": "table_preprocess.py"
    },
    {
      "from": "table_preprocess.py",
      "to": "embedding.py"
    },
    {
      "from": "table_preprocess.py",
      "to": "gradio_app.py"
    },
    {
      "from": "embedding.py",
      "to": "gradio_app.py"
    }
  ],
  "data_manifest": [
    {
      "filename": "ai_evaluate.py",
      "stored_relpath": "data/851e272e81a480e1_ai_evaluate.py"
    },
    {
      "filename": "clean_cache.py",
      "stored_relpath": "data/37a2cc59594426a7_clean_cache.py"
    },
    {
      "filename": "embedding.py",
      "stored_relpath": "data/e98f0f4ff6a57f83_embedding.py"
    },
    {
      "filename": "gradio_app.py",
      "stored_relpath": "data/cc4ebddb85bf4f99_gradio_app.py"
    },
    {
      "filename": "table_preprocess.py",
      "stored_relpath": "data/1d0b253770d8fa15_table_preprocess.py"
    }
  ],
  "tested_capabilities": [
    "Workspace Exploration",
    "Semantic Content Relations Understanding"
  ]
}