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CMDBench

This is the public repository for the CMDBench benchmark, which aims to evaluate data discovery agents within compound AI systems such as LllamaIndex that operate on an enterprise's complex data landscape. The repository contains the scripts for preparing the CMDBench benchmark dataset and tasks as well as the data discovery testing suite introduced at the GUIDE-AI workshop at SIGMOD'24 (paper here).

The core task we are focusing on is "Data Discovery" at varying granularities within enterprise data. We define enterprise data as silos of information stored in various modalities (e.g., tables, graphs, and documents) and organized based on downstream requirements of different teams/projects in different repositories such as data lakes, data warehouses, and data lakehouses. The coarse-grained data discovery methods essentially select the repositories (e.g., mongodb, postgresdb, neo4jdb) containing the relevant information to a query. The finer the granularity, the finer the result of the discover method. For example, a fine-grained discovery for text, table, and graphs is finding candidate document(s), table(s), and sub-graph(s), respectively. The finest granularity is the exact source, e.g., a paragraph in a document, (row, column) in a table, or (path, node) in a graph.

Serving the NBA Datalake

First clone the repository and navigate to the docker-compose directory:

git clone https://github.com/rit-git/cmdbench.git
cd cmdbench/docker/compose

Then run the following command to start the NBA datalake:

# to run in foreground
docker compose up 

# to run in detached mode
docker compose up -d

The following databases will be exposed at various ports:

  • Neo4j: localhost:7687 (bolt) and localhost:7474 (http)
  • MongoDB: localhost:27017
  • PostgreSQL: localhost:5432

When you are done, you can stop with Ctrl+C and then run:

docker compose down

Notes:

  • You can find the database credentials in docker/compose/.env
  • If there are port conflicts, you can change the ports in docker/compose/.env
  • For older versions of docker-compose, you may need to use docker-compose instead of docker compose

Running Experiments

1. Install dependencies

Create a virtual environment:

conda create -n cmdbench python=3.11
conda activate cmdbench

Install the dependencies:

pip install -r requirements.txt

2. Set openai api key and database credentials in .bashrc:

export OPENAI_API_KEY='your-api-key-here'
export NEO4J_USERNAME=neo4j
export NEO4J_PASSWORD=cmdbench
export PGUSER=megagon

Test database connections:

python tasks/test_connectivity.py

3. Run experiment pipelines

We implement experiment pipelines for each data discovery tasks. Each pipeline takes the data JSON file as input and outputs two JSON files:

  1. responses.json contains the outputs of the data discovery models.
  2. result.json additionally includes the evaluation metrics.

Here is a full list of implemented pipelines:

Here is a sample command to run the source selection task:

python tasks/run_source_selection.py \
  --modality_summary tasks/modality_summary_basic.json \
  --llm gpt-3.5-turbo-0125 \
  --output_dir outputs/ss/

Here is a sample command to run the nl2cypher task:

python tasks/run_nl2cypher.py \
  --llm gpt-3.5-turbo-0125 \
  --output_dir outputs/nl2cypher/

CMDBench Dataset

benchmark/q_graph.json contains the graph questions with annotated graph provenance. Here is a sample:

{
  "qid": "216f99dc-2826-4db8-92c3-f5066c8cf528",
  "question": "When did Dwyane Wade leave the Miami Heat?",
  "answer": "2016",
  "provenance_graph": {
    "cypher": "MATCH (p:Player {name: 'Dwyane Wade'})-[r:playsFor]->(t:Team {name: 'Miami Heat'}) RETURN r.end_time AS leave_time",
    "node_ids": [
      5231,
      1503
    ],
    "edge_ids": [
      11865
    ],
    "type": "path"
  }
}

⚠️ The node_ids and edge_ids here are not meaningful as they are specific to the Neo4j instance. They will be overwritten by the nl2cypher pipeline during evaluation when generating the results.json.

benchmark/q_doc.json contains the document questions. Here is a sample:

{
  "qid": "aab8025d-7414-42cc-bc4a-86bbb1cc2903",
  "question": "Named for it's proximity to the local NBA team, what is the name of the WNBA team in Phoenix?",
  "answer": "Mercury",
  "provenance_doc": {
    "paragraphs": [
      {
        "wikipedia_title": "Women's National Basketball Association",
        "paragraph_id": 2,
        "text": "Five WNBA teams have direct NBA counterparts and play in the same arena: the Atlanta Dream, Indiana Fever, Los Angeles Sparks, Minnesota Lynx, and Phoenix Mercury. The Chicago Sky, Connecticut Sun, Dallas Wings, Las Vegas Aces, New York Liberty, Seattle Storm, and Washington Mystics do not share an arena with a direct NBA counterpart, although four of the seven (the Sky, the Wings, the Liberty, and the Mystics) share a market with an NBA counterpart, and the Storm shared an arena and market with an NBA team at the time of its founding. The Sky, the Sun, the Wings, the Aces, the Sparks, and the Storm are all independently owned.\n"
      }
    ]
  }
}

benchmark/q_source.json contains the source selection questions. Here is a sample:

{
  "question": "When did Dwyane Wade leave the Miami Heat?",
  "answer": "2016",
  "provenance_sources": [
    "doc",
    "graph"
  ]
}

benchmark/q_table.json contains the table questions. Here is a sample:

  {
  "qid": "8",
  "question": "What is the average number of points scored by the Seattle Storm players in the 2005 season?",
  "answer": "208",
  "provenance_table": {
    "table": "t_1_24915964_4",
    "column": "['points']"
  }
}

Citation

If you use the CMDBench dataset or code, please cite the following paper:

@inproceedings{feng2024cmdbench,
  title={CMDBench: A Benchmark for Coarse-to-fine Multimodal Data Discovery in Compound AI Systems},
  author={Feng, Yanlin and Rahman, Sajjadur and Feng, Aaron and Chen, Vincent and Kandogan, Eser},
  booktitle={Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI},
  pages={16--25},
  year={2024}
}