README.md

E²GraphRAG: Streamlining Graph-based RAG for High Efficiency and Effectiveness

E²GraphRAG is a lightweight and modular framework designed to enhance both efficiency and effectiveness in Graph-based Retrieval-Augmented Generation (RAG). It streamlines the pipeline from document parsing to answer generation via structured graph reasoning.

📁 Project Structure

.
├── README.md
├── requirements.txt
├── main.py
├── build_tree.py
├── dataloader.py
├── extract_graph.py
├── GlobalConfig.py
├── process_utils.py
├── prompt_dict.py
├── query.py
└── utils.py

📦 Datasets

We use data from:

• 📚 NovelQA Partly open-source, to obtain the full dataset, please access via a request to the original authors.
• 🔁 InfiniteBench Fully open-source and publicly available.

You can find how to obtain the data in the ./data/README.md.

Note: After obtaining the datasets, specify the data path when initializing the Dataloader class.

🚀 Getting Started

1. Install Dependencies

Ensure your environment is set up by installing the required packages:

pip install -r requirements.txt

2. Run the Pipeline

The entire pipeline—tree construction, graph extraction, and answer generation—is executed via main.py.

Step-by-step:

1. Create a config file

Prepare a YAML configuration file to define key parameters.

👉 Example: ./configs/example_config.yaml

2. Run the pipeline

bash
python main.py --config <path_to_config_file>

📬 Contact & Citation

If you use this code or find it helpful in your research, please consider citing our work. For questions or dataset access (NovelQA), please contact the original authors.