GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation

📚 Table of Contents

• 📝 What is GraphGen?
• 🚀 Quick Start
• 📌 Latest Updates
• 🏗️ System Architecture
• 🍀 Acknowledgements
• 📚 Citation
• 📜 License

📝 What is GraphGen?

GraphGen is a framework for synthetic data generation guided by knowledge graphs. Here is our paper and best practice.

It begins by constructing a fine-grained knowledge graph from the source text，then identifies knowledge gaps in LLMs using the expected calibration error metric, prioritizing the generation of QA pairs that target high-value, long-tail knowledge. Furthermore, GraphGen incorporates multi-hop neighborhood sampling to capture complex relational information and employs style-controlled generation to diversify the resulting QA data.

🚀 Quick Start

Experience GraphGen through Web or Backup Web Entrance

For any questions, please check FAQ, open new issue or join our wechat group and ask.

Gradio Demo

python webui/app.py

Run from PyPI

1. Install GraphGen

   pip install graphg

2. Run in CLI

   SYNTHESIZER_MODEL=your_synthesizer_model_name \
   SYNTHESIZER_BASE_URL=your_base_url_for_synthesizer_model \
   SYNTHESIZER_API_KEY=your_api_key_for_synthesizer_model \
   TRAINEE_MODEL=your_trainee_model_name \
   TRAINEE_BASE_URL=your_base_url_for_trainee_model \
   TRAINEE_API_KEY=your_api_key_for_trainee_model \
   graphg --output_dir cache

Run from Source

1. Install dependencies

   pip install -r requirements.txt

2. Configure the environment
   • Create an .env file in the root directory

     cp .env.example .env

   • Set the following environment variables:

     # Synthesizer is the model used to construct KG and generate data
     SYNTHESIZER_MODEL=your_synthesizer_model_name
     SYNTHESIZER_BASE_URL=your_base_url_for_synthesizer_model
     SYNTHESIZER_API_KEY=your_api_key_for_synthesizer_model
     # Trainee is the model used to train with the generated data
     TRAINEE_MODEL=your_trainee_model_name
     TRAINEE_BASE_URL=your_base_url_for_trainee_model
     TRAINEE_API_KEY=your_api_key_for_trainee_model

3. (Optional) If you want to modify the default generated configuration, you can edit the content of the configs/graphgen_config.yaml file.

   # configs/graphgen_config.yaml
   # Example configuration
   data_type: "raw"
   input_file: "resources/examples/raw_demo.jsonl"
   # more configurations...

4. Run the generation script

   bash scripts/generate.sh

5. Get the generated data

   ls cache/data/graphgen

Run with Docker

1. Build the Docker image

   docker build -t graphgen .

2. Run the Docker container

    docker run -p 7860:7860 graphgen

📌 Latest Updates

• 2025.04.21: We have released the initial version of GraphGen.

🏗️ System Architecture

See analysis by deepwiki for a technical overview of the GraphGen system, its architecture, and core functionalities.

Workflow

🍀 Acknowledgements

• SiliconCloud Abundant LLM API, some models are free
• LightRAG Simple and efficient graph retrieval solution
• ROGRAG ROGRAG: A Robustly Optimized GraphRAG Framework

📚 Citation

If you find this repository useful, please consider citing our work:

@software{Chen_GraphGen_2025,
author = {Chen, Zihong and Jiang, Wanli and Li, Jingzhe and Yuan, Zhonghang and Wang, Chenyang and Kong, Huanjun and Dong, Nanqing},
month = apr,
title = {{GraphGen}},
url = {https://github.com/open-sciencelab/GraphGen},
year = {2025}
}

📜 License

This project is licensed under the Apache License 2.0.