The code for the paper "PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths".

Install

cd PathRAG
pip install -e . # or pip install -r requirements.txt 

RUN the project

Windows

python -m venv .venv    # create virtual environment
.venv\Scripts\activate  # activate the virtual environment
python v1_test.py       # to run the project

# if it doesn't works properly then try reinstalling the packages using the above installation command

Linux/Unix

python3 -m venv .venv    # create virtual environment
Source .venv\bin\activate  # activate the virtual environment
python3 v1_test.py       # to run the project

# if it doesn't works properly then try reinstalling the packages using the above installation command

Quick Start

• You can quickly experience this project in the v1_test.py file.
• Rename exampe.env to .env
• Set OpenAI API key in .env file and the BASE URL.
• Prepare your retrieval document text.txt. You can modify this in the code in v1_test.py.
• The v1_text.py file is the entry point to initialize PathRAG and perform queries.

Parameter modification

You can adjust the relevant parameters in the base.py and operate.py files.

Batch Insert

import os
folder_path = "your_folder_path"  

txt_files = [f for f in os.listdir(folder_path) if f.endswith(".txt")]
for file_name in txt_files:
    file_path = os.path.join(folder_path, file_name)
    with open(file_path, "r", encoding="utf-8") as file:
        rag.insert(file.read())

Cite

Please cite our paper if you use this code in your own work:

@article{chen2025pathrag,
  title={PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths},
  author={Chen, Boyu and Guo, Zirui and Yang, Zidan and Chen, Yuluo and Chen, Junze and Liu, Zhenghao and Shi, Chuan and Yang, Cheng},
  journal={arXiv preprint arXiv:2502.14902},
  year={2025}
}