LightRAG

Index of LightRAG

LightRAG is a snapshot from our experiments, with parameters, functions, and prompts fine-tuned to return statistical data and use unified prompts. To get started, you should first create a new environment and install the LightRAG dependencies.

conad create -n lightrag python=3.10
conda activate lightrag
cd LightRAG
pip install -e .

Similar to other RAG implementations, you need to create a main working directory called main_folder and place an input folder inside it to store your corpus files.

main_folder/
├── input/
│   ├── file1.md
│   ├── file2.txt
│   ├── file3.docx
│   └── ...

Then run

python -m Light_index -f path/to/main_folder

Answer and Evaluation

First, prepare your test questions according to the benchmark format. You’ll need to create a test set parquet file containing questions and their corresponding answer keys. Once ready, you can run the evaluation with:

python -m /eval/eval_light -f path/to/main_folder -q path/to/question_parquet