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