Final project for Yale CPSC 477. Using Retrieval-Augmented Generation to tackle BioLaySumm 2024, BioNLP task. -- Andrew Ton and Yuxuan Cheng

The repo is adapted from localGPT, see more detials on setting up here: https://github.com/PromtEngineer/localGPT

Environment Setup 🌍

1. 📥 Clone the repo using git:

[git clone https://github.com/PromtEngineer/localGPT.git](https://github.com/YUXUANCHENG/477RAG.git)

2. 🐍 Install conda for virtual environment management. Create and activate a new virtual environment.

conda create -n localGPT python=3.10.0
conda activate localGPT

3. 🛠️ Install the dependencies using pip

To set up your environment to run the code, first install all requirements:

pip install -r requirements.txt

Installing LLAMA-CPP :

LocalGPT uses LlamaCpp-Python for GGML (you will need llama-cpp-python <=0.1.76) and GGUF (llama-cpp-python >=0.1.83) models.

If you want to use BLAS or Metal with llama-cpp you can set appropriate flags:

For NVIDIA GPUs support, use cuBLAS

# Example: cuBLAS
CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python==0.1.83 --no-cache-dir

For Apple Metal (M1/M2) support, use

# Example: METAL
CMAKE_ARGS="-DLLAMA_METAL=on"  FORCE_CMAKE=1 pip install llama-cpp-python==0.1.83 --no-cache-dir

For more details, please refer to llama-cpp

To run LocalGPT:

python run_localGPT.py

To embed documents:

python ingest.py

To run evaluation:

python evaluate.py

Final Metric score results are stored in gpt_scores.txt and llama_scores.txt

Dependencies are listed in requirements.txt