OpenRAG

Lightweight code for Retrieval-Augmented Generation (RAG) chatbots over a PDF document catalogue.
OpenRAG pairs a local GGUF language model with a Qdrant vector store and a Panel web interface.

By defining a set of intructions in the prompt template, small locally deployed models can become reliable domain specific experts.

This project was inspired by following the following workshop tutorial: https://uw-ssec-tutorials.readthedocs.io/en/latest/AI_Postdoc_Workshop/module2/index.html

Project files:

• rag_engine.py -- core library handling the model downloading, PDF ingestion, vector indexing, chain construction, and the chat UI. For basic chatbot applications no modifications are required.
• mlip_advisor_example_chatbot.py -- Example usage script providing a "ready to-go" chatbot for advising which MLIP to use, given a chemical system and property type prompt from the user. Modify this code to make you own application.
• download_mlip_papers.py -- Download script for selected arxiv MLIP literature, used for the MLIP advisor example.

• conda
• Roughly 8 GB of free disk space for the model weights in present code

rag_engine.py will download quantized GGUF models from Hugging Face on first run. Three models are included out of the box, the model is selected by MODEL_NAME in the chatbot script. More model options can be included to the MODEL dict in rag_engine.py.

0. Clone repository, create and activate conda environment

git clone https://github.com/afouda11/OpenRAG.git
cd OpenRAG
conda env create -f environment.yml
conda activate openrag

The environment includes all required Python packages (LangChain, llama-cpp-python, Qdrant, Panel, PyMuPDF, sentence-transformers, and others).

1. Fetch the PDF catalouge of the MLIP literauture selected within download_mlip_papers.py:

python download_mlip_papers.py

2. Run the present configurations set in mlip_advisor_example_chatbot.py:

python mlip_advisor_example_chatbot.py

3. Follow the link to the chatbot server, ask which MLIP to use for any chemical system and property type. See what the model suggests and verify its suggestions.

1. Either modify the download_mlip_papers.py to download new set of papers of your choice, or move a collection of pdfs to a new folder catalogue.

2. Modify mlip_advisor_example_chatbot.py for a new PDF_FOLDER location and modify the RAG parameters and prompt template for your desired application.