RAGifyResearch

RAGifyResearch is a simple, fully local Retrieval-Augmented Generation (RAG) system designed to help interns in our research lab learn more about ongoing research. It enables users to extract and query research papers, abstracts, and documents using CLI.

Features

✅ Supports PDFs as a knowledge source
✅ Extracts and chunks text
✅ Stores data in ChromaDB for retrieval
✅ Enables local chatbot interaction
✅ Uses LM Studio with Meta Llama 3.1 7B-Instruct
✅ Uses Jina Embeddings v2 for vector storage
✅ Uses Discord Bot for easy user-friendly interaction

Planned Features

🚀 Support for additional models

Installation

1. Clone the Repository

   git clone https://github.com/yourusername/RAGifyResearch.git  
   cd RAGifyResearch  

2. Create virtual enviroment

   python3.8 -m venv venv
   source venv/bin/activate 

3. Install Dependencies

   pip install -r requirements.txt  

4. Run the System: First Time

   python main.py --create-db path/to/pdf/dir db_name

5. Run the System: Any Time

   python main.py --load-db db_name

Usage

1. Add research papers (PDFs) to the designated folder.
2. The system will extract, chunk, and store the text in ChromaDB.
3. Use the CLI to query the documents and get relevant information.
4. To exit the chat, use exit.

Contributing

Contributions are welcome! Feel free to submit a pull request or open an issue.