RAG Document Q&A with Groq and Llama3

This project is a web-based Question & Answer (Q&A) application that enables users to interactively query a collection of research papers. It uses Retrieval-Augmented Generation (RAG), which combines advanced document retrieval and natural language generation to deliver accurate, context-aware answers. The application allows users to ask questions about the content of research papers, retrieving relevant information and providing answers based on the documents' contents.

Built with Streamlit for an accessible web interface, this project integrates several machine learning and natural language processing tools:

1. Document Ingestion and Embedding: It loads and processes research papers in PDF format, splits them into manageable chunks, and converts them into vector embeddings for efficient search.
2. Vector Database and Retrieval: Uses FAISS (Facebook AI Similarity Search) as a vector database to store and retrieve document chunks relevant to the user's query.
3. Large Language Model (LLM) Integration: Utilizes the Groq-based Llama3-8b-8192 model for response generation. It answers user questions by focusing on the retrieved document context, ensuring accuracy.
4. Hugging Face Embeddings: Embedding generation is managed by Hugging Face's all-MiniLM-L6-v2 model, enabling efficient, contextual embeddings even in the absence of an OpenAI key.

How it Works
1. Data Ingestion and Embedding: When the user clicks "Document Embedding," research papers are processed, split into sections, and embedded into a FAISS vector database.
2. Query Handling: Upon entering a question, the application retrieves relevant document chunks, then generates a response using the Llama3 model, grounded in the specific context of the retrieved documents.
3. Similarity Search Display: Users can also view similar document sections to their query, gaining insight into how the answer was generated.