RAG System Using Llama2 with Hugging Face

This project demonstrates a Retrieval-Augmented Generation (RAG) system using Llama2 with Hugging Face tools for efficient document retrieval and question answering.

Key Features Data Loading: Reads and prepares documents for embedding. Model Configuration: Sets up Llama2 with HuggingFaceLLM for language generation and HuggingFaceEmbeddings for document embeddings. Query Engine: Uses VectorStoreIndex to support question answering, with sample queries for match results and statistics.