This project implements a Retrieval-Augmented Generation (RAG) pipeline that allows users to ask questions about a PDF document and receive accurate, context-aware answers using Large Language Models.
The system loads a PDF, splits it into semantic chunks, stores embeddings in a vector database, retrieves relevant context for a query, and generates a concise answer using an LLM.