A Survey on Retrieval-Augmented Generation for Multi-PDF Question Answering Systems
Retrieval-Augmented Generation (RAG) effectively separates knowledge retrieval from generation for private document question answering. This study explores the design space of document-grounded QA and details a multi-PDF RAG system. The pipeline uses PyMuPDF for text extraction with OCR fallback, BAAI/bge-small-en-v1.5...