An Investigation on RAG Question-Answering System Implementation

College of Liberal Arts & Sciences, University of Illinois Urbana-Champaign, Champaign, the United States

^* jiayi22@illinois.edu

Abstract

This paper presents the design and implementation of a Retrieval-Augmented Generation (RAG) question-answering system based on a Streamlit web application. The system allows users to upload one or more PDF files and ask questions in natural language. It retrieves and indexes PDF content using FAISS for semantic vector search and BM25 for keyword-based retrieval, combining both methods to improve accuracy. A cross-encoder is used to rerank the retrieved results, ensuring that the most relevant passages are used to generate the final answer. The OpenAI language model then produces short and clear responses grounded in the retrieved context. The system also supports notes generation, multilingual interfaces in English and Chinese, and memory for continuous dialogue. Experiments conducted on academic PDFs show that hybrid retrieval with reranking performs better than single retrieval methods, giving more precise and meaningful answers. The Streamlit interface and Cloudflare tunnel make the system easy to deploy and share. This project demonstrates how RAG technology can effectively extract information from complex documents and provide practical tools for education, research, and enterprise applications.