Hybrid Question-Answering System: A FAISS and BM25 Approach for Extracting Information from Technical Document

In this study, a hybrid question-answering system was developed to accelerate access to information contained in corporate technical documents and to generate appropriate responses to user queries. The system combines dense vector-based retrieval (FAISS) and sparse text-based retrieval (BM25) methods, integrated with the XLM-RoBERTa Large model. Evaluations conducted on a dataset consisting of 23 technical documents demonstrated the system's effectiveness in responding to both semantic and keyword-based queries. This study presents an innovative approach that enables fast and accurate access to information from technical documents, enhancing the efficiency of corporate knowledge management processes.