GARAG: A General adaptive question-answering system based on RAG

Large language models (LLMs) possess exceptional capabilities, but their effectiveness is limited by their knowledge base. Retrieval-Augmented Generation (RAG) techniques integrate external knowledge to enhance performance in question-answering tasks. However, RAG techniques may encounter difficulties in dealing with irrelevant content, complex queries, as well as accurately evaluating the importance and relevance of information. To address these challenges, we developed an optimization question-answering system General Adaptive RAG (GARAG), specifically tailored for question-and-answer tasks. Our system utilizes a retrieval agent to identify relevant content and a generation agent to construct answers based on the question and retrieved information. Additionally, we incorporated modules for question complexity measurement, question correction, query transformation, query routing, and adaptive retrieval to improve intent recognition and document retrieval. We implemented an answer corrector and discriminator, as well as a self-reflection mechanism, to enhance output quality and factuality. Empirical experiments conducted on real-world questions validate the effectiveness of our question-and-answer system.