URAG: Unified Retrieval-Augmented Generation

To address the issues of insufficient retrieval capabilities and "hallucinations" in responses generated by large models, this paper proposes a knowledge question-answering framework based on Unified Retrieval-Augmented Generation (URAG). The framework integrates three retrieval mechanisms—keyword retrieval, vector retrieval, and graph retrieval—enabling efficient and high-quality utilization of massive, multi-source, and heterogeneous data. It effectively overcomes the limitations of a single retrieval pathway. Experimental results demonstrate that the URAG framework excels across various task scenarios, enhancing the accuracy and comprehensiveness of knowledge retrieval. Its advantage is particularly evident when dealing with multi-dimensional and multi-layered information. In conclusion, the Unified Retrieval-Augmented Generation technique offers a new method for improving the performance of intelligent question-answering systems, with broad application prospects and research value. This study provides valuable insights into understanding RAG (Retrieval-Augmented Generation) technology and its application in large language models.