PowerRAG: Building Smarter and More Accurate RAG Systems with OceanBase's Multi-modal Retrieval

To reduce hallucinations and improve LLM response quality, the Retrieval-Augmented Generation (RAG) paradigm has emerged. RAG retrieves relevant content from external knowledge sources conditioned on the user's query, then concatenates the query with the retrieved context as model input (an augmented prompt) to obtain the LLM response. We propose PowerRAG, an RAG-based intelligent application development engine. PowerRAG leverages query rewriting, multi-format document preprocessing, RAPTOR-based document indexing, self-developed caching, and candidate retrieving and reranking powered by a proprietary high-performance database, effectively mitigating hallucinations caused by outdated parametric knowledge and gaps in long-tail knowledge. Using general-purpose intelligent chat assistant as example scenarios, we demonstrate the efficiency and comprehensive features of building AI applications with PowerRAG, as well as its ease and speed during the use of AI applications, contributing our efforts to the further improvement of the Agentic RAG ecosystem.