动态融合静态与实时知识，让大模型问答更准更可信。
DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation
- 用大模型判断检索结果是否够用，不够时自动调用外部API
- 在CRAG基准上，动态问题准确率显著提升，幻觉减少
- 适合需要实时信息的智能问答系统开发
我们提出DynaRAG，一种检索增强生成框架，通过动态知识融合处理静态与时效性信息需求。不同于仅依赖静态语料的传统RAG，DynaRAG在检索结果不足时，会主动调用外部API。系统采用基于LLM的重排序器评估文档相关性，使用充分性分类器判断是否需要调用工具，并借助Gorilla v2（当前最先进的API调用模型）实现精准工具调用。为增强鲁棒性，还引入基于FAISS的模式过滤机制，指导API选择。在CRAG基准上的评估显示，DynaRAG显著提升了动态问题的回答准确率，同时降低了幻觉。结果表明，在构建可靠、真实世界的问答系统中，动态感知路由和选择性工具使用至关重要。
原文摘要 · Abstract (English)
We present DynaRAG, a retrieval-augmented generation (RAG) framework designed to handle both static and time-sensitive information needs through dynamic knowledge integration. Unlike traditional RAG pipelines that rely solely on static corpora, DynaRAG selectively invokes external APIs when retrieved documents are insufficient for answering a query. The system employs an LLM-based reranker to assess document relevance, a sufficiency classifier to determine when fallback is necessary, and Gorilla v2 -- a state-of-the-art API calling model -- for accurate tool invocation. We further enhance robustness by incorporating schema filtering via FAISS to guide API selection. Evaluations on the CRAG benchmark demonstrate that DynaRAG significantly improves accuracy on dynamic questions, while also reducing hallucinations. Our results highlight the importance of dynamic-aware routing and selective tool use in building reliable, real-world question-answering systems.
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