Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA), yet they often struggle in domain-specific scenarios where accurate and up-to-date information is required. Retrieval-Augmented Generation (RAG) addresses this limitation by enriching LLMs with external knowledge, but existing systems primarily rely on unstructured documents, while largely overlooking relational databases, which provide precise, timely, and efficiently queryable factual information, serving as indispensable infrastructure in domains such as finance, healthcare, and scientific research. Motivated by this gap, we conduct a systematic analysis that reveals three central observations: (i) databases and documents offer complementary strengths across queries, (ii) naively combining both sources introduces noise and cost without consistent accuracy gains, and (iii) selecting the most suitable source for each query is crucial to balance effectiveness and efficiency. We further observe that query types show consistent regularities in their alignment with retrieval paths, suggesting that routing decisions can be effectively guided by systematic rules that capture these patterns. Building on these insights, we propose a rule-driven routing framework. A routing agent scores candidate augmentation paths based on explicit rules and selects the most suitable one; a rule-making expert agent refines the rules over time using QA feedback to maintain adaptability; and a path-level meta-cache reuses past routing decisions for semantically similar queries to reduce latency and cost. Experiments on three QA benchmarks demonstrate that our framework consistently outperforms static strategies and learned routing baselines, achieving higher accuracy while maintaining moderate computational cost.

翻译：大型语言模型（LLM）在通用问答（QA）任务中展现出卓越性能，但在需要准确且最新信息的特定领域场景中往往表现不佳。检索增强生成（RAG）通过为LLM注入外部知识来弥补这一局限，然而现有系统主要依赖非结构化文档，很大程度上忽略了关系型数据库——后者能提供精确、及时且可高效查询的事实信息，是金融、医疗和科学研究等领域不可或缺的基础设施。基于这一现状，我们进行了系统性分析，揭示了三个核心发现：（i）数据库与文档在不同查询中呈现互补优势；（ii）简单融合两种来源会引入噪声与成本，却无法持续提升准确率；（iii）为每个查询选择最合适的数据源对平衡效能与效率至关重要。我们进一步观察到，查询类型在与检索路径的匹配中呈现出稳定的规律性，这表明路由决策可以通过捕捉这些模式的系统性规则有效引导。基于这些发现，我们提出一种规则驱动的路由框架：路由智能体依据显式规则对候选增强路径进行评分并选择最优路径；规则制定专家智能体利用QA反馈持续优化规则以保持适应性；路径级元缓存则对语义相似的查询复用历史路由决策以降低延迟与成本。在三个QA基准测试上的实验表明，本框架始终优于静态策略与基于学习的路由基线，在保持适度计算成本的同时实现了更高的准确率。