MC-RAG系统：用于多约束查询的结构驱动RAG系统
MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries
AI总结：
针对RAG系统难以满足复杂多约束查询的问题，提出MC-RAG系统，将检索转化为知识图上的子图匹配问题，通过整合语义、结构嵌入与路径级索引，实现可解释、结构感知和约束一致的检索与生成，并提供交互式演示。
AI中文摘要：
检索增强生成（RAG）系统在问答中广泛应用，但常无法满足复杂多约束查询，导致约束违反、事实不一致或幻觉。我们提出用于多约束查询的结构驱动RAG系统（MC-RAG），将检索重新表述为知识图上的子图匹配问题。通过整合语义和结构嵌入与路径级索引，MC-RAG进行可解释、结构感知和约束一致的检索与生成。演示中，参与者可输入医学或百科多约束查询，可视化系统解析约束、进行结构匹配和生成答案的过程，体验端到端、交互式和可解释的RAG流程。
英文摘要：
Retrieval-Augmented Generation (RAG) systems are widely adopted in question answering, yet they often fail to satisfy complex multi-constraint queries, leading to constraint violations, factual inconsistencies, or hallucinations. We present Structure-Driven RAG System for Multi-Constraint Queries(MC-RAG), a structure-driven RAG system that reformulates retrieval as a subgraph matching problem over a knowledge graph. By integrating semantic and structural embeddings with path-level indexing, MC-RAG performs interpretable, structure-aware, and constraint-consistent retrieval and generation. During the demonstration, participants can input medical or encyclopedic multi-constraint queries, visualize how the system parses constraints, performs structural matching, and generates answers, thereby experiencing an end-to-end, interactive, and explainable RAG pipeline. A demo video is available at https://youtu.be/J8kahzmAnu0.
