MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries

Xiao Zhang Yangying Wan Yi Li Miao Xie Chunli Lv

Jul 2026 · arXiv.org · Vol abs/2607.10151 · 1 citation · 20 references

Computer Science

TL;DR

MC-RAG is presented, a structure-driven RAG system that reformulates retrieval as a subgraph matching problem over a knowledge graph that performs interpretable, structure-aware, and constraint-consistent retrieval and generation.

Abstract

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.

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