MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries
Xiao Zhang, Yang Wan, Yi Li, Miao Xie, Chunli Lv
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.
BibTeX
@inproceedings{ijcai2026_mcragsystemastru,
title = {MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries},
author = {Xiao Zhang and Yang Wan and Yi Li and Miao Xie and Chunli Lv},
booktitle = {IJCAI 2026},
year = {2026}
}