TriFuseRAG: Tri-Modal Hybrid Retrieval-Augmented Generation Using SQL, Vector and Graph Databases

Retrieval-Augmented Generation (RAG) systems typically route all queries to a single retrieval modality, limiting utility for heterogeneous query types that require simultaneous structured lookup, entity-relationship traversal, and text similarity search. We present TriFuseRAG, a prototype multi-modal RAG system integrating three retrieval backends—a relational SQL engine, an in-memory knowledge graph, and a TFIDF vector store—under a dual-stage adaptive router and weighted fusion layer. A two-tier safety gate filters adversarial inputs before retrieval. On a 1,126-query closed-world benchmark, the dual-stage router achieves 98.84% route accuracy. In strict no-leakage evaluation, TriFuseRAG achieves 91.3% overall answer accuracy (98.1% on supported queries), and 93.9% on composite Hybrid queries where all single-source baselines score 0%. Overall, TriFuseRAG outperforms the best singlesource baseline by 34.7 points (91.3% vs. 26.6%), with p95 query latency under 10ms on CPU hardware. We report full error analysis, latency profiles, and discussion of synthetic evaluation scope.