Vector-Graph-RAG: Dual-Engine Recall for Enterprise Q&A That Pure Vector Search Can't Handle
Teams with existing Milvus-based knowledge bases can step up to graph-enhanced RAG without re-architecting or running a heavy graph database; the main cost is entity extraction and ongoing graph curation, not infrastructure.
Pure vector RAG crumbles on questions that need cross-document links and multi-hop reasoning: 'Which vendors worked with Zhang San?', 'What caused similar equipment failures in Q3?' Testing shows accuracy drops to 50-60% on such questions, with hallucinations and entity confusion as the main culprits. vector-graph-rag addresses this by adding a knowledge graph layer to the existing Milvus pipeline, so retrieval happens along both semantic similarity and entity-relation paths.
The architecture is a five-layer design: document preprocessing, entity/relation extraction with normalization, dual-index storage (Milvus for vectors, a lightweight graph index for entities and edges), hybrid recall that fuses both paths, and answer generation with evidence-chain validation. A six-step workflow from ingestion to answer includes human-editable entity normalization, which alone lifts complex-question accuracy by over 15% in the evaluation's tests. The project is deliberately incremental: existing vector knowledge bases can add the graph layer without a rebuild, and small-to-medium deployments don't need a separate graph database.
Entity normalization is the highest-leverage step in this architecture, not the graph traversal itself; the evaluation attributes a 15% accuracy gain to normalization alone, and many GraphRAG implementations skip it.
The incremental upgrade path and lack of a graph DB requirement make this a pitch to system integrators and small teams, not just large enterprises; it trades graph depth for lower operational cost.
The claimed accuracy gains (20-30% on complex questions) stem from the evaluation's own tests, not published benchmarks; extraction quality still depends on the underlying LLM, so results will vary by domain and model.