Stop Using Graph Database to Build Your Graph RAG System — Vector Graph RAG Explained

🔗 GitHub: https://github.com/zilliztech/vector-graph-rag Vector Graph RAG is a lightweight framework that gives AI systems graph-style multi-hop reasoning through pure vector search, without requiring a graph database. Why vector-graph-rag: ✦ No graph database — pure vector search with Milvus, no Neo4j, no ArangoDB, no extra infra to manage ✦ Single-pass reranking — one LLM reranking call, no iterative agent loops, just 2 LLM calls total per query ✦ Multi-hop reasoning — expands subgraphs across documents to answer complex multi-hop questions ✦ Zero configuration — Milvus Lite runs as a local file, so you can just pip install and start ✦ State-of-the-art performance — 87.8% average Recall@5 on standard multi-hop QA benchmarks ✦ Visual explorer — interactive frontend shows step-by-step retrieval and how the system reasons ✨ If this project is helpful to you, we’d really appreciate a star on GitHub.We’d also love to hear your thoughts and feedback. Chapters: 00:00 Introduction of Vector Graph RAG 00:50 Two core advantages of Vector Graph RAG 01:40 Performance: Benchmark Metrics and Frontend Showcase 02:24 Live Demo: Vector Graph RAG in action 05:52 Wrap-up Find us on: Linkedin: / 67143129 X: https://x.com/milvusio GitHub: https://github.com/milvus-io/milvus Fully Managed Milvus (Zilliz Cloud): https://cloud.zilliz.com/signup