𝗬𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝗴𝗿𝗮𝗽𝗵 𝘁𝗿𝗮𝘃𝗲𝗿𝘀𝗮𝗹 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗴𝗿𝗮𝗽𝗵 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲. Here's how.

A knowledge graph is triplets: (Einstein, developed, Relativity). Typically you store them in Neo4j, traverse edges, and reason over connections. Vector Graph RAG skips the graph database entirely. It embeds every triplet into Milvus. Entities become vectors. Relations become vectors. The whole knowledge graph lives in one vector database. At query time: • Extract entities from the question. • Vector search finds the closest matching entities and relations. • 𝗦𝘂𝗯𝗴𝗿𝗮𝗽𝗵 𝗲𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻: if (Einstein → developed → Relativity) is retrieved, the system fetches neighboring triplets like (Relativity → revolutionized → Physics). • 𝗢𝗻𝗲 𝗟𝗟𝗠 𝗰𝗮𝗹𝗹 𝗿𝗲𝗿𝗮𝗻𝗸𝘀 the collected context. Answer generated. Two LLM calls total per query. 87.8% avg Recall@5 on standard multi-hop benchmarks (MuSiQue, HotpotQA, 2WikiMultiHopQA). pip install vector-graph-rag — runs locally with Milvus Lite. MIT licensed, fully open source. 🔗Try it on GitHub: https://github.com/zilliztech/vector-graph-rag