What is Vector Graph RAG?

Vector Graph RAG is a RAG technique that enables multi-hop reasoning by storing graph structures (entities, relations, passages) as linked collections in a vector DB like Milvus, skipping traditional graph databases.

How do you install Vector Graph RAG?

Run `pip install vector-graph-rag`, then `from vector_graph_rag import VectorGraphRAG; rag = VectorGraphRAG()` — uses Milvus Lite by default.

Does Vector Graph RAG beat standard RAG on benchmarks?

Yes, averages 87.8% Recall@5 across MuSiQue, HotpotQA, and 2WikiMultiHopQA, up from 73.4% for naive RAG, with fewer LLM calls.

☁️ Cloud & Infrastructure

Vector Graph RAG: Multi-Hop Reasoning Powered Purely by Vectors

Imagine chaining facts across documents without wrestling a graph database. Vector Graph RAG makes multi-hop RAG dead simple, all inside a vector store.

DevTools Feed Apr 03, 2026 3 min read 11 views

Architecture diagram showing Vector Graph RAG's three Milvus collections for entities, relations, and passages

⚡ Key Takeaways

Vector Graph RAG embeds full graph logic into Milvus for multi-hop RAG without graph DB overhead. 𝕏
Achieves top benchmark scores (87.8% average Recall@5) using just 2 LLM calls per query. 𝕏
Simple pip install and local Milvus Lite make it dev-friendly; scales to production effortlessly. 𝕏

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#Milvus #Vector Graph RAG #knowledge graphs #multi-hop RAG

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