𝗠𝘂𝗹𝘁𝗶-𝗵𝗼𝗽 𝗥𝗔𝗚 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗴𝗿𝗮𝗽𝗵 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 Multi-hop retrieval is often ...

Milvus on X: "𝗠𝘂𝗹𝘁𝗶-𝗵𝗼𝗽 𝗥𝗔𝗚 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗴𝗿𝗮𝗽𝗵 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 Multi-hop retrieval is often solved by adding a separate graph store. We wanted to see how far we could get with Milvus alone — so we built an open-source library and found out. Vector Graph RAG achieves multi-hop reasoning using only Milvus. Neo4j, Cypher queries, and the second system once needed to operate are all things of the past (in this scenario). Vector Graph RAG is built upon the fact that knowledge graph relations are just text. An example, (metformin, is the first-line drug for, type 2 diabetes) is a directed edge in a graph database — but it's also a sentence you can embed and store in Milvus, alongside entities and source passages. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚 𝘀𝘁𝗼𝗿𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗿𝗲𝗲 𝗠𝗶𝗹𝘃𝘂𝘀 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗜𝗗 𝗰𝗿𝗼𝘀𝘀-𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀:• 𝗘𝗻𝘁𝗶𝘁𝗶𝗲𝘀 — deduplicated nodes, each carrying a list of the relation IDs they participate in • 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀 — embedded triples pointing to the entity IDs and source passage IDs on each side • 𝗣𝗮𝘀𝘀𝗮𝗴𝗲𝘀 — original document chunks with back-references to the extracted entities and relations Those ID references are the graph structure. Subgraph expansion follows them to surface bridge entities that the question never mentions — the step that makes multi-hop reasoning work. Then a single LLM reranking pass filters the expanded candidate pool down to what actually answers the question, and one generation call produces the answer from the full source passages. That pipeline makes 𝟮 𝗟𝗟𝗠 𝗰𝗮𝗹𝗹𝘀 𝗽𝗲𝗿 𝗾𝘂𝗲𝗿𝘆 (rerank + generate), compared to 3-5 for IRCoT and 5-10+ for Agentic RAG. Against a 5-call iterative baseline, that works out to roughly 𝟲𝟬% 𝗹𝗼𝘄𝗲𝗿 𝗔𝗣𝗜 𝗰𝗼𝘀𝘁 𝗮𝗻𝗱 𝟮-𝟯𝘅 𝗳𝗮𝘀𝘁𝗲𝗿 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀, with predictable latency instead of spikes when an agent decides to loop again. On the benchmarks: 𝟴𝟳.𝟴% 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗥𝗲𝗰𝗮𝗹𝗹@𝟱 across MuSiQue, HotpotQA, and 2WikiMultiHopQA — against 𝟴𝟳.𝟭% for HippoRAG 2, under the same evaluation setup, with no graph database and no ColBERTv2. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝘁𝗵𝗶𝘀? You only need to type"pip install vector-graph-rag". It defaults to Milvus Lite — a local .db file, so you don't need to configure anything. 𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻, 𝘀𝗲𝗲 𝗶𝘁𝘀 𝗴𝗶𝘁𝗵𝘂𝗯 𝗽𝗮𝗴𝗲: https://t.co/uyBstuGBcq If your corpus is knowledge-dense — legal, biomedical, financial — and your questions routinely cross 2-4 document boundaries, this is the architecture worth testing first." / X

Milvus

@milvusio

𝗠𝘂𝗹𝘁𝗶-𝗵𝗼𝗽 𝗥𝗔𝗚 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗴𝗿𝗮𝗽𝗵 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 Multi-hop retrieval is often solved by adding a separate graph store. We wanted to see how far we could get with Milvus alone — so we built an open-source library and found out. Vector Graph RAG achieves multi-hop reasoning using only Milvus. Neo4j, Cypher queries, and the second system once needed to operate are all things of the past (in this scenario). Vector Graph RAG is built upon the fact that knowledge graph relations are just text. An example, (metformin, is the first-line drug for, type 2 diabetes) is a directed edge in a graph database — but it's also a sentence you can embed and store in Milvus, alongside entities and source passages. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚 𝘀𝘁𝗼𝗿𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗿𝗲𝗲 𝗠𝗶𝗹𝘃𝘂𝘀 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗜𝗗 𝗰𝗿𝗼𝘀𝘀-𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀:• 𝗘𝗻𝘁𝗶𝘁𝗶𝗲𝘀 — deduplicated nodes, each carrying a list of the relation IDs they participate in • 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀 — embedded triples pointing to the entity IDs and source passage IDs on each side • 𝗣𝗮𝘀𝘀𝗮𝗴𝗲𝘀 — original document chunks with back-references to the extracted entities and relations Those ID references are the graph structure. Subgraph expansion follows them to surface bridge entities that the question never mentions — the step that makes multi-hop reasoning work. Then a single LLM reranking pass filters the expanded candidate pool down to what actually answers the question, and one generation call produces the answer from the full source passages. That pipeline makes 𝟮 𝗟𝗟𝗠 𝗰𝗮𝗹𝗹𝘀 𝗽𝗲𝗿 𝗾𝘂𝗲𝗿𝘆 (rerank + generate), compared to 3-5 for IRCoT and 5-10+ for Agentic RAG. Against a 5-call iterative baseline, that works out to roughly 𝟲𝟬% 𝗹𝗼𝘄𝗲𝗿 𝗔𝗣𝗜 𝗰𝗼𝘀𝘁 𝗮𝗻𝗱 𝟮-𝟯𝘅 𝗳𝗮𝘀𝘁𝗲𝗿 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀, with predictable latency instead of spikes when an agent decides to loop again. On the benchmarks: 𝟴𝟳.𝟴% 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗥𝗲𝗰𝗮𝗹𝗹@𝟱 across MuSiQue, HotpotQA, and 2WikiMultiHopQA — against 𝟴𝟳.𝟭% for HippoRAG 2, under the same evaluation setup, with no graph database and no ColBERTv2. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝘁𝗵𝗶𝘀? You only need to type"pip install vector-graph-rag". It defaults to Milvus Lite — a local .db file, so you don't need to configure anything. 𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻, 𝘀𝗲𝗲 𝗶𝘁𝘀 𝗴𝗶𝘁𝗵𝘂𝗯 𝗽𝗮𝗴𝗲:

github.com/zilliztech/vec…

If your corpus is knowledge-dense — legal, biomedical, financial — and your questions routinely cross 2-4 document boundaries, this is the architecture worth testing first.

3:00 PM · Jul 23, 2026

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