Multi-hop questions break vector search. Here is a graph layer for Qdrant that fixes them.

A new open-source library, hubmesh, adds a graph layer on top of Qdrant to answer multi-hop questions that stymie vector search. The library builds an entity-document graph at index time and uses Personalized PageRank to fuse graph reachability with cosine similarity, improving recall@10 by up to 5.9 points on HotpotQA and outperforming HippoRAG-style PPR-only retrieval by 29.8 points. No LLM is used at query time, keeping retrieval deterministic and fast.

Qdrant answers "which vectors are near this query?" in milliseconds at billion scale. But there's a class of questions where nearness is the wrong criterion entirely: "Where did the founder of the company that acquired Slack study?" The passage that answers this talks about Marc Benioff and USC. It never mentions Slack. Cosine similarity — any similarity — ranks it low, because the answer doesn't look like the question. It's connected to the question, three entity-hops away: Slack → acquired by Salesforce → founded by Benioff → studied at USC. That's a topology problem, and no amount of ANN speed solves a topology problem. hubmesh https://github.com/DemigodDSK/hubmesh is a small MIT library that adds the topology layer on top of your existing Qdrant collection. Qdrant keeps doing what it's best at first-pass ANN ; hubmesh builds an entity–document graph at index time and, at query time, diffuses Personalized PageRank from the question's entities over that graph, fusing graph reachability with your cosine scores. No LLM is involved at query time — retrieval is one sparse matrix iteration, deterministic, roughly 100ms on a 30K-document corpus. pip install "hubmesh qdrant,kg " python -m spacy download en core web sm python from hubmesh import Planner from hubmesh.adapters import QdrantStore from hubmesh.kg import build entity kg import spacy embed = ... your embedding callable: text - np.ndarray any of: in-memory, on-disk, or your running Qdrant server store = QdrantStore.from documents docs, url="http://localhost:6333" entity-document graph via spaCy NER — zero LLM tokens to build nlp = spacy.load "en core web sm" kg = build entity kg store.get many store.all ids , nlp=nlp planner = Planner store=store, kg=kg, nlp=nlp, embed=embed result = planner.retrieve "Where did the founder of the company that acquired Slack study?", top k=10, for path in result.reasoning: print f"{path.score:.3f} " + " - ".join path.node ids 0.031 ent:slack - doc:acquisition - ent:salesforce - doc:benioff bio That reasoning field is not a post-hoc explanation — it's the actual graph route that surfaced each document, which means your RAG pipeline can show why a passage was retrieved. Each candidate document gets a composite of three signals, normalized and combined the formula descends from a network-topology paper — NNSI, ICOMP'25 — where the same lesson appeared: no single centrality metric identifies important nodes, but a weighted composite does : | Benchmark | recall@10 vs naive cosine, same embeddings | |---|---| | HotpotQA full dev 7,405 q | 75.2% vs 69.3% +5.9 pts | | MuSiQue 2/3/4-hop | +6.0 / +3.2 / +5.0 pts | | vs HippoRAG-style PPR-only, same graph | +29.8 pts | Disclosed trade-off: the convergence term buys depth recall with top-rank precision — recall@2 is 0.75 pts below naive on full dev. If you retrieve with top k=2 , disable it use convergence=False . Everything above reproduces with the scripts in benchmarks/ . retrieve accepts seed entities and exclude docs , so an agent can iterate: retrieve, read, then aim hop two at the entity it just discovered. There's an MCP server included hubmesh-mcp , listed in the official MCP Registry — the repo contains a field report of Perplexity driving a 3-hop chain through it, tool call by tool call. Single-hop corpora where similarity already wins; corpus-wide summary questions "what are the main themes?" — that's community-summary territory Microsoft GraphRAG's use case , a different query class. hubmesh is for multi-hop factual retrieval, and it deliberately keeps Qdrant as the geometry engine underneath. Repo: github.com/DemigodDSK/hubmesh · PyPI: pip install hubmesh · MIT