Hubmesh adds graph layer to Qdrant to improve multi-hop question retrieval

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A developer has released Hubmesh, an open-source Python library that extends Qdrant vector search with a knowledge graph layer to handle complex, multi-hop queries. Standard vector search struggles with questions requiring several reasoning steps, such as tracing a company acquisition back to a founder's education. Hubmesh combines cosine similarity, Personalized PageRank, and a convergence signal to rank documents that are reachable from all entities in a question, not just the closest one. On the HotpotQA benchmark, it achieved 75.2% recall@10 compared to 69.3% for naive cosine search using the same embeddings, with similar gains on the MuSiQue multi-hop dataset. The library is available on PyPI under the MIT license and uses spaCy for entity extraction, requiring no LLM calls to build the knowledge graph.

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