HubMesh enables multi-hop RAG using Chroma and entity graphs without LLM query calls

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A developer has released HubMesh, an open-source Python library that combines ChromaDB vector storage with a spaCy-built entity knowledge graph to perform multi-hop retrieval-augmented generation (RAG) locally. The tool is designed to answer complex, multi-step questions where the query and the relevant passage share little vocabulary overlap. HubMesh scores documents using a composite of cosine relevance and Personalized PageRank (PPR) convergence, requiring no additional LLM token calls during retrieval. On the full HotpotQA development benchmark of 7,405 questions, the library achieved 75.2% supporting-fact recall at top-10 results. The project is available on GitHub under the MIT license, with an optional MCP server extension for agent-steerable multi-hop navigation.

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