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arXiv AI By Ioannis E. Livieris

Quanta: A Self-Contained Python Library for Hybrid Retrieval over Quantised Embeddings, Lexical Indexes, and Knowledge Graphs

Read the original on arXiv AI →

Quanta is an open‑source Python library that unifies dense vector search over 4‑bit quantised embeddings, BM25 full‑text retrieval, and knowledge‑graph traversal behind a single retrieval API. It combines signals using weighted reciprocal rank fusion instead of normalising heterogeneous scores, arguing that such normalisations are query‑dependent. The library treats the graph as a candidate expander rather than a relevance scorer, widening the candidate pool and then re‑scoring documents with dense indexes under an identifier allowlist.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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