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Computer Science > Information Retrieval

arXiv:2308.14963 (cs)
[Submitted on 29 Aug 2023]

Title:Vector Search with OpenAI Embeddings: Lucene Is All You Need

Authors:Jimmy Lin, Ronak Pradeep, Tommaso Teofili, Jasper Xian
Download a PDF of the paper titled Vector Search with OpenAI
Embeddings: Lucene Is All You Need, by Jimmy Lin and 3 other authors
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    Abstract: We provide a reproducible, end-to-end demonstration of
    vector search with OpenAI embeddings using Lucene on the popular
    MS MARCO passage ranking test collection. The main goal of our
    work is to challenge the prevailing narrative that a dedicated
    vector store is necessary to take advantage of recent advances in
    deep neural networks as applied to search. Quite the contrary, we
    show that hierarchical navigable small-world network (HNSW)
    indexes in Lucene are adequate to provide vector search
    capabilities in a standard bi-encoder architecture. This suggests
    that, from a simple cost-benefit analysis, there does not appear
    to be a compelling reason to introduce a dedicated vector store
    into a modern "AI stack" for search, since such applications have
    already received substantial investments in existing, widely
    deployed infrastructure.

Subjects: Information Retrieval (cs.IR)
Cite as:  arXiv:2308.14963 [cs.IR]
          (or arXiv:2308.14963v1 [cs.IR] for this version)
          https://doi.org/10.48550/arXiv.2308.14963
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jimmy Lin [view email]
[v1] Tue, 29 Aug 2023 01:30:23 UTC (123 KB)
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