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Google releases GINGER library for faster graph-based vector search

By PulseAugur Editorial · [1 sources] ·

Google has developed GINGER, an open-source C++ library for graph-based approximate nearest neighbor search (ANNS). GINGER aims to improve speed and memory efficiency for large, high-dimensional embedding datasets. It achieves this through innovations like Nested TurboQuant for efficient navigation and reranking, and AdaptiveGap compression for proximity graphs, resulting in faster query throughput and reduced memory footprint compared to existing libraries. AI

IMPACT This library could accelerate the development and deployment of AI applications that rely on efficient similarity search in large vector datasets.

RANK_REASON Publication of a research paper detailing a new open-source library for graph-based vector search. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Google releases GINGER library for faster graph-based vector search

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Publication of a research paper detailing a new open-source library for graph-based vector search. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Vahab Mirrokni ·

    GINGER: Spicing Up Graph-Based Vector Search

    We present \textsc{Ginger}, a state-of-the-art open-source C++ library for graph-based approximate nearest neighbor search (ANNS). We developed \textsc{Ginger} at Google to push the speed and memory efficiency of graph search on large and high-dimensional embedding datasets. \tex…

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