ANNLib简化高效近邻搜索开发，支持灵活组合与高性能。
ANNLib: A Development Framework for Efficient Approximate Nearest Neighbor Search
- 解耦算法与数据结构，独立优化提升性能。
- 支持过滤搜索、动态更新等复杂场景，性能优于或媲美已有方案。
- 适合需要快速构建高效近邻搜索系统的开发者使用。
近似最近邻搜索（ANNS）在现代深度学习流程中起关键作用。尽管已有多种ANNS系统被提出，以实现广泛功能或高效率，但同时达成二者且编程开销低仍具挑战。本文提出ANNLib，一个基于主流图式ANNS算法的开发框架，旨在实现高性能与灵活功能的统一。通过精细解耦并独立优化算法与数据结构组件，ANNLib集成前沿算法与数据结构模块，并引入新设计。用户可自由组合组件，实现如过滤搜索、完全动态更新及快照历史查询等复杂应用。实验表明，该框架为各类应用提供简洁接口，性能达到甚至超越先前工作水平。
原文摘要 · Abstract (English)
Approximate Nearest Neighbor Search (ANNS) plays a pivotal role in modern deep learning pipelines. Recently, many ANNS systems have been proposed to either provide broad functionality or reach high performance. However, it is yet difficult to achieve both with minimal programming efforts. We propose ANNLib to address the gap. ANNLib is a library that provides a programming framework for achieving high performance and flexible functionality in ANNS systems, based on popular graph-based ANNS algorithms. We carefully decouple and independently optimize both the algorithm and the data structure components of an ANNS system. In addition, we integrate state-of-the-art algorithms and data structures into ANNLib as modules, along with our new designs. Users can choose combinations of components to implement sophisticated settings with high performance, such as filter search, fully dynamic updates, and historical queries on snapshots. Our experiments show that our new solution provides a simple interface for various applications and achieves comparable or even better performance than previous work, specifically for each application.
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