In approximate nearest neighbor search (ANNS) methods based on approximate
proximity graphs, DiskANN achieves good recall-speed balance for large-scale
datasets using both of RAM and storage. Despite it claims to save memory usage
by loading compressed vectors by product quantization (PQ), its memory usage
increases in proportion to the scale of datasets. In this paper, we propose
All-in-Storage ANNS with Product Quantization (AiSAQ), which offloads the
compressed vectors to storage. Our method achieves $\sim$10 MB memory usage in
query search even with billion-scale datasets with minor performance
degradation. AiSAQ also reduces the index load time before query search, which
enables the index switch between muitiple billion-scale datasets and
significantly enhances the flexibility of retrieval-augmented generation (RAG).
This method is applicable to all graph-based ANNS algorithms and can be
combined with higher-spec ANNS methods in the future.

本文提出了一种名为 All-in-Storage ANNS with Product Quantization (AiSAQ) 的方法，通过将压缩向量转移到存储器中，可以在十亿级别的数据集中实现约 10MB 的内存使用，同时还能减少查询之前的索引加载时间，增强检索辅助生成（RAG）的灵活性。该方法适用于所有基于图的近似最近邻搜索算法，未来还可以与更高规格的近似最近邻搜索方法相结合。