高效且有效的基于图的向量索引就地更新
Efficient and Effective In-place Graph-based Vector Index Updates
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中文总结 AI 辅助
大语言模型时代面临高效向量更新难题，本文提出Yi，通过分解促进合并思想及任务执行引擎等三个核心组件，实现基于图的向量索引就地高效更新，实验显示其在更新吞吐量等方面优于现有系统。
中文摘要 AI 辅助
在大语言模型时代，高效向量更新对于从快速演变的数据中捕获实时信息至关重要。处理频繁的向量插入和删除更新并同时保持高搜索召回率并非易事。基于聚类的向量索引方法更新吞吐量高但搜索结果质量低，现有基于图的向量索引更新方法因需定期合并更新批次到基础图索引而更新吞吐量差。本文提出Yi来实现高效就地更新。Yi支持基于图的向量索引就地更新，具有高更新吞吐量和良好搜索结果质量。其关键思想是分解促进合并，引入向量级更新机制并由任务执行引擎、异步缓冲区管理器和向量文件系统三个核心组件构成。实验表明，在800M数据集上，Yi的更新吞吐量比现有系统高1.75倍，并发搜索吞吐量高1.8倍，且仅使用73%的峰值内存和更少CPU核心。
英文摘要
In the era of Large Language Models (LLMs), efficient vector updates are critical for capturing real-time information from rapidly evolving data. However, it is not trivial to process frequent vector insert and delete updates and maintain a high recall of the search results simultaneously. Specifically, the cluster-based vector indexing methods have high update throughput but low search result quality. Existing out-of-place graph-based vector indexing update approaches suffer from poor update throughput due to the need to periodically merge update batches into the underlying graph index. Building a vector data system that supports efficient and effective in-place updates is inherently challenging. In this work, we propose Yi to achieve it. In particular, Yi supports in-place graph-based vector indexing updates with consistently high update throughput and good search result quality. The key idea of Yi is decomposition facilitates consolidation. In particular, we introduce a vector-level update mechanism and architect Yi with three core components: (i) a tasklet-based execution engine, (ii) an asynchronous buffer manager, and (iii) a vector file system. Experimental results demonstrate that Yi achieves 1.75x higher update throughput and 1.8x higher concurrent search throughput than the state-of-the-art systems on the 800M dataset, while using only 73% of the peak memory and fewer CPU cores.
