Yi: Efficient In-place Graph-based Vector Index Updates for the LLM Era
A new system called Yi is proposed for in-place graph-based vector index updates, addressing the challenge of maintaining high update throughput and search quality in dynamic vector databases. Yi introduces a vector-level update mechanism and is built with a tasklet-based execution engine, asynchronous buffer manager, and vector file system. Experiments on an 800M dataset show Yi achieves 1.75x higher update throughput and 1.8x higher concurrent search throughput than state-of-the-art systems, while using less memory and fewer CPU cores.
Why it matters: Yi's efficient in-place update capability addresses a key bottleneck for real-time vector databases, which are increasingly important for dynamic AI and LLM applications.
Full story at: arXiv Information Retrieval ↗