GaussDB-Vector: A Large-Scale Persistent Real-Time Vector Database for LLM Applications
Ji Sun, Guoliang Li, James Pan, Jiang Wang, Yongqing Xie, Ruicheng Liu, Wen Nie
Abstract
Vector databases are widely used as a fundamental tool for addressing the weaknesses of large language model (LLM) applications, specifically hallucinations and the high cost of inference. However, existing vector databases either cater to niche applications with low-latency in-memory search, or offer sophisticated data management capabilities but at the cost of low performance. To address these limitations, we propose GaussDB-Vector, a high-performance, real-time persistent vector database that excels in low-latency scalable search, real-time inserts and deletes, high availability, large-scale distributed search, and hybrid scalar-vector filtered search capabilities. These features are primarily achieved through an innovative storage architecture designed for a graph-based vector index, optimized for I/O operations and adaptable across various dataset sizes and dimensions, complemented by novel buffering strategies to further reduce I/O burdens. GaussDB-Vector supports product quantization, parallel search, and hardware acceleration via SIMD, GPUs, and NPUs in order to further accelerate queries. Experimental results show that GaussDB-Vector outperforms competitive baselines by a factor of 1 to 5 times.