PANNS: Enhancing Graph-based Approximate Nearest Neighbor Search through Recency-aware Construction and Parameterized Search

Approximate Nearest-Neighbor Search (ANNS) has become the standard querying method in vector databases, especially with the recent surge in large-scale, high-dimensional data driven by LLM-based applications. Recently, graph-based ANNS has shown improved throughput by constructing a graph from the dataset, with edges representing the distances between data points, and using best-first or beam search algorithms for query evaluation.