Despite the efficacy of graph-based algorithms for Approximate Nearest Neighbor (ANN) searches, the optimal tuning of such systems remains unclear. This study introduces a method to tune the performance of off-the-shelf graph-based indexes, focusing on the dimension of vectors, database size, and entry points of graph traversal. We utilize a black-box optimization algorithm to perform integrated tuning to meet the required levels of recall and Queries Per Second (QPS). We applied our approach to Task A of the SISAP 2023 Indexing Challenge and got second place in the 10M and 30M tracks. It improves performance substantially compared to brute force methods. This research offers a universally applicable tuning method for graph-based indexes, extending beyond the specific conditions of the competition to broader uses.

通过黑盒优化算法，本研究引入了一种方法来调整现成的基于图的索引的性能，重点关注向量的维度、数据库大小和图遍历的入口。我们将这种方法应用于SISAP 2023索引挑战的任务A，在10M和30M轨道上获得了第二名，与蛮力方法相比，显著提高了性能。该研究为基于图的索引提供了一种普适的调整方法，并推广到更广泛的用途。

通用且实用的调整方法用于现成的基于图的索引：UTokyo团队SISAP索引挑战报告