  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.
