Vector databases manage large collections of embedding vectors. As AI
applications are growing rapidly, so are the number of embeddings that need to
be stored and indexed. The Faiss library is dedicated to vector similarity
search, a core functionality of vector databases. Faiss is a toolkit of
indexing methods and related primitives used to search, cluster, compress and
transform vectors. This paper first describes the tradeoff space of vector
search, then the design principles of Faiss in terms of structure, approach to
optimization and interfacing. We benchmark key features of the library and
discuss a few selected applications to highlight its broad applicability.

Faiss 是一个用于向量相似度搜索的工具库，它管理大型的嵌入向量集合，描述了向量搜索的权衡空间以及 Faiss 的设计原则和关键特性。

Faiss 库

The Faiss library

Approximate nearest neighbor algorithms are used to speed up nearest neighbor
search in a wide array of applications. However, current indexing methods
feature several hyperparameters that need to be tuned to reach an acceptable
accuracy--speed trade-off. A grid search in the parameter space is often
impractically slow due to a time-consuming index-building procedure. Therefore,
we propose an algorithm for automatically tuning the hyperparameters of
indexing methods based on randomized space-partitioning trees. In particular,
we present results using randomized k-d trees, random projection trees and
randomized PCA trees. The tuning algorithm adds minimal overhead to the
index-building process but is able to find the optimal hyperparameters
accurately. We demonstrate that the algorithm is significantly faster than
existing approaches, and that the indexing methods used are competitive with
the state-of-the-art methods in query time while being faster to build.

本研究提出了一种使用随机空间划分树自动调整索引方法超参数的算法，相较于现有方法，该算法在查询时间上表现优异，且建立速度更快。