Vector Databases

Abstract

Vector databases represent a significant advancement in data management, tailored to efficiently handle high-dimensional vector embeddings. Unlike traditional databases, which face challenges with the complexity and scale of high-dimensional data, vector databases are engineered for optimal storage, indexing, and retrieval of vector data. This work explores the challenges posed by high-dimensional data, including the curse of dimensionality, and examines how vector databases address these issues through advanced indexing techniques such as Inverted File (IVF), Product Quantization (PQ), and Locality Sensitive Hashing (LSH). It highlights the importance of vector databases in facilitating rapid similarity searches, which are crucial for applications such as recommendation systems and search engines. The discussion also covers the evolution of vector databases and their impact on AI and machine learning.