First introduced with Milvus in 2019, vector databases have rapidly risen to prominence alongside the emergence of large language models (LLMs) and th

Emerging Trends in Vector Database Research and Development

submited by

Style Pass

2024-04-18 20:00:03

First introduced with Milvus in 2019, vector databases have rapidly risen to prominence alongside the emergence of large language models (LLMs) and the rise of generative AI (GenAI) applications. As an engineer deeply engaged in this sector, I've witnessed their evolution from basic implementations of Approximate Nearest Neighbors Search (ANNS) algorithms to sophisticated database systems integral to modern AI frameworks, from niche implementations to broad deployments.

As AI continues to mature, where is the future of vector databases headed? In my view, the development of this technology is intricately linked to product evolution, propelled by the changing demands of users. Understanding these shifts is crucial for directing the trajectory and objectives of technological developments.

In this article, I will discuss the development and anticipated future of vector databases from both technical and practical perspectives, focusing on cost-efficiency and business requirements.