In this guest post, Frank Liu, Head of ML&AI at Zilliz, explores the intricate realm of vector search, comparing vector databases, vector search plugins, and vector search libraries. Let’s dive in! The surge in ChatGPT and other large language models (LLMs) has driven the growth of vector search technologies, featuring specialized vector databases like
This article is too surface-level to influence any decisions. Simply using the analogy of adding vector search functionality to a preexisting database is akin to electrifying a ICE-powered vehicle (which often IS actually a great idea!) isn't nearly enough. There are far more tradeoffs that must be considered, especially if you're integrating into a preexisting system. I'm sure Milvus is a fine technology, but it doesn't have nearly the tenure that Postgres or Elasticsearch do. While those might not be "optimal" in retrieval times, they're battle-tested tools with far more capabilities than Milvus.
This article is too surface-level to influence any decisions. Simply using the analogy of adding vector search functionality to a preexisting database is akin to electrifying a ICE-powered vehicle (which often IS actually a great idea!) isn't nearly enough. There are far more tradeoffs that must be considered, especially if you're integrating into a preexisting system. I'm sure Milvus is a fine technology, but it doesn't have nearly the tenure that Postgres or Elasticsearch do. While those might not be "optimal" in retrieval times, they're battle-tested tools with far more capabilities than Milvus.
Nice post but I miss old TheSequence with vendor-neutral content.
Please single-space coding.