Vector Databases Explained: History, Internals, and Why Agentic AI Depends on Them
A lot of the recent attention on vector databases makes them sound like a brand-new invention created by the generative AI boom. That is not really true. What changed is not the underlying math. What changed is the workload. For more than a decade, industry and academia had already been working on large-scale nearest-neighbor search for recommendation systems, image retrieval, search, ads, and ranking. The generative AI wave did something different: it turned vector retrieval from a specialized backend capability into a mainstream application primitive. Once teams started building retrieval-augmented generation (RAG), long-term AI memory, semantic search, and tool-using agents, vector databases stopped being niche infrastructure and became part of the standard stack. ...