Vector indices have emerged as a valuable tool in the arsenal of AI researchers and developers. But they are hardly ever presented as a tool to use outside of this context. It's generally assumed that vector indices encode values via machine learning: trained data-to-vector models grouping instances by semantic relevance. These architectures tend to involve non-structured data and non-deterministic querying accuracy.
This document explores the use of vector indices for structured data and deterministic querying.
This document was designed to be understood by any technically-capable person. It is not a rigorous proof or a formal whitepaper; it's plain english with interactive examples.
This document was intended for the web, but it's also printer-friendly. The printed version of this document will not contain interactive examples, obviously.