Why Vector Databases Are the Future of Data Storage and Retrieval

No worries, non-members can read story here

Faced with the need to store vast amounts of data in a way that was not available to traditional databases, learning about vector databases appeared to be the only option. Relational databases are certainly fantastic for structured data — rotating images around, metadata like file type, date created, and even user-assigned tags. But the boundaries are extreme if you’re going to bridge the gap between computer-structured domains and the way people comprehend the semantics of information. What I would desire, e.g., like corresponding impressions or color categories in pictures, isn’t quite so with linear systems, and this distinction, which we referred to as the semantic gap, caused me to reconsider.

Evolving Semantic Representation from Vector Databases

I recognized that I needed a system that would be able to capture data’s nuances and not just deliver it in columns and rows. Vector databases exactly did that. With vector embeddings, they portray data as numeric arrays where each axis is an attribute or a feature. Images, text, or sound — vector embeddings get to the semantic heart of data in a way that is not possible for normal databases to achieve. The concept of putting similar items close to each…