Unveiling the Power of Vector Databases and Embeddings in the AI Landscape

Introduction

In the fascinating realm of computing, we face the challenge of enabling machines to comprehend non-numeric data such as text, images, and audio. Vectors and embeddings, vital elements in the development of generative artificial intelligence, address this enigma. As attention towards generative AI grows, it is crucial to understand why these vectors and embeddings have become fundamental in processing complex and unstructured information.

Vectors in the Computational World

Computers’ ability to understand unstructured data, such as text, images, and audio, is limited. This is where “vectors” come into play, numeric representations that allow machines to process this data efficiently. Traditional foundations of conventional databases are not designed to handle vectors, highlighting the need for new architectures, especially with the rise of generative AI.

Fundamentals of Vectors

At the core of this computational revolution lies the fundamental concept of a vector. From a mathematical perspective, a vector is a way to represent a set of numbers with magnitude and direction. Although visualising high-dimensional vectors in machine learning applications may be…