Vector Databases Exploring a New Way to Revolutionize Search
<p>If you’ve been anywhere near data management or computer science recently, you’ve probably heard murmurs about “Vector Databases”. The concept might seem intimidating at first. I mean, we already have a gazillion databases, right? Do we really need another one?</p>
<p>Well, stick around and I promise you’re going to find this new breed of databases fascinating. They are revamping the way we approach search functions, and in this article, we’ll deep dive into what they are, how they work, real-world examples, and how to implement them into a Spring Boot application.</p>
<h1>An Aerial View of Vector Databases</h1>
<p>Let’s start by untangling the term. A vector database is specifically engineered to efficiently deal with vector data. So, what’s vector data? It represents data points in multi-dimensional space, a mathematical approach to defining real-world information.</p>
<p>Consider this, you have an assortment of images. Each of these images can be represented as a vector in a high-dimensional space where each dimension relates to some feature of the image (like color, shape, or texture). By comparing these vectors, we can find similar images. Sounds neat, right?</p>
<p>This capability is pivotal because it enables similarity search — a type of search where you’re fishing for things that are similar, not necessarily exact replicas. This is a game-changer in many domains, like recommendation systems and machine learning.</p>
<h1>Dissecting Vector Databases</h1>
<p>Under the hood, vector databases employ a technique named “vector indexing.” This is a method of organizing and searching vector data that allows finding similar vectors in a snap. The lynchpin here is the concept of a “distance function”, which measures how similar two vectors are.</p>
<p>When you’re seeking vectors similar to a given vector, the database doesn’t compare the given vector to every single vector in the database. Instead, it uses the vector index to swiftly pinpoint a small subset of vectors that are likely to be similar. This feature makes the search much faster and more efficient.</p>
<p><a href="https://medium.com/@abhishekranjandev/vector-databases-exploring-a-new-way-to-revolutionize-search-85cb1b1fc7b">Click Here</a></p>