How Do Vector Databases Work?​

Vector databases are among the fastest-growing data management systems as the way we store and use data evolves. Modern business strategies rely on data, and their ability to organize, analyze, and make sense of this information sets them apart from their competitors.

Today, 80% of the data that is collected can’t simply be stored in tables and columns, and with companies looking to use this data to improve their services and train AI applications, the unique way vector databases store, organize, and search for information have made them increasingly popular in a wide range of industries, from ecommerce to healthcare. As a result, the vector database market is expected to grow by a CAGR of 16.33% between 2025 and 2034. In this article, we will examine how vector databases work, from how they store data to how they conduct a very specific type of search.

What is a Vector?

To understand how a vector database works, we need to understand how it stores data on a vector. A vector is a quantity with both magnitude and direction that can be broken down into components. In the context of a vector database, a vector is an ordered list or sequence of numbers where each number in the list represents a specific feature or attribute of that data. A vector can represent any data type, including text, images, audio, and video, and this is done by feeding the raw data into an embedding model. The embedding model then converts the data into a list of numbers that encapsulate the deeper semantic essence and the contextual nuances of the original data. If using an image of a cat as an example, one number will represent the cat, another the background, another the species of the cat, and even more detailed attributes like the average color of the image. Once this embedding is complete, the vectors are stored in a vector database.

How are Vectors Stored in a Vector Database

Vector databases store the vectors in a multi-dimensional space rather than the rows and columns of traditional databases. Vectors that are similar in subject or context will naturally cluster together using vector distance formulas to calculate similarities. Using the cat image example again, if you had a thousand cat images in the database, all the cats of the same breed would be clustered together, as would all images of kittens. It is this clustering that allows the vector data to use advanced indexing techniques to perform a vector search.

Using a Vector Search

A vector search allows the user to sort through data differently than traditional databases. Instead of hunting for precise matches between identical vectors, vector databases use a similarity search to identify vectors that reside in close proximity to the given query vector within the multidimensional space. This semantic understanding means that they can be matched even if two pieces of data aren’t identical but are contextually or semantically similar. Vector database searches for data through patterns and relationships that match how our brains think. A good example of this is how the brain would look for lost car keys. Your brain won’t methodically scan every room. Instead, it will quickly access relevant memories based on context and similarity. A vector database does the same thing, as algorithms optimized for a vector search, such as approximate nearest neighbor (ANN) search, can quickly identify the most similar vectors in this vast space without the need to scan every vector. This allows a vector database to handle data points spanning hundreds or even thousands of dimensions.

Use Cases of the Vector Database

The vector database is ideal for recommendation systems or image recognition applications, as they can fully utilize the similarity search. A recommendation system on an ecommerce platform will provide products similar to a user’s search query, allowing the platform to provide a better and more personalized customer experience. As visual content dominates our culture, vector databases are adept at sifting through vast repositories of images and videos to identify those that bear a striking resemblance to a given input. This makes them ideal for security features that use facial recognition to identify or authenticate individuals based on their facial features. Vector databases are also being increasingly used to train AI applications such as chatbots. Through being able to discern the semantic essence of phrases or sentences, the vector database can identify matches that might not be identical in terms of wording but are contextually similar, allowing the chatbot to answer a wide range of queries.

As data evolves along with technology, vector databases are quickly becoming one of the most efficient ways to store and manage data.