The world as we know it is rapidly changing as new technology is developed for every aspect of modern society. In today’s fast-paced and digitally-driven world, companies constantly amass information on customers, products, and operations through social media, websites, sensors, devices, and transactions.
The increase in the importance of data has caused a massive shift in how we collect and use this data. For example, data scientists now play a vital role in companies in all industries in the extraction, cleansing, processing, and visualization of data. There have also been big developments in the databases that can store the new data types produced by modern applications. With more and more data coming from videos, audio files, documents, emails, and social media posts, companies are looking for data management systems with more flexible schemas to store and organize this data. Even how we search for data is evolving, with semantic searches surpassing keyword matches for many applications.
This is why more industries and individuals are looking beyond traditional databases and moving towards the vector database. Simply put, a vector database converts information into a vector, which is then stored in a multidimensional database that allows the database to conduct a similarity search. Below, we will look at each part of a vector database and explain how it works.
Table of Contents
Vectors
In math and physics, a vector is a quantity with both magnitude (or size) and direction and can be broken down into components. For example, in a two-dimensional space, a vector has an X (horizontal) and Y (vertical) component. In a vector database, a vector is an ordered list or sequence of numbers representing data and can represent any type of data, including unstructured data (video, audio, documents). Each number in the vector represents a specific feature or attribute of that data, and an image vector would contain numbers representing the subject, the image size, the average colors, etc.
Embedding Model
An embedding model converts content into a vector, which breaks the content into a string of numbers that can be stored in a vector database. The embedding model doesn’t just turn the content into numerical translations; it also ensures that the vectors encapsulate the original data’s deeper semantic essence and contextual nuances.
How Vector Databases Store Vectors?
Unlike most databases, which store data in a table or a specific data model, vector databases store vectors as embeddings in a multidimensional space. A guide to vector databases uses this explanation to simplify the concept: imagine a vector database as a vast warehouse. In this warehouse, every item (data) is stored in a box (vector) and organized neatly on shelves in a multidimensional space. However, rather than being static, the vector databases allow vectors to automatically form clusters based on semantic essences and contextual nuances. Another example from the guide above explains how, in a dataset teeming with animal images, a trained convolutional neural network (CNN) would cluster all dog images close together, distinctly separate from clusters of cats or birds.
Similarity Searches
As mentioned above, vectors form clusters in the database. A similarity search identifies vectors that reside in close proximity to the given query vector within the multi-dimensional space to provide the user with a set of results that are similar but not just an exact match. Using the above example of a dataset of animal images, a similarity search for the query of ‘wolf’ would also include results that have a semantic and contextual relationship, such as wolf cubs, other members of the dog family, and even the prey of wolves. In the context of the above example, the advantage of a similarity search over an exact match would be that it would rapidly reduce the time looking for an image. This is achieved by presenting all the images related to the query rather than going through the entire dataset to find the image you want.
Real World Examples
Vector databases have several real-world applications that take advantage of the ability to store data as vectors and perform similarity searches. Ecommerce platforms use vector databases to enhance their databases recommendation engines. By converting product descriptions, user reviews, and user profiles into vectors, recommendation engines perform similarity searches to recommend products and services that closely match user preferences. Another example is how a customer support system can use a vector database to improve its natural language processing (NPL) capabilities. Converting customer queries and support documents into vectors, the database could quickly find relevant responses to user inquiries on NPL applications.
Vector databases are fast becoming vital systems for data management. As more data is made available, there will be an increasing need to use these databases to store large data volumes and improve services.