Data is fast becoming one of the most important commodities in the world.
Outlining the Benefits of Customer Data Platforms, we noted how businesses are constantly seeking ways to understand their customers better and deliver personalized experiences, and one way they do this is through data. As the world’s need to store and organize vast amounts of data increases, modern databases have evolved to handle these requirements. The vector database stores data as mathematical representations, compared to traditional relational databases that store data in rows and columns.
Because vector databases provide unparalleled speed, flexibility, and scalability for managing unstructured data, they have become increasingly popular for enterprises looking to advance in the AI-driven market. The LinkedIn post Understanding how Vector Databases are Transforming AI Applications reports that by 2026, more than 30% of enterprises will have adopted vector databases to ground their foundation models with relevant business data. With this new database form becoming a prominent technology across all industries, more people are asking how exactly vector databases work.
What is a Vector Database?
A vector database differs from SQL and NoSQL databases in that it stores and manipulates vector data. Simply put, a vector database “specializes in storing unstructured data such as text, images, or audio that are converted into numerical embeddings. These embeddings are large arrays of numbers across multiple dimensions that can be efficiently searched, processed, and analyzed”. SQL and NoSQL databases are made for the storage and retrieval of structured and unstructured data. This makes vector databases more suitable for generative AI applications such as image recognition and recommendation systems. SQL and NoSQL databases cannot perform real-time computations and handle high-dimensional vector data, which is typical in AI applications.
What is a Vector?
To understand how a vector database works exactly, we need to know what a vector is. As explained in ‘What Are Vector Databases?’ by MongoDB, in math and physics, a vector is a quantity that has both magnitude and direction and can be broken down into components.
In a two-dimensional space, a vector has an X (horizontal) and Y (vertical) component. However, in data science and machine learning, a vector is an ordered list or sequence of numbers representing data. The advantage of this is that a vector can represent any form of data, from text to images, audio, and video files. A vector represents this as a list of numbers where each list of numbers represents a specific feature or attribute of that data.
How Does a Vector Store Data
Vector embeddings convert data into numerical representations. For example, let’s use a large collection of car images to be added to a vector database. Each image is a piece of unstructured data and can be broken down into multiple data points that are represented by a vector in high-dimensional space. These data points could be the color of the car, the number of cars, or the average color in the background. Each data point is converted into a list of numbers to form a vector, which can then be stored and indexed on a vector database. For one image, you can have dozens or even hundreds of data points, giving you a much wider search parameter than a traditional database.
How Vector Databases Work
As clarified in an article on how ‘Gen AI is Raising the Popularity of Vector Databases’ by AI Business, a vector database creates indexes on all the vectors, which can use multiple algorithms to conduct an approximate nearest neighbor (ANN) search. This means data in the vector database can now be identified based on similarity metrics instead of exact matches. Using the above example, if you were to search through the car images for a red car on a traditional database, it would return results containing just red cars. In contrast, a vector database would find you all the images that are related to your search rather than just an exact match, such as cars that have red interiors or colors that are near to red, like orange and purple.
Use Cases of Vector Database
Vector databases are used for multiple applications that use their unique way of storing and searching for data.
Below are three examples.
- Voice recognition: Similar to the above image example, the paper Vector Database Management Systems by the Cognitive Systems Research journal details how vector databases can convert a whole audio file into a sequence of vectors. The vectorized spoken keyphrase is compared with other vectorized recordings to find a match.
- Recommendation systems: A vector database allows a recommendation system, such as the one used on Amazon, to study customer behavior and preferences and, therefore, make personalized product recommendations. This is because a vector database will link indexed data points to find products related to what the customer is searching for.
- Fraud detection: As Tommy Jacobs laid out in How Data Can Supercharge Business Growth for Financial Institutions, data is used to identify and assess potential financial risks. A vector search can pour through huge amounts of financial documents to find similar or anomalous items. This is because vector searches can identify similar data points and anomalies that would be challenging to discover using traditional methods.
Vector databases are fast becoming the future of data storage and sorting. With the rise of AI, we can expect an increase in use cases and applications.
