As technology continues to evolve, our need and ability to store different types of data has also developed. One data management system that is becoming increasingly crucial to institutions across industries are the vector Databases.
This modern database allows for the storage of different data points to provide users with a wide search canvas in which to find the best results. The vector database market is already worth over $1.5 billion and is projected to reach $4.3 billion by 2028 at a CAGR of 23.3%. With every industry — from healthcare to finance, retail to government agencies — looking to capitalize on the latest technology, such as generative AI and big data, vector databases are becoming a transformational technology. This is leading more developers and companies to ask what a vector database is and how it works.
A Vector Database Explained
To understand a vector database, it is vital to understand what a vector is. In physics, a vector is a quantity with both magnitude (or size) and direction. This allows for a vector to be broken down into many components. For example, in a two-dimensional space, a vector has an X (horizontal) and Y (vertical) component. When applied to data science and machine learning, a vector is an ordered list or sequence of numbers that can represent any type of data.
Most traditional databases are only built to store and organize structured data, data that can be fitted into a chart or table, while a vector database can store and organize unstructured data, which is data without a pre-defined data model or schema such as text, images, audio, and video. The vector takes the data from these sources and turns it into a list of numbers where each number in the list represents a specific feature or attribute of that data. This allows users to easily find data that is not only an exact match but also similar to what they are looking for, giving them a much wider search parameter.
How a Vector Database Works
An example of how a vector database can work is to imagine building an advanced film search function. As we mentioned in a previous post on the future of movie watching, viewers had access to vast film libraries through Netflix, Prime Video, Hulu, and other streaming services. We explained how a personalized SaaS for movie watching could provide users with a list of recommendations based on their analysis of your viewing habits (something that vector databases are also used for). A vector database could take this one step further.
As shown in the video below by MongoDB Senior Developer Advocate Jesse Hall, a vector database can be used to create an advanced search function for a film library. By imputing the film library into a vector database and using AI to convert each plot point into a vector, users can create a large database that they can instantly use to find films with similar plots. The example given in the video below is that if the user knows the film’s plot but not the exact film title, they can type in a short summary, and the vector database will find the closest results. Hall types in “A Boy and a Yellow Dog,” and the database returns the top results, including the film he was trying to remember the title of.
Vector Search: The Future of Data Querying Explained
Use Cases For Vector Databases
Vector databases can power a wide range of different applications. The most widely known are recommendation systems, image searches, and Large Language Models (LLMs). For the recommendation systems, the vector databases allow E-commerce platforms to suggest similar products for users. This is done by turning user purchase history and product details into vectors. Similar to the above film library example, a vector database can answer a query by finding similar images based on their visual characteristics. This allows users to have many more options compared to searching for images based on file names. LLMs, such as AI content generators, chatbots, and AI code writers, are all powered by vector databases. As explained by Kapil Uthra in his LinkedIn post, “When you interact with an LLM, your query is turned into a vector and compared to the database of text vectors. This allows the LLM to generate responses that are relevant and contextually aware.”
Vector databases are the future of data storage and AI applications. While still a relatively new technology, vector databases can expect to become increasingly essential for every industry.
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