As the world readies itself for a huge technological revolution due to the increasing integration of big data, artificial intelligence (AI), and machine learning (ML), more platforms and systems are being developed to help take advantage of these innovations. The vector database is one of the most important and transformational of these technologies.
It is changing how we store and utilize data and is developing how real-time applications that use generative AI, such as content creation and recommendation systems, are supported. This increasing demand for this new type of database led to the vector database market being valued at $1.5 billion in 2023, with a predicted increase of $4.3 billion by 2028. Yet, despite the use cases of vector databases increasing yearly, many are still unaware of how important these new databases are. Below are 5 reasons that will demonstrate why vector databases are a transformational technology.
Data Stored as Vectors
A vector database can store complex data, like text, images, and user behavior patterns, very efficiently. This is because the data is stored as a vector rather than in the rows or columns of a traditional database. A vector is a mathematical quantity that has both magnitude and direction, and in terms of data, a vector is an ordered list or sequence of numbers that represent the data. Unlike traditional databases that are limited to the type of data they can store, a vector can represent any type of data, including unstructured data, which is data without a pre-defined data model or schema. The vectors are arranged as lists of numbers where each sequence represents a specific feature or attribute of that data. For text documents, that can be individual words that are converted into vectors; for images, the vectors can be features like colors and shapes; and for behavior patterns, it can be browsing history or purchase transactions that are broken down into different vectors. By using vectors to store and organize data, vector databases can store vast amounts of various data types on one server.
Similarity Searches
For those unaware of the benefits of a vector, it may seem like an overly complicated way to store data. However, vector databases are not used like traditional databases for exact matches; instead, they search for data based on similarity. As per MongoDB, “This semantic understanding means that even if two pieces of data aren’t identical but are contextually or semantically similar, they can be matched.” For example, if searching through a vast library of images using specific vectors, the vector database would find all images with the same or similar features. This allows the user or program to easily and efficiently narrow down the search rather than look for an exact match. This is why applications like recommendation systems or image recognition can provide results based on similarity searches.
Accelerating AI Applications
The integration of AI applications into the business world is growing rapidly, with 83% of companies claiming that AI is a top priority in their business plans. Many businesses are implementing large language models (LLMs) that can recognize and generate text for content creation, translation services, and chatbots. Due to the vast amounts of instant data LLMs need, vector databases are able to support these AI applications. This is because “vector databases are optimized for real-time retrieval, allowing applications to provide instant recommendations, content suggestions, or search results.”
Both Scalable and Flexible
Vector databases are both easily scalable and flexible to facilitate these AI applications. While exact match searches can become increasingly slower due to the growing amount of data they store, vector searches maintain a consistent query performance throughout, guaranteeing timely results even with vast datasets. This is because most vector databases are built to scale horizontally as well as vertically, making them capable of handling massive collections of vectors across multiple nodes. Vector databases are also transformative because of how flexible they are. Alongside storing unstructured data like video and images, vector databases can also store non-vector data types. This allows them to provide versatility for a wide range of complex applications.
Multiple Industry Use Cases
As mentioned above, vector databases are extremely scalable and flexible, and this allows them to be used in multiple industries. In healthcare, vector databases help healthcare professionals provide personalized treatments to their patients by identifying similar patients, creating treatment plans, and promoting research efforts. In finance, the similarity search function of the vector database allows financial institutions to store patterns as vectors and detect anomalies such as fraud. Gaming devs are now using vector databases to manage player profiles and create realistic gaming environments. As vector databases continue to evolve with the latest technological applications, we can expect to see many more use cases of this transformative technology.