Along with advances in technology, our relationship with data has also evolved. Dorlenor Talrik’s smart data management post underscores how, in the modern business environment, data is not just a bargaining chip but a must-have to sustain a small business.
For businesses, this means staying ahead of the curve of data management innovations. While relational and NoSQL databases are the most widely used across industries, vector databases are becoming increasingly popular due to their benefits. With modern applications reliant on not just storing data but also using it to enhance their services, the way vector databases can store, organize, and use data gives them an advantage over other databases. In this post, we will provide an informative guide to vector databases and the benefits they provide.
Vector Data Storage
The major benefit of using a vector database is that it stores data on a vector. Using a machine learning embedding model, data that is to be stored in a vector database is converted into a string of numbers. Each number is attributed to a different aspect of the inputted data. Any type of data can be stored on a vector, and a Medium guide to vector databases details how they are designed to handle high-dimensional data efficiently, which is crucial for applications dealing with complex data types such as images, audio, or text embeddings.
Once the vector enters the vector database, it naturally clusters with vectors that are contextually or semantically similar. This clustering allows vector databases to perform a vector search.
Vector Search
A vector search finds relevant information differently from traditional databases. As demonstrated by the vector databases covered on MongoDB, a vector search operates based on similarity, and this semantic understanding means that even if two pieces of data aren’t identical, they can still be matched. Algorithms that are optimized for vector search of high-dimensional vectors, 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. A key benefit of this is that it allows the database to rapidly find results in vast datasets. When applied to applications like image retrieval software, the results are almost instantaneous, even if the image bank is made up of tens of thousands of images.
Scalability
Because vector databases are designed to handle large datasets without performance issues, they are also built to scale easily. This is achieved through horizontal scaling, where extra servers are simply added to the system rather than the need to install new hardware. This makes it ideal for applications that require large datasets, such as large language models (LLMs), which are trained on massive amounts of text data to generate human-quality text and perform various natural language processing tasks.
Generative AI Applications
Vector databases have become increasingly popular for their ability to power generative AI applications.
AI Business details how vector databases help enterprise apps effectively search large amounts of unstructured data, helping to reduce hallucinations and greatly improve the responses of LLMs. Hallucinations are when the generative AI application produces outputs that are incorrect, misleading, or entirely made-up. The hallucinations are a result of AI’s reliance on patterns learned from training data rather than access to up-to-date databases or real-time information. Vector databases that are connected to a generative AI application can ensure it is continuously updated. The application would enter the query into the vector database, which would use a vector search to find the latest relevant information. This is how chatbots are able to provide the latest information on services or products to customers.
Wide Range of Use Cases
Many industries are benefiting from vector databases, especially e-commerce platforms, security services, and financial management apps. E-commerce platforms employ vector databases to provide personalized recommendations to their customers based on previous purchasing and browsing history, which increases the chance of more purchases. As mentioned above, image retrieval is a common use for vector databases and allows security services to perform facial recognition in real-time as well as biometric access. In finance, vector databases are used for fraud detection. Through comparing data points, they can identify anomalies and patterns that point to potential fraud.