Part of:Bolster AI initiatives with vector databases
Top 10 industry use cases for vector databases
Vector database popularity is rising as generative AI use increases across all industries. Here are 10 top use cases for vector databases that generate organizational value.
Vector databases manage the vast data sets needed to power generative AI tools. As generative AI embeds itself across all industries, the use cases for vector databases increase as well.
A vector database is a specialized system designed to handle large volumes of unstructured data, such as text, images and audio. Unlike traditional relational databases, which store data in tables with rows and columns, vector databases represent data as points in a multidimensional space. Each dimension corresponds to a specific feature or characteristic of the data. It measures the distance between data points in the space, which enables the user to search and retrieve data based on similarity (or closeness) rather than traditional exact matching.
In a traditional database, someone might search for books based on author, title or publication date. With a vector database, they could search for books that are similar in terms of their content, tone, plot structure or style. The performance of potentially vast searches like these is made practical by algorithms such as approximate nearest neighbor. Vector databases may be familiar to users with experience using this approach with generative AI.
Vector databases play a crucial role in generative AI use, acting as the essential infrastructure for storing, managing and retrieving the massive amounts of data -- stored as vectors -- that power applications such as ChatGPT.
As the applications of generative AI grow, so do the uses for vector databases. Here are 10 of the most interesting use cases for vector databases, identified during research, where developers can take advantage of their unique value.
1. Natural language processing
Vector databases can represent complex semantic relationships that traditional data models can't capture, which provides a more nuanced understanding of language. The relationships enable the analysis of context and tone, improving the performance of tasks such as sentiment analysis and translation.
The meaning of words depends greatly on context and cultural nuances that are difficult to model using dictionary lookups. Vector databases can understand that "buy," "purchase" and "acquire" are all related, but also differ slightly in their contexts. For example, phrases such as "splurged on," carry a lot of sentiment.
[Vector databases are] the essential infrastructure for storing, managing and retrieving the massive amounts of data … that power applications such as ChatGPT.
2. Customer support
People who have experienced long waits with a pressing issue on a customer service call know that sentiment analysis is a critical skill for human customer service representatives. For online customer service, chatbots can now get a little closer to the human skill. A knowledge base using vectors can analyze and respond to customer support tickets by accurately categorizing issues. The bot can also note underlying sentiment using similar techniques to the natural language processing described above. Responses and prioritization are handled in a more satisfactory manner for customers.
3. Image and video recognition
Vector databases convert the pixel data of images and videos into vector representations. Vector databases can use the representations to perform complex tasks such as facial recognition, object detection and scene understanding with high accuracy. For example, an e-commerce site can tag products in images to enable visual search for shoppers. A social media platform can also use the technique for content moderation, detecting policy violations in visual content. The database can quickly flag explicit content in videos and images at scale without needing human case-by-case review.
4. Financial services fraud detection
Because vector databases represent transactions in a space with many dimensions, it becomes possible to detect subtle, nonlinear patterns typical of fraudulent behavior. Fraud detection systems have become quicker and more accurate in banking and financial services. For example, a bank can analyze customer transactions and activity patterns within a very complex space of dimensions, including time, location, amount and the relationship of one transaction to another. Banks can use the insights to identify anomalous vectors indicating potential fraud, but they can do so with more nuance than traditional rules-based fraud models. These new capabilities improve customer satisfaction, with fewer incorrect card issues raised and greater security for banks and card issuers.
5. E-commerce product recommendations
E-commerce sites already deliver personalized product recommendations by analyzing customer browsing and purchase history. Data mining can identify correlations, but is too slow to respond to customer behavior on the website in near real time. Vector databases can identify the correlations much faster and enable e-commerce sites to offer a more engaging and insightful experience. The recommendations may be so insightful that customers feel the system is reading their mind when it is simply finding patterns they may not have noticed themselves.
6. Autonomous vehicles
For autonomous vehicles, vector databases are crucial in processing sensor data to understand and navigate the vehicle's environment. They convert input from cameras, lidars and radars into vector data. Analysts can use the data to identify significant patterns such as pedestrians, traffic signals and obstacles.
7. Medical diagnostics
The techniques used in video and image processing are particularly powerful when applied to medical diagnostic scans such as MRIs and X-rays. By converting these images into vectors, it becomes possible to compare the scans with large data sets of known conditions. The system can reveal patterns imperceptible to humans and help make an accurate diagnosis. As a result, doctors could discover early indicators of cancer or other serious illnesses accurately without invasive tests.
8. Biometrics
Anyone who has recently traveled through an airport, applied for a driver's license or opened a bank account in person is familiar with biometric security. Vector analysis excels at biometric pattern recognition from fingerprints, facial characteristics and other identity attributes captured digitally for authentication. It converts slight, but distinctive qualities to vectors. The result is a system which is much less readily confused by cuts on a person's fingers, haircuts, makeup or simply natural aging.
9. Media recommendations
Entertainment services, such as streaming movie or music services, make recommendations much like e-commerce sites, but with some important differences. Entertainment users often consume media in longer sessions -- such as watching a movie or binge watching a series -- than online shoppers. Entertainment services may not be able to work with such simple variables as price and shipping options, but they do have temporal dynamics such as new releases, viral content and even the mood of the viewer or listener. The process may be similar in principle, but in practice vector databases are now essential for the complexity of media recommendations.
10. Video games
In gaming, vector databases enable designers to build dynamic and responsive game environments. Game developers use vector databases to power visual rendering and physics engines. Vectors help generate interactive, immersive worlds with nuanced, multifaceted experiences for players.
Vector databases are not just a technological innovation; they are reshaping how people interact with and benefit from the vast amounts of data generated in the contemporary world. Business applications are only glimpsing the potential of these models. The flexibility and scale of vector databases offer intriguing possibilities for adaptive, engaging and analytically powered experiences in the future.
Industries can look forward to creative, new applications in all the use cases presented here and many more as the technology evolves. Keep an eye on vector databases as it may affect every business sooner rather than later.
Donald Farmer is the principal of TreeHive Strategy, who advises software vendors, enterprises and investors on data and advanced analytics strategy. He has worked on some of the leading data technologies in the market and in award-winning startups. He previously led design and innovation teams at Microsoft and Qlik.
