Have you ever wondered how Spotify always seems to know your next favorite song or how Netflix curates the perfect movie suggestions? Or perhaps you’re curious about the cutting-edge technologies powering self-driving cars? The answer lies in vector databases—a core technology driving the rapid advancement of AI applications. From fraud detection in financial services to personalized recommendations by tech giants like Spotify and Netflix, vector databases have quickly become indispensable tools in the AI landscape.
What Are Vectors and Vector Databases?
Our computers don’t understand words the way we do. Instead, they use numbers. Vectors are just numerical representations of images, sentences, audio, and even more complex data like video clips or medical scans. For instance, a sentence can be represented as a vector of numbers that encapsulate its meaning, or an image can be broken down into a series of numbers representing its colors, shapes, and textures.
Vector databases are specialized for storing and managing these numerical representations, allowing for more advanced mathematical operations on these vectors. Think of them as high-dimensional libraries where each book (or data point) is represented by a unique set of numbers. Instead of searching for books by their title (exact match), you can search for books like one you’ve already read (similarity search).
This ability to quickly find similar items is crucial for many AI applications. A recommendation system might use a vector database to suggest songs similar to those a user enjoys, or an image search engine could find visually similar images based on a query image.
Vector Databases in Legal Documents and NLP
When it comes to legal documents, accuracy and speed are both vital. Natural Language Processing (NLP) can transform complex legal text into vectors, making it easier to compare, classify, and retrieve documents based on their content. For example, if a lawyer is searching for case laws related to a specific clause, a vector database can quickly sift through thousands of documents to find the most relevant ones.
This capability is useful in legal research, where time is of the essence, and the accuracy of information can make or break a case. By using a vector database, legal professionals can streamline their research process, reduce manual effort, and ensure they are working with the most relevant data.
Integrating with Pentaho+ and REST API
To build large-scale AI applications, integrating vector databases with existing business tools is key. Pentaho, a popular platform for data management and analysis, can be a powerful partner.
By connecting a vector database to Pentaho using a REST API, you can create a smooth workflow between your AI models and business operations. This means you can use your vector database to power AI applications while relying on Pentaho to manage and analyze the data.
Imagine building a product recommendation system. You could use a vector database to store information about products and customers in a way that allows for quick similarity searches. Pentaho could then be used to bring in additional customer data, analyze the recommendations, and create reports on the system’s performance.
The Future of AI Development
Legal documents are just one example of using vector databases to streamline traditionally time-intensive tasks, or for creating new possibilities altogether. There is much that AI has helped us accomplish, and much more that it will allow us to uncover. From fraud detection in financial institutes to new discoveries in neuroscience, vectors and vector databases have opened entire new possibilities for technology. Given that we are still in the beginning of AI’s abilities, it’s hard to predict where it can go from here. In the meantime, however, we can use the existing technologies to build new apps that empower our businesses.
This blog post was authored by Pratham Mehta as part of Hitachi Vantara Federal’s Pentaho+ Data Science 2024 summer internship program.
