How to Use Vector Databases to Build Intelligent Applications

In today’s digital society, data is considered to be the new oil due to its increasing importance across industries. As Gagan Bhangu outlined in his post on business customer data, in the current digital environment, businesses gather and examine enormous volumes of data to comprehend the demands, tastes, and behavior of their customers. They may then turn raw data into meaningful insights by utilizing artificial intelligence (AI).

Many of these businesses will use intelligent applications that utilize AI to enhance their functionality and provide an enhanced user experience. This includes historical and real-time data analytics, personalized experiences, and adaptive learning. In order to build an effective intelligent application that is able to gather, store, and use this data, developers will need to use an advanced data management system. Vector databases are increasingly being used in the creation of intelligent applications because of the unique way they store and search for data compared to more traditional databases.

What is a Vector Database

Vector databases are unique because they store data on vectors. A vector is an ordered list or sequence of numbers that represents the data. Each number in the list represents a specific feature or attribute of that data. A guide to MongoDB’s vector databases explains how a vector can represent any type of data, including unstructured data, which is data without a pre-defined data model or schema. This can range from documents and images to audio and video files. The data is converted into a vector using an embedding model, which then enters the vector into the database. Instead of rows and columns typical of relational databases, vector databases represent vectors as points in a multi-dimensional space. As a result, vectors that are similar to each other, through deeper semantic essences or contextual nuances, cluster together naturally. This allows a vector database to perform a similarity search where, instead of hunting for precise matches between identical vectors, a vector database uses a similarity search to identify vectors that reside near the given query vector. It is these storage and search abilities that make vector databases ideal for building intelligent applications.

How Vector Databases are Used to Build Intelligent Applications 

The way vector databases enable systems to store, index, and search high-dimensional vector embeddings means they can power everything from recommendation engines to generative AI. If the intelligent application being built requires a recommendation system for customers, a vector database with all the customer data inputted can produce recommendations by finding items whose embeddings closely match a user’s profile.

A Medium vector database article details how they can even search a product catalog using just an image. If the application being built is designed to provide generative AI services, such as a chatbot or virtual assistant, vector databases provide external knowledge to large language models. When a user submits a prompt, the system retrieves the most relevant context from the database and includes it in the model’s input, greatly enhancing accuracy and relevance.

A ResearchGate paper on vector databases for generative AI notes the importance of choosing the right vector database for a GenAI application. The paper states that it requires careful consideration to ensure it aligns with specific application requirements and technical capabilities. The vector databases should also be able to continuously train intelligent applications by adding vectors to ensure the application stays up to date.

Examples of Intelligent Applications Built by Vector Databases

Image Recognition Applications

Images stored in a vector database can be used to match images using a similarity search. This can range from platforms like Pinterest using image recognition to help users find similar items or products to facial recognition for security biometrics.

Healthcare Applications

Vector databases with patient information can perform similarity searches to immediately analyse patient information to find patterns that would have taken medical professionals much longer. These applications allow for a much faster and more accurate diagnosis. This provides more personalized treatment plans and reduces guesswork.

Financial Applications

A financial application that was built with a vector database can be used to analyze financial data, identify trends, and detect fraudulent activities. A robo-advisor is a common type of intelligent application that provides algorithm-driven financial management services. By running analyses on vectors with the latest financial information, these applications can provide accurate financial guidance.

As more companies invest in intelligent applications, vector databases will be increasingly used to build them. The way they store and organize data, along with the wide range of use cases, makes them ideal for creating intelligent applications.