Why is Vector Search Becoming So Critical?

Modern society is increasingly using and relying on generative AI models. 

A report from The Hill noted that generative AI “could drive a 7% (or almost $7 trillion) increase in global GDP and lift productivity growth by 15 percentage points over a 10-year period.” Generative AI describes algorithms that can be used to create new audio, code, images, text, videos, and simulations. The importance of generative AI for modern business is increasing at such a rate that Amazon CEO Andy Jassy disclosed that generative AI projects are now being worked on by every single one of Amazon’s divisions. 

With this rise in generative AI use cases comes a massive increase in the amount of data. The International Data Corporation predicts that by 2025, the global data sphere will grow to 163 zettabytes, 10 times the 16.1 zettabytes of data generated in 2016. In response to this increasing amount of data, more companies and developers who work in advanced fields are turning to vector searches as the most effective way to leverage this information. 

This article will examine what a vector search is and the critical ways it is being used by developers. 

How Do Vector Searches Work?

A vector search compiles a wide range of information from a vector database to create results outside of what would be expected from a regular search.

These vector databases are an ultramodern solution for storing, swiftly retrieving, and processing high-dimensional numerical data representations at scale.

Compared to a traditional SQL database, where a developer could use keywords to find what they are looking for, a vector database can effortlessly enable multimodal use cases from information of all types, ranging from text and images to statistics and music. This is done by turning the information into vectors.

As explained by MongoDB, a vector can be broken down into components, which means that it can represent any type of data. The vector is then usually characterized as a list of numbers where each number in the list represents a specific feature or attribute of that data. When a user does a vector search, it doesn’t just look for exact matches but recognizes content based on semantic similarity.

This means the database works better for identifying and retrieving information that is not just identical but similar to the request. A simple example of this would be that a keyword search for documents would only point to documents with that exact keyword, while a vector search would find similarities between documents, creating a much broader search.

Critical Use Cases For Vector Searches

Helping Clients Manage Large Datasets 

Vector databases are being offered to a wide range of clients to help efficiently manage and query large datasets in modern applications. A good example of this is Amazon Web Services (AWS), which has heavily invested in generative AI to help its clients. The services offer vector databases like Amazon OpenSearch, which can be used by clients for full-text search, log analytics, and application monitoring, allowing clients to get insights from their data in real time. 

Recommendations for Customers

Customer service is the cornerstone of every business, and ecommerce platforms are implementing vector searches to help their customers by using the data collected on them. In an article titled Why Developers Need Vector Search, The New Stack details how vector databases and vector searches can build a recommendation engine for their customers. This is done by seeking similarities across data in order to develop meaningful relationships. When a customer searches for a particular item, the vector database will also find and recommend similar items, improving the company’s customer service and increasing the chance of more sales. 

Tracking Down Copyright Infringement 

Due to the vast amount of unstructured data available online, developers are increasingly using vector searches to track and enforce copyright infringement. The example The New Stack gives is social media companies like Facebook. Every media that is uploaded to the platform creates a vector, which is then cross-checked against copyrighted vectors. Because a vector search can find similar data points in unstructured data like videos, it allows the user to filter through a much wider database with greater accuracy. This makes it much harder for those who want to share material they don’t have the rights to.

As more companies rely on data to reorganize and develop their businesses, vector searches will become increasingly more critical.