Episode 36: Searching by Meaning — What Is Vector Search and How It Moves Beyond Keywords

A New Way to Search: Finding by Meaning, Not Keywords

When you hear the word “search,” you might think of typing keywords into Google or Yahoo! to look something up. But recently, a new style of searching has been gaining attention—one that focuses not on “keywords” but on “meaning.” At the heart of this approach is a technology called “vector search.” While the term may sound unfamiliar, it’s gradually making its way into our everyday lives.

Understanding Meaning Through Numbers

In simple terms, vector search is a method for finding things that are similar in meaning. Here, “similar” doesn’t just mean words that match exactly—it refers to similarity in a way that feels more natural or intuitive to humans.

For example, if you search for “dog,” results like “Shiba Inu” or “Golden Retriever” might appear—specific breeds related in meaning. This is made possible by a technique called “embedding.” Embedding transforms text or images into sets of numbers—called vectors—that computers can work with. By measuring the distance between these vectors, we can determine how closely related their meanings are.

You can imagine it like a large map where pieces of information are scattered as dots. Each dot represents some content, and those with similar meanings are placed near each other. So if you look up “apple,” nearby dots might include “fruit,” “orange,” or “juice.” When you perform a search, the system finds the dot closest in meaning to what you’re looking for and shows you related information.

Real-World Uses Are Already Around Us

One strength of this method is its ability to handle vague or fuzzy questions—something traditional keyword searches often struggle with. For instance, if you ask for “summery music” or “a book that calms me down,” vector search may be able to suggest fitting options based on the overall feeling or context.

Because it can quickly find similar data from large datasets, this technology is also widely used in areas like AI-powered Q&A systems, chatbots, and image recognition. For example, it helps find products similar to a photo you’ve taken or choose expressions that fit the context in machine translation.

However, there are challenges too. Since vectors created through embedding are hard for humans to interpret directly, it’s often difficult to explain why certain results were shown. To address this issue, efforts like “Explainable AI” are being developed so we can better understand how AI reaches its conclusions.

If traditional keyword search is like looking up words in a dictionary, then vector search could be seen as a kind of dictionary that reads between the lines—it picks up on nuance and background meaning. While still evolving, its accuracy continues to improve every day. This technology also plays an important role in cutting-edge AI developments such as large language models (LLMs) and Retrieval-Augmented Generation (RAG), which we’ll explore in future articles.

Bringing People Closer to Information

We’re now entering an era where even the act of “searching” is starting to change. Instead of just typing out words, AI may soon be able to gently respond even when we can’t quite put our thoughts into words.

Vector search offers an intriguing glimpse into that future—a future where technology understands not just what we say but what we mean. Though it may not yet be widely known by name, this quiet technology is already working behind the scenes in many apps and services you use every day. And little by little, it’s helping bridge the gap between people and information.