Why AI understands you better: vector search, let’s learn about it!

Now, do you find yourself relying more and more on recommendation systems? Sometimes the results you searched for yourself don’t seem to be as accurate as those recommended by AI?

Some people may be curious about how the recommendation system is so "smart"? The answer is: "vector search". Today, let's talk about this technology and see how it changes the way we get information.

1. What is vector search?

First of all, what exactly is vector search? Simply put, it is a search technology that "understands you".

Traditional search engines generally look for content that matches the keywords you enter. But vector search is different. It is smarter. It not only matches keywords, but also tries to understand your true intention and context, and then finds the content that best meets your needs.

You can think of it as an "old friend" who understands you very well. It knows what you want even if you don't seem to have said anything clearly.

Two obvious applications of vector search are recommendation systems and knowledge bases. Whether it is shopping, music recommendations or knowledge base retrieval, vector search is working silently behind the scenes.

For example, when you listen to a song on a music platform, the platform will not only recommend songs of similar styles, but also recommend some more similar songs based on the emotions expressed in the song and the content of the lyrics.

2. The core of vector search is vector and dimension

So, how does vector search do this? The key lies in "vector" and "dimension".

In mathematics, a vector has direction and size, and in vector search, text or data is converted into a "high-dimensional vector".

Each dimension represents a different feature of the data, such as sentiment, semantics, or context. Imagine that these vectors become points in a high-dimensional space, and the search process is to find the point closest to your needs in this complex space.

The above explanation may be a bit abstract. It can be understood as follows: traditional search is like finding a precise point on a flat map, while vector search is like finding an approximate point in a 3D space (multi-dimensional space), and the factors considered are more numerous and complex.

3. Vector search changes the way you search

Vector search not only makes search smarter, but also changes the way we obtain information.

In the information society, information is growing explosively, and the data is not only large in quantity but also very chaotic.

Vector search can transform this data into a form that we can understand and manipulate. It can help us not only find precise information, but also find the closest information through multiple dimensions, including synonyms, meaning, intent, and context.

Vector search not only searches for individual words, but also analyzes the complex relationships between words to better understand whether each choice is closer to or farther away from the meaning of the retrieved sentence.

In this way, we not only obtain information, but also find more meaningful results.

In the past, we needed to enter very precise keywords to find the information we wanted, but now even if the description is vague, vector search can help us find the most relevant content.

This is very convenient for ordinary users. They don’t need to understand various professional terms. As long as they use natural language to roughly express their needs, they can get accurate results.

4. Summary

The emergence of vector search has quietly changed the way we obtain information, marking a leap forward in search technology. It has been widely used in fields such as recommendation systems and generative AI.