Semantic Search With Vectors

If you’re keeping up with the latest trends in search, you may have come across the term “vector search” at some point. And if you’re anything like most people, you probably tried to do some research on the topic only to come out feeling more confused than when you went in. 

The thing is, vector search is complicated. But that doesn’t mean you can’t understand it. And understanding that vector search isn’t the future of search – hybrid search is – is just as important.

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What Are Vectors?

Vectors in machine learning contexts represent groups of numbers that stand for something else, like an image, word, or just about anything else you can think of.

The questions, of course, are why those vectors are helpful and how they are created.

Let’s look first at where those vectors come from in machine learning. The short answer is that machine learning creates vectors by finding patterns in data.

Why Are Vectors Powerful?

Vectors are powerful because they can take into account a lot of data. For example, the Google Translate algorithm looks at billions of words and sentences to figure out how language works. 

That’s why people are excited about using vectors for search. With so much data, vectors can find connections that make algorithms smarter.

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Besides, some experts say that vector search will eventually replace traditional keyword search as we know it, but this doesn’t mean that keyword search will become irrelevant overnight. 

It would be too optimistic to assume that the new vector search method will completely take over and make keyword search redundant. Keyword search has been around for decades, and it’s not going to lose all its value anytime soon.

Vector search and keyword searches each have their own strengths, and they will work best when they work together.

Vector Search For Long Tail Queries

If you work in search, you are likely very familiar with the long tail of queries.

This concept, made popular by Chris Anderson, is used to describe digital content and says that there are some items that are much more popular than others, but there are also many individual items that people still want. And vector search is fantastic for more unique, longer queries – or what we like to call ‘long tail’ queries. This is because it can analyze relationships between products even without synonyms set up. For example, if someone searches for a mauve dress, it can also show results for pink or purple dresses.

Moreover, vector search is especially useful for long or natural language queries. For example, a query like “something to keep my drinks cold” will bring up refrigerators in a well-tuned vector search, whereas with a keyword search, you would only be able to find results that contain that text in the product description.

In other words, vector search enhances how many results are found, or the recall of search results.

How Vector Search Works

Vector search engines work by taking groups of numbers and asking, “If I were to graph these groups of numbers as lines, which would be closest together?”

To visualize this, think of groups that have just two numbers. Group [1,2] is going to be closer to group [2,2] than it would be to group [2,500]. Of course, since vectors have dozens of numbers within them, they are “graphed” in a huge number of dimensions, which makes it difficult to visualize them.

The Downsides of Vector Search

Although vector search has some advantages, there are also some disadvantages to using this method.

The main downside is the cost. All of the machine learning that is required for vector search increases the cost of this type of search. 

Additionally, storing the vectors is more expensive than storing a keyword-based search index. Furthermore, searching on those vectors is usually slower than a keyword search.

The Continued Usefulness Of Keyword Search

Although keyword search is faster than vector search, vector search provides more accurate results. 

Furthermore, it is simpler to comprehend why results are ranked in the manner that they are.

Keyword Search As Beneficial For Head Queries

For this reason, keyword search is ideal for head queries – those queries that are the most popular. 

Head queries tend to be shorter and easier to optimize for. That means if a keyword doesn’t match the right text inside a record, it’s often caught through analytics, and you can add a synonym.

Because vector search works best for long-tail queries, the two search methods work best in concert. This way, you can cover all your bases and ensure that all types of queries are being matched with the appropriate results.

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Ranking Records Across Search Sources

Ranking records from two different sources can be quite difficult. The reason is that the two approaches have different ways of scoring records by their very natures.

One example is that a vector search will return a score, while some keyword-based engines won’t. 

However, there is no assurance that the two scores are equivalent, even if the keyword-based algorithms do yield a score.

Another option would be to put all the results through the vector engine or the keyword engine’s scoring process. The advantage of this is that you would get the extra recall from the vector engine. However, there are a few disadvantages to this approach as well. For example, the extra results recalled from the vector engine likely won’t be rated as relevant based on a keyword score – otherwise, they would have appeared in the results set already.

Vector Search As A Fallback

Some search engines don’t bother with presenting blended results, and instead, show keyword results first and vector results second. The idea is that if a search doesn’t turn up many results, the vector results might still be helpful. Keep in mind that vector search is designed to boost recall or find more results, so it might uncover relevant results that the keyword search missed.

This is a decent stopgap measure, but it is not the future of true hybrid search. True hybrid search will rank multiple search sources in the same result set by creating a comparable score across different sources. This will allow for more accurate and comprehensive search results, giving users the best possible experience.

Conclusion

Vectors are very potent in their ability to search through large quantities of data and provide highly relevant results. With semantic search, all the data you have will be indexed and, when queried, will be able to produce results that are highly relevant, but it will still take a human to decide what is relevant and what is not.

So, if you want to learn more, please contact us anytime at EverRanks. Thank you for reading, we are always excited when one of our posts can provide helpful information on a topic like this!

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