🌱Combining with traditional search

A comparison of vector search and traditional search.

Assumed Knowledge: Vectors Target Audience: General Audience Reading Time: 3 minutes

If you are interested in comparing vector and traditional search, we recommend firstly looking over the playground which can be found here: https://playground.getvectorai.com/interact/search/text?collection=quora.

An example of traditional (left) vs vector search (right)

While the results may appear obvious, we will go over how this works and is built.

Firstly - what is traditional search?

Traditional search is reliant on words and the number of times a word may appear in a specific document. For example, if we are searching for the word "dog", sentences where the word "dog" appear more times will end up occurring. However, as a result, if we search for the question "what is a dog", we will note that no results will come up because there may be no results that contains all the words.

So how does vector search resolve this?

Vector search instead identifies the meaning behind the strung together text so instead of interpreting every word for what it is worth - it identifies the semantic meaning of the strung together text and outputs a vector for search on all the results. As a result - vector search becomes a useful tool.

However - we can sometimes get the best results from combining traditional search with vector search.

We can sometimes get better results from combining traditional search with vector search. This is useful because it takes into consideration the exact words we type a well as the meaning of the strung-together text. Vector AI allows us to do this using the hybrid search API. You can find out more about its use case and application in Combining Vector Search With Traditional Search.