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The frustration of trying to search for something and being unable to find it quickly and efficiently may be one of a user ’ s most disappointing experiences . You want to build a site where that rarely happens .
However , users make it very hard . Oftentimes , they do not know exactly what they are looking for . They have a picture in their mind of what they want but lack the precise terms , and their search ends up being submitted with keywords such as : “ the thing that tightens screws ."
A human respondent to that search will return an index of screwdrivers . What will your keyword-based search return ?
y Articles about tightening techniques . y Blog posts on different types of screws . y Tools that have nothing to do with screwdrivers .
This example happens all the time , every single day , countless times a day .
Facing this dilemma requires a new resource to improve the user experience and bring clarity even when users lack it . Vector search offers possibilities that are not feasible with traditional keyword search alone .
How vector search works
Vector search is a machine learning method that transforms textual data into high-dimensional vectors , capturing semantic relationships between words and phrases . It differs from traditional keywordbased search , which relies on exact matches , by understanding the context and meaning behind
Vector search offers possibilities that are not feasible with traditional keyword search alone .
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