What is vector search?
An introduction to vector search/nearest neighbors.
Last updated
Was this helpful?
An introduction to vector search/nearest neighbors.
Last updated
Was this helpful?
Assumed Knowledge: Vectors Target Audience: General developers Reading Time: 3 minutes
What is vector search?
Vector search is the process of finding the most similar vectors to itself. If you are unfamiliar with vectors, I recommend reading about them here.
Although we initially used the analogy of vectors as fingerprints in our introduction, vectors actually have additional properties that allow them to be useful in practical applications. These include:
Similar data have similar vectors.
You can measure the similarity of these vectors statistically in a number of different ways.
The most common algorithms that are used are called nearest neighbor algorithms. You can read more about them here.
Why is vector search important?
If you can find similar vectors based on the data - this means, you can provide different ways of linking data in ways individuals may have never considered. Linguistically - you can now link sentences based on semantics as opposed to relying on co-occurrences of words (used in traditional word search). Similarly, for image search, you can use reverse image search and personalised image search -- allowing for better recommendations for searches. If you are interested in vector search applications, you can read about them here.
