> For the complete documentation index, see [llms.txt](https://learn.getvectorai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://learn.getvectorai.com/what-are-vectors/what-is-vector-search.md).

# What is vector search?

**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](/what-are-vectors/untitled.md).

![Image Vector Search aims to identify the closest vectors based on given images.](/files/-MTIhIrLsO0E7gYiFD60)

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: 

1. Similar data have similar vectors.
2. 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](https://en.wikipedia.org/wiki/Nearest_neighbor_search).

**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](/what-are-vectors/untitled/applications-of-vectors.md).