# 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).


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://learn.getvectorai.com/what-are-vectors/what-is-vector-search.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.