# Introduction to vectors **Assumed Knowledge**: Vectors\ **Target Audience**: General Audience\ **Reading Time:** 3 minutes **What are vectors?** Vectors are a list of numbers that meaningfully and uniquely represent data. See below for an example. ``` [0.324, 0.241, 0.934, 0.424, 0.141, 0.242] #example of a vector ``` Although vectors may look like a series of random numbers, they are actually the result of carefully constructed and trained artificial neural networks (more details below). **Think of vectors as the fingerprint of data**. Much like how everyone has their own fingerprint, every piece of data (whether it is an image, video, text or audio) has its own vector. ![Vectors are much like fingerprints of data.](/files/-MTOAr1yYfYcDsNiEbYC) **How are vectors constructed?** Vectors can be constructed from:\ 1\) A row of data (for example - the machine learning model is an excellent example\ 2\) Extracting a layer in the middle of a neural network ![We can extract vectors from neural networks](/files/-MTOIHlsb8oUm4DJJmNA) Neural networks allow us to provide a new way to see the work done. --- # 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/untitled.md?ask= ``` 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.