# How to vector search

**Assumed Knowledge**: Vectors\
**Target Audience**: General audience\
**Reading Time:** 1 minute

Vector Search is reliant largely on index libraries and open-source models. Each vector search guide follows a template of: 

![Process of indexing and searching vectors](https://imgr.whimsical.com/object/BmHEJCcy6HtZYoNvRSgB8j)

The steps/materials can be summarised in the following way:\
**Data**: Obtain the data in a way that can be processed into a numerical representation and fed through the necessary model.\
**Encode**: Feed the data's numerical representation into a model and extract the vector which can be indexed and searched.\
**Index**: Indexing the vectors (from which the data has been encoded) in an efficient way that allows for retrieval.\
**Search**: Search the vectors that have been indexed by using a variety of Nearest Neighbor algorithms, filters, chunking and queries. 

**The Difficulties of Vector Search**\
While the above process appears simple, there are a lot of difficulties with actually using vector search for production. These difficulties include: 

* Deploying your index and search for production
* Usage of vectors to optimise search results 
* Optimising the way search is being done on the vectors 
* Optimising the matching of user intent and products