> 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/how-to-use-vector-search.md). # 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 --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## 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, and the optional `goal` query parameter: ``` GET https://learn.getvectorai.com/what-are-vectors/what-is-vector-search/how-to-use-vector-search.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.