> For the complete documentation index, see [llms.txt](https://docs.cosmocloud.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cosmocloud.io/resources/vector-search.md). # Vector Search ## What is Vector Search? Vector search is a search method which returns the result which are close to your search query in multi dimensional space. ## Difference between Full Text Search (FTS) and Vector Search The basic difference between Full Text search and Vector Search is that it in FTS it returns only data based on the text-matches, whereas in Vector Search you get the result which are close to the vectors in multi-dimensional field. ## Concepts ### Vector A vector is a 1-D numerical array that represents data across multiple dimensions. Vectors can encapsulate various types of data, including text, images, audio, and unstructured data. Semantic similarity between vectors is assessed by calculating the distance between them. ### Vector Embedding Vector Embedding also known as vectorization is the process of converting the data into vectors. ### Embedding Model The LLM Models which convert normal data to Vector Embedding, for example OpenAI GPT model, AWS Bedrock, Google Gemini AI, etc. ## Useful links * [Create a Vector Search Index on Cosmocloud](/resources/vector-search/create-a-vector-search-index.md) * [Edit a Vector Search Index](/resources/vector-search/edit-a-vector-search-index.md) * [Delete a Vector Search Index](/resources/vector-search/delete-a-vector-search-index.md) --- # 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://docs.cosmocloud.io/resources/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.