[How to: use a vector store to retrieve data](https://python.langchain.com/docs/how_to/vectorstores): LLM should read this page when building applications that require searching over large collections of text, when indexing and retrieving relevant information based on similarity between embeddings, and when working with vector databases and embeddings. The page covers how to create and query vector stores, which are used to store embedded vectors of text and search for similar embeddings. It explains how to initialize different vector store options like Chroma, FAISS, and LanceDB, and how to perform similarity searches on them. It also touches on asynchronous operations with vector stores.

