Vector Database

A vector database is a type of database designed to store and search data based on semantic similarity rather than exact matches. It's a key component of RAG systems: documents are converted into numerical representations (vectors) that capture their meaning, stored in the vector database, and retrieved based on how semantically similar they are to a query. Vector databases are what allow AI tools to answer "what does our policy say about X?" accurately — they find the most semantically relevant document sections even when the query doesn't exactly match the document wording.