Vector Search

Vector search works by converting text into high-dimensional numerical representations called embeddings, where semantically similar content is positioned close together in vector space. When a user asks a question, the system converts it into a vector and finds the most similar content — even if the words used are completely different from the stored documents. For example, searching for "how to handle an unhappy customer" would find documents about "client complaint resolution" even though they share no keywords. This is what makes modern AI knowledge assistants feel natural to use: employees can ask questions in their own words instead of guessing the right search terms. Vector search is a core component of retrieval-augmented generation (RAG) systems.