A vector database stores and searches data as mathematical vectors (embeddings). Unlike traditional databases that search for exact matches, vector databases find items that are semantically similar — meaning they understand meaning and context, not just keywords.
They’re essential for RAG systems, semantic search, recommendation engines, and any AI application that needs to find relevant information based on meaning rather than exact keywords.