Definition
A vector database stores vector representations and enables efficient similarity searches.
Suppose a customer says: “I am upset with your payment system and expect a refund.” A simple keyword search may look for documents containing the word “upset.” A semantic retrieval system can instead identify related concepts such as low ratings, payment failures, refunds, or customer dissatisfaction.
Because related concepts can be represented by nearby vectors, vector databases can help retrieve documents that are semantically relevant even when they do not contain the exact words used by the customer.
Vector databases are therefore commonly used as a retrieval layer in RAG systems.
Why It Matters
Vectors and vector databases help you understand semantic retrieval — how an AI product finds relevant information by meaning, not just keyword matching.