Glossary

Vector Database

A vector database is a specialized storage system designed to manage, index, and query high-dimensional vector embeddings. By representing unstructured data—such as text, images, or audio—as numerical arrays in a multi-dimensional space, these databases enable efficient similarity searches based on semantic meaning rather than exact keyword matches or structured relational queries.

Vector databases are essential to modern AI architecture because they provide the long-term memory required for Large Language Models and autonomous agents. Traditional relational databases struggle to handle the high-dimensional data generated by embedding models, leading to performance bottlenecks during retrieval-augmented generation (RAG) tasks. By offloading semantic search to a dedicated vector engine, enterprises can maintain context-rich interactions and reduce hallucinations, ensuring that AI agents retrieve relevant, domain-specific information from vast, unstructured datasets with low latency and high precision.

In practice, implementing a vector database requires careful consideration of indexing algorithms, such as Hierarchical Navigable Small Worlds (HNSW) or Inverted File Index (IVF), which balance search speed against recall accuracy. Practitioners must manage the lifecycle of embeddings, including synchronization between the source data and the vector index, and the selection of distance metrics like cosine similarity or Euclidean distance. Effective governance involves monitoring index drift and ensuring that access controls are strictly enforced, as vector representations can inadvertently expose sensitive information contained within the original source documents.

Last updated: 2026-08-25