vectorstore
Vector store is a specialized data store designed to hold high-dimensional vector representations, or embeddings, produced by machine learning models. Each stored item typically combines a vector with metadata such as an identifier, text, image link, or timestamp. The primary goal is efficient similarity search over large collections.
Core operations include insert, update, delete, and query. A query supplies a vector and returns a ranked
Indexing techniques commonly used include hierarchical navigable small world graphs (HNSW), inverted-file systems (IVF), and product
Typical use cases include semantic search over documents, retrieval-augmented generation, multimedia retrieval, recommendations, clustering, and anomaly
Operational considerations cover latency and throughput, memory footprint, index update capabilities, embedding freshness, and security controls.