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 list of items by proximity or similarity according to metrics such as cosine similarity, Euclidean distance, or inner product. Because exact nearest-neighbor search is costly in high dimensions, vector stores rely on approximate methods to scale.

Indexing techniques commonly used include hierarchical navigable small world graphs (HNSW), inverted-file systems (IVF), and product quantization (PQ), or combinations such as IVF-PQ. Implementations span libraries and services such as FAISS, HNSWlib, Annoy, Milvus, Weaviate, and Pinecone, with options for CPU or GPU acceleration and distributed deployment.

Typical use cases include semantic search over documents, retrieval-augmented generation, multimedia retrieval, recommendations, clustering, and anomaly detection. Vector stores are often integrated with AI pipelines via APIs or SDKs, enabling hybrid workflows that join semantic filtering with structured queries.

Operational considerations cover latency and throughput, memory footprint, index update capabilities, embedding freshness, and security controls. The choice of similarity metric, dimensionality, and index configuration affects accuracy and performance, while deployment options balance on-premises versus cloud.