Vector Database
A database optimized for storing and searching embedding vectors, enabling fast semantic similarity search across millions of text chunks or documents.
A vector database stores high-dimensional numerical vectors (embeddings) and provides efficient similarity search — given a query vector, find the most similar vectors in the database. This is the core infrastructure component of RAG systems, powering semantic search over private document collections, knowledge bases, and long-term memory for AI agents.
Popular vector databases include Pinecone, Weaviate, Qdrant, and Milvus (purpose-built), as well as pgvector (a PostgreSQL extension) and Chroma (lightweight and developer-friendly). They use approximate nearest neighbor (ANN) algorithms like HNSW and IVF to perform billion-scale searches in milliseconds.
The key metric for vector databases is recall at K — what fraction of the true top-K nearest neighbors are returned by the approximate search. Most production systems tune for a balance of recall and latency. As models improve and RAG architectures mature, vector databases have become core infrastructure for enterprise AI applications.
Read next
Retrieval-Augmented Generation (RAG) Explained
How RAG systems work, why they're the standard architecture for enterprise AI, the common failure modes, and how to build a production-quality RAG pipeline.
Embedding Models: The Unsung Heroes of AI Applications
What embedding models are, how they create vector representations of text and images, why they're essential for semantic search and RAG, and how to choose one.
LLMs for Business: A Decision-Maker's Guide
Strategic guidance for business leaders evaluating AI — from identifying high-ROI use cases and build-vs-buy decisions to governance, risk management, and change management.