Vector Database

Key facts

• Where traditional databases retrieve records by exact key or index match, a vector database retrieves records by semantic similarity — returning the vectors and associated documents closest to a query vector in the embedding space.
• This makes vector databases the foundational infrastructure layer for RAG systems, semantic search, recommendation engines, duplicate detection, and long-term agent memory.
• Common vector database systems include Pinecone, Weaviate, Qdrant, Milvus, Chroma, and pgvector (a PostgreSQL extension).
• Each makes different trade-offs between index algorithm (HNSW is the most widely used for low-latency high-recall search), filtering capabilities (metadata predicates alongside vector queries), scalability, and operational complexity.
• Performance is typically measured by recall@k — what fraction of the true top-k nearest neighbors are returned — balanced against query latency and indexing throughput.
• As RAG architectures have matured, vector databases are typically used in conjunction with an embedding model (to generate vectors at index and query time) and often with a re-ranker (to refine initial ANN results with a more precise cross-encoder pass).
• Vector search is also increasingly available natively inside general-purpose databases (PostgreSQL via pgvector, Redis, Elasticsearch), reducing the need for a dedicated specialized system.
• Understanding vector databases is prerequisite knowledge for building any retrieval-augmented LLM system.