How do vector databases support modern NLP systems?

Vector databases store embeddings for fast similarity search in NLP applications.

Vector databases index high-dimensional embeddings using approximate nearest neighbor algorithms like HNSW or IVF. They enable fast semantic search, retrieval-augmented generation, and recommendation systems at scale.