How do vector databases enable unsupervised learning at scale?

Vector databases store embeddings and enable fast similarity search for unsupervised models.

Vector databases like FAISS, Pinecone, and Weaviate store high-dimensional embeddings and support approximate nearest neighbor search. They enable unsupervised systems like clustering, recommendation, and retrieval-augmented generation by efficiently querying similarity in embedding space. These systems rely heavily on indexing structures like HNSW and quantization to scale to billions of vectors.