Vector Database

A Vector Database stores text, images and other material as numerical embeddings and retrieves semantically close items using measures such as cosine similarity. Dedicated products include Pinecone, Weaviate and Milvus; extensions such as PostgreSQL’s pgvector add vector capabilities to existing databases. These systems have become core infrastructure for large-scale RAG and recommendation engines. Selecting an appropriate approximate nearest neighbor (ANN) indexing algorithm, such as HNSW, is important to sustaining millisecond retrieval across millions of records.

Business applications and impact#

Core infrastructure for large-scale RAG systems and semantic recommendation engines.

Implementation guidance and considerations#

Choose suitable indexing algorithms such as HNSW and maintain consistent dimensionality with the embedding model.