Vector Database (Vector DB)

Vector Database (Vector DB) is a specialized database system designed to store, index, and query high-dimensional vector embeddings efficiently. These databases use advanced indexing algorithms like Hierarchical Navigable Small World (HNSW), Locality-Sensitive Hashing (LSH), or Inverted File Index (IVF) to enable fast similarity search and nearest neighbor retrieval. Vector databases are essential for AI applications including semantic search, recommendation systems, retrieval-augmented generation (RAG), and similarity matching. They support various distance metrics such as cosine similarity, Euclidean distance, and dot product to measure vector relationships. Popular vector databases include Pinecone, Weaviate, Chroma, and Qdrant. These systems handle massive-scale vector collections while maintaining sub-millisecond query performance, making them crucial infrastructure for modern AI workflows that require efficient similarity search across embeddings generated by machine learning models.