Vector Database

A vector database is a specialized database system designed to store, index, and search high-dimensional vectors (embeddings) efficiently. Unlike traditional databases that excel at exact matching and structured queries, vector databases are optimized for similarity search-finding vectors that are closest to a query vector in the vector space. Vector databases store embeddings created from text, images, or other data, along with metadata about the original content. They use specialized indexing techniques like approximate nearest neighbor (ANN) algorithms to enable fast similarity searches even with millions or billions of vectors. Common vector databases include Pinecone, Weaviate, Milvus, Qdrant, and Chroma. The key advantage of vector databases is speed and scalability. Without them, finding similar items would require computing distances between a query vector and every stored vector, which becomes prohibitively slow with large datasets. Vector databases use clever indexing structures to dramatically speed up this process while maintaining reasonable accuracy. Vector databases are essential for several AI applications. In retrieval-augmented generation (RAG), they store embeddings of documents and retrieve relevant context for language models. In semantic search, they find documents or products similar to a query. In recommendation systems, they identify similar users or items. In image search, they find visually similar images. As embeddings become increasingly central to AI applications, vector databases have become critical infrastructure for modern AI systems, enabling practical applications that would otherwise be too slow or expensive to implement.