Vector Database

A Vector Database is a specialized database system designed to store, index, and efficiently search high-dimensional vector embeddings that represent data in a semantic space. Unlike traditional databases that store structured data in rows and columns, vector databases store numerical representations (vectors) of unstructured data like text, images, or audio. These databases enable semantic search by finding vectors that are similar to a query vector, typically using techniques like approximate nearest neighbor (ANN) search. Vector databases are essential components of modern AI systems, particularly in applications like retrieval-augmented generation (RAG), recommendation systems, and similarity search. They can handle millions or billions of high-dimensional vectors while maintaining fast query performance. Popular vector databases include Pinecone, Weaviate, Chroma, and Milvus. These systems often include features like metadata filtering, real-time updates, and integration with machine learning pipelines. Vector databases have become increasingly important as organizations seek to leverage their unstructured data for AI applications while maintaining performance and scalability.