What Are Vector Databases? Key Concepts in Embeddings and Similarity Search

Vector databases store data as numerical vectors that represent the features of objects, enabling efficient similarity search across large datasets. Unlike traditional databases, they represent a fundamental shift in how data is stored and queried, making them especially valuable for AI-driven applications. Embeddings — lists of numbers capturing the meaning or characteristics of objects like words, images, or songs — are central to how these databases function. By calculating distances between vectors, the system identifies the most similar items, a technique used in recommendation engines, image search, and natural language processing. Tools like the Faiss library help developers implement optimized similarity search, and real-world applications range from music and movie recommendations to content discovery platforms.