Embeddings

Embeddings are numerical representations of text, images, or other data types that capture their meaning and relationships in a mathematical form. By converting information into vectors, embeddings allow machine learning models to understand similarity, context, and semantic connections beyond exact keyword matches. Items with similar meanings are positioned closer together in vector space, enabling more accurate search, recommendation, clustering, and classification. In enterprise applications, embeddings support capabilities such as semantic search, document retrieval, personalization, and knowledge discovery. They serve as a foundational component in modern AI systems, improving contextual understanding and enabling more intelligent data-driven outcomes.