Vector Embeddings Explained
What are vector embeddings? Vector embeddings are dense representations of objects (including words, images or user profiles) in a continuous vector space. Each object is represented by a point (or vector) in this space, where the distance and direction between points capture semantic or contextual relationships between the objects. For example in NLP, similar words are mapped close together in the embedding space. Types of vector embeddings Generative AI applications are built using vector embeddings and the data source can…