What is embedding retrieval architecture for classification systems?

Embedding retrieval architecture uses vector similarity search to retrieve relevant candidates before classification.

Instead of classifying directly over all labels, systems first map inputs into embedding space and retrieve nearest neighbors using vector search. These candidates are then passed to a classifier for final prediction. This reduces search space complexity and improves scalability in large label systems. It is widely used in recommendation systems and semantic classification.