What is hierarchical embedding architecture in large-scale systems?

It structures embeddings in multiple abstraction levels to improve scalability and semantic modeling.

Hierarchical embedding systems represent data at multiple granularity levels. Lower layers capture fine-grained details, while higher layers capture abstract semantics. This is used in hierarchical clustering, multi-level graph embeddings, and deep metric learning. It improves retrieval speed and semantic generalization by reducing search space at higher levels.