Semantic IDs: Product Understanding at Scale

This post details the implementation of Semantic IDs (SIDs) at Instacart, a system that compresses product embeddings into discrete codes using a Residual Vector Quantizer (RQ-VAE). It explains the contrastive training approach that leverages catalog structure for supervision, the hierarchical batch sampling strategy to ensure diverse training data, and the loss function combining reconstruction and contrastive terms. The post also introduces a two-flavor strategy for generating SIDs: ESCI for precision (substitution, search, reordering) and ESCI+Gemma for discovery, utilizing Gemini Flash for broader product understanding.