Selecting a model for semantic search at Dropbox scale

This post details the integration of semantic search into Dropbox's existing Nautilus search engine. It describes the shift from keyword-based search to understanding meaning and intent through embeddings. The authors evaluated text embedding models using a customized version of the Massive Text Embedding Benchmark (MTEB), adapting it for Dropbox's infrastructure, handling multiple embeddings per document, optimizing precision and dimensionality, and incorporating filename embeddings. The post highlights the observed metrics of a nearly 17% reduction in empty search sessions and a 2% lift in search session success rate.