  In this paper, we present OmniSearchSage, a versatile and scalable system for
understanding search queries, pins, and products for Pinterest search. We
jointly learn a unified query embedding coupled with pin and product
embeddings, leading to an improvement of $>8\%$ relevance, $>7\%$ engagement,
and $>5\%$ ads CTR in Pinterest's production search system. The main
contributors to these gains are improved content understanding, better
multi-task learning, and real-time serving. We enrich our entity
representations using diverse text derived from image captions from a
generative LLM, historical engagement, and user-curated boards. Our multitask
learning setup produces a single search query embedding in the same space as
pin and product embeddings and compatible with pre-existing pin and product
embeddings. We show the value of each feature through ablation studies, and
show the effectiveness of a unified model compared to standalone counterparts.
Finally, we share how these embeddings have been deployed across the Pinterest
search stack, from retrieval to ranking, scaling to serve $300k$ requests per
second at low latency. Our implementation of this work is available at
https://github.com/pinterest/atg-research/tree/main/omnisearchsage.
