  Search engine has become a fundamental component in various web and mobile
applications. Retrieving relevant documents from the massive datasets is
challenging for a search engine system, especially when faced with verbose or
tail queries. In this paper, we explore a vector space search framework for
document retrieval. Specifically, we trained a deep semantic matching model so
that each query and document can be encoded as a low dimensional embedding. Our
model was trained based on BERT architecture. We deployed a fast
k-nearest-neighbor index service for online serving. Both offline and online
metrics demonstrate that our method improved retrieval performance and search
quality considerably, particularly for tail
