Currently I have one vector per sentence (self.corpus_embeddings). I then use the same model to embed a query phrase. Then use cosine similarity to rank corpus embeddings with query:
co_dist = scipy.spatial.distance.cdist(query_embeddings, self.corpus_embeddings, "cosine")[0]