Creating Paragraph/Document Embedding with SBERT?

Hi, first thx of this awesome library. I'm new in NLP feld so if the following question is naive, please forgive me.

Since BERT works on sentence or 2 sentences (if my understanding is correct), and SBERT is based on bert, so is that possible that i use SBERT to get Embeddings for paragrah or document?

And as i see in your publication, its a siamese network (i understand it as that the pretrained bert here for both sentence 1 and 2 are the same or using same weight), is it possible to implement a pseudo-siamese network here? So that it can deal with more complicated problems, like accepting a document as sentence 1 and the document title as sentence 2 and judge if the document contect suits the title?

The issue asks whether SBERT can create paragraph or document embeddings and whether a pseudo-siamese document/title setup is possible. No files, tests, or entry points are named; first clarify the intended API and training behavior. Done would require a decided, testable scope for document embeddings or the proposed architecture.

Written by the indexing model from the issue text.