Doc2Vec Input from Paragraphs I understand how doc2vec works, but I am unclear the best practice on feeding in data.

Suppose we have a document with multiple sentences

['I really love football. Peyton Manning was a great player']

If we feed this into the algorithm as is, the window for 'Peyton' could include ['love','football','Manning','was']

This doesn't make intuitive sense to me, however, since the words come from different sentences.

Any suggestions?
The aim of Doc2Vec is to produce document level embeddings, thus even if words are sentence-separated if you include them in the same document it has to be considered part of the same semantic source for word similarities. If you don't want such a behavior, you might want to separate your documents in a sentence-level and maybe aggregate the embeddings later in different groups to form your initial paragraphs (depending on your plans)?