tuning size parameter for doc2vec
Your data – over a million texts, and perhaps tens-of-millions of words – is certainly large enough to try a default vector-size of 100 dimensions. 
People with smaller datasets may need to try even smaller vector-sizes, but that's getting far from the cases where Doc2Vec ('Paragraph Vectors') works well. 
But the actual best size for your dataset & goals is something you have to find out via experimentation. (If your dataset is dominated by 5-word texts, and if your vocabulary of unique words is quite small, maybe you'll need to try lower sizes, too.) 
There's no one answer – the variety of your texts/vocabulary, & the patterns in your data, will affect the best choice. Only having your own project-specific repeatable evaluation, which you can use to compare alternate choices, can guid you to what's best.
I have a dataset of 1.2mil single sentence descriptions (5-50 words) and I want to cluster these into n clusters. For vector conversion, I want to use doc2vec to get 1.2mil equal size vectors. However, I'm not sure what should be the size parameter. I've read, it should be between 100-300 however since each document, in this case, has fewer tokens (words) should the vector be small?