How would I implement a model in Keras to generate embeddings based on documents?
For starters, it depends on why you're trying to create Document Embeddings.
There's the TF-IDF algorithm; it's simple to grasp and implement, and it facilitates using cosine distance as a metric. To train in parallel, you could probably feed both documents to the model and have a synchronized counter doing the calculations such as Term Frequency and Inverse Document Frequency, and then in the end you can use those values to scale the elements of your TFIDF matrix.
Alternatively, you can use the Doc2Vec algorithm as explained here.
It's an extension of the Word2Vec model, but parallelizing the training for this one might be more complicated.
I'm sure there are more ways, but these are the two most popular that I know of.