What are key dataset requirements for topic models and word embeddings?
Similarity of documents can be done with varied approaches. 

As your documents are based on domain based words, you could employ a tfidf representation for each document and compute similarity based on this.

Previously, I have used word2vec representations of words and constructed document vectors by taking an average of all the vectors ( as one of the approaches ) and did a cosine similarity between these vectors. Basically an n*n similarity computation.

When you do topic modelling, even though the idea is to find documents which have similar topic distribution, there is a prior to the approach i.e identifying number of topics. Just using randomly 100 topics without looking at the distribution of the words might lead you on a wrong path. 

A very interesting approach I have recently come across is combining topic modelling and word vectors, here is the link to a blog by stitchfix : http://multithreaded.stitchfix.com/blog/2016/05/27/lda2vec/