ways to represent document by its keyword vectors I have documents, say for example, D1, D2, D3... Dm.

Every Di has its individual components or keywords k1, k2, k3,... kn, where ki is an n-dimensional vector. The number of individual components varies between documents.

What are the ways to find how close Di are? Or what is the best way to represent a document using its keyword vectors? Please note that I'm using a custom embedding here.
If you have the vectors for your keywords, you can aggregate those to get the document vectors. The simplest(and probably one of the most effective) way is to average out your keyword vectors to form the document vector. Once you have the vectors for each of the documents, you can use similarity measures like cosine similarity to see how close your documents are. 
A typical example of such a method is this :


Edit: Another interesting point would be to evaluate how you are getting your word vectors. Please look at pretrained word embeddings like word2vec, Glove or Fast Text.