I have now migrated it to TensorFlow v2 and I modified it to suit our needs.
Is there a way I could improve my search functionality further by looking at the question summary (which is a description of what the question covers) and the answer content as well?
I also have some alternative versions of the same questions (re-worded) and I have been thinking of creating additional dense_vectors and adding them in separate fields (
title_vector_3) and modifying the query so that it does a cosine similarity on each one and then take the max value - is that the best way to go about it? is there a way to not hard code separate vector fields and use a list or array type? What is the best way forward?
Also, the aforementioned post uses the universal-sentence-encoder v2 and I use the next version (v3). Would there be a benefit in using universal-sentence-encoder-multilingual-qa instead? I have tried but I am not sure how to make use of the extra embeddings and the product generated by
np.inner() in the example. Any ideas?