The neural network and django chatbot server are forked from Conchylicultor/DeepQA. For more info on this project see their project on Github.
Since our use case is more specific, we have a variety of scripts to accommodate iterative training and automated deployment.
For training, it is highly recommended to use a GPU with nvidia-docker. If you don't have this set up already, go ahead and install it.
In order to use a pre-trained word2vec file, you must first download it and place it in /data/embeddings. DeepQA supports both the .bin format of the Google News word2vec embeddings, and the .vec format of the Facebook fasttext embeddings. The vec2bin.py is a small utility script to convert a .vec to a .bin file, which reduces disk space and improve the loading time.
Google News embeddings: https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing