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Xatkit's NLU Engine is (or better said, it will be, as what we're releasing now is still an alpha version with limited functionality, which is good to play with and to learn, not so much to use it on production ;-) ) a flexible and pragmatic chatbot engine. A couple of examples of this flexible and pragmatic approach.
At the core of the Xatkit NLU engine we have a Keras/Tensorflow model.
(obviously) Create your own chatbots (pairing it up with Xatkit or any other chatbot platform for all the front-end and behaviour processing components)
Probably not. In fact, in Xatkit we aim to be a chatbot orchestration platform exactly to avoid reinventing the wheel and the non-invented here syndrome. So, in most cases, other existing platform (like DialogFlow or nlp.js) will work just fine. But we have also realized that there are always some particularly tricky bots for which you really need to be able to customize your engine to the specific chatbot semantics to get the results you want.
The main.py module is in charge of exposing our FastAPI methods. As an example, this is the method for training a bot. It relies on Pydantic to facilitate the processing of the JSON input and output parameters. Parameter types are the dto version of the dsl classes.
At the core of the Xatkit NLU engine we have a Keras/Tensorflow model.
Main.py holds the API definition (thanks to the FastAPI framework). The dsl package has the internal data structures storing the bot definition. The dto is a simplified version of the dsl classes to facilitate the API calls. Finally, the core package includes the configuration options and the core prediction and training functions.