aircode VS nlp.js

We'll use the "natural" NLP library; follow the previous session to understand how to set up your environment.

Several NLP libraries are available, each offering distinct features and functionalities. One popular choice in the JavaScript ecosystem is the Natural Language Toolkit for JavaScript (NLP.js), which provides a wide range of NLP capabilities.

nlp.js/docs/v4/nlp-intent-logics.md at master · axa-group/nlp.js · GitHub

For a JS based approach you could try NLP libraries like this one: https://github.com/axa-group/nlp.js

To determine which chatbot intent is the best match for the user textual input, we rely on nlp.js (in JS) though we are in the process of moving to our new Python NLP server for better optimization of the needs of eCommerce conversations. A preprocessor language model is also used to improve the chances of a matching.

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.

Also, if security is so important, you may want to configure Xatkit to work with nlp.js (see our wiki for instructions( so that even the intent matching part is done locally without sending the input text to the cloud (as it would happen if you decide to use, for instance, a NLP engine such as DialogFlowx)

Since the previous release, NLP.js pushed a lot of work and has released a major version, moving from a monolithic library to multiple independent packages. So I spent some time to make Leon's NLP compatible to the latest changes.