Rasa is an open-source conversational AI framework that uses machine learning to build chatbots and AI assistants. Today, I'm going to show you how to build your own simple chatbot using Rasa and deploying it as a bot to Facebook messenger -- all within an hour. All you need is some simple Python programming and a working internet connection. The code was developed and tested in Python 3.7. Rasa currently only supports Python up to 3.8 (see here for updates).

In this article, I will share my workflow for creating conversational AI agents using the Rasa framework. It uses VS Code and Docker to automate repetitive tasks and code in an OS-agnostic way. The code to reproduce the results can be found here. I assume the reader has a basic familiarity with Rasa and Docker. The goal of this workflow is to make it easy for developers to alternate between these 3 tasks.

When creating bots you have to converse with the users and maintain context of the conversation so you can understand the intent of the users, do the necessary actions and provide users with the information they need. Doing it all by yourself is a large undertaking as it involves doing Natural Language Process (NLP) and providing yourself with some easy mechanism to improve your conversation models. To solve this problem various services exist e.g. In this tutorial we will look into how to use Wit.ai to create conversation models and use them in Elixir. This can be used in your messaging bot, IoT device or any other project where you want to take some actions based on conversing with user.