Rasa is an open-source framework for building chatbots and conversational AI systems. It provides a set of tools and libraries that allow developers to build, train, and deploy chatbots and other conversational systems using natural language processing (NLP) and machine learning techniques.

Rasa consists of two main components:

Rasa Core: This is a library for building chatbots and conversational systems that can understand and respond to user input in a natural and intuitive way. It uses machine learning techniques to learn from example conversations and generate responses based on the user's input and the context of the conversation.

Rasa NLU: This is a library for natural language understanding that can be used to extract structured data from user input and understand the intent behind the user's messages. It can be used to classify user input into different categories, such as determining whether a user is asking a question or making a request.

Rasa is designed to be highly customizable and can be integrated with a variety of messaging platforms and chat tools, such as Slack, Facebook Messenger, and WhatsApp. It is often used to build customer service chatbots, virtual assistants, and other conversational AI systems that interact with users in a natural and intuitive way.

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Glad to see your interest in this topic. We know that Rasa can be integrated in Codesphere. Unfortunately our documentation is still being built up and this specific document does not exist yet.

We are working really hard to continuously improve our documentation, until we have caught up we are glad to assist you personally in setting this up.

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