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Rasa is a conversational AI platform that provides open-source software, the Rasa Stack, for creating contextual AI assistants and chatbots. In addition to the free Rasa Stack, there is also the Rasa Platform. This is also based on the Rasa Stack, but offers additional features for enterprise customers, such as a user interface with functionalities like the Training Data, Admin, Conversations and Models API. Rasa is developed by the Berlin-based start-up of the same name, and there are also over 300 contributors who promote Rasa in the open-source sense. [ 1][2]

Structure of the Rasa Stack

The Rasa Stack is divided into Rasa NLU and Rasa Core. These are structured in such a way that they can be used completely independently of each other. Thus, it is possible to build only part of the architecture on Rasa and to include additional services. Despite this fact, the two components are very well coordinated and are therefore quickly configured. The Rasa NLU takes over the tasks of intent recognition and entity extraction, while Rasa Core handles the complete session management, context handling and bot responses.[3]

Advantages and disadvantages of Rasa

First of all, the advantage of the open-source idea must be mentioned. On the one hand, one is not dependent on licences that are subject to a fee, and on the other hand, a very high degree of use-case-specific configuration is possible by viewing the entire source code. For example, industry-specific terms such as "cash" and "balance", which have the same meaning in the banking environment but must be differentiated in general, can be mapped much better in a chatbot. Of course, this also means that you have to work more intensively to get an optimal solution when you compare it to ready-made solutions such as Dialogflow from Google or LUIS from Microsoft.
In addition to ready-configured solutions, these also offer hosting of the service. Unlike these cloud solutions, Rasa runs on premise. This gives you complete control over the data generated by the chatbot, but you also have to provide a powerful server. Another advantage is the possibility to use Rasa completely offline. This means that it can be used in offline systems, for example IoT devices.[4]

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[1] https://www.gartner.com/en/documents/3879492
[2] https://rasa.com/about
[3] https://rasa.com/docs/
[4] https://rasa.com/docs/