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Rasa Core, together with Rasa NLU, forms the Rasa Stack. It is responsible for having a conversation with the user that is as natural as possible. [1]

assignments

Rasa Core is responsible for the conversation flow, context handling, bot responses and session management. It can be built on the Rasa NLU or other services that take over the intent recognition and entity extraction and make the results available to the Rasa Core. [1]

superstructure

The Rasa Core keeps a tracker for each session, i.e. for each user. This contains the current state of the conversations of the respective users. If the bot receives a message, it first runs through the interpreter, which receives the original text as input and returns the input, the intent and the extracted entities. Together with the current state of the tracker, the policy component now decides which action(bot response) should be executed next. This decision is not made by simple rules, but just like intents or entities, on the basis of a model trained with machine learning.

These processes can be influenced at several points. First of all, there is the configuration of the interpreter, or the Rasa NLU. This should reliably recognise the correct intents and extract all required entities. The policy component can also be configured specifically for use cases in a designated file (policy.yml).

You can choose between several policies, each of which can be adjusted even more precisely. The actions are the bot responses. These can be simple text responses, quick replies, images or action webhooks. The latter send a POST request to a previously defined interface and from where the responses are sent. In this way, API calls or database access can be realised, for example. [2] [3]

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Sources

[1] [https://rasa.com/docs/
[2] https://rasa.com/docs/core/policies/
[3] https://rasa.com/docs/core/architecture/