![]() □ Tired? Worry not we are done with concepts. Such actions are also called by Core by looking into actions.py script. Now, there are certain actions that require API calls and DB operations. Now this us the Core that calls appropriate action at appropriate times to respond to user based on input it gets from NLU. We train RASA Core after RASA NLU and training data for RASA Core is domain.yml because this is the universe of our chatbot whatever our core needs to decide regarding actions and response exist within "domain.yml". I mean, what will be the next sntence in the conversation when a user has entered certain response. Now we have an engine (RASA NLU) that detects the correct intents, entities and entity types in the sentence now we need something that can decide on "what next in conversation?".Since we are building a contextual chatbot we will provide a lot of training data so that our NLU after being trained is able to classify intents, entities and entity types in the sentence at its best. These are the docs where your NLU training data resides. Remember the "nlu.md" and "stories.md" docs from the first post? That is it !!!. Wait a minute "training data" what's this? It extracts intents, entity types and entities from the sentence based on what training data was provided when NLU was trained (We will soon train the NLU, don't worry □). ![]() NLU is the Natural Language Unit of the RASA framework.
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