The code follows the standard structure of a Rasa chatbot, which consists of two main components: the NLU (natural language understanding) and the dialogue management.
The NLU component is responsible for understanding user input, extracting relevant information, and mapping it to intents and entities. The NLU configuration is defined in the pipeline section of the config.yml file. The example pipeline consists of a WhitespaceTokenizer, a RegexFeaturizer, a CRFEntityExtractor, a DIETClassifier, and an EntitySynonymMapper.
The dialogue management component is responsible for managing the conversation with the user and generating appropriate responses. The dialogue management is defined using a set of stories that represent different conversation paths, and a set of rules that map intents and entities to actions. The example Rasa chatbot defines a simple set of stories and rules to handle the user's input and generate appropriate responses.
Overall, the Rasa chatbot follows a standard structure and can be easily customized to suit specific requirements. The actual implementation would require creating the necessary files and directories, installing the required dependencies, and training the model using the provided configuration and data files.
In this example, the chatbot starts by greeting the user and asking them to introduce themselves. It then offers to discuss their skills or work experience. The chatbot is designed to recognize various types of responses from the user, such as mentioning their name, skills, job roles, and company names. Finally, the chatbot thanks the user for their time and ends the conversation.
You can further customize this chatbot to match your resume and skills by updating the training examples and responses in the nlu and responses sections of the code.