from rasa.core.agent import Agent
from rasa.core.agent import Agent from rasa.core.interpreter import RasaNLUInterpreter from rasa.core.policies.memoization import MemoizationPolicy from rasa.core.policies.fallback import FallbackPolicy from rasa.core.domain import Domain from rasa.core.processor import MessageProcessor from rasa.core.utils import create_holdout_split interpreter = RasaNLUInterpreter(model_directory="/path/to/model") domain = Domain.load("domain.yml") training_data = await agent.load_data("data/stories.md") agent = Agent( domain=domain, policies=[ MemoizationPolicy(max_history=5), FallbackPolicy(nlu_threshold=0.6, core_threshold=0.3) ], interpreter=interpreter, ) training_data, validation_data = create_holdout_split(training_data, 0.2) agent.train( training_data, validation_data=validation_data, epochs=300, )In this example, we first load an NLU interpreter and a domain file. Then, we load some training data from a file called `stories.md`. We create an instance of the `Agent` class and pass in the domain, policies, and interpreter. We also create a training/validation split, and then call the `train` method on the `agent` object to train the model on the training data. Overall, Rasa is a powerful conversational AI framework that provides robust tools like the `Agent` class for managing dialogue models.