Beispiel #1
0
def test_agent_and_persist():
    policies = config.load("policies.yml")
    policies[0] = KerasPolicy(epochs=2)  # Keep training times low

    agent = Agent("domain.yml", policies=policies)
    training_data = agent.load_data("data/stories.md")
    agent.train(training_data, validation_split=0.0)
    agent.persist("./tests/models/dialogue")

    loaded = Agent.load("./tests/models/dialogue")

    assert agent.handle_text("/greet") is not None
    assert loaded.domain.action_names == agent.domain.action_names
    assert loaded.domain.intents == agent.domain.intents
    assert loaded.domain.entities == agent.domain.entities
    assert loaded.domain.templates == agent.domain.templates
Beispiel #2
0
from rasa_core.agent import Agent

# this will catch predictions the model isn't very certain about
# there is a threshold for the NLU predictions as well as the action predictions
fallback = FallbackPolicy(fallback_action_name="utter_unclear",
                          core_threshold=0.2,
                          nlu_threshold=0.6)

agent = Agent('domain.yml', policies=[MemoizationPolicy(), KerasPolicy()])

# loading our neatly defined training dialogues
training_data = agent.load_data('stories.md')

agent.train(
    training_data,
    validation_split=0.2,
    epochs=400
)

agent.persist('models/dialogue')

agent = Agent.load('models/dialogue', interpreter=model_directory)

print("Your bot is ready to talk! Type your messages here or send 'stop'")
while True:
    a = input()
    if a == 'stop':
        break
    responses = agent.handle_text(a)
    for response in responses:
        print(response["text"])