Pip list | grep rasa_core not returning anything

rasa-core

(Helga Sigurdardottir) #1

Hey,

I don’t get anything returned when I try to look at the version rasa_core is in. I wiped my macbook yesterday, reinstalled anaconda 3, xcode, tensorflow, rasa_nlu and rasa_core so those are the only installations on my computer as for now.

When I try to update rasa_core I get messages like this:

Requirement already satisfied, skipping upgrade: PyHamcrest>=1.9.0 in ./anaconda3/lib/python3.6/site-packages (from Twisted>=15.5->klein~=17.10->rasa-nlu~=0.14.0->rasa_core) (1.9.0)

The reason I’m asking is that the intents are not being found and reverting to fallback for every single utterance. I get:

DEBUG:rasa_core.featurizers:Feature 'intent_why do we meditate?' (value: '1.0') could not be found in feature map. Make sure you added all intents and entities to the domain

when I write “why do we meditate?” even though I have

## intent:why_meditate
- why do we meditate?

in my nlu file. I saw another discussion about this (Rasa-core not processing my stories properly) but his problem was that his rasa_core version wasn’t updated enough. Mine claims to be fully up-to-date but won’t tell me what the version is.

I’m using iPython and I want an interactive version so I’m running:

from rasa_nlu.training_data import load_data
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.model import Trainer
from rasa_nlu import config

training_data = load_data("nlupaths.md")
trainer = Trainer(config.load("config.yml"))
interpreter = trainer.train(training_data)
model_directory = trainer.persist("./models/nlu", fixed_model_name="current")

from rasa_core.policies import FallbackPolicy, KerasPolicy, MemoizationPolicy
from rasa_core.agent import Agent
fallback = FallbackPolicy(fallback_action_name="utter_unclear",
                          core_threshold=0.65,
                          nlu_threshold=0.65)

agent = Agent('domainpaths.yml', policies=[MemoizationPolicy(), KerasPolicy(), fallback])
training_data = agent.load_data('storiespaths.md')

agent.train(
    training_data,
    validation_split=0.0
)

agent.persist('models/dialogue')

Please, if there’s anything you can think of, help would be so greatly appreciated! I tried to get help on the Rasa Github but they say it’s a usage problem. I just can’t see how.