def test_luis_response_without_role():
em = LUISEmulator()
data = {
"text": "I want italian food",
"intent": {"name": "restaurant_search", "confidence": 0.737014589341683},
"intent_ranking": [
{"confidence": 0.737014589341683, "name": "restaurant_search"}
],
"entities": [{"entity": "cuisine", "value": "italian"}],
}
norm = em.normalise_response_json(data)
assert norm == {
"query": data["text"],
"prediction": {
"normalizedQuery": data["text"],
"topIntent": "restaurant_search",
"intents": {"restaurant_search": {"score": 0.737014589341683}},
"entities": {
"cuisine": ["italian"],
"$instance": {
"cuisine": [
{
"role": None,
"type": "cuisine",
"text": "italian",
"startIndex": None,
"length": None,
"score": None,
"modelType": None,
}
]
},
},
},
}
def test_emulators_can_handle_missing_data():
from rasa.nlu.emulators.luis import LUISEmulator
em = LUISEmulator()
norm = em.normalise_response_json({
"text": "this data doesn't contain an intent result"})
assert norm["topScoringIntent"] is None
assert norm["intents"] == []
def test_luis_response():
em = LUISEmulator()
data = {
"text": "I want italian food",
"intent": {"name": "restaurant_search", "confidence": 0.737014589341683},
"intent_ranking": [
{"confidence": 0.737014589341683, "name": "restaurant_search"},
{"confidence": 0.11605464483122209, "name": "goodbye"},
{"confidence": 0.08816417744097163, "name": "greet"},
{"confidence": 0.058766588386123204, "name": "affirm"},
],
"entities": [
{
"entity": "cuisine",
"value": "italian",
"role": "roleCuisine",
"extractor": "SpacyEntityExtractor",
"start": 7,
"end": 14,
}
],
}
norm = em.normalise_response_json(data)
assert norm == {
"query": data["text"],
"prediction": {
"normalizedQuery": data["text"],
"topIntent": "restaurant_search",
"intents": {
"restaurant_search": {"score": 0.737014589341683},
"goodbye": {"score": 0.11605464483122209},
"greet": {"score": 0.08816417744097163},
"affirm": {"score": 0.058766588386123204},
},
"entities": {
"roleCuisine": ["italian"],
"$instance": {
"roleCuisine": [
{
"role": "roleCuisine",
"type": "cuisine",
"text": "italian",
"startIndex": 7,
"length": len("italian"),
"score": None,
"modelType": "SpacyEntityExtractor",
}
]
},
},
},
}
def _create_emulator(mode: Optional[Text]) -> NoEmulator:
"""Create emulator for specified mode.
If no emulator is specified, we will use the Rasa NLU format."""
if mode is None:
return NoEmulator()
elif mode.lower() == "wit":
from rasa.nlu.emulators.wit import WitEmulator
return WitEmulator()
elif mode.lower() == "luis":
from rasa.nlu.emulators.luis import LUISEmulator
return LUISEmulator()
elif mode.lower() == "dialogflow":
from rasa.nlu.emulators.dialogflow import DialogflowEmulator
return DialogflowEmulator()
else:
raise ErrorResponse(
400,
"BadRequest",
"Invalid parameter value for 'emulation_mode'. "
"Should be one of 'WIT', 'LUIS', 'DIALOGFLOW'.",
{
"parameter": "emulation_mode",
"in": "query"
},
)
def _create_emulator(mode: Optional[Text]) -> NoEmulator:
"""Create emulator for specified mode.
If no emulator is specified, we will use the Rasa NLU format."""
if mode is None:
return NoEmulator()
elif mode.lower() == 'wit':
from rasa.nlu.emulators.wit import WitEmulator
return WitEmulator()
elif mode.lower() == 'luis':
from rasa.nlu.emulators.luis import LUISEmulator
return LUISEmulator()
elif mode.lower() == 'dialogflow':
from rasa.nlu.emulators.dialogflow import DialogflowEmulator
return DialogflowEmulator()
elif mode.lower() == 'lite':
from litemind.nlu.emulators.lite import LiteEmulator
return LiteEmulator()
elif mode.lower() == 'coref':
from litemind.nlu.emulators.coref import CorefEmulator
return CorefEmulator()
elif mode.lower() == 'entity':
from litemind.nlu.emulators.entity import EntityEmulator
return EntityEmulator()
elif mode.lower() == 'link':
from litemind.nlu.emulators.link import LinkEmulator
return LinkEmulator()
elif mode.lower() == 'relation':
from litemind.nlu.emulators.relation import RelationEmulator
return RelationEmulator()
else:
raise ValueError("unknown emulator mode : {0}".format(mode))
def _create_emulator(mode: Optional[Text]) -> NoEmulator:
"""Create emulator for specified mode.
If no emulator is specified, we will use the Rasa NLU format."""
if mode is None:
return NoEmulator()
elif mode.lower() == 'wit':
from rasa.nlu.emulators.wit import WitEmulator
return WitEmulator()
elif mode.lower() == 'luis':
from rasa.nlu.emulators.luis import LUISEmulator
return LUISEmulator()
elif mode.lower() == 'dialogflow':
from rasa.nlu.emulators.dialogflow import DialogflowEmulator
return DialogflowEmulator()
else:
raise ValueError("unknown mode : {0}".format(mode))
def test_luis_response():
from rasa.nlu.emulators.luis import LUISEmulator
em = LUISEmulator()
data = {
"text":
"I want italian food",
"intent": {
"name": "restaurant_search",
"confidence": 0.737014589341683
},
"intent_ranking": [
{
"confidence": 0.737014589341683,
"name": "restaurant_search"
},
{
"confidence": 0.11605464483122209,
"name": "goodbye"
},
{
"confidence": 0.08816417744097163,
"name": "greet"
},
{
"confidence": 0.058766588386123204,
"name": "affirm"
},
],
"entities": [{
"entity": "cuisine",
"value": "italian"
}],
}
norm = em.normalise_response_json(data)
assert norm == {
"query":
data["text"],
"topScoringIntent": {
"intent": "restaurant_search",
"score": 0.737014589341683
},
"intents": [
{
"intent": "restaurant_search",
"score": 0.737014589341683
},
{
"intent": "goodbye",
"score": 0.11605464483122209
},
{
"intent": "greet",
"score": 0.08816417744097163
},
{
"intent": "affirm",
"score": 0.058766588386123204
},
],
"entities": [{
"entity": e["value"],
"type": e["entity"],
"startIndex": None,
"endIndex": None,
"score": None,
} for e in data["entities"]],
}
def test_luis_request():
from rasa.nlu.emulators.luis import LUISEmulator
em = LUISEmulator()
norm = em.normalise_request_json({"text": ["arb text"]})
assert norm == {"text": "arb text", "time": None}
def test_luis_request():
em = LUISEmulator()
norm = em.normalise_request_json({"text": ["arb text"]})
assert norm == {"text": "arb text", "time": None}
