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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import re
from typing import Dict
import aiohttp
from botbuilder.ai.luis.activity_util import ActivityUtil
from botbuilder.ai.luis.luis_util import LuisUtil
from botbuilder.core import (
IntentScore,
RecognizerResult,
TurnContext,
)
from .luis_recognizer_internal import LuisRecognizerInternal
from .luis_recognizer_options_v3 import LuisRecognizerOptionsV3
from .luis_application import LuisApplication
# from .activity_util import ActivityUtil
class LuisRecognizerV3(LuisRecognizerInternal):
_dateSubtypes = [
"date",
"daterange",
"datetime",
"datetimerange",
"duration",
"set",
"time",
"timerange",
]
_geographySubtypes = ["poi", "city", "countryRegion", "continent", "state"]
_metadata_key = "$instance"
# The value type for a LUIS trace activity.
luis_trace_type: str = "https://www.luis.ai/schemas/trace"
# The context label for a LUIS trace activity.
luis_trace_label: str = "Luis Trace"
def __init__(
self,
luis_application: LuisApplication,
luis_recognizer_options_v3: LuisRecognizerOptionsV3 = None,
):
super().__init__(luis_application)
self.luis_recognizer_options_v3 = (
luis_recognizer_options_v3 or LuisRecognizerOptionsV3()
)
self._application = luis_application
async def recognizer_internal(self, turn_context: TurnContext):
recognizer_result: RecognizerResult = None
utterance: str = (
turn_context.activity.text if turn_context.activity is not None else None
)
url = self._build_url()
body = self._build_request(utterance)
headers = {
"Ocp-Apim-Subscription-Key": self.luis_application.endpoint_key,
"Content-Type": "application/json",
}
async with aiohttp.ClientSession() as session:
async with session.post(
url, json=body, headers=headers, ssl=False
) as result:
luis_result = await result.json()
recognizer_result = RecognizerResult(
text=utterance,
intents=self._get_intents(luis_result["prediction"]),
entities=self._extract_entities_and_metadata(
luis_result["prediction"]
),
)
if self.luis_recognizer_options_v3.include_instance_data:
recognizer_result.entities[self._metadata_key] = (
recognizer_result.entities[self._metadata_key]
if self._metadata_key in recognizer_result.entities
else {}
)
if "sentiment" in luis_result["prediction"]:
recognizer_result.properties["sentiment"] = self._get_sentiment(
luis_result["prediction"]
)
await self._emit_trace_info(
turn_context,
luis_result,
recognizer_result,
self.luis_recognizer_options_v3,
)
return recognizer_result
def _build_url(self):
base_uri = (
self._application.endpoint or "https://westus.api.cognitive.microsoft.com"
)
uri = "%s/luis/prediction/v3.0/apps/%s" % (
base_uri,
self._application.application_id,
)
if self.luis_recognizer_options_v3.version:
uri += "/versions/%s/predict" % (self.luis_recognizer_options_v3.version)
else:
uri += "/slots/%s/predict" % (self.luis_recognizer_options_v3.slot)
params = "?verbose=%s&show-all-intents=%s&log=%s" % (
(
"true"
if self.luis_recognizer_options_v3.include_instance_data
else "false"
),
"true" if self.luis_recognizer_options_v3.include_all_intents else "false",
"true" if self.luis_recognizer_options_v3.log else "false",
)
return uri + params
def _build_request(self, utterance: str):
body = {
"query": utterance,
"options": {
"preferExternalEntities": self.luis_recognizer_options_v3.prefer_external_entities,
},
}
if self.luis_recognizer_options_v3.datetime_reference:
body["options"][
"datetimeReference"
] = self.luis_recognizer_options_v3.datetime_reference
if self.luis_recognizer_options_v3.dynamic_lists:
body["dynamicLists"] = self.luis_recognizer_options_v3.dynamic_lists
if self.luis_recognizer_options_v3.external_entities:
body["externalEntities"] = self.luis_recognizer_options_v3.external_entities
return body
def _get_intents(self, luis_result):
intents = {}
if not luis_result["intents"]:
return intents
for intent in luis_result["intents"]:
intents[self._normalize_name(intent)] = IntentScore(
luis_result["intents"][intent]["score"]
)
return intents
def _normalize_name(self, name):
return re.sub(r"\.", "_", name)
def _normalize(self, entity):
split_entity = entity.split(":")
entity_name = split_entity[-1]
return self._normalize_name(entity_name)
def _extract_entities_and_metadata(self, luis_result):
entities = luis_result["entities"]
return self._map_properties(entities, False)
def _map_properties(self, source, in_instance):
if isinstance(source, (int, float, bool, str)):
return source
result = source
if isinstance(source, list):
narr = []
for item in source:
is_geography_v2 = ""
if (
isinstance(item, dict)
and "type" in item
and item["type"] in self._geographySubtypes
):
is_geography_v2 = item["type"]
if not in_instance and is_geography_v2:
geo_entity = {}
for item_props in item:
if item_props == "value":
geo_entity["location"] = item[item_props]
geo_entity["type"] = is_geography_v2
narr.append(geo_entity)
else:
narr.append(self._map_properties(item, in_instance))
result = narr
elif not isinstance(source, str):
nobj = {}
if (
not in_instance
and isinstance(source, dict)
and "type" in source
and isinstance(source["type"], str)
and source["type"] in self._dateSubtypes
):
timexs = source["values"]
arr = []
if timexs:
unique = []
for elt in timexs:
if elt["timex"] and elt["timex"] not in unique:
unique.append(elt["timex"])
for timex in unique:
arr.append(timex)
nobj["timex"] = arr
nobj["type"] = source["type"]
else:
for property in source:
name = self._normalize(property)
is_array = isinstance(source[property], list)
is_string = isinstance(source[property], str)
is_int = isinstance(source[property], (int, float))
val = self._map_properties(
source[property], in_instance or property == self._metadata_key
)
if name == "datetime" and is_array:
nobj["datetimeV1"] = val
elif name == "datetimeV2" and is_array:
nobj["datetime"] = val
elif in_instance:
if name == "length" and is_int:
nobj["endIndex"] = source[name] + source["startIndex"]
elif not (
(is_int and name == "modelTypeId")
or (is_string and name == "role")
):
nobj[name] = val
else:
if name == "unit" and is_string:
nobj["units"] = val
else:
nobj[name] = val
result = nobj
return result
def _get_sentiment(self, luis_result):
return {
"label": luis_result["sentiment"]["label"],
"score": luis_result["sentiment"]["score"],
}
async def _emit_trace_info(
self,
turn_context: TurnContext,
luis_result,
recognizer_result: RecognizerResult,
options: LuisRecognizerOptionsV3,
) -> None:
trace_info: Dict[str, object] = {
"recognizerResult": LuisUtil.recognizer_result_as_dict(recognizer_result),
"luisModel": {"ModelID": self._application.application_id},
"luisOptions": {"Slot": options.slot},
"luisResult": luis_result,
}
trace_activity = ActivityUtil.create_trace(
turn_context.activity,
"LuisRecognizer",
trace_info,
LuisRecognizerV3.luis_trace_type,
LuisRecognizerV3.luis_trace_label,
)
await turn_context.send_activity(trace_activity)
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