def determine_intent(self,
utterance,
num_results=1,
include_tags=False,
context_manager=None):
"""
Given an utterance, provide a valid intent.
Args:
utterance(str): an ascii or unicode string representing natural language speech
include_tags(list): includes the parsed tags (including position and confidence)
as part of result
context_manager(list): a context manager to provide context to the utterance
num_results(int): a maximum number of results to be returned.
Returns: A generator that yields dictionaries.
"""
parser = Parser(self.tokenizer, self.tagger)
parser.on('tagged_entities',
(lambda result: self.emit("tagged_entities", result)))
context = []
if context_manager:
context = context_manager.get_context()
for result in parser.parse(utterance, N=num_results, context=context):
self.emit("parse_result", result)
# create a context without entities used in result
remaining_context = self.__get_unused_context(result, context)
best_intent, tags = self.__best_intent(result, remaining_context)
if best_intent and best_intent.get('confidence', 0.0) > 0:
if include_tags:
best_intent['__tags__'] = tags
yield best_intent
def determine_intent(self, utterance, num_results=1, include_tags=False, context_manager=None):
"""
Given an utterance, provide a valid intent.
:param utterance: an ascii or unicode string representing natural language speech
:param include_tags: includes the parsed tags (including position and confidence)
as part of result
:param context_manager: a context manager to provide context to the utterance
:param num_results: a maximum number of results to be returned.
:return: A generator that yields dictionaries.
"""
parser = Parser(self.tokenizer, self.tagger)
parser.on('tagged_entities',
(lambda result:
self.emit("tagged_entities", result)))
context = []
if context_manager:
context = context_manager.get_context()
for result in parser.parse(utterance, N=num_results, context=context):
self.emit("parse_result", result)
# create a context without entities used in result
remaining_context = self.__get_unused_context(result, context)
best_intent, tags = self.__best_intent(result, remaining_context)
if best_intent and best_intent.get('confidence', 0.0) > 0:
if include_tags:
best_intent['__tags__'] = tags
yield best_intent
def determine_intent(self, utterance, num_results=1):
"""
Given an utterance, provide a valid intent.
:param utterance: an ascii or unicode string representing natural language speech
:param num_results: a maximum number of results to be returned.
:return: A generator the yields dictionaries.
"""
parser = Parser(self.tokenizer, self.tagger)
parser.on("tagged_entities", (lambda result: self.emit("tagged_entities", result)))
for result in parser.parse(utterance, N=num_results):
self.emit("parse_result", result)
best_intent = self.__best_intent(result)
if best_intent and best_intent.get("confidence", 0.0) > 0:
yield best_intent
def determine_intent(self, utterance, num_results=1):
"""
Given an utterance, provide a valid intent.
:param utterance: an ascii or unicode string representing natural language speech
:param num_results: a maximum number of results to be returned.
:return: A generator the yields dictionaries.
"""
parser = Parser(self.tokenizer, self.tagger)
parser.on('tagged_entities',
(lambda result: self.emit("tagged_entities", result)))
for result in parser.parse(utterance, N=num_results):
self.emit("parse_result", result)
best_intent = self.__best_intent(result)
if best_intent and best_intent.get('confidence', 0.0) > 0:
yield best_intent
def determine_good_intents(self,
utterance,
num_results=1,
include_tags=False,
context_manager=None):
"""
Given an utterance, provide a valid intent.
:param utterance: an ascii or unicode string representing natural language speech
:param include_tags: includes the parsed tags (including position and confidence)
as part of result
:param context_manager: a context manager to provide context to the utterance
:param num_results: a maximum number of results to be returned.
:return: A generator that yields dictionaries.
"""
parser = Parser(self.tokenizer, self.tagger)
parser.on('tagged_entities',
(lambda result: self.emit("tagged_entities", result)))
context = []
if context_manager:
context = context_manager.get_context()
all_good_intents = []
for result in parser.parse(utterance, N=num_results, context=context):
self.emit("parse_result", result)
# create a context without entities used in result
remaining_context = self.__get_unused_context(result, context)
good_intents = self.__good_intents(result, include_tags,
remaining_context)
all_good_intents += good_intents
all_good_intents = sorted(all_good_intents,
key=lambda i: i['confidence'],
reverse=True)
for intent in all_good_intents:
yield intent
