def frame_conversation(utterance=None, user_id=None):
last_system_utterance_info = db_linker.getLatestUtterance(user_id=user_id,
speaker='system')
now_user_utterance_info = db_linker.getLatestUtterance(user_id=user_id,
speaker='user')
last_user_utterance_info = db_linker.getLatestUtterance(
user_id=user_id, speaker='last_user')
#print(last_utterance_info)
print(last_user_utterance_info)
if last_system_utterance_info['intent_req'] == "frame_question":
return react_frame_answer(utterance,
last_user_utterance_info['utterance_id'],
now_user_utterance_info['utterance_id'])
else:
frames = sentence_parser.Frame_Interpreter(utterance, target='v')
print(frames)
if len(frames) > 0:
frame_log_id = save_frame_info(
frames, now_user_utterance_info['utterance_id'])
lu = frames[-1]['lu']
empty_argument_list = get_frame_core_empty_argument(frames[-1])
## frame이 잡혔고, 비어있는 core element가 존재하는 경우
if len(empty_argument_list) > 0:
dialog_act = "frame_question"
return frame_argument_question(
lu, empty_argument_list,
now_user_utterance_info['utterance_id'],
frame_log_id), dialog_act
return None, None
def user_access(user_name=None, user_id=None):
result = db_linker.LookUpUsers()
user_info = None
for user in result['user_list']:
if user_name:
if user['user_name'] == user_name:
user_info = user
if user_id:
if user['user_id'] == user_id:
user_info = user
if user_info:
print(user_info)
else:
print('user_not_found')
user_info = db_linker.AddNewUser(user_name)
session_info = db_linker.AddNewSession(user_info['user_id'])
result = {
'session_info': session_info,
'user_info': user_info
}
return result
def save_frame_answer(frames, frame_question, utterance_id):
print(frame_question)
for denotation in frames[-1]['denotations']:
datalist = []
if denotation['role'] == 'TARGET':
datadict = {
'frame_log_id': frame_question['frame_log_id'],
'utterance_id': utterance_id,
'object': frame_question['question_argument'],
'role': 'ARGUMENT'
}
datalist.append(datadict)
frame_answered_denotation_id = db_linker.InsertDataToTable(
'FRAME_ANSWERED_DENOTATION', datalist)
for span in denotation['token_span']:
datalist = []
datadict = {
'frame_answered_denotation_id':
frame_answered_denotation_id,
'token_span': span
}
datalist.append(datadict)
span_id = db_linker.InsertDataToTable(
'FRAME_ANSWERED_DENOTATION_SPAN', datalist)
return denotation['text']
def save_frame_info(frames, utterance_id):
for frame in frames:
datalist = []
datadict = {
'utterance_id': utterance_id,
'lu': frame['lu'],
'frame': frame['frame']
}
datalist.append(datadict)
frame_log_id = db_linker.InsertDataToTable('FRAME_LOG', datalist)
for denotation in frame['denotations']:
datalist = []
datadict = {
'frame_log_id': frame_log_id,
'object': denotation['obj'],
'role': denotation['role']
}
datalist.append(datadict)
denotation_log_id = db_linker.InsertDataToTable(
'FRAME_DENOTATION', datalist)
for token_span in denotation['token_span']:
datalist = []
datadict = {
'denotation_log_id': denotation_log_id,
'token_span': token_span
}
datalist.append(datadict)
span_id = db_linker.InsertDataToTable('FRAME_DENOTATION_SPAN',
datalist)
return frame_log_id
def react_frame_answer(sentence, last_user_utterance_id, user_utterance_id):
frames = sentence_parser.Frame_Interpreter(sentence, target='n')
print(frames)
print(user_utterance_id)
## 대답에서 frame을 잡은 경우
if len(frames) > 0:
frame_question = db_linker.getFrameQuestionByUtteranceID(
last_user_utterance_id)
obj = save_frame_answer(frames, frame_question, user_utterance_id)
if frame_question:
answer = frame_question['question_argument'] + '는 ' + obj + ' 이군요 '
answer = answer + '감사합니다.'
else:
answer = "그렇군요."
## 질문이 더 남은 경우
# if len(empty_argument_list) > 0:
# pre_system_dialog_act = 'frame_question'
# answer = answer + ' ' + frame_argument_question(frames)
## 질문이 더 남지 않은 경우
# else:
# pre_system_dialog_act = None
## 대답에서 frame을 잡지 못한 경우
else:
answer = '죄송한데, 잘 이해를 못했어요.'
return answer, "none"
return answer, "frame_answer"
def _lookUpSessionOfUser():
print('_saveUtterance')
data = request.data.decode('utf-8')
myjson = json.loads(data)
result = DB_Linker.LookUpSessionOfUser(user_id=myjson['user_id'],
user_name=myjson['user_name'])
return result
def _getUtterances():
print('_getUtterances')
data = request.data.decode('utf-8')
myjson = json.loads(data)
result = DB_Linker.GetUtterances(user_id=myjson['user_id'],
session_id=myjson['session_id'])
return result
def _addNewUser():
print('_addNewUser')
data = request.data.decode('utf-8')
myjson = json.loads(data)
user_name = myjson['user_name']
result = DB_Linker.AddNewUser(user_name)
return result
def _getUserInfo():
print('_getUserInfo')
data = request.data.decode('utf-8')
myjson = json.loads(data)
result = DB_Linker.GetUserInfo(user_id=myjson['user_id'],
user_name=myjson['user_name'])
return result
def kb_agent(user_id=None, user_utterance=None, modules=[]):
answer = '어떤 응답을 해야할지 모르겠어요.'
last_system_utterance_info = db_linker.getLatestUtterance(user_id=user_id, speaker='system')
if 'sparql_qa' in modules and last_system_utterance_info['intent_req'] not in ['frame_question', 'entity_question']:
print('sparql_qa')
sparql_answer, dialog_act = SPARQL_QA.sparql_conversation(user_utterance)
if sparql_answer is not None:
answer = sparql_answer
return answer, 'none'
if 'frame_qa' in modules and last_system_utterance_info['intent_req'] not in ['entity_question']:
print('frame_qa')
frame_answer, dialog_act = frame_QA.frame_conversation(user_id=user_id, utterance=user_utterance)
if frame_answer is not None:
answer = frame_answer
return answer, dialog_act
if 'knowledge_acquire' in modules:
print('knowledge_qa')
knowledge_answer, dialog_act = knowledge_question.knowledge_conversation(user_id=user_id,
user_utterance=user_utterance)
if knowledge_answer is not None:
answer = knowledge_answer
return answer, dialog_act
return answer, 'none'
def respond_to_user_utterance(user_id=None, user_name=None, user_utterance=None, session_id=None, modules=[]):
if user_id is None and user_name is None:
return False
user_info = db_linker.GetUserInfo(user_id=user_id, user_name=user_name)
db_linker.SaveUtterance(user_id=user_info['user_id'], speaker='user', utterance=user_utterance,
session_id=session_id)
answer, dialog_act = kb_agent(user_id=user_info['user_id'], user_utterance=user_utterance, modules=modules)
print('answer', answer)
db_linker.SaveUtterance(user_id=user_info['user_id'], speaker='system', utterance=answer, session_id=session_id,
intent_req=dialog_act)
response = {
'answer': answer
}
return response
def _addNewSession():
print('_addNewSession')
data = request.data.decode('utf-8')
myjson = json.loads(data)
result = DB_Linker.AddNewSession(user_id=myjson['user_id'],
model_id=myjson['model_id'],
mission_id=myjson['mission_id'],
feedback=myjson['feedback'])
return result
def _deleteUserListInfo():
print('_deleteUserListInfo')
data = request.data.decode('utf-8')
myjson = json.loads(data)
result = DB_Linker.DeleteUserListInfo(myjson['user_id'],
myjson['user_interest_celeb'],
myjson['user_interest_hobby'],
myjson['user_interest_location'],
myjson['user_topic'])
return result
def _queryToMasterKB():
print('QueryToMasterKB')
data = request.data.decode('utf-8')
myjson = json.loads(data)
query = myjson['query']
result = DB_Linker.QueryToMasterKB(query)
print(result)
return result
def _updateUserInfo():
print('_updateUserInfo')
data = request.data.decode('utf-8')
myjson = json.loads(data)
result = DB_Linker.UpdateUserInfo(
myjson['user_id'], myjson['user_name'], myjson['user_age'],
myjson['user_birth'], myjson['user_gender'],
myjson['user_current_city'], myjson['user_hometown'],
myjson['user_professional'], myjson['user_job_title'])
return result
def _insertKnowledgeToUserKB():
print('InsertKnowledgeToUserKB')
data = request.data.decode('utf-8')
myjson = json.loads(data)
user_name = myjson['user_name']
triple = myjson['triple']
print(triple)
result = DB_Linker.InsertKnowledgeToUserKB(user_name, triple)
return 'okay'
def _saveUtterance():
print('_saveUtterance')
data = request.data.decode('utf-8')
myjson = json.loads(data)
result = DB_Linker.SaveUtterance(user_id=myjson['user_id'],
utterance=myjson['utterance'],
session_id=myjson['session_id'],
speaker=myjson['speaker'],
emotion=myjson['emotion'],
intent_req=myjson['intent_req'],
intent_emp=myjson['intent_emp'])
return result
def frame_argument_question(lu, empty_argument_list, utterance_id,
frame_log_id):
question_argument = empty_argument_list.pop()
frame_question = lu + '의 ' + question_argument + '는 무엇인가요?'
datalist = []
datadict = {
'utterance_id': utterance_id,
'frame_log_id': frame_log_id,
'question_argument': question_argument
}
datalist.append(datadict)
frame_question_id = db_linker.InsertDataToTable('FRAME_QUESTION', datalist)
return frame_question
def get_entity_question_list(user_name, entities, entity_type):
question_property_list = prior_property[entity_type]
question_num = 0
question_list = []
for candidate_property in question_property_list:
if question_num == 3:
break
userdb_query = Knowledge_check(
[entities[0]['uri'], candidate_property, '?o'], user_name)
masterdb_query = Knowledge_check(
[entities[0]['uri'], candidate_property, '?o'])
masterdb_result = db_linker.QueryToMasterKB(masterdb_query)
userdb_result = db_linker.QueryToUserKB(userdb_query)
if masterdb_result['boolean'] == False and userdb_result[
'boolean'] == False:
question_list.append(
[entities[0]['uri'], candidate_property, '?o'])
question_num += 1
return question_list
def save_knowledge_to_database(triple, utterance_id):
s, p, o = triple
# s = s.split('/')[-1].rstrip('>')
# p = p.split('/')[-1].rstrip('>')
# o = o.split('/')[-1].rstrip('>')
datalist = []
datadict = {
'utterance_id': utterance_id,
'subject': s,
'property': p,
'object': o
}
datalist.append(datadict)
db_linker.InsertDataToTable('USERKB_LOG', datalist)
def sparql_conversation(sentence):
sentence_template, replaced_word_dict = sentence_to_template(sentence)
top_score, sparql_query = get_highest_similar_sparql(sentence_template)
print(top_score)
if top_score > 0.9:
sparql_query = get_complete_sparql(replaced_word_dict, sparql_query)
masterdb_result = db_linker.QueryToMasterKB(sparql_query)
print(masterdb_result)
if 'results' in masterdb_result:
if len(masterdb_result['results']['bindings']) == 0:
return '잘 모르겠어요'
answer = json.dumps(masterdb_result['results']['bindings'], indent=4)
return answer
elif 'boolean' in masterdb_result:
if masterdb_result['boolean'] == True:
return '네 맞아요'
else:
return '아닌것 같아요'
else:
return '질문이 어려워요'
return None, None
def _getSessionInfo():
print('_getSessionInfo')
data = request.data.decode('utf-8')
myjson = json.loads(data)
result = DB_Linker.GetSessionInfo(myjson['session_id'])
return result
def _lookUpUsers():
print('_lookUpUsers')
result = DB_Linker.LookUpUsers()
print(result)
return result
def knowledge_conversation(user_id=None, user_utterance=None):
entities = sentence_parser.Entity_Linking(user_utterance)
user_info = db_linker.GetUserInfo(user_id=user_id)
user_name = user_info['user_name']
print("entities: ", entities)
answer = ''
last_system_utterance_info = db_linker.getLatestUtterance(user_id=user_id,
speaker='system')
now_user_utterance_info = db_linker.getLatestUtterance(user_id=user_id,
speaker='user')
last_user_utterance_info = db_linker.getLatestUtterance(
user_id=user_id, speaker='last_user')
if last_system_utterance_info['intent_req'] == 'entity_question':
entities = sentence_parser.Entity_Linking(user_utterance)
answer = ''
if len(entities) > 0:
entity = entities[-1]['uri']
question_info = db_linker.getTripleQuestion(
last_user_utterance_info['utterance_id'])
if len(question_info) == 0:
return '질문이 뭐였는지 못찾았어요, 감사합니다.', 'entity_answer'
triple = [
question_info['subject'], question_info['property'], entity
]
save_knowledge_to_database(triple,
now_user_utterance_info['utterance_id'])
db_linker.InsertKnowledgeToUserKB(user_name, [triple])
answer += nlg_with_triple([triple], 'Knowledge_inform')
else:
answer = '무슨말씀이신지 잘 모르겠어요. 넘어갈게요!\n'
dialog_act = 'entity_answer'
answer += '감사합니다.'
# if len(self.entity_question_triple_list) > 0:
# self.question_triple = self.entity_question_triple_list.pop(0)
# answer = answer + self.triple_question_generation(self.question_triple)
#
return answer, dialog_act
## entity가 잡힌 경우
if len(entities) > 0:
## Entity summarization을 통해 정보 제공
print(entities[0]['text'])
# summarized_triples = entity_summarization.ES(entities[0]['text'])
#
# answer = nlg_with_triple(summarized_triples, 'Knowledge_inform')
# print("summarized_triples: ", summarized_triples)
if entities[0]['text'] in entity_summarized:
summarized_triples = entity_summarized[entities[0]['text']]['top5']
answer = nlg_with_triple(summarized_triples, 'Knowledge_inform')
entity_type = get_entity_type(entities)
print("entity_type: ", entity_type)
##entity type이 잡혀서 질문 목록 생성
if entity_type is not None:
entity_question_triple_list = get_entity_question_list(
user_name, entities, entity_type)
print("entity_question_triple_list: ", entity_question_triple_list)
## 질문 목록에 대해 질문 시작
if len(entity_question_triple_list) > 0:
answer = answer + entities[0]['text'] + '에 대해서 물어보고 싶은게 있어요.\n'
question_triple = entity_question_triple_list.pop(0)
print(question_triple)
save_knowledge_to_database(question_triple,
now_user_utterance_info['utterance_id'])
answer = answer + triple_question_generation(question_triple)
dialog_act = 'entity_question'
return answer, dialog_act
return None, None