def process_dialog(cls, msg, use_task=True):
"""
Dialog strategy: use sub-task to handle dialog firstly,
if failed, use retrieval or generational func to handle it.
"""
# Task response.
if use_task:
task_response, cls.dialog_status = TaskCore.task_handle(
msg, cls.dialog_status)
else:
task_response = None
# Search response.
if len(cls.dialog_status.context) >= 3 and ch_count(msg) <= 4:
user_msgs = cls.dialog_status.context[::2][-3:]
msg = "<s>".join(user_msgs)
mode = "cr"
else:
mode = "qa"
msg_tokens = NlpUtil.tokenize(msg, True)
search_response, sim_score = SearchCore.search(msg_tokens, mode=mode)
# Seq2seq response.
seq2seq_response = cls._predict_via_seq2seq(msg_tokens)
log_print("search_response=%s" % search_response)
log_print("seq2seq_response=%s" % seq2seq_response)
if task_response:
response = task_response
elif sim_score >= 1.0:
response = search_response
else:
response = seq2seq_response
return response
def task_handle(self, msg, dialog_status):
try:
response = None
if dialog_status.intent not in self.intent_not_reset:
dialog_status.intent = None
dialog_status = self._slots_update(msg, dialog_status)
task_type = label(" ".join(jieba.cut(msg)))
dialog_status.intent = task_type
if task_type != "not_task":
handle_func = self.intent_handle_func[dialog_status.intent]
response = eval(handle_func)(msg, dialog_status)
else:
response = None
# for intent, update_func in self.intent_update_func:
# dialog_status = self._slots_update(msg, dialog_status)
# dialog_status = eval(update_func)(msg, dialog_status)
# if dialog_status.intent == intent:
# handle_func = self.intent_handle_func[dialog_status.intent]
# response = eval(handle_func)(msg, dialog_status)
# if response:
# break
log_print("intent=%s, task_response=%s" %
(dialog_status.intent, response))
return response, dialog_status
except Exception as e:
log_print("[ERROR] msg=%s, errmsg=%s" % (msg, e))
return response, dialog_status
def _predict_via_seq2seq(self, msg_tokens):
user_msgs = " ".join(self.dialog_status.context[::2][-4:])
log_print("seq2seq_input=%s" % user_msgs)
n_layers, hidden_size, reverse = parseFilename(self.model_path, True)
response = predict(
n_layers,
hidden_size,
reverse,
self.model_path,
beam_size=1,
msg=user_msgs,
voc=self.voc,
)
# response = predict(self.seq2seq_inst, user_msgs, ret_size=1)
return response
def task_handle(cls, msg, dialog_status):
try:
response = None
if dialog_status.intent not in cls.intent_not_reset:
dialog_status.intent = None
for intent, update_func in cls.intent_update_func:
dialog_status = cls._slots_update(msg, dialog_status)
dialog_status = eval(update_func)(msg, dialog_status)
if dialog_status.intent == intent:
handle_func = cls.intent_handle_func[dialog_status.intent]
response = eval(handle_func)(msg, dialog_status)
if response:
break
log_print("intent=%s, task_response=%s" % (dialog_status.intent, response))
return response, dialog_status
except Exception as e:
log_print("[ERROR] msg=%s, errmsg=%s" % (msg, e))
return response, dialog_status
def search(cls, msg_tokens, mode="qa", filter_pattern=None):
query = [w for w in msg_tokens if w in cls.word2id]
search_inst = cls.qa_search_inst if mode == "qa" else cls.cr_search_inst
sim_items = search_inst.similarity(query, size=10)
docs, answers = search_inst.get_docs(sim_items)
# User filter pattern.
if filter_pattern:
new_docs, new_answers = [], []
for doc, ans in zip(docs, answers):
if not filter_pattern.search(ans):
new_docs.append(doc)
new_answers.append(ans)
docs, answers = new_docs, new_answers
log_print("init_query=%s, filter_query=%s" %
("".join(msg_tokens), "".join(query)))
response, score = answers[0], sim_items[0][1]
log_print("%s_search_sim_doc=%s, score=%.4f" %
(mode, "".join(docs[0]), score))
if score <= 1.0:
response, score = "亲爱哒，还有什么小妹可以帮您呢~", 2.0
return response, score
def _predict_via_seq2seq(cls, msg_tokens):
user_msgs = " ".join(cls.dialog_status.context[::2][-4:])
log_print("seq2seq_input=%s" % user_msgs)
response = predict(cls.seq2seq_inst, user_msgs, ret_size=1)
return response