class DialogManager:
def __init__(self):
print('Loading dialog manager...')
self.retriever = Retrival('data/qa_processed')
self.ranker = Ranker('localhost')
# self.ANSWER_TEMPLATE = "您可能想问：%s\n最佳答案：%s \n distance: %f" # debug
self.ANSWER_TEMPLATE = "您是说：“%s” 吗？ Friday想了想：%s"
# simple_state_tracker
self.tracker = Tracker()
self.threshold = 0.02 # 问题是否不确定的阈值
self.spider = None # TODO
print('DialogManager established.')
def get_answer(self, query):
# 输入query，进行文本处理和布尔搜索
query = simple_process(query)
bool_state = self.retriever.input_query(query)
query = self.retriever.query # 可能进行了文本纠错
# 检测最近一次对话，处理信息缺失情况. e.g. “那xx呢?”
if self.tracker.check(query):
tmp = self.tracker.fill_query()
if len(tmp) > 0:
query = tmp
bool_state = self.retriever.input_query(query)
# 对召回的结果进行更进一步的排序
candi_idx, candi_q = self.recall_candidates(bool_state)
if len(candi_idx) == 0:
if len(self.allowed_words) == 0:
return '非常抱歉，我不明白您的意思'
else:
return '非常抱歉，我不明白您的意思。你可以问问其他关于“{}”的问题'.format(
' '.join(self.allowed_words))
results = self.ranker.rank(query, candi_idx, candi_q, top=1) # (q_id, distance)
# 读取结果
best_match = self.retriever.data.iloc[results[0][0]].question
answer = self.retriever.data.iloc[results[0][0]].answer
distance = results[0][1]
# 保存最近一次对话
filted_words = self.retriever.allowed_words
self.tracker.previous_cache = (query, filted_words)
if distance < self.threshold:
return str(answer)
else:
return self.ANSWER_TEMPLATE % (best_match, answer)
# for debug
# return self.ANSWER_TEMPLATE % (best_match, answer, distance)
def recall_candidates(self, bool_state):
result_candidates = set()
# 处理当布尔搜索没有结果的情况。在input_query时，已经通过文本纠错和放宽约束
# 只进行词向量搜索。
if bool_state == True:
result_candidates.update(self.retriever.search_tfidf(top_k=10))
result_candidates.update(self.retriever.search_bm25(top_k=10))
result_candidates.update(self.retriever.search_editdist(top_k=10))
result_candidates.update(self.retriever.search_fasttext(top_k=10))
else:
result_candidates.update(self.retriever.search_fasttext(top_k=20))
candi_idx = list(result_candidates)
candi_q = []
for idx in result_candidates:
candi_q.append(self.retriever.data.iloc[idx].question)
return candi_idx, candi_q
def eval(self, query, topn=10):
# 输入query，进行文本处理和布尔搜索
query = simple_process(query)
bool_state = self.retriever.input_query(query)
# 检测最近一次对话，处理信息缺失情况. e.g. “那xx呢?”
cur_words = self.retriever.words
if self.tracker.check(cur_words):
query = self.tracker.fill_query(cur_words)
bool_state = self.retriever.input_query(query)
# 对召回的结果进行更进一步的排序
candi_idx, candi_q = self.recall_candidates(bool_state)
if len(candi_idx) == 0:
return ['非常抱歉，我不明白您的意思' for i in range(topn)]
results = self.ranker.rank(query, candi_idx, candi_q, top=topn) # (q_id, distance)
q_list = []
for idx, _ in results:
q_list.append(self.retriever.data.iloc[idx].question)
# 保存最近一次对话
filted_words = self.retriever.allowed_words
self.tracker.previous_cache = (query, filted_words)
return q_list
