-
Notifications
You must be signed in to change notification settings - Fork 19
/
demo.py
288 lines (218 loc) · 8.87 KB
/
demo.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
from pypdf import PdfReader
import numpy as np
import pandas as pd
from tqdm import tqdm
import docx
from dataclasses import dataclass
from enum import Enum, auto
from typing import List, Optional, Union, Tuple
from pathlib import Path
from glob import glob
import os
import re
from transformers import AutoTokenizer, AutoModel
import torch as t
from transformers import AutoTokenizer, AutoModel
import torch
# 对文本进行拆分
CHUNK_SIZE = 64
global_dir = "政策归档文件"
class FileType(Enum):
PDF = auto()
doc = auto()
docx = auto()
@dataclass
class TransOutput:
file_name: str
file_type: FileType
text_data: Union[pd.DataFrame, None]
def transpdf(pdf_path: str, show_progress_bar: bool = False):
reader = PdfReader(pdf_path)
number_of_pages = len(reader.pages)
def page2text(pageid: int):
page = reader.pages[pageid]
text = page.extract_text()
return text
data = pd.DataFrame({
# 'file_Path':pdf_path,
'text': [page2text(i) for i in tqdm(range(number_of_pages), disable=not show_progress_bar)],
'pageid': range(number_of_pages),
})
res = TransOutput(
file_name=pdf_path,
file_type=FileType.PDF,
text_data=data
)
return res
def transdocx(doc_path: str, show_progress_bar: bool = False):
doc = docx.Document(doc_path)
all_paras = doc.paragraphs
number_of_pages = len(all_paras)
data = pd.DataFrame({
'text': [i.text for i in tqdm(all_paras, disable=not show_progress_bar)],
'paraid': range(number_of_pages)
})
res = TransOutput(
file_name=doc_path,
file_type=FileType.docx,
text_data=data
)
return res
def cal_detail_in_dir(dir_name):
all_file_list = []
# all_file_size = []
for (root, dir, file_name) in os.walk(dir_name):
for temp_file in file_name:
standard_path = f"{root}/{temp_file}"
all_file_list.append(standard_path)
return all_file_list
def transfile(x: Path) -> TransOutput:
if x.suffix == ".docx":
return transdocx(x.__str__())
else:
return transpdf(x.__str__())
def cleanquestion(x: str) -> str:
if isinstance(x, str):
str_text = re.sub(
u"([^\u4e00-\u9fa5\u0030-\u0039\u0041-\u005a\u0061-\u007a])", "", x)
return str_text
else:
return None
def clean_text_data(transout: TransOutput) -> TransOutput:
text_df = transout.text_data
res = text_df.pipe(
lambda x: x.assign(**{
'new_text_': x['text'].apply(lambda j: cleanquestion(j))
})
).pipe(
lambda x: x.loc[x['new_text_'].apply(lambda j: len(j) > 0)]
)
transout.text_data = res
return transout
def chunk_text(x: str) -> Union[None, List[str]]:
if not isinstance(x, str):
x = str(x)
x_list = [x[startid:(startid + CHUNK_SIZE)] for startid in range(0, len(x), CHUNK_SIZE)]
return x_list
def chunk_text4TransOutput(x: TransOutput) -> TransOutput:
# try:
text_df = x.text_data
res = text_df.pipe(
lambda x: x.assign(**{
'chunk_text': x['new_text_'].apply(lambda j: chunk_text(j))
})
).explode(['chunk_text']).drop(columns=['new_text_'])
x.text_data = res
return x
# except Exception as e:
# return None
def numpy_cos_sim(a: np.ndarray, b: np.ndarray) -> np.ndarray:
if len(a.shape) == 1:
a = a.reshape(1, -1)
if len(b.shape) == 1:
b = b.reshape(1, -1)
a_norm = a / np.linalg.norm(a, ord=2, axis=1).reshape(-1, 1)
b_norm = b / np.linalg.norm(b, ord=2, axis=1).reshape(-1, 1)
return np.matmul(a_norm, b_norm.T)
class SentenceVector:
def __init__(self,
model_name_or_path: str = None,
device: str = "cuda:0") -> None:
self.model_name_or_path = model_name_or_path
self.device = device
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path)
self.model = AutoModel.from_pretrained(self.model_name_or_path)
self.model.to(self.device)
def encode_fun(self, texts: List[str]) -> np.ndarray:
texts = [cleanquestion(i) for i in texts]
inputs = self.tokenizer.batch_encode_plus(
texts, padding=True, truncation=True, return_tensors="pt", max_length=64)
inputs.to(device=self.device)
with t.no_grad():
embeddings = self.model(**inputs)
embeddings = embeddings.last_hidden_state[:, 0]
embeddings = embeddings.to('cpu').numpy()
return embeddings
def encode_fun_plus(self, texts: List[str], batch_size: int = 100) -> np.ndarray:
embeddings = np.concatenate([self.encode_fun(
texts[i:(i + batch_size)]) for i in tqdm(range(0, len(texts), batch_size))])
return embeddings
class KnowLedge:
def __init__(self,
global_dir: str = None,
gen_model_name_or_path: str = "THUDM/chatglm-6b",
sen_embedding_model_name_or_path: str = "hfl/chinese-roberta-wwm-ext",
batch_top_k=5
) -> None:
self.batch_top_k = batch_top_k
all_file_list = cal_detail_in_dir(global_dir)
all_file_list = [Path(i) for i in all_file_list]
all_file_list = [i for i in all_file_list if i.suffix in ['.pdf', '.docx']]
all_trans_data = [transfile(i) for i in tqdm(all_file_list)]
all_trans_data = [clean_text_data(i) for i in all_trans_data]
all_trans_data = [i for i in all_trans_data if i.text_data.shape[0] > 0]
all_trans_data = [chunk_text4TransOutput(i) for i in all_trans_data]
self.sv = SentenceVector(model_name_or_path=sen_embedding_model_name_or_path)
all_vector = [self.sv.encode_fun_plus(i.text_data['chunk_text'].tolist()) for i in all_trans_data]
self.all_trans_data = all_trans_data
self.all_vector = all_vector
self.gen_tokenizer = AutoTokenizer.from_pretrained(gen_model_name_or_path, trust_remote_code=True)
self.gen_model = AutoModel.from_pretrained(gen_model_name_or_path, trust_remote_code=True).half().cuda(1)
def search_top_info(self, index: int, question_vector: np.ndarray) -> pd.DataFrame:
# print("".format(index))
similar_score = numpy_cos_sim(self.all_vector[index], question_vector).flatten()
if similar_score.shape[0] < self.batch_top_k:
res = self.all_trans_data[index].text_data.reset_index(drop=True).pipe(
lambda x: x.assign(**{
'score': similar_score
})
).pipe(
lambda x: x.assign(**{
'file_name': self.all_trans_data[index].file_name,
'file_path': self.all_trans_data[index].file_type
})
)
else:
top_k_location = np.argpartition(similar_score, kth=-self.batch_top_k)[-self.batch_top_k:]
res = self.all_trans_data[index].text_data.reset_index(drop=True).iloc[top_k_location].pipe(
lambda x: x.assign(**{
'score': similar_score[top_k_location]
})
).pipe(
lambda x: x.assign(**{
'file_name': self.all_trans_data[index].file_name,
'file_path': self.all_trans_data[index].file_type
})
)
return res
def search_result(self, question_str: str) -> Tuple[str, pd.DataFrame]:
# question_str ="大学生创业有什么补贴" #"做集成电路的企业,有什么补贴"#
question_vector = self.sv.encode_fun([question_str])
# question_vector.shape
# index = 0
search_table_info = pd.concat(
[self.search_top_info(index, question_vector) for index in range(len(self.all_vector))]).pipe(
lambda x: x.sort_values(by=['score'], ascending=False)
)
search_table = search_table_info.drop_duplicates(['chunk_text']).head(30)
search_text_list = search_table['chunk_text'].tolist()
# len(search_text_list), search_text_list[:3]
prompt_template = """基于以下已知信息,简洁和专业的来回答用户的问题。
如果无法从中得到答案,请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息",不允许在答案中添加编造成分,答案请使用中文。
问题:
{question}
已知内容:
{context}
"""
text2chatglm = prompt_template.format_map({
'question': question_str,
'context': '\n'.join(search_text_list)
})
response, history = self.gen_model.chat(self.gen_tokenizer, text2chatglm, history=[])
torch.cuda.empty_cache()
return response, search_table
if __name__ == "__main__":
kl = KnowLedge(global_dir=global_dir)
res, data = kl.search_result("大学生创业有什么补贴")
print(res)
print(data)