Clean qa

scripts/qa_generation/Clean_QA.md

@@ -0,0 +1,11 @@
+# 清洗 QA 对
+调用qwen去判断当前QA对是否属于心理学范畴，去除非心理学范畴的 QA 对
+
+## Step 1
+1. 准备好需要清洗的 QA 对数据
+2. 将该数据放进 model 同级 data 文件夹下
+3. 根据文件夹名去修改 config/config.py 中的 judge_dir。我个人没有对文件名进行更改，所以我的judge_dir是 judge_dir = os.path.join(data_dir, '数据整合')
+
+## Step 2
+1. 运行QA_clean.py即可
+2. 清洗完的 QA 对会以 jsonl 的格式存在 data/cleaned 下

scripts/qa_generation/QA_clean.py

@@ -0,0 +1,111 @@
+import os
+import json
+import time
+from tqdm import tqdm
+import concurrent.futures
+from datetime import datetime
+import numpy as np
+
+from config.config import result_dir, clean_dir, storage_interval, window_size, overlap_size, multi_process_num
+from model.qwen import call_qwen_single_turn, call_qwen_Psychology_QA_Pairs
+from util.logger import get_logger
+from util.data_loader import get_jsonl_file_paths, get_file_list, get_QA_pairs, get_txt_content, capture_qa, merge_sub_qa_generation, save_to_file
+
+logger = get_logger()
+
+
+def single_thread_generate(thread_num, interval, model_caller, storage_jsonl_path, contents):
+
+ storage_counter = 0
+ judge_list = []
+ for content in tqdm(contents):
+ # print('content: ', content)
+ try:
+ # model_caller 函数的作用是调用某个预训练的问答生成模型，传递输入内容 content 给模型，然后获取模型的输出 response
+ response = model_caller(content)
+ # print('response: ', response)
+
+ if response == '1':
+ content = json.loads(content)
+ judge_list.append(content)
+ storage_counter += 1
+ else:
+ continue
+
+ # 在达到指定的 interval 后，将 storage_list 中的内容保存到指定的文件 storage_jsonl_path 中
+ if storage_counter % interval == 0:
+ save_to_file(storage_jsonl_path, judge_list)
+ storage_counter = 0
+ judge_list = []
+
+ except Exception as exc:
+ logger.error("QA generation error : %s" % (exc))
+
+ # 最后，如果 storage_list 中还有剩余内容，也会将其保存到文件中。
+ if judge_list:
+ save_to_file(storage_jsonl_path, judge_list)
+ judge_list = []
+
+
+"""
+生成 QA 对
+model_name: 可调用的模型名称，暂时只实现了 qwen
+interval: 存储间隔，即每隔多少条存一次文件，过密的间隔会增大 IO 开销
+"""
+def clean_qa(
+ model_name: str = 'qwen',
+ interval: int = 10,
+):
+ # current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+
+ if model_name == 'qwen':
+ model_caller = call_qwen_Psychology_QA_Pairs
+ else:
+ logger.warning('This model is currently not supported and will call the default model - qwen.')
+ model_caller = call_qwen_Psychology_QA_Pairs
+ model_name = 'qwen'
+
+ logger.info(f'The called model is: {model_name}.')
+ logger.info(f'The storage interval is: {interval}.')
+
+ file_lists = get_jsonl_file_paths() # 数据整合文件夹下所有.jsonl文件的地址
+
+ for file_path in file_lists:
+ # 一个jsonl文件的所有QA Pairs
+ contents = get_QA_pairs(file_path)
+ # print(contents)
+
+ file_name = os.path.basename(file_path)
+ print(file_name)
+ storage_jsonl_path = os.path.join(
+ clean_dir, f'{file_name}')
+
+ logger.info(f'The generated QA will be stored in {storage_jsonl_path}.')
+
+ contents_array = np.array(contents)
+ chunks = np.array_split(contents_array, multi_process_num)
+
+ # 构建并发参数 list
+ parameters_list = list()
+ for thread_num, chunk in enumerate(chunks):
+ parameters_list.append(
+ [thread_num, interval, model_caller, storage_jsonl_path, list(chunk)]
+ )
+
+ with concurrent.futures.ThreadPoolExecutor(max_workers=multi_process_num) as executor:
+ # 循环调用 single_thread_generate 函数，每次赋予参数 parameters
+ futures = [executor.submit(single_thread_generate, *parameters) for parameters in parameters_list]
+
+ for future in concurrent.futures.as_completed(futures):
+ try:
+ future.result()
+ except Exception as exc:
+ logger.error("Thread generated an exception: %s" % (exc))
+
+ merge_sub_qa_generation(result_dir, storage_jsonl_path)
+
+
+if __name__ == '__main__':
+ # 创建washed文件夹
+ os.makedirs('./data/cleaned', exist_ok=True)
+ clean_qa(interval=storage_interval)

scripts/qa_generation/choose_prompt.md

@@ -0,0 +1,8 @@
+你是一名经验丰富的心理咨询师，熟悉心理学相关知识。根据我提供的 QA 对，来判断这个 QA 对是否属于心理学范畴。
+
+标准如下：
+- 若当前 QA 对属于心理学范畴，则返回1
+- 若当前 QA 对不属于心理学范畴，则返回0
+
+
+以下是给定的心理学 QA 对内容：

scripts/qa_generation/config/config.py

@@ -10,15 +10,19 @@
 model_dir = os.path.join(base_dir, 'model') # model
 
 # data
-data_dir = os.path.join(base_dir, 'data') # data
+data_dir = os.path.join(base_dir, 'data')
+clean_dir = os.path.join(data_dir, 'cleaned')
+judge_dir = os.path.join(data_dir, '数据整合')
 result_dir = os.path.join(data_dir, 'generated') # result
 
 # log
 log_dir = os.path.join(base_dir, 'log') # log
 log_file_path = os.path.join(log_dir, 'log.log') # file
 
 # system prompt
+# Prompt内容
 system_prompt_file_path = os.path.join(base_dir, 'system_prompt_v2.md') # system prompt
+wash_prompt_file_path = os.path.join(base_dir, 'choose_prompt.md')
 
 
 """
@@ -28,11 +32,11 @@
 DASHSCOPE_API_KEY = ''
 
 
-
 """
 控制参数
 """
 storage_interval = 10
 window_size = 8
 overlap_size = 2
 multi_process_num = 3
+

scripts/qa_generation/model/qwen.py

@@ -5,7 +5,7 @@
 
 from config.config import DASHSCOPE_API_KEY
 from util.logger import get_logger
-from util.prompt_loader import load_system_prompt
+from util.prompt_loader import load_system_prompt, load_wash_prompt
 
 
 dashscope.api_key = DASHSCOPE_API_KEY
@@ -39,3 +39,31 @@ def call_qwen_single_turn(query: str) -> str:
  response.code, response.message
  ))
  return ""
+
+
+def call_qwen_Psychology_QA_Pairs(query: str) -> str:
+ messages = [
+ {
+ 'role': Role.SYSTEM,
+ 'content': load_wash_prompt()
+ },
+ {
+ 'role': Role.USER,
+ 'content': query
+ }
+ ]
+ response = Generation.call(
+ model='qwen-max-1201',
+ messages=messages,
+ result_format='message',
+ stream=False,
+ incremental_output=False
+ )
+ if response.status_code == HTTPStatus.OK:
+ return response.output.choices[0]['message']['content']
+ else:
+ logger.error('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
+ response.request_id, response.status_code,
+ response.code, response.message
+ ))
+ return ""

scripts/qa_generation/util/data_loader.py

@@ -4,11 +4,39 @@
 import glob
 from typing import List, Dict
 
-from config.config import data_dir
+from config.config import data_dir, judge_dir
 from util.logger import get_logger
 
 logger = get_logger()
 
+
+"""
+递归获取 数据整合 下的所有 .jsonl 文件列表
+"""
+def get_jsonl_file_paths() -> List[str]:
+ json_file_paths = []
+
+ # 遍历根目录及其所有子目录
+ for dirpath, dirnames, filenames in os.walk(judge_dir):
+ # 对每个文件进行检查
+ for filename in filenames:
+ # 使用正则表达式匹配以.jsonl结尾的文件名
+ if re.search(r'\.jsonl$', filename):
+ # 构建完整的文件路径并添加到列表中
+ json_file_path = os.path.join(dirpath, filename)
+ json_file_paths.append(json_file_path)
+
+ return json_file_paths
+
+def get_QA_pairs(json_path):
+ with open(json_path, 'r', encoding='utf-8') as f:
+ content = f.read().strip()
+
+ # 按照换行符分割字符串
+ QA_Pairs = content.split('\n')
+
+ return QA_Pairs
+
 """
 递归获取 data_dir 下的所有 .txt 文件列表
 """
@@ -47,7 +75,7 @@ def get_txt_content(
  res = []
  sentences_amount = len(sentences)
  start_index, end_index = 0, sentences_amount - window_size
- ## check length
+ # check length
  if window_size < overlap_size:
  logger.error("window_size must be greater than or equal to overlap_size")
  return None
@@ -56,7 +84,7 @@ def get_txt_content(
  return ['\n'.join(sentences)]
 
  for i in range(start_index, end_index + 1, overlap_size):
- res.append('\n'.join(sentences[i : i + window_size]))
+ res.append('\n'.join(sentences[i: i + window_size]))
  return res
 
 
@@ -80,6 +108,7 @@ def capture_qa(content: str) -> List[Dict]:
  logger.warning("No JSON block found.")
  return None
 
+
 """
 将 storage_list 存入到 storage_jsonl_path
 """
@@ -88,6 +117,7 @@ def save_to_file(storage_jsonl_path, storage_list):
  for item in storage_list:
  f.write(json.dumps(item, ensure_ascii=False) + '\n')
 
+
 """
 将并发产生的文件合并成为一个文件
 """
@@ -102,5 +132,4 @@ def merge_sub_qa_generation(directory, storage_jsonl_path):
  for line in f:
  file_contents.append(json.loads(line))
  os.remove(file_path)
- save_to_file(storage_jsonl_path, file_contents)
-
+ save_to_file(storage_jsonl_path, file_contents)

scripts/qa_generation/util/prompt_loader.py

@@ -1,7 +1,14 @@
 from config.config import system_prompt_file_path
+from config.config import wash_prompt_file_path
 
 
 def load_system_prompt() -> str:
  with open(system_prompt_file_path, 'r', encoding='utf-8') as f:
  system_prompt = f.read()
  return system_prompt
+
+
+def load_wash_prompt() -> str:
+ with open(wash_prompt_file_path, 'r', encoding='utf-8') as f:
+ wash_prompt = f.read()
+ return wash_prompt