新增对话处理功能，优化意图识别逻辑，添加结果保存至Excel的功能，更新依赖项以支持新的数据库驱动和ORM，重构代码以提高可读性和维护性，删除冗余文件以简化项目结构。
This commit is contained in:
@@ -118,10 +118,65 @@ def process_query(recognizer, query):
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}
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else:
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# 可以在这里添加延迟，避免过快重试
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time.sleep(10 * retry_count)
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time.sleep(10)
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def save_results_to_excel(results, output_file, is_final=False):
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"""
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将结果保存到Excel文件
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Args:
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results: 结果列表
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output_file: 输出文件路径
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is_final: 是否为最终保存，如果是则使用完整文件名，否则添加临时标记
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Returns:
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None
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"""
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# 过滤掉None值
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valid_results = [r for r in results if r is not None]
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if not valid_results:
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logging.warning("没有有效结果可保存")
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return
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# 创建DataFrame
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results_df = pd.DataFrame(valid_results)
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# 根据是否为最终保存确定文件名
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if not is_final:
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file_name, file_ext = os.path.splitext(output_file)
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temp_output_file = f"{file_name}_temp{file_ext}"
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else:
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temp_output_file = output_file
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# 使用ExcelWriter设置格式
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with pd.ExcelWriter(temp_output_file, engine='xlsxwriter') as writer:
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results_df.to_excel(writer, index=False, sheet_name='Sheet1')
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# 获取工作簿和工作表对象
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workbook = writer.book
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worksheet = writer.sheets['Sheet1']
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# 设置列宽（单位：像素）
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# 定义列宽（厘米转为Excel单位，1cm约等于4.7个Excel单位）
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worksheet.set_column('A:A', 60) # 提问列 60个Excel单位
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worksheet.set_column('B:B', 20) # 问题拆解 20个Excel单位
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worksheet.set_column('C:C', 20) # 一级分类 20个Excel单位
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worksheet.set_column('D:D', 20) # 二级分类 20个Excel单位
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worksheet.set_column('E:E', 60) # 问题改写 60个Excel单位
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worksheet.set_column('F:F', 60) # 检索到的关键词 60个Excel单位
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worksheet.set_column('G:G', 80) # 槽位填充 80个Excel单位
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# 设置所有行高为20磅
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for i in range(len(results_df) + 1): # +1 是为了包括表头
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worksheet.set_row(i, 20)
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logging.info(f"已保存{len(valid_results)}条结果至: {temp_output_file}")
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# 示例查询
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examples_query = """储能软件组合件界面，点击隐藏空项目划分后界面没有任何变化"""
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examples_query = """"锁标签号:811621005858, 注册单位:惠州电力勘察设计院有限公司,软件名称:广东迁改导则2022, 注册号:BW278-83834-58155-58339.迁改导则是要另外下载安装软件吗？"
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"""
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def main():
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"""
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@@ -138,10 +193,10 @@ def main():
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# 读取提问数据
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current_dir = os.path.dirname(os.path.abspath(__file__))
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data_file = os.path.join(current_dir, "..", "..", "data", "excel", "400条提问意图分类数据-原始.xlsx")
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data_file = os.path.join(current_dir, "..", "..", "data", "excel", "历史提问数据(dislike)_提问明确.xlsx")
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output_file = os.path.join(current_dir, "..", "..", "data", "excel", "测试提问数据_槽位填充结果.xlsx")
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# 检测是否为调试模式，调试模式下使用examples_query，否则从Excel读取
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is_debug = hasattr(sys, 'gettrace') and sys.gettrace() is not None
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if is_debug:
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examples = examples_query.strip().split("\n")
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@@ -149,11 +204,13 @@ def main():
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examples = load_questions_from_excel(data_file)
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if not is_debug:
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max_workers = 10 # 减少并发数以避免API限制
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max_workers = 20 # 减少并发数以避免API限制
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logging.info(f"共有 {len(examples)} 个问题需要处理，使用 {max_workers} 个并发线程")
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# 创建一个与输入顺序相同的结果列表
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results = [None] * len(examples)
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batch_size = 100 # 每100条保存一次
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# 使用线程池进行并发处理
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with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
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# 提交所有任务并记录它们的索引
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@@ -163,43 +220,27 @@ def main():
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future_to_index[future] = idx
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# 使用tqdm显示进度条
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completed = 0
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for future in tqdm(concurrent.futures.as_completed(future_to_index), total=len(examples), desc="处理进度"):
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idx = future_to_index[future]
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result = future.result()
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# 将结果放在与输入相同的位置
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results[idx] = result
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completed += 1
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# 每处理batch_size条数据保存一次
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if completed % batch_size == 0:
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logging.info(f"已完成 {completed}/{len(examples)} 条，保存中间结果...")
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save_results_to_excel(results, output_file, is_final=False)
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# 将结果保存到Excel文件
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results_df = pd.DataFrame(results)
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output_file = os.path.join(current_dir, "..", "..", "data", "excel", "测试提问数据_槽位填充结果.xlsx")
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# 使用ExcelWriter设置格式
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with pd.ExcelWriter(output_file, engine='xlsxwriter') as writer:
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results_df.to_excel(writer, index=False, sheet_name='Sheet1')
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# 获取工作簿和工作表对象
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workbook = writer.book
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worksheet = writer.sheets['Sheet1']
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# 设置列宽（单位：像素）
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# 定义列宽（厘米转为Excel单位，1cm约等于4.7个Excel单位）
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worksheet.set_column('A:A', 60) # 提问列 60个Excel单位
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worksheet.set_column('B:B', 20) # 问题拆解 20个Excel单位
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worksheet.set_column('C:C', 20) # 一级分类 20个Excel单位
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worksheet.set_column('D:D', 20) # 二级分类 20个Excel单位
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worksheet.set_column('E:E', 60) # 问题改写 60个Excel单位
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worksheet.set_column('F:F', 60) # 检索到的关键词 60个Excel单位
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worksheet.set_column('G:G', 80) # 槽位填充 80个Excel单位
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# 设置所有行高为20磅
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for i in range(len(results_df) + 1): # +1 是为了包括表头
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worksheet.set_row(i, 20)
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# 处理完所有数据后，保存最终结果
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save_results_to_excel(results, output_file, is_final=True)
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logging.info(f"所有处理完成，最终结果已保存至: {output_file}")
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else:
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for idx, query in enumerate(examples):
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if query.strip() == "":
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continue
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process_query(recognizer, query)
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logging.info(f"处理完成，结果已保存至: {output_file}")
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def setup_logging():
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# 配置日志输出到控制台
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@@ -1,4 +1,4 @@
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from rag2_0.dify.workflow_chat import NewWorkflowChat
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from rag2_0.dify.dify_tool import NewWorkflowChat
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import pandas as pd
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from concurrent.futures import ThreadPoolExecutor
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from tqdm import tqdm
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+304
-2
@@ -1,8 +1,17 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import psycopg2
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from psycopg2 import sql
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import os
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import json
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from datetime import timezone, timedelta
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from rag2_0.dify.dify_client import ChatClient
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from pydantic import BaseModel, Field
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from langchain.output_parsers import PydanticOutputParser
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class ContentSource(BaseModel):
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score: int = Field(description="相关性分数")
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reason: str = Field(description="评分理由")
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class PgSql:
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"""
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@@ -219,6 +228,299 @@ class DifyTool:
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finally:
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dify_pgsql.close_connection()
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class BaseWorkflowChat:
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"""
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工作流对话基类，封装了与Dify API交互的基本功能
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"""
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def __init__(self, api_key: str, base_url: str):
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"""
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初始化工作流对话基类
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Args:
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api_key: Dify API的密钥
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base_url: Dify API的基础URL
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"""
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self.chat_client = ChatClient(api_key=api_key, base_url=base_url)
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self.content_source_parser = PydanticOutputParser(pydantic_object=ContentSource)
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def create_chat_message(self, query: str):
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"""
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创建聊天消息
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Args:
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query: 问题内容
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Returns:
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tuple: (聊天响应, 消息ID)
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"""
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try:
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response = self.chat_client.create_chat_message(inputs={}, query=query, user="AutoTestDifyChat").json()
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return response, response["message_id"]
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except Exception as e:
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raise e
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def calculate_score(self, query: str, content: str) -> int:
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"""
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使用LLM判断query与content之间的相关性分数
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Args:
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query (str): 用户问题
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content (str): 检索内容
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Returns:
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int: 相关性分数，1-10分，10代表完全相关，1代表完全不相关；-1表示评分失败
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"""
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from rag2_0.tool.ModelTool import OpenAiLLM
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try:
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prompt = f"""你是一个专业的信息相关性评估助手。请根据以下标准对用户query和检索内容的相关性进行1-10评分（10=完全相关，1=完全不相关），并按指定格式输出JSON结果。
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【评分标准】
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10分：完全契合，主题/意图完全一致且涵盖所有关键信息
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8-9分：高度相关，核心要素匹配但存在少量信息缺失
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6-7分：部分相关，涉及相同主题但存在重要信息缺失
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4-5分：弱相关，仅次要信息点匹配
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1-3分：完全不相关或信息冲突
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【评估维度】
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1. 主题一致性：核心主题/意图的匹配程度
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2. 内容覆盖度：是否涵盖query的关键要素
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3. 信息准确性：是否存在矛盾/错误信息
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4. 细节丰富度：是否提供query要求的详细信息
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【输出格式】
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{{
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"score": 评分,
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"reason": "简明扼要的评分理由（中文）"
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}}
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【示例】
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query: "新冠疫苗的常见副作用"
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内容: "辉瑞疫苗常见反应包括注射部位疼痛（84.1%）、疲劳（62.9%）"
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输出: {{"score":8,"reason":"主题完全匹配，涵盖主要副作用但未提及发热等常见反应"}}
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现在评估：
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query: "{query}"
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content: "{content}"
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"""
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api_key = os.getenv("OPENAI_API_KEY")
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base_url = os.getenv("OPENAI_API_BASE")
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model = os.getenv("LLM_MODEL_NAME")
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llm = OpenAiLLM(api_key=api_key, base_url=base_url, model=model)
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response = llm.invoke(user_prompt=prompt, need_retry=True)
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# 解析JSON响应
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try:
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parsed_output = self.content_source_parser.parse(response.content)
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return parsed_output.score
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except Exception as e:
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return -1
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except Exception as e:
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return -1
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def get_retrieve_info(self, query: str, outputs: dict) -> tuple:
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"""
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获取检索信息并计算分数
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Args:
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query (str): 用户问题
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outputs (dict): 检索输出结果
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Returns:
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tuple: (检索内容列表, 最高分, 最低分, 平均分)
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"""
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max_score = 0
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min_score = 10
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total_score = 0
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valid_scores = 0
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retrieve_content = []
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# 使用线程池并发计算分数
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with ThreadPoolExecutor() as executor:
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# 创建任务列表
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future_to_content = {}
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for result in outputs["result"]:
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content = result["content"].strip()
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future = executor.submit(self.calculate_score, query=query, content=content)
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future_to_content[future] = content
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# 收集结果
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for future in as_completed(future_to_content):
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content = future_to_content[future]
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score = future.result()
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content_title = content.split("\n")[0]
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if score != -1:
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max_score = max(max_score, score)
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min_score = min(min_score, score)
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total_score += score
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valid_scores += 1
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if content_title:
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retrieve_content.append(content_title + f"--相关性得分({score}分)")
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avg_score = total_score / valid_scores if valid_scores > 0 else 0
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return retrieve_content, max_score, min_score, avg_score
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class NewWorkflowChat(BaseWorkflowChat):
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"""
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新工作流对话类，用于调用新工作流发送对话并解析获取相关数据
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"""
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def process_question(self, query: str) -> dict:
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"""
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处理问题，获取新工作流的回答和相关信息
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Args:
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query: 问题内容
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Returns:
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dict: 包含问题、回答和相关信息的字典
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"""
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response, message_id = self.create_chat_message(query)
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if isinstance(response, str) and response.startswith("error:"):
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raise RuntimeError(f"create_chat_message 出错：{response}")
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answer = response["answer"]
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workflow_info = self.get_workflow_info(query, message_id)
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if workflow_info is None:
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return None
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result = {
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"问题": query,
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"新流程答案": answer,
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"新问题改写": workflow_info["问题改写"],
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"新问题分类": workflow_info["问题分类"],
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"槽点信息": workflow_info["槽点信息"],
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"新检索词条": workflow_info["检索词条"],
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"检索内容": workflow_info["检索内容"],
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"message_id":message_id
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}
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return result
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def get_workflow_info(self, query: str, message_id: str) -> dict:
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"""
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获取新工作流的问题分类和检索信息
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Args:
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query (str): 用户问题
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message_id (str): 新工作流的消息ID
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Returns:
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dict: 包含问题分类结果的字典
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"""
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retrieve_title = []
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retrieve_content = []
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max_score = 0
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min_score = 0
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avg_score = 0
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rewrite_query = ""
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vertical_classification = ""
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sub_classification = ""
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slot_info = ""
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try:
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message_info = DifyTool.get_message_debug_info_by_id(message_id=message_id)
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for workflow_node in message_info["workflow_node_executions_info"]:
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if workflow_node["title"] == "知识检索结果后处理":
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outputs = json.loads(workflow_node["outputs"])
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retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs)
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retrieve_content = outputs["result"]
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elif workflow_node["title"] == "问题优化结果解析":
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outputs = json.loads(workflow_node["outputs"])
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rewrite_query = outputs["optimize_query"]
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llm_result_json = json.loads(workflow_node['inputs'])["llm_result"]
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json_result = json.loads(llm_result_json)
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vertical_classification = json_result['vertical_classification']
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sub_classification = json_result['sub_classification']
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slot_info = json.dumps(json_result["slot_filling"], ensure_ascii=False, indent=2)
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except Exception as e:
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raise e
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return {
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"问题改写": rewrite_query,
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"检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库",
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"检索内容": retrieve_content,
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"问题分类": f"{vertical_classification} - {sub_classification}",
|
||||
"槽点信息": slot_info,
|
||||
|
||||
}
|
||||
|
||||
class OldWorkFlowChat(BaseWorkflowChat):
|
||||
"""
|
||||
旧工作流对话类，用于调用旧工作流发送对话并解析获取相关数据
|
||||
"""
|
||||
def process_question(self, query: str) -> dict:
|
||||
"""
|
||||
处理问题，获取旧工作流的回答和相关信息
|
||||
|
||||
Args:
|
||||
query: 问题内容
|
||||
|
||||
Returns:
|
||||
dict: 包含问题、回答和相关信息的字典
|
||||
"""
|
||||
response, message_id = self.create_chat_message(query)
|
||||
|
||||
if isinstance(response, str) and response.startswith("error:"):
|
||||
return None
|
||||
|
||||
answer = response["answer"]
|
||||
workflow_info = self.get_workflow_info(query, message_id)
|
||||
|
||||
if workflow_info is None:
|
||||
return None
|
||||
|
||||
result = {
|
||||
"问题": query,
|
||||
"旧流程答案": answer,
|
||||
"旧问题改写": workflow_info["问题改写"],
|
||||
"旧检索词条": workflow_info["检索词条"],
|
||||
"检索内容": workflow_info["检索内容"],
|
||||
"message_id":message_id
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
def get_workflow_info(self, query: str, message_id: str) -> dict:
|
||||
"""
|
||||
获取旧工作流的问题改写和检索信息
|
||||
|
||||
Args:
|
||||
query (str): 用户问题
|
||||
message_id (str): 旧工作流的消息ID
|
||||
|
||||
Returns:
|
||||
dict: 包含问题改写和检索信息的字典
|
||||
"""
|
||||
retrieve_title = []
|
||||
retrieve_content = []
|
||||
max_score = 0
|
||||
min_score = 0
|
||||
avg_score = 0
|
||||
rewrite_query = ""
|
||||
|
||||
try:
|
||||
message_info = DifyTool.get_message_debug_info_by_id(message_id=message_id)
|
||||
for workflow_node in message_info["workflow_node_executions_info"]:
|
||||
if workflow_node["title"] == "知识检索结果后处理":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs)
|
||||
retrieve_content = outputs["result"]
|
||||
elif workflow_node["title"] == "问题优化结果解析":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
rewrite_query = outputs["optimize_query"]
|
||||
except Exception as e:
|
||||
return None
|
||||
|
||||
return {
|
||||
"问题改写": rewrite_query,
|
||||
"检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库",
|
||||
"检索内容": retrieve_content,
|
||||
}
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
|
||||
@@ -2,7 +2,8 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import os
|
||||
from rag2_0.dify.dify_client import ChatClient, DifyClient
|
||||
from rag2_0.dify.dify_client import DifyClient
|
||||
from rag2_0.dify.dify_tool import NewWorkflowChat, OldWorkFlowChat
|
||||
import pandas as pd
|
||||
# 使用线程池并发执行
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
@@ -44,8 +45,9 @@ class DifyComparisonTester:
|
||||
max_workers: 最大工作线程数
|
||||
"""
|
||||
self.excel_path = excel_path
|
||||
self.old_chat = ChatClient(api_key=old_workflow_api_key, base_url=baseurl)
|
||||
self.new_chat = ChatClient(api_key=new_workflow_api_key, base_url=baseurl)
|
||||
# 使用NewWorkflowChat和OldWorkFlowChat代替ChatClient
|
||||
self.old_chat = OldWorkFlowChat(api_key=old_workflow_api_key, base_url=baseurl)
|
||||
self.new_chat = NewWorkflowChat(api_key=new_workflow_api_key, base_url=baseurl)
|
||||
|
||||
# 评判相关参数
|
||||
self.output_path = output_path or os.path.join(os.path.dirname(self.excel_path), "dify问答_综合评判结果.xlsx")
|
||||
@@ -78,13 +80,13 @@ class DifyComparisonTester:
|
||||
"""
|
||||
def get_old_answer():
|
||||
try:
|
||||
return self.old_chat.create_chat_message(inputs={}, query=q, user="AutoTestDifyChat").json()
|
||||
return self.old_chat.process_question(query=q)
|
||||
except Exception as e:
|
||||
return f"error: {str(e)}"
|
||||
|
||||
def get_new_answer():
|
||||
try:
|
||||
return self.new_chat.create_chat_message(inputs={}, query=q, user="AutoTestDifyChat").json()
|
||||
return self.new_chat.process_question(query=q)
|
||||
except Exception as e:
|
||||
return f"error: {str(e)}"
|
||||
|
||||
@@ -95,14 +97,15 @@ class DifyComparisonTester:
|
||||
try:
|
||||
old_result = future_old.result()
|
||||
new_result = future_new.result()
|
||||
old_message_id = old_result["message_id"]
|
||||
new_message_id = new_result["message_id"]
|
||||
|
||||
if isinstance(old_result, str) and old_result.startswith("error:"):
|
||||
return None, None
|
||||
if isinstance(new_result, str) and new_result.startswith("error:"):
|
||||
return None, None
|
||||
|
||||
old_answer = old_result["answer"]
|
||||
new_answer = new_result["answer"]
|
||||
except Exception as e:
|
||||
return None, None, None
|
||||
return {"问题": q, "旧流程答案": old_answer, "新流程答案": new_answer}, old_message_id, new_message_id
|
||||
return future_old, future_new
|
||||
|
||||
def find_wiki_link(self, query) -> str | None:
|
||||
"""
|
||||
@@ -407,22 +410,24 @@ content: "{content}"
|
||||
Returns:
|
||||
dict: 包含问题分类结果的字典
|
||||
"""
|
||||
retrieve_title=[]
|
||||
retrieve_content=[]
|
||||
max_score=0
|
||||
min_score=0
|
||||
avg_score=0
|
||||
rewrite_query=""
|
||||
vertical_classification=""
|
||||
sub_classification=""
|
||||
slot_info=""
|
||||
try:
|
||||
# 使用DifyTool直接获取消息信息
|
||||
new_message_info = DifyTool.get_message_debug_info_by_id(message_id=new_message_id)
|
||||
|
||||
# 初始化变量
|
||||
retrieve_title = []
|
||||
retrieve_content = []
|
||||
rewrite_query = ""
|
||||
vertical_classification = ""
|
||||
sub_classification = ""
|
||||
slot_info = ""
|
||||
|
||||
# 解析工作流节点信息
|
||||
for workflow_node in new_message_info["workflow_node_executions_info"]:
|
||||
if workflow_node["title"] == "知识检索结果后处理":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs)
|
||||
retrieve_content=outputs["result"]
|
||||
retrieve_content = outputs["result"]
|
||||
elif workflow_node["title"] == "问题优化结果解析":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
rewrite_query = outputs["optimize_query"]
|
||||
@@ -430,20 +435,21 @@ content: "{content}"
|
||||
json_result = json.loads(llm_result_json)
|
||||
vertical_classification = json_result['vertical_classification']
|
||||
sub_classification = json_result['sub_classification']
|
||||
slot_info=json.dumps(json_result["slot_filling"],ensure_ascii=False,indent=2)
|
||||
slot_info = json.dumps(json_result["slot_filling"], ensure_ascii=False, indent=2)
|
||||
except Exception as e:
|
||||
return None
|
||||
|
||||
return {
|
||||
"问题改写": rewrite_query,
|
||||
"检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库",
|
||||
"检索内容": retrieve_content,
|
||||
"问题分类": f"{vertical_classification} - {sub_classification}",
|
||||
"槽点信息":slot_info
|
||||
"槽点信息": slot_info
|
||||
}
|
||||
|
||||
def get_old_workflow_info(self, query:str, old_message_id:str) -> dict:
|
||||
"""
|
||||
获取新流程的问题分类
|
||||
获取旧流程的问题分类
|
||||
|
||||
Args:
|
||||
query (str): 用户问题
|
||||
@@ -452,24 +458,27 @@ content: "{content}"
|
||||
Returns:
|
||||
dict: 包含问题分类结果的字典
|
||||
"""
|
||||
retrieve_title=[]
|
||||
retrieve_content=[]
|
||||
max_score=0
|
||||
min_score=0
|
||||
avg_score=0
|
||||
rewrite_query=""
|
||||
try:
|
||||
# 使用DifyTool直接获取消息信息
|
||||
old_message_info = DifyTool.get_message_debug_info_by_id(message_id=old_message_id)
|
||||
|
||||
# 初始化变量
|
||||
retrieve_title = []
|
||||
retrieve_content = []
|
||||
rewrite_query = ""
|
||||
|
||||
# 解析工作流节点信息
|
||||
for workflow_node in old_message_info["workflow_node_executions_info"]:
|
||||
if workflow_node["title"] == "知识检索结果后处理":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs)
|
||||
retrieve_content=outputs["result"]
|
||||
retrieve_content = outputs["result"]
|
||||
elif workflow_node["title"] == "问题优化结果解析":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
rewrite_query = outputs["optimize_query"]
|
||||
except Exception as e:
|
||||
return None
|
||||
|
||||
return {
|
||||
"问题改写": rewrite_query,
|
||||
"检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库",
|
||||
@@ -512,13 +521,13 @@ content: "{content}"
|
||||
dict: 包含问题、回答和评判结果的字典
|
||||
"""
|
||||
# 获取基本的问题和回答
|
||||
basic_result, old_message_id, new_message_id = self.process_question(q)
|
||||
if basic_result is None:
|
||||
future_old, future_new = self.process_question(q)
|
||||
if future_old is None or future_new is None:
|
||||
return None
|
||||
|
||||
query = basic_result["问题"]
|
||||
old_answer = basic_result["旧流程答案"]
|
||||
new_answer = basic_result["新流程答案"]
|
||||
query = future_old["问题"]
|
||||
old_answer = future_old["旧流程答案"]
|
||||
new_answer = future_new["新流程答案"]
|
||||
|
||||
# 获取词条链接和标准答案
|
||||
wiki_url = self.find_wiki_link(query)
|
||||
@@ -540,33 +549,23 @@ content: "{content}"
|
||||
|
||||
if judge_result is None:
|
||||
judge_result = ""
|
||||
|
||||
# retrieve_title_score = self.get_retrieve_title_similarity(old_retrieve_content=old_workflow_info["检索内容"], new_retrieve_content=new_workflow_info["检索内容"])
|
||||
|
||||
# 并行获取新旧流程信息
|
||||
with ThreadPoolExecutor(max_workers=2) as executor:
|
||||
future_new = executor.submit(self.get_new_workflow_info, query=query, new_message_id=new_message_id)
|
||||
future_old = executor.submit(self.get_old_workflow_info, query=query, old_message_id=old_message_id)
|
||||
|
||||
try:
|
||||
new_workflow_info = future_new.result()
|
||||
old_workflow_info = future_old.result()
|
||||
except Exception as e:
|
||||
print(f"处理问题 '{query}' 获取工作流信息时发生错误: {str(e)}")
|
||||
return None
|
||||
retrieve_title_score=self.get_retrieve_title_similarity(old_retrieve_content=old_workflow_info["检索内容"], new_retrieve_content=new_workflow_info["检索内容"])
|
||||
# 返回结果
|
||||
return {
|
||||
"问题": query,
|
||||
"新问题改写": new_workflow_info["问题改写"],
|
||||
"旧问题改写": old_workflow_info["问题改写"],
|
||||
"新问题分类": new_workflow_info["问题分类"],
|
||||
"槽点信息":new_workflow_info["槽点信息"],
|
||||
"新问题改写": future_new["问题改写"],
|
||||
"旧问题改写": future_old["问题改写"],
|
||||
"新问题分类": future_new["问题分类"],
|
||||
"槽点信息": future_new["槽点信息"],
|
||||
"新流程答案": new_answer,
|
||||
"旧流程答案": old_answer,
|
||||
"回答判断": judge_result,
|
||||
"词条检索相似度": retrieve_title_score,
|
||||
# "词条检索相似度": retrieve_title_score,
|
||||
"答案词条": answer_title if answer_title else "",
|
||||
"新检索词条": new_workflow_info["检索词条"],
|
||||
"旧检索词条": old_workflow_info["检索词条"],
|
||||
"新检索词条": future_new["检索词条"],
|
||||
"旧检索词条": future_old["检索词条"],
|
||||
}
|
||||
|
||||
def run_comparison(self, with_judge=False):
|
||||
@@ -670,5 +669,7 @@ if __name__ == "__main__":
|
||||
print(f"对比评判结果已保存至: {output_file}")
|
||||
|
||||
# 单个问题测试示例
|
||||
# c = DifyChat(baseurl="http://172.20.0.145/v1", api_key="app-LjJaeLoAfqa6aoGzqU9UvxSf")
|
||||
# c.chat("如何新建配电线路工程")
|
||||
# 使用新的工作流类进行测试
|
||||
# new_chat = NewWorkflowChat(api_key="app-qxsSybCs7ABiKlC1JabTYVn6", base_url="http://172.20.0.145/v1")
|
||||
# result = new_chat.process_question("如何新建配电线路工程")
|
||||
# print(json.dumps(result, ensure_ascii=False, indent=2))
|
||||
|
||||
@@ -1,310 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import os
|
||||
import json
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from rag2_0.dify.dify_client import ChatClient, DifyClient
|
||||
from rag2_0.dify.dify_tool import DifyTool
|
||||
from pydantic import BaseModel, Field
|
||||
from langchain.output_parsers import PydanticOutputParser
|
||||
from threading import Lock
|
||||
|
||||
class ContentSource(BaseModel):
|
||||
score: int = Field(description="相关性分数")
|
||||
reason: str = Field(description="评分理由")
|
||||
|
||||
class BaseWorkflowChat:
|
||||
"""
|
||||
工作流对话基类，封装了与Dify API交互的基本功能
|
||||
"""
|
||||
def __init__(self, api_key: str, base_url: str):
|
||||
"""
|
||||
初始化工作流对话基类
|
||||
|
||||
Args:
|
||||
api_key: Dify API的密钥
|
||||
base_url: Dify API的基础URL
|
||||
"""
|
||||
self.chat_client = ChatClient(api_key=api_key, base_url=base_url)
|
||||
self.content_source_parser = PydanticOutputParser(pydantic_object=ContentSource)
|
||||
|
||||
def create_chat_message(self, query: str):
|
||||
"""
|
||||
创建聊天消息
|
||||
|
||||
Args:
|
||||
query: 问题内容
|
||||
|
||||
Returns:
|
||||
tuple: (聊天响应, 消息ID)
|
||||
"""
|
||||
try:
|
||||
response = self.chat_client.create_chat_message(inputs={}, query=query, user="AutoTestDifyChat").json()
|
||||
return response, response["message_id"]
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
def calculate_score(self, query: str, content: str) -> int:
|
||||
"""
|
||||
使用LLM判断query与content之间的相关性分数
|
||||
|
||||
Args:
|
||||
query (str): 用户问题
|
||||
content (str): 检索内容
|
||||
|
||||
Returns:
|
||||
int: 相关性分数，1-10分，10代表完全相关，1代表完全不相关；-1表示评分失败
|
||||
"""
|
||||
from rag2_0.tool.ModelTool import OpenAiLLM
|
||||
|
||||
try:
|
||||
prompt = f"""你是一个专业的信息相关性评估助手。请根据以下标准对用户query和检索内容的相关性进行1-10评分（10=完全相关，1=完全不相关），并按指定格式输出JSON结果。
|
||||
|
||||
【评分标准】
|
||||
10分：完全契合，主题/意图完全一致且涵盖所有关键信息
|
||||
8-9分：高度相关，核心要素匹配但存在少量信息缺失
|
||||
6-7分：部分相关，涉及相同主题但存在重要信息缺失
|
||||
4-5分：弱相关，仅次要信息点匹配
|
||||
1-3分：完全不相关或信息冲突
|
||||
|
||||
【评估维度】
|
||||
1. 主题一致性：核心主题/意图的匹配程度
|
||||
2. 内容覆盖度：是否涵盖query的关键要素
|
||||
3. 信息准确性：是否存在矛盾/错误信息
|
||||
4. 细节丰富度：是否提供query要求的详细信息
|
||||
|
||||
【输出格式】
|
||||
{{
|
||||
"score": 评分,
|
||||
"reason": "简明扼要的评分理由（中文）"
|
||||
}}
|
||||
|
||||
【示例】
|
||||
query: "新冠疫苗的常见副作用"
|
||||
内容: "辉瑞疫苗常见反应包括注射部位疼痛（84.1%）、疲劳（62.9%）"
|
||||
输出: {{"score":8,"reason":"主题完全匹配，涵盖主要副作用但未提及发热等常见反应"}}
|
||||
|
||||
现在评估：
|
||||
query: "{query}"
|
||||
content: "{content}"
|
||||
"""
|
||||
api_key = os.getenv("OPENAI_API_KEY")
|
||||
base_url = os.getenv("OPENAI_API_BASE")
|
||||
model = os.getenv("LLM_MODEL_NAME")
|
||||
llm = OpenAiLLM(api_key=api_key, base_url=base_url, model=model)
|
||||
response = llm.invoke(user_prompt=prompt, need_retry=True)
|
||||
|
||||
# 解析JSON响应
|
||||
try:
|
||||
parsed_output = self.content_source_parser.parse(response.content)
|
||||
return parsed_output.score
|
||||
except Exception as e:
|
||||
return -1
|
||||
except Exception as e:
|
||||
return -1
|
||||
|
||||
def get_retrieve_info(self, query: str, outputs: dict) -> tuple:
|
||||
"""
|
||||
获取检索信息并计算分数
|
||||
|
||||
Args:
|
||||
query (str): 用户问题
|
||||
outputs (dict): 检索输出结果
|
||||
|
||||
Returns:
|
||||
tuple: (检索内容列表, 最高分, 最低分, 平均分)
|
||||
"""
|
||||
max_score = 0
|
||||
min_score = 10
|
||||
total_score = 0
|
||||
valid_scores = 0
|
||||
retrieve_content = []
|
||||
|
||||
# 使用线程池并发计算分数
|
||||
with ThreadPoolExecutor() as executor:
|
||||
# 创建任务列表
|
||||
future_to_content = {}
|
||||
for result in outputs["result"]:
|
||||
content = result["content"].strip()
|
||||
future = executor.submit(self.calculate_score, query=query, content=content)
|
||||
future_to_content[future] = content
|
||||
|
||||
# 收集结果
|
||||
for future in as_completed(future_to_content):
|
||||
content = future_to_content[future]
|
||||
score = future.result()
|
||||
content_title = content.split("\n")[0]
|
||||
|
||||
if score != -1:
|
||||
max_score = max(max_score, score)
|
||||
min_score = min(min_score, score)
|
||||
total_score += score
|
||||
valid_scores += 1
|
||||
|
||||
if content_title:
|
||||
retrieve_content.append(content_title + f"--相关性得分({score}分)")
|
||||
|
||||
avg_score = total_score / valid_scores if valid_scores > 0 else 0
|
||||
return retrieve_content, max_score, min_score, avg_score
|
||||
|
||||
|
||||
class NewWorkflowChat(BaseWorkflowChat):
|
||||
"""
|
||||
新工作流对话类，用于调用新工作流发送对话并解析获取相关数据
|
||||
"""
|
||||
def process_question(self, query: str) -> dict:
|
||||
"""
|
||||
处理问题，获取新工作流的回答和相关信息
|
||||
|
||||
Args:
|
||||
query: 问题内容
|
||||
|
||||
Returns:
|
||||
dict: 包含问题、回答和相关信息的字典
|
||||
"""
|
||||
response, message_id = self.create_chat_message(query)
|
||||
|
||||
if isinstance(response, str) and response.startswith("error:"):
|
||||
raise RuntimeError(f"create_chat_message 出错：{response}")
|
||||
|
||||
answer = response["answer"]
|
||||
workflow_info = self.get_workflow_info(query, message_id)
|
||||
|
||||
if workflow_info is None:
|
||||
return None
|
||||
|
||||
result = {
|
||||
"问题": query,
|
||||
"新流程答案": answer,
|
||||
"新问题改写": workflow_info["问题改写"],
|
||||
"新问题分类": workflow_info["问题分类"],
|
||||
"槽点信息": workflow_info["槽点信息"],
|
||||
"新检索词条": workflow_info["检索词条"],
|
||||
"检索内容": workflow_info["检索内容"],
|
||||
"message_id":message_id
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
def get_workflow_info(self, query: str, message_id: str) -> dict:
|
||||
"""
|
||||
获取新工作流的问题分类和检索信息
|
||||
|
||||
Args:
|
||||
query (str): 用户问题
|
||||
message_id (str): 新工作流的消息ID
|
||||
|
||||
Returns:
|
||||
dict: 包含问题分类结果的字典
|
||||
"""
|
||||
retrieve_title = []
|
||||
retrieve_content = []
|
||||
max_score = 0
|
||||
min_score = 0
|
||||
avg_score = 0
|
||||
rewrite_query = ""
|
||||
vertical_classification = ""
|
||||
sub_classification = ""
|
||||
slot_info = ""
|
||||
|
||||
try:
|
||||
message_info = DifyTool.get_message_debug_info_by_id(message_id=message_id)
|
||||
for workflow_node in message_info["workflow_node_executions_info"]:
|
||||
if workflow_node["title"] == "知识检索结果后处理":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs)
|
||||
retrieve_content = outputs["result"]
|
||||
elif workflow_node["title"] == "问题优化结果解析":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
rewrite_query = outputs["optimize_query"]
|
||||
llm_result_json = json.loads(workflow_node['inputs'])["llm_result"]
|
||||
json_result = json.loads(llm_result_json)
|
||||
vertical_classification = json_result['vertical_classification']
|
||||
sub_classification = json_result['sub_classification']
|
||||
slot_info = json.dumps(json_result["slot_filling"], ensure_ascii=False, indent=2)
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
return {
|
||||
"问题改写": rewrite_query,
|
||||
"检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库",
|
||||
"检索内容": retrieve_content,
|
||||
"问题分类": f"{vertical_classification} - {sub_classification}",
|
||||
"槽点信息": slot_info,
|
||||
|
||||
}
|
||||
|
||||
|
||||
class OldWorkFlowChat(BaseWorkflowChat):
|
||||
"""
|
||||
旧工作流对话类，用于调用旧工作流发送对话并解析获取相关数据
|
||||
"""
|
||||
def process_question(self, query: str) -> dict:
|
||||
"""
|
||||
处理问题，获取旧工作流的回答和相关信息
|
||||
|
||||
Args:
|
||||
query: 问题内容
|
||||
|
||||
Returns:
|
||||
dict: 包含问题、回答和相关信息的字典
|
||||
"""
|
||||
response, message_id = self.create_chat_message(query)
|
||||
|
||||
if isinstance(response, str) and response.startswith("error:"):
|
||||
return None
|
||||
|
||||
answer = response["answer"]
|
||||
workflow_info = self.get_workflow_info(query, message_id)
|
||||
|
||||
if workflow_info is None:
|
||||
return None
|
||||
|
||||
result = {
|
||||
"问题": query,
|
||||
"旧流程答案": answer,
|
||||
"旧问题改写": workflow_info["问题改写"],
|
||||
"旧检索词条": workflow_info["检索词条"],
|
||||
"检索内容": workflow_info["检索内容"],
|
||||
"message_id":message_id
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
def get_workflow_info(self, query: str, message_id: str) -> dict:
|
||||
"""
|
||||
获取旧工作流的问题改写和检索信息
|
||||
|
||||
Args:
|
||||
query (str): 用户问题
|
||||
message_id (str): 旧工作流的消息ID
|
||||
|
||||
Returns:
|
||||
dict: 包含问题改写和检索信息的字典
|
||||
"""
|
||||
retrieve_title = []
|
||||
retrieve_content = []
|
||||
max_score = 0
|
||||
min_score = 0
|
||||
avg_score = 0
|
||||
rewrite_query = ""
|
||||
|
||||
try:
|
||||
message_info = DifyTool.get_message_debug_info_by_id(message_id=message_id)
|
||||
for workflow_node in message_info["workflow_node_executions_info"]:
|
||||
if workflow_node["title"] == "知识检索结果后处理":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs)
|
||||
retrieve_content = outputs["result"]
|
||||
elif workflow_node["title"] == "问题优化结果解析":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
rewrite_query = outputs["optimize_query"]
|
||||
except Exception as e:
|
||||
return None
|
||||
|
||||
return {
|
||||
"问题改写": rewrite_query,
|
||||
"检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库",
|
||||
"检索内容": retrieve_content,
|
||||
}
|
||||
@@ -23,12 +23,12 @@ class SoftwareName(str, Enum):
|
||||
|
||||
# 软件别名映射
|
||||
ALIASES = {
|
||||
D3: ["配网D3", "D3软件", "配网工程软件"],
|
||||
C1: ["储能C1", "C1软件", "储能电站软件", "储能软件"],
|
||||
Z1: ["西藏Z1", "Z1软件", "西藏电力软件"],
|
||||
T1: ["技改T1", "T1软件", "技改检修软件"],
|
||||
T1_LIST: ["技改清单T1", "T1清单软件", "技改检修清单软件"],
|
||||
MAIN: ["主网软件", "电力建设软件", "主网建设软件", "主网软件"]
|
||||
D3: "别名包括：配网D3、D3软件、配网工程软件等 其他类似称呼",
|
||||
C1: "别名包括：储能C1、C1软件、储能电站软件、储能软件等 其他类似称呼",
|
||||
Z1: "别名包括：西藏Z1、Z1软件、西藏电力软件等 其他类似称呼",
|
||||
T1: "别名包括：技改T1、T1软件、技改检修软件等 其他类似称呼",
|
||||
T1_LIST: "别名包括：技改清单T1、T1清单软件、技改检修清单软件等 其他类似称呼",
|
||||
MAIN: "别名包括：主网软件、电力建设软件、主网建设软件、博微电力建设计价通等 其他类似称呼"
|
||||
}
|
||||
|
||||
# 定义输出模型
|
||||
@@ -58,7 +58,7 @@ class QueryRewrite(BaseModel):
|
||||
# 1. 软件问题
|
||||
# 1.1 软件功能
|
||||
class SoftwareFunction(BaseModel):
|
||||
software_name: SoftwareName = Field(description="软件名称")
|
||||
software_name: SoftwareName = Field(description="软件名称,只能从给定的范围中取值")
|
||||
function_name: str = Field(description="具体功能名称")
|
||||
operation: str = Field(description="用户操作意图(如何使用功能、功能入口、功能使用场景)")
|
||||
software_version: Optional[str] = Field(None, description="软件版本")
|
||||
@@ -77,7 +77,7 @@ class SoftwareFunction(BaseModel):
|
||||
|
||||
# 1.2 故障排查
|
||||
class TroubleShooting(BaseModel):
|
||||
software_name: SoftwareName = Field(description="软件名称")
|
||||
software_name: SoftwareName = Field(description="软件名称,只能从给定的范围中取值")
|
||||
function_name: str = Field(description="具体功能名称/操作描述")
|
||||
error_message: str = Field(description="报错信息/异常现象")
|
||||
software_version: Optional[str] = Field(None, description="软件版本")
|
||||
@@ -162,7 +162,7 @@ class SoftwareLock(BaseModel):
|
||||
# 3.3 安装下载类
|
||||
class InstallationDownload(BaseModel):
|
||||
|
||||
software_name: SoftwareName = Field(description="软件/插件名称，与file_name二选一")
|
||||
software_name: str = Field(description="软件/插件名称，与file_name二选一")
|
||||
file_name: str = Field(description="文件名，与software_name二选一")
|
||||
operation_stage: str = Field(description="操作阶段")
|
||||
os_version: Optional[str] = Field(None, description="操作系统版本")
|
||||
@@ -182,7 +182,7 @@ class InstallationDownload(BaseModel):
|
||||
# 3.4 问题排查类
|
||||
class ProblemDiagnosis(BaseModel):
|
||||
error_message: str = Field(description="报错信息/异常现象")
|
||||
software_name: Optional[SoftwareName] = Field(None, description="软件名称")
|
||||
software_name: Optional[SoftwareName] = Field(None, description="软件名称,只能从给定的范围中取值")
|
||||
os_version: Optional[str] = Field(None, description="操作系统版本")
|
||||
|
||||
def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
|
||||
|
||||
@@ -180,7 +180,7 @@ class IntentRecognizer:
|
||||
return reranked_terms
|
||||
|
||||
except Exception as e:
|
||||
return list(matched_terms)
|
||||
raise RuntimeError(f"SiliconFlowReRankerModel重排失败：{e}") from e
|
||||
|
||||
def match_keywords(self, query: str) -> Tuple[TermList, List[str]]:
|
||||
"""
|
||||
|
||||
@@ -18,26 +18,6 @@ import requests
|
||||
# sk-dvbaktabkdwdpjgxyoozlwnejosjyhdgqwllfeborqahndxs
|
||||
|
||||
API_KEY_LIST=[
|
||||
"sk-hrojkkkrrkmsajtnizokbcgexsfggdiqavbtvbayuwqbnmom",
|
||||
"sk-kkdklmnyompoiotzkfqahpayzlkgogfudjkyaebehtsowvid",
|
||||
"sk-sfxzvllifafbyfduupcdtcrjwhdyiyojnksyopnfslurnhsp",
|
||||
"sk-faqirxiszukfswqvzqawxnemqfacrkyurbxxkzwbbujqacdp",
|
||||
"sk-vonaanuueqiczppkntjuphateshrcpqpnvxmwxorkyihjmrb",
|
||||
"sk-qfpeoodgupcukcdstjcxgegwxnuhtxkkrupkogkcvhavxgny",
|
||||
"sk-fsvjnbpfgoadixympaabaukupuhjvbturcbxaqfdzjznemtr",
|
||||
"sk-fltvnbiqntfawjwkfnnhmyfiimzgzxkweqmefcfqkbucwrhi",
|
||||
"sk-oosswdriwyqkglwdigvcxgmcpyplcyowicbaugpizoscevdl",
|
||||
"sk-jswtxhkiralnyiukqimtyuurcaepulxdrfijadtxzrgsajyc",
|
||||
"sk-dcjuhoukdyrbneadtxtnyxzmigkpiqgtqqnreiprxpioftsv",
|
||||
"sk-yrhezyuxjblpaxzzudbowqmvcoxcammupcubghbodolikbdk",
|
||||
"sk-dsgvwpfagmarilmnewwbzhfzlqehburoupjaopucdvybpbdo",
|
||||
"sk-oljjlspuaurtoczyekztiidwtoerugadgepiufclpmrbdfqc",
|
||||
"sk-crgrimubjesthvxuqwedqqdoetljyrgeahxxpctfefgnkpyo",
|
||||
"sk-tubqhwgycxrdhwsqzjopxgeaqpsjdfppckckayvzornaluwq",
|
||||
"sk-amcxlmsdnadptpnehqnkvseolacipztmvovnmxojzohbjjil",
|
||||
"sk-pdyymhshpzmdduwxsezthnrgarnnhgzvmiflbpisfzxkiayt",
|
||||
"sk-qhwoorywmejumyudfxbrkegxtqifsbgcdkmpjckezepgyqnz",
|
||||
"sk-cpoctrgcnstaybeyuieuwjdgeakudhqdnnwdjavjudcbvvem",
|
||||
"sk-wqdpapdkisovziexgcyxvumpwzbjnhqbxvcqcspzctjhyhjk",
|
||||
"sk-bbntrnifrtdzhhgrtlrhvwbnaysuszviemshdakxonnnymnb",
|
||||
"sk-vmpnwjxersrwybmfhfxgsvbmhsmpjldxseiyxovnysrlbuzi",
|
||||
@@ -98,6 +78,25 @@ API_KEY_LIST=[
|
||||
"sk-nbksjgcngsayoumnsdbkcpnqivnvxjenwpzuazzrkhnsgeoo",
|
||||
"sk-iaafvpjyqiocgzchbdldbkgcffqniahkcbgoviuevuogulcm",
|
||||
"sk-muvjguqeshyimzowqnqgxwpsgujlpkqgrisxsimthtyrpypx",
|
||||
"sk-jgybgyayxlwoxeijgrjcneqlyusleohgbliuwpsuhocrjsmk",
|
||||
"sk-wzjsmwxcbbpcrqivqfzjwufqqjtlwejtncnvbpeicznkwiuh",
|
||||
"sk-izdjicdoyillktsihkiapuvwebisehtlgykozrvzfkgncwsc",
|
||||
"sk-fcsfmyivfuojsqsditvobfqprdpeunukycpcfnoxkraqevpx",
|
||||
"sk-szyjgyxrcvyxpvzfwgmbxnflxngxvcplitcctsdvvrqjgftk",
|
||||
"sk-jzbodthsnvjwbyrnynsxrudtqfnbdbrcxebjwjgajocnzqse",
|
||||
"sk-fxepossfzpmccibfwqpkluorzqlbtcaplepeugtfzfsctcbl",
|
||||
"sk-ympnflocrkxjrbubsxqdjqwicuyavvvysctlpfhunkcrzxjx",
|
||||
"sk-flhqvziknntednkcgjaxlyzzsrfzjhrzrmteqonajpbiinni",
|
||||
"sk-xfregpbbquqbxpiobjzanydsjivrjrnbokzxcqtnhxhyghhe",
|
||||
"sk-jrdzerhmvrtvzawkksowbgkggkubwfquplmrxbdhespqgtis",
|
||||
"sk-jjbpnkbeupsxyclcivbhizcfpfjrppddunbqynyjkqhtmpwu",
|
||||
"sk-oqehupcveovkjqqtxypqyifidcdissuyehwrkdwgruoyjkpq",
|
||||
"sk-orhfntzrbpmpavybcjyylofxncdvufdmvlznofmhxmnjymjl",
|
||||
"sk-kvgfuqeqvpmfsccykyoohheshclcrtvjlnewratvrjpkpbkc",
|
||||
"sk-zhnbqnpuumuuvegnvbgoggxafpukbzchpgrugpkobiwkzsar",
|
||||
"sk-kzhxlqvqcxlnbdgnpalqnzumkmspepkttkgbophnkqanainw",
|
||||
"sk-bzttugqtlskrvguvhckwamdssvgmgnrqpsialpdbskfsyyak",
|
||||
"sk-tovmogiablsoeabwgqyvevpcfichyjpuzqdymmvksspdrtqt",
|
||||
]
|
||||
|
||||
class APIKeyManager:
|
||||
|
||||
Reference in New Issue
Block a user