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"2b34e52cba181483fd7ab37d7d9ed92b4703aecc604befbd150d831e84de535d" diff --git a/pyproject.toml b/pyproject.toml index 745659e..b4c0db9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,6 +22,8 @@ tqdm = "^4.67.1" xlsxwriter = "^3.2.3" flask = "^3.1.1" psycopg2 = "^2.9.10" +gunicorn = "^23.0.0" +gevent = "^25.5.1" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/rag2_0/dify/chat_dify_by_workorder.py b/rag2_0/dify/chat_dify_by_workorder.py new file mode 100644 index 0000000..8a6d445 --- /dev/null +++ b/rag2_0/dify/chat_dify_by_workorder.py @@ -0,0 +1,151 @@ +from rag2_0.dify.workflow_chat import NewWorkflowChat +import pandas as pd +from concurrent.futures import ThreadPoolExecutor +from tqdm import tqdm +import concurrent.futures + + +class ChatDifyByWorkorder: + + def __init__(self, api_key=None, base_url="https://api.dify.ai/v1") -> None: + """ + 初始化ChatDifyByWorkorder类 + + Args: + api_key: Dify API密钥，默认为None + base_url: Dify API的基础URL，默认为"https://api.dify.ai/v1" + """ + baseurl = "http://172.20.0.145/v1" + new_workflow_api_key = "app-qxsSybCs7ABiKlC1JabTYVn6" + self.new_chat = NewWorkflowChat(api_key=new_workflow_api_key, base_url=baseurl) + self.new_chat_answer = NewWorkflowChat(api_key=new_workflow_api_key, base_url=baseurl) + + + def get_soft_name(self, row) -> str: + if "博微配网计价通D3" in row["产品线"]: + return "博微配网计价通D3" + elif "博微电力建设计价通软件" in row["产品线"]: + return "电力建设计价通软件" + elif "新能源系列" in row["产品线"] and "博微新型储能电站建设计价通C1软件" in row["产品名称"]: + return "储能C1软件" + elif "博微西藏计价通Z1" in row["产品线"]: + return "西藏计价通Z1" + elif "博微技改检修计价通T1软件" in row["产品线"] and "技改检修计价通T1软件-概预算" in row["产品名称"]: + return "技改检修工程计价通T1" + elif "博微技改检修计价通T1软件" in row["产品线"] and "技改检修计价通T1软件-清单" in row["产品名称"]: + return "检修清单计价通T1" + return "" + + def process_query(self, q:str) -> dict: + """ + 发送问题并获取回答及相关工作流信息 + + Args: + q: 用户问题 + + Returns: + dict: 包含问题、回答和工作流信息的字典 + """ + retry_count = 0 + max_retries = 2 + + while retry_count <= max_retries: + try: + # 发送问题获取回答和消息ID + result = self.new_chat.process_question(q) + return result + except Exception as e: + retry_count += 1 + if retry_count <= max_retries: + continue + else: + raise e + + def process_answer(self, q:str) -> dict: + """ + 发送问题并获取回答及相关工作流信息 + + Args: + q: 用户问题 + + Returns: + dict: 包含问题、回答和工作流信息的字典 + """ + retry_count = 0 + max_retries = 2 + + while retry_count <= max_retries: + try: + # 发送问题获取回答和消息ID + result = self.new_chat_answer.process_question(q) + return result + except Exception as e: + retry_count += 1 + if retry_count <= max_retries: + continue + else: + raise + + def process_row(self, row): + """处理单行数据""" + soft_name = self.get_soft_name(row=row) + if soft_name == "": + return None + + # 使用线程池并发执行查询 + with ThreadPoolExecutor() as executor: + try: + # 提交两个任务并获取Future对象 + query_future = executor.submit(self.process_query, q=f"{soft_name},{row['客户问题']}") + answer_future = executor.submit(self.process_answer, q=f"{soft_name},{row['解决方案']}") + + # 获取结果 + query_result = query_future.result() + answer_result = answer_future.result() + except Exception as e: + print(f"处理工单 {row.get('工单编号', '未知')} 时发生错误: {str(e)}") + return None + + worker_id = str(row["工单编号"]) + if query_result is None or answer_result is None: + print("处理对话出现错误") + return None + + worker_order_info = { + "工单编号": worker_id, + "用户问题": row['客户问题'], + "解决方案": row['解决方案'], + "AI回答": query_result["新流程答案"], + "用户问题检索到的词条": query_result["新检索词条"], + "解决方案检索到的词条": answer_result["新检索词条"], + } + return worker_order_info + + def run(self, excel_path:str): + df_data = pd.read_excel(excel_path) + list_worker_order_info = [] + + # 创建进度条 + with tqdm(total=len(df_data), desc="处理工单") as pbar: + # 创建线程池，最大并发数可以根据需要调整 + with ThreadPoolExecutor(max_workers=5) as executor: + # 提交所有任务 + future_to_row = {executor.submit(self.process_row, row): idx for idx, row in df_data.iterrows()} + + # 处理完成的任务 + for future in concurrent.futures.as_completed(future_to_row): + result = future.result() + if result is not None: + list_worker_order_info.append(result) + pbar.update(1) + + return list_worker_order_info + + + +if __name__=="__main__": + worker_chat = ChatDifyByWorkorder() + result = worker_chat.run(excel_path="data/excel/工单记录_均衡提取2000条.xlsx") + # 可以选择保存结果到Excel + if result: + pd.DataFrame(result).to_excel("data/excel/工单处理结果.xlsx", index=False) \ No newline at end of file diff --git a/rag2_0/dify/dify_tool.py b/rag2_0/dify/dify_tool.py index 1210be7..2dd2130 100644 --- a/rag2_0/dify/dify_tool.py +++ b/rag2_0/dify/dify_tool.py @@ -23,7 +23,7 @@ class PgSql: 连接到 PostgreSQL 数据库。 使用预定义的凭据连接到 'dify' 数据库。 - 如果连接失败，会打印错误信息。 + 如果连接失败，会抛出异常。 """ try: # 连接数据库 @@ -36,17 +36,16 @@ class PgSql: ) except (Exception, psycopg2.Error) as error: - print("Error while connecting to PostgreSQL", error) + raise Exception(f"Error while connecting to PostgreSQL: {error}") def close_connection(self): """ 关闭当前的 PostgreSQL 数据库连接。 - 如果存在活动的连接，则关闭它并打印确认信息。 + 如果存在活动的连接，则关闭它。 """ if self.connection: self.connection.close() - print("PostgreSQL connection is closed") def get_appinfo(self, appid:str)->dict | None: @@ -74,7 +73,7 @@ class PgSql: return dict(zip(colnames, result)) return None except (Exception, psycopg2.Error) as error: - print("Error while getting tenant_id by appid", error) + raise Exception(f"Error while getting tenant_id by appid: {error}") def get_messages_info(self, appid:str, query:str)->dict | None: @@ -103,7 +102,7 @@ class PgSql: return dict(zip(colnames, result)) return None except (Exception, psycopg2.Error) as error: - print("Error while getting messages_info", error) + raise Exception(f"Error while getting messages_info: {error}") def get_messages_info_by_id(self, message_id:str)->dict | None: """ @@ -123,7 +122,7 @@ class PgSql: return dict(zip(colnames, result)) return None except (Exception, psycopg2.Error) as error: - print("Error while getting messages_info", error) + raise Exception(f"Error while getting messages_info by id: {error}") def get_workflow_node_executions_info(self, workflow_run_id:str)->list[dict] | None: """ @@ -150,7 +149,7 @@ class PgSql: return [dict(zip(colnames, row)) for row in result] return None except (Exception, psycopg2.Error) as error: - print("Error while getting workflow_node_executions_info", error) + raise Exception(f"Error while getting workflow_node_executions_info: {error}") class DifyTool: """ @@ -165,16 +164,21 @@ class DifyTool: 根据消息 ID 从 'messages' 表中获取消息信息。 """ dify_pgsql = PgSql() - messages_info = dify_pgsql.get_messages_info_by_id(message_id) - if not messages_info: - return None - workflow_node_executions_info = dify_pgsql.get_workflow_node_executions_info(messages_info['workflow_run_id']) - if not workflow_node_executions_info: - return None - return { - "messages_info": messages_info, - "workflow_node_executions_info": workflow_node_executions_info - } + try: + messages_info = dify_pgsql.get_messages_info_by_id(message_id) + if not messages_info: + return None + workflow_node_executions_info = dify_pgsql.get_workflow_node_executions_info(messages_info['workflow_run_id']) + if not workflow_node_executions_info: + return None + return { + "messages_info": messages_info, + "workflow_node_executions_info": workflow_node_executions_info + } + except Exception as e: + raise Exception(f"Error in get_message_debug_info_by_id: {e}") + finally: + dify_pgsql.close_connection() @staticmethod @@ -195,21 +199,30 @@ class DifyTool: 查询到的应用数据、消息数据和节点执行数据。 """ dify_pgsql = PgSql() - appinfo = dify_pgsql.get_appinfo(appid) - if not appinfo: - return None - messages_info = dify_pgsql.get_messages_info(appid, query) - if not messages_info: - return None - workflow_node_executions_info = dify_pgsql.get_workflow_node_executions_info(messages_info['workflow_run_id']) - if not workflow_node_executions_info: - return None - return { - "appinfo": appinfo, - "messages_info": messages_info, - "workflow_node_executions_info": workflow_node_executions_info - } + try: + appinfo = dify_pgsql.get_appinfo(appid) + if not appinfo: + return None + messages_info = dify_pgsql.get_messages_info(appid, query) + if not messages_info: + return None + workflow_node_executions_info = dify_pgsql.get_workflow_node_executions_info(messages_info['workflow_run_id']) + if not workflow_node_executions_info: + return None + return { + "appinfo": appinfo, + "messages_info": messages_info, + "workflow_node_executions_info": workflow_node_executions_info + } + except Exception as e: + raise Exception(f"Error in get_message_debug_info_by_query: {e}") + finally: + dify_pgsql.close_connection() if __name__ == "__main__": - print(DifyTool.get_message_debug_info_by_query("ccf92b97-2789-4a3f-90e0-135a869a37c5", "电力建设计价通软件,导入结算后没有暂列金怎么办？要手动添加么？")) + try: + result = DifyTool.get_message_debug_info_by_query("ccf92b97-2789-4a3f-90e0-135a869a37c5", "电力建设计价通软件,导入结算后没有暂列金怎么办？要手动添加么？") + print(result) + except Exception as e: + print(f"执行出错: {e}") diff --git a/rag2_0/dify/intent_recognition_api.py b/rag2_0/dify/intent_recognition_api.py index 02e1278..daef05b 100644 --- a/rag2_0/dify/intent_recognition_api.py +++ b/rag2_0/dify/intent_recognition_api.py @@ -4,16 +4,32 @@ from dotenv import load_dotenv from rag2_0.intent_recognition import IntentRecognizer import json import time +import threading +import datetime + # 加载环境变量 load_dotenv() app = Flask(__name__) -# 初始化意图识别器 -api_key = os.getenv("OPENAI_API_KEY") -base_url = os.getenv("OPENAI_API_BASE") -model_name = os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo") -recognizer = IntentRecognizer(api_key=api_key, base_url=base_url, model_name=model_name) +# 创建线程锁，用于保护共享资源 +recognizer_lock = threading.Lock() + +# 使用单例模式创建意图识别器 +class RecognizerSingleton: + _instance = None + _lock = threading.Lock() + + @classmethod + def get_instance(cls): + if cls._instance is None: + with cls._lock: + if cls._instance is None: + api_key = os.getenv("OPENAI_API_KEY") + base_url = os.getenv("OPENAI_API_BASE") + model_name = os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo") + cls._instance = IntentRecognizer(api_key=api_key, base_url=base_url, model_name=model_name) + return cls._instance @app.route('/intent_recognize', methods=['POST']) def intent_recognize(): @@ -22,10 +38,16 @@ def intent_recognize(): query = data.get('query') if not query: return Response(json.dumps({"error": "缺少query参数"}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=400) + start_time = time.time() + + # 获取单例实例并使用线程锁保护关键操作 + recognizer = RecognizerSingleton.get_instance() result = recognizer.process_query_with_slots(query) + end_time = time.time() - print(f"意图识别耗时: {end_time - start_time:.2f}秒") + current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S %z") + print(f"[{current_time}] [{os.getpid()}] [INFO] 意图识别耗时: {end_time - start_time:.2f}秒") # 提取分类信息 classification = result["classification"] @@ -63,7 +85,10 @@ def intent_recognize(): } return Response(json.dumps(response_result, ensure_ascii=False), content_type='application/json; charset=utf-8') except Exception as e: + print(f"意图识别出错: {str(e)}") return Response(json.dumps({"error": str(e)}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=500) if __name__ == "__main__": - app.run(host="0.0.0.0", port=8001) \ No newline at end of file + # 开发环境使用Flask内置服务器 + # 生产环境使用gunicorn支持高并发 poetry run gunicorn -w 10 -k gevent -b 0.0.0.0:8001 rag2_0.dify.intent_recognition_api:app + app.run(host="0.0.0.0", port=8001, threaded=True) \ No newline at end of file diff --git a/rag2_0/dify/workflow_chat.py b/rag2_0/dify/workflow_chat.py new file mode 100644 index 0000000..fce903a --- /dev/null +++ b/rag2_0/dify/workflow_chat.py @@ -0,0 +1,310 @@ +#!/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, + } \ No newline at end of file diff --git a/rag2_0/tool/APIKeyManager.py b/rag2_0/tool/APIKeyManager.py index 696cbd4..3489e33 100644 --- a/rag2_0/tool/APIKeyManager.py +++ b/rag2_0/tool/APIKeyManager.py @@ -5,6 +5,18 @@ from typing import List, Optional, Dict from threading import Lock import requests +# 4090dify 中用的硅基流动 apikey +# sk-iuyfpcewztavgnivrllwnegffvrrsyeiuwvjrabngtejsqwy +# sk-skynrwfqvipknbcvjsxkhjaqlivmocpkdppkocjndbyulado +# sk-gjeanmnxtfcqezqagixyexarlxeztkazbrsciqescrfxrgpw +# sk-uapywdmjaylwwyufaivraqwbpqtxjpbsbkltlrmwqftvfech +# sk-dwmxnhaeephbxgsfncbonyajubuhuyhsfqwfsxahlepkiwas +# sk-lnxedlpzufrurrmvylugpccnppwyqdccgeiicoijrqnslcgm +# sk-duccaryfxcrpvwrbwvjbuwjwazyqleyebumhvrutksuqbxug +# sk-njcrhxpvevtxkzbmhkxshxcpwpnjzmccjgfdykdncaxjicez +# sk-bdagppigfxexcofiossccywvcqggbpywjapkdbtqycbgvqpz +# sk-dvbaktabkdwdpjgxyoozlwnejosjyhdgqwllfeborqahndxs + API_KEY_LIST=[ "sk-hrojkkkrrkmsajtnizokbcgexsfggdiqavbtvbayuwqbnmom", "sk-kkdklmnyompoiotzkfqahpayzlkgogfudjkyaebehtsowvid", @@ -56,6 +68,36 @@ API_KEY_LIST=[ "sk-zjwbwyocnuqxfshlpgfzdwlgjjrpewzgvoqwzyhufisidnos", "sk-kjxpzjbteiurpzhwjbbjqpjjfoewsahpjtmyqwectdubxhgf", "sk-sqdcnhapyzudneipdsuqlfawusrndxqkuwoaoumtonwdnppo", +"sk-yvyvoiegjrdlgihnxlaaznzdhnvpmfowtwmofomcodaoeaqs", +"sk-vccwuaomxhjcszjhheipoqqmsnuetasiveombkyrptstesbi", +"sk-mxbapcczwjsyrictwgigxgcvdgptyfrlynrewqioegqwrggv", +"sk-dujjzxrknevesbagqgqmuffxsosjoueviubnmodoormlmlzt", +"sk-rpptsvdeifcnyfkkrwnphgkrlchrqrbkglxrmztdzvfutdor", +"sk-lsukfggzghmdhtfhqcbmlfqabbtapwpuxnvtwshqqqlaesie", +"sk-aulumxzhvaladchcwgmsxidtdsvzytbpvzqgfuvcxlwbwcgl", +"sk-tzdqzroakecvseclcmrbhdnepveatybhhzxfpzxzgirpqcdy", +"sk-otxxemniwhxkdvroszmmkitswwuykosnqoldrkzdoflqpgvw", +"sk-zlruqobfdbjebyyvkmehakpcvfgnlfbdlbfrepusazzckbnv", +"sk-zryimztrlkgvcaiolarhvbcewmhwruhqfcndbylonzlqvdox", +"sk-rczjqufgdisqplkrmvhaxmdgcboluvxympvzljlreuqeeviq", +"sk-xfnvcksdgwufsktvmhpqrwpgovsxxtaeehtxnaqjtxmubqzl", +"sk-gcostftlutooxzsnqefcgyfxqytidvfjpxhbuxppgatwczoq", +"sk-wwonvjnowbcxmoyoluynnkjwerghspzdulyidskunkordaft", +"sk-rbuykocecbdoqteveeggwzvrhbvisgaerffexjsnyvjefhdk", +"sk-qmrkfvvbbfssuoreyvwqawoveyowuvxviqzqknotyweqmuog", +"sk-nprpuknjmikvoaxnwgyshwwwtnifvixpuqtzkzmcacdnvoib", +"sk-xanwnicepdxfqrfejzuxjcrhdsglfypkoxlcmmtamrtjkork", +"sk-lvtdgodiaurqyiwdxtdrgxifguychhccqlqkhqctscvqbfgi", +"sk-aedlbtlmqcttxwnvlfmxzaysamocamqxjceoyqjfgpcowybw", +"sk-fahdvndjblyjlizamvwcrxnilsgmbgbvwssxgquhkezgpqne", +"sk-tzludgttzxvpvwayazdbppbauvathdtccafjrhojpemucgyi", +"sk-hrbroidbfusidwnsmxenuzljxgdzzxiimlezygxplavnxjik", +"sk-ylgoiqxmtxeojdnonthxtweungyzldaqarvjxlqyztlvyrff", +"sk-asuqbqwdhjcqnvtjlwufyrkrwkobnrbmukzarvcctsgjipdp", +"sk-dpgpymiydutoexgvkajwgahagnfmcqzafwulccudnzvleifz", +"sk-nbksjgcngsayoumnsdbkcpnqivnvxjenwpzuazzrkhnsgeoo", +"sk-iaafvpjyqiocgzchbdldbkgcffqniahkcbgoviuevuogulcm", +"sk-muvjguqeshyimzowqnqgxwpsgujlpkqgrisxsimthtyrpypx", ] class APIKeyManager: