# Copyright 2023 The Qwen team, Alibaba Group. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import json import os import time from typing import Dict, Iterator, List, Literal, Optional, Union from qwen_agent.agents import Assistant from qwen_agent.llm.schema import ASSISTANT, FUNCTION, Message from qwen_agent.llm.oai import TextChatAtOAI class ModifiedAssistant(Assistant): """ 修改后的 Assistant 子类，改变循环判断逻辑： - 原始逻辑：如果没有使用工具，立即退出循环 - 修改后逻辑：如果没有使用工具，调用模型判断回答是否完整，如果不完整则继续循环 """ def _is_retryable_error(self, error: Exception) -> bool: """判断错误是否可重试 Args: error: 异常对象 Returns: bool: 是否可重试 """ error_str = str(error).lower() retryable_indicators = [ '502', '500', '503', '504', # HTTP错误代码 'internal server error', # 内部服务器错误 'timeout', # 超时 'connection', # 连接错误 'network', # 网络错误 'rate', # 速率限制和相关错误 'quota', # 配额限制 'service unavailable', # 服务不可用 'provider returned error', # Provider错误 'model service error', # 模型服务错误 'temporary', # 临时错误 'retry' # 明确提示重试 ] return any(indicator in error_str for indicator in retryable_indicators) def _call_llm_with_retry(self, messages: List[Message], functions=None, extra_generate_cfg=None, max_retries: int = 5) -> Iterator: """带重试机制的LLM调用 Args: messages: 消息列表 functions: 函数列表 extra_generate_cfg: 额外生成配置 max_retries: 最大重试次数 Returns: LLM响应流 Raises: Exception: 重试次数耗尽后重新抛出原始异常 """ for attempt in range(max_retries): try: return self._call_llm(messages=messages, functions=functions, extra_generate_cfg=extra_generate_cfg) except Exception as e: # 检查是否为可重试的错误 if self._is_retryable_error(e) and attempt < max_retries - 1: delay = 2 ** attempt # 指数退避: 1s, 2s, 4s print(f"LLM调用失败 (尝试 {attempt + 1}/{max_retries})，{delay}秒后重试: {str(e)}") time.sleep(delay) continue else: # 不可重试的错误或已达到最大重试次数 if attempt > 0: print(f"LLM调用重试失败，已达到最大重试次数 {max_retries}") raise def _run(self, messages: List[Message], lang: Literal['en', 'zh'] = 'en', **kwargs) -> Iterator[List[Message]]: message_list = copy.deepcopy(messages) response = [] # 保持原有的最大调用次数限制 total_num_llm_calls_available = self.MAX_LLM_CALL_PER_RUN if hasattr(self, 'MAX_LLM_CALL_PER_RUN') else 10 num_llm_calls_available = total_num_llm_calls_available while num_llm_calls_available > 0: num_llm_calls_available -= 1 extra_generate_cfg = {'lang': lang} if kwargs.get('seed') is not None: extra_generate_cfg['seed'] = kwargs['seed'] output_stream = self._call_llm_with_retry(messages=message_list, functions=[func.function for func in self.function_map.values()], extra_generate_cfg=extra_generate_cfg) output: List[Message] = [] for output in output_stream: if output: yield response + output if output: response.extend(output) message_list.extend(output) # 处理工具调用 used_any_tool = False for out in output: use_tool, tool_name, tool_args, _ = self._detect_tool(out) if use_tool: tool_result = self._call_tool(tool_name, tool_args, messages=message_list, **kwargs) fn_msg = Message(role=FUNCTION, name=tool_name, content=tool_result, extra={'function_id': out.extra.get('function_id', '1')}) message_list.append(fn_msg) response.append(fn_msg) yield response used_any_tool = True # 如果使用了工具，继续循环 if used_any_tool: continue # 如果没有使用工具，无调用次数，已经执行了2次以上循环，退出循环 if num_llm_calls_available ==0 or total_num_llm_calls_available - num_llm_calls_available >=2: break # 如果没有使用工具，还有调用次数，并只执行了1次，就需要调用模型判断回答是否完整（修复部分case不执行工具调用就停止的问题） if num_llm_calls_available > 0: # 构建判断消息 - 使用英文系统提示词，并在用户提示中包含具体内容 user_question = messages[-1].content if messages[-1].content else str(messages[-1]) assistant_response = output[-1].content if hasattr(output[-1], 'content') and output[-1].content else str(output[-1]) judge_messages = [ Message(role='system', content='''You are a professional conversation completeness evaluator. Your task is to determine whether an AI assistant has provided a complete and sufficient answer to a user's question based on strict evaluation criteria. ## Evaluation Criteria ### Complete Answer Characteristics (mark as "complete" if ANY of these are met): 1. **Information Completeness**: Provides core information users need, including specific parameters, specifications, data, or detailed descriptions 2. **Problem Resolution**: Directly addresses the user's question with clear answers or solutions 3. **Recommendation Adequacy**: When providing product/service recommendations, includes key information (model, parameters, price, features, etc.) 4. **Conclusion Clarity**: Provides clear conclusions, advice, or summaries ### Incomplete Answer Characteristics (mark as "incomplete" if ANY of these are met): 1. **Information Gaps**: Obviously lacks key information needed to answer the question 2. **Vague Responses**: Uses evasive language and avoids specific questions 3. **Unfulfilled Promises**: Promises to provide more information but doesn't deliver 4. **Needs Follow-up**: Clearly indicates more information is needed to continue answering ## Special Evaluation Rules - **Recommendation Questions**: Consider complete if specific recommendations with key information are provided - **Technical Questions**: Consider complete if technical details and specific data are provided - **Comparison Questions**: Consider complete if valuable comparative information is provided - **Inquiry Questions**: Consider complete if best effort answer is given based on available information ## Output Format Only output "complete" or "incomplete" with no additional text or explanation. Adhere strictly to these standards for objective evaluation.'''), Message(role='user', content=f'''Please evaluate whether the following assistant response is complete based on the evaluation criteria. USER QUESTION: {user_question} ASSISTANT RESPONSE: {assistant_response} Based on the evaluation criteria, is this response complete? Only reply with "complete" or "incomplete".''') ] # 调用模型进行判断（不使用工具） judge_stream = self._call_llm_with_retry(messages=judge_messages, functions=[], extra_generate_cfg={'lang': 'en'}) judge_output = [] for judge_msg in judge_stream: if judge_msg: judge_output.extend(judge_msg) # 分析判断结果 - 使用正则匹配检测 complete is_complete = False if judge_output: judge_content = judge_output[-1].content if hasattr(judge_output[-1], 'content') else str(judge_output[-1]) judge_content = judge_content.lower().strip() print(judge_content) # 使用正则匹配检测 complete import re if re.search(r'incomplete', judge_content): is_complete = False else: is_complete = True # 如果回答完整，退出循环；否则继续 if is_complete: break yield response # Utility functions def read_system_prompt(): """读取通用的无状态系统prompt""" with open("./prompt/system_prompt_default.md", "r", encoding="utf-8") as f: return f.read().strip() def read_mcp_settings(): """读取MCP工具配置""" with open("./mcp/mcp_settings.json", "r") as f: mcp_settings_json = json.load(f) return mcp_settings_json def update_agent_llm(agent, model_name: str, api_key: str = None, model_server: str = None, generate_cfg: Dict = None, system_prompt: str = None, mcp_settings: List[Dict] = None): """动态更新助手实例的LLM和配置，支持从接口传入参数""" # 获取基础配置 llm_config = { "model": model_name, "api_key": api_key, "model_server": model_server, "generate_cfg": generate_cfg if generate_cfg else {} } # 创建LLM实例 llm_instance = TextChatAtOAI(llm_config) # 动态设置LLM agent.llm = llm_instance # 更新系统消息（如果提供） if system_prompt: agent.system_message = system_prompt # 更新MCP设置（如果提供） if mcp_settings: agent.function_list = mcp_settings return agent # 向后兼容：保持原有的初始化函数接口 def init_modified_agent_service_with_files(rag_cfg=None, model_name="qwen3-next", api_key=None, model_server=None, generate_cfg=None, system_prompt=None, mcp=None): """创建支持预加载文件的修改版助手实例""" system = system_prompt if system_prompt else read_system_prompt() tools = mcp if mcp else read_mcp_settings() llm_config = { "model": model_name, "api_key": api_key, "model_server": model_server, "generate_cfg": generate_cfg if generate_cfg else {} } # 创建LLM实例 llm_instance = TextChatAtOAI(llm_config) bot = ModifiedAssistant( llm=llm_instance, name="修改版数据检索助手", description="基于智能判断循环终止的助手", system_message=system, function_list=tools, ) return bot