diff --git a/src/chat/heart_flow/heartFC_chat.py b/src/chat/heart_flow/heartFC_chat.py index a6813426..b95ec584 100644 --- a/src/chat/heart_flow/heartFC_chat.py +++ b/src/chat/heart_flow/heartFC_chat.py @@ -18,6 +18,8 @@ from src.chat.planner_actions.action_manager import ActionManager from src.chat.heart_flow.hfc_utils import CycleDetail from src.express.expression_learner import expression_learner_manager from src.chat.frequency_control.frequency_control import frequency_control_manager +from src.express.reflect_tracker import reflect_tracker_manager +from src.express.expression_reflector import expression_reflector_manager from src.jargon import extract_and_store_jargon from src.person_info.person_info import Person from src.plugin_system.base.component_types import EventType, ActionInfo @@ -397,8 +399,9 @@ class HeartFChatting: # ReflectTracker Check # 在每次回复前检查一次上下文，看是否有反思问题得到了解答 # ------------------------------------------------------------------------- - from src.express.reflect_tracker import reflect_tracker_manager + reflector = expression_reflector_manager.get_or_create_reflector(self.stream_id) + await reflector.check_and_ask() tracker = reflect_tracker_manager.get_tracker(self.stream_id) if tracker: resolved = await tracker.trigger_tracker() @@ -406,16 +409,8 @@ class HeartFChatting: reflect_tracker_manager.remove_tracker(self.stream_id) logger.info(f"{self.log_prefix} ReflectTracker resolved and removed.") - # ------------------------------------------------------------------------- - # Expression Reflection Check - # 检查是否需要提问表达反思 - # ------------------------------------------------------------------------- - from src.express.expression_reflector import expression_reflector_manager - reflector = expression_reflector_manager.get_or_create_reflector(self.stream_id) - asyncio.create_task(reflector.check_and_ask()) start_time = time.time() - async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()): asyncio.create_task(self.expression_learner.trigger_learning_for_chat()) asyncio.create_task( diff --git a/src/config/official_configs.py b/src/config/official_configs.py index c0a938a1..1536a03a 100644 --- a/src/config/official_configs.py +++ b/src/config/official_configs.py @@ -285,6 +285,14 @@ class ExpressionConfig(ConfigBase): reflect_operator_id: str = "" """表达反思操作员ID""" + allow_reflect: list[str] = field(default_factory=list) + """ + 允许进行表达反思的聊天流ID列表 + 格式: ["qq:123456:private", "qq:654321:group", ...] + 只有在此列表中的聊天流才会提出问题并跟踪 + 如果列表为空，则所有聊天流都可以进行表达反思（前提是 reflect = true） + """ + def _parse_stream_config_to_chat_id(self, stream_config_str: str) -> Optional[str]: """ 解析流配置字符串并生成对应的 chat_id diff --git a/src/express/express_utils.py b/src/express/express_utils.py index 20095561..e900baa1 100644 --- a/src/express/express_utils.py +++ b/src/express/express_utils.py @@ -63,13 +63,15 @@ def format_create_date(timestamp: float) -> str: def _compute_weights(population: List[Dict]) -> List[float]: """ - 根据表达的count计算权重，范围限定在1~3之间。 - count越高，权重越高，但最多为基础权重的3倍。 + 根据表达的count计算权重，范围限定在1~5之间。 + count越高，权重越高，但最多为基础权重的5倍。 + 如果表达已checked，权重会再乘以3倍。 """ if not population: return [] counts = [] + checked_flags = [] for item in population: count = item.get("count", 1) try: @@ -77,18 +79,29 @@ def _compute_weights(population: List[Dict]) -> List[float]: except (TypeError, ValueError): count_value = 1.0 counts.append(max(count_value, 0.0)) + # 获取checked状态 + checked = item.get("checked", False) + checked_flags.append(bool(checked)) min_count = min(counts) max_count = max(counts) if max_count == min_count: - return [1.0 for _ in counts] + base_weights = [1.0 for _ in counts] + else: + base_weights = [] + for count_value in counts: + # 线性映射到[1,5]区间 + normalized = (count_value - min_count) / (max_count - min_count) + base_weights.append(1.0 + normalized * 4.0) # 1~3 + # 如果checked，权重乘以3 weights = [] - for count_value in counts: - # 线性映射到[1,3]区间 - normalized = (count_value - min_count) / (max_count - min_count) - weights.append(1.0 + normalized * 2.0) # 1~3 + for base_weight, checked in zip(base_weights, checked_flags): + if checked: + weights.append(base_weight * 3.0) + else: + weights.append(base_weight) return weights diff --git a/src/express/expression_reflector.py b/src/express/expression_reflector.py index 4402c18d..9256b3dd 100644 --- a/src/express/expression_reflector.py +++ b/src/express/expression_reflector.py @@ -37,6 +37,12 @@ class ExpressionReflector: logger.info(f"[Expression Reflection] Operator ID 未配置，跳过") return False + # 检查是否在允许列表中 + allow_reflect = global_config.expression.allow_reflect + if allow_reflect and self.chat_id not in allow_reflect: + logger.info(f"[Expression Reflection] 当前聊天流 {self.chat_id} 不在允许列表中，跳过") + return False + # 检查上一次提问时间 current_time = time.time() time_since_last_ask = current_time - self.last_ask_time diff --git a/src/express/expression_selector.py b/src/express/expression_selector.py index d543025d..65d84f93 100644 --- a/src/express/expression_selector.py +++ b/src/express/expression_selector.py @@ -140,6 +140,7 @@ class ExpressionSelector: "source_id": expr.chat_id, "create_date": expr.create_date if expr.create_date is not None else expr.last_active_time, "count": expr.count if getattr(expr, "count", None) is not None else 1, + "checked": expr.checked if getattr(expr, "checked", None) is not None else False, } for expr in style_query ] diff --git a/src/memory_system/memory_retrieval.py b/src/memory_system/memory_retrieval.py index 7d129bbc..f2625c83 100644 --- a/src/memory_system/memory_retrieval.py +++ b/src/memory_system/memory_retrieval.py @@ -791,92 +791,6 @@ def _query_thinking_back(chat_id: str, question: str) -> Optional[Tuple[bool, st return None -async def _analyze_question_answer(question: str, answer: str, chat_id: str) -> None: - """异步分析问题和答案的类别，并存储到相应系统 - - Args: - question: 问题 - answer: 答案 - chat_id: 聊天ID - """ - try: - # 使用LLM分析类别 - analysis_prompt = f"""请分析以下问题和答案的类别： - -问题：{question} -答案：{answer} - -类别说明： -1. 人物信息：有关某个用户的个体信息（如某人的喜好、习惯、经历等） -2. 黑话：对特定概念、缩写词、谐音词、自创词的解释（如"yyds"、"社死"等） -3. 其他：除此之外的其他内容 - -请输出JSON格式： -{{ - "category": "人物信息" | "黑话" | "其他", - "jargon_keyword": "如果是黑话，提取关键词（如'yyds'），否则为空字符串", - "person_name": "如果是人物信息，提取人物名称，否则为空字符串", - "memory_content": "如果是人物信息，提取要存储的记忆内容（简短概括），否则为空字符串" -}} - -只输出JSON，不要输出其他内容：""" - - success, response, _, _ = await llm_api.generate_with_model( - analysis_prompt, - model_config=model_config.model_task_config.utils, - request_type="memory.analyze_qa", - ) - - if not success: - logger.error(f"分析问题和答案失败: {response}") - return - - # 解析JSON响应 - try: - json_pattern = r"```json\s*(.*?)\s*```" - matches = re.findall(json_pattern, response, re.DOTALL) - - if matches: - json_str = matches[0] - else: - json_str = response.strip() - - repaired_json = repair_json(json_str) - analysis_result = json.loads(repaired_json) - - category = analysis_result.get("category", "").strip() - - if category == "黑话": - # 处理黑话 - jargon_keyword = analysis_result.get("jargon_keyword", "").strip() - if jargon_keyword: - from src.jargon.jargon_miner import store_jargon_from_answer - - await store_jargon_from_answer(jargon_keyword, answer, chat_id) - else: - logger.warning(f"分析为黑话但未提取到关键词，问题: {question[:50]}...") - - elif category == "人物信息": - # 处理人物信息 - # person_name = analysis_result.get("person_name", "").strip() - # memory_content = analysis_result.get("memory_content", "").strip() - # if person_name and memory_content: - # from src.person_info.person_info import store_person_memory_from_answer - # await store_person_memory_from_answer(person_name, memory_content, chat_id) - # else: - # logger.warning(f"分析为人物信息但未提取到人物名称或记忆内容，问题: {question[:50]}...") - pass # 功能暂时禁用 - - else: - logger.info(f"问题和答案类别为'其他'，不进行存储，问题: {question[:50]}...") - - except Exception as e: - logger.error(f"解析分析结果失败: {e}, 响应: {response[:200]}...") - - except Exception as e: - logger.error(f"分析问题和答案时发生异常: {e}") - - def _store_thinking_back( chat_id: str, question: str, context: str, found_answer: bool, answer: str, thinking_steps: List[Dict[str, Any]] ) -> None: @@ -1013,8 +927,6 @@ async def _process_single_question(question: str, chat_id: str, context: str, in logger.info(f"ReAct Agent超时，不存储到数据库，问题: {question[:50]}...") if found_answer and answer: - # 创建异步任务分析问题和答案 - asyncio.create_task(_analyze_question_answer(question, answer, chat_id)) return f"问题：{question}\n答案：{answer}" return None diff --git a/template/bot_config_template.toml b/template/bot_config_template.toml index 97ce96ad..53b17f1e 100644 --- a/template/bot_config_template.toml +++ b/template/bot_config_template.toml @@ -1,5 +1,5 @@ [inner] -version = "6.23.0" +version = "6.23.1" #----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读---- #如果你想要修改配置文件，请递增version的值 @@ -82,6 +82,7 @@ expression_groups = [ reflect = false # 是否启用表达反思（Bot主动向管理员询问表达方式是否合适） reflect_operator_id = "" # 表达反思操作员ID，格式：platform:id:type (例如 "qq:123456:private" 或 "qq:654321:group") +allow_reflect = [] # 允许进行表达反思的聊天流ID列表，格式：["qq:123456:private", "qq:654321:group", ...]，只有在此列表中的聊天流才会提出问题并跟踪。如果列表为空，则所有聊天流都可以进行表达反思（前提是 reflect = true） [chat] #麦麦的聊天设置