From 87d4c7c38a396f6571d6ac013a6772b2da8fbd3a Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Mon, 12 Jan 2026 00:37:50 +0800 Subject: [PATCH] =?UTF-8?q?remove=EF=BC=9A=E7=A7=BB=E9=99=A4Planner?= =?UTF-8?q?=E9=97=AE=E9=A2=98=E9=85=8D=E7=BD=AE=E9=A1=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/chat/planner_actions/planner.py | 27 ++-------- src/chat/replyer/group_generator.py | 14 +---- src/chat/replyer/private_generator.py | 14 +---- src/config/config.py | 2 +- src/memory_system/memory_retrieval.py | 77 +++++++++++---------------- 5 files changed, 36 insertions(+), 98 deletions(-) diff --git a/src/chat/planner_actions/planner.py b/src/chat/planner_actions/planner.py index bdb33556..da02e845 100644 --- a/src/chat/planner_actions/planner.py +++ b/src/chat/planner_actions/planner.py @@ -53,7 +53,6 @@ reply 4.不要选择回复你自己发送的消息 5.不要单独对表情包进行回复 6.将上下文中所有含义不明的，疑似黑话的，缩写词均写入unknown_words中 -7.如果你对上下文存在疑问，有需要查询的问题，写入question中 {reply_action_example} no_reply @@ -225,24 +224,6 @@ class ActionPlanner: reasoning = "未提供原因" action_data = {key: value for key, value in action_json.items() if key not in ["action"]} - # 验证和清理 question - if "question" in action_data: - q = action_data.get("question") - if isinstance(q, str): - cleaned_q = q.strip() - if cleaned_q: - action_data["question"] = cleaned_q - else: - # 如果清理后为空字符串，移除该字段 - action_data.pop("question", None) - elif q is None: - # 如果为 None，移除该字段 - action_data.pop("question", None) - else: - # 如果不是字符串类型，记录警告并移除 - logger.warning(f"{self.log_prefix}question 格式不正确，应为字符串类型，已忽略") - action_data.pop("question", None) - # 非no_reply动作需要target_message_id target_message = None @@ -520,15 +501,14 @@ class ActionPlanner: name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。" # 根据 think_mode 配置决定 reply action 的示例 JSON - # 在 JSON 中直接作为 action 参数携带 unknown_words 和 question + # 在 JSON 中直接作为 action 参数携带 unknown_words if global_config.chat.think_mode == "classic": reply_action_example = "" if global_config.chat.llm_quote: reply_action_example += "5.如果要明确回复消息，使用quote，如果消息不多不需要明确回复，设置quote为false\n" reply_action_example += ( '{{"action":"reply", "target_message_id":"消息id(m+数字)", ' - '"unknown_words":["词语1","词语2"], ' - '"question":"需要查询的问题"' + '"unknown_words":["词语1","词语2"]' ) if global_config.chat.llm_quote: reply_action_example += ', "quote":"如果需要引用该message，设置为true"' @@ -542,8 +522,7 @@ class ActionPlanner: reply_action_example += ( '{{"action":"reply", "think_level":数值等级(0或1), ' '"target_message_id":"消息id(m+数字)", ' - '"unknown_words":["词语1","词语2"], ' - '"question":"需要查询的问题"' + '"unknown_words":["词语1","词语2"]' ) if global_config.chat.llm_quote: reply_action_example += ', "quote":"如果需要引用该message，设置为true"' diff --git a/src/chat/replyer/group_generator.py b/src/chat/replyer/group_generator.py index 5f69484e..89f2b263 100644 --- a/src/chat/replyer/group_generator.py +++ b/src/chat/replyer/group_generator.py @@ -845,18 +845,6 @@ class DefaultReplyer: chat_id, message_list_before_short, chat_talking_prompt_short, unknown_words ) - # 从 chosen_actions 中提取 question（仅在 reply 动作中） - question = None - if chosen_actions: - for action_info in chosen_actions: - if action_info.action_type == "reply" and isinstance(action_info.action_data, dict): - q = action_info.action_data.get("question") - if isinstance(q, str): - cleaned_q = q.strip() - if cleaned_q: - question = cleaned_q - break - # 并行执行构建任务（包括黑话解释，可配置关闭） task_results = await asyncio.gather( self._time_and_run_task( @@ -871,7 +859,7 @@ class DefaultReplyer: self._time_and_run_task(self.build_personality_prompt(), "personality_prompt"), self._time_and_run_task( build_memory_retrieval_prompt( - chat_talking_prompt_short, sender, target, self.chat_stream, think_level=think_level, unknown_words=unknown_words, question=question + chat_talking_prompt_short, sender, target, self.chat_stream, think_level=think_level, unknown_words=unknown_words ), "memory_retrieval", ), diff --git a/src/chat/replyer/private_generator.py b/src/chat/replyer/private_generator.py index 968e0291..d73883eb 100644 --- a/src/chat/replyer/private_generator.py +++ b/src/chat/replyer/private_generator.py @@ -710,18 +710,6 @@ class PrivateReplyer: else: jargon_coroutine = self._build_disabled_jargon_explanation() - # 从 chosen_actions 中提取 question（仅在 reply 动作中） - question = None - if chosen_actions: - for action_info in chosen_actions: - if action_info.action_type == "reply" and isinstance(action_info.action_data, dict): - q = action_info.action_data.get("question") - if isinstance(q, str): - cleaned_q = q.strip() - if cleaned_q: - question = cleaned_q - break - # 并行执行九个构建任务（包括黑话解释，可配置关闭） task_results = await asyncio.gather( self._time_and_run_task( @@ -736,7 +724,7 @@ class PrivateReplyer: self._time_and_run_task(self.build_personality_prompt(), "personality_prompt"), self._time_and_run_task( build_memory_retrieval_prompt( - chat_talking_prompt_short, sender, target, self.chat_stream, think_level=1, unknown_words=unknown_words, question=question + chat_talking_prompt_short, sender, target, self.chat_stream, think_level=1, unknown_words=unknown_words ), "memory_retrieval", ), diff --git a/src/config/config.py b/src/config/config.py index cafde1b6..0a3ba48c 100644 --- a/src/config/config.py +++ b/src/config/config.py @@ -57,7 +57,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template") # 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码 # 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/ -MMC_VERSION = "0.12.2" +MMC_VERSION = "0.13.0-snapshot.1" def get_key_comment(toml_table, key): diff --git a/src/memory_system/memory_retrieval.py b/src/memory_system/memory_retrieval.py index 4dd613e8..5473d53d 100644 --- a/src/memory_system/memory_retrieval.py +++ b/src/memory_system/memory_retrieval.py @@ -1097,7 +1097,6 @@ async def build_memory_retrieval_prompt( chat_stream, think_level: int = 1, unknown_words: Optional[List[str]] = None, - question: Optional[str] = None, ) -> str: """构建记忆检索提示 使用两段式查询：第一步生成问题，第二步使用ReAct Agent查询答案 @@ -1109,7 +1108,6 @@ async def build_memory_retrieval_prompt( chat_stream: 聊天流对象 think_level: 思考深度等级 unknown_words: Planner 提供的未知词语列表，优先使用此列表而不是从聊天记录匹配 - question: Planner 提供的问题，当 planner_question 配置开启时，直接使用此问题进行检索 Returns: str: 记忆检索结果字符串 @@ -1144,52 +1142,37 @@ async def build_memory_retrieval_prompt( if not recent_query_history: recent_query_history = "最近没有查询记录。" - # 第一步：生成问题或使用 Planner 提供的问题 + # 第一步：使用 LLM 生成问题 + question_prompt = await global_prompt_manager.format_prompt( + "memory_retrieval_question_prompt", + bot_name=bot_name, + time_now=time_now, + chat_history=message, + recent_query_history=recent_query_history, + sender=sender, + target_message=target, + ) + + success, response, reasoning_content, model_name = await llm_api.generate_with_model( + question_prompt, + model_config=model_config.model_task_config.tool_use, + request_type="memory.question", + ) + + if global_config.debug.show_memory_prompt: + logger.info(f"{log_prefix}记忆检索问题生成提示词: {question_prompt}") + # logger.info(f"记忆检索问题生成响应: {response}") + + if not success: + logger.error(f"{log_prefix}LLM生成问题失败: {response}") + return "" + + # 解析概念列表和问题列表，只取第一个问题 single_question: Optional[str] = None - - # 如果 planner_question 配置开启，只使用 Planner 提供的问题，不使用旧模式 - if global_config.memory.planner_question: - if question and isinstance(question, str) and question.strip(): - # 清理和验证 question - single_question = question.strip() - logger.info(f"{log_prefix}使用 Planner 提供的 question: {single_question}") - else: - # planner_question 开启但没有提供 question，跳过记忆检索 - logger.debug(f"{log_prefix}planner_question 已开启但未提供 question，跳过记忆检索") - end_time = time.time() - logger.info(f"{log_prefix}无当次查询，不返回任何结果，耗时: {(end_time - start_time):.3f}秒") - return "" - else: - # planner_question 关闭，使用旧模式：LLM 生成问题 - question_prompt = await global_prompt_manager.format_prompt( - "memory_retrieval_question_prompt", - bot_name=bot_name, - time_now=time_now, - chat_history=message, - recent_query_history=recent_query_history, - sender=sender, - target_message=target, - ) - - success, response, reasoning_content, model_name = await llm_api.generate_with_model( - question_prompt, - model_config=model_config.model_task_config.tool_use, - request_type="memory.question", - ) - - if global_config.debug.show_memory_prompt: - logger.info(f"{log_prefix}记忆检索问题生成提示词: {question_prompt}") - # logger.info(f"记忆检索问题生成响应: {response}") - - if not success: - logger.error(f"{log_prefix}LLM生成问题失败: {response}") - return "" - - # 解析概念列表和问题列表，只取第一个问题 - _, questions = parse_questions_json(response) - if questions and len(questions) > 0: - single_question = questions[0].strip() - logger.info(f"{log_prefix}解析到问题: {single_question}") + _, questions = parse_questions_json(response) + if questions and len(questions) > 0: + single_question = questions[0].strip() + logger.info(f"{log_prefix}解析到问题: {single_question}") # 初始阶段：使用 Planner 提供的 unknown_words 进行检索（如果提供） initial_info = ""