mirror of https://github.com/Mai-with-u/MaiBot.git
remove：移除Planner问题配置项
SengokuCola
parent
f591245540
commit
87d4c7c38a
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@ -53,7 +53,6 @@ reply
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4.不要选择回复你自己发送的消息
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5.不要单独对表情包进行回复
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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
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7.如果你对上下文存在疑问，有需要查询的问题，写入question中
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{reply_action_example}
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no_reply
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@ -225,24 +224,6 @@ class ActionPlanner:
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reasoning = "未提供原因"
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action_data = {key: value for key, value in action_json.items() if key not in ["action"]}
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# 验证和清理 question
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if "question" in action_data:
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q = action_data.get("question")
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if isinstance(q, str):
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cleaned_q = q.strip()
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if cleaned_q:
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action_data["question"] = cleaned_q
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else:
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# 如果清理后为空字符串，移除该字段
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action_data.pop("question", None)
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elif q is None:
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# 如果为 None，移除该字段
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action_data.pop("question", None)
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else:
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# 如果不是字符串类型，记录警告并移除
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logger.warning(f"{self.log_prefix}question 格式不正确，应为字符串类型，已忽略")
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action_data.pop("question", None)
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# 非no_reply动作需要target_message_id
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target_message = None
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@ -520,15 +501,14 @@ class ActionPlanner:
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name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
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# 根据 think_mode 配置决定 reply action 的示例 JSON
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# 在 JSON 中直接作为 action 参数携带 unknown_words 和 question
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# 在 JSON 中直接作为 action 参数携带 unknown_words
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if global_config.chat.think_mode == "classic":
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reply_action_example = ""
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if global_config.chat.llm_quote:
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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"
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reply_action_example += (
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'{{"action":"reply", "target_message_id":"消息id(m+数字)", '
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'"unknown_words":["词语1","词语2"], '
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'"question":"需要查询的问题"'
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'"unknown_words":["词语1","词语2"]'
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)
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if global_config.chat.llm_quote:
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reply_action_example += ', "quote":"如果需要引用该message，设置为true"'
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@ -542,8 +522,7 @@ class ActionPlanner:
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reply_action_example += (
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'{{"action":"reply", "think_level":数值等级(0或1), '
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'"target_message_id":"消息id(m+数字)", '
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'"unknown_words":["词语1","词语2"], '
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'"question":"需要查询的问题"'
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'"unknown_words":["词语1","词语2"]'
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)
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if global_config.chat.llm_quote:
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reply_action_example += ', "quote":"如果需要引用该message，设置为true"'
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@ -845,18 +845,6 @@ class DefaultReplyer:
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chat_id, message_list_before_short, chat_talking_prompt_short, unknown_words
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)
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# 从 chosen_actions 中提取 question（仅在 reply 动作中）
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question = None
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if chosen_actions:
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for action_info in chosen_actions:
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if action_info.action_type == "reply" and isinstance(action_info.action_data, dict):
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q = action_info.action_data.get("question")
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if isinstance(q, str):
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cleaned_q = q.strip()
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if cleaned_q:
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question = cleaned_q
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break
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# 并行执行构建任务（包括黑话解释，可配置关闭）
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task_results = await asyncio.gather(
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self._time_and_run_task(
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@ -871,7 +859,7 @@ class DefaultReplyer:
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self._time_and_run_task(self.build_personality_prompt(), "personality_prompt"),
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self._time_and_run_task(
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build_memory_retrieval_prompt(
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chat_talking_prompt_short, sender, target, self.chat_stream, think_level=think_level, unknown_words=unknown_words, question=question
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chat_talking_prompt_short, sender, target, self.chat_stream, think_level=think_level, unknown_words=unknown_words
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),
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"memory_retrieval",
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),
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@ -710,18 +710,6 @@ class PrivateReplyer:
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else:
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jargon_coroutine = self._build_disabled_jargon_explanation()
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# 从 chosen_actions 中提取 question（仅在 reply 动作中）
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question = None
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if chosen_actions:
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for action_info in chosen_actions:
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if action_info.action_type == "reply" and isinstance(action_info.action_data, dict):
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q = action_info.action_data.get("question")
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if isinstance(q, str):
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cleaned_q = q.strip()
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if cleaned_q:
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question = cleaned_q
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break
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# 并行执行九个构建任务（包括黑话解释，可配置关闭）
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task_results = await asyncio.gather(
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self._time_and_run_task(
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@ -736,7 +724,7 @@ class PrivateReplyer:
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self._time_and_run_task(self.build_personality_prompt(), "personality_prompt"),
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self._time_and_run_task(
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build_memory_retrieval_prompt(
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chat_talking_prompt_short, sender, target, self.chat_stream, think_level=1, unknown_words=unknown_words, question=question
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chat_talking_prompt_short, sender, target, self.chat_stream, think_level=1, unknown_words=unknown_words
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),
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"memory_retrieval",
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),
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@ -57,7 +57,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template")
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# 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
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# 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/
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MMC_VERSION = "0.12.2"
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MMC_VERSION = "0.13.0-snapshot.1"
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def get_key_comment(toml_table, key):
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@ -1097,7 +1097,6 @@ async def build_memory_retrieval_prompt(
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chat_stream,
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think_level: int = 1,
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unknown_words: Optional[List[str]] = None,
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question: Optional[str] = None,
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) -> str:
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"""构建记忆检索提示
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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
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@ -1109,7 +1108,6 @@ async def build_memory_retrieval_prompt(
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chat_stream: 聊天流对象
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think_level: 思考深度等级
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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
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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
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Returns:
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str: 记忆检索结果字符串
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@ -1144,52 +1142,37 @@ async def build_memory_retrieval_prompt(
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if not recent_query_history:
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recent_query_history = "最近没有查询记录。"
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# 第一步：生成问题或使用 Planner 提供的问题
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# 第一步：使用 LLM 生成问题
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question_prompt = await global_prompt_manager.format_prompt(
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"memory_retrieval_question_prompt",
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bot_name=bot_name,
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time_now=time_now,
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chat_history=message,
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recent_query_history=recent_query_history,
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sender=sender,
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target_message=target,
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)
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success, response, reasoning_content, model_name = await llm_api.generate_with_model(
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question_prompt,
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model_config=model_config.model_task_config.tool_use,
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request_type="memory.question",
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)
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if global_config.debug.show_memory_prompt:
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logger.info(f"{log_prefix}记忆检索问题生成提示词: {question_prompt}")
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# logger.info(f"记忆检索问题生成响应: {response}")
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if not success:
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logger.error(f"{log_prefix}LLM生成问题失败: {response}")
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return ""
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# 解析概念列表和问题列表，只取第一个问题
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single_question: Optional[str] = None
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# 如果 planner_question 配置开启，只使用 Planner 提供的问题，不使用旧模式
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if global_config.memory.planner_question:
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if question and isinstance(question, str) and question.strip():
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# 清理和验证 question
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single_question = question.strip()
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logger.info(f"{log_prefix}使用 Planner 提供的 question: {single_question}")
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else:
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# planner_question 开启但没有提供 question，跳过记忆检索
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logger.debug(f"{log_prefix}planner_question 已开启但未提供 question，跳过记忆检索")
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end_time = time.time()
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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")
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return ""
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else:
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# planner_question 关闭，使用旧模式：LLM 生成问题
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question_prompt = await global_prompt_manager.format_prompt(
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"memory_retrieval_question_prompt",
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bot_name=bot_name,
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time_now=time_now,
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chat_history=message,
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recent_query_history=recent_query_history,
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sender=sender,
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target_message=target,
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)
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success, response, reasoning_content, model_name = await llm_api.generate_with_model(
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question_prompt,
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model_config=model_config.model_task_config.tool_use,
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request_type="memory.question",
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)
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if global_config.debug.show_memory_prompt:
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logger.info(f"{log_prefix}记忆检索问题生成提示词: {question_prompt}")
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# logger.info(f"记忆检索问题生成响应: {response}")
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if not success:
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logger.error(f"{log_prefix}LLM生成问题失败: {response}")
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return ""
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# 解析概念列表和问题列表，只取第一个问题
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_, questions = parse_questions_json(response)
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if questions and len(questions) > 0:
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single_question = questions[0].strip()
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logger.info(f"{log_prefix}解析到问题: {single_question}")
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_, questions = parse_questions_json(response)
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if questions and len(questions) > 0:
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single_question = questions[0].strip()
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logger.info(f"{log_prefix}解析到问题: {single_question}")
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# 初始阶段：使用 Planner 提供的 unknown_words 进行检索（如果提供）
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initial_info = ""
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