fix：调整目录结构，优化hfc prompt，移除日程，移除动态和llm判断willing模式，

src/experimental/PFC/pfc.py

@@ -0,0 +1,345 @@
+from typing import List, Tuple, TYPE_CHECKING
+from src.common.logger import get_module_logger
+from src.chat.models.utils_model import LLMRequest
+from src.config.config import global_config
+from src.experimental.PFC.chat_observer import ChatObserver
+from src.experimental.PFC.pfc_utils import get_items_from_json
+from src.individuality.individuality import Individuality
+from src.experimental.PFC.conversation_info import ConversationInfo
+from src.experimental.PFC.observation_info import ObservationInfo
+from src.chat.utils.chat_message_builder import build_readable_messages
+from rich.traceback import install
+
+install(extra_lines=3)
+
+if TYPE_CHECKING:
+    pass
+
+logger = get_module_logger("pfc")
+
+
+def _calculate_similarity(goal1: str, goal2: str) -> float:
+    """简单计算两个目标之间的相似度
+
+    这里使用一个简单的实现，实际可以使用更复杂的文本相似度算法
+
+    Args:
+        goal1: 第一个目标
+        goal2: 第二个目标
+
+    Returns:
+        float: 相似度得分 (0-1)
+    """
+    # 简单实现：检查重叠字数比例
+    words1 = set(goal1)
+    words2 = set(goal2)
+    overlap = len(words1.intersection(words2))
+    total = len(words1.union(words2))
+    return overlap / total if total > 0 else 0
+
+
+class GoalAnalyzer:
+    """对话目标分析器"""
+
+    def __init__(self, stream_id: str, private_name: str):
+        self.llm = LLMRequest(
+            model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal"
+        )
+
+        self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
+        self.name = global_config.BOT_NICKNAME
+        self.nick_name = global_config.BOT_ALIAS_NAMES
+        self.private_name = private_name
+        self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
+
+        # 多目标存储结构
+        self.goals = []  # 存储多个目标
+        self.max_goals = 3  # 同时保持的最大目标数量
+        self.current_goal_and_reason = None
+
+    async def analyze_goal(self, conversation_info: ConversationInfo, observation_info: ObservationInfo):
+        """分析对话历史并设定目标
+
+        Args:
+            conversation_info: 对话信息
+            observation_info: 观察信息
+
+        Returns:
+            Tuple[str, str, str]: (目标, 方法, 原因)
+        """
+        # 构建对话目标
+        goals_str = ""
+        if conversation_info.goal_list:
+            for goal_reason in conversation_info.goal_list:
+                if isinstance(goal_reason, dict):
+                    goal = goal_reason.get("goal", "目标内容缺失")
+                    reasoning = goal_reason.get("reasoning", "没有明确原因")
+                else:
+                    goal = str(goal_reason)
+                    reasoning = "没有明确原因"
+
+                goal_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
+                goals_str += goal_str
+        else:
+            goal = "目前没有明确对话目标"
+            reasoning = "目前没有明确对话目标，最好思考一个对话目标"
+            goals_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
+
+        # 获取聊天历史记录
+        chat_history_text = observation_info.chat_history_str
+
+        if observation_info.new_messages_count > 0:
+            new_messages_list = observation_info.unprocessed_messages
+            new_messages_str = await build_readable_messages(
+                new_messages_list,
+                replace_bot_name=True,
+                merge_messages=False,
+                timestamp_mode="relative",
+                read_mark=0.0,
+            )
+            chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}"
+
+            # await observation_info.clear_unprocessed_messages()
+
+        persona_text = f"你的名字是{self.name}，{self.personality_info}。"
+        # 构建action历史文本
+        action_history_list = conversation_info.done_action
+        action_history_text = "你之前做的事情是："
+        for action in action_history_list:
+            action_history_text += f"{action}\n"
+
+        prompt = f"""{persona_text}。现在你在参与一场QQ聊天，请分析以下聊天记录，并根据你的性格特征确定多个明确的对话目标。
+这些目标应该反映出对话的不同方面和意图。
+
+{action_history_text}
+当前对话目标：
+{goals_str}
+
+聊天记录：
+{chat_history_text}
+
+请分析当前对话并确定最适合的对话目标。你可以：
+1. 保持现有目标不变
+2. 修改现有目标
+3. 添加新目标
+4. 删除不再相关的目标
+5. 如果你想结束对话，请设置一个目标，目标goal为"结束对话"，原因reasoning为你希望结束对话
+
+请以JSON数组格式输出当前的所有对话目标，每个目标包含以下字段：
+1. goal: 对话目标（简短的一句话）
+2. reasoning: 对话原因，为什么设定这个目标（简要解释）
+
+输出格式示例：
+[
+{{
+    "goal": "回答用户关于Python编程的具体问题",
+    "reasoning": "用户提出了关于Python的技术问题，需要专业且准确的解答"
+}},
+{{
+    "goal": "回答用户关于python安装的具体问题",
+    "reasoning": "用户提出了关于Python的技术问题，需要专业且准确的解答"
+}}
+]"""
+
+        logger.debug(f"[私聊][{self.private_name}]发送到LLM的提示词: {prompt}")
+        try:
+            content, _ = await self.llm.generate_response_async(prompt)
+            logger.debug(f"[私聊][{self.private_name}]LLM原始返回内容: {content}")
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]分析对话目标时出错: {str(e)}")
+            content = ""
+
+        # 使用改进后的get_items_from_json函数处理JSON数组
+        success, result = get_items_from_json(
+            content,
+            self.private_name,
+            "goal",
+            "reasoning",
+            required_types={"goal": str, "reasoning": str},
+            allow_array=True,
+        )
+
+        if success:
+            # 判断结果是单个字典还是字典列表
+            if isinstance(result, list):
+                # 清空现有目标列表并添加新目标
+                conversation_info.goal_list = []
+                for item in result:
+                    conversation_info.goal_list.append(item)
+
+                # 返回第一个目标作为当前主要目标（如果有）
+                if result:
+                    first_goal = result[0]
+                    return first_goal.get("goal", ""), "", first_goal.get("reasoning", "")
+            else:
+                # 单个目标的情况
+                conversation_info.goal_list.append(result)
+                return goal, "", reasoning
+
+        # 如果解析失败，返回默认值
+        return "", "", ""
+
+    async def _update_goals(self, new_goal: str, method: str, reasoning: str):
+        """更新目标列表
+
+        Args:
+            new_goal: 新的目标
+            method: 实现目标的方法
+            reasoning: 目标的原因
+        """
+        # 检查新目标是否与现有目标相似
+        for i, (existing_goal, _, _) in enumerate(self.goals):
+            if _calculate_similarity(new_goal, existing_goal) > 0.7:  # 相似度阈值
+                # 更新现有目标
+                self.goals[i] = (new_goal, method, reasoning)
+                # 将此目标移到列表前面（最主要的位置）
+                self.goals.insert(0, self.goals.pop(i))
+                return
+
+        # 添加新目标到列表前面
+        self.goals.insert(0, (new_goal, method, reasoning))
+
+        # 限制目标数量
+        if len(self.goals) > self.max_goals:
+            self.goals.pop()  # 移除最老的目标
+
+    async def get_all_goals(self) -> List[Tuple[str, str, str]]:
+        """获取所有当前目标
+
+        Returns:
+            List[Tuple[str, str, str]]: 目标列表，每项为(目标, 方法, 原因)
+        """
+        return self.goals.copy()
+
+    async def get_alternative_goals(self) -> List[Tuple[str, str, str]]:
+        """获取除了当前主要目标外的其他备选目标
+
+        Returns:
+            List[Tuple[str, str, str]]: 备选目标列表
+        """
+        if len(self.goals) <= 1:
+            return []
+        return self.goals[1:].copy()
+
+    async def analyze_conversation(self, goal, reasoning):
+        messages = self.chat_observer.get_cached_messages()
+        chat_history_text = await build_readable_messages(
+            messages,
+            replace_bot_name=True,
+            merge_messages=False,
+            timestamp_mode="relative",
+            read_mark=0.0,
+        )
+
+        persona_text = f"你的名字是{self.name}，{self.personality_info}。"
+        # ===> Persona 文本构建结束 <===
+
+        # --- 修改 Prompt 字符串，使用 persona_text ---
+        prompt = f"""{persona_text}。现在你在参与一场QQ聊天，
+        当前对话目标：{goal}
+        产生该对话目标的原因：{reasoning}
+        
+        请分析以下聊天记录，并根据你的性格特征评估该目标是否已经达到，或者你是否希望停止该次对话。
+        聊天记录：
+        {chat_history_text}
+        请以JSON格式输出，包含以下字段：
+        1. goal_achieved: 对话目标是否已经达到（true/false）
+        2. stop_conversation: 是否希望停止该次对话（true/false）
+        3. reason: 为什么希望停止该次对话（简要解释）   
+
+输出格式示例：
+{{
+    "goal_achieved": true,
+    "stop_conversation": false,
+    "reason": "虽然目标已达成，但对话仍然有继续的价值"
+}}"""
+
+        try:
+            content, _ = await self.llm.generate_response_async(prompt)
+            logger.debug(f"[私聊][{self.private_name}]LLM原始返回内容: {content}")
+
+            # 尝试解析JSON
+            success, result = get_items_from_json(
+                content,
+                self.private_name,
+                "goal_achieved",
+                "stop_conversation",
+                "reason",
+                required_types={"goal_achieved": bool, "stop_conversation": bool, "reason": str},
+            )
+
+            if not success:
+                logger.error(f"[私聊][{self.private_name}]无法解析对话分析结果JSON")
+                return False, False, "解析结果失败"
+
+            goal_achieved = result["goal_achieved"]
+            stop_conversation = result["stop_conversation"]
+            reason = result["reason"]
+
+            return goal_achieved, stop_conversation, reason
+
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]分析对话状态时出错: {str(e)}")
+            return False, False, f"分析出错: {str(e)}"
+
+
+# 先注释掉，万一以后出问题了还能开回来（（（
+# class DirectMessageSender:
+#     """直接发送消息到平台的发送器"""
+
+#     def __init__(self, private_name: str):
+#         self.logger = get_module_logger("direct_sender")
+#         self.storage = MessageStorage()
+#         self.private_name = private_name
+
+#     async def send_via_ws(self, message: MessageSending) -> None:
+#         try:
+#             await global_api.send_message(message)
+#         except Exception as e:
+#             raise ValueError(f"未找到平台：{message.message_info.platform} 的url配置，请检查配置文件") from e
+
+#     async def send_message(
+#         self,
+#         chat_stream: ChatStream,
+#         content: str,
+#         reply_to_message: Optional[Message] = None,
+#     ) -> None:
+#         """直接发送消息到平台
+
+#         Args:
+#             chat_stream: 聊天流
+#             content: 消息内容
+#             reply_to_message: 要回复的消息
+#         """
+#         # 构建消息对象
+#         message_segment = Seg(type="text", data=content)
+#         bot_user_info = UserInfo(
+#             user_id=global_config.BOT_QQ,
+#             user_nickname=global_config.BOT_NICKNAME,
+#             platform=chat_stream.platform,
+#         )
+
+#         message = MessageSending(
+#             message_id=f"dm{round(time.time(), 2)}",
+#             chat_stream=chat_stream,
+#             bot_user_info=bot_user_info,
+#             sender_info=reply_to_message.message_info.user_info if reply_to_message else None,
+#             message_segment=message_segment,
+#             reply=reply_to_message,
+#             is_head=True,
+#             is_emoji=False,
+#             thinking_start_time=time.time(),
+#         )
+
+#         # 处理消息
+#         await message.process()
+
+#         _message_json = message.to_dict()
+
+#         # 发送消息
+#         try:
+#             await self.send_via_ws(message)
+#             await self.storage.store_message(message, chat_stream)
+#             logger.success(f"[私聊][{self.private_name}]PFC消息已发送: {content}")
+#         except Exception as e:
+#             logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {str(e)}")