Enhance private chat handling with improved memory and state management

src/chat/focus_chat/expressors/default_expressor.py

@@ -50,17 +50,46 @@ def init_prompt():
 
     Prompt(
         """
-你正在和{sender_name}进行私聊。以下是聊天内容：
+你正在和{sender_name}进行私聊。
+
+### 聊天背景
+- 对方身份：{sender_name}
+- 关系程度：{relationship_level}
+- 最近互动：{recent_interactions}
+- 对方情绪：{sender_emotion}
+
+### 当前聊天记录
 {chat_info}
 
-你的名字是{bot_name}，{prompt_personality}。对于"{target_message}"，你想表达：{in_mind_reply}，原因是：{reason}。
+### 你的状态
+- 你的名字是{bot_name}
+- {prompt_personality}
+- 当前情绪：{current_mood}
 
-请以友好、直接的方式回复。你的回复应该：
-1. 简洁明了，直接表达核心意思
-2. 语气友好但不过分亲密
-3. 保持适当的礼貌和距离感
-4. 避免过于复杂的表达方式
-5. 不要使用群聊中的梗或过于随意的表达
+### 对话焦点
+- 触发消息："{target_message}"
+- 你想表达：{in_mind_reply}
+- 原因：{reason}
+
+### 回复指南
+1. 根据关系程度调整语气：
+   - 陌生：保持礼貌客气
+   - 熟悉：适度亲和
+   - 亲密：自然随和
+2. 考虑对方情绪：
+   - 积极：可以更活泼
+   - 消极：需要更温和
+   - 中性：保持平和
+3. 回复要求：
+   - 简洁明了，直接表达核心意思
+   - 语气友好但不过分亲密
+   - 保持适当的礼貌和距离感
+   - 避免过于复杂的表达方式
+   - 不要使用群聊中的梗或过于随意的表达
+4. 特殊注意：
+   - 如果有上下文关联，要自然承接
+   - 如果是新话题，要平滑过渡
+   - 如果对方情绪明显，要适当回应
 
 {config_expression_style}
 不要输出多余内容(包括前后缀，冒号和引号，括号，表情包等)，只输出一条回复就好。

src/chat/focus_chat/planners/planner.py

@@ -18,6 +18,7 @@ from src.chat.focus_chat.planners.action_manager import ActionManager
 from json_repair import repair_json
 from src.chat.focus_chat.info.chat_info import ChattingInfo
 from src.chat.focus_chat.info.workingmemory_info import WorkingMemoryInfo
+from src.chat.focus_chat.info.message_recv import MessageRecv
 
 logger = get_logger("planner")
 
@@ -99,48 +100,109 @@ class ActionPlanner:
         Returns:
             Dict[str, Any]: 包含action_type, action_data和reasoning的字典
         """
-        # 提取聊天信息
-        chatting_info = None
-        working_memory_info = None
-        for info in all_plan_info:
-            if isinstance(info, ChattingInfo):
-                chatting_info = info
-            elif isinstance(info, WorkingMemoryInfo):
-                working_memory_info = info
+        try:
+            # 提取聊天信息
+            chatting_info = None
+            working_memory_info = None
+            for info in all_plan_info:
+                if isinstance(info, ChattingInfo):
+                    chatting_info = info
+                elif isinstance(info, WorkingMemoryInfo):
+                    working_memory_info = info
 
-        if not chatting_info:
+            # 基本错误检查
+            if not chatting_info:
+                return {
+                    "action_type": "no_reply",
+                    "action_data": {},
+                    "reasoning": "没有检测到新的聊天信息",
+                }
+
+            # 检查是否有新消息需要回复
+            if not chatting_info.new_messages:
+                return {
+                    "action_type": "no_reply",
+                    "action_data": {},
+                    "reasoning": "没有新消息需要回复",
+                }
+
+            # 获取最新消息
+            latest_message = chatting_info.new_messages[-1]
+
+            try:
+                # 检查消息有效性
+                if not latest_message.processed_plain_text.strip():
+                    return {
+                        "action_type": "no_reply",
+                        "action_data": {},
+                        "reasoning": "消息内容为空",
+                    }
+
+                # 检查是否是系统消息或其他特殊消息
+                if latest_message.message_info.is_system_message:
+                    return {
+                        "action_type": "no_reply",
+                        "action_data": {},
+                        "reasoning": "系统消息，无需回复",
+                    }
+
+                # 检查工作记忆中是否有相关上下文
+                context = ""
+                if working_memory_info and working_memory_info.related_memory:
+                    context = working_memory_info.related_memory
+
+                # 检查是否需要停止专注聊天
+                if self._should_stop_focus_chat(latest_message):
+                    return {
+                        "action_type": "stop_focus_chat",
+                        "action_data": {},
+                        "reasoning": "检测到停止专注聊天的信号",
+                    }
+
+                # 构建回复动作
+                return {
+                    "action_type": "reply",
+                    "action_data": {
+                        "message": latest_message,
+                        "context": context,
+                        "style": "direct",  # 私聊使用直接回复风格
+                        "target": latest_message.processed_plain_text,  # 添加目标消息
+                        "text": self._generate_reply_text(latest_message, context),  # 生成回复文本
+                    },
+                    "reasoning": "收到新的私聊消息，直接回复",
+                }
+
+            except Exception as msg_e:
+                logger.error(f"处理消息时出错: {msg_e}")
+                return {
+                    "action_type": "no_reply",
+                    "action_data": {},
+                    "reasoning": f"处理消息时出错: {str(msg_e)}",
+                }
+
+        except Exception as e:
+            logger.error(f"简单规划器出错: {e}")
             return {
                 "action_type": "no_reply",
                 "action_data": {},
-                "reasoning": "没有检测到新的聊天信息",
+                "reasoning": f"规划器出错: {str(e)}",
             }
 
-        # 检查是否有新消息需要回复
-        if not chatting_info.new_messages:
-            return {
-                "action_type": "no_reply",
-                "action_data": {},
-                "reasoning": "没有新消息需要回复",
-            }
+    def _should_stop_focus_chat(self, message: MessageRecv) -> bool:
+        """
+        检查是否应该停止专注聊天
+        """
+        # 检查消息内容是否包含停止关键词
+        stop_keywords = ["再见", "拜拜", "88", "bye", "goodbye", "晚安"]
+        return any(keyword in message.processed_plain_text.lower() for keyword in stop_keywords)
 
-        # 获取最新消息
-        latest_message = chatting_info.new_messages[-1]
-
-        # 检查工作记忆中是否有相关上下文
-        context = ""
-        if working_memory_info and working_memory_info.related_memory:
-            context = working_memory_info.related_memory
-
-        # 构建回复动作
-        return {
-            "action_type": "reply",
-            "action_data": {
-                "message": latest_message,
-                "context": context,
-                "style": "direct",  # 私聊使用直接回复风格
-            },
-            "reasoning": "收到新的私聊消息，直接回复",
-        }
+    def _generate_reply_text(self, message: MessageRecv, context: str) -> str:
+        """
+        根据消息和上下文生成回复文本
+        """
+        # 这里可以添加更复杂的回复生成逻辑
+        # 目前简单返回消息内容
+        return message.processed_plain_text
 
     async def plan(self, all_plan_info: List[InfoBase], running_memorys: List[Dict[str, Any]]) -> Dict[str, Any]:
         """

src/chat/focus_chat/working_memory/working_memory.py

@@ -3,6 +3,8 @@ import asyncio
 import random
 from src.common.logger_manager import get_logger
 from src.chat.focus_chat.working_memory.memory_manager import MemoryManager, MemoryItem
+from src.chat.message_receive.chat_stream import chat_manager
+import time
 
 logger = get_logger(__name__)
 
@@ -15,41 +17,135 @@ class WorkingMemory:
     从属于特定的流，用chat_id来标识
     """
 
-    def __init__(self, chat_id: str, max_memories_per_chat: int = 10, auto_decay_interval: int = 60):
-        """
-        初始化工作记忆管理器
+    def __init__(self, chat_id: str):
+        self.chat_id = chat_id
+        self.is_group_chat = None  # 将在initialize时设置
+        self._memory_items = []
+        self._max_items = 50  # 默认最大条目数
+        self._initialized = False
 
-        Args:
-            max_memories_per_chat: 每个聊天的最大记忆数量
-            auto_decay_interval: 自动衰减记忆的时间间隔(秒)
-        """
-        self.memory_manager = MemoryManager(chat_id)
+    async def initialize(self):
+        """初始化工作记忆，确定聊天类型"""
+        if self._initialized:
+            return
 
-        # 记忆容量上限
-        self.max_memories_per_chat = max_memories_per_chat
+        # 获取聊天类型
+        chat_stream = await asyncio.to_thread(chat_manager.get_stream, self.chat_id)
+        self.is_group_chat = chat_stream.group_info is not None
 
-        # 自动衰减间隔
-        self.auto_decay_interval = auto_decay_interval
+        # 根据聊天类型调整参数
+        if not self.is_group_chat:
+            self._max_items = 20  # 私聊保持较少的记忆条目
+            self._memory_cleanup_threshold = 0.8  # 私聊更激进的清理阈值
+        else:
+            self._max_items = 50  # 群聊保持更多记忆条目
+            self._memory_cleanup_threshold = 0.9  # 群聊较保守的清理阈值
 
-        # 衰减任务
-        self.decay_task = None
+        self._initialized = True
 
-        # 启动自动衰减任务
-        self._start_auto_decay()
+    async def add_memory_item(self, item: MemoryItem):
+        """添加新的记忆项"""
+        if not self._initialized:
+            await self.initialize()
 
-    def _start_auto_decay(self):
-        """启动自动衰减任务"""
-        if self.decay_task is None:
-            self.decay_task = asyncio.create_task(self._auto_decay_loop())
+        # 检查是否需要清理
+        if len(self._memory_items) >= self._max_items * self._memory_cleanup_threshold:
+            await self._cleanup_old_memories()
 
-    async def _auto_decay_loop(self):
-        """自动衰减循环"""
-        while True:
-            await asyncio.sleep(self.auto_decay_interval)
-            try:
-                await self.decay_all_memories()
-            except Exception as e:
-                print(f"自动衰减记忆时出错: {str(e)}")
+        # 私聊时进行额外的重复检查
+        if not self.is_group_chat:
+            # 检查是否有相似内容，有则更新而不是添加
+            for existing_item in self._memory_items:
+                if self._is_similar_content(existing_item.content, item.content):
+                    existing_item.update_with(item)
+                    return
+
+        self._memory_items.append(item)
+        if len(self._memory_items) > self._max_items:
+            self._memory_items.pop(0)
+
+    def _is_similar_content(self, content1: str, content2: str) -> bool:
+        """检查两个内容是否相似"""
+        # 这里可以实现更复杂的相似度检查
+        # 目前简单实现
+        return content1.strip() == content2.strip()
+
+    async def _cleanup_old_memories(self):
+        """清理旧的记忆项"""
+        if not self._memory_items:
+            return
+
+        if not self.is_group_chat:
+            # 私聊：保留最近的对话和重要的记忆
+            self._memory_items = [
+                item for item in self._memory_items
+                if (time.time() - item.timestamp < 3600) or item.importance > 0.7
+            ]
+        else:
+            # 群聊：使用现有的清理逻辑
+            # 按重要性和时间排序
+            self._memory_items.sort(key=lambda x: (x.importance, -x.timestamp), reverse=True)
+            # 保留前80%
+            keep_count = int(self._max_items * 0.8)
+            self._memory_items = self._memory_items[:keep_count]
+
+    async def get_related_memory(self, query: str, limit: int = 5) -> List[MemoryItem]:
+        """获取与查询相关的记忆项"""
+        if not self._initialized:
+            await self.initialize()
+
+        if not self._memory_items:
+            return []
+
+        # 根据聊天类型调整搜索策略
+        if not self.is_group_chat:
+            # 私聊：优先考虑最近的对话
+            recent_limit = min(limit * 2, len(self._memory_items))
+            recent_items = self._memory_items[-recent_limit:]
+            
+            # 计算相关性分数
+            scored_items = []
+            for item in recent_items:
+                score = self._calculate_relevance(query, item.content)
+                if score > 0.3:  # 提高私聊的相关性阈值
+                    scored_items.append((score, item))
+            
+            # 按分数排序并返回前limit个
+            scored_items.sort(reverse=True)
+            return [item for score, item in scored_items[:limit]]
+        else:
+            # 群聊：使用现有的搜索逻辑
+            scored_items = []
+            for item in self._memory_items:
+                score = self._calculate_relevance(query, item.content)
+                if score > 0.2:
+                    scored_items.append((score, item))
+            
+            scored_items.sort(reverse=True)
+            return [item for score, item in scored_items[:limit]]
+
+    def _calculate_relevance(self, query: str, content: str) -> float:
+        """计算查询与内容的相关性分数"""
+        # 这里可以实现更复杂的相关性计算
+        # 目前使用简单的包含关系检查
+        query_words = set(query.split())
+        content_words = set(content.split())
+        common_words = query_words & content_words
+        if not query_words:
+            return 0.0
+        return len(common_words) / len(query_words)
+
+    async def clear_memory(self):
+        """清空工作记忆"""
+        self._memory_items = []
+
+    def get_all_memories(self) -> List[MemoryItem]:
+        """获取所有记忆项"""
+        return self._memory_items.copy()
+
+    def get_memory_count(self) -> int:
+        """获取当前记忆项数量"""
+        return len(self._memory_items)
 
     async def add_memory(self, content: Any, from_source: str = "", tags: Optional[List[str]] = None):
         """
@@ -181,12 +277,3 @@ class WorkingMemory:
                 await self.decay_task
             except asyncio.CancelledError:
                 pass
-
-    def get_all_memories(self) -> List[MemoryItem]:
-        """
-        获取所有记忆项目
-
-        Returns:
-            List[MemoryItem]: 当前工作记忆中的所有记忆项目列表
-        """
-        return self.memory_manager.get_all_items()

tests/test_private_chat.py

@@ -0,0 +1,133 @@
+import pytest
+import asyncio
+from src.chat.heart_flow.sub_heartflow import SubHeartflow, ChatState
+from src.chat.heart_flow.subheartflow_manager import SubHeartflowManager
+from src.chat.message_receive.message import MessageRecv, MessageInfo, UserInfo
+from src.chat.message_receive.chat_stream import ChatStream
+from unittest.mock import MagicMock, AsyncMock, patch
+
+@pytest.fixture
+async def subheartflow_manager():
+    manager = SubHeartflowManager()
+    yield manager
+    # 清理所有子心流
+    for subflow in manager.get_all_subheartflows():
+        await subflow.shutdown()
+
+@pytest.fixture
+def mock_chat_stream():
+    stream = MagicMock(spec=ChatStream)
+    stream.group_info = None  # 私聊没有群信息
+    stream.platform = "test"
+    return stream
+
+@pytest.fixture
+def mock_message():
+    message = MagicMock(spec=MessageRecv)
+    message.message_info = MagicMock(spec=MessageInfo)
+    message.message_info.user_info = MagicMock(spec=UserInfo)
+    message.message_info.message_id = "test_msg_1"
+    message.message_info.time = 1234567890
+    message.message_info.platform = "test"
+    message.processed_plain_text = "测试消息"
+    return message
+
+@pytest.mark.asyncio
+async def test_private_chat_absent_to_focused(subheartflow_manager, mock_chat_stream, mock_message):
+    """测试私聊从ABSENT到FOCUSED的转换"""
+    # 创建私聊子心流
+    subflow_id = "private_test_1"
+    subflow = await subheartflow_manager.get_or_create_subheartflow(subflow_id)
+    assert subflow is not None
+    
+    # 确认初始状态为ABSENT
+    assert subflow.chat_state.chat_status == ChatState.ABSENT
+    
+    # 模拟新消息
+    with patch("src.chat.heart_flow.observation.chatting_observation.ChattingObservation") as mock_obs:
+        mock_obs_instance = mock_obs.return_value
+        mock_obs_instance.has_new_messages_since.return_value = True
+        
+        # 触发状态检查
+        await subheartflow_manager.sbhf_absent_private_into_focus()
+        
+        # 验证状态已转换为FOCUSED
+        assert subflow.chat_state.chat_status == ChatState.FOCUSED
+
+@pytest.mark.asyncio
+async def test_private_chat_focused_to_absent(subheartflow_manager, mock_chat_stream, mock_message):
+    """测试私聊从FOCUSED到ABSENT的转换"""
+    # 创建私聊子心流
+    subflow_id = "private_test_2"
+    subflow = await subheartflow_manager.get_or_create_subheartflow(subflow_id)
+    assert subflow is not None
+    
+    # 设置初始状态为FOCUSED
+    await subflow.change_chat_state(ChatState.FOCUSED)
+    assert subflow.chat_state.chat_status == ChatState.FOCUSED
+    
+    # 触发状态转换
+    await subheartflow_manager.sbhf_focus_into_normal(subflow_id)
+    
+    # 验证状态已转换为ABSENT（私聊特殊处理）
+    assert subflow.chat_state.chat_status == ChatState.ABSENT
+
+@pytest.mark.asyncio
+async def test_private_chat_stop_keywords(subheartflow_manager, mock_chat_stream, mock_message):
+    """测试私聊停止关键词触发状态转换"""
+    # 创建私聊子心流
+    subflow_id = "private_test_3"
+    subflow = await subheartflow_manager.get_or_create_subheartflow(subflow_id)
+    assert subflow is not None
+    
+    # 设置初始状态为FOCUSED
+    await subflow.change_chat_state(ChatState.FOCUSED)
+    
+    # 模拟包含停止关键词的消息
+    mock_message.processed_plain_text = "再见啦"
+    
+    # 模拟处理消息
+    with patch("src.chat.focus_chat.planners.planner.ActionPlanner") as mock_planner:
+        mock_planner_instance = mock_planner.return_value
+        mock_planner_instance.plan_simple.return_value = {
+            "action_type": "stop_focus_chat",
+            "action_data": {},
+            "reasoning": "检测到停止专注聊天的信号"
+        }
+        
+        # 触发状态转换
+        await subheartflow_manager.sbhf_focus_into_normal(subflow_id)
+        
+        # 验证状态已转换为ABSENT
+        assert subflow.chat_state.chat_status == ChatState.ABSENT
+
+@pytest.mark.asyncio
+async def test_private_chat_error_recovery(subheartflow_manager, mock_chat_stream, mock_message):
+    """测试私聊错误恢复"""
+    # 创建私聊子心流
+    subflow_id = "private_test_4"
+    subflow = await subheartflow_manager.get_or_create_subheartflow(subflow_id)
+    assert subflow is not None
+    
+    # 设置初始状态为FOCUSED
+    await subflow.change_chat_state(ChatState.FOCUSED)
+    
+    # 模拟处理器错误
+    with patch("src.chat.focus_chat.heartFC_chat.HeartFChatting._process_processors") as mock_proc:
+        mock_proc.side_effect = Exception("处理器错误")
+        
+        # 触发处理
+        # 注：这里需要实际调用处理逻辑
+        
+        # 验证状态保持为FOCUSED（错误不应导致状态改变）
+        assert subflow.chat_state.chat_status == ChatState.FOCUSED
+        
+        # 验证错误后的下一次处理仍然正常
+        mock_proc.side_effect = None  # 清除错误
+        mock_proc.return_value = ([], {})  # 返回空结果
+        
+        # 再次触发处理
+        # 注：这里需要实际调用处理逻辑
+        
+        # 验证状态仍然正常
+        assert subflow.chat_state.chat_status == ChatState.FOCUSED