Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev

src/chat/heart_flow/heartFC_chat.py

@@ -183,7 +183,7 @@ class HeartFChatting:
         self.talk_threshold = think_len
         logger.info(f"{self.log_prefix} 思考频率阈值: {self.talk_threshold}")
 
-    async def _loopbody(self):
+    async def _loopbody(self):  # sourcery skip: hoist-if-from-if
         recent_messages_list = message_api.get_messages_by_time_in_chat(
             chat_id=self.stream_id,
             start_time=self.last_read_time,
@@ -195,11 +195,15 @@ class HeartFChatting:
         )
 
         if len(recent_messages_list) >= self.talk_threshold:
-            
             # !处理no_reply_until_call逻辑
             if self.no_reply_until_call:
                 for message in recent_messages_list:
-                    if message.is_mentioned or message.is_at or len(recent_messages_list) >= 8 or time.time() - self.last_read_time > 600:
+                    if (
+                        message.is_mentioned
+                        or message.is_at
+                        or len(recent_messages_list) >= 8
+                        or time.time() - self.last_read_time > 600
+                    ):
                         self.no_reply_until_call = False
                         break
                 # 没有提到，继续保持沉默
@@ -208,7 +212,6 @@ class HeartFChatting:
                     await asyncio.sleep(1)
                     return True
 
-            
             self.last_read_time = time.time()
             await self._observe(
                 recent_messages_list=recent_messages_list,
@@ -272,8 +275,8 @@ class HeartFChatting:
 
     async def _observe(
         self,  # interest_value: float = 0.0,
-        recent_messages_list: Optional[List["DatabaseMessages"]] = None
-    ) -> bool:
+        recent_messages_list: Optional[List["DatabaseMessages"]] = None,
+    ) -> bool:  # sourcery skip: merge-else-if-into-elif, remove-redundant-if
         if recent_messages_list is None:
             recent_messages_list = []
         reply_text = ""  # 初始化reply_text变量，避免UnboundLocalError
@@ -327,14 +330,12 @@ class HeartFChatting:
             if modified_message and modified_message._modify_flags.modify_llm_prompt:
                 prompt_info = (modified_message.llm_prompt, prompt_info[1])
 
-                
             with Timer("规划器", cycle_timers):
                 action_to_use_info, _ = await self.action_planner.plan(
                     loop_start_time=self.last_read_time,
                     available_actions=available_actions,
                 )
 
-            
             # !此处使at或者提及必定回复
             metioned_message = None
             for message in recent_messages_list:
@@ -358,7 +359,6 @@ class HeartFChatting:
                     )
                 )
 
-
             # 3. 并行执行所有动作
             action_tasks = [
                 asyncio.create_task(
@@ -521,7 +521,6 @@ class HeartFChatting:
         reply_text = ""
         first_replied = False
         for reply_content in reply_set.reply_data:
-            
             if reply_content.content_type != ReplyContentType.TEXT:
                 continue
             data: str = reply_content.content  # type: ignore
@@ -576,6 +575,7 @@ class HeartFChatting:
                 return {"action_type": "no_action", "success": True, "reply_text": "", "command": ""}
 
             elif action_planner_info.action_type == "wait_time":
+                action_planner_info.action_data = action_planner_info.action_data or {}
                 logger.info(f"{self.log_prefix} 等待{action_planner_info.action_data['time']}秒后回复")
                 await asyncio.sleep(action_planner_info.action_data["time"])
                 return {"action_type": "wait_time", "success": True, "reply_text": "", "command": ""}

src/chat/heart_flow/heartflow_message_processor.py

@@ -10,7 +10,6 @@ from src.chat.message_receive.message import MessageRecv
 from src.chat.message_receive.storage import MessageStorage
 from src.chat.heart_flow.heartflow import heartflow
 from src.chat.utils.utils import is_mentioned_bot_in_message
-from src.chat.utils.timer_calculator import Timer
 from src.chat.utils.chat_message_builder import replace_user_references
 from src.common.logger import get_logger
 from src.mood.mood_manager import mood_manager
@@ -36,7 +35,7 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, list[str]]:
         return 0.0, []
 
     is_mentioned, is_at, reply_probability_boost = is_mentioned_bot_in_message(message)
-    interested_rate = 0.0
+    # interested_rate = 0.0
     keywords = []
 
     message.interest_value = 1
@@ -113,10 +112,10 @@ class HeartFCMessageReceiver:
             logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[{interested_rate:.2f}]")  # type: ignore
 
             _ = Person.register_person(
-                platform=message.message_info.platform,
-                user_id=message.message_info.user_info.user_id,
-                nickname=userinfo.user_nickname,
-            )  # type: ignore
+                platform=message.message_info.platform,  # type: ignore
+                user_id=message.message_info.user_info.user_id,  # type: ignore
+                nickname=userinfo.user_nickname,  # type: ignore
+            )
 
         except Exception as e:
             logger.error(f"消息处理失败: {e}")

src/chat/planner_actions/planner.py

@@ -22,12 +22,12 @@ from src.chat.utils.chat_message_builder import (
 from src.chat.utils.utils import get_chat_type_and_target_info
 from src.chat.planner_actions.action_manager import ActionManager
 from src.chat.message_receive.chat_stream import get_chat_manager
-from src.plugin_system.base.component_types import ActionInfo, ChatMode, ComponentType, ActionActivationType
+from src.plugin_system.base.component_types import ActionInfo, ComponentType, ActionActivationType
 from src.plugin_system.core.component_registry import component_registry
 
 if TYPE_CHECKING:
     from src.common.data_models.info_data_model import TargetPersonInfo
-    from src.common.data_models.database_data_model import DatabaseMessages, DatabaseActionRecords
+    from src.common.data_models.database_data_model import DatabaseMessages
 
 logger = get_logger("planner")
 
@@ -121,7 +121,6 @@ no_reply_until_call
     )
 
 
-
 class ActionPlanner:
     def __init__(self, chat_id: str, action_manager: ActionManager):
         self.chat_id = chat_id
@@ -180,7 +179,6 @@ class ActionPlanner:
                 target_message = message_id_list[-1][1]
                 logger.info(f"{self.log_prefix}动作'{action}'缺少target_message_id，使用最新消息作为target_message")
 
-
             # 验证action是否可用
             available_action_names = [action_name for action_name, _ in current_available_actions]
             internal_action_names = ["no_reply", "reply", "wait_time", "no_reply_until_call"]
@@ -223,18 +221,17 @@ class ActionPlanner:
 
         return action_planner_infos
 
-
     async def plan(
         self,
         available_actions: Dict[str, ActionInfo],
         loop_start_time: float = 0.0,
     ) -> Tuple[List[ActionPlannerInfo], Optional["DatabaseMessages"]]:
+        # sourcery skip: use-named-expression
         """
         规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
         """
         target_message: Optional["DatabaseMessages"] = None
 
-        
         # 获取聊天上下文
         message_list_before_now = get_raw_msg_before_timestamp_with_chat(
             chat_id=self.chat_id,
@@ -264,9 +261,7 @@ class ActionPlanner:
         is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()
 
         # 应用激活类型过滤
-        filtered_actions = self._filter_actions_by_activation_type(
-            available_actions, chat_content_block_short
-        )
+        filtered_actions = self._filter_actions_by_activation_type(available_actions, chat_content_block_short)
 
         logger.info(f"{self.log_prefix}过滤后有{len(filtered_actions)}个可用动作")
 
@@ -286,7 +281,7 @@ class ActionPlanner:
             message_id_list=message_id_list,
             filtered_actions=filtered_actions,
             available_actions=available_actions,
-            loop_start_time=loop_start_time
+            loop_start_time=loop_start_time,
         )
 
         # 获取target_message（如果有非no_action的动作）
@@ -333,7 +328,9 @@ class ActionPlanner:
             moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
             time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
             bot_name = global_config.bot.nickname
-            bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
+            bot_nickname = (
+                f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
+            )
             name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
 
             # 获取主规划器模板并填充
@@ -379,11 +376,8 @@ class ActionPlanner:
 
         return is_group_chat, chat_target_info, current_available_actions
 
-    
     def _filter_actions_by_activation_type(
-        self, 
-        available_actions: Dict[str, ActionInfo], 
-        chat_content_block: str
+        self, available_actions: Dict[str, ActionInfo], chat_content_block: str
     ) -> Dict[str, ActionInfo]:
         """根据激活类型过滤动作"""
         filtered_actions = {}
@@ -409,6 +403,7 @@ class ActionPlanner:
         return filtered_actions
 
     async def _build_action_options_block(self, current_available_actions: Dict[str, ActionInfo]) -> str:
+        # sourcery skip: use-join
         """构建动作选项块"""
         if not current_available_actions:
             return ""
@@ -448,7 +443,7 @@ class ActionPlanner:
         message_id_list: List[Tuple[str, "DatabaseMessages"]],
         filtered_actions: Dict[str, ActionInfo],
         available_actions: Dict[str, ActionInfo],
-        loop_start_time: float
+        loop_start_time: float,
     ) -> List[ActionPlannerInfo]:
         """执行主规划器"""
         llm_content = None
@@ -487,16 +482,11 @@ class ActionPlanner:
         # 解析LLM响应
         if llm_content:
             try:
-                # 处理新的格式：多个```json包裹的JSON对象
-                json_objects = self._extract_json_from_markdown(llm_content)
-                
-                if json_objects:
+                if json_objects := self._extract_json_from_markdown(llm_content):
                     logger.info(f"{self.log_prefix}从响应中提取到{len(json_objects)}个JSON对象")
                     filtered_actions_list = list(filtered_actions.items())
                     for json_obj in json_objects:
-                        actions.extend(
-                            self._parse_single_action(json_obj, message_id_list, filtered_actions_list)
-                        )
+                        actions.extend(self._parse_single_action(json_obj, message_id_list, filtered_actions_list))
                 else:
                     # 尝试解析为直接的JSON
                     logger.warning(f"{self.log_prefix}LLM没有返回可用动作: {llm_content}")
@@ -509,12 +499,14 @@ class ActionPlanner:
         else:
             actions = self._create_no_reply("规划器没有获得LLM响应", available_actions)
 
-        
         # 添加循环开始时间到所有非no_action动作
         for action in actions:
+            action.action_data = action.action_data or {}
             action.action_data["loop_start_time"] = loop_start_time
 
-        logger.info(f"{self.log_prefix}规划器决定执行{len(actions)}个动作: {' '.join([a.action_type for a in actions])}")
+        logger.info(
+            f"{self.log_prefix}规划器决定执行{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
+        )
 
         return actions
 
@@ -531,21 +523,20 @@ class ActionPlanner:
         ]
 
     def _extract_json_from_markdown(self, content: str) -> List[dict]:
+        # sourcery skip: for-append-to-extend
         """从Markdown格式的内容中提取JSON对象"""
         json_objects = []
 
         # 使用正则表达式查找```json包裹的JSON内容
-        json_pattern = r'```json\s*(.*?)\s*```'
+        json_pattern = r"```json\s*(.*?)\s*```"
         matches = re.findall(json_pattern, content, re.DOTALL)
 
         for match in matches:
             try:
                 # 清理可能的注释和格式问题
-                json_str = re.sub(r'//.*?\n', '\n', match)  # 移除单行注释
-                json_str = re.sub(r'/\*.*?\*/', '', json_str, flags=re.DOTALL)  # 移除多行注释
-                json_str = json_str.strip()
-                
-                if json_str:
+                json_str = re.sub(r"//.*?\n", "\n", match)  # 移除单行注释
+                json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL)  # 移除多行注释
+                if json_str := json_str.strip():
                     json_obj = json.loads(repair_json(json_str))
                     if isinstance(json_obj, dict):
                         json_objects.append(json_obj)

src/chat/replyer/default_generator.py

@@ -368,37 +368,37 @@ class DefaultReplyer:
 
         return f"{expression_habits_title}\n{expression_habits_block}", selected_ids
 
-    async def build_memory_block(self, chat_history: List[DatabaseMessages], target: str) -> str:
-        """构建记忆块
+    # async def build_memory_block(self, chat_history: List[DatabaseMessages], target: str) -> str:
+    #     """构建记忆块
 
-        Args:
-            chat_history: 聊天历史记录
-            target: 目标消息内容
+    #     Args:
+    #         chat_history: 聊天历史记录
+    #         target: 目标消息内容
 
-        Returns:
-            str: 记忆信息字符串
-        """
+    #     Returns:
+    #         str: 记忆信息字符串
+    #     """
 
-        if not global_config.memory.enable_memory:
-            return ""
+    #     if not global_config.memory.enable_memory:
+    #         return ""
 
-        instant_memory = None
+    #     instant_memory = None
 
-        running_memories = await self.memory_activator.activate_memory_with_chat_history(
-            target_message=target, chat_history=chat_history
-        )
-        if not running_memories:
-            return ""
+    #     running_memories = await self.memory_activator.activate_memory_with_chat_history(
+    #         target_message=target, chat_history=chat_history
+    #     )
+    #     if not running_memories:
+    #         return ""
 
-        memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
-        for running_memory in running_memories:
-            keywords, content = running_memory
-            memory_str += f"- {keywords}：{content}\n"
+    #     memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
+    #     for running_memory in running_memories:
+    #         keywords, content = running_memory
+    #         memory_str += f"- {keywords}：{content}\n"
 
-        if instant_memory:
-            memory_str += f"- {instant_memory}\n"
+    #     if instant_memory:
+    #         memory_str += f"- {instant_memory}\n"
 
-        return memory_str
+    #     return memory_str
 
     async def build_tool_info(self, chat_history: str, sender: str, target: str, enable_tool: bool = True) -> str:
         """构建工具信息块

src/common/data_models/info_data_model.py

@@ -23,3 +23,4 @@ class ActionPlannerInfo(BaseDataModel):
     action_data: Optional[Dict] = None
     action_message: Optional["DatabaseMessages"] = None
     available_actions: Optional[Dict[str, "ActionInfo"]] = None
+    loop_start_time: Optional[float] = None

src/common/data_models/message_data_model.py

@@ -1,4 +1,4 @@
-from typing import Optional, TYPE_CHECKING, List, Tuple, Union, Dict
+from typing import Optional, TYPE_CHECKING, List, Tuple, Union, Dict, Any
 from dataclasses import dataclass, field
 from enum import Enum
 
@@ -50,10 +50,65 @@ class ReplyContentType(Enum):
         return self.value
 
 
+@dataclass
+class ForwardNode(BaseDataModel):
+    user_id: Optional[str] = None
+    user_nickname: Optional[str] = None
+    content: Union[List["ReplyContent"], str] = field(default_factory=list)
+
+    @classmethod
+    def construct_as_id_reference(cls, message_id: str) -> "ForwardNode":
+        return cls(user_id="", user_nickname="", content=message_id)
+
+    @classmethod
+    def construct_as_created_node(
+        cls, user_id: str, user_nickname: str, content: List["ReplyContent"]
+    ) -> "ForwardNode":
+        return cls(user_id=user_id, user_nickname=user_nickname, content=content)
+
+
 @dataclass
 class ReplyContent(BaseDataModel):
     content_type: ReplyContentType | str
-    content: Union[str, Dict, List["ReplyContent"]]  # 支持嵌套的 ReplyContent
+    content: Union[str, Dict, List[ForwardNode], List["ReplyContent"]]  # 支持嵌套的 ReplyContent
+
+    @classmethod
+    def construct_as_text(cls, text: str):
+        return cls(content_type=ReplyContentType.TEXT, content=text)
+
+    @classmethod
+    def construct_as_image(cls, image_base64: str):
+        return cls(content_type=ReplyContentType.IMAGE, content=image_base64)
+
+    @classmethod
+    def construct_as_voice(cls, voice_base64: str):
+        return cls(content_type=ReplyContentType.VOICE, content=voice_base64)
+
+    @classmethod
+    def construct_as_emoji(cls, emoji_str: str):
+        return cls(content_type=ReplyContentType.EMOJI, content=emoji_str)
+
+    @classmethod
+    def construct_as_command(cls, command_arg: Dict):
+        return cls(content_type=ReplyContentType.COMMAND, content=command_arg)
+
+    @classmethod
+    def construct_as_hybrid(cls, hybrid_content: List[Tuple[ReplyContentType | str, str]]):
+        hybrid_content_list: List[ReplyContent] = []
+        for content_type, content in hybrid_content:
+            assert content_type not in [
+                ReplyContentType.HYBRID,
+                ReplyContentType.FORWARD,
+                ReplyContentType.VOICE,
+                ReplyContentType.COMMAND,
+            ], "混合内容的每个项不能是混合、转发、语音或命令类型"
+            assert isinstance(content, str), "混合内容的每个项必须是字符串"
+            hybrid_content_list.append(ReplyContent(content_type=content_type, content=content))
+        return cls(content_type=ReplyContentType.HYBRID, content=hybrid_content_list)
+
+    @classmethod
+    def construct_as_forward(cls, forward_nodes: List[ForwardNode]):
+        return cls(content_type=ReplyContentType.FORWARD, content=forward_nodes)
 
     def __post_init__(self):
         if isinstance(self.content_type, ReplyContentType):
@@ -82,36 +137,70 @@ class ReplySetModel(BaseDataModel):
         return len(self.reply_data)
 
     def add_text_content(self, text: str):
-        """添加文本内容"""
+        """
+        添加文本内容
+        Args:
+            text: 文本内容
+        """
         self.reply_data.append(ReplyContent(content_type=ReplyContentType.TEXT, content=text))
 
     def add_image_content(self, image_base64: str):
-        """添加图片内容，base64编码的图片数据"""
+        """
+        添加图片内容，base64编码的图片数据
+        Args:
+            image_base64: base64编码的图片数据
+        """
         self.reply_data.append(ReplyContent(content_type=ReplyContentType.IMAGE, content=image_base64))
 
     def add_voice_content(self, voice_base64: str):
-        """添加语音内容，base64编码的音频数据"""
+        """
+        添加语音内容，base64编码的音频数据
+        Args:
+            voice_base64: base64编码的音频数据
+        """
         self.reply_data.append(ReplyContent(content_type=ReplyContentType.VOICE, content=voice_base64))
 
-    def add_hybrid_content(self, hybrid_content: List[Tuple[ReplyContentType, str]]):
+    def add_hybrid_content_by_raw(self, hybrid_content: List[Tuple[ReplyContentType | str, str]]):
         """
-        添加混合型内容，可以包含多种类型的内容
-
-        实际解析时只关注最外层，没有递归嵌套处理
+        添加混合型内容，可以包含text, image, emoji的任意组合
+        Args:
+            hybrid_content: 元组 (类型, 消息内容) 构成的列表，如[(ReplyContentType.TEXT, "Hello"), (ReplyContentType.IMAGE, "<base64")]
         """
+        hybrid_content_list: List[ReplyContent] = []
         for content_type, content in hybrid_content:
+            assert content_type not in [
+                ReplyContentType.HYBRID,
+                ReplyContentType.FORWARD,
+                ReplyContentType.VOICE,
+                ReplyContentType.COMMAND,
+            ], "混合内容的每个项不能是混合、转发、语音或命令类型"
             assert isinstance(content, str), "混合内容的每个项必须是字符串"
+            hybrid_content_list.append(ReplyContent(content_type=content_type, content=content))
+
+        self.reply_data.append(ReplyContent(content_type=ReplyContentType.HYBRID, content=hybrid_content_list))
+
+    def add_hybrid_content(self, hybrid_content: List[ReplyContent]):
+        """
+        添加混合型内容，使用已经构造好的 ReplyContent 列表
+        Args:
+            hybrid_content: ReplyContent 构成的列表，如[ReplyContent(ReplyContentType.TEXT, "Hello"), ReplyContent(ReplyContentType.IMAGE, "<base64")]
+        """
+        for content in hybrid_content:
+            assert content.content_type not in [
+                ReplyContentType.HYBRID,
+                ReplyContentType.FORWARD,
+                ReplyContentType.VOICE,
+                ReplyContentType.COMMAND,
+            ], "混合内容的每个项不能是混合、转发、语音或命令类型"
+            assert isinstance(content.content, str), "混合内容的每个项必须是字符串"
+
+        self.reply_data.append(ReplyContent(content_type=ReplyContentType.HYBRID, content=hybrid_content))
+
+    def add_custom_content(self, content_type: str, content: Any):
+        """
+        添加自定义类型的内容"""
         self.reply_data.append(ReplyContent(content_type=content_type, content=content))
 
-    def add_custom_content(self, content_type: str, content: str):
-        """添加自定义类型的内容"""
-        self.reply_data.append(ReplyContent(content_type=content_type, content=content))
-
-    def add_forward_content(self, forward_content: List[Tuple[ReplyContentType, Union[str, ReplyContent]]]):
+    def add_forward_content(self, forward_content: List[ForwardNode]):
         """添加转发内容，可以是字符串或ReplyContent，嵌套的转发内容需要自己构造放入"""
-        for content_type, content in forward_content:
-            if isinstance(content, ReplyContent):
-                self.reply_data.append(content)
-            else:
-                assert isinstance(content, str), "转发内容的每个data必须是字符串或ReplyContent"
-                self.reply_data.append(ReplyContent(content_type=content_type, content=content))
+        self.reply_data.append(ReplyContent(content_type=ReplyContentType.FORWARD, content=forward_content))

src/common/data_models/reply_set_doc.md

@@ -0,0 +1,57 @@
+# 有关转发消息和其他消息的构建类型说明
+```mermaid
+graph LR;
+    direction TB;
+    A[ReplySet] --- B[ReplyContent];
+    A --- C["ReplyContent"];
+    A --- K["ReplyContent"];
+    A --- L["ReplyContent"];
+    A --- N["ReplyContent"];
+    A --- D[...];
+    B --- E["Text (in str)"];
+    B --- F["Image (in base64)"];
+    C --- G["Voice (in base64)"];
+    B --- I["Emoji (in base64)"];
+    subgraph "可行内容(以下的任意组合)";
+        subgraph "转发消息(Forward)"
+            M["List[ForwardNode]"]
+        end
+        subgraph "混合消息(Hybrid)"
+            J["List[ReplyContent] (要求只能包含普通消息)"]
+        end
+        subgraph "命令消息(Command)"
+            H["Command (in Dict)"]
+        end
+        subgraph "语音消息"
+            G
+        end
+        subgraph "普通消息"
+            E
+            F
+            I
+        end
+    end
+    N --- H
+    K --- J
+    L --- M
+    subgraph ForwardNodes
+        O["ForwardNode"]
+        P["ForwardNode"]
+        Q["ForwardNode"]
+    end
+    M --- O
+    M --- P
+    M --- Q
+    subgraph "内容 (message_id引用法)"
+        P --- U["content: str, 引用已有消息的有效ID"];
+    end
+    subgraph "内容 (生成法)"
+        O --- R["user_id: str"];
+        O --- S["user_nickname: str"];
+        O --- T["content: List[ReplyContent], 为这个转发节点的消息内容"];
+    end
+```
+
+另外，自定义消息类型我们在这里不做讨论。
+
+以上列出了所有可能的ReplySet构建方式，下面我们来解释一下各个类型的含义。

src/config/official_configs.py

@@ -72,9 +72,6 @@ class ChatConfig(ConfigBase):
     interest_rate_mode: Literal["fast", "accurate"] = "fast"
     """兴趣值计算模式，fast为快速计算，accurate为精确计算"""
 
-    mentioned_bot_reply: float = 1
-    """提及 bot 必然回复，1为100%回复，0为不额外增幅"""
-
     planner_size: float = 1.5
     """副规划器大小，越小，麦麦的动作执行能力越精细，但是消耗更多token，调大可以缓解429类错误"""
 

src/main.py

@@ -132,13 +132,13 @@ class MainSystem:
 
             await asyncio.gather(*tasks)
 
-    async def forget_memory_task(self):
-        """记忆遗忘任务"""
-        while True:
-            await asyncio.sleep(global_config.memory.forget_memory_interval)
-            logger.info("[记忆遗忘] 开始遗忘记忆...")
-            await self.hippocampus_manager.forget_memory(percentage=global_config.memory.memory_forget_percentage)  # type: ignore
-            logger.info("[记忆遗忘] 记忆遗忘完成")
+    # async def forget_memory_task(self):
+    #     """记忆遗忘任务"""
+    #     while True:
+    #         await asyncio.sleep(global_config.memory.forget_memory_interval)
+    #         logger.info("[记忆遗忘] 开始遗忘记忆...")
+    #         await self.hippocampus_manager.forget_memory(percentage=global_config.memory.memory_forget_percentage)  # type: ignore
+    #         logger.info("[记忆遗忘] 记忆遗忘完成")
 
 
 async def main():

src/plugin_system/apis/send_api.py

@@ -21,7 +21,7 @@
 
 import traceback
 import time
-from typing import Optional, Union, Dict, List, TYPE_CHECKING
+from typing import Optional, Union, Dict, List, TYPE_CHECKING, Tuple
 
 from src.common.logger import get_logger
 from src.common.data_models.message_data_model import ReplyContentType
@@ -29,11 +29,11 @@ from src.config.config import global_config
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.chat.message_receive.uni_message_sender import UniversalMessageSender
 from src.chat.message_receive.message import MessageSending, MessageRecv
-from maim_message import Seg, UserInfo
+from maim_message import Seg, UserInfo, MessageBase, BaseMessageInfo
 
 if TYPE_CHECKING:
     from src.common.data_models.database_data_model import DatabaseMessages
-    from src.common.data_models.message_data_model import ReplySetModel
+    from src.common.data_models.message_data_model import ReplySetModel, ReplyContent, ForwardNode
 
 logger = get_logger("send_api")
 
@@ -367,48 +367,51 @@ async def custom_reply_set_to_stream(
     flag: bool = True
     for reply_content in reply_set.reply_data:
         status: bool = False
+        message_seg, need_typing = _parse_content_to_seg(reply_content)
+        status = await _send_to_target(
+            message_segment=message_seg,
+            stream_id=stream_id,
+            display_message=display_message,
+            typing=bool(need_typing and typing),
+            reply_message=reply_message,
+            set_reply=set_reply,
+            storage_message=storage_message,
+            show_log=show_log,
+        )
+        if not status:
+            flag = False
+            logger.error(
+                f"[SendAPI] 发送{repr(reply_content.content_type)}消息失败，消息内容：{str(reply_content.content)[:100]}"
+            )
+
+    return flag
+
+
+def _parse_content_to_seg(reply_content: "ReplyContent") -> Tuple[Seg, bool]:
+    """
+    把 ReplyContent 转换为 Seg 结构 (Forward 中仅递归一次)
+    Args:
+        reply_content: ReplyContent 对象
+    Returns:
+        Tuple[Seg, bool]: 转换后的 Seg 结构和是否需要typing的标志
+    """
     content_type = reply_content.content_type
-        message_data = reply_content.content
     if content_type == ReplyContentType.TEXT:
-            status = await _send_to_target(
-                message_segment=Seg(type="text", data=message_data),  # type: ignore
-                stream_id=stream_id,
-                display_message=display_message,
-                typing=typing,
-                reply_message=reply_message,
-                set_reply=set_reply,
-                storage_message=storage_message,
-                show_log=show_log,
-            )
-        elif content_type in [
-            ReplyContentType.IMAGE,
-            ReplyContentType.EMOJI,
-            ReplyContentType.COMMAND,
-            ReplyContentType.VOICE,
-        ]:
-            message_segment: Seg
-            if ReplyContentType == ReplyContentType.IMAGE:
-                message_segment = Seg(type="image", data=message_data)  # type: ignore
-            elif ReplyContentType == ReplyContentType.EMOJI:
-                message_segment = Seg(type="emoji", data=message_data)  # type: ignore
-            elif ReplyContentType == ReplyContentType.COMMAND:
-                message_segment = Seg(type="command", data=message_data)  # type: ignore
-            elif ReplyContentType == ReplyContentType.VOICE:
-                message_segment = Seg(type="voice", data=message_data)  # type: ignore
-            status = await _send_to_target(
-                message_segment=message_segment,
-                stream_id=stream_id,
-                display_message=display_message,
-                typing=False,
-                reply_message=reply_message,
-                set_reply=set_reply,
-                storage_message=storage_message,
-                show_log=show_log,
-            )
+        text_data: str = reply_content.content  # type: ignore
+        return Seg(type="text", data=text_data), True
+    elif content_type == ReplyContentType.IMAGE:
+        return Seg(type="image", data=reply_content.content), False  # type: ignore
+    elif content_type == ReplyContentType.EMOJI:
+        return Seg(type="emoji", data=reply_content.content), False  # type: ignore
+    elif content_type == ReplyContentType.COMMAND:
+        return Seg(type="command", data=reply_content.content), False  # type: ignore
+    elif content_type == ReplyContentType.VOICE:
+        return Seg(type="voice", data=reply_content.content), False  # type: ignore
     elif content_type == ReplyContentType.HYBRID:
-            assert isinstance(message_data, list), "混合类型内容必须是列表"
+        hybrid_message_list_data: List[ReplyContent] = reply_content.content  # type: ignore
+        assert isinstance(hybrid_message_list_data, list), "混合类型内容必须是列表"
         sub_seg_list: List[Seg] = []
-            for sub_content in message_data:
+        for sub_content in hybrid_message_list_data:
             sub_content_type = sub_content.content_type
             sub_content_data = sub_content.content
 
@@ -421,35 +424,27 @@ async def custom_reply_set_to_stream(
             else:
                 logger.warning(f"[SendAPI] 混合类型中不支持的子内容类型: {repr(sub_content_type)}")
                 continue
-            status = await _send_to_target(
-                message_segment=Seg(type="seglist", data=sub_seg_list),  # type: ignore
-                stream_id=stream_id,
-                display_message=display_message,
-                typing=typing,
-                reply_message=reply_message,
-                set_reply=set_reply,
-                storage_message=storage_message,
-                show_log=show_log,
-            )
+        return Seg(type="seglist", data=sub_seg_list), True
     elif content_type == ReplyContentType.FORWARD:
-            assert isinstance(message_data, list), "转发类型内容必须是列表"
-            # TODO: 完成转发消息的发送机制
+        forward_message_list_data: List["ForwardNode"] = reply_content.content  # type: ignore
+        assert isinstance(forward_message_list_data, list), "转发类型内容必须是列表"
+        forward_message_list: List[MessageBase] = []
+        for forward_node in forward_message_list_data:
+            message_segment = Seg(type="id", data=forward_node.content)  # type: ignore
+            user_info: Optional[UserInfo] = None
+            if forward_node.user_id and forward_node.user_nickname:
+                assert isinstance(forward_node.content, list), "转发节点内容必须是列表"
+                user_info = UserInfo(user_id=forward_node.user_id, user_nickname=forward_node.user_nickname)
+                single_node_content: List[Seg] = []
+                for sub_content in forward_node.content:
+                    if sub_content.content_type != ReplyContentType.FORWARD:
+                        sub_seg, _ = _parse_content_to_seg(sub_content)
+                        single_node_content.append(sub_seg)
+                message_segment = Seg(type="seglist", data=single_node_content)
+            forward_message_list.append(
+                MessageBase(message_segment=message_segment, message_info=BaseMessageInfo(user_info=user_info))
+            )
+        return Seg(type="forward", data=forward_message_list), False  # type: ignore
     else:
-            message_type_in_str = (
-                content_type.value if isinstance(content_type, ReplyContentType) else str(content_type)
-            )
-            return await _send_to_target(
-                message_segment=Seg(type=message_type_in_str, data=message_data),  # type: ignore
-                stream_id=stream_id,
-                display_message=display_message,
-                typing=typing,
-                reply_message=reply_message,
-                set_reply=set_reply,
-                storage_message=storage_message,
-                show_log=show_log,
-            )
-        if not status:
-            flag = False
-            logger.error(f"[SendAPI] 发送{repr(content_type)}消息失败，消息内容：{str(message_data)[:100]}")
-
-    return flag
+        message_type_in_str = content_type.value if isinstance(content_type, ReplyContentType) else str(content_type)
+        return Seg(type=message_type_in_str, data=reply_content.content), True  # type: ignore

src/plugins/built_in/relation/relation.py

@@ -179,7 +179,7 @@ class BuildRelationAction(BaseAction):
             chat_model_config = models.get("utils")
             success, update_memory, _, _ = await llm_api.generate_with_model(
                 prompt,
-                model_config=chat_model_config,
+                model_config=chat_model_config,  # type: ignore
                 request_type="relation.category.update",  # type: ignore
             )