删除已读标

修复重复聊天记录

src/plugins/PFC/action_planner.py

@@ -10,7 +10,7 @@ from ..models.utils_model import LLMRequest
 from src.config.config import global_config
 
 # 确保导入路径正确
-from .pfc_utils import get_items_from_json
+from .pfc_utils import get_items_from_json, build_chat_history_text
 from .chat_observer import ChatObserver
 from .observation_info import ObservationInfo
 from .conversation_info import ConversationInfo
@@ -210,7 +210,7 @@ class ActionPlanner:
             time_since_last_bot_message_info = self._get_bot_last_speak_time_info(observation_info)
             timeout_context = self._get_timeout_context(conversation_info)
             goals_str = self._build_goals_string(conversation_info)
-            chat_history_text = await self._build_chat_history_text(observation_info)
+            chat_history_text = await build_chat_history_text(observation_info, self.private_name)
             # 获取 sender_name, relationship_text, current_emotion_text
             sender_name_str = getattr(observation_info, "sender_name", "对方")  # 从 observation_info 获取
             if not sender_name_str:
@@ -467,50 +467,6 @@ class ActionPlanner:
             goals_str = "- 构建对话目标时出错。\n"
         return goals_str
 
-    async def _build_chat_history_text(self, observation_info: ObservationInfo) -> str:
-        """构建聊天历史记录文本 (包含未处理消息)"""
-
-        chat_history_text = ""
-        try:
-            if hasattr(observation_info, "chat_history_str") and observation_info.chat_history_str:
-                chat_history_text = observation_info.chat_history_str
-            elif hasattr(observation_info, "chat_history") and observation_info.chat_history:
-                history_slice = observation_info.chat_history[-20:]
-                chat_history_text = await build_readable_messages(
-                    history_slice, replace_bot_name=True, merge_messages=False, timestamp_mode="relative", read_mark=0.0
-                )
-            else:
-                chat_history_text = "还没有聊天记录。\n"
-            unread_count = getattr(observation_info, "new_messages_count", 0)
-            unread_messages = getattr(observation_info, "unprocessed_messages", [])
-            if unread_count > 0 and unread_messages:
-                bot_qq_str = str(global_config.BOT_QQ)
-                other_unread_messages = [
-                    msg for msg in unread_messages if msg.get("user_info", {}).get("user_id") != bot_qq_str
-                ]
-                other_unread_count = len(other_unread_messages)
-                if other_unread_count > 0:
-                    new_messages_str = await build_readable_messages(
-                        other_unread_messages,
-                        replace_bot_name=True,
-                        merge_messages=False,
-                        timestamp_mode="relative",
-                        read_mark=0.0,
-                    )
-                    chat_history_text += (
-                        f"\n--- 以下是 {other_unread_count} 条你需要处理的新消息 ---\n{new_messages_str}\n------\n"
-                    )
-                    logger.debug(f"[私聊][{self.private_name}] 向 LLM 追加了 {other_unread_count} 条未读消息。")
-                else:
-                    chat_history_text += "\n--- 以上均为已读消息，未读消息均已处理完毕 ---\n"
-        except AttributeError as e:
-            logger.warning(f"[私聊][{self.private_name}] 构建聊天记录文本时属性错误: {e}")
-            chat_history_text = "[获取聊天记录时出错]\n"
-        except Exception as e:
-            logger.error(f"[私聊][{self.private_name}] 处理聊天记录时发生未知错误: {e}")
-            chat_history_text = "[处理聊天记录时出错]\n"
-        return chat_history_text
-
     def _build_action_history_context(self, conversation_info: ConversationInfo) -> Tuple[str, str]:
         """构建行动历史概要和上一次行动详细情况"""
 

src/plugins/PFC/conversation.py

@@ -25,7 +25,6 @@ from .observation_info import ObservationInfo
 from .conversation_info import ConversationInfo
 from .reply_generator import ReplyGenerator
 from .PFC_idle.idle_chat import IdleChat
-from .pfc_KnowledgeFetcher import KnowledgeFetcher
 from .waiter import Waiter
 from .reply_checker import ReplyChecker
 
@@ -74,7 +73,6 @@ class Conversation:
         self.action_planner: Optional[ActionPlanner] = None
         self.goal_analyzer: Optional[GoalAnalyzer] = None
         self.reply_generator: Optional[ReplyGenerator] = None
-        self.knowledge_fetcher: Optional[KnowledgeFetcher] = None
         self.waiter: Optional[Waiter] = None
         self.direct_sender: Optional[DirectMessageSender] = None
         self.idle_chat: Optional[IdleChat] = None

src/plugins/PFC/conversation_initializer.py

@@ -18,7 +18,6 @@ from .observation_info import ObservationInfo
 from .conversation_info import ConversationInfo
 from .reply_generator import ReplyGenerator
 from .PFC_idle.idle_chat import IdleChat
-from .pfc_KnowledgeFetcher import KnowledgeFetcher  # 修正大小写
 from .waiter import Waiter
 from .pfc_utils import get_person_id
 from .reply_checker import ReplyChecker
@@ -166,9 +165,6 @@ async def initialize_core_components(conversation_instance: "Conversation"):
             conversation_instance.stream_id, conversation_instance.private_name
         )
 
-        logger.debug(f"[私聊][{conversation_instance.private_name}] (Initializer) 初始化 KnowledgeFetcher...")
-        conversation_instance.knowledge_fetcher = KnowledgeFetcher(conversation_instance.private_name)
-
         logger.debug(f"[私聊][{conversation_instance.private_name}] (Initializer) 初始化 Waiter...")
         conversation_instance.waiter = Waiter(conversation_instance.stream_id, conversation_instance.private_name)
 

src/plugins/PFC/pfc_KnowledgeFetcher.py

@@ -1,85 +0,0 @@
-from typing import List, Tuple
-from src.common.logger import get_module_logger
-from src.plugins.memory_system.Hippocampus import HippocampusManager
-from ..models.utils_model import LLMRequest
-from ...config.config import global_config
-from ..chat.message import Message
-from ..knowledge.knowledge_lib import qa_manager
-from ..utils.chat_message_builder import build_readable_messages
-
-logger = get_module_logger("knowledge_fetcher")
-
-
-class KnowledgeFetcher:
-    """知识调取器"""
-
-    def __init__(self, private_name: str):
-        self.llm = LLMRequest(
-            model=global_config.llm_normal,
-            temperature=global_config.llm_normal["temp"],
-            max_tokens=1000,
-            request_type="knowledge_fetch",
-        )
-        self.private_name = private_name
-
-    def _lpmm_get_knowledge(self, query: str) -> str:
-        """获取相关知识
-
-        Args:
-            query: 查询内容
-
-        Returns:
-            str: 构造好的,带相关度的知识
-        """
-
-        logger.debug(f"[私聊][{self.private_name}]正在从LPMM知识库中获取知识")
-        try:
-            knowledge_info = qa_manager.get_knowledge(query)
-            logger.debug(f"[私聊][{self.private_name}]LPMM知识库查询结果: {knowledge_info:150}")
-            return knowledge_info
-        except Exception as e:
-            logger.error(f"[私聊][{self.private_name}]LPMM知识库搜索工具执行失败: {str(e)}")
-            return "未找到匹配的知识"
-
-    async def fetch(self, query: str, chat_history: List[Message]) -> Tuple[str, str]:
-        """获取相关知识
-
-        Args:
-            query: 查询内容
-            chat_history: 聊天历史
-
-        Returns:
-            Tuple[str, str]: (获取的知识, 知识来源)
-        """
-        # 构建查询上下文
-        chat_history_text = await build_readable_messages(
-            chat_history,
-            replace_bot_name=True,
-            merge_messages=False,
-            timestamp_mode="relative",
-            read_mark=0.0,
-        )
-
-        # 从记忆中获取相关知识
-        related_memory = await HippocampusManager.get_instance().get_memory_from_text(
-            text=f"{query}\n{chat_history_text}",
-            max_memory_num=3,
-            max_memory_length=2,
-            max_depth=3,
-            fast_retrieval=False,
-        )
-        knowledge_text = ""
-        sources_text = "无记忆匹配"  # 默认值
-        if related_memory:
-            sources = []
-            for memory in related_memory:
-                knowledge_text += memory[1] + "\n"
-                sources.append(f"记忆片段{memory[0]}")
-            knowledge_text = knowledge_text.strip()
-            sources_text = "，".join(sources)
-
-        knowledge_text += "\n现在有以下**知识**可供参考：\n "
-        knowledge_text += self._lpmm_get_knowledge(query)
-        knowledge_text += "\n请记住这些**知识**，并根据**知识**回答问题。\n"
-
-        return knowledge_text or "未找到相关知识", sources_text or "无记忆匹配"

src/plugins/PFC/pfc_utils.py

@@ -8,6 +8,9 @@ from src.plugins.heartFC_chat.heartflow_prompt_builder import prompt_builder  #
 from src.plugins.chat.chat_stream import ChatStream
 from ..person_info.person_info import person_info_manager
 import math
+from src.plugins.utils.chat_message_builder import build_readable_messages
+from .observation_info import ObservationInfo
+from src.config.config import global_config
 
 logger = get_logger("pfc_utils")
 
@@ -339,3 +342,42 @@ async def adjust_relationship_value_nonlinear(old_value: float, raw_adjustment:
             value = 0
 
     return value
+
+async def build_chat_history_text(observation_info: ObservationInfo, private_name: str) -> str:
+    """构建聊天历史记录文本 (包含未处理消息)"""
+
+    chat_history_text = ""
+    try:
+        if hasattr(observation_info, "chat_history_str") and observation_info.chat_history_str:
+            chat_history_text = observation_info.chat_history_str
+        elif hasattr(observation_info, "chat_history") and observation_info.chat_history:
+            history_slice = observation_info.chat_history[-20:]
+            chat_history_text = await build_readable_messages(
+                history_slice, replace_bot_name=True, merge_messages=False, timestamp_mode="relative", read_mark=0.0
+            )
+        else:
+            chat_history_text = "还没有聊天记录。\n"
+        unread_count = getattr(observation_info, "new_messages_count", 0)
+        unread_messages = getattr(observation_info, "unprocessed_messages", [])
+        if unread_count > 0 and unread_messages:
+            bot_qq_str = str(global_config.BOT_QQ)
+            other_unread_messages = [
+                msg for msg in unread_messages if msg.get("user_info", {}).get("user_id") != bot_qq_str
+            ]
+            other_unread_count = len(other_unread_messages)
+            if other_unread_count > 0:
+                new_messages_str = await build_readable_messages(
+                    other_unread_messages,
+                    replace_bot_name=True,
+                    merge_messages=False,
+                    timestamp_mode="relative",
+                    read_mark=0.0,
+                )
+                chat_history_text += f"\n{new_messages_str}\n------\n"
+    except AttributeError as e:
+        logger.warning(f"[私聊][{private_name}] 构建聊天记录文本时属性错误: {e}")
+        chat_history_text = "[获取聊天记录时出错]\n"
+    except Exception as e:
+        logger.error(f"[私聊][{private_name}] 处理聊天记录时发生未知错误: {e}")
+        chat_history_text = "[处理聊天记录时出错]\n"
+    return chat_history_text

src/plugins/PFC/reply_generator.py

@@ -1,4 +1,6 @@
 import random
+
+from networkx import bull_graph
 from .pfc_utils import retrieve_contextual_info
 
 from src.common.logger_manager import get_logger
@@ -9,7 +11,7 @@ from .reply_checker import ReplyChecker
 from src.individuality.individuality import Individuality
 from .observation_info import ObservationInfo
 from .conversation_info import ConversationInfo
-from src.plugins.utils.chat_message_builder import build_readable_messages
+from .pfc_utils import build_chat_history_text
 
 logger = get_logger("reply_generator")
 
@@ -214,21 +216,7 @@ class ReplyGenerator:
         else:
             goals_str = "- 目前没有明确对话目标\n"
 
-        chat_history_text = observation_info.chat_history_str
-        if observation_info.new_messages_count > 0 and observation_info.unprocessed_messages:
-            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}"
-        elif not chat_history_text:
-            chat_history_text = "还没有聊天记录。"
-        else:
-            chat_history_text += "\n--- 以上均为已读消息，未读消息均已处理完毕 ---\n"
+        chat_history_text = await build_chat_history_text(observation_info, self.private_name)
 
         sender_name_str = getattr(observation_info, "sender_name", "对方")
         if not sender_name_str: