Merge branch 'dev' into type-fix

.github/workflows/docker-image.yml

@@ -9,6 +9,8 @@ on:
     tags:
       - "v*.*.*"
       - "v*"
+      - "*.*.*"
+      - "*.*.*-*"
 
 jobs:
   build-amd64:
@@ -47,6 +49,7 @@ jobs:
           tags: |
             type=ref,event=branch
             type=ref,event=tag
+            type=raw,value=latest,enable=${{ github.ref == 'refs/heads/main' }}
             type=semver,pattern={{version}}
             type=semver,pattern={{major}}.{{minor}}
             type=semver,pattern={{major}}
@@ -109,6 +112,7 @@ jobs:
           tags: |
             type=ref,event=branch
             type=ref,event=tag
+            type=raw,value=latest,enable=${{ github.ref == 'refs/heads/main' }}
             type=semver,pattern={{version}}
             type=semver,pattern={{major}}.{{minor}}
             type=semver,pattern={{major}}
@@ -153,6 +157,7 @@ jobs:
           tags: |
             type=ref,event=branch
             type=ref,event=tag
+            type=raw,value=latest,enable=${{ github.ref == 'refs/heads/main' }}
             type=semver,pattern={{version}}
             type=semver,pattern={{major}}.{{minor}}
             type=semver,pattern={{major}}

.gitignore

@@ -40,6 +40,7 @@ config/lpmm_config.toml.bak
 （测试版）麦麦生成人格.bat
 （临时版）麦麦开始学习.bat
 src/plugins/utils/statistic.py
+CLAUDE.md
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

CLAUDE.md

@@ -1,20 +0,0 @@
-# CLAUDE.md
-
-This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
-
-## Commands
-- **Run Bot**: `python bot.py`
-- **Lint**: `ruff check --fix .` or `ruff format .`
-- **Run Tests**: `python -m unittest discover -v`
-- **Run Single Test**: `python -m unittest src/plugins/message/test.py`
-
-## Code Style
-- **Formatting**: Line length 120 chars, use double quotes for strings
-- **Imports**: Group standard library, external packages, then internal imports
-- **Naming**: snake_case for functions/variables, PascalCase for classes
-- **Error Handling**: Use try/except blocks with specific exceptions
-- **Types**: Use type hints where possible
-- **Docstrings**: Document classes and complex functions
-- **Linting**: Follow ruff rules (E, F, B) with ignores E711, E501
-
-When making changes, run `ruff check --fix .` to ensure code follows style guidelines. The codebase uses Ruff for linting and formatting.

changelogs/changelog.md

@@ -1,8 +1,9 @@
 # Changelog
 
-## [0.7.0] -2025-5-19
+## [0.7.0] -2025-6-1
 - 重构数据库，弃用MongoDB，采用轻量sqlite,无需额外安装
-- 重构HFC，可扩展的聊天模式，初步支持插件v0.1
+- 重构HFC，可扩展的聊天模式
+- HFC初步支持插件v0.1（测试版）
 - 重构表情包模块
 - 移除日程系统
 

docs/HeartFC_readme.md

@@ -4,8 +4,8 @@ HeartFC_chat 是一个基于心流理论的聊天系统，通过模拟人类的
 
 ## 核心工作流程
 
-### 1. 消息处理与存储 (HeartFCProcessor)
+### 1. 消息处理与存储 (HeartFCMessageReceiver)
-[代码位置: src/plugins/focus_chat/heartflow_processor.py]
+[代码位置: src/plugins/focus_chat/heartflow_message_receiver.py]
 
 消息处理器负责接收和预处理消息，主要完成以下工作：
 ```mermaid
@@ -132,7 +132,7 @@ graph TD
 
 ### 关键参数
 - LLM配置：`model_normal` [heartFC_generator.py 行号: 32-37]
-- 过滤规则：`_check_ban_words()`, `_check_ban_regex()` [heartflow_processor.py 行号: 196-215]
+- 过滤规则：`_check_ban_words()`, `_check_ban_regex()` [heartflow_message_receiver.py 行号: 196-215]
 - 状态控制：`INITIAL_DURATION = 60.0` [focus_chat.py 行号: 11]
 
 ### 优化建议

docs/HeartFC_system.md

@@ -113,7 +113,7 @@ c HeartFChatting工作方式
 ### 1.5. 消息处理与回复流程 (Message Processing vs. Replying Flow)
 - **关注点分离**: 系统严格区分了接收和处理传入消息的流程与决定和生成回复的流程。
     - **消息处理 (Processing)**: 
-        - 由一个独立的处理器（例如 `HeartFCProcessor`）负责接收原始消息数据。
+        - 由一个独立的处理器（例如 `HeartFCMessageReceiver`）负责接收原始消息数据。
         - 职责包括：消息解析 (`MessageRecv`)、过滤（屏蔽词、正则表达式）、基于记忆系统的初步兴趣计算 (`HippocampusManager`)、消息存储 (`MessageStorage`) 以及用户关系更新 (`RelationshipManager`)。
         - 处理后的消息信息（如计算出的兴趣度）会传递给对应的 `SubHeartflow`。
     - **回复决策与生成 (Replying)**:
@@ -121,7 +121,7 @@ c HeartFChatting工作方式
         - 基于其内部状态 (`ChatState`、`SubMind` 的思考结果)、观察到的信息 (`Observation` 提供的内容) 以及 `InterestChatting` 的状态来决定是否回复、何时回复以及如何回复。
 - **消息缓冲 (Message Caching)**:
     - `message_buffer` 模块会对某些传入消息进行临时缓存，尤其是在处理连续的多部分消息（如多张图片）时。
-    - 这个缓冲机制发生在 `HeartFCProcessor` 处理流程中，确保消息的完整性，然后才进行后续的存储和兴趣计算。
+    - 这个缓冲机制发生在 `HeartFCMessageReceiver` 处理流程中，确保消息的完整性，然后才进行后续的存储和兴趣计算。
     - 缓存的消息最终仍会流向对应的 `ChatStream`（与 `SubHeartflow` 关联），但核心的消息处理与回复决策仍然是分离的步骤。
 
 ## 2. 核心控制与状态管理 (Core Control and State Management)
@@ -148,7 +148,7 @@ c HeartFChatting工作方式
 - **管理对象**: 每个 `SubHeartflow` 实例内部维护其 `ChatStateInfo`，包含当前的 `ChatState`。
 - **状态及含义**:
     - `ChatState.ABSENT` (不参与/没在看): 初始或停用状态。子心流不观察新信息，不进行思考，也不回复。
-    - `ChatState.CHAT` (随便看看/水群): 普通聊天模式。激活 `NormalChatInstance`。
+    - `ChatState.NORMAL` (随便看看/水群): 普通聊天模式。激活 `NormalChatInstance`。
     *   `ChatState.FOCUSED` (专注/认真聊天): 专注聊天模式。激活 `HeartFlowChatInstance`。
 - **选择**: 子心流可以根据外部指令（来自 `SubHeartflowManager`）或内部逻辑（未来的扩展）选择进入 `ABSENT` 状态（不回复不观察），或进入 `CHAT` / `FOCUSED` 中的一种回复模式。
 - **状态转换机制** (由 `SubHeartflowManager` 驱动，更细致的说明):
@@ -156,7 +156,7 @@ c HeartFChatting工作方式
     - **`ABSENT` -> `CHAT` (激活闲聊)**:
         - **触发条件**: `Heartflow` 的主状态 (`MaiState`) 允许 `CHAT` 模式，且当前 `CHAT` 状态的子心流数量未达上限。
         - **判定机制**: `SubHeartflowManager` 中的 `sbhf_absent_into_chat` 方法调用大模型(LLM)。LLM 读取该群聊的近期内容和结合自身个性信息，判断是否"想"在该群开始聊天。
-        - **执行**: 若 LLM 判断为是，且名额未满，`SubHeartflowManager` 调用 `change_chat_state(ChatState.CHAT)`。
+        - **执行**: 若 LLM 判断为是，且名额未满，`SubHeartflowManager` 调用 `change_chat_state(ChatState.NORMAL)`。
     - **`CHAT` -> `FOCUSED` (激活专注)**:
         - **触发条件**: 子心流处于 `CHAT` 状态，其内部维护的"开屎热聊"概率 (`InterestChatting.start_hfc_probability`) 达到预设阈值（表示对当前聊天兴趣浓厚），同时 `Heartflow` 的主状态允许 `FOCUSED` 模式，且 `FOCUSED` 名额未满。
         - **判定机制**: `SubHeartflowManager` 中的 `sbhf_absent_into_focus` 方法定期检查满足条件的 `CHAT` 子心流。

src/api/config_api.py

@@ -41,7 +41,7 @@ class APIBotConfig:
     allow_focus_mode: bool  # 是否允许专注聊天状态
     base_normal_chat_num: int  # 最多允许多少个群进行普通聊天
     base_focused_chat_num: int  # 最多允许多少个群进行专注聊天
-    chat.observation_context_size: int  # 观察到的最长上下文大小
+    observation_context_size: int  # 观察到的最长上下文大小
     message_buffer: bool  # 是否启用消息缓冲
     ban_words: List[str]  # 禁止词列表
     ban_msgs_regex: List[str]  # 禁止消息的正则表达式列表
@@ -128,7 +128,7 @@ class APIBotConfig:
     llm_reasoning: Dict[str, Any]  # 推理模型配置
     llm_normal: Dict[str, Any]  # 普通模型配置
     llm_topic_judge: Dict[str, Any]  # 主题判断模型配置
-    model.summary: Dict[str, Any]  # 总结模型配置
+    summary: Dict[str, Any]  # 总结模型配置
     vlm: Dict[str, Any]  # VLM模型配置
     llm_heartflow: Dict[str, Any]  # 心流模型配置
     llm_observation: Dict[str, Any]  # 观察模型配置
@@ -203,7 +203,7 @@ class APIBotConfig:
             "llm_reasoning",
             "llm_normal",
             "llm_topic_judge",
-            "model.summary",
+            "summary",
             "vlm",
             "llm_heartflow",
             "llm_observation",

src/chat/__init__.py

@@ -1,5 +1,5 @@
 """
-MaiMBot插件系统
+MaiBot模块系统
 包含聊天、情绪、记忆、日程等功能模块
 """
 

src/chat/focus_chat/expressors/default_expressor.py

@@ -17,7 +17,7 @@ from src.manager.mood_manager import mood_manager
 from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info
 from src.chat.message_receive.chat_stream import ChatStream
 from src.chat.focus_chat.hfc_utils import parse_thinking_id_to_timestamp
-from src.individuality.individuality import Individuality
+from src.individuality.individuality import individuality
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
 import time
@@ -281,7 +281,6 @@ class DefaultExpressor:
         in_mind_reply,
         target_message,
     ) -> str:
-        individuality = Individuality.get_instance()
         prompt_personality = individuality.get_prompt(x_person=0, level=2)
 
         # Determine if it's a group chat
@@ -294,7 +293,7 @@ class DefaultExpressor:
         message_list_before_now = get_raw_msg_before_timestamp_with_chat(
             chat_id=chat_stream.stream_id,
             timestamp=time.time(),
-            limit=global_config.chat.observation_context_size,
+            limit=global_config.focus_chat.observation_context_size,
         )
         chat_talking_prompt = await build_readable_messages(
             message_list_before_now,

src/chat/focus_chat/expressors/exprssion_learner.py

@@ -36,24 +36,6 @@ def init_prompt() -> None:
 """
     Prompt(learn_style_prompt, "learn_style_prompt")
 
-    personality_expression_prompt = """
-{personality}
-
-请从以上人设中总结出这个角色可能的语言风格
-思考回复的特殊内容和情感
-思考有没有特殊的梗，一并总结成语言风格
-总结成如下格式的规律，总结的内容要详细，但具有概括性：
-当"xxx"时，可以"xxx", xxx不超过10个字
-
-例如：
-当"表示十分惊叹"时，使用"我嘞个xxxx"
-当"表示讽刺的赞同，不想讲道理"时，使用"对对对"
-当"想说明某个观点，但懒得明说"，使用"懂的都懂"
-
-现在请你概括
-"""
-    Prompt(personality_expression_prompt, "personality_expression_prompt")
-
     learn_grammar_prompt = """
 {chat_str}
 
@@ -278,44 +260,6 @@ class ExpressionLearner:
             expressions.append((chat_id, situation, style))
         return expressions
 
-    async def extract_and_store_personality_expressions(self):
-        """
-        检查data/expression/personality目录，不存在则创建。
-        用peronality变量作为chat_str，调用LLM生成表达风格，解析后count=100，存储到expressions.json。
-        """
-        dir_path = os.path.join("data", "expression", "personality")
-        os.makedirs(dir_path, exist_ok=True)
-        file_path = os.path.join(dir_path, "expressions.json")
-
-        # 构建prompt
-        prompt = await global_prompt_manager.format_prompt(
-            "personality_expression_prompt",
-            personality=global_config.personality.expression_style,
-        )
-        # logger.info(f"个性表达方式提取prompt: {prompt}")
-
-        try:
-            response, _ = await self.express_learn_model.generate_response_async(prompt)
-        except Exception as e:
-            logger.error(f"个性表达方式提取失败: {e}")
-            return
-
-        logger.info(f"个性表达方式提取response: {response}")
-        # chat_id用personality
-        expressions = self.parse_expression_response(response, "personality")
-        # 转为dict并count=100
-        result = []
-        for _, situation, style in expressions:
-            result.append({"situation": situation, "style": style, "count": 100})
-        # 超过50条时随机删除多余的，只保留50条
-        if len(result) > 50:
-            remove_count = len(result) - 50
-            remove_indices = set(random.sample(range(len(result)), remove_count))
-            result = [item for idx, item in enumerate(result) if idx not in remove_indices]
-        with open(file_path, "w", encoding="utf-8") as f:
-            json.dump(result, f, ensure_ascii=False, indent=2)
-        logger.info(f"已写入{len(result)}条表达到{file_path}")
-
 
 init_prompt()
 

src/chat/focus_chat/heartFC_chat.py

@@ -3,7 +3,7 @@ import contextlib
 import time
 import traceback
 from collections import deque
-from typing import List, Optional, Dict, Any, Deque, Callable, Coroutine
+from typing import List, Optional, Dict, Any, Deque
 from src.chat.message_receive.chat_stream import ChatStream
 from src.chat.message_receive.chat_stream import chat_manager
 from rich.traceback import install
@@ -25,10 +25,21 @@ from src.chat.focus_chat.info_processors.self_processor import SelfProcessor
 from src.chat.focus_chat.planners.planner import ActionPlanner
 from src.chat.focus_chat.planners.action_manager import ActionManager
 from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
+from src.config.config import global_config
 
 install(extra_lines=3)
 
 
+# 定义处理器映射
+PROCESSOR_CLASSES = {
+    "ChattingInfoProcessor": ChattingInfoProcessor,
+    "MindProcessor": MindProcessor,
+    "ToolProcessor": ToolProcessor,
+    "WorkingMemoryProcessor": WorkingMemoryProcessor,
+    "SelfProcessor": SelfProcessor,
+}
+
+
 WAITING_TIME_THRESHOLD = 300  # 等待新消息时间阈值，单位秒
 
 EMOJI_SEND_PRO = 0.3  # 设置一个概率，比如 30% 才真的发
@@ -89,6 +100,29 @@ class HeartFChatting:
             observe_id=self.stream_id, working_memory=self.working_memory
         )
 
+        # 根据配置文件和默认规则确定启用的处理器
+        PROCESSOR_NAME_TO_CONFIG_KEY_MAP = {
+            "SelfProcessor": "self_identify_processor",
+            "ToolProcessor": "tool_use_processor",
+            "WorkingMemoryProcessor": "working_memory_processor",
+        }
+        self.enabled_processor_names: List[str] = []
+        config_processor_settings = global_config.focus_chat_processor  # 获取处理器配置，若不存在则为空字典
+
+        for proc_name in PROCESSOR_CLASSES.keys():
+            config_key = PROCESSOR_NAME_TO_CONFIG_KEY_MAP.get(proc_name)
+            if config_key:
+                # 此处理器可通过配置控制
+                # getattr(config_processor_settings, config_key, True)
+                # 如果config_processor_settings是字典，则用 config_processor_settings.get(config_key, True)
+                if getattr(config_processor_settings, config_key, True):  # 默认启用，如果配置中未指定
+                    self.enabled_processor_names.append(proc_name)
+            else:
+                # 此处理器不在配置映射中，默认启用
+                self.enabled_processor_names.append(proc_name)
+
+        logger.info(f"{self.log_prefix} 将启用的处理器: {self.enabled_processor_names}")
+
         self.expressor = DefaultExpressor(chat_id=self.stream_id)
         self.action_manager = ActionManager()
         self.action_planner = ActionPlanner(log_prefix=self.log_prefix, action_manager=self.action_manager)
@@ -149,13 +183,37 @@ class HeartFChatting:
         return True
 
     def _register_default_processors(self):
-        """注册默认的信息处理器"""
+        """根据 self.enabled_processor_names 注册信息处理器"""
-        self.processors.append(ChattingInfoProcessor())
+        self.processors = []  # 清空已有的
-        self.processors.append(MindProcessor(subheartflow_id=self.stream_id))
+
-        self.processors.append(ToolProcessor(subheartflow_id=self.stream_id))
+        # self.enabled_processor_names 由 __init__ 保证是一个列表
-        self.processors.append(WorkingMemoryProcessor(subheartflow_id=self.stream_id))
+        for name in self.enabled_processor_names:  # 'name' is "ChattingInfoProcessor", etc.
-        self.processors.append(SelfProcessor(subheartflow_id=self.stream_id))
+            processor_class = PROCESSOR_CLASSES.get(name)
-        logger.info(f"{self.log_prefix} 已注册默认处理器: {[p.__class__.__name__ for p in self.processors]}")
+            if processor_class:
+                # 根据处理器类名判断是否需要 subheartflow_id
+                if name in ["MindProcessor", "ToolProcessor", "WorkingMemoryProcessor", "SelfProcessor"]:
+                    self.processors.append(processor_class(subheartflow_id=self.stream_id))
+                elif name == "ChattingInfoProcessor":
+                    self.processors.append(processor_class())
+                else:
+                    # 对于PROCESSOR_CLASSES中定义但此处未明确处理构造的处理器
+                    try:
+                        self.processors.append(processor_class())  # 尝试无参构造
+                        logger.debug(f"{self.log_prefix} 注册处理器 {name} (尝试无参构造).")
+                    except TypeError:
+                        logger.error(
+                            f"{self.log_prefix} 处理器 {name} 构造失败。它可能需要参数（如 subheartflow_id）但未在注册逻辑中明确处理。"
+                        )
+            else:
+                # 这理论上不应该发生，因为 enabled_processor_names 是从 PROCESSOR_CLASSES 的键生成的
+                logger.warning(f"{self.log_prefix} 在 PROCESSOR_CLASSES 中未找到名为 '{name}' 的处理器，将跳过注册。")
+
+        if self.processors:
+            logger.info(
+                f"{self.log_prefix} 已根据配置和默认规则注册处理器: {[p.__class__.__name__ for p in self.processors]}"
+            )
+        else:
+            logger.warning(f"{self.log_prefix} 没有注册任何处理器。这可能是由于配置错误或所有处理器都被禁用了。")
 
     async def start(self):
         """
@@ -258,6 +316,8 @@ class HeartFChatting:
                     + (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "")
                 )
 
+                await asyncio.sleep(global_config.focus_chat.think_interval)
+
         except asyncio.CancelledError:
             # 设置了关闭标志位后被取消是正常流程
             if not self._shutting_down:

src/chat/focus_chat/heartflow_message_revceiver.py

@@ -5,7 +5,6 @@ from ...config.config import global_config
 from ..message_receive.message import MessageRecv
 from ..message_receive.storage import MessageStorage
 from ..utils.utils import is_mentioned_bot_in_message
-from maim_message import Seg
 from src.chat.heart_flow.heartflow import heartflow
 from src.common.logger_manager import get_logger
 from ..message_receive.chat_stream import chat_manager
@@ -79,26 +78,26 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool]:
     return interested_rate, is_mentioned
 
 
-def _get_message_type(message: MessageRecv) -> str:
+# def _get_message_type(message: MessageRecv) -> str:
-    """获取消息类型
+#     """获取消息类型
 
-    Args:
+#     Args:
-        message: 消息对象
+#         message: 消息对象
 
-    Returns:
+#     Returns:
-        str: 消息类型
+#         str: 消息类型
-    """
+#     """
-    if message.message_segment.type != "seglist":
+#     if message.message_segment.type != "seglist":
-        return message.message_segment.type
+#         return message.message_segment.type
 
-    if (
+#     if (
-        isinstance(message.message_segment.data, list)
+#         isinstance(message.message_segment.data, list)
-        and all(isinstance(x, Seg) for x in message.message_segment.data)
+#         and all(isinstance(x, Seg) for x in message.message_segment.data)
-        and len(message.message_segment.data) == 1
+#         and len(message.message_segment.data) == 1
-    ):
+#     ):
-        return message.message_segment.data[0].type
+#         return message.message_segment.data[0].type
 
-    return "seglist"
+#     return "seglist"
 
 
 def _check_ban_words(text: str, chat, userinfo) -> bool:
@@ -112,7 +111,7 @@ def _check_ban_words(text: str, chat, userinfo) -> bool:
     Returns:
         bool: 是否包含过滤词
     """
-    for word in global_config.chat.ban_words:
+    for word in global_config.message_receive.ban_words:
         if word in text:
             chat_name = chat.group_info.group_name if chat.group_info else "私聊"
             logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}")
@@ -132,7 +131,7 @@ def _check_ban_regex(text: str, chat, userinfo) -> bool:
     Returns:
         bool: 是否匹配过滤正则
     """
-    for pattern in global_config.chat.ban_msgs_regex:
+    for pattern in global_config.message_receive.ban_msgs_regex:
         if pattern.search(text):
             chat_name = chat.group_info.group_name if chat.group_info else "私聊"
             logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}")
@@ -141,7 +140,7 @@ def _check_ban_regex(text: str, chat, userinfo) -> bool:
     return False
 
 
-class HeartFCProcessor:
+class HeartFCMessageReceiver:
     """心流处理器，负责处理接收到的消息并计算兴趣度"""
 
     def __init__(self):

src/chat/focus_chat/heartflow_prompt_builder.py

@@ -1,20 +1,16 @@
 from src.config.config import global_config
 from src.common.logger_manager import get_logger
-from src.individuality.individuality import Individuality
+from src.individuality.individuality import individuality
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
 from src.chat.person_info.relationship_manager import relationship_manager
-from src.chat.utils.utils import get_embedding
 import time
-from typing import Union, Optional
+from typing import Optional
 from src.chat.utils.utils import get_recent_group_speaker
 from src.manager.mood_manager import mood_manager
 from src.chat.memory_system.Hippocampus import HippocampusManager
 from src.chat.knowledge.knowledge_lib import qa_manager
 import random
-import json
-import math
-from src.common.database.database_model import Knowledges
 
 
 logger = get_logger("prompt")
@@ -103,7 +99,6 @@ class PromptBuilder:
         return None
 
     async def _build_prompt_normal(self, chat_stream, message_txt: str, sender_name: str = "某人") -> str:
-        individuality = Individuality.get_instance()
         prompt_personality = individuality.get_prompt(x_person=2, level=2)
         is_group_chat = bool(chat_stream.group_info)
 
@@ -112,7 +107,7 @@ class PromptBuilder:
             who_chat_in_group = get_recent_group_speaker(
                 chat_stream.stream_id,
                 (chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None,
-                limit=global_config.chat.observation_context_size,
+                limit=global_config.focus_chat.observation_context_size,
             )
         elif chat_stream.user_info:
             who_chat_in_group.append(
@@ -160,7 +155,7 @@ class PromptBuilder:
         message_list_before_now = get_raw_msg_before_timestamp_with_chat(
             chat_id=chat_stream.stream_id,
             timestamp=time.time(),
-            limit=global_config.chat.observation_context_size,
+            limit=global_config.focus_chat.observation_context_size,
         )
         chat_talking_prompt = await build_readable_messages(
             message_list_before_now,
@@ -264,129 +259,6 @@ class PromptBuilder:
 
         return prompt
 
-    async def get_prompt_info_old(self, message: str, threshold: float):
-        start_time = time.time()
-        related_info = ""
-        logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
-        # 1. 先从LLM获取主题，类似于记忆系统的做法
-        topics = []
-
-        # 如果无法提取到主题，直接使用整个消息
-        if not topics:
-            logger.info("未能提取到任何主题，使用整个消息进行查询")
-            embedding = await get_embedding(message, request_type="prompt_build")
-            if not embedding:
-                logger.error("获取消息嵌入向量失败")
-                return ""
-
-            related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
-            logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
-            return related_info
-
-        # 2. 对每个主题进行知识库查询
-        logger.info(f"开始处理{len(topics)}个主题的知识库查询")
-
-        # 优化：批量获取嵌入向量，减少API调用
-        embeddings = {}
-        topics_batch = [topic for topic in topics if len(topic) > 0]
-        if message:  # 确保消息非空
-            topics_batch.append(message)
-
-        # 批量获取嵌入向量
-        embed_start_time = time.time()
-        for text in topics_batch:
-            if not text or len(text.strip()) == 0:
-                continue
-
-            try:
-                embedding = await get_embedding(text, request_type="prompt_build")
-                if embedding:
-                    embeddings[text] = embedding
-                else:
-                    logger.warning(f"获取'{text}'的嵌入向量失败")
-            except Exception as e:
-                logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
-
-        logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
-
-        if not embeddings:
-            logger.error("所有嵌入向量获取失败")
-            return ""
-
-        # 3. 对每个主题进行知识库查询
-        all_results = []
-        query_start_time = time.time()
-
-        # 首先添加原始消息的查询结果
-        if message in embeddings:
-            original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
-            if original_results:
-                for result in original_results:
-                    result["topic"] = "原始消息"
-                all_results.extend(original_results)
-                logger.info(f"原始消息查询到{len(original_results)}条结果")
-
-        # 然后添加每个主题的查询结果
-        for topic in topics:
-            if not topic or topic not in embeddings:
-                continue
-
-            try:
-                topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
-                if topic_results:
-                    # 添加主题标记
-                    for result in topic_results:
-                        result["topic"] = topic
-                    all_results.extend(topic_results)
-                    logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
-            except Exception as e:
-                logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
-
-        logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
-
-        # 4. 去重和过滤
-        process_start_time = time.time()
-        unique_contents = set()
-        filtered_results = []
-        for result in all_results:
-            content = result["content"]
-            if content not in unique_contents:
-                unique_contents.add(content)
-                filtered_results.append(result)
-
-        # 5. 按相似度排序
-        filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
-
-        # 6. 限制总数量（最多10条）
-        filtered_results = filtered_results[:10]
-        logger.info(
-            f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果"
-        )
-
-        # 7. 格式化输出
-        if filtered_results:
-            format_start_time = time.time()
-            grouped_results = {}
-            for result in filtered_results:
-                topic = result["topic"]
-                if topic not in grouped_results:
-                    grouped_results[topic] = []
-                grouped_results[topic].append(result)
-
-            # 按主题组织输出
-            for topic, results in grouped_results.items():
-                related_info += f"【主题: {topic}】\n"
-                for _i, result in enumerate(results, 1):
-                    _similarity = result["similarity"]
-                    content = result["content"].strip()
-                    related_info += f"{content}\n"
-                related_info += "\n"
-
-            logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
-
-        logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
-        return related_info
-
     async def get_prompt_info(self, message: str, threshold: float):
         related_info = ""
         start_time = time.time()
@@ -406,93 +278,11 @@ class PromptBuilder:
                 logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
                 return related_info
             else:
-                logger.debug("从LPMM知识库获取知识失败，使用旧版数据库进行检索")
+                logger.debug("从LPMM知识库获取知识失败，可能是从未导入过知识，返回空知识...")
-                knowledge_from_old = await self.get_prompt_info_old(message, threshold=threshold)
+                return "未检索到知识"
-                related_info += knowledge_from_old
-                logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
-                return related_info
         except Exception as e:
             logger.error(f"获取知识库内容时发生异常: {str(e)}")
-            try:
+            return "未检索到知识"
-                knowledge_from_old = await self.get_prompt_info_old(message, threshold=threshold)
-                related_info += knowledge_from_old
-                logger.debug(
-                    f"异常后使用旧版数据库获取知识，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}"
-                )
-                return related_info
-            except Exception as e2:
-                logger.error(f"使用旧版数据库获取知识时也发生异常: {str(e2)}")
-                return ""
-
-    @staticmethod
-    def get_info_from_db(
-        query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
-    ) -> Union[str, list]:
-        if not query_embedding:
-            return "" if not return_raw else []
-
-        results_with_similarity = []
-        try:
-            # Fetch all knowledge entries
-            # This might be inefficient for very large databases.
-            # Consider strategies like FAISS or other vector search libraries if performance becomes an issue.
-            all_knowledges = Knowledges.select()
-
-            if not all_knowledges:
-                return [] if return_raw else ""
-
-            query_embedding_magnitude = math.sqrt(sum(x * x for x in query_embedding))
-            if query_embedding_magnitude == 0:  # Avoid division by zero
-                return "" if not return_raw else []
-
-            for knowledge_item in all_knowledges:
-                try:
-                    db_embedding_str = knowledge_item.embedding
-                    db_embedding = json.loads(db_embedding_str)
-
-                    if len(db_embedding) != len(query_embedding):
-                        logger.warning(
-                            f"Embedding length mismatch for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}. Skipping."
-                        )
-                        continue
-
-                    # Calculate Cosine Similarity
-                    dot_product = sum(q * d for q, d in zip(query_embedding, db_embedding))
-                    db_embedding_magnitude = math.sqrt(sum(x * x for x in db_embedding))
-
-                    if db_embedding_magnitude == 0:  # Avoid division by zero
-                        similarity = 0.0
-                    else:
-                        similarity = dot_product / (query_embedding_magnitude * db_embedding_magnitude)
-
-                    if similarity >= threshold:
-                        results_with_similarity.append({"content": knowledge_item.content, "similarity": similarity})
-                except json.JSONDecodeError:
-                    logger.error(
-                        f"Failed to parse embedding for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}"
-                    )
-                except Exception as e:
-                    logger.error(f"Error processing knowledge item: {e}")
-
-            # Sort by similarity in descending order
-            results_with_similarity.sort(key=lambda x: x["similarity"], reverse=True)
-
-            # Limit results
-            limited_results = results_with_similarity[:limit]
-
-            logger.debug(f"知识库查询结果数量 (after Peewee processing): {len(limited_results)}")
-
-            if not limited_results:
-                return "" if not return_raw else []
-
-            if return_raw:
-                return limited_results
-            else:
-                return "\n".join(str(result["content"]) for result in limited_results)
-
-        except Exception as e:
-            logger.error(f"Error querying Knowledges with Peewee: {e}")
-            return "" if not return_raw else []
 
 
 init_prompt()

src/chat/focus_chat/info/action_info.py

@@ -80,4 +80,4 @@ class ActionInfo(InfoBase):
         Returns:
             bool: 如果有任何动作需要添加或移除则返回True
         """
-        return bool(self.get_add_actions() or self.get_remove_actions()) 
+        return bool(self.get_add_actions() or self.get_remove_actions())

src/chat/focus_chat/info_processors/action_processor.py

@@ -1,14 +1,10 @@
 from typing import List, Optional, Any
-from src.chat.focus_chat.info.obs_info import ObsInfo
 from src.chat.heart_flow.observation.observation import Observation
 from src.chat.focus_chat.info.info_base import InfoBase
 from src.chat.focus_chat.info.action_info import ActionInfo
 from .base_processor import BaseProcessor
 from src.common.logger_manager import get_logger
-from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
 from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
-from src.chat.focus_chat.info.cycle_info import CycleInfo
-from datetime import datetime
 from typing import Dict
 from src.chat.models.utils_model import LLMRequest
 from src.config.config import global_config
@@ -55,10 +51,7 @@ class ActionProcessor(BaseProcessor):
         # 处理Observation对象
         if observations:
             for obs in observations:
-
                 if isinstance(obs, HFCloopObservation):
-
-
                     # 创建动作信息
                     action_info = ActionInfo()
                     action_changes = await self.analyze_loop_actions(obs)
@@ -75,7 +68,6 @@ class ActionProcessor(BaseProcessor):
 
         return processed_infos
 
-
     async def analyze_loop_actions(self, obs: HFCloopObservation) -> Dict[str, List[str]]:
         """分析最近的循环内容并决定动作的增减
 
@@ -87,29 +79,29 @@ class ActionProcessor(BaseProcessor):
                 }
         """
         result = {"add": [], "remove": []}
-        
+
         # 获取最近10次循环
         recent_cycles = obs.history_loop[-10:] if len(obs.history_loop) > 10 else obs.history_loop
         if not recent_cycles:
             return result
-            
+
         # 统计no_reply的数量
         no_reply_count = 0
         reply_sequence = []  # 记录最近的动作序列
-        
+
         for cycle in recent_cycles:
             action_type = cycle.loop_plan_info["action_result"]["action_type"]
             if action_type == "no_reply":
                 no_reply_count += 1
             reply_sequence.append(action_type == "reply")
-            
+
         # 检查no_reply比例
         if len(recent_cycles) >= 5 and (no_reply_count / len(recent_cycles)) >= 0.8:
             result["add"].append("exit_focus_chat")
-            
+
         # 获取最近三次的reply状态
         last_three = reply_sequence[-3:] if len(reply_sequence) >= 3 else reply_sequence
-        
+
         # 根据最近的reply情况决定是否移除reply动作
         if len(last_three) >= 3 and all(last_three):
             # 如果最近三次都是reply，直接移除
@@ -122,5 +114,5 @@ class ActionProcessor(BaseProcessor):
             # 如果最近一次是reply，20%概率移除
             if random.random() < 0.2:
                 result["remove"].append("reply")
-                
+
         return result

src/chat/focus_chat/info_processors/mind_processor.py

@@ -5,7 +5,7 @@ from src.config.config import global_config
 import time
 import traceback
 from src.common.logger_manager import get_logger
-from src.individuality.individuality import Individuality
+from src.individuality.individuality import individuality
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.utils.json_utils import safe_json_dumps
 from src.chat.message_receive.chat_stream import chat_manager
@@ -194,7 +194,6 @@ class MindProcessor(BaseProcessor):
 
         # ---------- 3. 准备个性化数据 ----------
         # 获取个性化信息
-        individuality = Individuality.get_instance()
 
         relation_prompt = ""
         for person in person_list:

src/chat/focus_chat/info_processors/self_processor.py

@@ -5,7 +5,7 @@ from src.config.config import global_config
 import time
 import traceback
 from src.common.logger_manager import get_logger
-from src.individuality.individuality import Individuality
+from src.individuality.individuality import individuality
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.message_receive.chat_stream import chat_manager
 from src.chat.person_info.relationship_manager import relationship_manager
@@ -21,8 +21,9 @@ logger = get_logger("processor")
 
 def init_prompt():
     indentify_prompt = """
-你的名字是{bot_name},你是一个AI，但是你伪装成了一个人类，你的人格是，{prompt_personality}。
+{name_block}
-你的头像形象是一只橙色的鱼，头上有绿色的树叶。
+你是一个AI，但是你伪装成了一个人类，你的人格是，{prompt_personality}。
+{indentify_block}
 
 {relation_prompt}
 {memory_str}
@@ -36,8 +37,8 @@ def init_prompt():
 3. 你的自我认同是否有助于你的回答，如果你需要自我相关的信息来帮你参与聊天，请输出，否则请输出十个字以内的简短自我认同
 4. 一般情况下不用输出自我认同，只需要输出十几个字的简短自我认同就好，除非有明显需要自我认同的场景
 
-请回复的平淡一些，简短一些，说中文，不要浮夸，平淡一些。
+请思考的平淡一些，简短一些，说中文，不要浮夸，平淡一些。
-请注意不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出内容。
+请注意不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出自我认同内容。
 
 """
     Prompt(indentify_prompt, "indentify_prompt")
@@ -125,16 +126,22 @@ class SelfProcessor(BaseProcessor):
                 # hfcloop_observe_info = observation.get_observe_info()
                 pass
 
-        individuality = Individuality.get_instance()
+        nickname_str = ""
-        personality_block = individuality.get_prompt(x_person=2, level=2)
+        for nicknames in global_config.bot.alias_names:
+            nickname_str += f"{nicknames},"
+        name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。"
+
+        personality_block = individuality.get_personality_prompt(x_person=2, level=2)
+        identity_block = individuality.get_identity_prompt(x_person=2, level=2)
 
         relation_prompt = ""
         for person in person_list:
             relation_prompt += await relationship_manager.build_relationship_info(person, is_id=True)
 
         prompt = (await global_prompt_manager.get_prompt_async("indentify_prompt")).format(
-            bot_name=individuality.name,
+            name_block=name_block,
             prompt_personality=personality_block,
+            indentify_block=identity_block,
             memory_str=memory_str,
             relation_prompt=relation_prompt,
             time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),

src/chat/focus_chat/info_processors/tool_processor.py

@@ -3,7 +3,7 @@ from src.chat.models.utils_model import LLMRequest
 from src.config.config import global_config
 import time
 from src.common.logger_manager import get_logger
-from src.individuality.individuality import Individuality
+from src.individuality.individuality import individuality
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.tools.tool_use import ToolUser
 from src.chat.utils.json_utils import process_llm_tool_calls
@@ -133,7 +133,7 @@ class ToolProcessor(BaseProcessor):
             relation_prompt += await relationship_manager.build_relationship_info(person, is_id=True)
 
         # 获取个性信息
-        individuality = Individuality.get_instance()
+
         # prompt_personality = individuality.get_prompt(x_person=2, level=2)
 
         # 获取时间信息

src/chat/focus_chat/planners/action_manager.py

@@ -1,9 +1,8 @@
-from typing import Dict, List, Optional, Callable, Coroutine, Type, Any
+from typing import Dict, List, Optional, Type, Any
 from src.chat.focus_chat.planners.actions.base_action import BaseAction, _ACTION_REGISTRY
 from src.chat.heart_flow.observation.observation import Observation
 from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor
 from src.chat.message_receive.chat_stream import ChatStream
-from src.chat.focus_chat.heartFC_Cycleinfo import CycleDetail
 from src.common.logger_manager import get_logger
 import importlib
 import pkgutil

src/chat/focus_chat/planners/actions/exit_focus_chat_action.py

@@ -1,12 +1,9 @@
 import asyncio
 import traceback
 from src.common.logger_manager import get_logger
-from src.chat.utils.timer_calculator import Timer
 from src.chat.focus_chat.planners.actions.base_action import BaseAction, register_action
-from typing import Tuple, List, Callable, Coroutine
+from typing import Tuple, List
 from src.chat.heart_flow.observation.observation import Observation
-from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
-from src.chat.heart_flow.sub_heartflow import SubHeartFlow
 from src.chat.message_receive.chat_stream import ChatStream
 from src.chat.heart_flow.heartflow import heartflow
 from src.chat.heart_flow.sub_heartflow import ChatState
@@ -61,8 +58,6 @@ class ExitFocusChatAction(BaseAction):
         self._shutting_down = shutting_down
         self.chat_id = chat_stream.stream_id
 
-        
-
     async def handle_action(self) -> Tuple[bool, str]:
         """
         处理退出专注聊天的情况
@@ -95,7 +90,6 @@ class ExitFocusChatAction(BaseAction):
                 logger.warning(f"{self.log_prefix} {warning_msg}")
                 return False, warning_msg
 
-
             return True, status_message
 
         except asyncio.CancelledError:
@@ -105,4 +99,4 @@ class ExitFocusChatAction(BaseAction):
             error_msg = f"处理 'exit_focus_chat' 时发生错误: {str(e)}"
             logger.error(f"{self.log_prefix} {error_msg}")
             logger.error(traceback.format_exc())
-            return False, error_msg
+            return False, error_msg

src/chat/focus_chat/planners/actions/no_reply_action.py

@@ -3,7 +3,7 @@ import traceback
 from src.common.logger_manager import get_logger
 from src.chat.utils.timer_calculator import Timer
 from src.chat.focus_chat.planners.actions.base_action import BaseAction, register_action
-from typing import Tuple, List, Callable, Coroutine
+from typing import Tuple, List
 from src.chat.heart_flow.observation.observation import Observation
 from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
 from src.chat.focus_chat.hfc_utils import parse_thinking_id_to_timestamp

src/chat/focus_chat/planners/planner.py

@@ -12,7 +12,7 @@ from src.chat.focus_chat.info.action_info import ActionInfo
 from src.chat.focus_chat.info.structured_info import StructuredInfo
 from src.common.logger_manager import get_logger
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
-from src.individuality.individuality import Individuality
+from src.individuality.individuality import individuality
 from src.chat.focus_chat.planners.action_manager import ActionManager
 
 logger = get_logger("planner")
@@ -92,37 +92,37 @@ class ActionPlanner:
         try:
             # 获取观察信息
             extra_info: list[str] = []
-            
+
             # 首先处理动作变更
             for info in all_plan_info:
                 if isinstance(info, ActionInfo) and info.has_changes():
                     add_actions = info.get_add_actions()
                     remove_actions = info.get_remove_actions()
                     reason = info.get_reason()
-                    
+
                     # 处理动作的增加
                     for action_name in add_actions:
                         if action_name in self.action_manager.get_registered_actions():
                             self.action_manager.add_action_to_using(action_name)
                             logger.debug(f"{self.log_prefix}添加动作: {action_name}, 原因: {reason}")
-                            
+
                     # 处理动作的移除
                     for action_name in remove_actions:
                         self.action_manager.remove_action_from_using(action_name)
                         logger.debug(f"{self.log_prefix}移除动作: {action_name}, 原因: {reason}")
-                        
+
                     # 如果当前选择的动作被移除了，更新为no_reply
                     if action in remove_actions:
                         action = "no_reply"
                         reasoning = f"之前选择的动作{action}已被移除，原因: {reason}"
-            
+
             # 继续处理其他信息
             for info in all_plan_info:
                 if isinstance(info, ObsInfo):
                     observed_messages = info.get_talking_message()
                     observed_messages_str = info.get_talking_message_str_truncate()
                     chat_type = info.get_chat_type()
-                    is_group_chat = (chat_type == "group")
+                    is_group_chat = chat_type == "group"
                 elif isinstance(info, MindInfo):
                     current_mind = info.get_current_mind()
                 elif isinstance(info, CycleInfo):
@@ -134,20 +134,16 @@ class ActionPlanner:
 
             # 获取当前可用的动作
             current_available_actions = self.action_manager.get_using_actions()
-            
+
             # 如果没有可用动作，直接返回no_reply
             if not current_available_actions:
                 logger.warning(f"{self.log_prefix}没有可用的动作，将使用no_reply")
                 action = "no_reply"
                 reasoning = "没有可用的动作"
                 return {
-                    "action_result": {
+                    "action_result": {"action_type": action, "action_data": action_data, "reasoning": reasoning},
-                        "action_type": action,
-                        "action_data": action_data,
-                        "reasoning": reasoning
-                    },
                     "current_mind": current_mind,
-                    "observed_messages": observed_messages
+                    "observed_messages": observed_messages,
                 }
 
             # --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
@@ -271,7 +267,6 @@ class ActionPlanner:
             else:
                 mind_info_block = "你刚参与聊天"
 
-            individuality = Individuality.get_instance()
             personality_block = individuality.get_prompt(x_person=2, level=2)
 
             action_options_block = ""

src/chat/heart_flow/background_tasks.py

@@ -4,7 +4,7 @@ from typing import Optional, Coroutine, Callable, Any, List
 from src.common.logger_manager import get_logger
 from src.chat.heart_flow.mai_state_manager import MaiStateManager, MaiStateInfo
 from src.chat.heart_flow.subheartflow_manager import SubHeartflowManager
-
+from src.config.config import global_config
 
 logger = get_logger("background_tasks")
 
@@ -94,13 +94,6 @@ class BackgroundTaskManager:
                 f"清理任务已启动 间隔:{CLEANUP_INTERVAL_SECONDS}s",
                 "_cleanup_task",
             ),
-            # 新增兴趣评估任务配置
-            (
-                self._run_into_focus_cycle,
-                "debug",  # 设为debug，避免过多日志
-                f"专注评估任务已启动 间隔:{INTEREST_EVAL_INTERVAL_SECONDS}s",
-                "_into_focus_task",
-            ),
             # 新增私聊激活任务配置
             (
                 # Use lambda to pass the interval to the runner function
@@ -111,6 +104,19 @@ class BackgroundTaskManager:
             ),
         ]
 
+        # 根据 chat_mode 条件添加专注评估任务
+        if not (global_config.chat.chat_mode == "normal"):
+            task_configs.append(
+                (
+                    self._run_into_focus_cycle,
+                    "debug",  # 设为debug，避免过多日志
+                    f"专注评估任务已启动 间隔:{INTEREST_EVAL_INTERVAL_SECONDS}s",
+                    "_into_focus_task",
+                )
+            )
+        else:
+            logger.info("聊天模式为 normal，跳过启动专注评估任务")
+
         # 统一启动所有任务
         for task_func, log_level, log_msg, task_attr_name in task_configs:
             # 检查任务变量是否存在且未完成
@@ -183,7 +189,6 @@ class BackgroundTaskManager:
                 logger.info("检测到离线，停用所有子心流")
                 await self.subheartflow_manager.deactivate_all_subflows()
 
-
     async def _perform_cleanup_work(self):
         """执行子心流清理任务
         1. 获取需要清理的不活跃子心流列表
@@ -209,18 +214,15 @@ class BackgroundTaskManager:
         # 记录最终清理结果
         logger.info(f"[清理任务] 清理完成, 共停止 {stopped_count}/{len(flows_to_stop)} 个子心流")
 
-
     # --- 新增兴趣评估工作函数 ---
     async def _perform_into_focus_work(self):
         """执行一轮子心流兴趣评估与提升检查。"""
         # 直接调用 subheartflow_manager 的方法，并传递当前状态信息
-        await self.subheartflow_manager.sbhf_absent_into_focus()
+        await self.subheartflow_manager.sbhf_normal_into_focus()
-        
+
     async def _run_state_update_cycle(self, interval: int):
         await _run_periodic_loop(task_name="State Update", interval=interval, task_func=self._perform_state_update_work)
 
-
-
     async def _run_cleanup_cycle(self):
         await _run_periodic_loop(
             task_name="Subflow Cleanup", interval=CLEANUP_INTERVAL_SECONDS, task_func=self._perform_cleanup_work

src/chat/heart_flow/chat_state_info.py

@@ -4,13 +4,13 @@ import enum
 
 class ChatState(enum.Enum):
     ABSENT = "没在看群"
-    CHAT = "随便水群"
+    NORMAL = "随便水群"
     FOCUSED = "认真水群"
 
 
 class ChatStateInfo:
     def __init__(self):
-        self.chat_status: ChatState = ChatState.CHAT
+        self.chat_status: ChatState = ChatState.NORMAL
         self.current_state_time = 120
 
         self.mood_manager = mood_manager

src/chat/heart_flow/heartflow.py

@@ -1,9 +1,6 @@
 from src.chat.heart_flow.sub_heartflow import SubHeartflow, ChatState
-from src.chat.models.utils_model import LLMRequest
-from src.config.config import global_config
 from src.common.logger_manager import get_logger
 from typing import Any, Optional
-from src.tools.tool_use import ToolUser
 from src.chat.heart_flow.mai_state_manager import MaiStateInfo, MaiStateManager
 from src.chat.heart_flow.subheartflow_manager import SubHeartflowManager
 from src.chat.heart_flow.background_tasks import BackgroundTaskManager  # Import BackgroundTaskManager

src/chat/heart_flow/mai_state_manager.py

@@ -4,21 +4,10 @@ import random
 from typing import List, Tuple, Optional
 from src.common.logger_manager import get_logger
 from src.manager.mood_manager import mood_manager
-from src.config.config import global_config
 
 logger = get_logger("mai_state")
 
 
-# -- 状态相关的可配置参数 (可以从 glocal_config 加载) --
-# The line `enable_unlimited_hfc_chat = False` is setting a configuration parameter that controls
-# whether a specific debugging feature is enabled or not. When `enable_unlimited_hfc_chat` is set to
-# `False`, it means that the debugging feature for unlimited focused chatting is disabled.
-# enable_unlimited_hfc_chat = True  # 调试用：无限专注聊天
-enable_unlimited_hfc_chat = False
-prevent_offline_state = True
-# 目前默认不启用OFFLINE状
-
-
 class MaiState(enum.Enum):
     """
     聊天状态:
@@ -97,7 +86,6 @@ class MaiStateManager:
         current_time = time.time()
         current_status = current_state_info.mai_status
         time_in_current_status = current_time - current_state_info.last_status_change_time
-        _time_since_last_min_check = current_time - current_state_info.last_min_check_time
         next_state: Optional[MaiState] = None
 
         def _resolve_offline(candidate_state: MaiState) -> MaiState:
@@ -141,10 +129,6 @@ class MaiStateManager:
                 )
                 next_state = resolved_candidate
 
-            if enable_unlimited_hfc_chat:
-                logger.debug("调试用：开挂了，强制切换到专注聊天")
-                next_state = MaiState.FOCUSED_CHAT
-
         if next_state is not None and next_state != current_status:
             return next_state
         else:

src/chat/heart_flow/observation/chatting_observation.py

@@ -57,7 +57,7 @@ class ChattingObservation(Observation):
         self.talking_message_str_truncate = ""
         self.name = global_config.bot.nickname
         self.nick_name = global_config.bot.alias_names
-        self.max_now_obs_len = global_config.chat.observation_context_size
+        self.max_now_obs_len = global_config.focus_chat.observation_context_size
         self.overlap_len = global_config.focus_chat.compressed_length
         self.mid_memories = []
         self.max_mid_memory_len = global_config.focus_chat.compress_length_limit

src/chat/heart_flow/observation/hfcloop_observation.py

@@ -18,7 +18,7 @@ class HFCloopObservation:
         self.last_observe_time = datetime.now().timestamp()  # 初始化为当前时间
         self.history_loop: List[CycleDetail] = []
         self.action_manager: ActionManager = None
-        
+
         self.all_actions = {}
 
     def get_observe_info(self):

src/chat/heart_flow/sub_heartflow.py

@@ -2,7 +2,7 @@ from .observation.observation import Observation
 from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
 import asyncio
 import time
-from typing import Optional, List, Dict, Tuple, Callable, Coroutine
+from typing import Optional, List, Dict, Tuple
 import traceback
 from src.common.logger_manager import get_logger
 from src.chat.message_receive.message import MessageRecv
@@ -13,6 +13,7 @@ from src.chat.heart_flow.mai_state_manager import MaiStateInfo
 from src.chat.heart_flow.chat_state_info import ChatState, ChatStateInfo
 from .utils_chat import get_chat_type_and_target_info
 from .interest_chatting import InterestChatting
+from src.config.config import global_config
 
 
 logger = get_logger("sub_heartflow")
@@ -23,7 +24,6 @@ class SubHeartflow:
         self,
         subheartflow_id,
         mai_states: MaiStateInfo,
-        hfc_no_reply_callback: Callable[[], Coroutine[None, None, None]],
     ):
         """子心流初始化函数
 
@@ -35,7 +35,6 @@ class SubHeartflow:
         # 基础属性，两个值是一样的
         self.subheartflow_id = subheartflow_id
         self.chat_id = subheartflow_id
-        self.hfc_no_reply_callback = hfc_no_reply_callback
 
         # 麦麦的状态
         self.mai_states = mai_states
@@ -89,13 +88,13 @@ class SubHeartflow:
         await self.interest_chatting.initialize()
         logger.debug(f"{self.log_prefix} InterestChatting 实例已初始化。")
 
-        # 创建并初始化 normal_chat_instance
+        # 根据配置决定初始状态
-        chat_stream = chat_manager.get_stream(self.chat_id)
+        if global_config.chat.chat_mode == "focus":
-        if chat_stream:
+            logger.info(f"{self.log_prefix} 配置为 focus 模式，将直接尝试进入 FOCUSED 状态。")
-            self.normal_chat_instance = NormalChat(chat_stream=chat_stream,interest_dict=self.get_interest_dict())
+            await self.change_chat_state(ChatState.FOCUSED)
-            await self.normal_chat_instance.initialize()
+        else:  # "auto" 或其他模式保持原有逻辑或默认为 NORMAL
-            await self.normal_chat_instance.start_chat()
+            logger.info(f"{self.log_prefix} 配置为 auto 或其他模式，将尝试进入 NORMAL 状态。")
-            logger.info(f"{self.log_prefix} NormalChat 实例已创建并启动。")
+            await self.change_chat_state(ChatState.NORMAL)
 
     def update_last_chat_state_time(self):
         self.chat_state_last_time = time.time() - self.chat_state_changed_time
@@ -128,10 +127,9 @@ class SubHeartflow:
             if not chat_stream:
                 logger.error(f"{log_prefix} 无法获取 chat_stream，无法启动 NormalChat。")
                 return False
-            if rewind:
+            # 在 rewind 为 True 或 NormalChat 实例尚未创建时，创建新实例
+            if rewind or not self.normal_chat_instance:
                 self.normal_chat_instance = NormalChat(chat_stream=chat_stream, interest_dict=self.get_interest_dict())
-            else:
-                self.normal_chat_instance = NormalChat(chat_stream=chat_stream)
 
             # 进行异步初始化
             await self.normal_chat_instance.initialize()
@@ -187,9 +185,10 @@ class SubHeartflow:
         logger.info(f"{log_prefix} 麦麦准备开始专注聊天...")
         try:
             # 创建 HeartFChatting 实例，并传递 从构造函数传入的 回调函数
+
             self.heart_fc_instance = HeartFChatting(
                 chat_id=self.subheartflow_id,
-                observations=self.observations, 
+                observations=self.observations,
             )
 
             # 初始化并启动 HeartFChatting
@@ -216,7 +215,7 @@ class SubHeartflow:
         state_changed = False
         log_prefix = f"[{self.log_prefix}]"
 
-        if new_state == ChatState.CHAT:
+        if new_state == ChatState.NORMAL:
             logger.debug(f"{log_prefix} 准备进入或保持 普通聊天 状态")
             if await self._start_normal_chat():
                 logger.debug(f"{log_prefix} 成功进入或保持 NormalChat 状态。")
@@ -260,20 +259,6 @@ class SubHeartflow:
                 f"{log_prefix} 尝试将状态从 {current_state.value} 变为 {new_state.value}，但未成功或未执行更改。"
             )
 
-    async def subheartflow_start_working(self):
-        """启动子心流的后台任务
-
-        功能说明:
-        - 负责子心流的主要后台循环
-        - 每30秒检查一次停止标志
-        """
-        logger.trace(f"{self.log_prefix} 子心流开始工作...")
-
-        while not self.should_stop:
-            await asyncio.sleep(30)  # 30秒检查一次停止标志
-
-        logger.info(f"{self.log_prefix} 子心流后台任务已停止。")
-
     def add_observation(self, observation: Observation):
         for existing_obs in self.observations:
             if existing_obs.observe_id == observation.observe_id:

src/chat/heart_flow/subheartflow_manager.py

@@ -2,13 +2,11 @@ import asyncio
 import time
 import random
 from typing import Dict, Any, Optional, List
-import functools
 from src.common.logger_manager import get_logger
 from src.chat.message_receive.chat_stream import chat_manager
 from src.chat.heart_flow.sub_heartflow import SubHeartflow, ChatState
 from src.chat.heart_flow.mai_state_manager import MaiStateInfo
 from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
-from src.config.config import global_config
 
 
 # 初始化日志记录器
@@ -62,7 +60,6 @@ class SubHeartflowManager:
         self._lock = asyncio.Lock()  # 用于保护 self.subheartflows 的访问
         self.mai_state_info: MaiStateInfo = mai_state_info  # 存储传入的 MaiStateInfo 实例
 
-
     async def force_change_state(self, subflow_id: Any, target_state: ChatState) -> bool:
         """强制改变指定子心流的状态"""
         async with self._lock:
@@ -101,35 +98,25 @@ class SubHeartflowManager:
                 return subflow
 
             try:
-                # --- 使用 functools.partial 创建 HFC 回调 --- #
+                # 初始化子心流, 传入 mai_state_info
-                # 将 manager 的 _handle_hfc_no_reply 方法与当前的 subheartflow_id 绑定
-                hfc_callback = functools.partial(self._handle_hfc_no_reply, subheartflow_id)
-                # --- 结束创建回调 --- #
-
-                # 初始化子心流, 传入 mai_state_info 和 partial 创建的回调
                 new_subflow = SubHeartflow(
                     subheartflow_id,
                     self.mai_state_info,
-                    hfc_callback,  # <-- 传递 partial 创建的回调
                 )
 
-                # 异步初始化
+                # 首先创建并添加聊天观察者
-                await new_subflow.initialize()
-
-                # 添加聊天观察者
                 observation = ChattingObservation(chat_id=subheartflow_id)
                 await observation.initialize()
-
                 new_subflow.add_observation(observation)
 
+                # 然后再进行异步初始化，此时 SubHeartflow 内部若需启动 HeartFChatting，就能拿到 observation
+                await new_subflow.initialize()
+
                 # 注册子心流
                 self.subheartflows[subheartflow_id] = new_subflow
                 heartflow_name = chat_manager.get_stream_name(subheartflow_id) or subheartflow_id
                 logger.info(f"[{heartflow_name}] 开始接收消息")
 
-                # 启动后台任务
-                asyncio.create_task(new_subflow.subheartflow_start_working())
-
                 return new_subflow
             except Exception as e:
                 logger.error(f"创建子心流 {subheartflow_id} 失败: {e}", exc_info=True)
@@ -199,22 +186,14 @@ class SubHeartflowManager:
             f"{log_prefix} 完成，共处理 {processed_count} 个子心流，成功将 {changed_count} 个非 ABSENT 子心流的状态更改为 ABSENT。"
         )
 
-    async def sbhf_absent_into_focus(self):
+    async def sbhf_normal_into_focus(self):
         """评估子心流兴趣度，满足条件则提升到FOCUSED状态（基于start_hfc_probability）"""
         try:
-            current_state = self.mai_state_info.get_current_state()
-
-            # 检查是否允许进入 FOCUS 模式
-            if not global_config.chat.allow_focus_mode:
-                if int(time.time()) % 60 == 0:  # 每60秒输出一次日志避免刷屏
-                    logger.trace("未开启 FOCUSED 状态 (allow_focus_mode=False)")
-                return
-
             for sub_hf in list(self.subheartflows.values()):
                 flow_id = sub_hf.subheartflow_id
                 stream_name = chat_manager.get_stream_name(flow_id) or flow_id
 
-                # 跳过非CHAT状态或已经是FOCUSED状态的子心流
+                # 跳过已经是FOCUSED状态的子心流
                 if sub_hf.chat_state.chat_status == ChatState.FOCUSED:
                     continue
 
@@ -225,13 +204,6 @@ class SubHeartflowManager:
                         f"{stream_name}，现在状态: {sub_hf.chat_state.chat_status.value}，进入专注概率: {sub_hf.interest_chatting.start_hfc_probability}"
                     )
 
-                # 调试用
-                from .mai_state_manager import enable_unlimited_hfc_chat
-
-                if not enable_unlimited_hfc_chat:
-                    if sub_hf.chat_state.chat_status != ChatState.CHAT:
-                        continue
-
                 if random.random() >= sub_hf.interest_chatting.start_hfc_probability:
                     continue
 
@@ -250,12 +222,11 @@ class SubHeartflowManager:
         except Exception as e:
             logger.error(f"启动HFC 兴趣评估失败: {e}", exc_info=True)
 
-
+    async def sbhf_focus_into_normal(self, subflow_id: Any):
-    async def sbhf_focus_into_absent_or_chat(self, subflow_id: Any):
         """
-        接收来自 HeartFChatting 的请求，将特定子心流的状态转换为 CHAT。
+        接收来自 HeartFChatting 的请求，将特定子心流的状态转换为 NORMAL。
         通常在连续多次 "no_reply" 后被调用。
-        对于私聊和群聊，都转换为 CHAT。
+        对于私聊和群聊，都转换为 NORMAL。
 
         Args:
             subflow_id: 需要转换状态的子心流 ID。
@@ -263,15 +234,15 @@ class SubHeartflowManager:
         async with self._lock:
             subflow = self.subheartflows.get(subflow_id)
             if not subflow:
-                logger.warning(f"[状态转换请求] 尝试转换不存在的子心流 {subflow_id} 到 CHAT")
+                logger.warning(f"[状态转换请求] 尝试转换不存在的子心流 {subflow_id} 到 NORMAL")
                 return
 
             stream_name = chat_manager.get_stream_name(subflow_id) or subflow_id
             current_state = subflow.chat_state.chat_status
 
             if current_state == ChatState.FOCUSED:
-                target_state = ChatState.CHAT
+                target_state = ChatState.NORMAL
-                log_reason = "转为CHAT"
+                log_reason = "转为NORMAL"
 
                 logger.info(
                     f"[状态转换请求] 接收到请求，将 {stream_name} (当前: {current_state.value}) 尝试转换为 {target_state.value} ({log_reason})"
@@ -292,34 +263,10 @@ class SubHeartflowManager:
                         f"[状态转换请求] 转换 {stream_name} 到 {target_state.value} 时出错: {e}", exc_info=True
                     )
             elif current_state == ChatState.ABSENT:
-                logger.debug(f"[状态转换请求] {stream_name} 处于 ABSENT 状态，尝试转为 CHAT")
+                logger.debug(f"[状态转换请求] {stream_name} 处于 ABSENT 状态，尝试转为 NORMAL")
-                await subflow.change_chat_state(ChatState.CHAT)
+                await subflow.change_chat_state(ChatState.NORMAL)
             else:
-                logger.debug(
+                logger.debug(f"[状态转换请求] {stream_name} 当前状态为 {current_state.value}，无需转换")
-                    f"[状态转换请求] {stream_name} 当前状态为 {current_state.value}，无需转换"
-                )
-
-    def count_subflows_by_state(self, state: ChatState) -> int:
-        """统计指定状态的子心流数量"""
-        count = 0
-        # 遍历所有子心流实例
-        for subheartflow in self.subheartflows.values():
-            # 检查子心流状态是否匹配
-            if subheartflow.chat_state.chat_status == state:
-                count += 1
-        return count
-
-    def count_subflows_by_state_nolock(self, state: ChatState) -> int:
-        """
-        统计指定状态的子心流数量 (不上锁版本)。
-        警告：仅应在已持有 self._lock 的上下文中使用此方法。
-        """
-        count = 0
-        for subheartflow in self.subheartflows.values():
-            if subheartflow.chat_state.chat_status == state:
-                count += 1
-        return count
-
 
     async def delete_subflow(self, subheartflow_id: Any):
         """删除指定的子心流。"""
@@ -336,28 +283,14 @@ class SubHeartflowManager:
             else:
                 logger.warning(f"尝试删除不存在的 SubHeartflow: {subheartflow_id}")
 
-
-    async def _handle_hfc_no_reply(self, subheartflow_id: Any):
-        """处理来自 HeartFChatting 的连续无回复信号 (通过 partial 绑定 ID)"""
-        # 注意：这里不需要再获取锁，因为 sbhf_focus_into_absent_or_chat 内部会处理锁
-        logger.debug(f"[管理器 HFC 处理器] 接收到来自 {subheartflow_id} 的 HFC 无回复信号")
-        await self.sbhf_focus_into_absent_or_chat(subheartflow_id)
-
     # --- 新增：处理私聊从 ABSENT 直接到 FOCUSED 的逻辑 --- #
     async def sbhf_absent_private_into_focus(self):
-        """检查 ABSENT 状态的私聊子心流是否有新活动，若有且未达 FOCUSED 上限，则直接转换为 FOCUSED。"""
+        """检查 ABSENT 状态的私聊子心流是否有新活动，若有则直接转换为 FOCUSED。"""
         log_prefix_task = "[私聊激活检查]"
         transitioned_count = 0
         checked_count = 0
 
-        # --- 检查是否允许 FOCUS 模式 --- #
-        if not global_config.chat.allow_focus_mode:
-            return
-
         async with self._lock:
-            # --- 获取当前 FOCUSED 计数 (不上锁版本) --- #
-            current_focused_count = self.count_subflows_by_state_nolock(ChatState.FOCUSED)
-
             # --- 筛选出所有 ABSENT 状态的私聊子心流 --- #
             eligible_subflows = [
                 hf
@@ -372,7 +305,6 @@ class SubHeartflowManager:
 
             # --- 遍历评估每个符合条件的私聊 --- #
             for sub_hf in eligible_subflows:
-
                 flow_id = sub_hf.subheartflow_id
                 stream_name = chat_manager.get_stream_name(flow_id) or flow_id
                 log_prefix = f"[{stream_name}]({log_prefix_task})"
@@ -393,13 +325,12 @@ class SubHeartflowManager:
                     else:
                         logger.warning(f"{log_prefix} 无法获取主要观察者来检查活动状态。")
 
-                    # --- 如果活跃且未达上限，则尝试转换 --- #
+                    # --- 如果活跃，则尝试转换 --- #
                     if is_active:
                         await sub_hf.change_chat_state(ChatState.FOCUSED)
                         # 确认转换成功
                         if sub_hf.chat_state.chat_status == ChatState.FOCUSED:
                             transitioned_count += 1
-                            current_focused_count += 1  # 更新计数器以供本轮后续检查
                             logger.info(f"{log_prefix} 成功进入 FOCUSED 状态。")
                         else:
                             logger.warning(

src/chat/knowledge/src/qa_manager.py

@@ -121,5 +121,5 @@ class QAManager:
                 found_knowledge = found_knowledge[:MAX_KNOWLEDGE_LENGTH] + "\n"
             return found_knowledge
         else:
-            logger.info("LPMM知识库并未初始化，使用旧版数据库进行检索")
+            logger.info("LPMM知识库并未初始化，可能是从未导入过知识...")
             return None

src/chat/memory_system/Hippocampus.py

@@ -11,7 +11,6 @@ import jieba
 import networkx as nx
 import numpy as np
 from collections import Counter
-from ...common.database.database import memory_db as db
 from ...chat.models.utils_model import LLMRequest
 from src.common.logger_manager import get_logger
 from src.chat.memory_system.sample_distribution import MemoryBuildScheduler  # 分布生成器

src/chat/message_receive/bot.py

@@ -7,7 +7,7 @@ from src.chat.message_receive.chat_stream import chat_manager
 from src.chat.message_receive.message import MessageRecv
 from src.experimental.only_message_process import MessageProcessor
 from src.experimental.PFC.pfc_manager import PFCManager
-from src.chat.focus_chat.heartflow_processor import HeartFCProcessor
+from src.chat.focus_chat.heartflow_message_revceiver import HeartFCMessageReceiver
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.config.config import global_config
 
@@ -23,7 +23,7 @@ class ChatBot:
         self.bot = None  # bot 实例引用
         self._started = False
         self.mood_manager = mood_manager  # 获取情绪管理器单例
-        self.heartflow_processor = HeartFCProcessor()  # 新增
+        self.heartflow_message_receiver = HeartFCMessageReceiver()  # 新增
 
         # 创建初始化PFC管理器的任务，会在_ensure_started时执行
         self.only_process_chat = MessageProcessor()
@@ -110,11 +110,11 @@ class ChatBot:
                     # 禁止PFC，进入普通的心流消息处理逻辑
                     else:
                         logger.trace("进入普通心流私聊处理")
-                        await self.heartflow_processor.process_message(message_data)
+                        await self.heartflow_message_receiver.process_message(message_data)
                 # 群聊默认进入心流消息处理逻辑
                 else:
                     logger.trace(f"检测到群聊消息，群ID: {group_info.group_id}")
-                    await self.heartflow_processor.process_message(message_data)
+                    await self.heartflow_message_receiver.process_message(message_data)
 
             if template_group_name:
                 async with global_prompt_manager.async_message_scope(template_group_name):

src/chat/normal_chat/normal_chat.py

@@ -26,7 +26,7 @@ logger = get_logger("normal_chat")
 
 
 class NormalChat:
-    def __init__(self, chat_stream: ChatStream, interest_dict: dict = {}):
+    def __init__(self, chat_stream: ChatStream, interest_dict: dict = None):
         """初始化 NormalChat 实例。只进行同步操作。"""
 
         # Basic info from chat_stream (sync)
@@ -38,10 +38,8 @@ class NormalChat:
         # Interest dict
         self.interest_dict = interest_dict
 
-        # --- Initialize attributes (defaults) ---
         self.is_group_chat: bool = False
         self.chat_target_info: Optional[dict] = None
-        # --- End Initialization ---
 
         # Other sync initializations
         self.gpt = NormalChatGenerator()
@@ -51,9 +49,6 @@ class NormalChat:
         self._chat_task: Optional[asyncio.Task] = None
         self._initialized = False  # Track initialization status
 
-        # logger.info(f"[{self.stream_name}] NormalChat 实例 __init__ 完成 (同步部分)。")
-        # Avoid logging here as stream_name might not be final
-
     async def initialize(self):
         """异步初始化，获取聊天类型和目标信息。"""
         if self._initialized:
@@ -200,7 +195,6 @@ class NormalChat:
                 logger.info(f"[{self.stream_name}] 兴趣监控任务被取消或置空，退出")
                 break
 
-
             items_to_process = list(self.interest_dict.items())
             if not items_to_process:
                 continue
@@ -463,10 +457,11 @@ class NormalChat:
             await self.initialize()  # Ensure initialized before starting tasks
 
         if self._chat_task is None or self._chat_task.done():
-            logger.info(f"[{self.stream_name}] 开始后台处理初始兴趣消息和轮询任务...")
+            logger.info(f"[{self.stream_name}] 开始回顾消息...")
             # Process initial messages first
             await self._process_initial_interest_messages()
             # Then start polling task
+            logger.info(f"[{self.stream_name}] 开始处理兴趣消息...")
             polling_task = asyncio.create_task(self._reply_interested_message())
             polling_task.add_done_callback(lambda t: self._handle_task_completion(t))
             self._chat_task = polling_task

src/chat/normal_chat/willing/mode_classical.py

@@ -49,7 +49,7 @@ class ClassicalWillingManager(BaseWillingManager):
         # 检查群组权限（如果是群聊）
         if (
             willing_info.group_info
-            and willing_info.group_info.group_id in global_config.chat_target.talk_frequency_down_groups
+            and willing_info.group_info.group_id in global_config.normal_chat.talk_frequency_down_groups
         ):
             reply_probability = reply_probability / global_config.normal_chat.down_frequency_rate
 

src/chat/normal_chat/willing/mode_mxp.py

@@ -180,7 +180,7 @@ class MxpWillingManager(BaseWillingManager):
             if w_info.is_emoji:
                 probability *= global_config.normal_chat.emoji_response_penalty
 
-            if w_info.group_info and w_info.group_info.group_id in global_config.chat_target.talk_frequency_down_groups:
+            if w_info.group_info and w_info.group_info.group_id in global_config.normal_chat.talk_frequency_down_groups:
                 probability /= global_config.normal_chat.down_frequency_rate
 
             self.temporary_willing = current_willing

src/chat/person_info/person_info.py

@@ -9,7 +9,7 @@ import asyncio
 import numpy as np
 from src.chat.models.utils_model import LLMRequest
 from src.config.config import global_config
-from src.individuality.individuality import Individuality
+from src.individuality.individuality import individuality
 
 import matplotlib
 
@@ -257,7 +257,6 @@ class PersonInfoManager:
         current_name_set = set(self.person_name_list.values())
 
         while current_try < max_retries:
-            individuality = Individuality.get_instance()
             prompt_personality = individuality.get_prompt(x_person=2, level=1)
             bot_name = individuality.personality.bot_nickname
 

src/chat/utils/info_catcher.py

@@ -127,7 +127,7 @@ class InfoCatcher:
             Messages.select()
             .where((Messages.chat_id == chat_id_val) & (Messages.message_id < message_id_val))
             .order_by(Messages.time.desc())
-            .limit(global_config.chat.observation_context_size * 3)
+            .limit(global_config.focus_chat.observation_context_size * 3)
         )
 
         return list(messages_before_query)

src/chat/utils/utils.py

@@ -6,16 +6,13 @@ from collections import Counter
 import jieba
 import numpy as np
 from maim_message import UserInfo
-from pymongo.errors import PyMongoError
 
 from src.common.logger import get_module_logger
 from src.manager.mood_manager import mood_manager
 from ..message_receive.message import MessageRecv
 from ..models.utils_model import LLMRequest
 from .typo_generator import ChineseTypoGenerator
-from ...common.database.database import db
 from ...config.config import global_config
-from ...common.database.database_model import Messages
 from ...common.message_repository import find_messages, count_messages
 
 logger = get_module_logger("chat_utils")
@@ -112,11 +109,7 @@ async def get_embedding(text, request_type="embedding"):
 def get_recent_group_detailed_plain_text(chat_stream_id: str, limit: int = 12, combine=False):
     filter_query = {"chat_id": chat_stream_id}
     sort_order = [("time", -1)]
-    recent_messages = find_messages(
+    recent_messages = find_messages(message_filter=filter_query, sort=sort_order, limit=limit)
-        message_filter=filter_query,
-        sort=sort_order,
-        limit=limit
-    )
 
     if not recent_messages:
         return []
@@ -141,23 +134,21 @@ def get_recent_group_speaker(chat_stream_id: str, sender, limit: int = 12) -> li
     # 获取当前群聊记录内发言的人
     filter_query = {"chat_id": chat_stream_id}
     sort_order = [("time", -1)]
-    recent_messages = find_messages(
+    recent_messages = find_messages(message_filter=filter_query, sort=sort_order, limit=limit)
-        message_filter=filter_query,
-        sort=sort_order,
-        limit=limit
-    )
 
     if not recent_messages:
         return []
 
     who_chat_in_group = []
     for msg_db_data in recent_messages:
-        user_info = UserInfo.from_dict({
+        user_info = UserInfo.from_dict(
-            "platform": msg_db_data["user_platform"],
+            {
-            "user_id": msg_db_data["user_id"],
+                "platform": msg_db_data["user_platform"],
-            "user_nickname": msg_db_data["user_nickname"],
+                "user_id": msg_db_data["user_id"],
-            "user_cardname": msg_db_data.get("user_cardname", "")
+                "user_nickname": msg_db_data["user_nickname"],
-        })
+                "user_cardname": msg_db_data.get("user_cardname", ""),
+            }
+        )
         if (
             (user_info.platform, user_info.user_id) != sender
             and user_info.user_id != global_config.bot.qq_account
@@ -579,14 +570,13 @@ def count_messages_between(start_time: float, end_time: float, stream_id: str) -
 
     # 使用message_repository中的count_messages和find_messages函数
 
-
     # 构建查询条件
     filter_query = {"chat_id": stream_id, "time": {"$gt": start_time, "$lte": end_time}}
 
     try:
         # 先获取消息数量
         count = count_messages(filter_query)
-        
+
         # 获取消息内容计算总长度
         messages = find_messages(message_filter=filter_query)
         total_length = sum(len(msg.get("processed_plain_text", "")) for msg in messages)

src/chat/zhishi/knowledge_library.py

@@ -1,312 +0,0 @@
-import os
-import sys
-import requests
-from dotenv import load_dotenv
-import hashlib
-from datetime import datetime
-from tqdm import tqdm
-from rich.console import Console
-from rich.table import Table
-from rich.traceback import install
-
-install(extra_lines=3)
-
-# 添加项目根目录到 Python 路径
-root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
-sys.path.append(root_path)
-
-# 现在可以导入src模块
-from common.database.database import db  # noqa E402
-
-
-# 加载根目录下的env.edv文件
-env_path = os.path.join(root_path, ".env")
-if not os.path.exists(env_path):
-    raise FileNotFoundError(f"配置文件不存在: {env_path}")
-load_dotenv(env_path)
-
-
-class KnowledgeLibrary:
-    def __init__(self):
-        self.raw_info_dir = "data/raw_info"
-        self._ensure_dirs()
-        self.api_key = os.getenv("SILICONFLOW_KEY")
-        if not self.api_key:
-            raise ValueError("SILICONFLOW_API_KEY 环境变量未设置")
-        self.console = Console()
-
-    def _ensure_dirs(self):
-        """确保必要的目录存在"""
-        os.makedirs(self.raw_info_dir, exist_ok=True)
-
-    @staticmethod
-    def read_file(file_path: str) -> str:
-        """读取文件内容"""
-        with open(file_path, "r", encoding="utf-8") as f:
-            return f.read()
-
-    @staticmethod
-    def split_content(content: str, max_length: int = 512) -> list:
-        """将内容分割成适当大小的块，按空行分割
-
-        Args:
-            content: 要分割的文本内容
-            max_length: 每个块的最大长度
-
-        Returns:
-            list: 分割后的文本块列表
-        """
-        # 按空行分割内容
-        paragraphs = [p.strip() for p in content.split("\n\n") if p.strip()]
-        chunks = []
-
-        for para in paragraphs:
-            para_length = len(para)
-
-            # 如果段落长度小于等于最大长度，直接添加
-            if para_length <= max_length:
-                chunks.append(para)
-            else:
-                # 如果段落超过最大长度，则按最大长度切分
-                for i in range(0, para_length, max_length):
-                    chunks.append(para[i : i + max_length])
-
-        return chunks
-
-    def get_embedding(self, text: str) -> list:
-        """获取文本的embedding向量"""
-        url = "https://api.siliconflow.cn/v1/embeddings"
-        payload = {"model": "BAAI/bge-m3", "input": text, "encoding_format": "float"}
-        headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
-
-        response = requests.post(url, json=payload, headers=headers)
-        if response.status_code != 200:
-            print(f"获取embedding失败: {response.text}")
-            return None
-
-        return response.json()["data"][0]["embedding"]
-
-    def process_files(self, knowledge_length: int = 512):
-        """处理raw_info目录下的所有txt文件"""
-        txt_files = [f for f in os.listdir(self.raw_info_dir) if f.endswith(".txt")]
-
-        if not txt_files:
-            self.console.print("[red]警告：在 {} 目录下没有找到任何txt文件[/red]".format(self.raw_info_dir))
-            self.console.print("[yellow]请将需要处理的文本文件放入该目录后再运行程序[/yellow]")
-            return
-
-        total_stats = {"processed_files": 0, "total_chunks": 0, "failed_files": [], "skipped_files": []}
-
-        self.console.print(f"\n[bold blue]开始处理知识库文件 - 共{len(txt_files)}个文件[/bold blue]")
-
-        for filename in tqdm(txt_files, desc="处理文件进度"):
-            file_path = os.path.join(self.raw_info_dir, filename)
-            result = self.process_single_file(file_path, knowledge_length)
-            self._update_stats(total_stats, result, filename)
-
-        self._display_processing_results(total_stats)
-
-    def process_single_file(self, file_path: str, knowledge_length: int = 512):
-        """处理单个文件"""
-        result = {"status": "success", "chunks_processed": 0, "error": None}
-
-        try:
-            current_hash = self.calculate_file_hash(file_path)
-            processed_record = db.processed_files.find_one({"file_path": file_path})
-
-            if processed_record:
-                if processed_record.get("hash") == current_hash:
-                    if knowledge_length in processed_record.get("split_by", []):
-                        result["status"] = "skipped"
-                        return result
-
-            content = self.read_file(file_path)
-            chunks = self.split_content(content, knowledge_length)
-
-            for chunk in tqdm(chunks, desc=f"处理 {os.path.basename(file_path)} 的文本块", leave=False):
-                embedding = self.get_embedding(chunk)
-                if embedding:
-                    knowledge = {
-                        "content": chunk,
-                        "embedding": embedding,
-                        "source_file": file_path,
-                        "split_length": knowledge_length,
-                        "created_at": datetime.now(),
-                    }
-                    db.knowledges.insert_one(knowledge)
-                    result["chunks_processed"] += 1
-
-            split_by = processed_record.get("split_by", []) if processed_record else []
-            if knowledge_length not in split_by:
-                split_by.append(knowledge_length)
-
-            db.knowledges.processed_files.update_one(
-                {"file_path": file_path},
-                {"$set": {"hash": current_hash, "last_processed": datetime.now(), "split_by": split_by}},
-                upsert=True,
-            )
-
-        except Exception as e:
-            result["status"] = "failed"
-            result["error"] = str(e)
-
-        return result
-
-    @staticmethod
-    def _update_stats(total_stats, result, filename):
-        """更新总体统计信息"""
-        if result["status"] == "success":
-            total_stats["processed_files"] += 1
-            total_stats["total_chunks"] += result["chunks_processed"]
-        elif result["status"] == "failed":
-            total_stats["failed_files"].append((filename, result["error"]))
-        elif result["status"] == "skipped":
-            total_stats["skipped_files"].append(filename)
-
-    def _display_processing_results(self, stats):
-        """显示处理结果统计"""
-        self.console.print("\n[bold green]处理完成！统计信息如下：[/bold green]")
-
-        table = Table(show_header=True, header_style="bold magenta")
-        table.add_column("统计项", style="dim")
-        table.add_column("数值")
-
-        table.add_row("成功处理文件数", str(stats["processed_files"]))
-        table.add_row("处理的知识块总数", str(stats["total_chunks"]))
-        table.add_row("跳过的文件数", str(len(stats["skipped_files"])))
-        table.add_row("失败的文件数", str(len(stats["failed_files"])))
-
-        self.console.print(table)
-
-        if stats["failed_files"]:
-            self.console.print("\n[bold red]处理失败的文件：[/bold red]")
-            for filename, error in stats["failed_files"]:
-                self.console.print(f"[red]- {filename}: {error}[/red]")
-
-        if stats["skipped_files"]:
-            self.console.print("\n[bold yellow]跳过的文件（已处理）：[/bold yellow]")
-            for filename in stats["skipped_files"]:
-                self.console.print(f"[yellow]- {filename}[/yellow]")
-
-    @staticmethod
-    def calculate_file_hash(file_path):
-        """计算文件的MD5哈希值"""
-        hash_md5 = hashlib.md5()
-        with open(file_path, "rb") as f:
-            for chunk in iter(lambda: f.read(4096), b""):
-                hash_md5.update(chunk)
-        return hash_md5.hexdigest()
-
-    def search_similar_segments(self, query: str, limit: int = 5) -> list:
-        """搜索与查询文本相似的片段"""
-        query_embedding = self.get_embedding(query)
-        if not query_embedding:
-            return []
-
-        # 使用余弦相似度计算
-        pipeline = [
-            {
-                "$addFields": {
-                    "dotProduct": {
-                        "$reduce": {
-                            "input": {"$range": [0, {"$size": "$embedding"}]},
-                            "initialValue": 0,
-                            "in": {
-                                "$add": [
-                                    "$$value",
-                                    {
-                                        "$multiply": [
-                                            {"$arrayElemAt": ["$embedding", "$$this"]},
-                                            {"$arrayElemAt": [query_embedding, "$$this"]},
-                                        ]
-                                    },
-                                ]
-                            },
-                        }
-                    },
-                    "magnitude1": {
-                        "$sqrt": {
-                            "$reduce": {
-                                "input": "$embedding",
-                                "initialValue": 0,
-                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
-                            }
-                        }
-                    },
-                    "magnitude2": {
-                        "$sqrt": {
-                            "$reduce": {
-                                "input": query_embedding,
-                                "initialValue": 0,
-                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
-                            }
-                        }
-                    },
-                }
-            },
-            {"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
-            {"$sort": {"similarity": -1}},
-            {"$limit": limit},
-            {"$project": {"content": 1, "similarity": 1, "file_path": 1}},
-        ]
-
-        results = list(db.knowledges.aggregate(pipeline))
-        return results
-
-
-# 创建单例实例
-knowledge_library = KnowledgeLibrary()
-
-if __name__ == "__main__":
-    console = Console()
-    console.print("[bold green]知识库处理工具[/bold green]")
-
-    while True:
-        console.print("\n请选择要执行的操作：")
-        console.print("[1] 麦麦开始学习")
-        console.print("[2] 麦麦全部忘光光（仅知识）")
-        console.print("[q] 退出程序")
-
-        choice = input("\n请输入选项: ").strip()
-
-        if choice.lower() == "q":
-            console.print("[yellow]程序退出[/yellow]")
-            sys.exit(0)
-        elif choice == "2":
-            confirm = input("确定要删除所有知识吗？这个操作不可撤销！(y/n): ").strip().lower()
-            if confirm == "y":
-                db.knowledges.delete_many({})
-                console.print("[green]已清空所有知识！[/green]")
-            continue
-        elif choice == "1":
-            if not os.path.exists(knowledge_library.raw_info_dir):
-                console.print(f"[yellow]创建目录：{knowledge_library.raw_info_dir}[/yellow]")
-                os.makedirs(knowledge_library.raw_info_dir, exist_ok=True)
-
-            # 询问分割长度
-            while True:
-                try:
-                    length_input = input("请输入知识分割长度（默认512，输入q退出，回车使用默认值）: ").strip()
-                    if length_input.lower() == "q":
-                        break
-                    if not length_input:  # 如果直接回车，使用默认值
-                        knowledge_length = 512
-                        break
-                    knowledge_length = int(length_input)
-                    if knowledge_length <= 0:
-                        print("分割长度必须大于0，请重新输入")
-                        continue
-                    break
-                except ValueError:
-                    print("请输入有效的数字")
-                    continue
-
-            if length_input.lower() == "q":
-                continue
-
-            # 测试知识库功能
-            print(f"开始处理知识库文件，使用分割长度: {knowledge_length}...")
-            knowledge_library.process_files(knowledge_length=knowledge_length)
-        else:
-            console.print("[red]无效的选项，请重新选择[/red]")
-            continue

src/common/remote.py

@@ -71,9 +71,9 @@ class TelemetryHeartBeatTask(AsyncTask):
                 )
 
                 logger.debug(f"{TELEMETRY_SERVER_URL}/stat/reg_client")
-                
+
                 logger.debug(local_storage["deploy_time"])
-                
+
                 logger.debug(response)
 
                 if response.status_code == 200:
@@ -111,7 +111,7 @@ class TelemetryHeartBeatTask(AsyncTask):
             }
 
             logger.debug(f"正在发送心跳到服务器: {self.server_url}")
-            
+
             logger.debug(headers)
 
             response = requests.post(
@@ -119,7 +119,7 @@ class TelemetryHeartBeatTask(AsyncTask):
                 headers=headers,
                 json=self.info_dict,
             )
-            
+
             logger.debug(response)
 
             # 处理响应

src/config/config.py

@@ -14,10 +14,9 @@ from rich.traceback import install
 from src.config.config_base import ConfigBase
 from src.config.official_configs import (
     BotConfig,
-    ChatTargetConfig,
     PersonalityConfig,
     IdentityConfig,
-    PlatformsConfig,
+    ExpressionConfig,
     ChatConfig,
     NormalChatConfig,
     FocusChatConfig,
@@ -30,6 +29,8 @@ from src.config.official_configs import (
     TelemetryConfig,
     ExperimentalConfig,
     ModelConfig,
+    FocusChatProcessorConfig,
+    MessageReceiveConfig,
 )
 
 install(extra_lines=3)
@@ -139,14 +140,15 @@ class Config(ConfigBase):
     MMC_VERSION: str = field(default=MMC_VERSION, repr=False, init=False)  # 硬编码的版本信息
 
     bot: BotConfig
-    chat_target: ChatTargetConfig
     personality: PersonalityConfig
     identity: IdentityConfig
-    platforms: PlatformsConfig
     chat: ChatConfig
+    message_receive: MessageReceiveConfig
     normal_chat: NormalChatConfig
     focus_chat: FocusChatConfig
+    focus_chat_processor: FocusChatProcessorConfig
     emoji: EmojiConfig
+    expression: ExpressionConfig
     memory: MemoryConfig
     mood: MoodConfig
     keyword_reaction: KeywordReactionConfig

src/config/official_configs.py

@@ -26,23 +26,6 @@ class BotConfig(ConfigBase):
     """别名列表"""
 
 
-@dataclass
-class ChatTargetConfig(ConfigBase):
-    """
-    聊天目标配置类
-    此类中有聊天的群组和用户配置
-    """
-
-    talk_allowed_groups: set[str] = field(default_factory=lambda: set())
-    """允许聊天的群组列表"""
-
-    talk_frequency_down_groups: set[str] = field(default_factory=lambda: set())
-    """降低聊天频率的群组列表"""
-
-    ban_user_id: set[str] = field(default_factory=lambda: set())
-    """禁止聊天的用户列表"""
-
-
 @dataclass
 class PersonalityConfig(ConfigBase):
     """人格配置类"""
@@ -50,9 +33,6 @@ class PersonalityConfig(ConfigBase):
     personality_core: str
     """核心人格"""
 
-    expression_style: str
-    """表达风格"""
-
     personality_sides: list[str] = field(default_factory=lambda: [])
     """人格侧写"""
 
@@ -80,32 +60,17 @@ class IdentityConfig(ConfigBase):
     """身份特征"""
 
 
-@dataclass
-class PlatformsConfig(ConfigBase):
-    """平台配置类"""
-
-    qq: str
-    """QQ适配器连接URL配置"""
-
-
 @dataclass
 class ChatConfig(ConfigBase):
     """聊天配置类"""
 
-    allow_focus_mode: bool = True
+    chat_mode: str = "normal"
-    """是否允许专注聊天状态"""
+    """聊天模式"""
 
-    base_normal_chat_num: int = 3
-    """最多允许多少个群进行普通聊天"""
 
-    base_focused_chat_num: int = 2
+@dataclass
-    """最多允许多少个群进行专注聊天"""
+class MessageReceiveConfig(ConfigBase):
-
+    """消息接收配置类"""
-    observation_context_size: int = 12
-    """可观察到的最长上下文大小，超过这个值的上下文会被压缩"""
-
-    message_buffer: bool = True
-    """消息缓冲器"""
 
     ban_words: set[str] = field(default_factory=lambda: set())
     """过滤词列表"""
@@ -124,6 +89,12 @@ class NormalChatConfig(ConfigBase):
     选择普通模型的概率为 1 - reasoning_normal_model_probability
     """
 
+    max_context_size: int = 15
+    """上下文长度"""
+
+    message_buffer: bool = True
+    """消息缓冲器"""
+
     emoji_chance: float = 0.2
     """发送表情包的基础概率"""
 
@@ -139,6 +110,9 @@ class NormalChatConfig(ConfigBase):
     response_interested_rate_amplifier: float = 1.0
     """回复兴趣度放大系数"""
 
+    talk_frequency_down_groups: list[str] = field(default_factory=lambda: [])
+    """降低回复频率的群组"""
+
     down_frequency_rate: float = 3.0
     """降低回复频率的群组回复意愿降低系数"""
 
@@ -162,6 +136,9 @@ class FocusChatConfig(ConfigBase):
     default_decay_rate_per_second: float = 0.98
     """默认衰减率，越大衰减越快"""
 
+    observation_context_size: int = 12
+    """可观察到的最长上下文大小，超过这个值的上下文会被压缩"""
+
     consecutive_no_reply_threshold: int = 3
     """连续不回复的次数阈值"""
 
@@ -171,6 +148,37 @@ class FocusChatConfig(ConfigBase):
     compress_length_limit: int = 5
     """最多压缩份数，超过该数值的压缩上下文会被删除"""
 
+    think_interval: int = 1
+    """思考间隔（秒）"""
+
+
+@dataclass
+class FocusChatProcessorConfig(ConfigBase):
+    """专注聊天处理器配置类"""
+
+    self_identify_processor: bool = True
+    """是否启用自我识别处理器"""
+
+    tool_use_processor: bool = True
+    """是否启用工具使用处理器"""
+
+    working_memory_processor: bool = True
+    """是否启用工作记忆处理器"""
+
+
+@dataclass
+class ExpressionConfig(ConfigBase):
+    """表达配置类"""
+
+    expression_style: str = ""
+    """表达风格"""
+
+    learning_interval: int = 300
+    """学习间隔（秒）"""
+
+    enable_expression_learning: bool = True
+    """是否启用表达学习"""
+
 
 @dataclass
 class EmojiConfig(ConfigBase):
@@ -340,11 +348,8 @@ class TelemetryConfig(ConfigBase):
 class ExperimentalConfig(ConfigBase):
     """实验功能配置类"""
 
-    # enable_friend_chat: bool = False
+    enable_friend_chat: bool = False
-    # """是否启用好友聊天"""
+    """是否启用好友聊天"""
-
-    # talk_allowed_private: set[str] = field(default_factory=lambda: set())
-    # """允许聊天的私聊列表"""
 
     pfc_chatting: bool = False
     """是否启用PFC"""

src/experimental/PFC/action_planner.py

@@ -5,7 +5,7 @@ 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.individuality.individuality import individuality
 from src.experimental.PFC.observation_info import ObservationInfo
 from src.experimental.PFC.conversation_info import ConversationInfo
 from src.chat.utils.chat_message_builder import build_readable_messages
@@ -113,7 +113,7 @@ class ActionPlanner:
             max_tokens=1500,
             request_type="action_planning",
         )
-        self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
+        self.personality_info = individuality.get_prompt(x_person=2, level=3)
         self.name = global_config.bot.nickname
         self.private_name = private_name
         self.chat_observer = ChatObserver.get_instance(stream_id, private_name)

src/experimental/PFC/pfc.py

@@ -4,7 +4,7 @@ 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.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
@@ -47,7 +47,7 @@ class GoalAnalyzer:
             model=global_config.model.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.personality_info = individuality.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

src/experimental/PFC/reply_generator.py

@@ -4,7 +4,7 @@ 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.reply_checker import ReplyChecker
-from src.individuality.individuality import Individuality
+from src.individuality.individuality import individuality
 from .observation_info import ObservationInfo
 from .conversation_info import ConversationInfo
 from src.chat.utils.chat_message_builder import build_readable_messages
@@ -92,7 +92,7 @@ class ReplyGenerator:
             max_tokens=300,
             request_type="reply_generation",
         )
-        self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
+        self.personality_info = individuality.get_prompt(x_person=2, level=3)
         self.name = global_config.bot.nickname
         self.private_name = private_name
         self.chat_observer = ChatObserver.get_instance(stream_id, private_name)

src/experimental/PFC/waiter.py

@@ -2,7 +2,7 @@ from src.common.logger import get_module_logger
 from .chat_observer import ChatObserver
 from .conversation_info import ConversationInfo
 
-# from src.individuality.individuality import Individuality # 不再需要
+# from src.individuality.individuality import individuality,Individuality # 不再需要
 from src.config.config import global_config
 import time
 import asyncio

src/individuality/expression_style.py

@@ -0,0 +1,159 @@
+import random
+from src.common.logger_manager import get_logger
+from src.chat.models.utils_model import LLMRequest
+from src.config.config import global_config
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+from typing import List, Tuple
+import os
+import json
+
+logger = get_logger("expressor")
+
+
+def init_prompt() -> None:
+    personality_expression_prompt = """
+{personality}
+
+请从以上人设中总结出这个角色可能的语言风格
+思考回复的特殊内容和情感
+思考有没有特殊的梗，一并总结成语言风格
+总结成如下格式的规律，总结的内容要详细，但具有概括性：
+当"xxx"时，可以"xxx", xxx不超过10个字
+
+例如：
+当"表示十分惊叹"时，使用"我嘞个xxxx"
+当"表示讽刺的赞同，不想讲道理"时，使用"对对对"
+当"想说明某个观点，但懒得明说"，使用"懂的都懂"
+
+现在请你概括
+"""
+    Prompt(personality_expression_prompt, "personality_expression_prompt")
+
+
+class PersonalityExpression:
+    def __init__(self):
+        self.express_learn_model: LLMRequest = LLMRequest(
+            model=global_config.model.normal,
+            temperature=0.1,
+            max_tokens=256,
+            request_type="response_heartflow",
+        )
+        self.meta_file_path = os.path.join("data", "expression", "personality", "expression_style_meta.json")
+        self.expressions_file_path = os.path.join("data", "expression", "personality", "expressions.json")
+        self.max_calculations = 5
+
+    def _read_meta_data(self):
+        if os.path.exists(self.meta_file_path):
+            try:
+                with open(self.meta_file_path, "r", encoding="utf-8") as f:
+                    return json.load(f)
+            except json.JSONDecodeError:
+                logger.warning(f"无法解析 {self.meta_file_path} 中的JSON数据，将重新开始。")
+                return {"last_style_text": None, "count": 0}
+        return {"last_style_text": None, "count": 0}
+
+    def _write_meta_data(self, data):
+        os.makedirs(os.path.dirname(self.meta_file_path), exist_ok=True)
+        with open(self.meta_file_path, "w", encoding="utf-8") as f:
+            json.dump(data, f, ensure_ascii=False, indent=2)
+
+    async def extract_and_store_personality_expressions(self):
+        """
+        检查data/expression/personality目录，不存在则创建。
+        用peronality变量作为chat_str，调用LLM生成表达风格，解析后count=100，存储到expressions.json。
+        如果expression_style发生变化，则删除旧的expressions.json并重置计数。
+        对于相同的expression_style，最多计算self.max_calculations次。
+        """
+        os.makedirs(os.path.dirname(self.expressions_file_path), exist_ok=True)
+
+        current_style_text = global_config.expression.expression_style
+        meta_data = self._read_meta_data()
+
+        last_style_text = meta_data.get("last_style_text")
+        count = meta_data.get("count", 0)
+
+        if current_style_text != last_style_text:
+            logger.info(f"表达风格已从 '{last_style_text}' 变为 '{current_style_text}'。重置计数。")
+            count = 0
+            if os.path.exists(self.expressions_file_path):
+                try:
+                    os.remove(self.expressions_file_path)
+                    logger.info(f"已删除旧的表达文件: {self.expressions_file_path}")
+                except OSError as e:
+                    logger.error(f"删除旧的表达文件 {self.expressions_file_path} 失败: {e}")
+
+        if count >= self.max_calculations:
+            logger.info(f"对于风格 '{current_style_text}' 已达到最大计算次数 ({self.max_calculations})。跳过提取。")
+            # 即使跳过，也更新元数据以反映当前风格已被识别且计数已满
+            self._write_meta_data({"last_style_text": current_style_text, "count": count})
+            return
+
+        # 构建prompt
+        prompt = await global_prompt_manager.format_prompt(
+            "personality_expression_prompt",
+            personality=current_style_text,
+        )
+        # logger.info(f"个性表达方式提取prompt: {prompt}")
+
+        try:
+            response, _ = await self.express_learn_model.generate_response_async(prompt)
+        except Exception as e:
+            logger.error(f"个性表达方式提取失败: {e}")
+            # 如果提取失败，保存当前的风格和未增加的计数
+            self._write_meta_data({"last_style_text": current_style_text, "count": count})
+            return
+
+        logger.info(f"个性表达方式提取response: {response}")
+        # chat_id用personality
+        expressions = self.parse_expression_response(response, "personality")
+        # 转为dict并count=100
+        result = []
+        for _, situation, style in expressions:
+            result.append({"situation": situation, "style": style, "count": 100})
+        # 超过50条时随机删除多余的，只保留50条
+        if len(result) > 50:
+            remove_count = len(result) - 50
+            remove_indices = set(random.sample(range(len(result)), remove_count))
+            result = [item for idx, item in enumerate(result) if idx not in remove_indices]
+
+        with open(self.expressions_file_path, "w", encoding="utf-8") as f:
+            json.dump(result, f, ensure_ascii=False, indent=2)
+        logger.info(f"已写入{len(result)}条表达到{self.expressions_file_path}")
+
+        # 成功提取后更新元数据
+        count += 1
+        self._write_meta_data({"last_style_text": current_style_text, "count": count})
+        logger.info(f"成功处理。风格 '{current_style_text}' 的计数现在是 {count}。")
+
+    def parse_expression_response(self, response: str, chat_id: str) -> List[Tuple[str, str, str]]:
+        """
+        解析LLM返回的表达风格总结，每一行提取"当"和"使用"之间的内容，存储为(situation, style)元组
+        """
+        expressions: List[Tuple[str, str, str]] = []
+        for line in response.splitlines():
+            line = line.strip()
+            if not line:
+                continue
+            # 查找"当"和下一个引号
+            idx_when = line.find('当"')
+            if idx_when == -1:
+                continue
+            idx_quote1 = idx_when + 1
+            idx_quote2 = line.find('"', idx_quote1 + 1)
+            if idx_quote2 == -1:
+                continue
+            situation = line[idx_quote1 + 1 : idx_quote2]
+            # 查找"使用"
+            idx_use = line.find('使用"', idx_quote2)
+            if idx_use == -1:
+                continue
+            idx_quote3 = idx_use + 2
+            idx_quote4 = line.find('"', idx_quote3 + 1)
+            if idx_quote4 == -1:
+                continue
+            style = line[idx_quote3 + 1 : idx_quote4]
+            expressions.append((chat_id, situation, style))
+        return expressions
+
+
+init_prompt()

src/individuality/individuality.py

@@ -1,6 +1,9 @@
 from typing import Optional
+
+from numpy import double
 from .personality import Personality
 from .identity import Identity
+from .expression_style import PersonalityExpression
 import random
 from rich.traceback import install
 
@@ -10,43 +13,22 @@ install(extra_lines=3)
 class Individuality:
     """个体特征管理类"""
 
-    _instance = None
-
     def __init__(self):
-        if Individuality._instance is not None:
-            raise RuntimeError("Individuality 类是单例，请使用 get_instance() 方法获取实例。")
-
         # 正常初始化实例属性
         self.personality: Optional[Personality] = None
         self.identity: Optional[Identity] = None
+        self.express_style: PersonalityExpression = PersonalityExpression()
 
         self.name = ""
 
-    @classmethod
+    async def initialize(
-    def get_instance(cls) -> "Individuality":
-        """获取Individuality单例实例
-
-        Returns:
-            Individuality: 单例实例
-        """
-        if cls._instance is None:
-            # 实例不存在，调用 cls() 创建新实例
-            # cls() 会调用 __init__
-            # 因为此时 cls._instance 仍然是 None，__init__ 会正常执行初始化
-            new_instance = cls()
-            # 将新创建的实例赋值给类变量 _instance
-            cls._instance = new_instance
-        # 返回（新创建的或已存在的）单例实例
-        return cls._instance
-
-    def initialize(
         self,
         bot_nickname: str,
         personality_core: str,
         personality_sides: list,
         identity_detail: list,
         height: int,
-        weight: float,
+        weight: double,
         age: int,
         gender: str,
         appearance: str,
@@ -74,6 +56,8 @@ class Individuality:
             identity_detail=identity_detail, height=height, weight=weight, age=age, gender=gender, appearance=appearance
         )
 
+        await self.express_style.extract_and_store_personality_expressions()
+
         self.name = bot_nickname
 
     def to_dict(self) -> dict:
@@ -86,7 +70,7 @@ class Individuality:
     @classmethod
     def from_dict(cls, data: dict) -> "Individuality":
         """从字典创建个体特征实例"""
-        instance = cls.get_instance()
+        instance = cls()
         if data.get("personality"):
             instance.personality = Personality.from_dict(data["personality"])
         if data.get("identity"):
@@ -174,6 +158,10 @@ class Individuality:
                 identity_parts.append(f"年龄大约{self.identity.age}岁")
             if self.identity.gender:
                 identity_parts.append(f"性别是{self.identity.gender}")
+            if self.identity.height:
+                identity_parts.append(f"身高大约{self.identity.height}厘米")
+            if self.identity.weight:
+                identity_parts.append(f"体重大约{self.identity.weight}千克")
 
         if identity_parts:
             details_str = "，".join(identity_parts)
@@ -250,3 +238,6 @@ class Individuality:
         elif factor == "neuroticism":
             return self.personality.neuroticism
         return None
+
+
+individuality = Individuality()

src/individuality/not_using/per_bf_gen.py

@@ -17,9 +17,9 @@ with open(config_path, "r", encoding="utf-8") as f:
     config = toml.load(f)
 
 # 现在可以导入src模块
-from src.individuality.scene import get_scene_by_factor, PERSONALITY_SCENES  # noqa E402
+from individuality.not_using.scene import get_scene_by_factor, PERSONALITY_SCENES  # noqa E402
-from src.individuality.questionnaire import FACTOR_DESCRIPTIONS  # noqa E402
+from individuality.not_using.questionnaire import FACTOR_DESCRIPTIONS  # noqa E402
-from src.individuality.offline_llm import LLMRequestOff  # noqa E402
+from individuality.not_using.offline_llm import LLMRequestOff  # noqa E402
 
 # 加载环境变量
 env_path = os.path.join(root_path, ".env")

src/main.py

@@ -16,7 +16,7 @@ from .chat.message_receive.storage import MessageStorage
 from .config.config import global_config
 from .chat.message_receive.bot import chat_bot
 from .common.logger_manager import get_logger
-from .individuality.individuality import Individuality
+from .individuality.individuality import individuality, Individuality
 from .common.server import global_server, Server
 from rich.traceback import install
 from .chat.focus_chat.expressors.exprssion_learner import expression_learner
@@ -30,7 +30,7 @@ logger = get_logger("main")
 class MainSystem:
     def __init__(self):
         self.hippocampus_manager: HippocampusManager = HippocampusManager.get_instance()
-        self.individuality: Individuality = Individuality.get_instance()
+        self.individuality: Individuality = individuality
 
         # 使用消息API替代直接的FastAPI实例
         from src.common.message import global_api
@@ -91,7 +91,7 @@ class MainSystem:
         self.app.register_message_handler(chat_bot.message_process)
 
         # 初始化个体特征
-        self.individuality.initialize(
+        await self.individuality.initialize(
             bot_nickname=global_config.bot.nickname,
             personality_core=global_config.personality.personality_core,
             personality_sides=global_config.personality.personality_sides,
@@ -104,9 +104,6 @@ class MainSystem:
         )
         logger.success("个体特征初始化成功")
 
-        # 初始化表达方式
-        await expression_learner.extract_and_store_personality_expressions()
-
         try:
             # 启动全局消息管理器 (负责消息发送/排队)
             await message_manager.start()
@@ -169,7 +166,7 @@ class MainSystem:
     async def learn_and_store_expression_task():
         """学习并存储表达方式任务"""
         while True:
-            await asyncio.sleep(60)
+            await asyncio.sleep(global_config.expression.learning_interval)
             print("\033[1;32m[表达方式学习]\033[0m 开始学习表达方式...")
             await expression_learner.learn_and_store_expression()
             print("\033[1;32m[表达方式学习]\033[0m 表达方式学习完成")

src/manager/mood_manager.py

@@ -7,7 +7,7 @@ from typing import Dict, Tuple
 from ..config.config import global_config
 from ..common.logger_manager import get_logger
 from ..manager.async_task_manager import AsyncTask
-from ..individuality.individuality import Individuality
+from ..individuality.individuality import individuality
 
 logger = get_logger("mood")
 
@@ -54,7 +54,7 @@ class MoodUpdateTask(AsyncTask):
         agreeableness_bias = 0  # 宜人性偏置
         neuroticism_factor = 0.5  # 神经质系数
         # 获取人格特质
-        personality = Individuality.get_instance().personality
+        personality = individuality.personality
         if personality:
             # 神经质：影响情绪变化速度
             neuroticism_factor = 1 + (personality.neuroticism - 0.5) * 0.4

src/tools/not_used/mid_chat_mem.py

@@ -1,40 +0,0 @@
-from src.tools.tool_can_use.base_tool import BaseTool
-from src.common.logger import get_module_logger
-from typing import Any
-
-logger = get_module_logger("get_mid_memory_tool")
-
-
-class GetMidMemoryTool(BaseTool):
-    """从记忆系统中获取相关记忆的工具"""
-
-    name = "mid_chat_mem"
-    description = "之前的聊天内容概述id中获取具体信息，如果没有聊天内容概述id，就不要使用"
-    parameters = {
-        "type": "object",
-        "properties": {
-            "id": {"type": "integer", "description": "要查询的聊天记录概述id"},
-        },
-        "required": ["id"],
-    }
-
-    async def execute(self, function_args: dict[str, Any], message_txt: str = "") -> dict[str, Any]:
-        """执行记忆获取
-
-        Args:
-            function_args: 工具参数
-            message_txt: 原始消息文本
-
-        Returns:
-            dict: 工具执行结果
-        """
-        try:
-            id = function_args.get("id")
-            return {"name": "mid_chat_mem", "content": str(id)}
-        except Exception as e:
-            logger.error(f"聊天记录获取工具执行失败: {str(e)}")
-            return {"name": "mid_chat_mem", "content": f"聊天记录获取失败: {str(e)}"}
-
-
-# 注册工具
-# register_tool(GetMemoryTool)

src/tools/not_used/send_emoji.py

@@ -1,25 +0,0 @@
-from src.tools.tool_can_use.base_tool import BaseTool
-from src.common.logger import get_module_logger
-
-from typing import Any
-
-logger = get_module_logger("send_emoji_tool")
-
-
-class SendEmojiTool(BaseTool):
-    """发送表情包的工具"""
-
-    name = "send_emoji"
-    description = "当你觉得需要表达情感，或者帮助表达，可以使用这个工具发送表情包"
-    parameters = {
-        "type": "object",
-        "properties": {"text": {"type": "string", "description": "要发送的表情包描述"}},
-        "required": ["text"],
-    }
-
-    async def execute(self, function_args: dict[str, Any], message_txt: str = "") -> dict[str, Any]:
-        text = function_args.get("text", message_txt)
-        return {
-            "name": "send_emoji",
-            "content": text,
-        }

src/tools/tool_can_use/get_time_date.py

@@ -1,39 +0,0 @@
-from src.tools.tool_can_use.base_tool import BaseTool
-from src.common.logger_manager import get_logger
-from typing import Dict, Any
-from datetime import datetime
-import time
-
-logger = get_logger("get_time_date")
-
-
-class GetCurrentDateTimeTool(BaseTool):
-    """获取当前时间、日期、年份和星期的工具"""
-
-    name = "get_current_date_time"
-    description = "当有人询问或者涉及到具体时间或者日期的时候，必须使用这个工具"
-    parameters = {
-        "type": "object",
-        "properties": {},
-        "required": [],
-    }
-
-    async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
-        """执行获取当前时间、日期、年份和星期
-
-        Args:
-            function_args: 工具参数（此工具不使用）
-
-        Returns:
-            Dict: 工具执行结果
-        """
-        current_time = datetime.now().strftime("%H:%M:%S")
-        current_date = datetime.now().strftime("%Y-%m-%d")
-        current_year = datetime.now().strftime("%Y")
-        current_weekday = datetime.now().strftime("%A")
-
-        return {
-            "type": "time_info",
-            "id": f"time_info_{time.time()}",
-            "content": f"当前时间: {current_time}, 日期: {current_date}, 年份: {current_year}, 星期: {current_weekday}",
-        }

src/tools/tool_use.py

@@ -3,11 +3,6 @@ from src.config.config import global_config
 import json
 from src.common.logger_manager import get_logger
 from src.tools.tool_can_use import get_all_tool_definitions, get_tool_instance
-import traceback
-from src.chat.person_info.relationship_manager import relationship_manager
-from src.chat.utils.utils import parse_text_timestamps
-from src.chat.message_receive.chat_stream import ChatStream
-from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
 
 logger = get_logger("tool_use")
 
@@ -18,41 +13,6 @@ class ToolUser:
             model=global_config.model.tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use"
         )
 
-    @staticmethod
-    async def _build_tool_prompt(
-        message_txt: str, chat_stream: ChatStream = None, observation: ChattingObservation = None
-    ):
-        """构建工具使用的提示词
-
-        Args:
-            message_txt: 用户消息文本
-            subheartflow: 子心流对象
-
-        Returns:
-            str: 构建好的提示词
-        """
-
-        if observation:
-            mid_memory_info = observation.mid_memory_info
-            # print(f"intol111111111111111111111111111111111222222222222mid_memory_info：{mid_memory_info}")
-
-        # 这些信息应该从调用者传入，而不是从self获取
-        bot_name = global_config.bot.nickname
-        prompt = ""
-        prompt += mid_memory_info
-        prompt += "你正在思考如何回复群里的消息。\n"
-        prompt += "之前群里进行了如下讨论:\n"
-        prompt += message_txt
-        # prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
-        prompt += f"注意你就是{bot_name}，{bot_name}是你的名字。根据之前的聊天记录补充问题信息，搜索时避开你的名字。\n"
-        # prompt += "必须调用 'lpmm_get_knowledge' 工具来获取知识。\n"
-        prompt += "你现在需要对群里的聊天内容进行回复，请你思考应该使用什么工具，然后选择工具来对消息和你的回复进行处理，你是否需要额外的信息，比如回忆或者搜寻已有的知识，改变关系和情感，或者了解你现在正在做什么。"
-
-        prompt = await relationship_manager.convert_all_person_sign_to_person_name(prompt)
-        prompt = parse_text_timestamps(prompt, mode="lite")
-
-        return prompt
-
     @staticmethod
     def _define_tools():
         """获取所有已注册工具的定义
@@ -100,93 +60,3 @@ class ToolUser:
         except Exception as e:
             logger.error(f"执行工具调用时发生错误: {str(e)}")
             return None
-
-    async def use_tool(self, message_txt: str, chat_stream: ChatStream = None, observation: ChattingObservation = None):
-        """使用工具辅助思考，判断是否需要额外信息
-
-        Args:
-            message_txt: 用户消息文本
-            chat_stream: 聊天流对象
-            observation: 观察对象（可选）
-
-        Returns:
-            dict: 工具使用结果，包含结构化的信息
-        """
-        try:
-            # 构建提示词
-            prompt = await self._build_tool_prompt(
-                message_txt=message_txt,
-                chat_stream=chat_stream,
-                observation=observation,
-            )
-
-            # 定义可用工具
-            tools = self._define_tools()
-            logger.trace(f"工具定义: {tools}")
-
-            # 使用llm_model_tool发送带工具定义的请求
-            payload = {
-                "model": self.llm_model_tool.model_name,
-                "messages": [{"role": "user", "content": prompt}],
-                "tools": tools,
-                "temperature": 0.2,
-            }
-
-            logger.trace(f"发送工具调用请求，模型: {self.llm_model_tool.model_name}")
-            # 发送请求获取模型是否需要调用工具
-            response = await self.llm_model_tool._execute_request(
-                endpoint="/chat/completions", payload=payload, prompt=prompt
-            )
-
-            # 根据返回值数量判断是否有工具调用
-            if len(response) == 3:
-                content, reasoning_content, tool_calls = response
-                # logger.info(f"工具思考: {tool_calls}")
-                # logger.debug(f"工具思考: {content}")
-
-                # 检查响应中工具调用是否有效
-                if not tool_calls:
-                    logger.debug("模型返回了空的tool_calls列表")
-                    return {"used_tools": False}
-
-                tool_calls_str = ""
-                for tool_call in tool_calls:
-                    tool_calls_str += f"{tool_call['function']['name']}\n"
-                logger.info(
-                    f"根据:\n{prompt}\n\n内容：{content}\n\n模型请求调用{len(tool_calls)}个工具: {tool_calls_str}"
-                )
-                tool_results = []
-                structured_info = {}  # 动态生成键
-
-                # 执行所有工具调用
-                for tool_call in tool_calls:
-                    result = await self._execute_tool_call(tool_call)
-                    if result:
-                        tool_results.append(result)
-                        # 使用工具名称作为键
-                        tool_name = result["name"]
-                        if tool_name not in structured_info:
-                            structured_info[tool_name] = []
-                        structured_info[tool_name].append({"name": result["name"], "content": result["content"]})
-
-                # 如果有工具结果，返回结构化的信息
-                if structured_info:
-                    logger.debug(f"工具调用收集到结构化信息: {json.dumps(structured_info, ensure_ascii=False)}")
-                    return {"used_tools": True, "structured_info": structured_info}
-            else:
-                # 没有工具调用
-                content, reasoning_content = response
-                logger.debug("模型没有请求调用任何工具")
-
-            # 如果没有工具调用或处理失败，直接返回原始思考
-            return {
-                "used_tools": False,
-            }
-
-        except Exception as e:
-            logger.error(f"工具调用过程中出错: {str(e)}")
-            logger.error(f"工具调用过程中出错: {traceback.format_exc()}")
-            return {
-                "used_tools": False,
-                "error": str(e),
-            }

start_personality.bat

@@ -1,56 +0,0 @@
-@echo off
-chcp 65001 > nul
-setlocal enabledelayedexpansion
-cd /d %~dp0
-
-title 麦麦人格生成
-
-cls
-echo ======================================
-echo    警告提示
-echo ======================================
-echo  1.这是一个demo系统,仅供体验，特性可能会在将来移除
-echo ======================================
-
-echo.
-echo ======================================
-echo    请选择Python环境:
-echo    1 - venv (推荐)
-echo    2 - conda
-echo ======================================
-choice /c 12 /n /m "请输入数字选择(1或2): "
-
-if errorlevel 2 (
-    echo ======================================
-    set "CONDA_ENV="
-    set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
-    
-    :: 检查输入是否为空
-    if "!CONDA_ENV!"=="" (
-        echo 错误：环境名称不能为空
-        pause
-        exit /b 1
-    )
-    
-    call conda activate !CONDA_ENV!
-    if errorlevel 1 (
-        echo 激活 conda 环境失败
-        pause
-        exit /b 1
-    )
-    
-    echo Conda 环境 "!CONDA_ENV!" 激活成功
-    python src/individuality/per_bf_gen.py
-) else (
-    if exist "venv\Scripts\python.exe" (
-        venv\Scripts\python src/individuality/per_bf_gen.py
-    ) else (
-        echo ======================================
-        echo 错误: venv环境不存在，请先创建虚拟环境
-        pause
-        exit /b 1
-    )
-)
-
-endlocal
-pause

template/bot_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "2.2.0"
+version = "2.3.0"
 
 #----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
 #如果你想要修改配置文件，请在修改后将version的值进行变更
@@ -15,12 +15,9 @@ version = "2.2.0"
 [bot]
 qq_account = 1145141919810
 nickname = "麦麦"
-alias_names = ["麦叠", "牢麦"] #该选项还在调试中，暂时未生效
+alias_names = ["麦叠", "牢麦"] #仅在 专注聊天 有效
 
-[chat_target]
+[personality]
-talk_frequency_down_groups = []  #降低回复频率的群号码
-
-[personality] #未完善
 personality_core = "用一句话或几句话描述人格的核心特点" # 建议20字以内，谁再写3000字小作文敲谁脑袋
 personality_sides = [
     "用一句话或几句话描述人格的一些细节",
@@ -30,35 +27,30 @@ personality_sides = [
     "用一句话或几句话描述人格的一些细节",
 ]# 条数任意，不能为0, 该选项还在调试中，可能未完全生效
 
-# 表达方式
+# 身份特点 部分选项仅在 专注聊天 有效
-expression_style = "描述麦麦说话的表达风格，表达习惯"
-
-
 [identity] #アイデンティティがない 生まれないらららら
-# 兴趣爱好 未完善，有些条目未使用
 identity_detail = [
     "身份特点",
     "身份特点",
-]# 条数任意，不能为0, 该选项还在调试中
+]# 条数任意，不能为0
 
 #外貌特征
 age = 18 # 年龄 单位岁
 gender = "女" # 性别
 height = "170" # 身高（单位cm）
 weight = "50" # 体重（单位kg）
-appearance = "用一句或几句话描述外貌特征" # 外貌特征 该选项还在调试中，暂时未生效
+appearance = "用一句或几句话描述外貌特征" # 外貌特征
-
-[platforms] # 必填项目，填写每个平台适配器提供的链接
-qq="http://127.0.0.1:18002/api/message"
 
 [chat] #麦麦的聊天通用设置
-allow_focus_mode = false # 是否允许专注聊天状态
+chat_mode = "normal" # 聊天模式 —— 普通模式：normal，专注模式：focus，在普通模式和专注模式之间自动切换
-# 是否启用heart_flowC(HFC)模式
+# chat_mode = "focus"
-# 启用后麦麦会自主选择进入heart_flowC模式（持续一段时间），进行主动的观察和回复，并给出回复，比较消耗token
+# chat_mode = "auto"
 
-chat.observation_context_size = 15 # 观察到的最长上下文大小,建议15，太短太长都会导致脑袋尖尖
+# 普通模式下，麦麦会针对感兴趣的消息进行回复，token消耗量较低
-message_buffer = true # 启用消息缓冲器？启用此项以解决消息的拆分问题，但会使麦麦的回复延迟
+# 专注模式下，麦麦会进行主动的观察和回复，并给出回复，token消耗量较高
+# 自动模式下，麦麦会根据消息内容自动切换到专注模式或普通模式
 
+[message_receive]
 # 以下是消息过滤，可以根据规则过滤特定消息，将不会读取这些消息
 ban_words = [
     # "403","张三"
@@ -74,9 +66,10 @@ ban_msgs_regex = [
 [normal_chat] #普通聊天
 #一般回复参数
 reasoning_model_probability = 0.3 # 麦麦回答时选择推理模型的概率（与之相对的，普通模型的概率为1 - reasoning_model_probability）
-
+max_context_size = 15 #上下文长度
 emoji_chance = 0.2 # 麦麦一般回复时使用表情包的概率，设置为1让麦麦自己决定发不发
 thinking_timeout = 120 # 麦麦最长思考时间，超过这个时间的思考会放弃（往往是api反应太慢）
+message_buffer = true # 启用消息缓冲器？启用此项以解决消息的拆分问题，但会使麦麦的回复延迟
 
 willing_mode = "classical" # 回复意愿模式 —— 经典模式：classical，mxp模式：mxp，自定义模式：custom（需要你自己实现）
 response_willing_amplifier = 1 # 麦麦回复意愿放大系数，一般为1
@@ -85,16 +78,32 @@ down_frequency_rate = 3 # 降低回复频率的群组回复意愿降低系数
 emoji_response_penalty = 0 # 表情包回复惩罚系数，设为0为不回复单个表情包，减少单独回复表情包的概率
 mentioned_bot_inevitable_reply = false # 提及 bot 必然回复
 at_bot_inevitable_reply = false # @bot 必然回复
+talk_frequency_down_groups = []  #降低回复频率的群号码
 
 [focus_chat] #专注聊天
 reply_trigger_threshold = 3.0 # 专注聊天触发阈值，越低越容易进入专注聊天
 default_decay_rate_per_second = 0.98 # 默认衰减率，越大衰减越快，越高越难进入专注聊天
 consecutive_no_reply_threshold = 3 # 连续不回复的阈值，越低越容易结束专注聊天
 
-# 以下选项暂时无效
+think_interval = 1 # 思考间隔 单位秒
+
+observation_context_size = 15 # 观察到的最长上下文大小,建议15，太短太长都会导致脑袋尖尖
 compressed_length = 5 # 不能大于chat.observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
 compress_length_limit = 5 #最多压缩份数，超过该数值的压缩上下文会被删除
 
+[focus_chat_processor] # 专注聊天处理器，打开可以实现更多功能，但是会增加token消耗
+self_identify_processor = true # 是否启用自我识别处理器
+tool_use_processor = true # 是否启用工具使用处理器
+working_memory_processor = true # 是否启用工作记忆处理器
+
+
+
+[expression]
+# 表达方式
+expression_style = "描述麦麦说话的表达风格，表达习惯"
+enable_expression_learning = true # 是否启用表达学习
+learning_interval = 300 # 学习间隔 单位秒
+
 
 [emoji]
 max_reg_num = 40 # 表情包最大注册数量
@@ -124,7 +133,7 @@ consolidation_check_percentage = 0.01 # 检查节点比例
 #不希望记忆的词，已经记忆的不会受到影响
 memory_ban_words = [ "表情包", "图片", "回复", "聊天记录" ]
 
-[mood]
+[mood] # 仅在 普通聊天 有效
 mood_update_interval = 1.0 # 情绪更新间隔 单位秒
 mood_decay_rate = 0.95 # 情绪衰减率
 mood_intensity_factor = 1.0 # 情绪强度因子
@@ -164,6 +173,7 @@ enable_kaomoji_protection = false # 是否启用颜文字保护
 enable = true
 
 [experimental] #实验性功能
+enable_friend_chat = false # 是否启用好友聊天
 pfc_chatting = false # 是否启用PFC聊天，该功能仅作用于私聊，与回复模式独立
 
 #下面的模型若使用硅基流动则不需要更改，使用ds官方则改成.env自定义的宏，使用自定义模型则选择定位相似的模型自己填写

tests/common/test_message_repository.py

@@ -1,5 +1,4 @@
 import unittest
-from unittest.mock import patch, MagicMock
 import datetime
 import sys
 import os

tests/test_build_readable_messages.py

@@ -1,11 +1,9 @@
 import unittest
 import sys
 import os
-import datetime
 import time
 import asyncio
 import traceback
-import json
 import copy
 
 # 添加项目根目录到Python路径

tests/test_extract_messages.py

@@ -9,8 +9,6 @@ sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")
 
 from src.common.message_repository import find_messages
 from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_with_chat
-from peewee import SqliteDatabase
-from src.common.database.database import db  # 导入实际的数据库连接
 
 
 class TestExtractMessages(unittest.TestCase):