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

scripts/import_openie.py

@@ -10,13 +10,13 @@ from time import sleep
 
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 
-from src.plugins.knowledge.src.lpmmconfig import PG_NAMESPACE, global_config
-from src.plugins.knowledge.src.embedding_store import EmbeddingManager
-from src.plugins.knowledge.src.llm_client import LLMClient
-from src.plugins.knowledge.src.open_ie import OpenIE
-from src.plugins.knowledge.src.kg_manager import KGManager
+from src.chat.knowledge.src.lpmmconfig import PG_NAMESPACE, global_config
+from src.chat.knowledge.src.embedding_store import EmbeddingManager
+from src.chat.knowledge.src.llm_client import LLMClient
+from src.chat.knowledge.src.open_ie import OpenIE
+from src.chat.knowledge.src.kg_manager import KGManager
 from src.common.logger import get_module_logger
-from src.plugins.knowledge.src.utils.hash import get_sha256
+from src.chat.knowledge.src.utils.hash import get_sha256
 
 
 # 添加项目根目录到 sys.path

scripts/info_extraction.py

@@ -13,11 +13,11 @@ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 from rich.progress import Progress  # 替换为 rich 进度条
 
 from src.common.logger import get_module_logger
-from src.plugins.knowledge.src.lpmmconfig import global_config
-from src.plugins.knowledge.src.ie_process import info_extract_from_str
-from src.plugins.knowledge.src.llm_client import LLMClient
-from src.plugins.knowledge.src.open_ie import OpenIE
-from src.plugins.knowledge.src.raw_processing import load_raw_data
+from src.chat.knowledge.src.lpmmconfig import global_config
+from src.chat.knowledge.src.ie_process import info_extract_from_str
+from src.chat.knowledge.src.llm_client import LLMClient
+from src.chat.knowledge.src.open_ie import OpenIE
+from src.chat.knowledge.src.raw_processing import load_raw_data
 from rich.progress import (
     BarColumn,
     TimeElapsedColumn,

scripts/raw_data_preprocessor.py

@@ -6,7 +6,7 @@ import datetime  # 新增导入
 
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 from src.common.logger_manager import get_logger
-from src.plugins.knowledge.src.lpmmconfig import global_config
+from src.chat.knowledge.src.lpmmconfig import global_config
 
 logger = get_logger("lpmm")
 ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))

src/api/config_api.py

@@ -34,14 +34,6 @@ class APIBotConfig:
     gender: str  # 性别
     appearance: str  # 外貌特征描述
 
-    # schedule
-    enable_schedule_gen: bool  # 是否启用日程表
-    enable_schedule_interaction: bool  # 日程表是否影响回复模式
-    prompt_schedule_gen: str  # 日程生成提示词
-    schedule_doing_update_interval: int  # 日程表更新间隔（秒）
-    schedule_temperature: float  # 日程表温度
-    time_zone: str  # 时区
-
     # platforms
     platforms: Dict[str, str]  # 平台信息
 
@@ -164,7 +156,6 @@ class APIBotConfig:
             "groups",
             "personality",
             "identity",
-            "schedule",
             "platforms",
             "chat",
             "normal_chat",

src/chat/__init__.py

@@ -0,0 +1,17 @@
+"""
+MaiMBot插件系统
+包含聊天、情绪、记忆、日程等功能模块
+"""
+
+from src.chat.message_receive.chat_stream import chat_manager
+from src.chat.emoji_system.emoji_manager import emoji_manager
+from src.chat.person_info.relationship_manager import relationship_manager
+from src.chat.normal_chat.willing.willing_manager import willing_manager
+
+# 导出主要组件供外部使用
+__all__ = [
+    "chat_manager",
+    "emoji_manager",
+    "relationship_manager",
+    "willing_manager",
+]

src/chat/emoji_system/emoji_manager.py

@@ -12,7 +12,7 @@ import re
 
 from ...common.database import db
 from ...config.config import global_config
-from ..chat.utils_image import image_path_to_base64, image_manager
+from ..utils.utils_image import image_path_to_base64, image_manager
 from ..models.utils_model import LLMRequest
 from src.common.logger_manager import get_logger
 from rich.traceback import install

src/chat/focus_chat/cycle_analyzer.py

@@ -1,7 +1,7 @@
 import os
 import time
 from typing import List, Dict, Any, Tuple
-from src.plugins.heartFC_chat.heartFC_Cycleinfo import CycleInfo
+from src.chat.focus_chat.heartFC_Cycleinfo import CycleInfo
 from src.common.logger_manager import get_logger
 
 logger = get_logger("cycle_analyzer")

src/chat/focus_chat/cycle_viewer.py

@@ -1,6 +1,6 @@
 import os
 import argparse
-from src.plugins.heartFC_chat.cycle_analyzer import CycleAnalyzer
+from src.chat.focus_chat.cycle_analyzer import CycleAnalyzer
 
 
 def print_section(title: str, width: int = 80):

src/chat/focus_chat/expressors/default_expressor.py

@@ -1,23 +1,23 @@
 import time
 import traceback
 from typing import List, Optional, Dict, Any
-from src.plugins.chat.message import MessageRecv, MessageThinking, MessageSending
-from src.plugins.chat.message import Seg  # Local import needed after move
-from src.plugins.chat.message import UserInfo
-from src.plugins.chat.chat_stream import chat_manager
+from src.chat.message_receive.message import MessageRecv, MessageThinking, MessageSending
+from src.chat.message_receive.message import Seg  # Local import needed after move
+from src.chat.message_receive.message import UserInfo
+from src.chat.message_receive.chat_stream import chat_manager
 from src.common.logger_manager import get_logger
-from src.plugins.models.utils_model import LLMRequest
+from src.chat.models.utils_model import LLMRequest
 from src.config.config import global_config
-from src.plugins.chat.utils_image import image_path_to_base64  # Local import needed after move
-from src.plugins.utils.timer_calculator import Timer  # <--- Import Timer
-from src.plugins.emoji_system.emoji_manager import emoji_manager
-from src.plugins.heartFC_chat.heartflow_prompt_builder import prompt_builder
-from src.plugins.heartFC_chat.heartFC_sender import HeartFCSender
-from src.plugins.chat.utils import process_llm_response
-from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
+from src.chat.utils.utils_image import image_path_to_base64  # Local import needed after move
+from src.chat.utils.timer_calculator import Timer  # <--- Import Timer
+from src.chat.emoji_system.emoji_manager import emoji_manager
+from src.chat.focus_chat.heartflow_prompt_builder import prompt_builder
+from src.chat.focus_chat.heartFC_sender import HeartFCSender
+from src.chat.utils.utils import process_llm_response
+from src.chat.utils.info_catcher import info_catcher_manager
 from src.manager.mood_manager import mood_manager
 from src.heart_flow.utils_chat import get_chat_type_and_target_info
-from src.plugins.chat.chat_stream import ChatStream
+from src.chat.message_receive.chat_stream import ChatStream
 
 logger = get_logger("expressor")
 
@@ -99,6 +99,7 @@ class DefaultExpressor:
                         anchor_message=anchor_message,
                         thinking_id=thinking_id,
                         reason=reasoning,
+                        action_data=action_data,
                     )
 
                 if reply:
@@ -135,6 +136,7 @@ class DefaultExpressor:
         reason: str,
         anchor_message: MessageRecv,
         thinking_id: str,
+        action_data: Dict[str, Any],
     ) -> Optional[List[str]]:
         """
         回复器 (Replier): 核心逻辑，负责生成回复文本。
@@ -160,6 +162,8 @@ class DefaultExpressor:
                 )
             # --- End determining sender_name ---
 
+            target_message = action_data.get("target", "")
+
             # 3. 构建 Prompt
             with Timer("构建Prompt", {}):  # 内部计时器，可选保留
                 prompt = await prompt_builder.build_prompt(
@@ -170,6 +174,7 @@ class DefaultExpressor:
                     current_mind_info="",
                     structured_info="",
                     sender_name=sender_name_for_prompt,  # Pass determined name
+                    target_message=target_message,
                 )
 
             # 4. 调用 LLM 生成回复
@@ -182,9 +187,14 @@ class DefaultExpressor:
 
             try:
                 with Timer("LLM生成", {}):  # 内部计时器，可选保留
+                    # logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n")
                     content, reasoning_content, model_name = await self.express_model.generate_response(prompt)
-                # logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n生成回复: {content}\n")
-                # 捕捉 LLM 输出信息
+
+                    logger.info(f"{self.log_prefix}\nPrompt:\n{prompt}\n---------------------------\n")
+
+                    logger.info(f"想要表达：{in_mind_reply}")
+                    logger.info(f"理由：{reason}")
+                    logger.info(f"生成回复: {content}\n")
                 info_catcher.catch_after_llm_generated(
                     prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=model_name
                 )

src/chat/focus_chat/expressors/exprssion_learner.py

@@ -2,10 +2,10 @@ import time
 import random
 from typing import List, Dict, Optional, Any, Tuple
 from src.common.logger_manager import get_logger
-from src.plugins.models.utils_model import LLMRequest
+from src.chat.models.utils_model import LLMRequest
 from src.config.config import global_config
-from src.plugins.utils.chat_message_builder import get_raw_msg_by_timestamp_random, build_readable_messages
-from src.plugins.heartFC_chat.heartflow_prompt_builder import Prompt, global_prompt_manager
+from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_random, build_readable_messages
+from src.chat.focus_chat.heartflow_prompt_builder import Prompt, global_prompt_manager
 import os
 import json
 
@@ -16,12 +16,12 @@ logger = get_logger("expressor")
 
 
 def init_prompt() -> None:
-    learn_expression_prompt = """
+    learn_style_prompt = """
 {chat_str}
 
 请从上面这段群聊中概括除了人名为"麦麦"之外的人的语言风格，只考虑文字，不要考虑表情包和图片
 不要涉及具体的人名，只考虑语言风格
-思考回复语法，长度和情感
+语言风格包含特殊内容和情感
 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
@@ -29,20 +29,18 @@ def init_prompt() -> None:
 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"想表达某个观点，但不想明说"，使用"反讽的句式"
 当"想说明某个观点，但懒得明说"，使用"懂的都懂"
-当"想搞笑的表现高深的感觉"，使用"文言文句式"
 
 注意不要总结你自己的发言
 现在请你概括
 """
-    Prompt(learn_expression_prompt, "learn_expression_prompt")
+    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
-思考回复语法，长度和情感
+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
@@ -50,20 +48,38 @@ def init_prompt() -> None:
 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"想表达某个观点，但不想明说"，使用"反讽的句式"
 当"想说明某个观点，但懒得明说"，使用"懂的都懂"
-当"想搞笑的表现高深的感觉"，使用"文言文句式"
 
 现在请你概括
 """
     Prompt(personality_expression_prompt, "personality_expression_prompt")
 
+    learn_grammar_prompt = """
+{chat_str}
+
+请从上面这段群聊中概括除了人名为"麦麦"之外的人的语法和句法特点，只考虑纯文字，不要考虑表情包和图片
+不要总结【图片】，【动画表情】，[图片]，[动画表情]，不总结 表情符号
+不要涉及具体的人名，只考虑语法和句法特点,
+语法和句法特点要包括，句子长短（具体字数），如何分局，有何种语病，如何拆分句子。
+总结成如下格式的规律，总结的内容要简洁，不浮夸：
+当"xxx"时，可以"xxx"
+
+例如：
+当"表达观点较复杂"时，使用"省略主语"的句法
+当"不用详细说明的一般表达"时，使用"非常简洁的句子"的句法
+当"需要单纯简单的确认"时，使用"单字或几个字的肯定"的句法
+
+注意不要总结你自己的发言
+现在请你概括
+"""
+    Prompt(learn_grammar_prompt, "learn_grammar_prompt")
+
 
 class ExpressionLearner:
     def __init__(self) -> None:
         self.express_learn_model: LLMRequest = LLMRequest(
             model=global_config.llm_normal,
-            temperature=global_config.llm_normal["temp"],
+            temperature=0.1,
             max_tokens=256,
             request_type="response_heartflow",
         )
@@ -73,17 +89,22 @@ class ExpressionLearner:
         读取/data/expression/learnt/{chat_id}/expressions.json和/data/expression/personality/expressions.json
         返回(learnt_expressions, personality_expressions)
         """
-        learnt_file = os.path.join("data", "expression", "learnt", str(chat_id), "expressions.json")
+        learnt_style_file = os.path.join("data", "expression", "learnt_style", str(chat_id), "expressions.json")
+        learnt_grammar_file = os.path.join("data", "expression", "learnt_grammar", str(chat_id), "expressions.json")
         personality_file = os.path.join("data", "expression", "personality", "expressions.json")
-        learnt_expressions = []
+        learnt_style_expressions = []
+        learnt_grammar_expressions = []
         personality_expressions = []
-        if os.path.exists(learnt_file):
-            with open(learnt_file, "r", encoding="utf-8") as f:
-                learnt_expressions = json.load(f)
+        if os.path.exists(learnt_style_file):
+            with open(learnt_style_file, "r", encoding="utf-8") as f:
+                learnt_style_expressions = json.load(f)
+        if os.path.exists(learnt_grammar_file):
+            with open(learnt_grammar_file, "r", encoding="utf-8") as f:
+                learnt_grammar_expressions = json.load(f)
         if os.path.exists(personality_file):
             with open(personality_file, "r", encoding="utf-8") as f:
                 personality_expressions = json.load(f)
-        return learnt_expressions, personality_expressions
+        return learnt_style_expressions, learnt_grammar_expressions, personality_expressions
 
     def is_similar(self, s1: str, s2: str) -> bool:
         """
@@ -98,23 +119,48 @@ class ExpressionLearner:
         return same / min_len > 0.8
 
     async def learn_and_store_expression(self) -> List[Tuple[str, str, str]]:
-        """选择从当前到最近1小时内的随机10条消息，然后学习这些消息的表达方式"""
-        logger.info("开始学习表达方式...")
-        expressions: Optional[List[Tuple[str, str, str]]] = await self.learn_expression()
-        logger.info(f"学习到{len(expressions) if expressions else 0}条表达方式")
-        # expressions: List[(chat_id, situation, style)]
-        if not expressions:
-            logger.info("没有学习到表达方式")
+        """
+        学习并存储表达方式，分别学习语言风格和句法特点
+        """
+        learnt_style: Optional[List[Tuple[str, str, str]]] = await self.learn_and_store(type="style", num=3)
+        if not learnt_style:
             return []
+
+        learnt_grammar: Optional[List[Tuple[str, str, str]]] = await self.learn_and_store(type="grammar", num=2)
+        if not learnt_grammar:
+            return []
+
+        return learnt_style, learnt_grammar
+
+    async def learn_and_store(self, type: str, num: int = 10) -> List[Tuple[str, str, str]]:
+        """
+        选择从当前到最近1小时内的随机num条消息，然后学习这些消息的表达方式
+        type: "style" or "grammar"
+        """
+        if type == "style":
+            type_str = "语言风格"
+        elif type == "grammar":
+            type_str = "句法特点"
+        else:
+            raise ValueError(f"Invalid type: {type}")
+        logger.info(f"开始学习{type_str}...")
+        learnt_expressions: Optional[List[Tuple[str, str, str]]] = await self.learn_expression(type, num)
+        logger.info(f"学习到{len(learnt_expressions) if learnt_expressions else 0}条{type_str}")
+        # learnt_expressions: List[(chat_id, situation, style)]
+
+        if not learnt_expressions:
+            logger.info(f"没有学习到{type_str}")
+            return []
+
         # 按chat_id分组
         chat_dict: Dict[str, List[Dict[str, str]]] = {}
-        for chat_id, situation, style in expressions:
+        for chat_id, situation, style in learnt_expressions:
             if chat_id not in chat_dict:
                 chat_dict[chat_id] = []
             chat_dict[chat_id].append({"situation": situation, "style": style})
         # 存储到/data/expression/对应chat_id/expressions.json
         for chat_id, expr_list in chat_dict.items():
-            dir_path = os.path.join("data", "expression", "learnt", str(chat_id))
+            dir_path = os.path.join("data", "expression", f"learnt_{type}", str(chat_id))
             os.makedirs(dir_path, exist_ok=True)
             file_path = os.path.join(dir_path, "expressions.json")
             # 若已存在，先读出合并
@@ -154,17 +200,26 @@ class ExpressionLearner:
                     old_data.append(new_expr)
             with open(file_path, "w", encoding="utf-8") as f:
                 json.dump(old_data, f, ensure_ascii=False, indent=2)
-        return expressions
+        return learnt_expressions
 
-    async def learn_expression(self) -> Optional[List[Tuple[str, str, str]]]:
-        """选择从当前到最近1小时内的随机10条消息，然后学习这些消息的表达方式
+    async def learn_expression(self, type: str, num: int = 10) -> Optional[List[Tuple[str, str, str]]]:
+        """选择从当前到最近1小时内的随机num条消息，然后学习这些消息的表达方式
 
         Args:
-            chat_stream (ChatStream): _description_
+            type: "style" or "grammar"
         """
+        if type == "style":
+            type_str = "语言风格"
+            prompt = "learn_style_prompt"
+        elif type == "grammar":
+            type_str = "句法特点"
+            prompt = "learn_grammar_prompt"
+        else:
+            raise ValueError(f"Invalid type: {type}")
+
         current_time = time.time()
         random_msg: Optional[List[Dict[str, Any]]] = get_raw_msg_by_timestamp_random(
-            current_time - 3600 * 24, current_time, limit=10
+            current_time - 3600 * 24, current_time, limit=num
         )
         if not random_msg:
             return None
@@ -173,15 +228,19 @@ class ExpressionLearner:
         random_msg_str: str = await build_readable_messages(random_msg, timestamp_mode="normal")
 
         prompt: str = await global_prompt_manager.format_prompt(
-            "learn_expression_prompt",
+            prompt,
             chat_str=random_msg_str,
         )
 
-        logger.debug(f"学习表达方式的prompt: {prompt}")
+        logger.debug(f"学习{type_str}的prompt: {prompt}")
 
-        response, _ = await self.express_learn_model.generate_response_async(prompt)
+        try:
+            response, _ = await self.express_learn_model.generate_response_async(prompt)
+        except Exception as e:
+            logger.error(f"学习{type_str}失败: {e}")
+            return None
 
-        logger.debug(f"学习表达方式的response: {response}")
+        logger.debug(f"学习{type_str}的response: {response}")
 
         expressions: List[Tuple[str, str, str]] = self.parse_expression_response(response, chat_id)
 
@@ -232,7 +291,13 @@ class ExpressionLearner:
             personality=global_config.expression_style,
         )
         logger.info(f"个性表达方式提取prompt: {prompt}")
-        response, _ = await self.express_learn_model.generate_response_async(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")

src/chat/focus_chat/heartFC_chat.py

@@ -6,32 +6,32 @@ import time
 import traceback
 from collections import deque
 from typing import List, Optional, Dict, Any, Deque, Callable, Coroutine
-from src.plugins.chat.chat_stream import ChatStream
-from src.plugins.chat.chat_stream import chat_manager
+from src.chat.message_receive.chat_stream import ChatStream
+from src.chat.message_receive.chat_stream import chat_manager
 from rich.traceback import install
 from src.common.logger_manager import get_logger
-from src.plugins.models.utils_model import LLMRequest
+from src.chat.models.utils_model import LLMRequest
 from src.config.config import global_config
-from src.plugins.utils.timer_calculator import Timer
+from src.chat.utils.timer_calculator import Timer
 from src.heart_flow.observation.observation import Observation
-from src.plugins.heartFC_chat.heartflow_prompt_builder import prompt_builder
-from src.plugins.heartFC_chat.heartFC_Cycleinfo import CycleDetail
+from src.chat.focus_chat.heartflow_prompt_builder import prompt_builder
+from src.chat.focus_chat.heartFC_Cycleinfo import CycleDetail
 from src.heart_flow.observation.chatting_observation import ChattingObservation
 from src.heart_flow.utils_chat import get_chat_type_and_target_info
-from src.heart_flow.info.info_base import InfoBase
-from src.heart_flow.info.obs_info import ObsInfo
-from src.heart_flow.info.cycle_info import CycleInfo
-from src.heart_flow.info.mind_info import MindInfo
-from src.heart_flow.info.structured_info import StructuredInfo
-from src.plugins.heartFC_chat.info_processors.chattinginfo_processor import ChattingInfoProcessor
-from src.plugins.heartFC_chat.info_processors.mind_processor import MindProcessor
+from src.chat.focus_chat.info.info_base import InfoBase
+from src.chat.focus_chat.info.obs_info import ObsInfo
+from src.chat.focus_chat.info.cycle_info import CycleInfo
+from src.chat.focus_chat.info.mind_info import MindInfo
+from src.chat.focus_chat.info.structured_info import StructuredInfo
+from src.chat.focus_chat.info_processors.chattinginfo_processor import ChattingInfoProcessor
+from src.chat.focus_chat.info_processors.mind_processor import MindProcessor
 from src.heart_flow.observation.memory_observation import MemoryObservation
 from src.heart_flow.observation.hfcloop_observation import HFCloopObservation
 from src.heart_flow.observation.working_observation import WorkingObservation
-from src.plugins.heartFC_chat.info_processors.tool_processor import ToolProcessor
-from src.plugins.heartFC_chat.expressors.default_expressor import DefaultExpressor
-from src.plugins.heartFC_chat.hfc_utils import _create_empty_anchor_message
-from src.plugins.heartFC_chat.memory_activator import MemoryActivator
+from src.chat.focus_chat.info_processors.tool_processor import ToolProcessor
+from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor
+from src.chat.focus_chat.hfc_utils import _create_empty_anchor_message
+from src.chat.focus_chat.memory_activator import MemoryActivator
 
 install(extra_lines=3)
 

src/chat/focus_chat/heartFC_readme.md

@@ -5,7 +5,7 @@ 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)
-[代码位置: src/plugins/heartFC_chat/heartflow_processor.py]
+[代码位置: src/plugins/focus_chat/heartflow_processor.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
@@ -23,7 +23,7 @@ graph TD
   - 消息存储：`storage.store_message()` [行号: 108]
 
 ### 2. 对话管理循环 (HeartFChatting)
-[代码位置: src/plugins/heartFC_chat/heartFC_chat.py]
+[代码位置: src/plugins/focus_chat/focus_chat.py]
 
 HeartFChatting是系统的核心组件，实现了完整的对话管理循环：
 
@@ -55,7 +55,7 @@ graph TD
   * 处理表情：`_handle_emoji()` [行号: 527-567]
 
 ### 3. 回复生成机制 (HeartFCGenerator)
-[代码位置: src/plugins/heartFC_chat/heartFC_generator.py]
+[代码位置: src/plugins/focus_chat/heartFC_generator.py]
 
 回复生成器负责产生高质量的回复内容：
 
@@ -74,7 +74,7 @@ graph TD
   * 响应处理：`_process_response()` [行号: 97-106]
 
 ### 4. 提示词构建系统 (HeartFlowPromptBuilder)
-[代码位置: src/plugins/heartFC_chat/heartflow_prompt_builder.py]
+[代码位置: src/plugins/focus_chat/heartflow_prompt_builder.py]
 
 提示词构建器支持两种工作模式，HeartFC_chat专门使用Focus模式，而Normal模式是为normal_chat设计的：
 
@@ -106,8 +106,8 @@ graph TD
 ## 智能特性
 
 ### 1. 对话决策机制
-- LLM决策工具定义：`PLANNER_TOOL_DEFINITION` [heartFC_chat.py 行号: 13-42]
-- 决策执行：`_planner()` [heartFC_chat.py 行号: 282-386]
+- LLM决策工具定义：`PLANNER_TOOL_DEFINITION` [focus_chat.py 行号: 13-42]
+- 决策执行：`_planner()` [focus_chat.py 行号: 282-386]
 - 考虑因素：
   * 上下文相关性
   * 情感状态
@@ -115,7 +115,7 @@ graph TD
   * 对话时机
 
 ### 2. 状态管理
-[代码位置: src/plugins/heartFC_chat/heartFC_chat.py]
+[代码位置: src/plugins/focus_chat/focus_chat.py]
 - 状态机实现：`HeartFChatting`类 [行号: 44-567]
 - 核心功能：
   * 初始化：`_initialize()` [行号: 89-112]
@@ -123,7 +123,7 @@ graph TD
   * 状态转换：`_handle_loop_completion()` [行号: 166-190]
 
 ### 3. 回复生成策略
-[代码位置: src/plugins/heartFC_chat/heartFC_generator.py]
+[代码位置: src/plugins/focus_chat/heartFC_generator.py]
 - 温度调节：`current_model.temperature = global_config.llm_normal["temp"] * arousal_multiplier` [行号: 48]
 - 生成控制：`_generate_response_with_model()` [行号: 69-95]
 - 响应处理：`_process_response()` [行号: 97-106]
@@ -133,7 +133,7 @@ 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]
-- 状态控制：`INITIAL_DURATION = 60.0` [heartFC_chat.py 行号: 11]
+- 状态控制：`INITIAL_DURATION = 60.0` [focus_chat.py 行号: 11]
 
 ### 优化建议
 1. 调整LLM参数：`temperature`和`max_tokens`

src/chat/focus_chat/heartFC_sender.py

@@ -1,14 +1,11 @@
-# src/plugins/heartFC_chat/heartFC_sender.py
-import asyncio  # 重新导入 asyncio
+import asyncio
 from typing import Dict, Optional  # 重新导入类型
-from ..chat.message import MessageSending, MessageThinking  # 只保留 MessageSending 和 MessageThinking
-
-# from ..message import global_api
-from src.plugins.message.api import global_api
-from ..storage.storage import MessageStorage
-from ..chat.utils import truncate_message
+from src.chat.message_receive.message import MessageSending, MessageThinking
+from src.common.message.api import global_api
+from src.chat.message_receive.storage import MessageStorage
+from src.chat.utils.utils import truncate_message
 from src.common.logger_manager import get_logger
-from src.plugins.chat.utils import calculate_typing_time
+from src.chat.utils.utils import calculate_typing_time
 from rich.traceback import install
 
 install(extra_lines=3)

src/chat/focus_chat/heartflow_processor.py

@@ -2,16 +2,16 @@ import time
 import traceback
 from ..memory_system.Hippocampus import HippocampusManager
 from ...config.config import global_config
-from ..chat.message import MessageRecv
-from ..storage.storage import MessageStorage
-from ..chat.utils import is_mentioned_bot_in_message
+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.heart_flow.heartflow import heartflow
 from src.common.logger_manager import get_logger
-from ..chat.chat_stream import chat_manager
-from ..chat.message_buffer import message_buffer
+from ..message_receive.chat_stream import chat_manager
+from ..message_receive.message_buffer import message_buffer
 from ..utils.timer_calculator import Timer
-from src.plugins.person_info.relationship_manager import relationship_manager
+from src.chat.person_info.relationship_manager import relationship_manager
 from typing import Optional, Tuple, Dict, Any
 
 logger = get_logger("chat")
@@ -215,7 +215,7 @@ class HeartFCProcessor:
                 f"[{current_time}][{mes_name}]"
                 f"{userinfo.user_nickname}:"
                 f"{message.processed_plain_text}"
-                f"[兴趣度: {interested_rate:.2f}]"
+                f"[激活: {interested_rate:.1f}]"
             )
 
             # 8. 关系处理

src/chat/focus_chat/heartflow_prompt_builder.py

@@ -1,21 +1,20 @@
-import random
-from ...config.config import global_config
+from src.config.config import global_config
 from src.common.logger_manager import get_logger
-from ...individuality.individuality import Individuality
-from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
-from src.plugins.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
-from src.plugins.person_info.relationship_manager import relationship_manager
-from src.plugins.chat.utils import get_embedding
+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, Dict, Any
-from ...common.database import db
-from ..chat.utils import get_recent_group_speaker
+from src.common.database import db
+from src.chat.utils.utils import get_recent_group_speaker
 from src.manager.mood_manager import mood_manager
-from ..memory_system.Hippocampus import HippocampusManager
-from ..schedule.schedule_generator import bot_schedule
-from ..knowledge.knowledge_lib import qa_manager
-from .expressors.exprssion_learner import expression_learner
+from src.chat.memory_system.Hippocampus import HippocampusManager
+from src.chat.knowledge.knowledge_lib import qa_manager
+from src.chat.focus_chat.expressors.exprssion_learner import expression_learner
 import traceback
+import random
 
 
 logger = get_logger("prompt")
@@ -24,21 +23,22 @@ logger = get_logger("prompt")
 def init_prompt():
     Prompt(
         """
-你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用：
-{language_habits}
-
-不要生硬使用，而是结合到表达中
+你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中：
+{style_habbits}
 
+你现在正在群里聊天，以下是群里正在进行的聊天内容：
 {chat_info}
 
-你只需要了解聊天记录中的内容就好
+以上是聊天内容，你需要了解聊天记录中的内容
 
 {chat_target}
-你的名字是{bot_name}，{prompt_personality}，你想表达：{in_mind_reply},原因是：{reason}
-
-请注意，请根据你想表达的内容和原因，参考聊天内容，组织一条日常且口语化的回复
-要求回复尽量简短一些。{reply_style2}。{prompt_ger}。可以参考贴吧，知乎或者微博的回复风格，你可以完全重组回复，保留最基本的表达含义就好，但注意简短，保持一个话题。
-{reply_style1}，说中文，不要刻意突出自身学科背景。不要浮夸，不要用夸张修辞，平淡一些。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 )，只输出一条回复就好。
+你的名字是{bot_name}，{prompt_personality}，在这聊天中，"{target_message}"引起了你的注意，你想表达：{in_mind_reply},原因是：{reason}。你现在要思考怎么回复
+你需要使用合适的语法和句法，参考聊天内容，组织一条日常且口语化的回复。
+请你根据情景使用以下句法：
+{grammar_habbits}
+回复尽量简短一些。可以参考贴吧，知乎和微博的回复风格，你可以完全重组回复，保留最基本的表达含义就好，但注意回复要简短。
+回复不要浮夸，不要用夸张修辞，平淡一些。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 )，只输出一条回复就好。
+现在，你说：
 """,
         "heart_flow_prompt",
     )
@@ -119,7 +119,6 @@ def init_prompt():
 {memory_prompt}
 {relation_prompt}
 {prompt_info}
-{schedule_prompt}
 {chat_target}
 {chat_talking_prompt}
 现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言或者回复这条消息。\n
@@ -137,7 +136,7 @@ def init_prompt():
         "你回忆起：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n",
         "memory_prompt",
     )
-    Prompt("你现在正在做的事情是：{schedule_info}", "schedule_prompt")
+
     Prompt("\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n", "knowledge_prompt")
 
     # --- Template for HeartFChatting (FOCUSED mode) ---
@@ -168,7 +167,6 @@ def init_prompt():
 {memory_prompt}
 {relation_prompt}
 {prompt_info}
-{schedule_prompt}
 你正在和 {sender_name} 私聊。
 聊天记录如下：
 {chat_talking_prompt}
@@ -186,7 +184,7 @@ def init_prompt():
 
 
 async def _build_prompt_focus(
-    reason, current_mind_info, structured_info, chat_stream, sender_name, in_mind_reply
+    reason, current_mind_info, structured_info, chat_stream, sender_name, in_mind_reply, target_message
 ) -> str:
     individuality = Individuality.get_instance()
     prompt_personality = individuality.get_prompt(x_person=0, level=2)
@@ -206,38 +204,12 @@ async def _build_prompt_focus(
     chat_talking_prompt = await build_readable_messages(
         message_list_before_now,
         replace_bot_name=True,
-        merge_messages=False,
-        timestamp_mode="normal",
+        merge_messages=True,
+        timestamp_mode="relative",
         read_mark=0.0,
         truncate=True,
     )
 
-    prompt_ger = ""
-    if random.random() < 0.04:
-        prompt_ger += "你喜欢用倒装句"
-    if random.random() < 0.02:
-        prompt_ger += "你喜欢用反问句"
-
-    reply_styles1 = [
-        ("给出日常且口语化的回复，平淡一些", 0.4),
-        ("给出非常简短的回复", 0.4),
-        ("给出缺失主语的回复，简短", 0.15),
-        ("给出带有语病的回复，朴实平淡", 0.05),
-    ]
-    reply_style1_chosen = random.choices(
-        [style[0] for style in reply_styles1], weights=[style[1] for style in reply_styles1], k=1
-    )[0]
-
-    reply_styles2 = [
-        ("不要回复的太有条理，可以有个性", 0.7),
-        ("不要回复的太有条理，可以复读，但是不要复读自己说的话", 0.1),
-        ("回复的认真一些", 0.1),
-        ("可以回复单个表情符号", 0.05),
-    ]
-    reply_style2_chosen = random.choices(
-        [style[0] for style in reply_styles2], weights=[style[1] for style in reply_styles2], k=1
-    )[0]
-
     if structured_info:
         structured_info_prompt = await global_prompt_manager.format_prompt(
             "info_from_tools", structured_info=structured_info
@@ -246,33 +218,36 @@ async def _build_prompt_focus(
         structured_info_prompt = ""
 
     # 从/data/expression/对应chat_id/expressions.json中读取表达方式
-    learnt_expressions, personality_expressions = await expression_learner.get_expression_by_chat_id(
-        chat_stream.stream_id
-    )
-    language_habits = []
-    # 1. learnt_expressions加权随机选5条
-    if learnt_expressions:
-        weights = [expr["count"] for expr in learnt_expressions]
-        selected_learnt = weighted_sample_no_replacement(learnt_expressions, weights, 5)
+    (
+        learnt_style_expressions,
+        learnt_grammar_expressions,
+        personality_expressions,
+    ) = await expression_learner.get_expression_by_chat_id(chat_stream.stream_id)
+
+    style_habbits = []
+    grammar_habbits = []
+    # 1. learnt_expressions加权随机选3条
+    if learnt_style_expressions:
+        weights = [expr["count"] for expr in learnt_style_expressions]
+        selected_learnt = weighted_sample_no_replacement(learnt_style_expressions, weights, 3)
         for expr in selected_learnt:
             if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-                language_habits.append(f"当{expr['situation']}时，使用 {expr['style']}")
-    # 2. personality_expressions随机选1条
+                style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
+    # 2. learnt_grammar_expressions加权随机选3条
+    if learnt_grammar_expressions:
+        weights = [expr["count"] for expr in learnt_grammar_expressions]
+        selected_learnt = weighted_sample_no_replacement(learnt_grammar_expressions, weights, 3)
+        for expr in selected_learnt:
+            if isinstance(expr, dict) and "situation" in expr and "style" in expr:
+                grammar_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
+    # 3. personality_expressions随机选1条
     if personality_expressions:
         expr = random.choice(personality_expressions)
         if isinstance(expr, dict) and "situation" in expr and "style" in expr:
-            language_habits.append(f"当{expr['situation']}时，使用 {expr['style']}")
-    # 3. 如果都没有，给默认
-    if not language_habits:
-        language_habits = [
-            "当表示惊讶无语是，使用 不是？",
-            "当表示无语时，使用 阿这",
-            "当用调侃的方式称呼某人，使用 哈基X",
-            "当表示讽刺的赞同时，使用 对对对",
-            "当你想说明某个观点，但懒得明说，使用 懂的都懂",
-        ]
-        language_habits = random.sample(language_habits, 6)
-    language_habits_str = "\n".join(language_habits)
+            style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}")
+
+    style_habbits_str = "\n".join(style_habbits)
+    grammar_habbits_str = "\n".join(grammar_habbits)
 
     logger.debug("开始构建 focus prompt")
 
@@ -286,20 +261,17 @@ async def _build_prompt_focus(
         prompt = await global_prompt_manager.format_prompt(
             template_name,
             # info_from_tools=structured_info_prompt,
-            language_habits=language_habits_str,
+            style_habbits=style_habbits_str,
+            grammar_habbits=grammar_habbits_str,
             chat_target=chat_target_1,  # Used in group template
             # chat_talking_prompt=chat_talking_prompt,
             chat_info=chat_talking_prompt,
             bot_name=global_config.BOT_NICKNAME,
             # prompt_personality=prompt_personality,
             prompt_personality="",
-            # chat_target_2=chat_target_2,  # Used in group template
-            # current_mind_info=current_mind_info,
-            reply_style2=reply_style2_chosen,
-            reply_style1=reply_style1_chosen,
             reason=reason,
             in_mind_reply=in_mind_reply,
-            prompt_ger=prompt_ger,
+            target_message=target_message,
             # moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
             # sender_name is not used in the group template
         )
@@ -314,10 +286,7 @@ async def _build_prompt_focus(
             prompt_personality=prompt_personality,
             # chat_target and chat_target_2 are not used in private template
             current_mind_info=current_mind_info,
-            reply_style2=reply_style2_chosen,
-            reply_style1=reply_style1_chosen,
             reason=reason,
-            prompt_ger=prompt_ger,
             moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
         )
     # --- End choosing template ---
@@ -464,13 +433,6 @@ class PromptBuilder:
         end_time = time.time()
         logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
 
-        if global_config.ENABLE_SCHEDULE_GEN:
-            schedule_prompt = await global_prompt_manager.format_prompt(
-                "schedule_prompt", schedule_info=bot_schedule.get_current_num_task(num=1, time_info=False)
-            )
-        else:
-            schedule_prompt = ""
-
         logger.debug("开始构建 normal prompt")
 
         # --- Choose template and format based on chat type ---
@@ -486,7 +448,6 @@ class PromptBuilder:
                 sender_name=effective_sender_name,
                 memory_prompt=memory_prompt,
                 prompt_info=prompt_info,
-                schedule_prompt=schedule_prompt,
                 chat_target=chat_target_1,
                 chat_target_2=chat_target_2,
                 chat_talking_prompt=chat_talking_prompt,
@@ -511,7 +472,6 @@ class PromptBuilder:
                 sender_name=effective_sender_name,
                 memory_prompt=memory_prompt,
                 prompt_info=prompt_info,
-                schedule_prompt=schedule_prompt,
                 chat_talking_prompt=chat_talking_prompt,
                 message_txt=message_txt,
                 bot_name=global_config.BOT_NICKNAME,
@@ -861,7 +821,7 @@ class PromptBuilder:
             return "[构建 Planner Prompt 时出错]"
 
 
-def weighted_sample_no_replacement(items, weights, k):
+def weighted_sample_no_replacement(items, weights, k) -> list:
     """
     加权且不放回地随机抽取k个元素。
 
@@ -872,6 +832,8 @@ def weighted_sample_no_replacement(items, weights, k):
     返回：
         selected: 按权重加权且不重复抽取的k个元素组成的列表
 
+        如果 items 中的元素不足 k 个，就只会返回所有可用的元素
+
     实现思路：
         每次从当前池中按权重加权随机选出一个元素，选中后将其从池中移除，重复k次。
         这样保证了：

src/chat/focus_chat/hfc_utils.py

@@ -1,9 +1,9 @@
 import time
 import traceback
 from typing import Optional
-from src.plugins.chat.message import MessageRecv, BaseMessageInfo
-from src.plugins.chat.chat_stream import ChatStream
-from src.plugins.chat.message import UserInfo
+from src.chat.message_receive.message import MessageRecv, BaseMessageInfo
+from src.chat.message_receive.chat_stream import ChatStream
+from src.chat.message_receive.message import UserInfo
 from src.common.logger_manager import get_logger
 import json
 

src/chat/focus_chat/info_processors/base_processor.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 from typing import List, Any, Optional, Dict
-from src.heart_flow.info.info_base import InfoBase
+from src.chat.focus_chat.info.info_base import InfoBase
 from src.heart_flow.observation.observation import Observation
 from src.common.logger_manager import get_logger
 

src/chat/focus_chat/info_processors/chattinginfo_processor.py

@@ -1,15 +1,15 @@
 from typing import List, Optional, Any
-from src.heart_flow.info.obs_info import ObsInfo
+from src.chat.focus_chat.info.obs_info import ObsInfo
 from src.heart_flow.observation.observation import Observation
-from src.heart_flow.info.info_base import InfoBase
+from src.chat.focus_chat.info.info_base import InfoBase
 from .base_processor import BaseProcessor
 from src.common.logger_manager import get_logger
 from src.heart_flow.observation.chatting_observation import ChattingObservation
 from src.heart_flow.observation.hfcloop_observation import HFCloopObservation
-from src.heart_flow.info.cycle_info import CycleInfo
+from src.chat.focus_chat.info.cycle_info import CycleInfo
 from datetime import datetime
 from typing import Dict
-from src.plugins.models.utils_model import LLMRequest
+from src.chat.models.utils_model import LLMRequest
 from src.config.config import global_config
 
 logger = get_logger("observation")

src/chat/focus_chat/info_processors/mind_processor.py

@@ -1,28 +1,28 @@
 from src.heart_flow.observation.chatting_observation import ChattingObservation
 from src.heart_flow.observation.observation import Observation
-from src.plugins.models.utils_model import LLMRequest
+from src.chat.models.utils_model import LLMRequest
 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
 import random
-from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
-from src.plugins.utils.json_utils import safe_json_dumps
-from src.plugins.chat.chat_stream import chat_manager
+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
 import difflib
-from src.plugins.person_info.relationship_manager import relationship_manager
+from src.chat.person_info.relationship_manager import relationship_manager
 from .base_processor import BaseProcessor
-from src.heart_flow.info.mind_info import MindInfo
+from src.chat.focus_chat.info.mind_info import MindInfo
 from typing import List, Optional
 from src.heart_flow.observation.hfcloop_observation import HFCloopObservation
-from src.plugins.heartFC_chat.info_processors.processor_utils import (
+from src.chat.focus_chat.info_processors.processor_utils import (
     calculate_similarity,
     calculate_replacement_probability,
     get_spark,
 )
 from typing import Dict
-from src.heart_flow.info.info_base import InfoBase
+from src.chat.focus_chat.info.info_base import InfoBase
 
 logger = get_logger("sub_heartflow")
 

src/chat/focus_chat/info_processors/tool_processor.py

@@ -1,18 +1,18 @@
 from src.heart_flow.observation.chatting_observation import ChattingObservation
-from src.plugins.models.utils_model import LLMRequest
+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.plugins.utils.prompt_builder import Prompt, global_prompt_manager
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.tools.tool_use import ToolUser
-from src.plugins.utils.json_utils import process_llm_tool_calls
-from src.plugins.person_info.relationship_manager import relationship_manager
+from src.chat.utils.json_utils import process_llm_tool_calls
+from src.chat.person_info.relationship_manager import relationship_manager
 from .base_processor import BaseProcessor
 from typing import List, Optional, Dict
 from src.heart_flow.observation.observation import Observation
 from src.heart_flow.observation.working_observation import WorkingObservation
-from src.heart_flow.info.structured_info import StructuredInfo
+from src.chat.focus_chat.info.structured_info import StructuredInfo
 
 logger = get_logger("tool_use")
 

src/chat/focus_chat/memory_activator.py

@@ -1,12 +1,12 @@
 from src.heart_flow.observation.chatting_observation import ChattingObservation
 from src.heart_flow.observation.working_observation import WorkingObservation
 from src.heart_flow.observation.hfcloop_observation import HFCloopObservation
-from src.plugins.models.utils_model import LLMRequest
+from src.chat.models.utils_model import LLMRequest
 from src.config.config import global_config
 from src.common.logger_manager import get_logger
-from src.plugins.utils.prompt_builder import Prompt
+from src.chat.utils.prompt_builder import Prompt
 from datetime import datetime
-from src.plugins.memory_system.Hippocampus import HippocampusManager
+from src.chat.memory_system.Hippocampus import HippocampusManager
 from typing import List, Dict
 
 

src/chat/memory_system/Hippocampus.py

@@ -11,14 +11,14 @@ import networkx as nx
 import numpy as np
 from collections import Counter
 from ...common.database import db
-from ...plugins.models.utils_model import LLMRequest
+from ...chat.models.utils_model import LLMRequest
 from src.common.logger_manager import get_logger
-from src.plugins.memory_system.sample_distribution import MemoryBuildScheduler  # 分布生成器
+from src.chat.memory_system.sample_distribution import MemoryBuildScheduler  # 分布生成器
 from ..utils.chat_message_builder import (
     get_raw_msg_by_timestamp,
     build_readable_messages,
 )  # 导入 build_readable_messages
-from ..chat.utils import translate_timestamp_to_human_readable
+from ..utils.utils import translate_timestamp_to_human_readable
 from .memory_config import MemoryConfig
 from rich.traceback import install
 

src/chat/memory_system/debug_memory.py

@@ -6,7 +6,7 @@ import os
 
 # 添加项目根目录到系统路径
 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))))
-from src.plugins.memory_system.Hippocampus import HippocampusManager
+from src.chat.memory_system.Hippocampus import HippocampusManager
 from src.config.config import global_config
 from rich.traceback import install
 

src/chat/message_receive/__init__.py

@@ -2,7 +2,7 @@ from ..emoji_system.emoji_manager import emoji_manager
 from ..person_info.relationship_manager import relationship_manager
 from .chat_stream import chat_manager
 from .message_sender import message_manager
-from ..storage.storage import MessageStorage
+from .storage import MessageStorage
 
 
 __all__ = [

src/chat/message_receive/bot.py

@@ -3,13 +3,13 @@ from typing import Dict, Any
 
 from src.common.logger_manager import get_logger
 from src.manager.mood_manager import mood_manager  # 导入情绪管理器
-from .chat_stream import chat_manager
-from .message import MessageRecv
-from .only_message_process import MessageProcessor
-from ..PFC.pfc_manager import PFCManager
-from ..heartFC_chat.heartflow_processor import HeartFCProcessor
-from ..utils.prompt_builder import Prompt, global_prompt_manager
-from ...config.config import global_config
+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.utils.prompt_builder import Prompt, global_prompt_manager
+from src.config.config import global_config
 
 # 定义日志配置
 

src/chat/message_receive/message.py

@@ -7,7 +7,7 @@ import urllib3
 
 from src.common.logger_manager import get_logger
 from .chat_stream import ChatStream
-from .utils_image import image_manager
+from ..utils.utils_image import image_manager
 from maim_message import Seg, UserInfo, BaseMessageInfo, MessageBase
 from rich.traceback import install
 

src/chat/message_receive/message_sender.py

@@ -3,14 +3,14 @@ import asyncio
 import time
 from asyncio import Task
 from typing import Union
-from src.plugins.message.api import global_api
+from src.common.message.api import global_api
 
 # from ...common.database import db # 数据库依赖似乎不需要了，注释掉
 from .message import MessageSending, MessageThinking, MessageSet
 
-from ..storage.storage import MessageStorage
+from .storage import MessageStorage
 from ...config.config import global_config
-from .utils import truncate_message, calculate_typing_time, count_messages_between
+from ..utils.utils import truncate_message, calculate_typing_time, count_messages_between
 
 from src.common.logger_manager import get_logger
 from rich.traceback import install

src/chat/message_receive/storage.py

@@ -2,8 +2,8 @@ import re
 from typing import Union
 
 from ...common.database import db
-from ..chat.message import MessageSending, MessageRecv
-from ..chat.chat_stream import ChatStream
+from .message import MessageSending, MessageRecv
+from .chat_stream import ChatStream
 from src.common.logger import get_module_logger
 
 logger = get_module_logger("message_storage")

src/chat/models/utils_model.py

@@ -157,7 +157,7 @@ class LLMRequest:
             completion_tokens: 输出token数
             total_tokens: 总token数
             user_id: 用户ID，默认为system
-            request_type: 请求类型(chat/embedding/image/topic/schedule)
+            request_type: 请求类型
             endpoint: API端点
         """
         # 如果 request_type 为 None，则使用实例变量中的值

src/chat/normal_chat/normal_chat.py

@@ -10,17 +10,17 @@ from maim_message import UserInfo, Seg
 from src.common.logger_manager import get_logger
 from src.heart_flow.utils_chat import get_chat_type_and_target_info
 from src.manager.mood_manager import mood_manager
-from src.plugins.chat.chat_stream import ChatStream, chat_manager
-from src.plugins.person_info.relationship_manager import relationship_manager
-from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
-from src.plugins.utils.timer_calculator import Timer
+from src.chat.message_receive.chat_stream import ChatStream, chat_manager
+from src.chat.person_info.relationship_manager import relationship_manager
+from src.chat.utils.info_catcher import info_catcher_manager
+from src.chat.utils.timer_calculator import Timer
 from .normal_chat_generator import NormalChatGenerator
-from ..chat.message import MessageSending, MessageRecv, MessageThinking, MessageSet
-from ..chat.message_sender import message_manager
-from ..chat.utils_image import image_path_to_base64
-from ..emoji_system.emoji_manager import emoji_manager
-from ..willing.willing_manager import willing_manager
-from ...config.config import global_config
+from ..message_receive.message import MessageSending, MessageRecv, MessageThinking, MessageSet
+from src.chat.message_receive.message_sender import message_manager
+from src.chat.utils.utils_image import image_path_to_base64
+from src.chat.emoji_system.emoji_manager import emoji_manager
+from src.chat.normal_chat.willing.willing_manager import willing_manager
+from src.config.config import global_config
 
 logger = get_logger("chat")
 

src/chat/normal_chat/normal_chat_generator.py

@@ -2,12 +2,12 @@ from typing import List, Optional, Tuple, Union
 import random
 from ..models.utils_model import LLMRequest
 from ...config.config import global_config
-from ..chat.message import MessageThinking
-from .heartflow_prompt_builder import prompt_builder
-from ..chat.utils import process_llm_response
-from ..utils.timer_calculator import Timer
+from ..message_receive.message import MessageThinking
+from src.chat.focus_chat.heartflow_prompt_builder import prompt_builder
+from src.chat.utils.utils import process_llm_response
+from src.chat.utils.timer_calculator import Timer
 from src.common.logger_manager import get_logger
-from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
+from src.chat.utils.info_catcher import info_catcher_manager
 
 
 logger = get_logger("llm")

src/chat/normal_chat/willing/willing_manager.py

@@ -1,9 +1,9 @@
 from src.common.logger import LogConfig, WILLING_STYLE_CONFIG, LoguruLogger, get_module_logger
 from dataclasses import dataclass
-from ...config.config import global_config, BotConfig
-from ..chat.chat_stream import ChatStream, GroupInfo
-from ..chat.message import MessageRecv
-from ..person_info.person_info import person_info_manager, PersonInfoManager
+from src.config.config import global_config, BotConfig
+from src.chat.message_receive.chat_stream import ChatStream, GroupInfo
+from src.chat.message_receive.message import MessageRecv
+from src.chat.person_info.person_info import person_info_manager, PersonInfoManager
 from abc import ABC, abstractmethod
 import importlib
 from typing import Dict, Optional

src/chat/person_info/person_info.py

@@ -6,7 +6,7 @@ from typing import Any, Callable, Dict
 import datetime
 import asyncio
 import numpy as np
-from src.plugins.models.utils_model import LLMRequest
+from src.chat.models.utils_model import LLMRequest
 from src.config.config import global_config
 from src.individuality.individuality import Individuality
 

src/chat/person_info/relationship_manager.py

@@ -1,5 +1,5 @@
 from src.common.logger_manager import get_logger
-from ..chat.chat_stream import ChatStream
+from ..message_receive.chat_stream import ChatStream
 import math
 from bson.decimal128 import Decimal128
 from .person_info import person_info_manager

src/chat/utils/chat_message_builder.py

@@ -1,24 +1,11 @@
 from src.config.config import global_config
-
-# 不再直接使用 db
-# from src.common.database import db
-# 移除 logger 和 traceback，因为错误处理移至 repository
-# from src.common.logger import get_module_logger
-# import traceback
 from typing import List, Dict, Any, Tuple  # 确保类型提示被导入
 import time  # 导入 time 模块以获取当前时间
 import random
 import re
-
-# 导入新的 repository 函数
 from src.common.message_repository import find_messages, count_messages
-
-# 导入 PersonInfoManager 和时间转换工具
-from src.plugins.person_info.person_info import person_info_manager
-from src.plugins.chat.utils import translate_timestamp_to_human_readable
-
-# 不再需要文件级别的 logger
-# logger = get_module_logger(__name__)
+from src.chat.person_info.person_info import person_info_manager
+from src.chat.utils.utils import translate_timestamp_to_human_readable
 
 
 def get_raw_msg_by_timestamp(

src/chat/utils/info_catcher.py

@@ -1,5 +1,5 @@
 from src.config.config import global_config
-from src.plugins.chat.message import MessageRecv, MessageSending, Message
+from src.chat.message_receive.message import MessageRecv, MessageSending, Message
 from src.common.database import db
 import time
 import traceback

src/chat/utils/utils.py

@@ -10,9 +10,9 @@ from pymongo.errors import PyMongoError
 
 from src.common.logger import get_module_logger
 from src.manager.mood_manager import mood_manager
-from .message import MessageRecv
+from ..message_receive.message import MessageRecv
 from ..models.utils_model import LLMRequest
-from ..utils.typo_generator import ChineseTypoGenerator
+from .typo_generator import ChineseTypoGenerator
 from ...common.database import db
 from ...config.config import global_config
 

src/common/logger.py

@@ -8,7 +8,7 @@ from dotenv import load_dotenv
 
 
 # 加载 .env 文件
-env_path = Path(__file__).resolve().parent.parent.parent / ".env"
+env_path = Path(os.getcwd()) / ".env"
 load_dotenv(dotenv_path=env_path)
 
 # 保存原生处理器ID
@@ -29,8 +29,7 @@ _handler_registry: dict[str, List[int]] = {}
 _custom_style_handlers: dict[Tuple[str, str], List[int]] = {}  # 记录自定义样式处理器ID
 
 # 获取日志存储根地址
-current_file_path = Path(__file__).resolve()
-ROOT_PATH = os.path.abspath(os.path.join(current_file_path, "..", ".."))
+ROOT_PATH = os.getcwd()
 LOG_ROOT = str(ROOT_PATH) + "/" + "logs"
 
 SIMPLE_OUTPUT = os.getenv("SIMPLE_OUTPUT", "false").strip().lower()

src/common/logger_manager.py

@@ -9,7 +9,6 @@ from src.common.logger import (
     RELATION_STYLE_CONFIG,
     CONFIG_STYLE_CONFIG,
     HEARTFLOW_STYLE_CONFIG,
-    SCHEDULE_STYLE_CONFIG,
     LLM_STYLE_CONFIG,
     CHAT_STYLE_CONFIG,
     EMOJI_STYLE_CONFIG,
@@ -56,7 +55,6 @@ MODULE_LOGGER_CONFIGS = {
     "relation": RELATION_STYLE_CONFIG,  # 关系
     "config": CONFIG_STYLE_CONFIG,  # 配置
     "heartflow": HEARTFLOW_STYLE_CONFIG,  # 麦麦大脑袋
-    "schedule": SCHEDULE_STYLE_CONFIG,  # 在干嘛
     "llm": LLM_STYLE_CONFIG,  # 麦麦组织语言
     "chat": CHAT_STYLE_CONFIG,  # 见闻
     "emoji": EMOJI_STYLE_CONFIG,  # 表情包

src/config/config.py

@@ -2,7 +2,6 @@ import os
 import re
 from dataclasses import dataclass, field
 from typing import Dict, List, Optional
-from dateutil import tz
 
 import tomli
 import tomlkit
@@ -167,13 +166,6 @@ class BotConfig:
     gender: str = "男"  # 性别
     appearance: str = "用几句话描述外貌特征"  # 外貌特征
 
-    # schedule
-    ENABLE_SCHEDULE_GEN: bool = False  # 是否启用日程生成
-    PROMPT_SCHEDULE_GEN = "无日程"
-    SCHEDULE_DOING_UPDATE_INTERVAL: int = 300  # 日程表更新间隔 单位秒
-    SCHEDULE_TEMPERATURE: float = 0.5  # 日程表温度，建议0.5-1.0
-    TIME_ZONE: str = "Asia/Shanghai"  # 时区
-
     # chat
     allow_focus_mode: bool = True  # 是否允许专注聊天状态
 
@@ -374,24 +366,6 @@ class BotConfig:
                 config.gender = identity_config.get("gender", config.gender)
                 config.appearance = identity_config.get("appearance", config.appearance)
 
-        def schedule(parent: dict):
-            schedule_config = parent["schedule"]
-            config.ENABLE_SCHEDULE_GEN = schedule_config.get("enable_schedule_gen", config.ENABLE_SCHEDULE_GEN)
-            config.PROMPT_SCHEDULE_GEN = schedule_config.get("prompt_schedule_gen", config.PROMPT_SCHEDULE_GEN)
-            config.SCHEDULE_DOING_UPDATE_INTERVAL = schedule_config.get(
-                "schedule_doing_update_interval", config.SCHEDULE_DOING_UPDATE_INTERVAL
-            )
-            logger.info(
-                f"载入自定义日程prompt:{schedule_config.get('prompt_schedule_gen', config.PROMPT_SCHEDULE_GEN)}"
-            )
-            if config.INNER_VERSION in SpecifierSet(">=1.0.2"):
-                config.SCHEDULE_TEMPERATURE = schedule_config.get("schedule_temperature", config.SCHEDULE_TEMPERATURE)
-                time_zone = schedule_config.get("time_zone", config.TIME_ZONE)
-                if tz.gettz(time_zone) is None:
-                    logger.error(f"无效的时区: {time_zone}，使用默认值: {config.TIME_ZONE}")
-                else:
-                    config.TIME_ZONE = time_zone
-
         def emoji(parent: dict):
             emoji_config = parent["emoji"]
             config.EMOJI_CHECK_INTERVAL = emoji_config.get("check_interval", config.EMOJI_CHECK_INTERVAL)
@@ -681,7 +655,6 @@ class BotConfig:
             "groups": {"func": groups, "support": ">=0.0.0"},
             "personality": {"func": personality, "support": ">=0.0.0"},
             "identity": {"func": identity, "support": ">=1.2.4"},
-            "schedule": {"func": schedule, "support": ">=0.0.11", "necessary": False},
             "emoji": {"func": emoji, "support": ">=0.0.0"},
             "model": {"func": model, "support": ">=0.0.0"},
             "memory": {"func": memory, "support": ">=0.0.0", "necessary": False},

src/experimental/PFC/action_planner.py

@@ -1,14 +1,14 @@
 import time
 from typing import Tuple, Optional  # 增加了 Optional
 from src.common.logger_manager import get_logger
-from ..models.utils_model import LLMRequest
-from ...config.config import global_config
-from .chat_observer import ChatObserver
-from .pfc_utils import get_items_from_json
+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 .observation_info import ObservationInfo
-from .conversation_info import ConversationInfo
-from src.plugins.utils.chat_message_builder import build_readable_messages
+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
 
 
 logger = get_logger("pfc_action_planner")

src/experimental/PFC/chat_observer.py

@@ -4,9 +4,13 @@ import traceback
 from typing import Optional, Dict, Any, List
 from src.common.logger import get_module_logger
 from maim_message import UserInfo
-from ...config.config import global_config
-from .chat_states import NotificationManager, create_new_message_notification, create_cold_chat_notification
-from .message_storage import MongoDBMessageStorage
+from src.config.config import global_config
+from src.experimental.PFC.chat_states import (
+    NotificationManager,
+    create_new_message_notification,
+    create_cold_chat_notification,
+)
+from src.experimental.PFC.message_storage import MongoDBMessageStorage
 from rich.traceback import install
 
 install(extra_lines=3)

src/experimental/PFC/conversation.py

@@ -3,11 +3,11 @@ import asyncio
 import datetime
 
 # from .message_storage import MongoDBMessageStorage
-from src.plugins.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
+from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
 
 # from ...config.config import global_config
 from typing import Dict, Any, Optional
-from ..chat.message import Message
+from src.chat.message_receive.message import Message
 from .pfc_types import ConversationState
 from .pfc import ChatObserver, GoalAnalyzer
 from .message_sender import DirectMessageSender
@@ -16,9 +16,9 @@ from .action_planner import ActionPlanner
 from .observation_info import ObservationInfo
 from .conversation_info import ConversationInfo  # 确保导入 ConversationInfo
 from .reply_generator import ReplyGenerator
-from ..chat.chat_stream import ChatStream
-from maim_message import UserInfo
-from src.plugins.chat.chat_stream import chat_manager
+from src.chat.message_receive.chat_stream import ChatStream
+from src.chat.message_receive.message import UserInfo
+from src.chat.message_receive.chat_stream import chat_manager
 from .pfc_KnowledgeFetcher import KnowledgeFetcher
 from .waiter import Waiter
 

src/experimental/PFC/message_sender.py

@@ -1,13 +1,13 @@
 import time
 from typing import Optional
 from src.common.logger import get_module_logger
-from ..chat.chat_stream import ChatStream
-from ..chat.message import Message
+from src.chat.message_receive.chat_stream import ChatStream
+from src.chat.message_receive.message import Message
 from maim_message import UserInfo, Seg
-from src.plugins.chat.message import MessageSending, MessageSet
-from src.plugins.chat.message_sender import message_manager
-from ..storage.storage import MessageStorage
-from ...config.config import global_config
+from src.chat.message_receive.message import MessageSending, MessageSet
+from src.chat.message_receive.message_sender import message_manager
+from src.chat.message_receive.storage import MessageStorage
+from src.config.config import global_config
 from rich.traceback import install
 
 install(extra_lines=3)

src/experimental/PFC/observation_info.py

@@ -2,9 +2,9 @@ from typing import List, Optional, Dict, Any, Set
 from maim_message import UserInfo
 import time
 from src.common.logger import get_module_logger
-from .chat_observer import ChatObserver
-from .chat_states import NotificationHandler, NotificationType, Notification
-from src.plugins.utils.chat_message_builder import build_readable_messages
+from src.experimental.PFC.chat_observer import ChatObserver
+from src.experimental.PFC.chat_states import NotificationHandler, NotificationType, Notification
+from src.chat.utils.chat_message_builder import build_readable_messages
 import traceback  # 导入 traceback 用于调试
 
 logger = get_module_logger("observation_info")

src/experimental/PFC/pfc.py

@@ -1,13 +1,13 @@
 from typing import List, Tuple, TYPE_CHECKING
 from src.common.logger import get_module_logger
-from ..models.utils_model import LLMRequest
-from ...config.config import global_config
-from .chat_observer import ChatObserver
-from .pfc_utils import get_items_from_json
+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 .conversation_info import ConversationInfo
-from .observation_info import ObservationInfo
-from src.plugins.utils.chat_message_builder import build_readable_messages
+from src.experimental.PFC.conversation_info import ConversationInfo
+from src.experimental.PFC.observation_info import ObservationInfo
+from src.chat.utils.chat_message_builder import build_readable_messages
 from rich.traceback import install
 
 install(extra_lines=3)

src/experimental/PFC/pfc_KnowledgeFetcher.py

@@ -1,11 +1,11 @@
 from typing import List, Tuple
 from src.common.logger import get_module_logger
-from src.plugins.memory_system.Hippocampus import HippocampusManager
-from ..models.utils_model import LLMRequest
-from ...config.config import global_config
-from ..chat.message import Message
-from ..knowledge.knowledge_lib import qa_manager
-from ..utils.chat_message_builder import build_readable_messages
+from src.chat.memory_system.Hippocampus import HippocampusManager
+from src.chat.models.utils_model import LLMRequest
+from src.config.config import global_config
+from src.chat.message_receive.message import Message
+from src.chat.knowledge.knowledge_lib import qa_manager
+from src.chat.utils.chat_message_builder import build_readable_messages
 
 logger = get_module_logger("knowledge_fetcher")