改各种小问题

src/do_tool/tool_use.py

@@ -1,4 +1,4 @@
-from src.plugins.models.utils_model import LLM_request
+from src.plugins.models.utils_model import LLMRequest
 from src.plugins.config.config import global_config
 from src.plugins.chat.chat_stream import ChatStream
 from src.common.database import db
@@ -18,7 +18,7 @@ logger = get_module_logger("tool_use", config=tool_use_config)
 
 class ToolUser:
     def __init__(self):
-        self.llm_model_tool = LLM_request(
+        self.llm_model_tool = LLMRequest(
             model=global_config.llm_tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use"
         )
 
@@ -107,7 +107,7 @@ class ToolUser:
             return None
 
     async def use_tool(
-        self, message_txt: str, sender_name: str, chat_stream: ChatStream, subheartflow: SubHeartflow = None
+        self, message_txt: str, sender_name: str, chat_stream: ChatStream, sub_heartflow: SubHeartflow = None
     ):
         """使用工具辅助思考，判断是否需要额外信息
 
@@ -115,13 +115,14 @@ class ToolUser:
             message_txt: 用户消息文本
             sender_name: 发送者名称
             chat_stream: 聊天流对象
+            sub_heartflow: 子心流对象（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, sender_name, chat_stream, subheartflow)
+            prompt = await self._build_tool_prompt(message_txt, sender_name, chat_stream, sub_heartflow)
 
             # 定义可用工具
             tools = self._define_tools()

src/heart_flow/heartflow.py

@@ -1,7 +1,7 @@
 from .sub_heartflow import SubHeartflow
 from .observation import ChattingObservation
 from src.plugins.moods.moods import MoodManager
-from src.plugins.models.utils_model import LLM_request
+from src.plugins.models.utils_model import LLMRequest
 from src.plugins.config.config import global_config
 from src.plugins.schedule.schedule_generator import bot_schedule
 from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
@@ -60,7 +60,7 @@ class Heartflow:
         self.current_mind = "你什么也没想"
         self.past_mind = []
         self.current_state: CurrentState = CurrentState()
-        self.llm_model = LLM_request(
+        self.llm_model = LLMRequest(
             model=global_config.llm_heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow"
         )
 

src/heart_flow/observation.py

@@ -1,7 +1,7 @@
 # 定义了来自外部世界的信息
 # 外部世界可以是某个聊天 不同平台的聊天 也可以是任意媒体
 from datetime import datetime
-from src.plugins.models.utils_model import LLM_request
+from src.plugins.models.utils_model import LLMRequest
 from src.plugins.config.config import global_config
 from src.common.database import db
 from src.common.logger import get_module_logger
@@ -40,7 +40,7 @@ class ChattingObservation(Observation):
 
         self.updating_old = False
 
-        self.llm_summary = LLM_request(
+        self.llm_summary = LLMRequest(
             model=global_config.llm_observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
         )
 

src/heart_flow/sub_heartflow.py

@@ -1,7 +1,7 @@
 from .observation import Observation, ChattingObservation
 import asyncio
 from src.plugins.moods.moods import MoodManager
-from src.plugins.models.utils_model import LLM_request
+from src.plugins.models.utils_model import LLMRequest
 from src.plugins.config.config import global_config
 import time
 from src.plugins.chat.message import UserInfo
@@ -79,7 +79,7 @@ class SubHeartflow:
         self.current_mind = ""
         self.past_mind = []
         self.current_state: CurrentState = CurrentState()
-        self.llm_model = LLM_request(
+        self.llm_model = LLMRequest(
             model=global_config.llm_sub_heartflow,
             temperature=global_config.llm_sub_heartflow["temp"],
             max_tokens=600,

src/individuality/offline_llm.py

@@ -10,7 +10,7 @@ from src.common.logger import get_module_logger
 logger = get_module_logger("offline_llm")
 
 
-class LLM_request_off:
+class LLMRequestOff:
     def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs):
         self.model_name = model_name
         self.params = kwargs

src/individuality/per_bf_gen.py

@@ -19,7 +19,7 @@ with open(config_path, "r", encoding="utf-8") as f:
 # 现在可以导入src模块
 from src.individuality.scene import get_scene_by_factor, PERSONALITY_SCENES  # noqa E402
 from src.individuality.questionnaire import FACTOR_DESCRIPTIONS  # noqa E402
-from src.individuality.offline_llm import LLM_request_off  # noqa E402
+from src.individuality.offline_llm import LLMRequestOff  # noqa E402
 
 # 加载环境变量
 env_path = os.path.join(root_path, ".env")
@@ -65,7 +65,7 @@ def adapt_scene(scene: str) -> str:
 现在，请你给出改编后的场景描述
 """
 
-        llm = LLM_request_off(model_name=config["model"]["llm_normal"]["name"])
+        llm = LLMRequestOff(model_name=config["model"]["llm_normal"]["name"])
         adapted_scene, _ = llm.generate_response(prompt)
 
         # 检查返回的场景是否为空或错误信息
@@ -79,7 +79,7 @@ def adapt_scene(scene: str) -> str:
         return scene
 
 
-class PersonalityEvaluator_direct:
+class PersonalityEvaluatorDirect:
     def __init__(self):
         self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
         self.scenarios = []
@@ -110,7 +110,7 @@ class PersonalityEvaluator_direct:
                     {"场景": scene["scenario"], "评估维度": [trait, secondary_trait], "场景编号": scene_key}
                 )
 
-        self.llm = LLM_request_off()
+        self.llm = LLMRequestOff()
 
     def evaluate_response(self, scenario: str, response: str, dimensions: List[str]) -> Dict[str, float]:
         """
@@ -269,7 +269,7 @@ class PersonalityEvaluator_direct:
 
 
 def main():
-    evaluator = PersonalityEvaluator_direct()
+    evaluator = PersonalityEvaluatorDirect()
     result = evaluator.run_evaluation()
 
     # 准备简化的结果数据

src/main.py

@@ -9,7 +9,7 @@ from .plugins.willing.willing_manager import willing_manager
 from .plugins.chat.chat_stream import chat_manager
 from .heart_flow.heartflow import heartflow
 from .plugins.memory_system.Hippocampus import HippocampusManager
-from .plugins.chat.message_sender import message_manager
+from .plugins.chat.messagesender import message_manager
 from .plugins.storage.storage import MessageStorage
 from .plugins.config.config import global_config
 from .plugins.chat.bot import chat_bot

src/plugins/PFC/action_planner.py

@@ -1,6 +1,6 @@
 from typing import Tuple
 from src.common.logger import get_module_logger
-from ..models.utils_model import LLM_request
+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
@@ -23,7 +23,7 @@ class ActionPlanner:
     """行动规划器"""
 
     def __init__(self, stream_id: str):
-        self.llm = LLM_request(
+        self.llm = LLMRequest(
             model=global_config.llm_normal,
             temperature=global_config.llm_normal["temp"],
             max_tokens=1000,

src/plugins/PFC/message_sender.py

@@ -4,7 +4,7 @@ from ..chat.chat_stream import ChatStream
 from ..chat.message import Message
 from ..message.message_base import Seg
 from src.plugins.chat.message import MessageSending, MessageSet
-from src.plugins.chat.message_sender import message_manager
+from src.plugins.chat.messagesender import message_manager
 
 logger = get_module_logger("message_sender")
 

src/plugins/PFC/observation_info.py

@@ -120,6 +120,10 @@ class ObservationInfo:
     # #spec
     # meta_plan_trigger: bool = False
 
+    def __init__(self):
+        self.last_message_id = None
+        self.chat_observer = None
+
     def __post_init__(self):
         """初始化后创建handler"""
         self.chat_observer = None
@@ -129,7 +133,7 @@ class ObservationInfo:
         """绑定到指定的chat_observer
 
         Args:
-            stream_id: 聊天流ID
+            chat_observer: 要绑定的ChatObserver实例
         """
         self.chat_observer = chat_observer
         self.chat_observer.notification_manager.register_handler(
@@ -171,7 +175,8 @@ class ObservationInfo:
             self.last_bot_speak_time = message["time"]
         else:
             self.last_user_speak_time = message["time"]
-            self.active_users.add(user_info.user_id)
+            if user_info.user_id is not None:
+                self.active_users.add(str(user_info.user_id))
 
         self.new_messages_count += 1
         self.unprocessed_messages.append(message)
@@ -227,7 +232,7 @@ class ObservationInfo:
         """清空未处理消息列表"""
         # 将未处理消息添加到历史记录中
         for message in self.unprocessed_messages:
-            self.chat_history.append(message)
+            self.chat_history.append(message) # TODO NEED FIX TYPE???
         # 清空未处理消息列表
         self.has_unread_messages = False
         self.unprocessed_messages.clear()

src/plugins/PFC/pfc.py

@@ -8,7 +8,7 @@ from src.common.logger import get_module_logger
 from ..chat.chat_stream import ChatStream
 from ..message.message_base import UserInfo, Seg
 from ..chat.message import Message
-from ..models.utils_model import LLM_request
+from ..models.utils_model import LLMRequest
 from ..config.config import global_config
 from src.plugins.chat.message import MessageSending
 from ..message.api import global_api
@@ -30,7 +30,7 @@ class GoalAnalyzer:
     """对话目标分析器"""
 
     def __init__(self, stream_id: str):
-        self.llm = LLM_request(
+        self.llm = LLMRequest(
             model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal"
         )
 
@@ -350,7 +350,7 @@ class DirectMessageSender:
                 # logger.info(f"发送消息到{end_point}")
                 # logger.info(message_json)
                 try:
-                    await global_api.send_message_REST(end_point, message_json)
+                    await global_api.send_message_rest(end_point, message_json)
                 except Exception as e:
                     logger.error(f"REST方式发送失败，出现错误: {str(e)}")
                     logger.info("尝试使用ws发送")

src/plugins/PFC/pfc_KnowledgeFetcher.py

@@ -1,7 +1,7 @@
 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 LLM_request
+from ..models.utils_model import LLMRequest
 from ..config.config import global_config
 from ..chat.message import Message
 
@@ -12,7 +12,7 @@ class KnowledgeFetcher:
     """知识调取器"""
 
     def __init__(self):
-        self.llm = LLM_request(
+        self.llm = LLMRequest(
             model=global_config.llm_normal,
             temperature=global_config.llm_normal["temp"],
             max_tokens=1000,

src/plugins/PFC/reply_checker.py

@@ -2,7 +2,7 @@ import json
 import datetime
 from typing import Tuple
 from src.common.logger import get_module_logger
-from ..models.utils_model import LLM_request
+from ..models.utils_model import LLMRequest
 from ..config.config import global_config
 from .chat_observer import ChatObserver
 from ..message.message_base import UserInfo
@@ -14,7 +14,7 @@ class ReplyChecker:
     """回复检查器"""
 
     def __init__(self, stream_id: str):
-        self.llm = LLM_request(
+        self.llm = LLMRequest(
             model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="reply_check"
         )
         self.name = global_config.BOT_NICKNAME

src/plugins/PFC/reply_generator.py

@@ -1,6 +1,6 @@
 from typing import Tuple
 from src.common.logger import get_module_logger
-from ..models.utils_model import LLM_request
+from ..models.utils_model import LLMRequest
 from ..config.config import global_config
 from .chat_observer import ChatObserver
 from .reply_checker import ReplyChecker
@@ -15,7 +15,7 @@ class ReplyGenerator:
     """回复生成器"""
 
     def __init__(self, stream_id: str):
-        self.llm = LLM_request(
+        self.llm = LLMRequest(
             model=global_config.llm_normal,
             temperature=global_config.llm_normal["temp"],
             max_tokens=300,

src/plugins/chat/__init__.py

@@ -1,7 +1,7 @@
 from .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 .messagesender import message_manager
 from ..storage.storage import MessageStorage
 
 

src/plugins/chat/bot.py

@@ -42,7 +42,7 @@ class ChatBot:
 
             self._started = True
 
-    async def _create_PFC_chat(self, message: MessageRecv):
+    async def _create_pfc_chat(self, message: MessageRecv):
         try:
             chat_id = str(message.chat_stream.stream_id)
 
@@ -112,7 +112,7 @@ class ChatBot:
                                 )
                                 message.update_chat_stream(chat)
                                 await self.only_process_chat.process_message(message)
-                                await self._create_PFC_chat(message)
+                                await self._create_pfc_chat(message)
                         else:
                             if groupinfo.group_id in global_config.talk_allowed_groups:
                                 # logger.debug(f"开始群聊模式{str(message_data)[:50]}...")

src/plugins/chat/emoji_manager.py

@@ -13,7 +13,7 @@ from ...common.database import db
 from ..config.config import global_config
 from ..chat.utils import get_embedding
 from ..chat.utils_image import ImageManager, image_path_to_base64
-from ..models.utils_model import LLM_request
+from ..models.utils_model import LLMRequest
 from src.common.logger import get_module_logger
 
 logger = get_module_logger("emoji")
@@ -34,8 +34,8 @@ class EmojiManager:
 
     def __init__(self):
         self._scan_task = None
-        self.vlm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000, request_type="emoji")
+        self.vlm = LLMRequest(model=global_config.vlm, temperature=0.3, max_tokens=1000, request_type="emoji")
-        self.llm_emotion_judge = LLM_request(
+        self.llm_emotion_judge = LLMRequest(
             model=global_config.llm_emotion_judge, max_tokens=600, temperature=0.8, request_type="emoji"
         )  # 更高的温度，更少的token（后续可以根据情绪来调整温度）
 

src/plugins/chat/message_buffer.py

@@ -59,20 +59,20 @@ class MessageBuffer:
                     logger.debug(f"被新消息覆盖信息id: {cache_msg.message.message_info.message_id}")
 
             # 查找最近的处理成功消息(T)
-            recent_F_count = 0
+            recent_f_count = 0
             for msg_id in reversed(self.buffer_pool[person_id_]):
                 msg = self.buffer_pool[person_id_][msg_id]
                 if msg.result == "T":
                     break
                 elif msg.result == "F":
-                    recent_F_count += 1
+                    recent_f_count += 1
 
             # 判断条件：最近T之后有超过3-5条F
-            if recent_F_count >= random.randint(3, 5):
+            if recent_f_count >= random.randint(3, 5):
                 new_msg = CacheMessages(message=message, result="T")
                 new_msg.cache_determination.set()
                 self.buffer_pool[person_id_][message.message_info.message_id] = new_msg
-                logger.debug(f"快速处理消息(已堆积{recent_F_count}条F): {message.message_info.message_id}")
+                logger.debug(f"快速处理消息(已堆积{recent_f_count}条F): {message.message_info.message_id}")
                 return
 
             # 添加新消息

src/plugins/chat/messagesender.py

@@ -23,7 +23,7 @@ sender_config = LogConfig(
 logger = get_module_logger("msg_sender", config=sender_config)
 
 
-class Message_Sender:
+class MessageSender:
     """发送器"""
 
     def __init__(self):
@@ -83,7 +83,7 @@ class Message_Sender:
                         # logger.info(f"发送消息到{end_point}")
                         # logger.info(message_json)
                         try:
-                            await global_api.send_message_REST(end_point, message_json)
+                            await global_api.send_message_rest(end_point, message_json)
                         except Exception as e:
                             logger.error(f"REST方式发送失败，出现错误: {str(e)}")
                             logger.info("尝试使用ws发送")
@@ -286,4 +286,4 @@ class MessageManager:
 # 创建全局消息管理器实例
 message_manager = MessageManager()
 # 创建全局发送器实例
-message_sender = Message_Sender()
+message_sender = MessageSender()

src/plugins/chat/utils.py

@@ -8,7 +8,7 @@ import jieba
 import numpy as np
 from src.common.logger import get_module_logger
 
-from ..models.utils_model import LLM_request
+from ..models.utils_model import LLMRequest
 from ..utils.typo_generator import ChineseTypoGenerator
 from ..config.config import global_config
 from .message import MessageRecv, Message
@@ -91,7 +91,7 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
 
 async def get_embedding(text, request_type="embedding"):
     """获取文本的embedding向量"""
-    llm = LLM_request(model=global_config.embedding, request_type=request_type)
+    llm = LLMRequest(model=global_config.embedding, request_type=request_type)
     # return llm.get_embedding_sync(text)
     try:
         embedding = await llm.get_embedding(text)
@@ -105,7 +105,7 @@ async def get_recent_group_messages(chat_id: str, limit: int = 12) -> list:
     """从数据库获取群组最近的消息记录
 
     Args:
-        group_id: 群组ID
+        chat_id: 群组ID
         limit: 获取消息数量，默认12条
 
     Returns:

src/plugins/chat/utils_image.py

@@ -9,7 +9,7 @@ import io
 
 from ...common.database import db
 from ..config.config import global_config
-from ..models.utils_model import LLM_request
+from ..models.utils_model import LLMRequest
 
 from src.common.logger import get_module_logger
 
@@ -32,7 +32,7 @@ class ImageManager:
             self._ensure_description_collection()
             self._ensure_image_dir()
             self._initialized = True
-            self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300, request_type="image")
+            self._llm = LLMRequest(model=global_config.vlm, temperature=0.4, max_tokens=300, request_type="image")
 
     def _ensure_image_dir(self):
         """确保图像存储目录存在"""

src/plugins/chat_module/reasoning_chat/reasoning_chat.py

@@ -8,7 +8,7 @@ from ...config.config import global_config
 from ...chat.emoji_manager import emoji_manager
 from .reasoning_generator import ResponseGenerator
 from ...chat.message import MessageSending, MessageRecv, MessageThinking, MessageSet
-from ...chat.message_sender import message_manager
+from ...chat.messagesender import message_manager
 from ...storage.storage import MessageStorage
 from ...chat.utils import is_mentioned_bot_in_message
 from ...chat.utils_image import image_path_to_base64

src/plugins/chat_module/reasoning_chat/reasoning_generator.py

@@ -1,7 +1,7 @@
 from typing import List, Optional, Tuple, Union
 import random
 
-from ...models.utils_model import LLM_request
+from ...models.utils_model import LLMRequest
 from ...config.config import global_config
 from ...chat.message import MessageThinking
 from .reasoning_prompt_builder import prompt_builder
@@ -22,20 +22,20 @@ logger = get_module_logger("llm_generator", config=llm_config)
 
 class ResponseGenerator:
     def __init__(self):
-        self.model_reasoning = LLM_request(
+        self.model_reasoning = LLMRequest(
             model=global_config.llm_reasoning,
             temperature=0.7,
             max_tokens=3000,
             request_type="response_reasoning",
         )
-        self.model_normal = LLM_request(
+        self.model_normal = LLMRequest(
             model=global_config.llm_normal,
             temperature=global_config.llm_normal["temp"],
             max_tokens=256,
             request_type="response_reasoning",
         )
 
-        self.model_sum = LLM_request(
+        self.model_sum = LLMRequest(
             model=global_config.llm_summary_by_topic, temperature=0.7, max_tokens=3000, request_type="relation"
         )
         self.current_model_type = "r1"  # 默认使用 R1
@@ -68,7 +68,7 @@ class ResponseGenerator:
             logger.info(f"{self.current_model_type}思考，失败")
             return None
 
-    async def _generate_response_with_model(self, message: MessageThinking, model: LLM_request, thinking_id: str):
+    async def _generate_response_with_model(self, message: MessageThinking, model: LLMRequest, thinking_id: str):
         sender_name = ""
 
         info_catcher = info_catcher_manager.get_info_catcher(thinking_id)

src/plugins/chat_module/think_flow_chat/think_flow_chat.py

@@ -8,7 +8,7 @@ from ...config.config import global_config
 from ...chat.emoji_manager import emoji_manager
 from .think_flow_generator import ResponseGenerator
 from ...chat.message import MessageSending, MessageRecv, MessageThinking, MessageSet
-from ...chat.message_sender import message_manager
+from ...chat.messagesender import message_manager
 from ...storage.storage import MessageStorage
 from ...chat.utils import is_mentioned_bot_in_message, get_recent_group_detailed_plain_text
 from ...chat.utils_image import image_path_to_base64

src/plugins/chat_module/think_flow_chat/think_flow_generator.py

@@ -2,7 +2,7 @@ from typing import List, Optional
 import random
 
 
-from ...models.utils_model import LLM_request
+from ...models.utils_model import LLMRequest
 from ...config.config import global_config
 from ...chat.message import MessageRecv
 from .think_flow_prompt_builder import prompt_builder
@@ -25,14 +25,14 @@ logger = get_module_logger("llm_generator", config=llm_config)
 
 class ResponseGenerator:
     def __init__(self):
-        self.model_normal = LLM_request(
+        self.model_normal = LLMRequest(
             model=global_config.llm_normal,
             temperature=global_config.llm_normal["temp"],
             max_tokens=256,
             request_type="response_heartflow",
         )
 
-        self.model_sum = LLM_request(
+        self.model_sum = LLMRequest(
             model=global_config.llm_summary_by_topic, temperature=0.6, max_tokens=2000, request_type="relation"
         )
         self.current_model_type = "r1"  # 默认使用 R1
@@ -94,7 +94,7 @@ class ResponseGenerator:
             return None
 
     async def _generate_response_with_model(
-        self, message: MessageRecv, model: LLM_request, thinking_id: str, mode: str = "normal"
+        self, message: MessageRecv, model: LLMRequest, thinking_id: str, mode: str = "normal"
     ) -> str:
         sender_name = ""
 

src/plugins/config/config.py

@@ -62,8 +62,7 @@ def update_config():
         shutil.copy2(template_path, old_config_path)
         logger.info(f"已创建新配置文件，请填写后重新运行: {old_config_path}")
         # 如果是新创建的配置文件,直接返回
-        quit()
+        return quit()
-        return
 
     # 读取旧配置文件和模板文件
     with open(old_config_path, "r", encoding="utf-8") as f:

src/plugins/memory_system/Hippocampus.py

@@ -9,7 +9,7 @@ import networkx as nx
 import numpy as np
 from collections import Counter
 from ...common.database import db
-from ...plugins.models.utils_model import LLM_request
+from ...plugins.models.utils_model import LLMRequest
 from src.common.logger import get_module_logger, LogConfig, MEMORY_STYLE_CONFIG
 from src.plugins.memory_system.sample_distribution import MemoryBuildScheduler  # 分布生成器
 from .memory_config import MemoryConfig
@@ -91,7 +91,7 @@ memory_config = LogConfig(
 logger = get_module_logger("memory_system", config=memory_config)
 
 
-class Memory_graph:
+class MemoryGraph:
     def __init__(self):
         self.G = nx.Graph()  # 使用 networkx 的图结构
 
@@ -229,7 +229,7 @@ class Memory_graph:
 # 海马体
 class Hippocampus:
     def __init__(self):
-        self.memory_graph = Memory_graph()
+        self.memory_graph = MemoryGraph()
         self.llm_topic_judge = None
         self.llm_summary_by_topic = None
         self.entorhinal_cortex = None
@@ -243,8 +243,8 @@ class Hippocampus:
         self.parahippocampal_gyrus = ParahippocampalGyrus(self)
         # 从数据库加载记忆图
         self.entorhinal_cortex.sync_memory_from_db()
-        self.llm_topic_judge = LLM_request(self.config.llm_topic_judge, request_type="memory")
+        self.llm_topic_judge = LLMRequest(self.config.llm_topic_judge, request_type="memory")
-        self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic, request_type="memory")
+        self.llm_summary_by_topic = LLMRequest(self.config.llm_summary_by_topic, request_type="memory")
 
     def get_all_node_names(self) -> list:
         """获取记忆图中所有节点的名字列表"""
@@ -346,7 +346,8 @@ class Hippocampus:
 
         Args:
             text (str): 输入文本
-            num (int, optional): 需要返回的记忆数量。默认为5。
+            max_memory_num (int, optional): 记忆数量限制。默认为3。
+            max_memory_length (int, optional): 记忆长度限制。默认为2。
             max_depth (int, optional): 记忆检索深度。默认为2。
             fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
                 如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。
@@ -540,7 +541,6 @@ class Hippocampus:
 
         Args:
             text (str): 输入文本
-            num (int, optional): 需要返回的记忆数量。默认为5。
             max_depth (int, optional): 记忆检索深度。默认为2。
             fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
                 如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。
@@ -937,7 +937,7 @@ class EntorhinalCortex:
 # 海马体
 class Hippocampus:
     def __init__(self):
-        self.memory_graph = Memory_graph()
+        self.memory_graph = MemoryGraph()
         self.llm_topic_judge = None
         self.llm_summary_by_topic = None
         self.entorhinal_cortex = None
@@ -951,8 +951,8 @@ class Hippocampus:
         self.parahippocampal_gyrus = ParahippocampalGyrus(self)
         # 从数据库加载记忆图
         self.entorhinal_cortex.sync_memory_from_db()
-        self.llm_topic_judge = LLM_request(self.config.llm_topic_judge, request_type="memory")
+        self.llm_topic_judge = LLMRequest(self.config.llm_topic_judge, request_type="memory")
-        self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic, request_type="memory")
+        self.llm_summary_by_topic = LLMRequest(self.config.llm_summary_by_topic, request_type="memory")
 
     def get_all_node_names(self) -> list:
         """获取记忆图中所有节点的名字列表"""
@@ -1054,8 +1054,9 @@ class Hippocampus:
 
         Args:
             text (str): 输入文本
-            num (int, optional): 需要返回的记忆数量。默认为5。
+            max_memory_num (int, optional): 返回的记忆条目数量上限。默认为3，表示最多返回3条与输入文本相关度最高的记忆。
-            max_depth (int, optional): 记忆检索深度。默认为2。
+            max_memory_length (int, optional): 每个主题最多返回的记忆条目数量。默认为2，表示每个主题最多返回2条相似度最高的记忆。
+            max_depth (int, optional): 记忆检索深度。默认为3。值越大，检索范围越广，可以获取更多间接相关的记忆，但速度会变慢。
             fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
                 如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。
                 如果为False，使用LLM提取关键词，速度较慢但更准确。
@@ -1248,7 +1249,6 @@ class Hippocampus:
 
         Args:
             text (str): 输入文本
-            num (int, optional): 需要返回的记忆数量。默认为5。
             max_depth (int, optional): 记忆检索深度。默认为2。
             fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
                 如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。

src/plugins/memory_system/manually_alter_memory.py

@@ -177,7 +177,7 @@ def remove_mem_edge(hippocampus: Hippocampus):
 
 # 修改节点信息
 def alter_mem_node(hippocampus: Hippocampus):
-    batchEnviroment = dict()
+    batch_environment = dict()
     while True:
         concept = input("请输入节点概念名（输入'终止'以结束）:\n")
         if concept.lower() == "终止":
@@ -229,7 +229,7 @@ def alter_mem_node(hippocampus: Hippocampus):
                 break
 
             try:
-                user_exec(command, node_environment, batchEnviroment)
+                user_exec(command, node_environment, batch_environment)
             except Exception as e:
                 console.print(e)
                 console.print(
@@ -239,7 +239,7 @@ def alter_mem_node(hippocampus: Hippocampus):
 
 # 修改边信息
 def alter_mem_edge(hippocampus: Hippocampus):
-    batchEnviroment = dict()
+    batch_enviroment = dict()
     while True:
         source = input("请输入 **第一个节点** 名称（输入'终止'以结束）：\n")
         if source.lower() == "终止":
@@ -262,21 +262,21 @@ def alter_mem_edge(hippocampus: Hippocampus):
         console.print("[yellow]你将获得一个执行任意代码的环境[/yellow]")
         console.print("[red]你已经被警告过了。[/red]\n")
 
-        edgeEnviroment = {"source": "<节点名>", "target": "<节点名>", "strength": "<强度值,装在一个list里>"}
+        edge_environment = {"source": "<节点名>", "target": "<节点名>", "strength": "<强度值,装在一个list里>"}
         console.print(
             "[green]环境变量中会有env与batchEnv两个dict, env在切换节点时会清空, batchEnv在操作终止时才会清空[/green]"
         )
         console.print(
-            f"[green] env 会被初始化为[/green]\n{edgeEnviroment}\n[green]且会在用户代码执行完毕后被提交 [/green]"
+            f"[green] env 会被初始化为[/green]\n{edge_environment}\n[green]且会在用户代码执行完毕后被提交 [/green]"
         )
         console.print(
             "[yellow]为便于书写临时脚本，请手动在输入代码通过Ctrl+C等方式触发KeyboardInterrupt来结束代码执行[/yellow]"
         )
 
         # 拷贝数据以防操作炸了
-        edgeEnviroment["strength"] = [edge["strength"]]
+        edge_environment["strength"] = [edge["strength"]]
-        edgeEnviroment["source"] = source
+        edge_environment["source"] = source
-        edgeEnviroment["target"] = target
+        edge_environment["target"] = target
 
         while True:
 
@@ -288,8 +288,8 @@ def alter_mem_edge(hippocampus: Hippocampus):
             except KeyboardInterrupt:
                 # 稍微防一下小天才
                 try:
-                    if isinstance(edgeEnviroment["strength"][0], int):
+                    if isinstance(edge_environment["strength"][0], int):
-                        edge["strength"] = edgeEnviroment["strength"][0]
+                        edge["strength"] = edge_environment["strength"][0]
                     else:
                         raise Exception
 
@@ -301,7 +301,7 @@ def alter_mem_edge(hippocampus: Hippocampus):
                 break
 
             try:
-                user_exec(command, edgeEnviroment, batchEnviroment)
+                user_exec(command, edge_environment, batch_enviroment)
             except Exception as e:
                 console.print(e)
                 console.print(

src/plugins/memory_system/offline_llm.py

@@ -10,7 +10,7 @@ from src.common.logger import get_module_logger
 logger = get_module_logger("offline_llm")
 
 
-class LLM_request_off:
+class LLMRequestOff:
     def __init__(self, model_name="deepseek-ai/DeepSeek-V3", **kwargs):
         self.model_name = model_name
         self.params = kwargs

src/plugins/message/api.py

@@ -233,7 +233,8 @@ class MessageServer(BaseMessageHandler):
     async def send_message(self, message: MessageBase):
         await self.broadcast_to_platform(message.message_info.platform, message.to_dict())
 
-    async def send_message_REST(self, url: str, data: Dict[str, Any]) -> Dict[str, Any]:
+    @staticmethod
+    async def send_message_rest(url: str, data: Dict[str, Any]) -> Dict[str, Any]:
         """发送消息到指定端点"""
         async with aiohttp.ClientSession() as session:
             try:

src/plugins/models/utils_model.py

@@ -16,7 +16,7 @@ from ..config.config import global_config
 logger = get_module_logger("model_utils")
 
 
-class LLM_request:
+class LLMRequest:
     # 定义需要转换的模型列表，作为类变量避免重复
     MODELS_NEEDING_TRANSFORMATION = [
         "o3-mini",

src/plugins/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 LLM_request
+from src.plugins.models.utils_model import LLMRequest
 from src.plugins.config.config import global_config
 from src.individuality.individuality import Individuality
 
@@ -56,7 +56,7 @@ person_info_default = {
 class PersonInfoManager:
     def __init__(self):
         self.person_name_list = {}
-        self.qv_name_llm = LLM_request(
+        self.qv_name_llm = LLMRequest(
             model=global_config.llm_normal,
             max_tokens=256,
             request_type="qv_name",
@@ -107,7 +107,7 @@ class PersonInfoManager:
 
         db.person_info.insert_one(_person_info_default)
 
-    async def update_one_field(self, person_id: str, field_name: str, value, Data: dict = None):
+    async def update_one_field(self, person_id: str, field_name: str, value, data: dict = None):
         """更新某一个字段，会补全"""
         if field_name not in person_info_default.keys():
             logger.debug(f"更新'{field_name}'失败，未定义的字段")
@@ -118,11 +118,12 @@ class PersonInfoManager:
         if document:
             db.person_info.update_one({"person_id": person_id}, {"$set": {field_name: value}})
         else:
-            Data[field_name] = value
+            data[field_name] = value
             logger.debug(f"更新时{person_id}不存在，已新建")
-            await self.create_person_info(person_id, Data)
+            await self.create_person_info(person_id, data)
 
-    async def has_one_field(self, person_id: str, field_name: str):
+    @staticmethod
+    async def has_one_field(person_id: str, field_name: str):
         """判断是否存在某一个字段"""
         document = db.person_info.find_one({"person_id": person_id}, {field_name: 1})
         if document:

src/plugins/personality_s/renqingziji.py

@@ -38,7 +38,7 @@ else:
     print("将使用默认配置")
 
 
-class PersonalityEvaluator_direct:
+class PersonalityEvaluatorDirect:
     def __init__(self):
         self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
         self.scenarios = []
@@ -135,7 +135,7 @@ def main():
     print("\n准备好了吗？按回车键开始...")
     input()
 
-    evaluator = PersonalityEvaluator_direct()
+    evaluator = PersonalityEvaluatorDirect()
     final_scores = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
     dimension_counts = {trait: 0 for trait in final_scores.keys()}
 

src/plugins/remote/remote.py

@@ -125,12 +125,12 @@ def main():
     if global_config.remote_enable:
         """主函数，启动心跳线程"""
         # 配置
-        SERVER_URL = "http://hyybuth.xyz:10058"
+        server_url = "http://hyybuth.xyz:10058"
-        # SERVER_URL = "http://localhost:10058"
+        # server_url = "http://localhost:10058"
-        HEARTBEAT_INTERVAL = 300  # 5分钟（秒）
+        heartbeat_interval = 300  # 5分钟（秒）
 
         # 创建并启动心跳线程
-        heartbeat_thread = HeartbeatThread(SERVER_URL, HEARTBEAT_INTERVAL)
+        heartbeat_thread = HeartbeatThread(server_url, heartbeat_interval)
         heartbeat_thread.start()
 
         return heartbeat_thread  # 返回线程对象，便于外部控制

src/plugins/schedule/schedule_generator.py

@@ -11,7 +11,7 @@ sys.path.append(root_path)
 
 from src.common.database import db  # noqa: E402
 from src.common.logger import get_module_logger, SCHEDULE_STYLE_CONFIG, LogConfig  # noqa: E402
-from src.plugins.models.utils_model import LLM_request  # noqa: E402
+from src.plugins.models.utils_model import LLMRequest  # noqa: E402
 from src.plugins.config.config import global_config  # noqa: E402
 
 TIME_ZONE = tz.gettz(global_config.TIME_ZONE)  # 设置时区
@@ -30,13 +30,13 @@ class ScheduleGenerator:
 
     def __init__(self):
         # 使用离线LLM模型
-        self.llm_scheduler_all = LLM_request(
+        self.llm_scheduler_all = LLMRequest(
             model=global_config.llm_reasoning,
             temperature=global_config.SCHEDULE_TEMPERATURE + 0.3,
             max_tokens=7000,
             request_type="schedule",
         )
-        self.llm_scheduler_doing = LLM_request(
+        self.llm_scheduler_doing = LLMRequest(
             model=global_config.llm_normal,
             temperature=global_config.SCHEDULE_TEMPERATURE,
             max_tokens=2048,

src/plugins/topic_identify/topic_identifier.py

@@ -1,7 +1,7 @@
 from typing import List, Optional
 
 
-from ..models.utils_model import LLM_request
+from ..models.utils_model import LLMRequest
 from ..config.config import global_config
 from src.common.logger import get_module_logger, LogConfig, TOPIC_STYLE_CONFIG
 
@@ -17,7 +17,7 @@ logger = get_module_logger("topic_identifier", config=topic_config)
 
 class TopicIdentifier:
     def __init__(self):
-        self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge, request_type="topic")
+        self.llm_topic_judge = LLMRequest(model=global_config.llm_topic_judge, request_type="topic")
 
     async def identify_topic_llm(self, text: str) -> Optional[List[str]]:
         """识别消息主题，返回主题列表"""