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🤖 自动格式化代码 [skip ci]

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github-actions[bot]
2025-04-12 16:46:11 +00:00
parent e1f272d9c5
commit 46da415d98
43 changed files with 498 additions and 532 deletions
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8
src/common/logger.py
View File

@@ -283,17 +283,13 @@ WILLING_STYLE_CONFIG = {
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 意愿 | {message}"),
},
"simple": {
"console_format": (
"<green>{time:MM-DD HH:mm}</green> | <light-blue>意愿</light-blue> | {message}"
), # noqa: E501
"console_format": ("<green>{time:MM-DD HH:mm}</green> | <light-blue>意愿</light-blue> | {message}"), # noqa: E501
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 意愿 | {message}"),
},
}
CONFIRM_STYLE_CONFIG = {
"console_format": (
"<RED>{message}</RED>"
), # noqa: E501
"console_format": ("<RED>{message}</RED>"), # noqa: E501
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | EULA与PRIVACY确认 | {message}"),
}

14
src/do_tool/tool_can_use/__init__.py
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@@ -4,16 +4,16 @@ from src.do_tool.tool_can_use.base_tool import (
discover_tools,
get_all_tool_definitions,
get_tool_instance,
TOOL_REGISTRY
TOOL_REGISTRY,
)
__all__ = [
'BaseTool',
'register_tool',
'discover_tools',
'get_all_tool_definitions',
'get_tool_instance',
'TOOL_REGISTRY'
"BaseTool",
"register_tool",
"discover_tools",
"get_all_tool_definitions",
"get_tool_instance",
"TOOL_REGISTRY",
]
# 自动发现并注册工具

8
src/do_tool/tool_can_use/base_tool.py
View File

@@ -10,8 +10,10 @@ logger = get_module_logger("base_tool")
# 工具注册表
TOOL_REGISTRY = {}
class BaseTool:
"""所有工具的基类"""
# 工具名称，子类必须重写
name = None
# 工具描述，子类必须重写
@@ -31,11 +33,7 @@ class BaseTool:
return {
"type": "function",
"function": {
"name": cls.name,
"description": cls.description,
"parameters": cls.parameters
}
"function": {"name": cls.name, "description": cls.description, "parameters": cls.parameters},
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:

23
src/do_tool/tool_can_use/fibonacci_sequence_tool.py
View File

@@ -4,20 +4,16 @@ from typing import Dict, Any
logger = get_module_logger("fibonacci_sequence_tool")
class FibonacciSequenceTool(BaseTool):
"""生成斐波那契数列的工具"""
name = "fibonacci_sequence"
description = "生成指定长度的斐波那契数列"
parameters = {
"type": "object",
"properties": {
"n": {
"type": "integer",
"description": "斐波那契数列的长度",
"minimum": 1
}
},
"required": ["n"]
"properties": {"n": {"type": "integer", "description": "斐波那契数列的长度", "minimum": 1}},
"required": ["n"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
@@ -41,16 +37,11 @@ class FibonacciSequenceTool(BaseTool):
sequence.append(a)
a, b = b, a + b
return {
"name": self.name,
"content": sequence
}
return {"name": self.name, "content": sequence}
except Exception as e:
logger.error(f"fibonacci_sequence工具执行失败: {str(e)}")
return {
"name": self.name,
"content": f"执行失败: {str(e)}"
}
return {"name": self.name, "content": f"执行失败: {str(e)}"}
# 注册工具
register_tool(FibonacciSequenceTool)

15
src/do_tool/tool_can_use/generate_buddha_emoji_tool.py
View File

@@ -4,8 +4,10 @@ from typing import Dict, Any
logger = get_module_logger("generate_buddha_emoji_tool")
class GenerateBuddhaEmojiTool(BaseTool):
"""生成佛祖颜文字的工具类"""
name = "generate_buddha_emoji"
description = "生成一个佛祖的颜文字表情"
parameters = {
@@ -13,7 +15,7 @@ class GenerateBuddhaEmojiTool(BaseTool):
"properties": {
# 无参数
},
"required": []
"required": [],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
@@ -29,16 +31,11 @@ class GenerateBuddhaEmojiTool(BaseTool):
try:
buddha_emoji = "这是一个佛祖emoji：༼ つ ◕_◕ ༽つ"
return {
"name": self.name,
"content": buddha_emoji
}
return {"name": self.name, "content": buddha_emoji}
except Exception as e:
logger.error(f"generate_buddha_emoji工具执行失败: {str(e)}")
return {
"name": self.name,
"content": f"执行失败: {str(e)}"
}
return {"name": self.name, "content": f"执行失败: {str(e)}"}
# 注册工具
register_tool(GenerateBuddhaEmojiTool)

19
src/do_tool/tool_can_use/generate_cmd_tutorial_tool.py
View File

@@ -4,15 +4,13 @@ from typing import Dict, Any
logger = get_module_logger("generate_cmd_tutorial_tool")
class GenerateCmdTutorialTool(BaseTool):
"""生成Windows CMD基本操作教程的工具"""
name = "generate_cmd_tutorial"
description = "生成关于Windows命令提示符(CMD)的基本操作教程，包括常用命令和使用方法"
parameters = {
"type": "object",
"properties": {},
"required": []
}
parameters = {"type": "object", "properties": {}, "required": []}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行工具功能
@@ -58,16 +56,11 @@ class GenerateCmdTutorialTool(BaseTool):
注意：使用命令时要小心，特别是删除操作。
"""
return {
"name": self.name,
"content": tutorial_content
}
return {"name": self.name, "content": tutorial_content}
except Exception as e:
logger.error(f"generate_cmd_tutorial工具执行失败: {str(e)}")
return {
"name": self.name,
"content": f"执行失败: {str(e)}"
}
return {"name": self.name, "content": f"执行失败: {str(e)}"}
# 注册工具
register_tool(GenerateCmdTutorialTool)

25
src/do_tool/tool_can_use/get_current_task.py
View File

@@ -5,23 +5,19 @@ from typing import Dict, Any
logger = get_module_logger("get_current_task_tool")
class GetCurrentTaskTool(BaseTool):
"""获取当前正在做的事情/最近的任务工具"""
name = "get_current_task"
description = "获取当前正在做的事情/最近的任务"
parameters = {
"type": "object",
"properties": {
"num": {
"type": "integer",
"description": "要获取的任务数量"
"num": {"type": "integer", "description": "要获取的任务数量"},
"time_info": {"type": "boolean", "description": "是否包含时间信息"},
},
"time_info": {
"type": "boolean",
"description": "是否包含时间信息"
}
},
"required": []
"required": [],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
@@ -48,16 +44,11 @@ class GetCurrentTaskTool(BaseTool):
else:
task_info = "当前没有正在进行的任务"
return {
"name": "get_current_task",
"content": f"当前任务信息: {task_info}"
}
return {"name": "get_current_task", "content": f"当前任务信息: {task_info}"}
except Exception as e:
logger.error(f"获取当前任务工具执行失败: {str(e)}")
return {
"name": "get_current_task",
"content": f"获取当前任务失败: {str(e)}"
}
return {"name": "get_current_task", "content": f"获取当前任务失败: {str(e)}"}
# 注册工具
register_tool(GetCurrentTaskTool)

30
src/do_tool/tool_can_use/get_knowledge.py
View File

@@ -6,23 +6,19 @@ from typing import Dict, Any, Union
logger = get_module_logger("get_knowledge_tool")
class SearchKnowledgeTool(BaseTool):
"""从知识库中搜索相关信息的工具"""
name = "search_knowledge"
description = "从知识库中搜索相关信息"
parameters = {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "搜索查询关键词"
"query": {"type": "string", "description": "搜索查询关键词"},
"threshold": {"type": "number", "description": "相似度阈值，0.0到1.0之间"},
},
"threshold": {
"type": "number",
"description": "相似度阈值，0.0到1.0之间"
}
},
"required": ["query"]
"required": ["query"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
@@ -47,20 +43,11 @@ class SearchKnowledgeTool(BaseTool):
content = f"你知道这些知识: {knowledge_info}"
else:
content = f"你不太了解有关{query}的知识"
return {
"name": "search_knowledge",
"content": content
}
return {
"name": "search_knowledge",
"content": f"无法获取关于'{query}'的嵌入向量"
}
return {"name": "search_knowledge", "content": content}
return {"name": "search_knowledge", "content": f"无法获取关于'{query}'的嵌入向量"}
except Exception as e:
logger.error(f"知识库搜索工具执行失败: {str(e)}")
return {
"name": "search_knowledge",
"content": f"知识库搜索失败: {str(e)}"
}
return {"name": "search_knowledge", "content": f"知识库搜索失败: {str(e)}"}
def get_info_from_db(
self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
@@ -143,5 +130,6 @@ class SearchKnowledgeTool(BaseTool):
# 返回所有找到的内容，用换行分隔
return "\n".join(str(result["content"]) for result in results)
# 注册工具
register_tool(SearchKnowledgeTool)

31
src/do_tool/tool_can_use/get_memory.py
View File

@@ -5,23 +5,19 @@ from typing import Dict, Any
logger = get_module_logger("get_memory_tool")
class GetMemoryTool(BaseTool):
"""从记忆系统中获取相关记忆的工具"""
name = "get_memory"
description = "从记忆系统中获取相关记忆"
parameters = {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "要查询的相关文本"
"text": {"type": "string", "description": "要查询的相关文本"},
"max_memory_num": {"type": "integer", "description": "最大返回记忆数量"},
},
"max_memory_num": {
"type": "integer",
"description": "最大返回记忆数量"
}
},
"required": ["text"]
"required": ["text"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
@@ -40,11 +36,7 @@ class GetMemoryTool(BaseTool):
# 调用记忆系统
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
text=text,
max_memory_num=max_memory_num,
max_memory_length=2,
max_depth=3,
fast_retrieval=False
text=text, max_memory_num=max_memory_num, max_memory_length=2, max_depth=3, fast_retrieval=False
)
memory_info = ""
@@ -57,16 +49,11 @@ class GetMemoryTool(BaseTool):
else:
content = f"你不太记得有关{text}的记忆，你对此不太了解"
return {
"name": "get_memory",
"content": content
}
return {"name": "get_memory", "content": content}
except Exception as e:
logger.error(f"记忆获取工具执行失败: {str(e)}")
return {
"name": "get_memory",
"content": f"记忆获取失败: {str(e)}"
}
return {"name": "get_memory", "content": f"记忆获取失败: {str(e)}"}
# 注册工具
register_tool(GetMemoryTool)

12
src/do_tool/tool_use.py
View File

@@ -28,9 +28,7 @@ class ToolUser:
str: 构建好的提示词
"""
new_messages = list(
db.messages.find({"chat_id": chat_stream.stream_id, "time": {"$gt": time.time()}})
.sort("time", 1)
.limit(15)
db.messages.find({"chat_id": chat_stream.stream_id, "time": {"$gt": time.time()}}).sort("time", 1).limit(15)
)
new_messages_str = ""
for msg in new_messages:
@@ -82,7 +80,7 @@ class ToolUser:
"tool_call_id": tool_call["id"],
"role": "tool",
"name": function_name,
"content": result["content"]
"content": result["content"],
}
return None
except Exception as e:
@@ -113,15 +111,13 @@ class ToolUser:
"messages": [{"role": "user", "content": prompt}],
"max_tokens": global_config.max_response_length,
"tools": tools,
"temperature": 0.2
"temperature": 0.2,
}
logger.debug(f"发送工具调用请求，模型: {self.llm_model_tool.model_name}")
# 发送请求获取模型是否需要调用工具
response = await self.llm_model_tool._execute_request(
endpoint="/chat/completions",
payload=payload,
prompt=prompt
endpoint="/chat/completions", payload=payload, prompt=prompt
)
# 根据返回值数量判断是否有工具调用

5
src/heart_flow/heartflow.py
View File

@@ -43,7 +43,6 @@ def init_prompt():
class CurrentState:
def __init__(self):
self.current_state_info = ""
self.mood_manager = MoodManager()
@@ -67,9 +66,6 @@ class Heartflow:
self._subheartflows: Dict[Any, SubHeartflow] = {}
async def _cleanup_inactive_subheartflows(self):
"""定期清理不活跃的子心流"""
while True:
@@ -103,7 +99,6 @@ class Heartflow:
await asyncio.sleep(global_config.heart_flow_update_interval) # 5分钟思考一次
async def heartflow_start_working(self):
# 启动清理任务
asyncio.create_task(self._cleanup_inactive_subheartflows())

25
src/heart_flow/sub_heartflow.py
View File

@@ -5,9 +5,11 @@ from src.plugins.models.utils_model import LLM_request
from src.plugins.config.config import global_config
import re
import time
# from src.plugins.schedule.schedule_generator import bot_schedule
# from src.plugins.memory_system.Hippocampus import HippocampusManager
from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402
# from src.plugins.chat.utils import get_embedding
# from src.common.database import db
# from typing import Union
@@ -26,6 +28,7 @@ subheartflow_config = LogConfig(
)
logger = get_module_logger("subheartflow", config=subheartflow_config)
def init_prompt():
prompt = ""
# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
@@ -52,8 +55,7 @@ def init_prompt():
prompt += "你现在{mood_info}"
prompt += "现在你接下去继续思考，产生新的想法，记得保留你刚刚的想法，不要分点输出，输出连贯的内心独白"
prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:"
Prompt(prompt,'sub_heartflow_prompt_after')
Prompt(prompt, "sub_heartflow_prompt_after")
class CurrentState:
@@ -79,7 +81,6 @@ class SubHeartflow:
model=global_config.llm_sub_heartflow, temperature=0.2, max_tokens=600, request_type="sub_heart_flow"
)
self.main_heartflow_info = ""
self.last_reply_time = time.time()
@@ -145,11 +146,11 @@ class SubHeartflow:
): # 5分钟无回复/不在场，销毁
logger.info(f"子心流 {self.subheartflow_id} 已经5分钟没有激活，正在销毁...")
break # 退出循环，销毁自己
async def do_observe(self):
observation = self.observations[0]
await observation.observe()
async def do_thinking_before_reply(self, message_txt: str, sender_name: str, chat_stream: ChatStream):
current_thinking_info = self.current_mind
mood_info = self.current_state.mood
@@ -204,7 +205,7 @@ class SubHeartflow:
# f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
# f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
# )
relation_prompt_all = (await global_prompt_manager.get_prompt_async('relationship_prompt')).format(
relation_prompt_all = (await global_prompt_manager.get_prompt_async("relationship_prompt")).format(
relation_prompt, sender_name
)
@@ -225,8 +226,15 @@ class SubHeartflow:
# prompt += f"记得结合上述的消息，生成内心想法，文字不要浮夸，注意你就是{self.bot_name}，{self.bot_name}指的就是你。"
prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_before")).format(
collected_info,relation_prompt_all,prompt_personality,current_thinking_info,chat_observe_info,mood_info,sender_name,
message_txt,self.bot_name
collected_info,
relation_prompt_all,
prompt_personality,
current_thinking_info,
chat_observe_info,
mood_info,
sender_name,
message_txt,
self.bot_name,
)
try:
@@ -281,7 +289,7 @@ class SubHeartflow:
# prompt += f"你现在{mood_info}"
# prompt += "现在你接下去继续思考，产生新的想法，记得保留你刚刚的想法，不要分点输出，输出连贯的内心独白"
# prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:"
prompt=(await global_prompt_manager.get_prompt_async('sub_heartflow_prompt_after')).format(
prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_after")).format(
prompt_personality, chat_observe_info, current_thinking_info, message_new_info, reply_info, mood_info
)
@@ -343,5 +351,6 @@ class SubHeartflow:
self.past_mind.append(self.current_mind)
self.current_mind = response
init_prompt()
# subheartflow = SubHeartflow()

39
src/plugins/PFC/action_planner.py
View File

@@ -53,8 +53,8 @@ class ActionPlanner:
goal = goal_reason[0]
reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
elif isinstance(goal_reason, dict):
goal = goal_reason.get('goal')
reasoning = goal_reason.get('reasoning', "没有明确原因")
goal = goal_reason.get("goal")
reasoning = goal_reason.get("reasoning", "没有明确原因")
else:
# 如果是其他类型，尝试转为字符串
goal = str(goal_reason)
@@ -68,7 +68,11 @@ class ActionPlanner:
goals_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
# 获取聊天历史记录
chat_history_list = observation_info.chat_history[-20:] if len(observation_info.chat_history) >= 20 else observation_info.chat_history
chat_history_list = (
observation_info.chat_history[-20:]
if len(observation_info.chat_history) >= 20
else observation_info.chat_history
)
chat_history_text = ""
for msg in chat_history_list:
chat_history_text += f"{msg.get('detailed_plain_text', '')}\n"
@@ -85,15 +89,21 @@ class ActionPlanner:
personality_text = f"你的名字是{self.name}，{self.personality_info}"
# 构建action历史文本
action_history_list = conversation_info.done_action[-10:] if len(conversation_info.done_action) >= 10 else conversation_info.done_action
action_history_list = (
conversation_info.done_action[-10:]
if len(conversation_info.done_action) >= 10
else conversation_info.done_action
)
action_history_text = "你之前做的事情是："
for action in action_history_list:
if isinstance(action, dict):
action_type = action.get('action')
action_reason = action.get('reason')
action_status = action.get('status')
action_type = action.get("action")
action_reason = action.get("reason")
action_status = action.get("status")
if action_status == "recall":
action_history_text += f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
action_history_text += (
f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
)
elif action_status == "done":
action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
elif isinstance(action, tuple):
@@ -102,7 +112,9 @@ class ActionPlanner:
action_reason = action[1] if len(action) > 1 else "未知原因"
action_status = action[2] if len(action) > 2 else "done"
if action_status == "recall":
action_history_text += f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
action_history_text += (
f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
)
elif action_status == "done":
action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
@@ -147,7 +159,14 @@ end_conversation: 结束对话，长时间没回复或者当你觉得谈话暂
reason = result["reason"]
# 验证action类型
if action not in ["direct_reply", "fetch_knowledge", "wait", "listening", "rethink_goal", "end_conversation"]:
if action not in [
"direct_reply",
"fetch_knowledge",
"wait",
"listening",
"rethink_goal",
"end_conversation",
]:
logger.warning(f"未知的行动类型: {action}，默认使用listening")
action = "listening"

8
src/plugins/PFC/chat_observer.py
View File

@@ -51,7 +51,6 @@ class ChatObserver:
self.waiting_start_time: float = time.time() # 等待开始时间，初始化为当前时间
# 运行状态
self._running: bool = False
self._task: Optional[asyncio.Task] = None
@@ -94,10 +93,11 @@ class ChatObserver:
message: 消息数据
"""
try:
# 发送新消息通知
# logger.info(f"发送新ccchandleer消息通知: {message}")
notification = create_new_message_notification(sender="chat_observer", target="observation_info", message=message)
notification = create_new_message_notification(
sender="chat_observer", target="observation_info", message=message
)
# logger.info(f"发送新消ddddd息通知: {notification}")
# print(self.notification_manager)
await self.notification_manager.send_notification(notification)
@@ -131,7 +131,6 @@ class ChatObserver:
notification = create_cold_chat_notification(sender="chat_observer", target="pfc", is_cold=is_cold)
await self.notification_manager.send_notification(notification)
def new_message_after(self, time_point: float) -> bool:
"""判断是否在指定时间点后有新消息
@@ -347,7 +346,6 @@ class ChatObserver:
return time_info
def get_cached_messages(self, limit: int = 50) -> List[Dict[str, Any]]:
"""获取缓存的消息历史

3
src/plugins/PFC/chat_states.py
View File

@@ -141,7 +141,6 @@ class NotificationManager:
else:
self._active_states.discard(notification.type)
# 调用目标接收者的处理器
target = notification.target
if target in self._handlers:
@@ -295,5 +294,3 @@ class ChatStateManager:
current_time = datetime.now().timestamp()
return (current_time - self.state_info.last_message_time) <= threshold

7
src/plugins/PFC/conversation.py
View File

@@ -65,7 +65,6 @@ class Conversation:
self.observation_info.bind_to_chat_observer(self.chat_observer)
# print(self.chat_observer.get_cached_messages(limit=)
self.conversation_info = ConversationInfo()
except Exception as e:
logger.error(f"初始化对话实例：注册信息组件失败: {e}")
@@ -174,7 +173,6 @@ class Conversation:
await self._send_reply()
conversation_info.done_action[-1].update(
{
"status": "done",
@@ -208,7 +206,6 @@ class Conversation:
logger.info("倾听对方发言...")
await self.waiter.wait_listening(conversation_info)
elif action == "end_conversation":
self.should_continue = False
logger.info("决定结束对话...")
@@ -239,9 +236,7 @@ class Conversation:
return
try:
await self.direct_sender.send_message(
chat_stream=self.chat_stream, content=self.generated_reply
)
await self.direct_sender.send_message(chat_stream=self.chat_stream, content=self.generated_reply)
self.chat_observer.trigger_update() # 触发立即更新
if not await self.chat_observer.wait_for_update():
logger.warning("等待消息更新超时")

1
src/plugins/PFC/message_storage.py
View File

@@ -2,6 +2,7 @@ from abc import ABC, abstractmethod
from typing import List, Dict, Any
from src.common.database import db
class MessageStorage(ABC):
"""消息存储接口"""

3
src/plugins/PFC/observation_info.py
View File

@@ -41,7 +41,7 @@ class ObservationInfoHandler(NotificationHandler):
"processed_plain_text": processed_plain_text,
"detailed_plain_text": detailed_plain_text,
"user_info": user_info,
"time": time_value
"time": time_value,
}
self.observation_info.update_from_message(message)
@@ -233,4 +233,3 @@ class ObservationInfo:
self.unprocessed_messages.clear()
self.chat_history_count = len(self.chat_history)
self.new_messages_count = 0

10
src/plugins/PFC/pfc.py
View File

@@ -1,6 +1,7 @@
# Programmable Friendly Conversationalist
# Prefrontal cortex
import datetime
# import asyncio
from typing import List, Optional, Tuple, TYPE_CHECKING
from src.common.logger import get_module_logger
@@ -63,8 +64,8 @@ class GoalAnalyzer:
goal = goal_reason[0]
reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
elif isinstance(goal_reason, dict):
goal = goal_reason.get('goal')
reasoning = goal_reason.get('reasoning', "没有明确原因")
goal = goal_reason.get("goal")
reasoning = goal_reason.get("reasoning", "没有明确原因")
else:
# 如果是其他类型，尝试转为字符串
goal = str(goal_reason)
@@ -143,9 +144,7 @@ class GoalAnalyzer:
# 使用改进后的get_items_from_json函数处理JSON数组
success, result = get_items_from_json(
content, "goal", "reasoning",
required_types={"goal": str, "reasoning": str},
allow_array=True
content, "goal", "reasoning", required_types={"goal": str, "reasoning": str}, allow_array=True
)
if success:
@@ -293,7 +292,6 @@ class GoalAnalyzer:
return False, False, f"分析出错: {str(e)}"
class DirectMessageSender:
"""直接发送消息到平台的发送器"""

30
src/plugins/PFC/reply_generator.py
View File

@@ -49,8 +49,8 @@ class ReplyGenerator:
goal = goal_reason[0]
reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
elif isinstance(goal_reason, dict):
goal = goal_reason.get('goal')
reasoning = goal_reason.get('reasoning', "没有明确原因")
goal = goal_reason.get("goal")
reasoning = goal_reason.get("reasoning", "没有明确原因")
else:
# 如果是其他类型，尝试转为字符串
goal = str(goal_reason)
@@ -64,7 +64,11 @@ class ReplyGenerator:
goals_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
# 获取聊天历史记录
chat_history_list = observation_info.chat_history[-20:] if len(observation_info.chat_history) >= 20 else observation_info.chat_history
chat_history_list = (
observation_info.chat_history[-20:]
if len(observation_info.chat_history) >= 20
else observation_info.chat_history
)
chat_history_text = ""
for msg in chat_history_list:
chat_history_text += f"{msg.get('detailed_plain_text', '')}\n"
@@ -81,15 +85,21 @@ class ReplyGenerator:
personality_text = f"你的名字是{self.name}，{self.personality_info}"
# 构建action历史文本
action_history_list = conversation_info.done_action[-10:] if len(conversation_info.done_action) >= 10 else conversation_info.done_action
action_history_list = (
conversation_info.done_action[-10:]
if len(conversation_info.done_action) >= 10
else conversation_info.done_action
)
action_history_text = "你之前做的事情是："
for action in action_history_list:
if isinstance(action, dict):
action_type = action.get('action')
action_reason = action.get('reason')
action_status = action.get('status')
action_type = action.get("action")
action_reason = action.get("reason")
action_status = action.get("status")
if action_status == "recall":
action_history_text += f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
action_history_text += (
f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
)
elif action_status == "done":
action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
elif isinstance(action, tuple):
@@ -98,7 +108,9 @@ class ReplyGenerator:
action_reason = action[1] if len(action) > 1 else "未知原因"
action_status = action[2] if len(action) > 2 else "done"
if action_status == "recall":
action_history_text += f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
action_history_text += (
f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
)
elif action_status == "done":
action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"

5
src/plugins/PFC/waiter.py
View File

@@ -42,7 +42,7 @@ class Waiter:
logger.info("等待超过300秒，结束对话")
wait_goal = {
"goal": f"你等待了{self.wait_accumulated_time / 60}分钟，思考接下来要做什么",
"reason": "对方很久没有回复你的消息了"
"reason": "对方很久没有回复你的消息了",
}
conversation_info.goal_list.append(wait_goal)
print(f"添加目标: {wait_goal}")
@@ -74,7 +74,7 @@ class Waiter:
logger.info("等待超过300秒，结束对话")
wait_goal = {
"goal": f"你等待了{self.wait_accumulated_time / 60}分钟，思考接下来要做什么",
"reason": "对方话说一半消失了，很久没有回复"
"reason": "对方话说一半消失了，很久没有回复",
}
conversation_info.goal_list.append(wait_goal)
print(f"添加目标: {wait_goal}")
@@ -83,4 +83,3 @@ class Waiter:
await asyncio.sleep(1)
logger.info("等待中...")

1
src/plugins/chat/utils.py
View File

@@ -87,7 +87,6 @@ async def get_embedding(text, request_type="embedding"):
return embedding
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

8
src/plugins/chat_module/reasoning_chat/reasoning_generator.py
View File

@@ -96,11 +96,8 @@ class ResponseGenerator:
content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
info_catcher.catch_after_llm_generated(
prompt=prompt,
response=content,
reasoning_content=reasoning_content,
model_name=self.current_model_name)
prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
)
except Exception:
logger.exception("生成回复时出错")
@@ -118,7 +115,6 @@ class ResponseGenerator:
return content
# def _save_to_db(
# self,
# message: MessageRecv,

8
src/plugins/chat_module/reasoning_chat/reasoning_prompt_builder.py
View File

@@ -144,12 +144,10 @@ class PromptBuilder:
for pattern in rule.get("regex", []):
result = pattern.search(message_txt)
if result:
reaction = rule.get('reaction', '')
reaction = rule.get("reaction", "")
for name, content in result.groupdict().items():
reaction = reaction.replace(f'[{name}]', content)
logger.info(
f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}"
)
reaction = reaction.replace(f"[{name}]", content)
logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
keywords_reaction_prompt += reaction + "，"
break

12
src/plugins/chat_module/think_flow_chat/think_flow_chat.py
View File

@@ -59,11 +59,7 @@ class ThinkFlowChat:
return thinking_id
async def _send_response_messages(self,
message,
chat,
response_set:List[str],
thinking_id) -> MessageSending:
async def _send_response_messages(self, message, chat, response_set: List[str], thinking_id) -> MessageSending:
"""发送回复消息"""
container = message_manager.get_container(chat.stream_id)
thinking_message = None
@@ -261,8 +257,6 @@ class ThinkFlowChat:
try:
do_reply = True
# 回复前处理
await willing_manager.before_generate_reply_handle(message.message_info.message_id)
@@ -297,7 +291,7 @@ class ThinkFlowChat:
current_mind, past_mind = await heartflow.get_subheartflow(chat.stream_id).do_thinking_before_reply(
message_txt=message.processed_plain_text,
sender_name=message.message_info.user_info.user_nickname,
chat_stream = chat
chat_stream=chat,
)
timer2 = time.time()
timing_results["思考前脑内状态"] = timer2 - timer1
@@ -327,10 +321,8 @@ class ThinkFlowChat:
except Exception as e:
logger.error(f"心流发送消息失败: {e}")
info_catcher.catch_after_response(timing_results["发送消息"], response_set, first_bot_msg)
info_catcher.done_catch()
# 处理表情包

39
src/plugins/chat_module/think_flow_chat/think_flow_generator.py
View File

@@ -38,7 +38,6 @@ class ResponseGenerator:
async def generate_response(self, message: MessageRecv, thinking_id: str) -> Optional[List[str]]:
"""根据当前模型类型选择对应的生成函数"""
logger.info(
f"思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
)
@@ -52,7 +51,9 @@ class ResponseGenerator:
checked = False
current_model = self.model_normal
current_model.temperature = 0.3 * arousal_multiplier # 激活度越高，温度越高
model_response = await self._generate_response_with_model(message, current_model,thinking_id,mode="normal")
model_response = await self._generate_response_with_model(
message, current_model, thinking_id, mode="normal"
)
model_checked_response = model_response
else:
@@ -60,16 +61,22 @@ class ResponseGenerator:
current_model = self.model_normal
current_model.temperature = 0.3 * arousal_multiplier # 激活度越高，温度越高
print(f"生成{message.processed_plain_text}回复温度是：{current_model.temperature}")
model_response = await self._generate_response_with_model(message, current_model,thinking_id,mode="simple")
model_response = await self._generate_response_with_model(
message, current_model, thinking_id, mode="simple"
)
current_model.temperature = 0.3
model_checked_response = await self._check_response_with_model(message, model_response, current_model,thinking_id)
model_checked_response = await self._check_response_with_model(
message, model_response, current_model, thinking_id
)
time2 = time.time()
if model_response:
if checked:
logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}，思忖后，回复是：{model_checked_response},生成回复时间: {time2 - time1}秒")
logger.info(
f"{global_config.BOT_NICKNAME}的回复是：{model_response}，思忖后，回复是：{model_checked_response},生成回复时间: {time2 - time1}秒"
)
else:
logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response},生成回复时间: {time2 - time1}秒")
@@ -80,7 +87,9 @@ class ResponseGenerator:
logger.info(f"{self.current_model_type}思考，失败")
return None
async def _generate_response_with_model(self, message: MessageRecv, model: LLM_request,thinking_id:str,mode:str = "normal") -> str:
async def _generate_response_with_model(
self, message: MessageRecv, model: LLM_request, thinking_id: str, mode: str = "normal"
) -> str:
sender_name = ""
info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
@@ -117,22 +126,19 @@ class ResponseGenerator:
try:
content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
info_catcher.catch_after_llm_generated(
prompt=prompt,
response=content,
reasoning_content=reasoning_content,
model_name=self.current_model_name)
prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
)
except Exception:
logger.exception("生成回复时出错")
return None
return content
async def _check_response_with_model(self, message: MessageRecv, content:str, model: LLM_request,thinking_id:str) -> str:
async def _check_response_with_model(
self, message: MessageRecv, content: str, model: LLM_request, thinking_id: str
) -> str:
_info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
sender_name = ""
@@ -146,7 +152,6 @@ class ResponseGenerator:
else:
sender_name = f"用户({message.chat_stream.user_info.user_id})"
# 构建prompt
timer1 = time.time()
prompt = await prompt_builder._build_prompt_check_response(
@@ -154,7 +159,7 @@ class ResponseGenerator:
message_txt=message.processed_plain_text,
sender_name=sender_name,
stream_id=message.chat_stream.stream_id,
content=content
content=content,
)
timer2 = time.time()
logger.info(f"构建check_prompt: {prompt}")
@@ -163,7 +168,6 @@ class ResponseGenerator:
try:
checked_content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
# info_catcher.catch_after_llm_generated(
# prompt=prompt,
# response=content,
@@ -174,7 +178,6 @@ class ResponseGenerator:
logger.exception("检查回复时出错")
return None
return checked_content
async def _get_emotion_tags(self, content: str, processed_plain_text: str):

8
src/plugins/chat_module/think_flow_chat/think_flow_prompt_builder.py
View File

@@ -110,12 +110,10 @@ class PromptBuilder:
for pattern in rule.get("regex", []):
result = pattern.search(message_txt)
if result:
reaction = rule.get('reaction', '')
reaction = rule.get("reaction", "")
for name, content in result.groupdict().items():
reaction = reaction.replace(f'[{name}]', content)
logger.info(
f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}"
)
reaction = reaction.replace(f"[{name}]", content)
logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
keywords_reaction_prompt += reaction + "，"
break

2
src/plugins/memory_system/Hippocampus.py
View File

@@ -225,6 +225,7 @@ class Memory_graph:
return None
# 海马体
class Hippocampus:
def __init__(self):
@@ -653,7 +654,6 @@ class Hippocampus:
return activation_ratio
# 负责海马体与其他部分的交互
class EntorhinalCortex:
def __init__(self, hippocampus: Hippocampus):

1
src/plugins/memory_system/debug_memory.py
View File

@@ -27,7 +27,6 @@ async def test_memory_system():
# 测试记忆检索
test_text = "千石可乐在群里聊天"
# test_text = '''千石可乐：分不清AI的陪伴和人类的陪伴,是这样吗？'''
print(f"开始测试记忆检索，测试文本: {test_text}\n")
memories = await hippocampus_manager.get_memory_from_text(

4
src/plugins/models/utils_model.py
View File

@@ -657,9 +657,7 @@ class LLM_request:
**kwargs,
}
response = await self._execute_request(
endpoint="/chat/completions", payload=data, prompt=prompt
)
response = await self._execute_request(endpoint="/chat/completions", payload=data, prompt=prompt)
# 原样返回响应，不做处理
return response

44
src/plugins/respon_info_catcher/info_catcher.py
View File

@@ -5,6 +5,7 @@ import time
import traceback
from typing import List
class InfoCatcher:
def __init__(self):
self.chat_history = [] # 聊天历史，长度为三倍使用的上下文
@@ -32,16 +33,11 @@ class InfoCatcher:
"sub_heartflow_model": "",
"prompt": "",
"response": "",
"model": ""
"model": "",
}
# 使用字典来存储 reasoning 模式的数据
self.reasoning_data = {
"thinking_log": "",
"prompt": "",
"response": "",
"model": ""
}
self.reasoning_data = {"thinking_log": "", "prompt": "", "response": "", "model": ""}
# 耗时
self.timing_results = {
@@ -76,10 +72,7 @@ class InfoCatcher:
self.heartflow_data["sub_heartflow_before"] = past_mind[-1]
self.heartflow_data["sub_heartflow_now"] = current_mind
def catch_after_llm_generated(self,prompt:str,
response:str,
reasoning_content:str = "",
model_name:str = ""):
def catch_after_llm_generated(self, prompt: str, response: str, reasoning_content: str = "", model_name: str = ""):
if self.response_mode == "heart_flow":
self.heartflow_data["prompt"] = prompt
self.heartflow_data["response"] = response
@@ -95,17 +88,17 @@ class InfoCatcher:
def catch_after_generate_response(self, response_duration: float):
self.timing_results["make_response_time"] = response_duration
def catch_after_response(self,response_duration:float,
response_message:List[str],
first_bot_msg:MessageSending):
def catch_after_response(
self, response_duration: float, response_message: List[str], first_bot_msg: MessageSending
):
self.timing_results["make_response_time"] = response_duration
self.response_time = time.time()
for msg in response_message:
self.response_messages.append(msg)
self.chat_history_in_thinking = self.get_message_from_db_between_msgs(self.trigger_response_message,first_bot_msg)
self.chat_history_in_thinking = self.get_message_from_db_between_msgs(
self.trigger_response_message, first_bot_msg
)
def get_message_from_db_between_msgs(self, message_start: Message, message_end: Message):
try:
@@ -118,10 +111,7 @@ class InfoCatcher:
# 查询数据库，获取 chat_id 相同且时间在 start 和 end 之间的数据
messages_between = db.messages.find(
{
"chat_id": chat_id,
"time": {"$gt": time_start, "$lt": time_end}
}
{"chat_id": chat_id, "time": {"$gt": time_start, "$lt": time_end}}
).sort("time", -1)
result = list(messages_between)
@@ -140,9 +130,11 @@ class InfoCatcher:
chat_id = message.chat_stream.stream_id
# 查询数据库，获取 chat_id 相同且 message_id 小于当前消息的 30 条数据
messages_before = db.messages.find(
{"chat_id": chat_id, "message_id": {"$lt": message_id}}
).sort("time", -1).limit(self.context_length*3) #获取更多历史信息
messages_before = (
db.messages.find({"chat_id": chat_id, "message_id": {"$lt": message_id}})
.sort("time", -1)
.limit(self.context_length * 3)
) # 获取更多历史信息
return list(messages_before)
@@ -198,7 +190,7 @@ class InfoCatcher:
"timing_results": self.timing_results,
"chat_history": self.message_list_to_dict(self.chat_history),
"chat_history_in_thinking": self.message_list_to_dict(self.chat_history_in_thinking),
"chat_history_after_response": self.message_list_to_dict(self.chat_history_after_response)
"chat_history_after_response": self.message_list_to_dict(self.chat_history_after_response),
}
# 根据不同的响应模式添加相应的数据
@@ -216,6 +208,7 @@ class InfoCatcher:
print(traceback.format_exc())
return False
class InfoCatcherManager:
def __init__(self):
self.info_catchers = {}
@@ -225,4 +218,5 @@ class InfoCatcherManager:
self.info_catchers[thinking_id] = InfoCatcher()
return self.info_catchers[thinking_id]
info_catcher_manager = InfoCatcherManager()

1
src/plugins/storage/storage.py
View File

@@ -8,6 +8,7 @@ from src.common.logger import get_module_logger
logger = get_module_logger("message_storage")
class MessageStorage:
async def store_message(self, message: Union[MessageSending, MessageRecv], chat_stream: ChatStream) -> None:
"""存储消息到数据库"""

5
src/plugins/utils/prompt_builder.py
View File

@@ -5,6 +5,7 @@ from typing import Dict, Any, Optional, List, Union
from contextlib import asynccontextmanager
import asyncio
class PromptContext:
def __init__(self):
self._context_prompts: Dict[str, Dict[str, "Prompt"]] = {}
@@ -129,7 +130,9 @@ class Prompt(str):
return obj
@classmethod
async def create_async(cls, fstr: str, name: Optional[str] = None, args: Union[List[Any], tuple[Any, ...]] = None, **kwargs):
async def create_async(
cls, fstr: str, name: Optional[str] = None, args: Union[List[Any], tuple[Any, ...]] = None, **kwargs
):
"""异步创建Prompt实例"""
prompt = cls(fstr, name, args, **kwargs)
if global_prompt_manager._context._current_context:

12
src/plugins/willing/mode_classical.py
View File

@@ -1,6 +1,7 @@
import asyncio
from .willing_manager import BaseWillingManager
class ClassicalWillingManager(BaseWillingManager):
def __init__(self):
super().__init__()
@@ -41,10 +42,15 @@ class ClassicalWillingManager(BaseWillingManager):
self.chat_reply_willing[chat_id] = min(current_willing, 3.0)
reply_probability = min(max((current_willing - 0.5), 0.01) * self.global_config.response_willing_amplifier * 2, 1)
reply_probability = min(
max((current_willing - 0.5), 0.01) * self.global_config.response_willing_amplifier * 2, 1
)
# 检查群组权限（如果是群聊）
if willing_info.group_info and willing_info.group_info.group_id in self.global_config.talk_frequency_down_groups:
if (
willing_info.group_info
and willing_info.group_info.group_id in self.global_config.talk_frequency_down_groups
):
reply_probability = reply_probability / self.global_config.down_frequency_rate
if is_emoji_not_reply:
@@ -74,5 +80,3 @@ class ClassicalWillingManager(BaseWillingManager):
async def set_variable_parameters(self, parameters):
return await super().set_variable_parameters(parameters)

1
src/plugins/willing/mode_custom.py
View File

@@ -4,4 +4,3 @@ from .willing_manager import BaseWillingManager
class CustomWillingManager(BaseWillingManager):
def __init__(self):
super().__init__()

9
src/plugins/willing/mode_dynamic.py
View File

@@ -20,7 +20,6 @@ class DynamicWillingManager(BaseWillingManager):
self._decay_task = None
self._mode_switch_task = None
async def async_task_starter(self):
if self._decay_task is None:
self._decay_task = asyncio.create_task(self._decay_reply_willing())
@@ -84,7 +83,9 @@ class DynamicWillingManager(BaseWillingManager):
self.chat_high_willing_mode[chat_id] = True
self.chat_reply_willing[chat_id] = 1.0 # 设置为较高回复意愿
self.chat_high_willing_duration[chat_id] = random.randint(180, 240) # 3-4分钟
self.logger.debug(f"聊天流 {chat_id} 切换到高回复意愿期，持续 {self.chat_high_willing_duration[chat_id]} 秒")
self.logger.debug(
f"聊天流 {chat_id} 切换到高回复意愿期，持续 {self.chat_high_willing_duration[chat_id]} 秒"
)
self.chat_last_mode_change[chat_id] = time.time()
self.chat_msg_count[chat_id] = 0 # 重置消息计数
@@ -148,7 +149,9 @@ class DynamicWillingManager(BaseWillingManager):
# 根据话题兴趣度适当调整
if willing_info.interested_rate > 0.5:
current_willing += (willing_info.interested_rate - 0.5) * 0.5 * self.global_config.response_interested_rate_amplifier
current_willing += (
(willing_info.interested_rate - 0.5) * 0.5 * self.global_config.response_interested_rate_amplifier
)
# 根据当前模式计算回复概率
base_probability = 0.0

45
src/plugins/willing/mode_mxp.py
View File

@@ -17,14 +17,17 @@ Mxp 模式：梦溪畔独家赞助
中策是发issue
下下策是询问一个菜鸟（@梦溪畔）
"""
from .willing_manager import BaseWillingManager
from typing import Dict
import asyncio
import time
import math
class MxpWillingManager(BaseWillingManager):
"""Mxp意愿管理器"""
def __init__(self):
super().__init__()
self.chat_person_reply_willing: Dict[str, Dict[str, float]] = {} # chat_id: {person_id: 意愿值}
@@ -73,9 +76,13 @@ class MxpWillingManager(BaseWillingManager):
w_info = self.ongoing_messages[message_id]
if w_info.is_mentioned_bot:
self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += 0.2
if w_info.chat_id in self.last_response_person and self.last_response_person[w_info.chat_id][0] == w_info.person_id:
self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] +=\
self.single_chat_gain * (2 * self.last_response_person[w_info.chat_id][1] + 1)
if (
w_info.chat_id in self.last_response_person
and self.last_response_person[w_info.chat_id][0] == w_info.person_id
):
self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += self.single_chat_gain * (
2 * self.last_response_person[w_info.chat_id][1] + 1
)
now_chat_new_person = self.last_response_person.get(w_info.chat_id, ["", 0])
if now_chat_new_person[0] != w_info.person_id:
self.last_response_person[w_info.chat_id] = [w_info.person_id, 0]
@@ -98,7 +105,10 @@ class MxpWillingManager(BaseWillingManager):
rel_level = self._get_relationship_level_num(rel_value)
current_willing += rel_level * 0.1
if w_info.chat_id in self.last_response_person and self.last_response_person[w_info.chat_id][0] == w_info.person_id:
if (
w_info.chat_id in self.last_response_person
and self.last_response_person[w_info.chat_id][0] == w_info.person_id
):
current_willing += self.single_chat_gain * (2 * self.last_response_person[w_info.chat_id][1] + 1)
chat_ongoing_messages = [msg for msg in self.ongoing_messages.values() if msg.chat_id == w_info.chat_id]
@@ -141,16 +151,22 @@ class MxpWillingManager(BaseWillingManager):
self.logger.debug(f"聊天流{chat_id}不存在，错误")
continue
basic_willing = self.chat_reply_willing[chat_id]
person_willing[person_id] = basic_willing + (willing - basic_willing) * self.intention_decay_rate
person_willing[person_id] = (
basic_willing + (willing - basic_willing) * self.intention_decay_rate
)
def setup(self, message, chat, is_mentioned_bot, interested_rate):
super().setup(message, chat, is_mentioned_bot, interested_rate)
self.chat_reply_willing[chat.stream_id] = self.chat_reply_willing.get(chat.stream_id, self.basic_maximum_willing)
self.chat_reply_willing[chat.stream_id] = self.chat_reply_willing.get(
chat.stream_id, self.basic_maximum_willing
)
self.chat_person_reply_willing[chat.stream_id] = self.chat_person_reply_willing.get(chat.stream_id, {})
self.chat_person_reply_willing[chat.stream_id][self.ongoing_messages[message.message_info.message_id].person_id] = \
self.chat_person_reply_willing[chat.stream_id].get(self.ongoing_messages[message.message_info.message_id].person_id,
self.chat_reply_willing[chat.stream_id])
self.chat_person_reply_willing[chat.stream_id][
self.ongoing_messages[message.message_info.message_id].person_id
] = self.chat_person_reply_willing[chat.stream_id].get(
self.ongoing_messages[message.message_info.message_id].person_id, self.chat_reply_willing[chat.stream_id]
)
if chat.stream_id not in self.chat_new_message_time:
self.chat_new_message_time[chat.stream_id] = []
@@ -174,10 +190,11 @@ class MxpWillingManager(BaseWillingManager):
await asyncio.sleep(update_time)
async with self.lock:
for chat_id, message_times in self.chat_new_message_time.items():
# 清理过期消息
current_time = time.time()
message_times = [msg_time for msg_time in message_times if current_time - msg_time < self.message_expiration_time]
message_times = [
msg_time for msg_time in message_times if current_time - msg_time < self.message_expiration_time
]
self.chat_new_message_time[chat_id] = message_times
if len(message_times) < self.number_of_message_storage:
@@ -185,7 +202,9 @@ class MxpWillingManager(BaseWillingManager):
update_time = 20
elif len(message_times) == self.number_of_message_storage:
time_interval = current_time - message_times[0]
basic_willing = self.basic_maximum_willing * math.sqrt(time_interval / self.message_expiration_time)
basic_willing = self.basic_maximum_willing * math.sqrt(
time_interval / self.message_expiration_time
)
self.chat_reply_willing[chat_id] = basic_willing
update_time = 17 * math.sqrt(time_interval / self.message_expiration_time) + 3
else:
@@ -203,7 +222,7 @@ class MxpWillingManager(BaseWillingManager):
"interest_willing_gain": "兴趣意愿增益",
"emoji_response_penalty": "表情包回复惩罚",
"down_frequency_rate": "降低回复频率的群组惩罚系数",
"single_chat_gain": "单聊增益（不仅是私聊）"
"single_chat_gain": "单聊增益（不仅是私聊）",
}
async def set_variable_parameters(self, parameters: Dict[str, any]):

11
src/plugins/willing/willing_manager.py
View File

@@ -1,4 +1,3 @@
from src.common.logger import LogConfig, WILLING_STYLE_CONFIG, LoguruLogger, get_module_logger
from dataclasses import dataclass
from ..config.config import global_config, BotConfig
@@ -38,6 +37,7 @@ willing_config = LogConfig(
)
logger = get_module_logger("willing", config=willing_config)
@dataclass
class WillingInfo:
"""此类保存意愿模块常用的参数
@@ -53,6 +53,7 @@ class WillingInfo:
is_emoji (bool): 是否为表情包
interested_rate (float): 兴趣度
"""
message: MessageRecv
chat: ChatStream
person_info_manager: PersonInfoManager
@@ -64,18 +65,17 @@ class WillingInfo:
interested_rate: float
# current_mood: float 当前心情？
class BaseWillingManager(ABC):
"""回复意愿管理基类"""
@classmethod
def create(cls, manager_type: str) -> 'BaseWillingManager':
def create(cls, manager_type: str) -> "BaseWillingManager":
try:
module = importlib.import_module(f".mode_{manager_type}", __package__)
manager_class = getattr(module, f"{manager_type.capitalize()}WillingManager")
if not issubclass(manager_class, cls):
raise TypeError(
f"Manager class {manager_class.__name__} is not a subclass of {cls.__name__}"
)
raise TypeError(f"Manager class {manager_class.__name__} is not a subclass of {cls.__name__}")
else:
logger.info(f"成功载入willing模式：{manager_type}")
return manager_class()
@@ -173,5 +173,6 @@ def init_willing_manager() -> BaseWillingManager:
mode = global_config.willing_mode.lower()
return BaseWillingManager.create(mode)
# 全局willing_manager对象
willing_manager = init_willing_manager()