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Merge branch 'dev' of https://github.com/SnowindMe/MaiBot into dev

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SnowindMe
2025-04-13 01:07:50 +08:00
parent e37e678167 46da415d98
commit 6da41d99e2
45 changed files with 667 additions and 552 deletions
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14
.github/workflows/ruff.yml vendored
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@@ -1,9 +1,23 @@
name: Ruff
on: [ push, pull_request ]
permissions:
contents: write
jobs:
ruff:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: astral-sh/ruff-action@v3
- run: ruff check --fix
- run: ruff format
- name: Commit changes
if: success()
run: |
git config --local user.email "github-actions[bot]@users.noreply.github.com"
git config --local user.name "github-actions[bot]"
git add -A
git diff --quiet && git diff --staged --quiet || git commit -m "🤖 自动格式化代码 [skip ci]"
git push

8
src/common/logger.py
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@@ -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}"),
}

16
src/do_tool/tool_can_use/__init__.py
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@@ -4,17 +4,17 @@ 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
discover_tools()
discover_tools()

42
src/do_tool/tool_can_use/base_tool.py
View File

@@ -10,41 +10,39 @@ 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
description = None
# 工具参数定义，子类必须重写
parameters = None
@classmethod
def get_tool_definition(cls) -> Dict[str, Any]:
"""获取工具定义，用于LLM工具调用
Returns:
Dict: 工具定义字典
"""
if not cls.name or not cls.description or not cls.parameters:
raise NotImplementedError(f"工具类 {cls.__name__} 必须定义 name, description 和 parameters 属性")
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]:
"""执行工具函数
Args:
function_args: 工具调用参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
@@ -53,17 +51,17 @@ class BaseTool:
def register_tool(tool_class: Type[BaseTool]):
"""注册工具到全局注册表
Args:
tool_class: 工具类
"""
if not issubclass(tool_class, BaseTool):
raise TypeError(f"{tool_class.__name__} 不是 BaseTool 的子类")
tool_name = tool_class.name
if not tool_name:
raise ValueError(f"工具类 {tool_class.__name__} 没有定义 name 属性")
TOOL_REGISTRY[tool_name] = tool_class
logger.info(f"已注册工具: {tool_name}")
@@ -73,27 +71,27 @@ def discover_tools():
# 获取当前目录路径
current_dir = os.path.dirname(os.path.abspath(__file__))
package_name = os.path.basename(current_dir)
# 遍历包中的所有模块
for _, module_name, _ in pkgutil.iter_modules([current_dir]):
# 跳过当前模块和__pycache__
if module_name == "base_tool" or module_name.startswith("__"):
continue
# 导入模块
module = importlib.import_module(f"src.do_tool.{package_name}.{module_name}")
# 查找模块中的工具类
for _, obj in inspect.getmembers(module):
if inspect.isclass(obj) and issubclass(obj, BaseTool) and obj != BaseTool:
register_tool(obj)
logger.info(f"工具发现完成，共注册 {len(TOOL_REGISTRY)} 个工具")
def get_all_tool_definitions() -> List[Dict[str, Any]]:
"""获取所有已注册工具的定义
Returns:
List[Dict]: 工具定义列表
"""
@@ -102,14 +100,14 @@ def get_all_tool_definitions() -> List[Dict[str, Any]]:
def get_tool_instance(tool_name: str) -> Optional[BaseTool]:
"""获取指定名称的工具实例
Args:
tool_name: 工具名称
Returns:
Optional[BaseTool]: 工具实例，如果找不到则返回None
"""
tool_class = TOOL_REGISTRY.get(tool_name)
if not tool_class:
return None
return tool_class()
return tool_class()

35
src/do_tool/tool_can_use/fibonacci_sequence_tool.py
View File

@@ -4,29 +4,25 @@ 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]:
"""执行工具功能
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
@@ -34,23 +30,18 @@ class FibonacciSequenceTool(BaseTool):
n = function_args.get("n")
if n <= 0:
raise ValueError("参数n必须大于0")
sequence = []
a, b = 0, 1
for _ in range(n):
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)
register_tool(FibonacciSequenceTool)

25
src/do_tool/tool_can_use/generate_buddha_emoji_tool.py
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@@ -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 = {
@@ -13,32 +15,27 @@ class GenerateBuddhaEmojiTool(BaseTool):
"properties": {
# 无参数
},
"required": []
"required": [],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行工具功能，生成佛祖颜文字
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
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)
register_tool(GenerateBuddhaEmojiTool)

29
src/do_tool/tool_can_use/generate_cmd_tutorial_tool.py
View File

@@ -4,23 +4,21 @@ 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
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
@@ -57,17 +55,12 @@ 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)
register_tool(GenerateCmdTutorialTool)

39
src/do_tool/tool_can_use/get_current_task.py
View File

@@ -5,32 +5,28 @@ 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"
},
"time_info": {
"type": "boolean",
"description": "是否包含时间信息"
}
"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"},
},
"required": []
"required": [],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行获取当前任务
Args:
function_args: 工具参数
message_txt: 原始消息文本，此工具不使用
Returns:
Dict: 工具执行结果
"""
@@ -38,26 +34,21 @@ class GetCurrentTaskTool(BaseTool):
# 获取参数，如果没有提供则使用默认值
num = function_args.get("num", 1)
time_info = function_args.get("time_info", False)
# 调用日程系统获取当前任务
current_task = bot_schedule.get_current_num_task(num=num, time_info=time_info)
# 格式化返回结果
if current_task:
task_info = current_task
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)
register_tool(GetCurrentTaskTool)

46
src/do_tool/tool_can_use/get_knowledge.py
View File

@@ -6,39 +6,35 @@ 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"
},
"threshold": {
"type": "number",
"description": "相似度阈值，0.0到1.0之间"
}
"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"},
},
"required": ["query"]
"required": ["query"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行知识库搜索
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
try:
query = function_args.get("query", message_txt)
threshold = function_args.get("threshold", 0.4)
# 调用知识库搜索
embedding = await get_embedding(query, request_type="info_retrieval")
if embedding:
@@ -47,38 +43,29 @@ 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
) -> Union[str, list]:
"""从数据库中获取相关信息
Args:
query_embedding: 查询的嵌入向量
limit: 最大返回结果数
threshold: 相似度阈值
return_raw: 是否返回原始结果
Returns:
Union[str, list]: 格式化的信息字符串或原始结果列表
"""
if not query_embedding:
return "" if not return_raw else []
# 使用余弦相似度计算
pipeline = [
{
@@ -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)

47
src/do_tool/tool_can_use/get_memory.py
View File

@@ -5,68 +5,55 @@ 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"
},
"max_memory_num": {
"type": "integer",
"description": "最大返回记忆数量"
}
"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"},
},
"required": ["text"]
"required": ["text"],
}
async def execute(self, function_args: Dict[str, Any], message_txt: str = "") -> Dict[str, Any]:
"""执行记忆获取
Args:
function_args: 工具参数
message_txt: 原始消息文本
Returns:
Dict: 工具执行结果
"""
try:
text = function_args.get("text", message_txt)
max_memory_num = function_args.get("max_memory_num", 2)
# 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 = ""
if related_memory:
for memory in related_memory:
memory_info += memory[1] + "\n"
if memory_info:
content = f"你记得这些事情: {memory_info}"
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)
register_tool(GetMemoryTool)

66
src/do_tool/tool_use.py
View File

@@ -16,21 +16,19 @@ class ToolUser:
model=global_config.llm_heartflow, temperature=0.2, max_tokens=1000, request_type="tool_use"
)
async def _build_tool_prompt(self, message_txt:str, sender_name:str, chat_stream:ChatStream):
async def _build_tool_prompt(self, message_txt: str, sender_name: str, chat_stream: ChatStream):
"""构建工具使用的提示词
Args:
message_txt: 用户消息文本
sender_name: 发送者名称
chat_stream: 聊天流对象
Returns:
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:
@@ -44,37 +42,37 @@ class ToolUser:
prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
prompt += f"注意你就是{bot_name}，{bot_name}指的就是你。"
prompt += "你现在需要对群里的聊天内容进行回复，现在请你思考，你是否需要额外的信息，或者一些工具来帮你回复，比如回忆或者搜寻已有的知识，或者了解你现在正在做什么，请输出你需要的工具，或者你需要的额外信息。"
return prompt
def _define_tools(self):
"""获取所有已注册工具的定义
Returns:
list: 工具定义列表
"""
return get_all_tool_definitions()
async def _execute_tool_call(self, tool_call, message_txt:str):
async def _execute_tool_call(self, tool_call, message_txt: str):
"""执行特定的工具调用
Args:
tool_call: 工具调用对象
message_txt: 原始消息文本
Returns:
dict: 工具调用结果
"""
try:
function_name = tool_call["function"]["name"]
function_args = json.loads(tool_call["function"]["arguments"])
# 获取对应工具实例
tool_instance = get_tool_instance(function_name)
if not tool_instance:
logger.warning(f"未知工具名称: {function_name}")
return None
# 执行工具
result = await tool_instance.execute(function_args, message_txt)
if result:
@@ -82,62 +80,60 @@ 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:
logger.error(f"执行工具调用时发生错误: {str(e)}")
return None
async def use_tool(self, message_txt:str, sender_name:str, chat_stream:ChatStream):
async def use_tool(self, message_txt: str, sender_name: str, chat_stream: ChatStream):
"""使用工具辅助思考，判断是否需要额外信息
Args:
message_txt: 用户消息文本
sender_name: 发送者名称
chat_stream: 聊天流对象
Returns:
dict: 工具使用结果
"""
try:
# 构建提示词
prompt = await self._build_tool_prompt(message_txt, sender_name, chat_stream)
# 定义可用工具
tools = self._define_tools()
# 使用llm_model_tool发送带工具定义的请求
payload = {
"model": self.llm_model_tool.model_name,
"messages": [{"role": "user", "content": prompt}],
"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
if len(response) == 3:
content, reasoning_content, tool_calls = response
logger.info(f"工具思考: {tool_calls}")
# 检查响应中工具调用是否有效
if not tool_calls:
logger.info("模型返回了空的tool_calls列表")
return {"used_tools": False}
logger.info(f"模型请求调用{len(tool_calls)}个工具")
tool_results = []
collected_info = ""
# 执行所有工具调用
for tool_call in tool_calls:
result = await self._execute_tool_call(tool_call, message_txt)
@@ -145,7 +141,7 @@ class ToolUser:
tool_results.append(result)
# 将工具结果添加到收集的信息中
collected_info += f"\n{result['name']}返回结果: {result['content']}\n"
# 如果有工具结果，直接返回收集的信息
if collected_info:
logger.info(f"工具调用收集到信息: {collected_info}")
@@ -157,15 +153,15 @@ class ToolUser:
# 没有工具调用
content, reasoning_content = response
logger.info("模型没有请求调用任何工具")
# 如果没有工具调用或处理失败，直接返回原始思考
return {
"used_tools": False,
}
except Exception as e:
logger.error(f"工具调用过程中出错: {str(e)}")
return {
"used_tools": False,
"error": str(e),
}
}

15
src/heart_flow/heartflow.py
View File

@@ -43,12 +43,11 @@ def init_prompt():
class CurrentState:
def __init__(self):
self.current_state_info = ""
self.mood_manager = MoodManager()
self.mood = self.mood_manager.get_prompt()
self.attendance_factor = 0
self.engagement_factor = 0
@@ -66,9 +65,6 @@ class Heartflow:
)
self._subheartflows: Dict[Any, SubHeartflow] = {}
async def _cleanup_inactive_subheartflows(self):
"""定期清理不活跃的子心流"""
@@ -90,7 +86,7 @@ class Heartflow:
logger.info(f"已清理不活跃的子心流: {subheartflow_id}")
await asyncio.sleep(30) # 每分钟检查一次
async def _sub_heartflow_update(self):
while True:
# 检查是否存在子心流
@@ -103,13 +99,12 @@ 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())
# 启动子心流更新任务
asyncio.create_task(self._sub_heartflow_update())
async def _update_current_state(self):
print("TODO")
@@ -155,7 +150,7 @@ class Heartflow:
# prompt += f"你现在{mood_info}。"
# prompt += "现在你接下去继续思考，产生新的想法，但是要基于原有的主要想法，不要分点输出，"
# prompt += "输出连贯的内心独白，不要太长，但是记得结合上述的消息，关注新内容:"
prompt = global_prompt_manager.get_prompt("thinking_prompt").format(
prompt = (await global_prompt_manager.get_prompt_async("thinking_prompt")).format(
schedule_info, personality_info, related_memory_info, current_thinking_info, sub_flows_info, mood_info
)
@@ -212,7 +207,7 @@ class Heartflow:
# prompt += f"你现在{mood_info}\n"
# prompt += """现在请你总结这些聊天内容，注意关注聊天内容对原有的想法的影响，输出连贯的内心独白
# 不要太长，但是记得结合上述的消息，要记得你的人设，关注新内容:"""
prompt = global_prompt_manager.get_prompt("mind_summary_prompt").format(
prompt = (await global_prompt_manager.get_prompt_async("mind_summary_prompt")).format(
personality_info, global_config.BOT_NICKNAME, self.current_mind, minds_str, mood_info
)

2
src/heart_flow/observation.py
View File

@@ -150,7 +150,7 @@ class ChattingObservation(Observation):
except Exception as e:
print(f"获取总结失败: {e}")
updated_observe_info = ""
return updated_observe_info
# print(f"prompt：{prompt}")
# print(f"self.observe_info：{self.observe_info}")

125
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
@@ -16,7 +18,8 @@ import random
from src.plugins.chat.chat_stream import ChatStream
from src.plugins.person_info.relationship_manager import relationship_manager
from src.plugins.chat.utils import get_recent_group_speaker
from src.do_tool.tool_use import ToolUser
from src.do_tool.tool_use import ToolUser
from ..plugins.utils.prompt_builder import Prompt, global_prompt_manager
subheartflow_config = LogConfig(
# 使用海马体专用样式
@@ -26,6 +29,35 @@ 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"
prompt += "{collected_info}\n"
prompt += "{relation_prompt_all}\n"
prompt += "{prompt_personality}\n"
prompt += "刚刚你的想法是{current_thinking_info}。如果有新的内容，记得转换话题\n"
prompt += "-----------------------------------\n"
prompt += "现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
prompt += "你现在{mood_info}\n"
prompt += "你注意到{sender_name}刚刚说：{message_txt}\n"
prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白"
prompt += "思考时可以想想如何对群聊内容进行回复。回复的要求是：平淡一些，简短一些，说中文，尽量不要说你说过的话\n"
prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号， 表情，等)，不要带有括号和动作描写"
prompt += "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{bot_name}，{bot_name}指的就是你。"
Prompt(prompt, "sub_heartflow_prompt_before")
prompt = ""
# prompt += f"你现在正在做的事情是：{schedule_info}\n"
prompt += "{prompt_personality}\n"
prompt += "现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
prompt += "刚刚你的想法是{current_thinking_info}。"
prompt += "你现在看到了网友们发的新消息:{message_new_info}\n"
prompt += "你刚刚回复了群友们:{reply_info}"
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")
class CurrentState:
def __init__(self):
self.willing = 0
@@ -48,7 +80,6 @@ class SubHeartflow:
self.llm_model = LLM_request(
model=global_config.llm_sub_heartflow, temperature=0.2, max_tokens=600, request_type="sub_heart_flow"
)
self.main_heartflow_info = ""
@@ -63,9 +94,9 @@ class SubHeartflow:
self.observations: list[Observation] = []
self.running_knowledges = []
self.bot_name = global_config.BOT_NICKNAME
self.tool_user = ToolUser()
def add_observation(self, observation: Observation):
@@ -115,12 +146,12 @@ 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):
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
# mood_info = "你很生气，很愤怒"
@@ -130,12 +161,12 @@ class SubHeartflow:
# 首先尝试使用工具获取更多信息
tool_result = await self.tool_user.use_tool(message_txt, sender_name, chat_stream)
# 如果工具被使用且获得了结果，将收集到的信息合并到思考中
collected_info = ""
if tool_result.get("used_tools", False):
logger.info("使用工具收集了信息")
# 如果有收集到的信息，将其添加到当前思考中
if "collected_info" in tool_result:
collected_info = tool_result["collected_info"]
@@ -155,7 +186,7 @@ class SubHeartflow:
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
# 关系
who_chat_in_group = [
(chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
@@ -170,26 +201,41 @@ class SubHeartflow:
for person in who_chat_in_group:
relation_prompt += await relationship_manager.build_relationship_info(person)
relation_prompt_all = (
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 = (
# 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, sender_name
)
prompt = ""
# prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
if tool_result.get("used_tools", False):
prompt += f"{collected_info}\n"
prompt += f"{relation_prompt_all}\n"
prompt += f"{prompt_personality}\n"
prompt += f"刚刚你的想法是{current_thinking_info}。如果有新的内容，记得转换话题\n"
prompt += "-----------------------------------\n"
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
prompt += f"你现在{mood_info}\n"
prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白"
prompt += "思考时可以想想如何对群聊内容进行回复。回复的要求是：平淡一些，简短一些，说中文，尽量不要说你说过的话\n"
prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号， 表情，等)，不要带有括号和动作描写"
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 = ""
# # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n"
# if tool_result.get("used_tools", False):
# prompt += f"{collected_info}\n"
# prompt += f"{relation_prompt_all}\n"
# prompt += f"{prompt_personality}\n"
# prompt += f"刚刚你的想法是{current_thinking_info}。如果有新的内容，记得转换话题\n"
# prompt += "-----------------------------------\n"
# prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
# prompt += f"你现在{mood_info}\n"
# prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
# prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白"
# prompt += "思考时可以想想如何对群聊内容进行回复。回复的要求是：平淡一些，简短一些，说中文，尽量不要说你说过的话\n"
# prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号， 表情，等)，不要带有括号和动作描写"
# 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,
)
try:
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
@@ -233,16 +279,20 @@ class SubHeartflow:
reply_info = reply_content
# schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False)
prompt = ""
# prompt += f"你现在正在做的事情是：{schedule_info}\n"
prompt += f"{prompt_personality}\n"
prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
prompt += f"刚刚你的想法是{current_thinking_info}。"
prompt += f"你现在看到了网友们发的新消息:{message_new_info}\n"
prompt += f"你刚刚回复了群友们:{reply_info}"
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 = ""
# # prompt += f"你现在正在做的事情是：{schedule_info}\n"
# prompt += f"{prompt_personality}\n"
# prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n"
# prompt += f"刚刚你的想法是{current_thinking_info}。"
# prompt += f"你现在看到了网友们发的新消息:{message_new_info}\n"
# prompt += f"你刚刚回复了群友们:{reply_info}"
# 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_personality, chat_observe_info, current_thinking_info, message_new_info, reply_info, mood_info
)
try:
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
except Exception as e:
@@ -302,4 +352,5 @@ class SubHeartflow:
self.current_mind = response
init_prompt()
# subheartflow = SubHeartflow()

41
src/plugins/PFC/action_planner.py
View File

@@ -53,13 +53,13 @@ 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)
reasoning = "没有明确原因"
goal_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
goals_str += goal_str
else:
@@ -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"

20
src/plugins/PFC/chat_observer.py
View File

@@ -1,12 +1,12 @@
import time
import asyncio
import traceback
from typing import Optional, Dict, Any, List
from typing import Optional, Dict, Any, List
from src.common.logger import get_module_logger
from ..message.message_base 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 .message_storage import MongoDBMessageStorage
logger = get_module_logger("chat_observer")
@@ -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
@@ -197,7 +196,7 @@ class ChatObserver:
if new_messages:
self.last_message_read = new_messages[-1]
self.last_message_time = new_messages[-1]["time"]
# print(f"获取数据库中找到的新消息: {new_messages}")
return new_messages
@@ -215,7 +214,7 @@ class ChatObserver:
if new_messages:
self.last_message_read = new_messages[-1]["message_id"]
logger.debug(f"获取指定时间点111之前的消息: {new_messages}")
return new_messages
@@ -239,7 +238,7 @@ class ChatObserver:
try:
# print("等待事件")
await asyncio.wait_for(self._update_event.wait(), timeout=1)
except asyncio.TimeoutError:
# print("超时")
pass # 超时后也执行一次检查
@@ -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
@@ -368,6 +366,6 @@ class ChatObserver:
if not self.message_cache:
return None
return self.message_cache[0]
def __str__(self):
return f"ChatObserver for {self.stream_id}"

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

@@ -140,7 +140,6 @@ class NotificationManager:
self._active_states.add(notification.type)
else:
self._active_states.discard(notification.type)
# 调用目标接收者的处理器
target = notification.target
@@ -181,7 +180,7 @@ class NotificationManager:
history = history[-limit:]
return history
def __str__(self):
str = ""
for target, handlers in self._handlers.items():
@@ -295,5 +294,3 @@ class ChatStateManager:
current_time = datetime.now().timestamp()
return (current_time - self.state_info.last_message_time) <= threshold

15
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}")
@@ -96,7 +95,7 @@ class Conversation:
# 执行行动
await self._handle_action(action, reason, self.observation_info, self.conversation_info)
for goal in self.conversation_info.goal_list:
# 检查goal是否为元组类型，如果是元组则使用索引访问，如果是字典则使用get方法
if isinstance(goal, tuple):
@@ -151,7 +150,7 @@ class Conversation:
if action == "direct_reply":
self.waiter.wait_accumulated_time = 0
self.state = ConversationState.GENERATING
self.generated_reply = await self.reply_generator.generate(observation_info, conversation_info)
print(f"生成回复: {self.generated_reply}")
@@ -174,7 +173,6 @@ class Conversation:
await self._send_reply()
conversation_info.done_action[-1].update(
{
"status": "done",
@@ -184,7 +182,7 @@ class Conversation:
elif action == "fetch_knowledge":
self.waiter.wait_accumulated_time = 0
self.state = ConversationState.FETCHING
knowledge = "TODO:知识"
topic = "TODO:关键词"
@@ -199,7 +197,7 @@ class Conversation:
elif action == "rethink_goal":
self.waiter.wait_accumulated_time = 0
self.state = ConversationState.RETHINKING
await self.goal_analyzer.analyze_goal(conversation_info, observation_info)
@@ -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"""

13
src/plugins/PFC/observation_info.py
View File

@@ -26,24 +26,24 @@ class ObservationInfoHandler(NotificationHandler):
# 获取通知类型和数据
notification_type = notification.type
data = notification.data
if notification_type == NotificationType.NEW_MESSAGE:
# 处理新消息通知
logger.debug(f"收到新消息通知data: {data}")
message_id = data.get("message_id")
processed_plain_text = data.get("processed_plain_text")
detailed_plain_text = data.get("detailed_plain_text")
detailed_plain_text = data.get("detailed_plain_text")
user_info = data.get("user_info")
time_value = data.get("time")
message = {
"message_id": message_id,
"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)
elif notification_type == NotificationType.COLD_CHAT:
@@ -161,7 +161,7 @@ class ObservationInfo:
# logger.debug(f"更新信息from_message: {message}")
self.last_message_time = message["time"]
self.last_message_id = message["message_id"]
self.last_message_content = message.get("processed_plain_text", "")
user_info = UserInfo.from_dict(message.get("user_info", {}))
@@ -233,4 +233,3 @@ class ObservationInfo:
self.unprocessed_messages.clear()
self.chat_history_count = len(self.chat_history)
self.new_messages_count = 0

20
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
@@ -63,13 +64,13 @@ 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)
reasoning = "没有明确原因"
goal_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
goals_str += goal_str
else:
@@ -140,14 +141,12 @@ class GoalAnalyzer:
except Exception as e:
logger.error(f"分析对话目标时出错: {str(e)}")
content = ""
# 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:
# 判断结果是单个字典还是字典列表
if isinstance(result, list):
@@ -157,7 +156,7 @@ class GoalAnalyzer:
goal = item.get("goal", "")
reasoning = item.get("reasoning", "")
conversation_info.goal_list.append((goal, reasoning))
# 返回第一个目标作为当前主要目标（如果有）
if result:
first_goal = result[0]
@@ -168,7 +167,7 @@ class GoalAnalyzer:
reasoning = result.get("reasoning", "")
conversation_info.goal_list.append((goal, reasoning))
return (goal, "", reasoning)
# 如果解析失败，返回默认值
return ("", "", "")
@@ -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"""

16
src/plugins/PFC/pfc_utils.py
View File

@@ -27,7 +27,7 @@ def get_items_from_json(
"""
content = content.strip()
result = {}
# 设置默认值
if default_values:
result.update(default_values)
@@ -41,7 +41,7 @@ def get_items_from_json(
if array_match:
array_content = array_match.group()
json_array = json.loads(array_content)
# 确认是数组类型
if isinstance(json_array, list):
# 验证数组中的每个项目是否包含所有必需字段
@@ -49,7 +49,7 @@ def get_items_from_json(
for item in json_array:
if not isinstance(item, dict):
continue
# 检查是否有所有必需字段
if all(field in item for field in items):
# 验证字段类型
@@ -59,22 +59,22 @@ def get_items_from_json(
if field in item and not isinstance(item[field], expected_type):
type_valid = False
break
if not type_valid:
continue
# 验证字符串字段不为空
string_valid = True
for field in items:
if isinstance(item[field], str) and not item[field].strip():
string_valid = False
break
if not string_valid:
continue
valid_items.append(item)
if valid_items:
return True, valid_items
except json.JSONDecodeError:

34
src/plugins/PFC/reply_generator.py
View File

@@ -49,22 +49,26 @@ 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)
reasoning = "没有明确原因"
goal_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
goals_str += goal_str
else:
goal = "目前没有明确对话目标"
reasoning = "目前没有明确对话目标，最好思考一个对话目标"
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"

19
src/plugins/PFC/waiter.py
View File

@@ -16,7 +16,7 @@ class Waiter:
self.chat_observer = ChatObserver.get_instance(stream_id)
self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=2)
self.name = global_config.BOT_NICKNAME
self.wait_accumulated_time = 0
async def wait(self, conversation_info: ConversationInfo) -> bool:
@@ -38,20 +38,20 @@ class Waiter:
# 检查是否超时
if time.time() - wait_start_time > 300:
self.wait_accumulated_time += 300
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"
"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",
}
conversation_info.goal_list.append(wait_goal)
print(f"添加目标: {wait_goal}")
return True
await asyncio.sleep(1)
logger.info("等待中...")
async def wait_listening(self, conversation_info: ConversationInfo) -> bool:
"""等待倾听
@@ -73,14 +73,13 @@ class Waiter:
self.wait_accumulated_time += 300
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"
"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",
}
conversation_info.goal_list.append(wait_goal)
print(f"添加目标: {wait_goal}")
return True
await asyncio.sleep(1)
logger.info("等待中...")

94
src/plugins/chat/bot.py
View File

@@ -8,6 +8,7 @@ from ..chat_module.only_process.only_message_process import MessageProcessor
from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
from ..chat_module.think_flow_chat.think_flow_chat import ThinkFlowChat
from ..chat_module.reasoning_chat.reasoning_chat import ReasoningChat
from ..utils.prompt_builder import Prompt, global_prompt_manager
import traceback
# 定义日志配置
@@ -89,52 +90,71 @@ class ChatBot:
logger.debug(f"用户{userinfo.user_id}被禁止回复")
return
if global_config.enable_pfc_chatting:
try:
if message.message_info.template_info and not message.message_info.template_info.template_default:
template_group_name = message.message_info.template_info.template_name
template_items = message.message_info.template_info.template_items
async with global_prompt_manager.async_message_scope(template_group_name):
if isinstance(template_items, dict):
for k in template_items.keys():
await Prompt.create_async(template_items[k], k)
print(f"注册{template_items[k]},{k}")
else:
template_group_name = None
async def preprocess():
if global_config.enable_pfc_chatting:
try:
if groupinfo is None:
if global_config.enable_friend_chat:
userinfo = message.message_info.user_info
messageinfo = message.message_info
# 创建聊天流
chat = await chat_manager.get_or_create_stream(
platform=messageinfo.platform,
user_info=userinfo,
group_info=groupinfo,
)
message.update_chat_stream(chat)
await self.only_process_chat.process_message(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]}...")
if global_config.response_mode == "heart_flow":
await self.think_flow_chat.process_message(message_data)
elif global_config.response_mode == "reasoning":
# logger.debug(f"开始推理模式{str(message_data)[:50]}...")
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
except Exception as e:
logger.error(f"处理PFC消息失败: {e}")
else:
if groupinfo is None:
if global_config.enable_friend_chat:
userinfo = message.message_info.user_info
messageinfo = message.message_info
# 创建聊天流
chat = await chat_manager.get_or_create_stream(
platform=messageinfo.platform,
user_info=userinfo,
group_info=groupinfo,
)
message.update_chat_stream(chat)
await self.only_process_chat.process_message(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]}...")
# 私聊处理流程
# await self._handle_private_chat(message)
if global_config.response_mode == "heart_flow":
await self.think_flow_chat.process_message(message_data)
elif global_config.response_mode == "reasoning":
# logger.debug(f"开始推理模式{str(message_data)[:50]}...")
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
except Exception as e:
logger.error(f"处理PFC消息失败: {e}")
else: # 群聊处理
if groupinfo.group_id in global_config.talk_allowed_groups:
if global_config.response_mode == "heart_flow":
await self.think_flow_chat.process_message(message_data)
elif global_config.response_mode == "reasoning":
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
if template_group_name:
async with global_prompt_manager.async_message_scope(template_group_name):
await preprocess()
else:
if groupinfo is None:
if global_config.enable_friend_chat:
# 私聊处理流程
# await self._handle_private_chat(message)
if global_config.response_mode == "heart_flow":
await self.think_flow_chat.process_message(message_data)
elif global_config.response_mode == "reasoning":
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
else: # 群聊处理
if groupinfo.group_id in global_config.talk_allowed_groups:
if global_config.response_mode == "heart_flow":
await self.think_flow_chat.process_message(message_data)
elif global_config.response_mode == "reasoning":
await self.reasoning_chat.process_message(message_data)
else:
logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
await preprocess()
except Exception as e:
logger.error(f"预处理消息失败: {e}")
traceback.print_exc()

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

28
src/plugins/chat_module/reasoning_chat/reasoning_prompt_builder.py
View File

@@ -106,7 +106,7 @@ class PromptBuilder:
for memory in related_memory:
related_memory_info += memory[1]
# memory_prompt = f"你想起你之前见过的事情：{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 = global_prompt_manager.format_prompt(
memory_prompt = await global_prompt_manager.format_prompt(
"memory_prompt", related_memory_info=related_memory_info
)
else:
@@ -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
@@ -168,7 +166,7 @@ class PromptBuilder:
prompt_info = await self.get_prompt_info(message_txt, threshold=0.38)
if prompt_info:
# prompt_info = f"""\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"""
prompt_info = global_prompt_manager.format_prompt("knowledge_prompt", prompt_info=prompt_info)
prompt_info = await global_prompt_manager.format_prompt("knowledge_prompt", prompt_info=prompt_info)
end_time = time.time()
logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
@@ -194,22 +192,22 @@ class PromptBuilder:
# 请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
# {moderation_prompt}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 )。"""
prompt = global_prompt_manager.format_prompt(
prompt = await global_prompt_manager.format_prompt(
"reasoning_prompt_main",
relation_prompt_all=global_prompt_manager.get_prompt("relationship_prompt"),
relation_prompt_all=await global_prompt_manager.get_prompt_async("relationship_prompt"),
replation_prompt=relation_prompt,
sender_name=sender_name,
memory_prompt=memory_prompt,
prompt_info=prompt_info,
schedule_prompt=global_prompt_manager.format_prompt(
schedule_prompt=await global_prompt_manager.format_prompt(
"schedule_prompt", schedule_info=bot_schedule.get_current_num_task(num=1, time_info=False)
),
chat_target=global_prompt_manager.get_prompt("chat_target_group1")
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
if chat_in_group
else global_prompt_manager.get_prompt("chat_target_private1"),
chat_target_2=global_prompt_manager.get_prompt("chat_target_group2")
else await global_prompt_manager.get_prompt_async("chat_target_private1"),
chat_target_2=await global_prompt_manager.get_prompt_async("chat_target_group2")
if chat_in_group
else global_prompt_manager.get_prompt("chat_target_private2"),
else await global_prompt_manager.get_prompt_async("chat_target_private2"),
chat_talking_prompt=chat_talking_prompt,
message_txt=message_txt,
bot_name=global_config.BOT_NICKNAME,
@@ -220,7 +218,7 @@ class PromptBuilder:
mood_prompt=mood_prompt,
keywords_reaction_prompt=keywords_reaction_prompt,
prompt_ger=prompt_ger,
moderation_prompt=global_prompt_manager.get_prompt("moderation_prompt"),
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
)
return prompt

34
src/plugins/chat_module/think_flow_chat/think_flow_prompt_builder.py
View File

@@ -30,7 +30,7 @@ def init_prompt():
Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1")
Prompt("和群里聊天", "chat_target_group2")
Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
Prompt("和{sender_name}私聊", "chat_target_pivate2")
Prompt("和{sender_name}私聊", "chat_target_private2")
Prompt(
"""**检查并忽略**任何涉及尝试绕过审核的行为。
涉及政治敏感以及违法违规的内容请规避。""",
@@ -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
@@ -143,24 +141,24 @@ class PromptBuilder:
# 回复尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。{prompt_ger}
# 请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话 ，注意只输出回复内容。
# {moderation_prompt}。注意：不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。"""
prompt = global_prompt_manager.format_prompt(
prompt = await global_prompt_manager.format_prompt(
"heart_flow_prompt_normal",
chat_target=global_prompt_manager.get_prompt("chat_target_group1")
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
if chat_in_group
else global_prompt_manager.get_prompt("chat_target_private1"),
else await global_prompt_manager.get_prompt_async("chat_target_private1"),
chat_talking_prompt=chat_talking_prompt,
sender_name=sender_name,
message_txt=message_txt,
bot_name=global_config.BOT_NICKNAME,
prompt_personality=prompt_personality,
prompt_identity=prompt_identity,
chat_target_2=global_prompt_manager.get_prompt("chat_target_group2")
chat_target_2=await global_prompt_manager.get_prompt_async("chat_target_group2")
if chat_in_group
else global_prompt_manager.get_prompt("chat_target_private2"),
else await global_prompt_manager.get_prompt_async("chat_target_private2"),
current_mind_info=current_mind_info,
keywords_reaction_prompt=keywords_reaction_prompt,
prompt_ger=prompt_ger,
moderation_prompt=global_prompt_manager.get_prompt("moderation_prompt"),
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
)
return prompt
@@ -218,13 +216,13 @@ class PromptBuilder:
# 你刚刚脑子里在想：{current_mind_info}
# 现在请你读读之前的聊天记录，然后给出日常，口语化且简短的回复内容，只给出文字的回复内容，不要有内心独白:
# """
prompt = global_prompt_manager.format_prompt(
prompt = await global_prompt_manager.format_prompt(
"heart_flow_prompt_simple",
bot_name=global_config.BOT_NICKNAME,
prompt_personality=prompt_personality,
chat_target=global_prompt_manager.get_prompt("chat_target_group1")
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
if chat_in_group
else global_prompt_manager.get_prompt("chat_target_private1"),
else await global_prompt_manager.get_prompt_async("chat_target_private1"),
chat_talking_prompt=chat_talking_prompt,
sender_name=sender_name,
message_txt=message_txt,
@@ -266,14 +264,14 @@ class PromptBuilder:
# {chat_target}，你希望在群里回复：{content}。现在请你根据以下信息修改回复内容。将这个回复修改的更加日常且口语化的回复，平淡一些，回复尽量简短一些。不要回复的太有条理。
# {prompt_ger}，不要刻意突出自身学科背景，注意只输出回复内容。
# {moderation_prompt}。注意：不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。"""
prompt = global_prompt_manager.format_prompt(
prompt = await global_prompt_manager.format_prompt(
"heart_flow_prompt_response",
bot_name=global_config.BOT_NICKNAME,
prompt_identity=prompt_identity,
chat_target=global_prompt_manager.get_prompt("chat_target_group1"),
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1"),
content=content,
prompt_ger=prompt_ger,
moderation_prompt=global_prompt_manager.get_prompt("moderation_prompt"),
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
)
return prompt

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(

2
src/plugins/message/message_base.py
View File

@@ -137,7 +137,7 @@ class FormatInfo:
class TemplateInfo:
"""模板信息类"""
template_items: Optional[List[Dict]] = None
template_items: Optional[Dict] = None
template_name: Optional[str] = None
template_default: bool = True

8
src/plugins/models/utils_model.py
View File

@@ -574,7 +574,7 @@ class LLM_request:
reasoning_content = message.get("reasoning_content", "")
if not reasoning_content:
reasoning_content = reasoning
# 提取工具调用信息
tool_calls = message.get("tool_calls", None)
@@ -592,7 +592,7 @@ class LLM_request:
request_type=request_type if request_type is not None else self.request_type,
endpoint=endpoint,
)
# 只有当tool_calls存在且不为空时才返回
if tool_calls:
return content, reasoning_content, tool_calls
@@ -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

6
src/plugins/moods/moods.py
View File

@@ -238,14 +238,14 @@ class MoodManager:
base_prompt += "情绪比较平静。"
return base_prompt
def get_arousal_multiplier(self) -> float:
"""根据当前情绪状态返回唤醒度乘数"""
if self.current_mood.arousal > 0.4:
multiplier = 1 + min(0.15,(self.current_mood.arousal - 0.4)/3)
multiplier = 1 + min(0.15, (self.current_mood.arousal - 0.4) / 3)
return multiplier
elif self.current_mood.arousal < -0.4:
multiplier = 1 - min(0.15,((0 - self.current_mood.arousal) - 0.4)/3)
multiplier = 1 - min(0.15, ((0 - self.current_mood.arousal) - 0.4) / 3)
return multiplier
return 1.0

124
src/plugins/respon_info_catcher/info_catcher.py
View File

@@ -1,28 +1,29 @@
from src.plugins.config.config import global_config
from src.plugins.chat.message import MessageRecv,MessageSending,Message
from src.plugins.chat.message import MessageRecv, MessageSending, Message
from src.common.database import db
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
self.chat_history = [] # 聊天历史，长度为三倍使用的上下文
self.context_length = global_config.MAX_CONTEXT_SIZE
self.chat_history_in_thinking = [] # 思考期间的聊天内容
self.chat_history_after_response = [] # 回复后的聊天内容，长度为一倍上下文
self.chat_history_in_thinking = [] # 思考期间的聊天内容
self.chat_history_after_response = [] # 回复后的聊天内容，长度为一倍上下文
self.chat_id = ""
self.response_mode = global_config.response_mode
self.trigger_response_text = ""
self.response_text = ""
self.trigger_response_time = 0
self.trigger_response_message = None
self.response_time = 0
self.response_messages = []
# 使用字典来存储 heartflow 模式的数据
self.heartflow_data = {
"heart_flow_prompt": "",
@@ -32,17 +33,12 @@ 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 = {
"interested_rate_time": 0,
@@ -50,24 +46,24 @@ class InfoCatcher:
"sub_heartflow_step_time": 0,
"make_response_time": 0,
}
def catch_decide_to_response(self,message:MessageRecv):
def catch_decide_to_response(self, message: MessageRecv):
# 搜集决定回复时的信息
self.trigger_response_message = message
self.trigger_response_text = message.detailed_plain_text
self.trigger_response_time = time.time()
self.chat_id = message.chat_stream.stream_id
self.chat_history = self.get_message_from_db_before_msg(message)
def catch_after_observe(self,obs_duration:float):#这里可以有更多信息
def catch_after_observe(self, obs_duration: float): # 这里可以有更多信息
self.timing_results["sub_heartflow_observe_time"] = obs_duration
# def catch_shf
def catch_afer_shf_step(self,step_duration:float,past_mind:str,current_mind:str):
def catch_afer_shf_step(self, step_duration: float, past_mind: str, current_mind: str):
self.timing_results["sub_heartflow_step_time"] = step_duration
if len(past_mind) > 1:
self.heartflow_data["sub_heartflow_before"] = past_mind[-1]
@@ -75,11 +71,8 @@ class InfoCatcher:
else:
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
@@ -89,41 +82,38 @@ class InfoCatcher:
self.reasoning_data["prompt"] = prompt
self.reasoning_data["response"] = response
self.reasoning_data["model"] = model_name
self.response_text = response
def catch_after_generate_response(self,response_duration:float):
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:
# 从数据库中获取消息的时间戳
time_start = message_start.message_info.time
time_end = message_end.message_info.time
chat_id = message_start.chat_stream.stream_id
print(f"查询参数: time_start={time_start}, time_end={time_end}, chat_id={chat_id}")
# 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)
print(f"查询结果数量: {len(result)}")
if result:
@@ -133,21 +123,23 @@ class InfoCatcher:
except Exception as e:
print(f"获取消息时出错: {str(e)}")
return []
def get_message_from_db_before_msg(self, message: MessageRecv):
# 从数据库中获取消息
message_id = message.message_info.message_id
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)
def message_list_to_dict(self, message_list):
#存储简化的聊天记录
# 存储简化的聊天记录
result = []
for message in message_list:
if not isinstance(message, dict):
@@ -160,7 +152,7 @@ class InfoCatcher:
"processed_plain_text": message["processed_plain_text"],
}
result.append(lite_message)
return result
def message_to_dict(self, message):
@@ -176,12 +168,12 @@ class InfoCatcher:
"processed_plain_text": message.processed_plain_text,
# "detailed_plain_text": message.detailed_plain_text
}
def done_catch(self):
"""将收集到的信息存储到数据库的 thinking_log 集合中"""
try:
# 将消息对象转换为可序列化的字典
thinking_log_data = {
"chat_id": self.chat_id,
"response_mode": self.response_mode,
@@ -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
@@ -209,20 +201,22 @@ class InfoCatcher:
# 将数据插入到 thinking_log 集合中
db.thinking_log.insert_one(thinking_log_data)
return True
except Exception as e:
print(f"存储思考日志时出错: {str(e)}")
print(traceback.format_exc())
return False
class InfoCatcherManager:
def __init__(self):
self.info_catchers = {}
def get_info_catcher(self,thinking_id:str) -> InfoCatcher:
def get_info_catcher(self, thinking_id: str) -> InfoCatcher:
if thinking_id not in self.info_catchers:
self.info_catchers[thinking_id] = InfoCatcher()
return self.info_catchers[thinking_id]
info_catcher_manager = InfoCatcherManager()
info_catcher_manager = InfoCatcherManager()

2
src/plugins/schedule/schedule_generator.py
View File

@@ -32,7 +32,7 @@ class ScheduleGenerator:
# 使用离线LLM模型
self.llm_scheduler_all = LLM_request(
model=global_config.llm_reasoning,
temperature=global_config.SCHEDULE_TEMPERATURE+0.3,
temperature=global_config.SCHEDULE_TEMPERATURE + 0.3,
max_tokens=7000,
request_type="schedule",
)

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"""

96
src/plugins/utils/prompt_builder.py
View File

@@ -2,16 +2,69 @@
import ast
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"]] = {}
self._current_context: Optional[str] = None
self._context_lock = asyncio.Lock() # 添加异步锁
@asynccontextmanager
async def async_scope(self, context_id: str):
"""创建一个异步的临时提示模板作用域"""
async with self._context_lock:
if context_id not in self._context_prompts:
self._context_prompts[context_id] = {}
previous_context = self._current_context
self._current_context = context_id
try:
yield self
finally:
async with self._context_lock:
self._current_context = previous_context
async def get_prompt_async(self, name: str) -> Optional["Prompt"]:
"""异步获取当前作用域中的提示模板"""
async with self._context_lock:
if self._current_context and name in self._context_prompts[self._current_context]:
return self._context_prompts[self._current_context][name]
return None
async def register_async(self, prompt: "Prompt", context_id: Optional[str] = None) -> None:
"""异步注册提示模板到指定作用域"""
async with self._context_lock:
target_context = context_id or self._current_context
if target_context:
self._context_prompts.setdefault(target_context, {})[prompt.name] = prompt
class PromptManager:
_instance = None
def __init__(self):
self._prompts = {}
self._counter = 0
self._context = PromptContext()
self._lock = asyncio.Lock()
def __new__(cls):
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance._prompts = {}
cls._instance._counter = 0
return cls._instance
@asynccontextmanager
async def async_message_scope(self, message_id: str):
"""为消息处理创建异步临时作用域"""
async with self._context.async_scope(message_id):
yield self
async def get_prompt_async(self, name: str) -> "Prompt":
# 首先尝试从当前上下文获取
context_prompt = await self._context.get_prompt_async(name)
if context_prompt is not None:
return context_prompt
# 如果上下文中不存在，则使用全局提示模板
async with self._lock:
if name not in self._prompts:
raise KeyError(f"Prompt '{name}' not found")
return self._prompts[name]
def generate_name(self, template: str) -> str:
"""为未命名的prompt生成名称"""
@@ -29,13 +82,8 @@ class PromptManager:
self._prompts[prompt.name] = prompt
return prompt
def get_prompt(self, name: str) -> "Prompt":
if name not in self._prompts:
raise KeyError(f"Prompt '{name}' not found")
return self._prompts[name]
def format_prompt(self, name: str, **kwargs) -> str:
prompt = self.get_prompt(name)
async def format_prompt(self, name: str, **kwargs) -> str:
prompt = await self.get_prompt_async(name)
return prompt.format(**kwargs)
@@ -71,10 +119,26 @@ class Prompt(str):
obj._args = args or []
obj._kwargs = kwargs
# 自动注册到全局管理器
global_prompt_manager.register(obj)
# 修改自动注册逻辑
if global_prompt_manager._context._current_context:
# 如果存在当前上下文，则注册到上下文中
# asyncio.create_task(global_prompt_manager._context.register_async(obj))
pass
else:
# 否则注册到全局管理器
global_prompt_manager.register(obj)
return obj
@classmethod
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:
await global_prompt_manager._context.register_async(prompt)
return prompt
@classmethod
def _format_template(cls, template: str, args: List[Any] = None, kwargs: Dict[str, Any] = None) -> str:
fmt_str = f"f'''{template}'''"

2
src/plugins/utils/statistic.py
View File

@@ -337,7 +337,7 @@ class LLMStatistics:
stats_output = self._format_stats_section_lite(
hour_stats, "最近1小时统计：详细信息见根目录文件：llm_statistics.txt"
)
logger.info("\n" + stats_output + "\n" + "=" * 50)
logger.debug("\n" + stats_output + "\n" + "=" * 50)
except Exception:
logger.exception("控制台统计数据输出失败")

16
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__()
@@ -41,17 +42,22 @@ 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:
reply_probability = 0
return reply_probability
async def before_generate_reply_handle(self, message_id):
chat_id = self.ongoing_messages[message_id].chat_id
current_willing = self.chat_reply_willing.get(chat_id, 0)
@@ -71,8 +77,6 @@ class ClassicalWillingManager(BaseWillingManager):
async def get_variable_parameters(self):
return await super().get_variable_parameters()
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__()

15
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
@@ -228,12 +231,12 @@ class DynamicWillingManager(BaseWillingManager):
async def bombing_buffer_message_handle(self, message_id):
return await super().bombing_buffer_message_handle(message_id)
async def after_generate_reply_handle(self, message_id):
return await super().after_generate_reply_handle(message_id)
async def get_variable_parameters(self):
return await super().get_variable_parameters()
async def set_variable_parameters(self, parameters):
return await super().set_variable_parameters(parameters)
return await super().set_variable_parameters(parameters)

60
src/plugins/willing/mode_mxp.py
View File

@@ -17,19 +17,22 @@ 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}
self.chat_new_message_time: Dict[str, list[float]] = {} # 聊天流ID: 消息时间
self.last_response_person: Dict[str, tuple[str, int]] = {} # 上次回复的用户信息
self.last_response_person: Dict[str, tuple[str, int]] = {} # 上次回复的用户信息
self.temporary_willing: float = 0 # 临时意愿值
# 可变参数
@@ -39,8 +42,8 @@ class MxpWillingManager(BaseWillingManager):
self.basic_maximum_willing = 0.5 # 基础最大意愿值
self.mention_willing_gain = 0.6 # 提及意愿增益
self.interest_willing_gain = 0.3 # 兴趣意愿增益
self.emoji_response_penalty = self.global_config.emoji_response_penalty # 表情包回复惩罚
self.down_frequency_rate = self.global_config.down_frequency_rate # 降低回复频率的群组惩罚系数
self.emoji_response_penalty = self.global_config.emoji_response_penalty # 表情包回复惩罚
self.down_frequency_rate = self.global_config.down_frequency_rate # 降低回复频率的群组惩罚系数
self.single_chat_gain = 0.12 # 单聊增益
async def async_task_starter(self) -> None:
@@ -73,9 +76,16 @@ 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]
async def get_reply_probability(self, message_id: str):
"""获取回复概率"""
@@ -95,7 +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]
@@ -138,16 +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] = []
@@ -163,7 +182,7 @@ class MxpWillingManager(BaseWillingManager):
else:
probability = math.atan(willing * 4) / math.pi * 2
return probability
async def _chat_new_message_to_change_basic_willing(self):
"""聊天流新消息改变基础意愿"""
while True:
@@ -171,10 +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:
@@ -182,7 +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:
@@ -200,7 +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]):
@@ -212,7 +234,7 @@ class MxpWillingManager(BaseWillingManager):
self.logger.debug(f"参数 {key} 已更新为 {value}")
else:
self.logger.debug(f"尝试设置未知参数 {key}")
def _get_relationship_level_num(self, relationship_value) -> int:
"""关系等级计算"""
if -1000 <= relationship_value < -227:
@@ -232,4 +254,4 @@ class MxpWillingManager(BaseWillingManager):
return level_num - 2
async def get_willing(self, chat_id):
return self.temporary_willing
return self.temporary_willing

25
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
@@ -38,10 +37,11 @@ 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
Attributes:
message (MessageRecv): 原始消息对象
chat (ChatStream): 聊天流对象
@@ -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
@@ -60,22 +61,21 @@ class WillingInfo:
person_id: str
group_info: Optional[GroupInfo]
is_mentioned_bot: bool
is_emoji: bool
is_emoji: bool
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()
@@ -85,7 +85,7 @@ class BaseWillingManager(ABC):
logger.info(f"载入当前意愿模式{manager_type}失败，使用经典配方~~~~")
logger.debug(f"加载willing模式{manager_type}失败，原因: {str(e)}。")
return manager_class()
def __init__(self):
self.chat_reply_willing: Dict[str, float] = {} # 存储每个聊天流的回复意愿(chat_id)
self.ongoing_messages: Dict[str, WillingInfo] = {} # 当前正在进行的消息(message_id)
@@ -136,17 +136,17 @@ class BaseWillingManager(ABC):
async def get_reply_probability(self, message_id: str):
"""抽象方法：获取回复概率"""
raise NotImplementedError
@abstractmethod
async def bombing_buffer_message_handle(self, message_id: str):
"""抽象方法：炸飞消息处理"""
pass
async def get_willing(self, chat_id: str):
"""获取指定聊天流的回复意愿"""
async with self.lock:
return self.chat_reply_willing.get(chat_id, 0)
async def set_willing(self, chat_id: str, willing: float):
"""设置指定聊天流的回复意愿"""
async with self.lock:
@@ -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()

4
（临时版）麦麦开始学习.bat
View File

@@ -42,8 +42,8 @@ if errorlevel 2 (
echo Conda 环境 "!CONDA_ENV!" 激活成功
python src/plugins/zhishi/knowledge_library.py
) else (
if exist "venv\Scripts\python.exe" (
venv\Scripts\python src/plugins/zhishi/knowledge_library.py
if exist "..\maibot_env\Scripts\python.exe" (
..\maibot_env\Scripts\python src/plugins/zhishi/knowledge_library.py
) else (
echo ======================================
echo 错误: venv环境不存在，请先创建虚拟环境

4
（测试版）麦麦生成人格.bat
View File

@@ -42,8 +42,8 @@ if errorlevel 2 (
echo Conda 环境 "!CONDA_ENV!" 激活成功
python src/individuality/per_bf_gen.py
) else (
if exist "venv\Scripts\python.exe" (
venv\Scripts\python src/individuality/per_bf_gen.py
if exist "..\maibot_env\Scripts\python.exe" (
..\maibot_env\Scripts\python src/individuality/per_bf_gen.py
) else (
echo ======================================
echo 错误: venv环境不存在，请先创建虚拟环境