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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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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
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@@ -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
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 = {
@@ -13,32 +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
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@@ -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),
}
}