mirror of https://github.com/Mai-with-u/MaiBot.git
426 lines
15 KiB
Python
426 lines
15 KiB
Python
import time
|
||
from typing import List, Dict, Tuple, Optional, Any
|
||
from src.plugin_system.apis.tool_api import get_llm_available_tool_definitions, get_tool_instance
|
||
from src.plugin_system.base.base_tool import BaseTool
|
||
from src.plugin_system.core.global_announcement_manager import global_announcement_manager
|
||
from src.llm_models.utils_model import LLMRequest
|
||
from src.llm_models.payload_content import ToolCall
|
||
from src.config.config import global_config, model_config
|
||
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
|
||
from src.chat.message_receive.chat_stream import get_chat_manager
|
||
from src.common.logger import get_logger
|
||
|
||
logger = get_logger("tool_use")
|
||
|
||
|
||
def init_tool_executor_prompt():
|
||
"""初始化工具执行器的提示词"""
|
||
tool_executor_prompt = """
|
||
你是一个专门执行工具的助手。你的名字是{bot_name}。现在是{time_now}。
|
||
群里正在进行的聊天内容：
|
||
{chat_history}
|
||
|
||
现在，{sender}发送了内容:{target_message},你想要回复ta。
|
||
请仔细分析聊天内容，考虑以下几点：
|
||
1. 内容中是否包含需要查询信息的问题
|
||
2. 是否有明确的工具使用指令
|
||
|
||
If you need to use a tool, please directly call the corresponding tool function. If you do not need to use any tool, simply output "No tool needed".
|
||
"""
|
||
Prompt(tool_executor_prompt, "tool_executor_prompt")
|
||
|
||
|
||
# 初始化提示词
|
||
init_tool_executor_prompt()
|
||
|
||
|
||
class ToolExecutor:
|
||
"""独立的工具执行器组件
|
||
|
||
可以直接输入聊天消息内容，自动判断并执行相应的工具，返回结构化的工具执行结果。
|
||
"""
|
||
|
||
def __init__(self, chat_id: str, enable_cache: bool = True, cache_ttl: int = 3):
|
||
"""初始化工具执行器
|
||
|
||
Args:
|
||
executor_id: 执行器标识符，用于日志记录
|
||
enable_cache: 是否启用缓存机制
|
||
cache_ttl: 缓存生存时间（周期数）
|
||
"""
|
||
self.chat_id = chat_id
|
||
self.chat_stream = get_chat_manager().get_stream(self.chat_id)
|
||
self.log_prefix = f"[{get_chat_manager().get_stream_name(self.chat_id) or self.chat_id}]"
|
||
|
||
self.llm_model = LLMRequest(model_set=model_config.model_task_config.tool_use, request_type="tool_executor")
|
||
|
||
# 缓存配置
|
||
self.enable_cache = enable_cache
|
||
self.cache_ttl = cache_ttl
|
||
self.tool_cache = {} # 格式: {cache_key: {"result": result, "ttl": ttl, "timestamp": timestamp}}
|
||
|
||
logger.info(f"{self.log_prefix}工具执行器初始化完成，缓存{'启用' if enable_cache else '禁用'}，TTL={cache_ttl}")
|
||
|
||
async def execute_from_chat_message(
|
||
self, target_message: str, chat_history: str, sender: str, return_details: bool = False
|
||
) -> Tuple[List[Dict[str, Any]], List[str], str]:
|
||
"""从聊天消息执行工具
|
||
|
||
Args:
|
||
target_message: 目标消息内容
|
||
chat_history: 聊天历史
|
||
sender: 发送者
|
||
return_details: 是否返回详细信息(使用的工具列表和提示词)
|
||
|
||
Returns:
|
||
如果return_details为False: Tuple[List[Dict], List[str], str] - (工具执行结果列表, 空, 空)
|
||
如果return_details为True: Tuple[List[Dict], List[str], str] - (结果列表, 使用的工具, 提示词)
|
||
"""
|
||
|
||
# 首先检查缓存
|
||
cache_key = self._generate_cache_key(target_message, chat_history, sender)
|
||
if cached_result := self._get_from_cache(cache_key):
|
||
logger.info(f"{self.log_prefix}使用缓存结果，跳过工具执行")
|
||
if not return_details:
|
||
return cached_result, [], ""
|
||
|
||
# 从缓存结果中提取工具名称
|
||
used_tools = [result.get("tool_name", "unknown") for result in cached_result]
|
||
return cached_result, used_tools, ""
|
||
|
||
# 缓存未命中，执行工具调用
|
||
# 获取可用工具
|
||
tools = self._get_tool_definitions()
|
||
|
||
# print(f"tools: {tools}")
|
||
|
||
# 获取当前时间
|
||
time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
|
||
|
||
bot_name = global_config.bot.nickname
|
||
|
||
# 构建工具调用提示词
|
||
prompt = await global_prompt_manager.format_prompt(
|
||
"tool_executor_prompt",
|
||
target_message=target_message,
|
||
chat_history=chat_history,
|
||
sender=sender,
|
||
bot_name=bot_name,
|
||
time_now=time_now,
|
||
)
|
||
|
||
logger.debug(f"{self.log_prefix}开始LLM工具调用分析")
|
||
|
||
# 调用LLM进行工具决策
|
||
response, (reasoning_content, model_name, tool_calls) = await self.llm_model.generate_response_async(
|
||
prompt=prompt, tools=tools, raise_when_empty=False
|
||
)
|
||
|
||
# 执行工具调用
|
||
tool_results, used_tools = await self.execute_tool_calls(tool_calls)
|
||
|
||
# 缓存结果
|
||
if tool_results:
|
||
self._set_cache(cache_key, tool_results)
|
||
|
||
if used_tools:
|
||
logger.info(f"{self.log_prefix}工具执行完成，共执行{len(used_tools)}个工具: {used_tools}")
|
||
|
||
if return_details:
|
||
return tool_results, used_tools, prompt
|
||
else:
|
||
return tool_results, [], ""
|
||
|
||
def _get_tool_definitions(self) -> List[Dict[str, Any]]:
|
||
all_tools = get_llm_available_tool_definitions()
|
||
user_disabled_tools = global_announcement_manager.get_disabled_chat_tools(self.chat_id)
|
||
return [definition for name, definition in all_tools if name not in user_disabled_tools]
|
||
|
||
async def execute_tool_calls(self, tool_calls: Optional[List[ToolCall]]) -> Tuple[List[Dict[str, Any]], List[str]]:
|
||
"""执行工具调用
|
||
|
||
Args:
|
||
tool_calls: LLM返回的工具调用列表
|
||
|
||
Returns:
|
||
Tuple[List[Dict], List[str]]: (工具执行结果列表, 使用的工具名称列表)
|
||
"""
|
||
tool_results: List[Dict[str, Any]] = []
|
||
used_tools = []
|
||
|
||
if not tool_calls:
|
||
logger.debug(f"{self.log_prefix}无需执行工具")
|
||
return [], []
|
||
|
||
# 提取tool_calls中的函数名称
|
||
func_names = [call.func_name for call in tool_calls if call.func_name]
|
||
|
||
logger.info(f"{self.log_prefix}开始执行工具调用: {func_names}")
|
||
|
||
# 执行每个工具调用
|
||
for tool_call in tool_calls:
|
||
try:
|
||
tool_name = tool_call.func_name
|
||
logger.debug(f"{self.log_prefix}执行工具: {tool_name}")
|
||
|
||
# 执行工具
|
||
result = await self.execute_tool_call(tool_call)
|
||
|
||
if result:
|
||
tool_info = {
|
||
"type": result.get("type", "unknown_type"),
|
||
"id": result.get("id", f"tool_exec_{time.time()}"),
|
||
"content": result.get("content", ""),
|
||
"tool_name": tool_name,
|
||
"timestamp": time.time(),
|
||
}
|
||
content = tool_info["content"]
|
||
if not isinstance(content, (str, list, tuple)):
|
||
tool_info["content"] = str(content)
|
||
|
||
tool_results.append(tool_info)
|
||
used_tools.append(tool_name)
|
||
logger.info(f"{self.log_prefix}工具{tool_name}执行成功，类型: {tool_info['type']}")
|
||
preview = content[:200]
|
||
logger.debug(f"{self.log_prefix}工具{tool_name}结果内容: {preview}...")
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix}工具{tool_name}执行失败: {e}")
|
||
# 添加错误信息到结果中
|
||
error_info = {
|
||
"type": "tool_error",
|
||
"id": f"tool_error_{time.time()}",
|
||
"content": f"工具{tool_name}执行失败: {str(e)}",
|
||
"tool_name": tool_name,
|
||
"timestamp": time.time(),
|
||
}
|
||
tool_results.append(error_info)
|
||
|
||
return tool_results, used_tools
|
||
|
||
async def execute_tool_call(
|
||
self, tool_call: ToolCall, tool_instance: Optional[BaseTool] = None
|
||
) -> Optional[Dict[str, Any]]:
|
||
# sourcery skip: use-assigned-variable
|
||
"""执行单个工具调用
|
||
|
||
Args:
|
||
tool_call: 工具调用对象
|
||
|
||
Returns:
|
||
Optional[Dict]: 工具调用结果，如果失败则返回None
|
||
"""
|
||
try:
|
||
function_name = tool_call.func_name
|
||
function_args = tool_call.args or {}
|
||
function_args["llm_called"] = True # 标记为LLM调用
|
||
|
||
# 获取对应工具实例
|
||
tool_instance = tool_instance or 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)
|
||
if result:
|
||
return {
|
||
"tool_call_id": tool_call.call_id,
|
||
"role": "tool",
|
||
"name": function_name,
|
||
"type": "function",
|
||
"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)}")
|
||
raise e
|
||
|
||
def _generate_cache_key(self, target_message: str, chat_history: str, sender: str) -> str:
|
||
"""生成缓存键
|
||
|
||
Args:
|
||
target_message: 目标消息内容
|
||
chat_history: 聊天历史
|
||
sender: 发送者
|
||
|
||
Returns:
|
||
str: 缓存键
|
||
"""
|
||
import hashlib
|
||
|
||
# 使用消息内容和群聊状态生成唯一缓存键
|
||
content = f"{target_message}_{chat_history}_{sender}"
|
||
return hashlib.md5(content.encode()).hexdigest()
|
||
|
||
def _get_from_cache(self, cache_key: str) -> Optional[List[Dict]]:
|
||
"""从缓存获取结果
|
||
|
||
Args:
|
||
cache_key: 缓存键
|
||
|
||
Returns:
|
||
Optional[List[Dict]]: 缓存的结果，如果不存在或过期则返回None
|
||
"""
|
||
if not self.enable_cache or cache_key not in self.tool_cache:
|
||
return None
|
||
|
||
cache_item = self.tool_cache[cache_key]
|
||
if cache_item["ttl"] <= 0:
|
||
# 缓存过期，删除
|
||
del self.tool_cache[cache_key]
|
||
logger.debug(f"{self.log_prefix}缓存过期，删除缓存键: {cache_key}")
|
||
return None
|
||
|
||
# 减少TTL
|
||
cache_item["ttl"] -= 1
|
||
logger.debug(f"{self.log_prefix}使用缓存结果，剩余TTL: {cache_item['ttl']}")
|
||
return cache_item["result"]
|
||
|
||
def _set_cache(self, cache_key: str, result: List[Dict]):
|
||
"""设置缓存
|
||
|
||
Args:
|
||
cache_key: 缓存键
|
||
result: 要缓存的结果
|
||
"""
|
||
if not self.enable_cache:
|
||
return
|
||
|
||
self.tool_cache[cache_key] = {"result": result, "ttl": self.cache_ttl, "timestamp": time.time()}
|
||
logger.debug(f"{self.log_prefix}设置缓存，TTL: {self.cache_ttl}")
|
||
|
||
def _cleanup_expired_cache(self):
|
||
"""清理过期的缓存"""
|
||
if not self.enable_cache:
|
||
return
|
||
|
||
expired_keys = []
|
||
expired_keys.extend(cache_key for cache_key, cache_item in self.tool_cache.items() if cache_item["ttl"] <= 0)
|
||
for key in expired_keys:
|
||
del self.tool_cache[key]
|
||
|
||
if expired_keys:
|
||
logger.debug(f"{self.log_prefix}清理了{len(expired_keys)}个过期缓存")
|
||
|
||
async def execute_specific_tool_simple(self, tool_name: str, tool_args: Dict) -> Optional[Dict]:
|
||
"""直接执行指定工具
|
||
|
||
Args:
|
||
tool_name: 工具名称
|
||
tool_args: 工具参数
|
||
validate_args: 是否验证参数
|
||
|
||
Returns:
|
||
Optional[Dict]: 工具执行结果，失败时返回None
|
||
"""
|
||
try:
|
||
tool_call = ToolCall(
|
||
call_id=f"direct_tool_{time.time()}",
|
||
func_name=tool_name,
|
||
args=tool_args,
|
||
)
|
||
|
||
logger.info(f"{self.log_prefix}直接执行工具: {tool_name}")
|
||
|
||
result = await self.execute_tool_call(tool_call)
|
||
|
||
if result:
|
||
tool_info = {
|
||
"type": result.get("type", "unknown_type"),
|
||
"id": result.get("id", f"direct_tool_{time.time()}"),
|
||
"content": result.get("content", ""),
|
||
"tool_name": tool_name,
|
||
"timestamp": time.time(),
|
||
}
|
||
logger.info(f"{self.log_prefix}直接工具执行成功: {tool_name}")
|
||
return tool_info
|
||
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix}直接工具执行失败 {tool_name}: {e}")
|
||
|
||
return None
|
||
|
||
def clear_cache(self):
|
||
"""清空所有缓存"""
|
||
if self.enable_cache:
|
||
cache_count = len(self.tool_cache)
|
||
self.tool_cache.clear()
|
||
logger.info(f"{self.log_prefix}清空了{cache_count}个缓存项")
|
||
|
||
def get_cache_status(self) -> Dict:
|
||
"""获取缓存状态信息
|
||
|
||
Returns:
|
||
Dict: 包含缓存统计信息的字典
|
||
"""
|
||
if not self.enable_cache:
|
||
return {"enabled": False, "cache_count": 0}
|
||
|
||
# 清理过期缓存
|
||
self._cleanup_expired_cache()
|
||
|
||
total_count = len(self.tool_cache)
|
||
ttl_distribution = {}
|
||
|
||
for cache_item in self.tool_cache.values():
|
||
ttl = cache_item["ttl"]
|
||
ttl_distribution[ttl] = ttl_distribution.get(ttl, 0) + 1
|
||
|
||
return {
|
||
"enabled": True,
|
||
"cache_count": total_count,
|
||
"cache_ttl": self.cache_ttl,
|
||
"ttl_distribution": ttl_distribution,
|
||
}
|
||
|
||
def set_cache_config(self, enable_cache: Optional[bool] = None, cache_ttl: int = -1):
|
||
"""动态修改缓存配置
|
||
|
||
Args:
|
||
enable_cache: 是否启用缓存
|
||
cache_ttl: 缓存TTL
|
||
"""
|
||
if enable_cache is not None:
|
||
self.enable_cache = enable_cache
|
||
logger.info(f"{self.log_prefix}缓存状态修改为: {'启用' if enable_cache else '禁用'}")
|
||
|
||
if cache_ttl > 0:
|
||
self.cache_ttl = cache_ttl
|
||
logger.info(f"{self.log_prefix}缓存TTL修改为: {cache_ttl}")
|
||
|
||
|
||
"""
|
||
ToolExecutor使用示例：
|
||
|
||
# 1. 基础使用 - 从聊天消息执行工具（启用缓存，默认TTL=3）
|
||
executor = ToolExecutor(executor_id="my_executor")
|
||
results, _, _ = await executor.execute_from_chat_message(
|
||
talking_message_str="今天天气怎么样？现在几点了？",
|
||
is_group_chat=False
|
||
)
|
||
|
||
# 2. 禁用缓存的执行器
|
||
no_cache_executor = ToolExecutor(executor_id="no_cache", enable_cache=False)
|
||
|
||
# 3. 自定义缓存TTL
|
||
long_cache_executor = ToolExecutor(executor_id="long_cache", cache_ttl=10)
|
||
|
||
# 4. 获取详细信息
|
||
results, used_tools, prompt = await executor.execute_from_chat_message(
|
||
talking_message_str="帮我查询Python相关知识",
|
||
is_group_chat=False,
|
||
return_details=True
|
||
)
|
||
|
||
# 5. 直接执行特定工具
|
||
result = await executor.execute_specific_tool_simple(
|
||
tool_name="get_knowledge",
|
||
tool_args={"query": "机器学习"}
|
||
)
|
||
|
||
# 6. 缓存管理
|
||
cache_status = executor.get_cache_status() # 查看缓存状态
|
||
executor.clear_cache() # 清空缓存
|
||
executor.set_cache_config(cache_ttl=5) # 动态修改缓存配置
|
||
"""
|