408 lines
14 KiB
Python
408 lines
14 KiB
Python
from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config
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import time
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from src.common.logger import get_logger
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.tools.tool_use import ToolUser
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from src.chat.utils.json_utils import process_llm_tool_calls
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from typing import List, Dict, Tuple, Optional
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from src.chat.message_receive.chat_stream import get_chat_manager
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logger = get_logger("tool_executor")
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def init_tool_executor_prompt():
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"""初始化工具执行器的提示词"""
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tool_executor_prompt = """
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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}。
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群里正在进行的聊天内容：
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{chat_history}
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现在，{sender}发送了内容:{target_message},你想要回复ta。
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请仔细分析聊天内容，考虑以下几点：
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1. 内容中是否包含需要查询信息的问题
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2. 是否有明确的工具使用指令
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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".
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"""
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Prompt(tool_executor_prompt, "tool_executor_prompt")
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class ToolExecutor:
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"""独立的工具执行器组件
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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。
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"""
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def __init__(self, chat_id: str, enable_cache: bool = True, cache_ttl: int = 3):
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"""初始化工具执行器
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Args:
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executor_id: 执行器标识符，用于日志记录
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enable_cache: 是否启用缓存机制
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cache_ttl: 缓存生存时间（周期数）
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"""
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self.chat_id = chat_id
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self.chat_stream = get_chat_manager().get_stream(self.chat_id)
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self.log_prefix = f"[{get_chat_manager().get_stream_name(self.chat_id) or self.chat_id}]"
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self.llm_model = LLMRequest(
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model=global_config.model.tool_use,
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request_type="tool_executor",
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)
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# 初始化工具实例
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self.tool_instance = ToolUser()
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# 缓存配置
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self.enable_cache = enable_cache
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self.cache_ttl = cache_ttl
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self.tool_cache = {} # 格式: {cache_key: {"result": result, "ttl": ttl, "timestamp": timestamp}}
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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}")
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async def execute_from_chat_message(
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self, target_message: str, chat_history: str, sender: str, return_details: bool = False
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) -> Tuple[List[Dict], List[str], str]:
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"""从聊天消息执行工具
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Args:
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target_message: 目标消息内容
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chat_history: 聊天历史
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sender: 发送者
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return_details: 是否返回详细信息(使用的工具列表和提示词)
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Returns:
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如果return_details为False: List[Dict] - 工具执行结果列表
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如果return_details为True: Tuple[List[Dict], List[str], str] - (结果列表, 使用的工具, 提示词)
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"""
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# 首先检查缓存
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cache_key = self._generate_cache_key(target_message, chat_history, sender)
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if cached_result := self._get_from_cache(cache_key):
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logger.info(f"{self.log_prefix}使用缓存结果，跳过工具执行")
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if not return_details:
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return cached_result, [], "使用缓存结果"
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# 从缓存结果中提取工具名称
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used_tools = [result.get("tool_name", "unknown") for result in cached_result]
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return cached_result, used_tools, "使用缓存结果"
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# 缓存未命中，执行工具调用
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# 获取可用工具
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tools = self.tool_instance._define_tools()
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# 获取当前时间
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time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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bot_name = global_config.bot.nickname
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# 构建工具调用提示词
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prompt = await global_prompt_manager.format_prompt(
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"tool_executor_prompt",
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target_message=target_message,
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chat_history=chat_history,
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sender=sender,
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bot_name=bot_name,
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time_now=time_now,
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)
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logger.debug(f"{self.log_prefix}开始LLM工具调用分析")
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# 调用LLM进行工具决策
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response, other_info = await self.llm_model.generate_response_async(prompt=prompt, tools=tools)
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# 解析LLM响应
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if len(other_info) == 3:
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reasoning_content, model_name, tool_calls = other_info
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else:
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reasoning_content, model_name = other_info
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tool_calls = None
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# 执行工具调用
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tool_results, used_tools = await self._execute_tool_calls(tool_calls)
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# 缓存结果
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if tool_results:
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self._set_cache(cache_key, tool_results)
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if used_tools:
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logger.info(f"{self.log_prefix}工具执行完成，共执行{len(used_tools)}个工具: {used_tools}")
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if return_details:
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return tool_results, used_tools, prompt
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else:
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return tool_results, [], ""
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async def _execute_tool_calls(self, tool_calls) -> Tuple[List[Dict], List[str]]:
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"""执行工具调用
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Args:
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tool_calls: LLM返回的工具调用列表
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Returns:
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Tuple[List[Dict], List[str]]: (工具执行结果列表, 使用的工具名称列表)
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"""
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tool_results = []
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used_tools = []
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if not tool_calls:
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logger.debug(f"{self.log_prefix}无需执行工具")
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return tool_results, used_tools
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logger.info(f"{self.log_prefix}开始执行工具调用: {tool_calls}")
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# 处理工具调用
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success, valid_tool_calls, error_msg = process_llm_tool_calls(tool_calls)
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if not success:
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logger.error(f"{self.log_prefix}工具调用解析失败: {error_msg}")
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return tool_results, used_tools
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if not valid_tool_calls:
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logger.debug(f"{self.log_prefix}无有效工具调用")
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return tool_results, used_tools
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# 执行每个工具调用
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for tool_call in valid_tool_calls:
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try:
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tool_name = tool_call.get("name", "unknown_tool")
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used_tools.append(tool_name)
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logger.debug(f"{self.log_prefix}执行工具: {tool_name}")
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# 执行工具
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result = await self.tool_instance._execute_tool_call(tool_call)
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if result:
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tool_info = {
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"type": result.get("type", "unknown_type"),
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"id": result.get("id", f"tool_exec_{time.time()}"),
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"content": result.get("content", ""),
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"tool_name": tool_name,
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"timestamp": time.time(),
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}
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tool_results.append(tool_info)
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logger.info(f"{self.log_prefix}工具{tool_name}执行成功，类型: {tool_info['type']}")
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content = tool_info["content"]
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if not isinstance(content, (str, list, tuple)):
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content = str(content)
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preview = content[:200]
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logger.debug(f"{self.log_prefix}工具{tool_name}结果内容: {preview}...")
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except Exception as e:
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logger.error(f"{self.log_prefix}工具{tool_name}执行失败: {e}")
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# 添加错误信息到结果中
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error_info = {
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"type": "tool_error",
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"id": f"tool_error_{time.time()}",
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"content": f"工具{tool_name}执行失败: {str(e)}",
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"tool_name": tool_name,
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"timestamp": time.time(),
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}
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tool_results.append(error_info)
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return tool_results, used_tools
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def _generate_cache_key(self, target_message: str, chat_history: str, sender: str) -> str:
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"""生成缓存键
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Args:
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target_message: 目标消息内容
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chat_history: 聊天历史
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sender: 发送者
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Returns:
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str: 缓存键
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"""
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import hashlib
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# 使用消息内容和群聊状态生成唯一缓存键
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content = f"{target_message}_{chat_history}_{sender}"
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return hashlib.md5(content.encode()).hexdigest()
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def _get_from_cache(self, cache_key: str) -> Optional[List[Dict]]:
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"""从缓存获取结果
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Args:
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cache_key: 缓存键
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Returns:
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Optional[List[Dict]]: 缓存的结果，如果不存在或过期则返回None
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"""
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if not self.enable_cache or cache_key not in self.tool_cache:
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return None
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cache_item = self.tool_cache[cache_key]
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if cache_item["ttl"] <= 0:
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# 缓存过期，删除
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del self.tool_cache[cache_key]
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logger.debug(f"{self.log_prefix}缓存过期，删除缓存键: {cache_key}")
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return None
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# 减少TTL
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cache_item["ttl"] -= 1
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logger.debug(f"{self.log_prefix}使用缓存结果，剩余TTL: {cache_item['ttl']}")
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return cache_item["result"]
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def _set_cache(self, cache_key: str, result: List[Dict]):
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"""设置缓存
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Args:
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cache_key: 缓存键
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result: 要缓存的结果
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"""
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if not self.enable_cache:
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return
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self.tool_cache[cache_key] = {"result": result, "ttl": self.cache_ttl, "timestamp": time.time()}
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logger.debug(f"{self.log_prefix}设置缓存，TTL: {self.cache_ttl}")
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def _cleanup_expired_cache(self):
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"""清理过期的缓存"""
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if not self.enable_cache:
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return
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expired_keys = []
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expired_keys.extend(cache_key for cache_key, cache_item in self.tool_cache.items() if cache_item["ttl"] <= 0)
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for key in expired_keys:
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del self.tool_cache[key]
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if expired_keys:
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logger.debug(f"{self.log_prefix}清理了{len(expired_keys)}个过期缓存")
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def get_available_tools(self) -> List[str]:
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"""获取可用工具列表
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Returns:
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List[str]: 可用工具名称列表
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"""
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tools = self.tool_instance._define_tools()
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return [tool.get("function", {}).get("name", "unknown") for tool in tools]
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async def execute_specific_tool(
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self, tool_name: str, tool_args: Dict, validate_args: bool = True
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) -> Optional[Dict]:
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"""直接执行指定工具
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Args:
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tool_name: 工具名称
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tool_args: 工具参数
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validate_args: 是否验证参数
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Returns:
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Optional[Dict]: 工具执行结果，失败时返回None
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"""
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try:
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tool_call = {"name": tool_name, "arguments": tool_args}
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logger.info(f"{self.log_prefix}直接执行工具: {tool_name}")
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result = await self.tool_instance._execute_tool_call(tool_call)
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if result:
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tool_info = {
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"type": result.get("type", "unknown_type"),
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"id": result.get("id", f"direct_tool_{time.time()}"),
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"content": result.get("content", ""),
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"tool_name": tool_name,
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"timestamp": time.time(),
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}
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logger.info(f"{self.log_prefix}直接工具执行成功: {tool_name}")
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return tool_info
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except Exception as e:
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logger.error(f"{self.log_prefix}直接工具执行失败 {tool_name}: {e}")
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return None
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def clear_cache(self):
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"""清空所有缓存"""
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if self.enable_cache:
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cache_count = len(self.tool_cache)
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self.tool_cache.clear()
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logger.info(f"{self.log_prefix}清空了{cache_count}个缓存项")
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def get_cache_status(self) -> Dict:
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"""获取缓存状态信息
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Returns:
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Dict: 包含缓存统计信息的字典
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"""
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if not self.enable_cache:
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return {"enabled": False, "cache_count": 0}
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# 清理过期缓存
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self._cleanup_expired_cache()
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total_count = len(self.tool_cache)
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ttl_distribution = {}
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for cache_item in self.tool_cache.values():
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ttl = cache_item["ttl"]
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ttl_distribution[ttl] = ttl_distribution.get(ttl, 0) + 1
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return {
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"enabled": True,
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"cache_count": total_count,
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"cache_ttl": self.cache_ttl,
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"ttl_distribution": ttl_distribution,
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}
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def set_cache_config(self, enable_cache: Optional[bool] = None, cache_ttl: int = -1):
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"""动态修改缓存配置
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Args:
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enable_cache: 是否启用缓存
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cache_ttl: 缓存TTL
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"""
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if enable_cache is not None:
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self.enable_cache = enable_cache
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logger.info(f"{self.log_prefix}缓存状态修改为: {'启用' if enable_cache else '禁用'}")
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if cache_ttl > 0:
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self.cache_ttl = cache_ttl
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logger.info(f"{self.log_prefix}缓存TTL修改为: {cache_ttl}")
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# 初始化提示词
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init_tool_executor_prompt()
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"""
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使用示例：
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# 1. 基础使用 - 从聊天消息执行工具（启用缓存，默认TTL=3）
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executor = ToolExecutor(executor_id="my_executor")
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results, _, _ = await executor.execute_from_chat_message(
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talking_message_str="今天天气怎么样？现在几点了？",
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is_group_chat=False
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)
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# 2. 禁用缓存的执行器
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no_cache_executor = ToolExecutor(executor_id="no_cache", enable_cache=False)
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# 3. 自定义缓存TTL
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long_cache_executor = ToolExecutor(executor_id="long_cache", cache_ttl=10)
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# 4. 获取详细信息
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results, used_tools, prompt = await executor.execute_from_chat_message(
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talking_message_str="帮我查询Python相关知识",
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is_group_chat=False,
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return_details=True
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)
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# 5. 直接执行特定工具
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result = await executor.execute_specific_tool(
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tool_name="get_knowledge",
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tool_args={"query": "机器学习"}
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)
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# 6. 缓存管理
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available_tools = executor.get_available_tools()
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cache_status = executor.get_cache_status() # 查看缓存状态
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executor.clear_cache() # 清空缓存
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executor.set_cache_config(cache_ttl=5) # 动态修改缓存配置
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"""
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