159 lines
4.7 KiB
Python
159 lines
4.7 KiB
Python
"""
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Agent 工具基类
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"""
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from abc import ABC, abstractmethod
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from typing import Any, Dict, Optional, Type
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from dataclasses import dataclass, field
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from pydantic import BaseModel
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import logging
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import time
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logger = logging.getLogger(__name__)
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@dataclass
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class ToolResult:
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"""工具执行结果"""
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success: bool
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data: Any = None
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error: Optional[str] = None
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duration_ms: int = 0
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metadata: Dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> Dict[str, Any]:
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return {
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"success": self.success,
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"data": self.data,
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"error": self.error,
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"duration_ms": self.duration_ms,
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"metadata": self.metadata,
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}
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def to_string(self, max_length: int = 5000) -> str:
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"""转换为字符串（用于 LLM 输出）"""
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if not self.success:
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return f"Error: {self.error}"
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if isinstance(self.data, str):
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result = self.data
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elif isinstance(self.data, (dict, list)):
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import json
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result = json.dumps(self.data, ensure_ascii=False, indent=2)
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else:
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result = str(self.data)
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if len(result) > max_length:
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result = result[:max_length] + f"\n... (truncated, total {len(result)} chars)"
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return result
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class AgentTool(ABC):
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"""
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Agent 工具基类
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所有工具需要继承此类并实现必要的方法
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"""
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def __init__(self):
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self._call_count = 0
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self._total_duration_ms = 0
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@property
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@abstractmethod
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def name(self) -> str:
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"""工具名称"""
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pass
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@property
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@abstractmethod
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def description(self) -> str:
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"""工具描述（用于 Agent 理解工具功能）"""
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pass
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@property
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def args_schema(self) -> Optional[Type[BaseModel]]:
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"""参数 Schema（Pydantic 模型）"""
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return None
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@abstractmethod
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async def _execute(self, **kwargs) -> ToolResult:
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"""执行工具（子类实现）"""
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pass
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async def execute(self, **kwargs) -> ToolResult:
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"""执行工具（带计时和日志）"""
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start_time = time.time()
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try:
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logger.debug(f"Tool '{self.name}' executing with args: {kwargs}")
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result = await self._execute(**kwargs)
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except Exception as e:
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logger.error(f"Tool '{self.name}' error: {e}", exc_info=True)
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error_msg = str(e)
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result = ToolResult(
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success=False,
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data=f"工具执行异常: {error_msg}", # 🔥 修复：设置 data 字段避免 None
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error=error_msg,
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)
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duration_ms = int((time.time() - start_time) * 1000)
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result.duration_ms = duration_ms
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self._call_count += 1
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self._total_duration_ms += duration_ms
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logger.debug(f"Tool '{self.name}' completed in {duration_ms}ms, success={result.success}")
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return result
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def get_langchain_tool(self):
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"""转换为 LangChain Tool"""
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from langchain.tools import Tool, StructuredTool
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import asyncio
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def sync_wrapper(**kwargs):
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"""同步包装器"""
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loop = asyncio.get_event_loop()
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if loop.is_running():
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import concurrent.futures
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with concurrent.futures.ThreadPoolExecutor() as executor:
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future = executor.submit(asyncio.run, self.execute(**kwargs))
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result = future.result()
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else:
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result = asyncio.run(self.execute(**kwargs))
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return result.to_string()
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async def async_wrapper(**kwargs):
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"""异步包装器"""
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result = await self.execute(**kwargs)
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return result.to_string()
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if self.args_schema:
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return StructuredTool(
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name=self.name,
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description=self.description,
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func=sync_wrapper,
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coroutine=async_wrapper,
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args_schema=self.args_schema,
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)
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else:
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return Tool(
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name=self.name,
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description=self.description,
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func=lambda x: sync_wrapper(query=x),
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coroutine=lambda x: async_wrapper(query=x),
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)
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@property
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def stats(self) -> Dict[str, Any]:
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"""工具使用统计"""
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return {
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"name": self.name,
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"call_count": self._call_count,
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"total_duration_ms": self._total_duration_ms,
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"avg_duration_ms": self._total_duration_ms // max(1, self._call_count),
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}
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