101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
"""
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工具执行节点模块
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负责执行 AI 调用的工具函数
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"""
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import asyncio
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from typing import Any, Dict
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from langchain_core.messages import AIMessage, ToolMessage
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from langgraph.config import get_stream_writer
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# 本地模块
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from app.graph.state import MessagesState
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from app.utils.logging import log_state_change
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from app.logger import debug, info
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def create_tool_call_node(tools_by_name: Dict[str, Any]):
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"""
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工厂函数：创建工具执行节点
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Args:
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tools_by_name: 名称到工具函数的映射字典
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Returns:
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异步节点函数
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"""
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from langchain_core.runnables.config import RunnableConfig
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async def call_tools(state: MessagesState, config: RunnableConfig) -> Dict[str, Any]:
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"""
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工具执行节点（异步方法）
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Args:
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state: 当前对话状态
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config: 运行时配置
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Returns:
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包含 ToolMessage 的状态更新
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"""
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log_state_change("tool_node", state, "进入")
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last_message = state['messages'][-1]
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if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
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log_state_change("tool_node", state, "离开（无工具调用）")
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return {"messages": []}
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results = []
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loop = asyncio.get_event_loop()
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info(f"🛠️ [工具调用] 准备执行 {len(last_message.tool_calls)} 个工具")
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for tool_call in last_message.tool_calls:
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tool_name = tool_call["name"]
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tool_args = tool_call["args"]
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tool_id = tool_call["id"]
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tool_func = tools_by_name.get(tool_name)
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debug(f" ├─ 调用工具: {tool_name} 参数: {tool_args}")
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if tool_func is None:
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err_msg = f"Tool {tool_name} not found"
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debug(f" └─ ❌ {err_msg}")
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results.append(ToolMessage(content=err_msg, tool_call_id=tool_id))
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continue
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# 获取流式写入器并发送工具调用开始事件
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writer = get_stream_writer()
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writer({"type": "custom", "data": {"type": "tool_start", "tool": tool_name}})
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try:
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# 修复闭包问题：将变量作为默认参数传入 lambda
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# 如果工具支持异步 (ainvoke)，优先使用异步调用
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if hasattr(tool_func, 'ainvoke'):
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observation = await tool_func.ainvoke(tool_args)
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else:
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observation = await loop.run_in_executor(
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None,
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lambda args=tool_args: tool_func.invoke(args) # 默认参数捕获当前值
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)
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# 字符打印
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result_preview = str(observation).replace("\n", " ")
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debug(f" └─ ✅ 结果: {result_preview}")
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results.append(ToolMessage(content=str(observation), tool_call_id=tool_id))
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# 发送工具调用完成事件
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writer({"type": "custom", "data": {"type": "tool_end", "tool": tool_name, "success": True}})
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except Exception as e:
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debug(f" └─ ❌ 异常: {e}")
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results.append(ToolMessage(content=f"Error: {e}", tool_call_id=tool_id))
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# 发送工具调用失败事件
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writer({"type": "custom", "data": {"type": "tool_end", "tool": tool_name, "success": False, "error": str(e)}})
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info(f"🛠️ [工具调用] 执行完成，返回 {len(results)} 条 ToolMessage")
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result = {"messages": results}
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log_state_change("tool_node", {**state, **result}, "离开")
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return result
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return call_tools |