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+"""
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+基于 StateGraph 的、具备上下文感知能力的 React Agent 核心实现
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+"""
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+import logging
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+import json
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+import pandas as pd
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+from typing import List, Optional, Dict, Any, Tuple
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+from contextlib import AsyncExitStack
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+
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+from langchain_openai import ChatOpenAI
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+from langchain_core.messages import HumanMessage, ToolMessage, BaseMessage
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+from langgraph.graph import StateGraph, END
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+from langgraph.prebuilt import ToolNode
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+from redis.asyncio import Redis
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+try:
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+ from langgraph.checkpoint.redis import AsyncRedisSaver
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+except ImportError:
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+ AsyncRedisSaver = None
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+
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+# 从新模块导入配置、状态和工具
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+from . import config
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+from .state import AgentState
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+from .sql_tools import sql_tools
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+
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+logger = logging.getLogger(__name__)
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+
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+class CustomReactAgent:
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+ """
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+ 一个使用 StateGraph 构建的、具备上下文感知和持久化能力的 Agent。
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+ """
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+ def __init__(self):
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+ """私有构造函数，请使用 create() 类方法来创建实例。"""
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+ self.llm = None
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+ self.tools = None
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+ self.agent_executor = None
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+ self.checkpointer = None
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+ self._exit_stack = None
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+
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+ @classmethod
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+ async def create(cls):
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+ """异步工厂方法，创建并初始化 CustomReactAgent 实例。"""
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+ instance = cls()
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+ await instance._async_init()
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+ return instance
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+
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+ async def _async_init(self):
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+ """异步初始化所有组件。"""
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+ logger.info("🚀 开始初始化 CustomReactAgent...")
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+
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+ # 1. 初始化 LLM
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+ self.llm = ChatOpenAI(
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+ api_key=config.QWEN_API_KEY,
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+ base_url=config.QWEN_BASE_URL,
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+ model=config.QWEN_MODEL,
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+ temperature=0.1,
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+ extra_body={"enable_thinking": False}
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+ )
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+ logger.info(f" LLM 已初始化，模型: {config.QWEN_MODEL}")
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+
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+ # 2. 绑定工具
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+ self.tools = sql_tools
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+ self.llm_with_tools = self.llm.bind_tools(self.tools)
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+ logger.info(f" 已绑定 {len(self.tools)} 个工具。")
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+
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+ # 3. 初始化 Redis Checkpointer
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+ if config.REDIS_ENABLED and AsyncRedisSaver is not None:
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+ try:
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+ self._exit_stack = AsyncExitStack()
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+ checkpointer_manager = AsyncRedisSaver.from_conn_string(config.REDIS_URL)
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+ self.checkpointer = await self._exit_stack.enter_async_context(checkpointer_manager)
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+ await self.checkpointer.asetup()
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+ logger.info(f" AsyncRedisSaver 持久化已启用: {config.REDIS_URL}")
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+ except Exception as e:
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+ logger.error(f" ❌ RedisSaver 初始化失败: {e}", exc_info=True)
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+ if self._exit_stack:
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+ await self._exit_stack.aclose()
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+ self.checkpointer = None
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+ else:
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+ logger.warning(" Redis 持久化功能已禁用。")
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+
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+ # 4. 构建 StateGraph
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+ self.agent_executor = self._create_graph()
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+ logger.info(" StateGraph 已构建并编译。")
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+ logger.info("✅ CustomReactAgent 初始化完成。")
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+
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+ async def close(self):
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+ """清理资源，关闭 Redis 连接。"""
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+ if self._exit_stack:
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+ await self._exit_stack.aclose()
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+ self._exit_stack = None
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+ self.checkpointer = None
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+ logger.info("✅ RedisSaver 资源已通过 AsyncExitStack 释放。")
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+
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+ def _create_graph(self):
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+ """定义并编译 StateGraph。"""
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+ builder = StateGraph(AgentState)
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+
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+ # 定义节点
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+ builder.add_node("agent", self._agent_node)
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+ builder.add_node("tools", ToolNode(self.tools))
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+
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+ # 定义边
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+ builder.set_entry_point("agent")
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+ builder.add_conditional_edges(
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+ "agent",
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+ self._should_continue,
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+ {"continue": "tools", "end": END}
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+ )
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+ builder.add_edge("tools", "agent")
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+
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+ # 编译图，并传入 checkpointer
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+ return builder.compile(checkpointer=self.checkpointer)
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+
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+ def _should_continue(self, state: AgentState) -> str:
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+ """判断是否需要继续调用工具。"""
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+ last_message = state["messages"][-1]
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+ if not hasattr(last_message, "tool_calls") or not last_message.tool_calls:
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+ return "end"
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+ return "continue"
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+
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+ def _agent_node(self, state: AgentState) -> Dict[str, Any]:
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+ """Agent 节点：调用 LLM 进行思考和决策。"""
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+ logger.info(f"🧠 [Node] agent - Thread: {state['thread_id']}")
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+
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+ # LangChain 会自动从 state 中提取 messages 传递给 LLM
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+ response = self.llm_with_tools.invoke(state["messages"])
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+ logger.info(f" LLM 返回: {response.pretty_print()}")
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+ return {"messages": [response]}
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+
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+ async def chat(self, message: str, user_id: str, thread_id: Optional[str] = None) -> Dict[str, Any]:
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+ """
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+ 处理用户聊天请求。
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+ """
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+ if not thread_id:
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+ thread_id = f"{user_id}:{pd.Timestamp.now().strftime('%Y%m%d%H%M%S%f')}"
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+ logger.info(f"🆕 新建会话，Thread ID: {thread_id}")
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+
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+ config = {"configurable": {"thread_id": thread_id}}
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+
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+ # 定义输入
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+ inputs = {
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+ "messages": [HumanMessage(content=message)],
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+ "user_id": user_id,
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+ "thread_id": thread_id,
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+ }
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+
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+ final_state = None
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+ try:
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+ logger.info(f"🔄 开始处理 - Thread: {thread_id}, User: {user_id}, Message: '{message}'")
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+ # 使用 ainvoke 来执行完整的图流程
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+ final_state = await self.agent_executor.ainvoke(inputs, config)
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+
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+ if final_state and final_state.get("messages"):
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+ answer = final_state["messages"][-1].content
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+ logger.info(f"✅ 处理完成 - Thread: {thread_id}, Final Answer: '{answer}'")
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+ return {"success": True, "answer": answer, "thread_id": thread_id}
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+ else:
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+ logger.error(f"❌ 处理异常结束，最终状态为空 - Thread: {thread_id}")
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+ return {"success": False, "error": "Agent failed to produce a final answer.", "thread_id": thread_id}
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+
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+ except Exception as e:
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+ logger.error(f"❌ 处理过程中发生严重错误 - Thread: {thread_id}: {e}", exc_info=True)
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+ return {"success": False, "error": str(e), "thread_id": thread_id}
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+
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+ async def get_conversation_history(self, thread_id: str) -> List[Dict[str, Any]]:
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+ """从 checkpointer 获取指定线程的对话历史。"""
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+ if not self.checkpointer:
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+ return []
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+
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+ config = {"configurable": {"thread_id": thread_id}}
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+ conversation_state = await self.checkpointer.get(config)
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+
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+ if not conversation_state:
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+ return []
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+
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+ history = []
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+ for msg in conversation_state['values'].get('messages', []):
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+ if isinstance(msg, HumanMessage):
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+ role = "human"
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+ elif isinstance(msg, ToolMessage):
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+ role = "tool"
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+ else: # AIMessage
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+ role = "ai"
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+
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+ history.append({
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+ "type": role,
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+ "content": msg.content,
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+ "tool_calls": getattr(msg, 'tool_calls', None)
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+ })
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+ return history
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wangxq