citu_vanna/test/custom_react_agent/agent.py

"""
基于 StateGraph 的、具备上下文感知能力的 React Agent 核心实现
"""
import logging
import json
import pandas as pd
from typing import List, Optional, Dict, Any, Tuple
from contextlib import AsyncExitStack

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, ToolMessage, BaseMessage, SystemMessage, AIMessage
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
from redis.asyncio import Redis
try:
    from langgraph.checkpoint.redis import AsyncRedisSaver
except ImportError:
    AsyncRedisSaver = None

# 从新模块导入配置、状态和工具
from . import config
from .state import AgentState
from .sql_tools import sql_tools
from langchain_core.runnables import RunnablePassthrough

logger = logging.getLogger(__name__)

class CustomReactAgent:
    """
    一个使用 StateGraph 构建的、具备上下文感知和持久化能力的 Agent。
    """
    def __init__(self):
        """私有构造函数，请使用 create() 类方法来创建实例。"""
        self.llm = None
        self.tools = None
        self.agent_executor = None
        self.checkpointer = None
        self._exit_stack = None

    @classmethod
    async def create(cls):
        """异步工厂方法，创建并初始化 CustomReactAgent 实例。"""
        instance = cls()
        await instance._async_init()
        return instance

    async def _async_init(self):
        """异步初始化所有组件。"""
        logger.info("🚀 开始初始化 CustomReactAgent...")

        # 1. 初始化 LLM
        self.llm = ChatOpenAI(
            api_key=config.QWEN_API_KEY,
            base_url=config.QWEN_BASE_URL,
            model=config.QWEN_MODEL,
            temperature=0.1,
            model_kwargs={
                "extra_body": {
                    "enable_thinking": False,
                    "misc": {
                        "ensure_ascii": False
                    }
                }
            }
        )
        logger.info(f"   LLM 已初始化，模型: {config.QWEN_MODEL}")

        # 2. 绑定工具
        self.tools = sql_tools
        self.llm_with_tools = self.llm.bind_tools(self.tools)
        logger.info(f"   已绑定 {len(self.tools)} 个工具。")

        # 3. 初始化 Redis Checkpointer
        if config.REDIS_ENABLED and AsyncRedisSaver is not None:
            try:
                self._exit_stack = AsyncExitStack()
                checkpointer_manager = AsyncRedisSaver.from_conn_string(config.REDIS_URL)
                self.checkpointer = await self._exit_stack.enter_async_context(checkpointer_manager)
                await self.checkpointer.asetup()
                logger.info(f"   AsyncRedisSaver 持久化已启用: {config.REDIS_URL}")
            except Exception as e:
                logger.error(f"   ❌ RedisSaver 初始化失败: {e}", exc_info=True)
                if self._exit_stack:
                    await self._exit_stack.aclose()
                self.checkpointer = None
        else:
            logger.warning("   Redis 持久化功能已禁用。")

        # 4. 构建 StateGraph
        self.agent_executor = self._create_graph()
        logger.info("   StateGraph 已构建并编译。")
        logger.info("✅ CustomReactAgent 初始化完成。")

    async def close(self):
        """清理资源，关闭 Redis 连接。"""
        if self._exit_stack:
            await self._exit_stack.aclose()
            self._exit_stack = None
            self.checkpointer = None
            logger.info("✅ RedisSaver 资源已通过 AsyncExitStack 释放。")

    def _create_graph(self):
        """定义并编译最终的、正确的 StateGraph 结构。"""
        builder = StateGraph(AgentState)

        # 定义所有需要的节点
        builder.add_node("agent", self._agent_node)
        builder.add_node("prepare_tool_input", self._prepare_tool_input_node)
        builder.add_node("tools", ToolNode(self.tools))
        builder.add_node("update_state_after_tool", self._update_state_after_tool_node)
        builder.add_node("format_final_response", self._format_final_response_node)

        # 建立正确的边连接
        builder.set_entry_point("agent")
        builder.add_conditional_edges(
            "agent",
            self._should_continue,
            {
                "continue": "prepare_tool_input",
                "end": "format_final_response"
            }
        )
        builder.add_edge("prepare_tool_input", "tools")
        builder.add_edge("tools", "update_state_after_tool")
        builder.add_edge("update_state_after_tool", "agent")
        builder.add_edge("format_final_response", END)

        return builder.compile(checkpointer=self.checkpointer)

    def _should_continue(self, state: AgentState) -> str:
        """判断是继续调用工具还是结束。"""
        last_message = state["messages"][-1]
        if hasattr(last_message, "tool_calls") and last_message.tool_calls:
            return "continue"
        return "end"

    def _agent_node(self, state: AgentState) -> Dict[str, Any]:
        """Agent 节点：只负责调用 LLM 并返回其输出。"""
        logger.info(f"🧠 [Node] agent - Thread: {state['thread_id']}")
        
        messages_for_llm = list(state["messages"])
        if state.get("suggested_next_step"):
            instruction = f"提示：建议下一步使用工具 '{state['suggested_next_step']}'。"
            messages_for_llm.append(SystemMessage(content=instruction))

        response = self.llm_with_tools.invoke(messages_for_llm)
        logger.info(f"   LLM Response: {response.pretty_print()}")
        
        # 只返回消息，不承担其他职责
        return {"messages": [response]}
    
    def _print_state_info(self, state: AgentState, node_name: str) -> None:
        """
        打印 state 的全部信息，用于调试
        """
        logger.info(" ~" * 10 + " State Print Start" + "~" * 10)
        logger.info(f"📋 [State Debug] {node_name} - 当前状态信息:")
        
        # 🎯 打印 state 中的所有字段
        logger.info("   State中的所有字段:")
        for key, value in state.items():
            if key == "messages":
                logger.info(f"     {key}: {len(value)} 条消息")
            else:
                logger.info(f"     {key}: {value}")
        
        # 原有的详细消息信息
        logger.info(f"   用户ID: {state.get('user_id', 'N/A')}")
        logger.info(f"   线程ID: {state.get('thread_id', 'N/A')}")
        logger.info(f"   建议下一步: {state.get('suggested_next_step', 'N/A')}")
        
        messages = state.get("messages", [])
        logger.info(f"   消息历史数量: {len(messages)}")
        
        if messages:
            logger.info("   最近的消息:")
            for i, msg in enumerate(messages[-10:], start=max(0, len(messages)-10)):  # 显示最后3条消息
                msg_type = type(msg).__name__
                content_preview = str(msg.content)[:100] + "..." if len(str(msg.content)) > 100 else str(msg.content)
                logger.info(f"     [{i}] {msg_type}: {content_preview}")
                
                # 如果是 AIMessage 且有工具调用，显示工具调用信息
                if hasattr(msg, 'tool_calls') and msg.tool_calls:
                    for tool_call in msg.tool_calls:
                        tool_name = tool_call.get('name', 'Unknown')
                        tool_args = tool_call.get('args', {})
                        logger.info(f"         工具调用: {tool_name}")
                        logger.info(f"         参数: {str(tool_args)[:200]}...")
        
        logger.info(" ~" * 10 + " State Print End" + "~" * 10)

    def _prepare_tool_input_node(self, state: AgentState) -> Dict[str, Any]:
        """
        信息组装节点：为需要上下文的工具注入历史消息。
        """
        logger.info(f"🛠️ [Node] prepare_tool_input - Thread: {state['thread_id']}")
        
        # 🎯 打印 state 全部信息
        # self._print_state_info(state, "prepare_tool_input")
        
        last_message = state["messages"][-1]
        if not hasattr(last_message, "tool_calls") or not last_message.tool_calls:
            return {"messages": [last_message]}

        # 创建一个新的 AIMessage 来替换，避免直接修改 state 中的对象
        new_tool_calls = []
        for tool_call in last_message.tool_calls:
            if tool_call["name"] == "generate_sql":
                logger.info("   检测到 generate_sql 调用，注入历史消息。")
                # 复制一份以避免修改原始 tool_call
                modified_args = tool_call["args"].copy()
                
                # 🎯 改进的消息过滤逻辑：只保留有用的对话上下文
                clean_history = []
                for msg in state["messages"]:
                    if isinstance(msg, HumanMessage):
                        # 保留所有用户消息
                        clean_history.append({
                            "type": "human",
                            "content": msg.content
                        })
                    elif isinstance(msg, AIMessage):
                        # 只保留最终的、面向用户的回答（包含"[Formatted Output]"的消息）
                        if msg.content and "[Formatted Output]" in msg.content:
                            # 去掉 "[Formatted Output]" 标记，只保留真正的回答
                            clean_content = msg.content.replace("[Formatted Output]\n", "")
                            clean_history.append({
                                "type": "ai",
                                "content": clean_content
                            })
                        # 跳过包含工具调用的 AIMessage（中间步骤）
                    # 跳过所有 ToolMessage（工具执行结果）
                
                modified_args["history_messages"] = clean_history
                logger.info(f"   注入了 {len(clean_history)} 条过滤后的历史消息")
                
                new_tool_calls.append({
                    "name": tool_call["name"],
                    "args": modified_args,
                    "id": tool_call["id"],
                })
            else:
                new_tool_calls.append(tool_call)
        
        # 用包含修改后参数的新消息替换掉原来的
        last_message.tool_calls = new_tool_calls
        return {"messages": [last_message]}

    def _update_state_after_tool_node(self, state: AgentState) -> Dict[str, Any]:
        """在工具执行后，更新 suggested_next_step 并清理参数。"""
        logger.info(f"📝 [Node] update_state_after_tool - Thread: {state['thread_id']}")
        
        # 🎯 打印 state 全部信息
        self._print_state_info(state, "update_state_after_tool")
        
        last_tool_message = state['messages'][-1]
        tool_name = last_tool_message.name
        tool_output = last_tool_message.content
        next_step = None

        if tool_name == 'generate_sql':
            if "失败" in tool_output or "无法生成" in tool_output:
                next_step = 'answer_with_common_sense'
            else:
                next_step = 'valid_sql'
            
            # 🎯 清理 generate_sql 的 history_messages 参数，设置为空字符串
            # self._clear_history_messages_parameter(state['messages'])
        
        elif tool_name == 'valid_sql':
            if "失败" in tool_output:
                next_step = 'analyze_validation_error'
            else:
                next_step = 'run_sql'

        elif tool_name == 'run_sql':
            next_step = 'summarize_final_answer'
            
        logger.info(f"   Tool '{tool_name}' executed. Suggested next step: {next_step}")
        return {"suggested_next_step": next_step}

    def _clear_history_messages_parameter(self, messages: List[BaseMessage]) -> None:
        """
        将 generate_sql 工具的 history_messages 参数设置为空字符串
        """
        for message in messages:
            if hasattr(message, "tool_calls") and message.tool_calls:
                for tool_call in message.tool_calls:
                    if tool_call["name"] == "generate_sql" and "history_messages" in tool_call["args"]:
                        tool_call["args"]["history_messages"] = ""
                        logger.info(f"   已将 generate_sql 的 history_messages 设置为空字符串")

    def _format_final_response_node(self, state: AgentState) -> Dict[str, Any]:
        """最终输出格式化节点。"""
        logger.info(f"🎨 [Node] format_final_response - Thread: {state['thread_id']}")
        last_message = state['messages'][-1]
        last_message.content = f"[Formatted Output]\n{last_message.content}"
        return {"messages": [last_message]}

    def _extract_latest_sql_data(self, messages: List[BaseMessage]) -> Optional[str]:
        """从消息历史中提取最近的run_sql执行结果，但仅限于当前对话轮次。"""
        logger.info("🔍 提取最新的SQL执行结果...")
        
        # 🎯 只查找最后一个HumanMessage之后的SQL执行结果
        last_human_index = -1
        for i in range(len(messages) - 1, -1, -1):
            if isinstance(messages[i], HumanMessage):
                last_human_index = i
                break
        
        if last_human_index == -1:
            logger.info("   未找到用户消息，跳过SQL数据提取")
            return None
        
        # 只在当前对话轮次中查找SQL结果
        current_conversation = messages[last_human_index:]
        logger.info(f"   当前对话轮次包含 {len(current_conversation)} 条消息")
        
        for msg in reversed(current_conversation):
            if isinstance(msg, ToolMessage) and msg.name == 'run_sql':
                logger.info(f"   找到当前对话轮次的run_sql结果: {msg.content[:100]}...")
                
                # 🎯 处理Unicode转义序列，将其转换为正常的中文字符
                try:
                    # 先尝试解析JSON以验证格式
                    parsed_data = json.loads(msg.content)
                    # 重新序列化，确保中文字符正常显示
                    formatted_content = json.dumps(parsed_data, ensure_ascii=False, separators=(',', ':'))
                    logger.info(f"   已转换Unicode转义序列为中文字符")
                    return formatted_content
                except json.JSONDecodeError:
                    # 如果不是有效JSON，直接返回原内容
                    logger.warning(f"   SQL结果不是有效JSON格式，返回原始内容")
                    return msg.content
        
        logger.info("   当前对话轮次中未找到run_sql执行结果")
        return None

    async def chat(self, message: str, user_id: str, thread_id: Optional[str] = None) -> Dict[str, Any]:
        """
        处理用户聊天请求。
        """
        if not thread_id:
            now = pd.Timestamp.now()
            milliseconds = int(now.microsecond / 1000)
            thread_id = f"{user_id}:{now.strftime('%Y%m%d%H%M%S')}{milliseconds:03d}"
            logger.info(f"🆕 新建会话，Thread ID: {thread_id}")
        
        config = {
            "configurable": {
                "thread_id": thread_id,
            }
        }
        
        inputs = {
            "messages": [HumanMessage(content=message)],
            "user_id": user_id,
            "thread_id": thread_id,
            "suggested_next_step": None,
        }

        try:
            final_state = await self.agent_executor.ainvoke(inputs, config)
            answer = final_state["messages"][-1].content
            
            # 🎯 提取最近的 run_sql 执行结果（不修改messages）
            sql_data = self._extract_latest_sql_data(final_state["messages"])
            
            logger.info(f"✅ 处理完成 - Final Answer: '{answer}'")
            
            # 构建返回结果
            result = {
                "success": True, 
                "answer": answer, 
                "thread_id": thread_id
            }
            
            # 只有当存在SQL数据时才添加到返回结果中
            if sql_data:
                result["sql_data"] = sql_data
                logger.info("   📊 已包含SQL原始数据")
            
            return result
            
        except Exception as e:
            logger.error(f"❌ 处理过程中发生严重错误 - Thread: {thread_id}: {e}", exc_info=True)
            return {"success": False, "error": str(e), "thread_id": thread_id}
    
    async def get_conversation_history(self, thread_id: str) -> List[Dict[str, Any]]:
        """从 checkpointer 获取指定线程的对话历史。"""
        if not self.checkpointer:
            return []
        
        config = {"configurable": {"thread_id": thread_id}}
        conversation_state = await self.checkpointer.get(config)
        
        if not conversation_state:
            return []
            
        history = []
        for msg in conversation_state['values'].get('messages', []):
            if isinstance(msg, HumanMessage):
                role = "human"
            elif isinstance(msg, ToolMessage):
                role = "tool"
            else: # AIMessage
                role = "ai"
            
            history.append({
                "type": role,
                "content": msg.content,
                "tool_calls": getattr(msg, 'tool_calls', None)
            })
        return history