citu_vanna/agent/citu_agent.py

# agent/citu_agent.py
from typing import Dict, Any, Literal
from langgraph.graph import StateGraph, END
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import SystemMessage, HumanMessage

from agent.state import AgentState
from agent.classifier import QuestionClassifier
from agent.tools import TOOLS, generate_sql, execute_sql, generate_summary, general_chat
from agent.utils import get_compatible_llm

class CituLangGraphAgent:
    """Citu LangGraph智能助手主类 - 使用@tool装饰器 + Agent工具调用"""
    
    def __init__(self):
        # 加载配置
        try:
            from agent.config import get_current_config, get_nested_config
            self.config = get_current_config()
            print("[CITU_AGENT] 加载Agent配置完成")
        except ImportError:
            self.config = {}
            print("[CITU_AGENT] 配置文件不可用，使用默认配置")
        
        self.classifier = QuestionClassifier()
        self.tools = TOOLS
        self.llm = get_compatible_llm()
        
        # 注意：现在使用直接工具调用模式，不再需要预创建Agent执行器
        print("[CITU_AGENT] 使用直接工具调用模式")
        
        self.workflow = self._create_workflow()
        print("[CITU_AGENT] LangGraph Agent with Direct Tools初始化完成")
    
    def _create_workflow(self) -> StateGraph:
        """创建LangGraph工作流"""
        workflow = StateGraph(AgentState)
        
        # 添加节点
        workflow.add_node("classify_question", self._classify_question_node)
        workflow.add_node("agent_chat", self._agent_chat_node)
        workflow.add_node("agent_database", self._agent_database_node)
        workflow.add_node("format_response", self._format_response_node)
        
        # 设置入口点
        workflow.set_entry_point("classify_question")
        
        # 添加条件边：分类后的路由
        # 完全信任QuestionClassifier的决策，不再进行二次判断
        workflow.add_conditional_edges(
            "classify_question",
            self._route_after_classification,
            {
                "DATABASE": "agent_database",
                "CHAT": "agent_chat"  # CHAT分支处理所有非DATABASE的情况（包括UNCERTAIN）
            }
        )
        
        # 添加边
        workflow.add_edge("agent_chat", "format_response")
        workflow.add_edge("agent_database", "format_response")
        workflow.add_edge("format_response", END)
        
        return workflow.compile()
    
    def _classify_question_node(self, state: AgentState) -> AgentState:
        """问题分类节点"""
        try:
            print(f"[CLASSIFY_NODE] 开始分类问题: {state['question']}")
            
            classification_result = self.classifier.classify(state["question"])
            
            # 更新状态
            state["question_type"] = classification_result.question_type
            state["classification_confidence"] = classification_result.confidence
            state["classification_reason"] = classification_result.reason
            state["classification_method"] = classification_result.method
            state["current_step"] = "classified"
            state["execution_path"].append("classify")
            
            print(f"[CLASSIFY_NODE] 分类结果: {classification_result.question_type}, 置信度: {classification_result.confidence}")
            
            return state
            
        except Exception as e:
            print(f"[ERROR] 问题分类异常: {str(e)}")
            state["error"] = f"问题分类失败: {str(e)}"
            state["error_code"] = 500
            state["execution_path"].append("classify_error")
            return state
    
    def _agent_database_node(self, state: AgentState) -> AgentState:
        """数据库Agent节点 - 直接工具调用模式"""
        try:
            print(f"[DATABASE_AGENT] 开始处理数据库查询: {state['question']}")
            
            question = state["question"]
            
            # 步骤1：生成SQL
            print(f"[DATABASE_AGENT] 步骤1：生成SQL")
            sql_result = generate_sql.invoke({"question": question, "allow_llm_to_see_data": True})
            
            if not sql_result.get("success"):
                print(f"[DATABASE_AGENT] SQL生成失败: {sql_result.get('error')}")
                state["error"] = sql_result.get("error", "SQL生成失败")
                state["error_code"] = 500
                state["current_step"] = "database_error"
                state["execution_path"].append("agent_database_error")
                return state
            
            sql = sql_result.get("sql")
            state["sql"] = sql
            print(f"[DATABASE_AGENT] SQL生成成功: {sql}")
            
            # 步骤1.5：检查是否为解释性响应而非SQL
            error_type = sql_result.get("error_type")
            if error_type == "llm_explanation":
                # LLM返回了解释性文本，直接作为最终答案
                explanation = sql_result.get("error", "")
                state["summary"] = explanation + " 请尝试提问其它问题。"
                state["current_step"] = "database_completed"
                state["execution_path"].append("agent_database")
                print(f"[DATABASE_AGENT] 返回LLM解释性答案: {explanation}")
                return state
            
            # 额外验证：检查SQL格式（防止工具误判）
            from agent.utils import _is_valid_sql_format
            if not _is_valid_sql_format(sql):
                # 内容看起来不是SQL，当作解释性响应处理
                state["summary"] = sql + " 请尝试提问其它问题。"
                state["current_step"] = "database_completed"  
                state["execution_path"].append("agent_database")
                print(f"[DATABASE_AGENT] 内容不是有效SQL，当作解释返回: {sql}")
                return state
            
            # 步骤2：执行SQL
            print(f"[DATABASE_AGENT] 步骤2：执行SQL")
            execute_result = execute_sql.invoke({"sql": sql, "max_rows": 200})
            
            if not execute_result.get("success"):
                print(f"[DATABASE_AGENT] SQL执行失败: {execute_result.get('error')}")
                state["error"] = execute_result.get("error", "SQL执行失败")
                state["error_code"] = 500
                state["current_step"] = "database_error"
                state["execution_path"].append("agent_database_error")
                return state
            
            data_result = execute_result.get("data_result")
            state["data_result"] = data_result
            print(f"[DATABASE_AGENT] SQL执行成功，返回 {data_result.get('row_count', 0)} 行数据")
            
            # 步骤3：生成摘要
            print(f"[DATABASE_AGENT] 步骤3：生成摘要")
            summary_result = generate_summary.invoke({
                "question": question,
                "data_result": data_result,
                "sql": sql
            })
            
            if not summary_result.get("success"):
                print(f"[DATABASE_AGENT] 摘要生成失败: {summary_result.get('message')}")
                # 摘要生成失败不是致命错误，使用默认摘要
                state["summary"] = f"查询执行完成，共返回 {data_result.get('row_count', 0)} 条记录。"
            else:
                state["summary"] = summary_result.get("summary")
                print(f"[DATABASE_AGENT] 摘要生成成功")
            
            state["current_step"] = "database_completed"
            state["execution_path"].append("agent_database")
            
            print(f"[DATABASE_AGENT] 数据库查询完成")
            return state
            
        except Exception as e:
            print(f"[ERROR] 数据库Agent异常: {str(e)}")
            import traceback
            print(f"[ERROR] 详细错误信息: {traceback.format_exc()}")
            state["error"] = f"数据库查询失败: {str(e)}"
            state["error_code"] = 500
            state["current_step"] = "database_error"
            state["execution_path"].append("agent_database_error")
            return state
    
    def _agent_chat_node(self, state: AgentState) -> AgentState:
        """聊天Agent节点 - 直接工具调用模式"""
        try:
            print(f"[CHAT_AGENT] 开始处理聊天: {state['question']}")
            
            question = state["question"]
            
            # 构建上下文
            enable_context_injection = self.config.get("chat_agent", {}).get("enable_context_injection", True)
            context = None
            if enable_context_injection and state.get("classification_reason"):
                context = f"分类原因: {state['classification_reason']}"
            
            # 直接调用general_chat工具
            print(f"[CHAT_AGENT] 调用general_chat工具")
            chat_result = general_chat.invoke({
                "question": question,
                "context": context
            })
            
            if chat_result.get("success"):
                state["chat_response"] = chat_result.get("response", "")
                print(f"[CHAT_AGENT] 聊天处理成功")
            else:
                # 处理失败，使用备用响应
                state["chat_response"] = chat_result.get("response", "抱歉，我暂时无法处理您的问题。请稍后再试。")
                print(f"[CHAT_AGENT] 聊天处理失败，使用备用响应: {chat_result.get('error')}")
            
            state["current_step"] = "chat_completed"
            state["execution_path"].append("agent_chat")
            
            print(f"[CHAT_AGENT] 聊天处理完成")
            return state
            
        except Exception as e:
            print(f"[ERROR] 聊天Agent异常: {str(e)}")
            import traceback
            print(f"[ERROR] 详细错误信息: {traceback.format_exc()}")
            state["chat_response"] = "抱歉，我暂时无法处理您的问题。请稍后再试，或者尝试询问数据相关的问题。"
            state["current_step"] = "chat_error"
            state["execution_path"].append("agent_chat_error")
            return state
    
    def _format_response_node(self, state: AgentState) -> AgentState:
        """格式化最终响应节点"""
        try:
            print(f"[FORMAT_NODE] 开始格式化响应，问题类型: {state['question_type']}")
            
            state["current_step"] = "completed"
            state["execution_path"].append("format_response")
            
            # 根据问题类型和执行状态格式化响应
            if state.get("error"):
                # 有错误的情况
                state["final_response"] = {
                    "success": False,
                    "error": state["error"],
                    "error_code": state.get("error_code", 500),
                    "question_type": state["question_type"],
                    "execution_path": state["execution_path"],
                    "classification_info": {
                        "confidence": state.get("classification_confidence", 0),
                        "reason": state.get("classification_reason", ""),
                        "method": state.get("classification_method", "")
                    }
                }
            
            elif state["question_type"] == "DATABASE":
                # 数据库查询类型
                if state.get("summary"):
                    # 有摘要的情况（包括解释性响应和完整查询结果）
                    state["final_response"] = {
                        "success": True,
                        "response": state["summary"],
                        "type": "DATABASE",
                        "sql": state.get("sql"),
                        "data_result": state.get("data_result"),  # 可能为None（解释性响应）
                        "summary": state["summary"],
                        "execution_path": state["execution_path"],
                        "classification_info": {
                            "confidence": state["classification_confidence"],
                            "reason": state["classification_reason"],
                            "method": state["classification_method"]
                        }
                    }
                else:
                    # 数据库查询失败，没有任何结果
                    state["final_response"] = {
                        "success": False,
                        "error": state.get("error", "数据库查询未完成"),
                        "type": "DATABASE",
                        "sql": state.get("sql"),
                        "execution_path": state["execution_path"]
                    }
            
            else:
                # 聊天类型
                state["final_response"] = {
                    "success": True,
                    "response": state.get("chat_response", ""),
                    "type": "CHAT",
                    "execution_path": state["execution_path"],
                    "classification_info": {
                        "confidence": state["classification_confidence"],
                        "reason": state["classification_reason"],
                        "method": state["classification_method"]
                    }
                }
            
            print(f"[FORMAT_NODE] 响应格式化完成")
            return state
            
        except Exception as e:
            print(f"[ERROR] 响应格式化异常: {str(e)}")
            state["final_response"] = {
                "success": False,
                "error": f"响应格式化异常: {str(e)}",
                "error_code": 500,
                "execution_path": state["execution_path"]
            }
            return state
    
    def _route_after_classification(self, state: AgentState) -> Literal["DATABASE", "CHAT"]:
        """
        分类后的路由决策
        
        完全信任QuestionClassifier的决策：
        - DATABASE类型 → 数据库Agent
        - CHAT和UNCERTAIN类型 → 聊天Agent
        
        这样避免了双重决策的冲突，所有分类逻辑都集中在QuestionClassifier中
        """
        question_type = state["question_type"]
        confidence = state["classification_confidence"]
        
        print(f"[ROUTE] 分类路由: {question_type}, 置信度: {confidence} (完全信任分类器决策)")
        
        if question_type == "DATABASE":
            return "DATABASE"
        else:
            # 将 "CHAT" 和 "UNCERTAIN" 类型都路由到聊天流程
            # 聊天Agent可以处理不确定的情况，并在必要时引导用户提供更多信息
            return "CHAT"
    
    def process_question(self, question: str, session_id: str = None) -> Dict[str, Any]:
        """
        统一的问题处理入口
        
        Args:
            question: 用户问题
            session_id: 会话ID
            
        Returns:
            Dict包含完整的处理结果
        """
        try:
            print(f"[CITU_AGENT] 开始处理问题: {question}")
            
            # 初始化状态
            initial_state = self._create_initial_state(question, session_id)
            
            # 执行工作流
            final_state = self.workflow.invoke(
                initial_state,
                config={
                    "configurable": {"session_id": session_id}
                } if session_id else None
            )
            
            # 提取最终结果
            result = final_state["final_response"]
            
            print(f"[CITU_AGENT] 问题处理完成: {result.get('success', False)}")
            
            return result
            
        except Exception as e:
            print(f"[ERROR] Agent执行异常: {str(e)}")
            return {
                "success": False,
                "error": f"Agent系统异常: {str(e)}",
                "error_code": 500,
                "execution_path": ["error"]
            }
    
    def _create_initial_state(self, question: str, session_id: str = None) -> AgentState:
        """创建初始状态"""
        return AgentState(
            # 输入信息
            question=question,
            session_id=session_id,
            
            # 分类结果
            question_type="",
            classification_confidence=0.0,
            classification_reason="",
            classification_method="",
            
            # 数据库查询流程状态
            sql=None,
            sql_generation_attempts=0,
            data_result=None,
            summary=None,
            
            # 聊天响应
            chat_response=None,
            
            # 最终输出
            final_response={},
            
            # 错误处理
            error=None,
            error_code=None,
            
            # 流程控制
            current_step="start",
            execution_path=[],
            retry_count=0,
            max_retries=2,
            
            # 调试信息
            debug_info={}
        )
    
    def health_check(self) -> Dict[str, Any]:
        """健康检查"""
        try:
            # 从配置获取健康检查参数
            from agent.config import get_nested_config
            test_question = get_nested_config(self.config, "health_check.test_question", "你好")
            enable_full_test = get_nested_config(self.config, "health_check.enable_full_test", True)
            
            if enable_full_test:
                # 完整流程测试
                test_result = self.process_question(test_question, "health_check")
                
                return {
                    "status": "healthy" if test_result.get("success") else "degraded",
                    "test_result": test_result.get("success", False),
                    "workflow_compiled": self.workflow is not None,
                    "tools_count": len(self.tools),
                    "agent_reuse_enabled": False,
                    "message": "Agent健康检查完成"
                }
            else:
                # 简单检查
                return {
                    "status": "healthy",
                    "test_result": True,
                    "workflow_compiled": self.workflow is not None,
                    "tools_count": len(self.tools),
                    "agent_reuse_enabled": False,
                    "message": "Agent简单健康检查完成"
                }
            
        except Exception as e:
            return {
                "status": "unhealthy",
                "error": str(e),
                "workflow_compiled": self.workflow is not None,
                "tools_count": len(self.tools) if hasattr(self, 'tools') else 0,
                "agent_reuse_enabled": False,
                "message": "Agent健康检查失败"
            }