from typing import Dict, Any, List, Optional
from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
from langgraph.graph import StateGraph, END
from .base_agent import BaseAgent, AgentState


class ConversationAgent(BaseAgent):
    """处理多轮对话的代理"""
    
    def __init__(self, model_section: Optional[str] = None):
        super().__init__(model_section)
        self.conversation_history: List[BaseMessage] = []
    
    def _build_graph(self) -> StateGraph:
        """构建对话专用图"""
        workflow = StateGraph(AgentState)
        
        # 添加节点
        workflow.add_node("analyze_intent", self._analyze_intent)
        workflow.add_node("generate_response", self._generate_response)
        workflow.add_node("update_context", self._update_context)
        
        # 定义边
        workflow.add_edge("analyze_intent", "generate_response")
        workflow.add_edge("generate_response", "update_context")
        workflow.add_edge("update_context", END)
        
        # 设置入口节点
        workflow.set_entry_point("analyze_intent")
        
        return workflow.compile()
    
    def _analyze_intent(self, state: AgentState) -> AgentState:
        """分析用户意图与对话上下文"""
        # 简单意图分析，可用更复杂逻辑增强
        user_message = state.messages[-1] if state.messages else None
        
        if user_message and isinstance(user_message, HumanMessage):
            content = user_message.content.lower()
            
            # 基础意图识别
            if any(word in content for word in ["hello", "hi", "hey", "greetings"]):
                state.context["intent"] = "greeting"
            elif any(word in content for word in ["help", "assist", "support"]):
                state.context["intent"] = "help"
            elif "?" in content:
                state.context["intent"] = "question"
            else:
                """兼容导出：请优先使用 agent 包"""

                from agent.conversation import ConversationAgent

                __all__ = ["ConversationAgent"]