from typing import Any, Dict, Optional
from langchain_core.messages import HumanMessage
from langgraph.graph import StateGraph, END

from services.llm_factory import create_chat_model
from .state import AgentState
from . import nodes


class BaseAgent:
    """包含通用功能的基础代理类"""

    def __init__(self, model_section: Optional[str] = None):
        self.model = create_chat_model(model_section)
        self.graph = self._build_graph()

    def _build_graph(self) -> StateGraph:
        """构建代理状态图"""
        workflow = StateGraph(AgentState)

        workflow.add_node("process_input", nodes.process_input)
        workflow.add_node("normalize_input", self._normalize_input)
        workflow.add_node("generate_sql", self._generate_sql)
        workflow.add_node("generate_response", self._generate_response)

        workflow.add_edge("process_input", "normalize_input")
        workflow.add_edge("normalize_input", "generate_sql")
        workflow.add_edge("generate_sql", "generate_response")
        workflow.add_edge("generate_response", END)

        workflow.set_entry_point("process_input")

        return workflow.compile()

    def _generate_response(self, state: AgentState) -> AgentState:
        """使用 LLM 生成回复"""
        return nodes.generate_response(state, self.model)

    def _normalize_input(self, state: AgentState) -> AgentState:
        """规范化用户输入"""
        return nodes.normalize_input(state, self.model)

    def _generate_sql(self, state: AgentState) -> AgentState:
        """生成 SQL"""
        return nodes.generate_sql(state, self.model)

    def run(self, user_input: str, **kwargs) -> Dict[str, Any]:
        """运行代理并处理用户输入"""
        initial_state = AgentState(
            messages=[HumanMessage(content=user_input)],
            context=kwargs
        )

        result = self.graph.invoke(initial_state)

        return {
            "messages": result.get("messages", []),
            "context": result.get("context", {}),
            "final_step": result.get("current_step", "unknown")
        }