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

from services.core.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) -> Any:
        """构建代理状态图"""
        workflow = StateGraph(cast(Any, AgentState))

        self._add_shared_sql_nodes(workflow)
        self._add_shared_sql_edges(workflow, start_node="process_input", end_node="generate_response")
        workflow.add_edge("generate_response", END)

        workflow.set_entry_point("process_input")

        return workflow.compile()

    def _add_shared_sql_nodes(self, workflow: Any) -> None:
        """注册 SQL 规划相关共享节点。"""
        workflow.add_node("process_input", cast(Any, nodes.process_input))
        workflow.add_node("normalize_input", cast(Any, self._normalize_input))
        workflow.add_node("classify_query_mode", cast(Any, nodes.classify_query_mode))
        workflow.add_node("match_table", cast(Any, nodes.match_table))
        workflow.add_node("load_sql_prompt", cast(Any, nodes.load_sql_prompt))
        workflow.add_node("build_sql_plan", cast(Any, nodes.build_sql_plan))
        workflow.add_node("generate_sql", cast(Any, self._generate_sql))
        workflow.add_node("execute_sql", cast(Any, nodes.execute_sql))
        workflow.add_node("check_empty_result", cast(Any, nodes.check_empty_result))
        workflow.add_node("generate_response", cast(Any, self._generate_response))

    @staticmethod
    def _add_shared_sql_edges(workflow: Any, start_node: str, end_node: str) -> None:
        """串联标准 SQL 工作流。"""
        workflow.add_edge(start_node, "normalize_input")
        workflow.add_edge("normalize_input", "classify_query_mode")
        workflow.add_edge("classify_query_mode", "match_table")
        workflow.add_edge("match_table", "load_sql_prompt")
        workflow.add_edge("load_sql_prompt", "build_sql_plan")
        workflow.add_edge("build_sql_plan", "generate_sql")
        workflow.add_edge("generate_sql", "execute_sql")
        workflow.add_edge("execute_sql", "check_empty_result")
        workflow.add_edge("check_empty_result", end_node)

    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)

    @staticmethod
    def _coerce_state(initial_state: AgentState, result: Any) -> AgentState:
        """兼容 LangGraph 返回 AgentState 或 dict。"""
        if isinstance(result, AgentState):
            return result
        return initial_state.apply_graph_result(result)

    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)
        final_state = self._coerce_state(initial_state, result)

        return {
            "messages": final_state.messages,
            "context": final_state.sync_context(),
            "final_step": final_state.current_step,
        }