"""
Agent 编排层 —— 调度四层架构完成分析

Layer 1: Planner      意图规划
Layer 2: Explorer     自适应探索
Layer 3: Insight      异常洞察
Layer 4: Context      上下文记忆
"""
from typing import Optional

from config import DB_PATH, MAX_EXPLORATION_ROUNDS
from schema_extractor import extract_schema, schema_to_text
from sandbox_executor import SandboxExecutor
from planner import Planner
from explorer import Explorer
from insights import InsightEngine, quick_detect
from reporter import ReportGenerator
from context import ContextManager


class DataAnalysisAgent:
    """
    数据分析 Agent

    四层架构：
    1. Planner  - 理解用户意图，生成分析计划
    2. Explorer - 基于计划多轮迭代探索
    3. Insights - 从结果中检测异常、输出主动洞察
    4. Context  - 管理多轮对话上下文

    Agent 负责编排这四层，从问题到报告。
    """

    def __init__(self, db_path: str):
        # 数据层
        self.db_path = db_path
        self.schema = extract_schema(db_path)
        self.schema_text = schema_to_text(self.schema)
        self.executor = SandboxExecutor(db_path)

        # 四层组件
        self.planner = Planner()
        self.explorer = Explorer(self.executor)
        self.insight_engine = InsightEngine()
        self.reporter = ReportGenerator()
        self.context = ContextManager()

    def analyze(self, question: str, max_rounds: Optional[int] = None) -> str:
        """
        完整分析流程

        Args:
            question: 用户分析问题
            max_rounds: 最大探索轮数（默认用配置值）

        Returns:
            格式化的分析报告
        """
        max_rounds = max_rounds or MAX_EXPLORATION_ROUNDS

        print(f"\n{'='*60}")
        print(f"📊 {question}")
        print(f"{'='*60}")

        # ── Layer 0: 检查上下文 ──────────────────────────
        prev_context = self.context.get_context_for(question)
        if prev_context:
            print("📎 发现历史分析上下文，将结合之前的发现")

        # ── Layer 1: 意图规划 ────────────────────────────
        print("\n🎯 [Layer 1] 意图规划...")
        plan = self.planner.plan(question, self.schema_text)

        analysis_type = plan.get("analysis_type", "unknown")
        dimensions = plan.get("dimensions", [])
        rationale = plan.get("rationale", "")
        print(f"   类型: {analysis_type}")
        print(f"   维度: {', '.join(dimensions) if dimensions else '自动发现'}")
        print(f"   理由: {rationale[:80]}{'...' if len(rationale) > 80 else ''}")

        # ── Layer 2: 自适应探索 ──────────────────────────
        print(f"\n🔍 [Layer 2] 自适应探索 (最多 {max_rounds} 轮)...")
        steps = self.explorer.explore(plan, self.schema_text, max_rounds=max_rounds)

        successful = sum(1 for s in steps if s.success)
        print(f"\n   完成: {len(steps)} 轮, {successful} 条成功查询")

        # ── Layer 3: 异常洞察 ────────────────────────────
        print("\n🔎 [Layer 3] 异常洞察...")

        # 先做规则检测
        rule_alerts = quick_detect(steps)
        for alert in rule_alerts:
            print(f"   {alert}")

        # 再做 LLM 深度分析
        insights = self.insight_engine.analyze(steps, question)
        if insights:
            print(f"   发现 {len(insights)} 条洞察")
            for insight in insights:
                print(f"   {insight}")
        else:
            print("   未发现异常")

        # ── 生成报告 ────────────────────────────────────
        print("\n📝 正在生成报告...")
        report = self.reporter.generate(question, plan, steps, insights)

        # 追加主动洞察
        if insights:
            insight_text = self.insight_engine.format_insights(insights)
            report += f"\n\n---\n\n{insight_text}"

        # ── Layer 4: 记录上下文 ──────────────────────────
        self.context.add_session(
            question=question,
            plan=plan,
            steps=steps,
            insights=insights,
            report=report,
        )

        return report

    def get_schema(self) -> str:
        """获取 Schema 文本"""
        return self.schema_text

    def get_history(self) -> str:
        """获取分析历史摘要"""
        return self.context.get_history_summary()

    def get_audit(self) -> str:
        """获取执行审计日志"""
        return self.executor.get_execution_summary()

    def clear_history(self):
        """清空分析历史"""
        self.context.clear()