# -*- coding: utf-8 -*-
"""Ti 场景编排：报警解释 / 交接班摘要 / NL 查询驾驶舱（EPIC #11）。

复用内核组件（零内核改动，换行业只改模板资产）：
- ``llm_gateway.LLMGateway``：混合网关主编排（敏感度路由 → 生成 → 幻觉校验 → DLP）；
- ``llm_gateway.prompts.PromptRegistry``：提示词版本库（alarm_explain / shift_handover / qa）；
- ``llm_gateway.router.SensitivityRouter``：敏感度路由（行业规则 + 内置保底）；
- ``rag_kb.RagKnowledgeBase``：领域 RAG（工艺规范 / SOP / 国标三类知识源）。

三个场景均通过 ``TiScenarioRunner`` 暴露，业务方只需一行调用：
    runner = TiScenarioRunner()
    result = runner.explain_alarm("氯化炉炉温异常")
"""
from __future__ import annotations

import os
from typing import Callable, Dict, List, Optional

from llm_gateway.gateway import GatewayResult, LLMGateway
from llm_gateway.prompts import PromptRegistry
from llm_gateway.router import SensitivityRouter
from rag_kb import RagKnowledgeBase, load_kb_config

# ---------------------------------------------------------------------------
# 场景配置资产路径
# ---------------------------------------------------------------------------


def _scenarios_dir() -> str:
    return os.path.dirname(os.path.abspath(__file__))


def _repo_root() -> str:
    """仓库根目录（scenarios.py 向上 4 层：llm-scenarios → ti-cl4 → templates → 根）。"""
    return os.path.dirname(os.path.dirname(os.path.dirname(
        os.path.dirname(os.path.abspath(__file__)))))


def scenarios_config_path() -> str:
    """场景配置资产（scenarios.template.yaml）路径。"""
    return os.path.join(_scenarios_dir(), "config", "scenarios.template.yaml")


def prompts_config_path() -> str:
    """复用 llm-gateway 提示词版本库资产路径。"""
    return os.path.join(_repo_root(), "core", "llm-gateway", "config",
                        "prompts.template.yaml")


def kb_config_path() -> str:
    """复用 rag-kb 知识库模板资产路径。"""
    return os.path.join(_repo_root(), "core", "rag-kb", "config",
                        "kb.template.yaml")


# ---------------------------------------------------------------------------
# 演示知识文档集（Ti 场景开箱即用；接入真实客户数据时替换 loader 即可）
# ---------------------------------------------------------------------------

DEMO_KB_DOCS: Dict[str, str] = {
    "沸腾氯化工艺规范": (
        "沸腾氯化炉采用流态化氯化工艺，炉温控制在 850±50℃，"
        "氯气流量按加料量比例调节，加料比保持 1:2.4~1:2.8。"
        "炉温异常时优先检查氯气流量与加料系统。"
    ),
    "沸腾氯化炉操作手册": (
        "开机前确认氯气缓冲罐压力、炉体密封与尾气处理系统正常；"
        "运行中每 30 分钟记录一次炉温、氯气流量与出料量。"
    ),
    "沸腾氯化炉异常处置SOP": (
        "SOP-CL-001：炉温超上限（>900℃）时立即降低氯气流量并减少加料，"
        "若 10 分钟内未回落则按紧急停机流程处理，并通知当班班长。"
    ),
    "交接班报告生成规范": (
        "交接班报告须包含：当班生产概况、设备运行状态、异常与处置记录、"
        "安全注意事项、待办事项。异常事项必须标注发生时间与处理人。"
    ),
    "GB/T 氯气安全使用标准": (
        "氯气属于剧毒气体，作业场所应配备气体泄漏检测与报警装置，"
        "作业人员须佩戴防护用品，泄漏时启动应急程序并疏散无关人员。"
    ),
    "GB/T 钛及钛合金加工标准": (
        "海绵钛产品纯度按 GB/T 标准分级，钛纯度 ≥99.5% 为一级品；"
        "氯化产物杂质含量影响最终钛纯度，须按批次检验并留样。"
    ),
}


def demo_kb_loader(title: str) -> str:
    """演示文档加载器：按标题返回内置演示文本（换数据源时替换本函数）。"""
    return DEMO_KB_DOCS.get(title, "")


# ---------------------------------------------------------------------------
# Ti 场景编排
# ---------------------------------------------------------------------------


class TiScenarioRunner:
    """Template-Ti 一期 LLM 场景统一入口（EPIC #11 主体交付）。

    构造参数均可注入（便于测试与替换真实组件）；缺省使用仓库模板资产
    （提示词版本库 + 领域 RAG + 行业路由规则）构建完整场景编排。
    """

    def __init__(
        self,
        gateway: Optional[LLMGateway] = None,
        kb: Optional[RagKnowledgeBase] = None,
        prompts: Optional[PromptRegistry] = None,
        router: Optional[SensitivityRouter] = None,
        kb_loader: Callable[[str], str] = demo_kb_loader,
    ) -> None:
        prompts = prompts or PromptRegistry.from_template_config(prompts_config_path())
        kb = kb or RagKnowledgeBase.from_template_config(
            load_kb_config(kb_config_path()), loader=kb_loader,
        )
        router = router or SensitivityRouter.from_template_config(router_config_path())
        self.kb = kb
        if gateway is not None:
            # 注入 gateway：由调用方负责 prompt_name / 高利害配置
            self._gateways = {
                "alarm_explain": gateway,
                "shift_handover": gateway,
                "nl_query": gateway,
            }
        else:
            # 每个场景绑定各自的提示词模板版本（可复现）
            base = dict(prompts=prompts, router=router)
            self._gateways = {
                # 报警解释：高利害，命中即启用幻觉校验信度阈值 → 低信度转人工
                "alarm_explain": LLMGateway(
                    prompt_name="alarm_explain",
                    high_stakes_names={"alarm_explain"}, **base,
                ),
                "shift_handover": LLMGateway(
                    prompt_name="shift_handover", **base,
                ),
                "nl_query": LLMGateway(prompt_name="qa", **base),
            }

    # -- 场景 1：报警根因解释（高利害） -----------------------------------

    def explain_alarm(self, alarm: str, confidence: float = 1.0,
                      top_k: int = 3) -> GatewayResult:
        """解释一条报警的可能原因与处置建议（RAG 检索异常处置 SOP）。"""
        hits = self.kb.search(alarm, top_k=top_k,
                              categories=None)
        sources = [h.source for h in hits]
        return self._gateways["alarm_explain"].ask(
            alarm, rag_context=sources, confidence=confidence,
        )

    # -- 场景 2：交接班自动摘要 -------------------------------------------

    def generate_handover(self, shift_desc: str, confidence: float = 1.0,
                          top_k: int = 3) -> GatewayResult:
        """按班次情况生成交接班摘要（RAG 检索交接班规范）。"""
        hits = self.kb.search(shift_desc, top_k=top_k, categories=None)
        sources = [h.source for h in hits]
        return self._gateways["shift_handover"].ask(
            shift_desc, rag_context=sources, confidence=confidence,
        )

    # -- 场景 3：自然语言查询驾驶舱（NL → 查询意图/指标） -----------------

    def query_cockpit(self, question: str, confidence: float = 1.0,
                      top_k: int = 3) -> GatewayResult:
        """把自然语言问题映射为驾驶舱查询意图（RAG 检索工艺指标定义）。"""
        hits = self.kb.search(question, top_k=top_k, categories=None)
        sources = [h.source for h in hits]
        return self._gateways["nl_query"].ask(
            question, rag_context=sources, confidence=confidence,
        )

    # -- 审计 --------------------------------------------------------------

    def drain_audits(self) -> Dict[str, List[Dict[str, object]]]:
        """取走网关各组件审计记录（DLP/路由/Prompt/幻觉校验）。"""
        audits: Dict[str, List[Dict[str, object]]] = {
            "dlp": [], "router": [], "prompts": [], "guard": [],
        }
        for gw in set(self._gateways.values()):
            for key, rows in gw.drain_audits().items():
                audits.setdefault(key, []).extend(rows)
        return audits


def router_config_path() -> str:
    """复用 llm-gateway 行业路由规则资产路径。"""
    return os.path.join(_repo_root(), "core", "llm-gateway", "config",
                        "router.template.yaml")