"""简历两阶段并行提取

第一阶段：5路并行提取主表短字段 + 各子表标识名（极快，输出极短）。
第二阶段：N+1路并行提取每条子表记录的短字段 + description，以及 profile 补充（skills/certificates/summary）。
description/summary 由 AI 直接按原文结构输出为字符串数组（不再返回行号区间），代码只做类型兜底与清理。
最终组装为与原方案一致的 dict 结构（description 为 list[str]，summary 为 str），上下游无感知。
"""

import asyncio
import time

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

from app.ai.model_config import ResumeExtractorModel
from app.ai.resume_extractor.prompts import (
    OVERVIEW_PROFILE_PROMPT, OVERVIEW_EDUCATION_PROMPT, OVERVIEW_WORK_PROMPT,
    OVERVIEW_PROJECT_PROMPT, OVERVIEW_COMPETITION_PROMPT,
    DETAIL_PROFILE_PROMPT, DETAIL_EDUCATION_PROMPT, DETAIL_WORK_PROMPT,
    DETAIL_INTERNSHIP_PROMPT, DETAIL_PROJECT_PROMPT, DETAIL_COMPETITION_PROMPT,
)
from app.core.logger import log
from app.tool.json_helper import parse_llm_json


# ==================== LLM 调用工具 ====================

def _build_chain(prompt: str):
    """构建提取链：prompt → LLM → 文本输出"""
    return ChatPromptTemplate.from_messages([("system", prompt), ("human", "{text}")]) | ResumeExtractorModel.PARSE | StrOutputParser()


async def _safe_invoke(chain, inp: dict, label: str):
    """单个链调用，记录耗时，失败返回空"""
    start = time.perf_counter()
    try:
        raw = await chain.ainvoke(inp)
        log.info(f"AI提取[{label}]完成，耗时: {time.perf_counter() - start:.2f}s")
        return parse_llm_json(raw)
    except Exception as e:
        log.warning(f"AI提取[{label}]失败，耗时: {time.perf_counter() - start:.2f}s，错误: {e}")
        return None


def _clean_str_list(value) -> list[str]:
    """将 AI 返回值规整为字符串数组：过滤非字符串与空白元素，去除首尾空白"""
    if not isinstance(value, list):
        return []
    return [s.strip() for s in value if isinstance(s, str) and s.strip()]


# ==================== 第一阶段：概览 ====================

_overview_profile_chain = _build_chain(OVERVIEW_PROFILE_PROMPT)
_overview_education_chain = _build_chain(OVERVIEW_EDUCATION_PROMPT)
_overview_work_chain = _build_chain(OVERVIEW_WORK_PROMPT)
_overview_project_chain = _build_chain(OVERVIEW_PROJECT_PROMPT)
_overview_competition_chain = _build_chain(OVERVIEW_COMPETITION_PROMPT)


async def _extract_overview(text: str) -> dict:
    """第一阶段：5路并行提取概览信息"""
    inp = {"text": text}
    profile, edu_names, work_names, proj_names, comp_names = await asyncio.gather(
        _safe_invoke(_overview_profile_chain, inp, "概览-个人信息"),
        _safe_invoke(_overview_education_chain, inp, "概览-教育"),
        _safe_invoke(_overview_work_chain, inp, "概览-工作实习"),
        _safe_invoke(_overview_project_chain, inp, "概览-项目"),
        _safe_invoke(_overview_competition_chain, inp, "概览-竞赛"),
    )
    return {
        "profile": profile if isinstance(profile, dict) else {},
        "education": edu_names if isinstance(edu_names, list) else [],
        "work": work_names.get("work", []) if isinstance(work_names, dict) else [],
        "internship": work_names.get("internship", []) if isinstance(work_names, dict) else [],
        "project": proj_names if isinstance(proj_names, list) else [],
        "competition": comp_names if isinstance(comp_names, list) else [],
    }


# ==================== 第二阶段：详情 ====================

# 子表模块统一配置（单一数据源）：(模块键, 详情 prompt 模板, 日志标签)
# 概览/详情/组装/日志均复用此清单，新增子表只需在此追加一行。
_DETAIL_MODULES: tuple[tuple[str, str, str], ...] = (
    ("education", DETAIL_EDUCATION_PROMPT, "教育"),
    ("work", DETAIL_WORK_PROMPT, "工作"),
    ("internship", DETAIL_INTERNSHIP_PROMPT, "实习"),
    ("project", DETAIL_PROJECT_PROMPT, "项目"),
    ("competition", DETAIL_COMPETITION_PROMPT, "竞赛"),
)
_SUB_MODULES: tuple[str, ...] = tuple(m[0] for m in _DETAIL_MODULES)


async def _extract_detail(prompt_tpl: str, name: str, text: str, label: str) -> dict | None:
    """单条子表记录详情提取：用 name 替换 prompt 中的 {name}"""
    chain = _build_chain(prompt_tpl.replace("{name}", name))
    return await _safe_invoke(chain, {"text": text}, label)


async def _extract_all_details(overview: dict, text: str) -> dict:
    """第二阶段：根据概览结果，N+1路并行提取所有子表记录详情 + 个人信息补充"""
    # 第 0 路固定为 profile 补充（skills/certificates/summary），其余按子表记录展开
    tasks = [_extract_detail(DETAIL_PROFILE_PROMPT, "", text, "详情-个人信息补充")]
    task_modules = ["profile_extra"]
    for module, prompt_tpl, label in _DETAIL_MODULES:
        for name in overview[module]:
            tasks.append(_extract_detail(prompt_tpl, name, text, f"详情-{label}-{name}"))
            task_modules.append(module)

    results = await asyncio.gather(*tasks)
    details: dict[str, list] = {"profile_extra": [], **{m: [] for m in _SUB_MODULES}}
    for module, result in zip(task_modules, results):
        details[module].append(result if isinstance(result, dict) else {})
    return details


# ==================== 组装 ====================

def _assemble(overview: dict, details: dict) -> dict:
    """将两阶段结果组装为与原方案一致的 dict 结构（description 为 list[str]，summary 为 str）"""
    profile = overview["profile"]
    profile_extra = details.get("profile_extra", [{}])[0] if details.get("profile_extra") else {}
    profile["skills"] = _clean_str_list(profile_extra.get("skills"))[:5]
    profile["certificates"] = _clean_str_list(profile_extra.get("certificates"))
    summary_paras = _clean_str_list(profile_extra.get("summary"))
    profile["summary"] = "\n".join(summary_paras) if summary_paras else None

    result = dict(profile)
    for module in _SUB_MODULES:
        items = []
        for item in details.get(module, []):
            item["description"] = _clean_str_list(item.get("description"))
            items.append(item)
        result[module] = items
    return result


# ==================== 入口 ====================

async def extract_all(text: str) -> dict:
    """两阶段并行提取简历，返回与原方案完全一致的结构化数据

    text: 简历纯文本全文（PyMuPDF/docx/txt 提取，保留原始换行）。
    """
    log.info("第一阶段：5路并行概览提取")
    overview = await _extract_overview(text)
    log.info(
        "概览完成 - " + " ".join(f"{label}:{len(overview[module])}" for module, _, label in _DETAIL_MODULES)
    )

    total = sum(len(overview[m]) for m in _SUB_MODULES)
    log.info(f"第二阶段：{total + 1}路并行详情提取")
    details = await _extract_all_details(overview, text)

    result = _assemble(overview, details)
    log.info("两阶段提取完成，数据组装完毕")
    return result