cfc/cfc-langgraph/app/agents/report_parse_agent.py

"""
报告解析 Agent：算法解析 + LLM 兜底 + 多类型报告支持
"""
import json
import logging
import base64
from typing import Optional

from app.config import settings
from app.parsers.pdf_parser import parse_report_pdf_with_fallback

logger = logging.getLogger(__name__)

# 算法解析器使用中文键名（与 extract_full_report_v5.py 一致），
# Java 消费端期望英文键名；在此做双向映射，Java 读取英文键即可。
_CN_TO_EN_OVERVIEW: dict[str, str] = {
    '健康总分': 'overallScore',
    '菌群健康': 'gutHealthScore',
    '慢病控制': 'chronicDiseaseScore',
    '营养均衡': 'nutritionScore',
    '肠道菌群平衡': 'balanceScore',
    '菌群多样性': 'diversityScore',
    '有益菌': 'beneficialScore',
    '有害菌': 'harmfulScore',
    '核心菌属': 'coreGenusScore',
}

# 指标分区 → Java 消费端 indicators 的 category（与 Java PdfParseService 兜底路径保持一致）
_INDICATOR_SECTION_CATEGORY: list[tuple[str, str]] = [
    ('nutrition', '主要营养评估'),
    ('amino_acids', '氨基酸评估'),
    ('vitamins', '维生素评估'),
    ('trace_elements', '微量元素评估'),
    ('抗生素风险评估', '抗生素耐药'),
    ('肠道屏障及代谢物', '肠道屏障功能'),
    ('短链脂肪酸', '短链脂肪酸'),
    ('神经递质及激素', '神经递质与激素'),
]

# 菌群检出详细列表分组 → Java 消费端键
_FLORA_GROUP_TO_KEY: dict[str, str] = {
    '核心菌属': 'gut_flora',
    '益生菌': 'probiotic_species',
    '菌纲构成': 'taxonomy_class',
    '菌目构成': 'taxonomy_order',
    '菌科构成': 'taxonomy_family',
    '菌属构成': 'taxonomy_genus',
    '菌种构成': 'taxonomy_species',
    '病原菌属': 'pathogen_genus',
    '病原菌检出': 'pathogen_detection',
}

# Java 端无独立字段、需并入 gut_flora（带 category 区分）的分组
_FLORA_EXTRA_GROUPS: list[str] = ['有害菌属', '其它重要菌属']


class ReportParseAgent:
    """报告解析 Agent"""

    def __init__(self):
        self.llm_api_key = getattr(settings, 'llm_api_key', '')

    @staticmethod
    def _normalize_overview_keys(result: dict) -> None:
        """将 overview 中的中文键名映射为英文键名（双向写入），兼容 Java 消费端。"""
        overview = result.get('overview')
        if not overview:
            return
        for _cn, _en in _CN_TO_EN_OVERVIEW.items():
            if _cn in overview and _en not in overview:
                overview[_en] = overview[_cn]

    @staticmethod
    def _normalize_for_java(result: dict) -> None:
        """将算法解析结果归一化为 Java 消费端 snake_case 键。

        保留中文键供调试/LLM 兜底；仅当构建出非空列表时才覆盖 result 对应键，
        避免覆盖 LLM 兜底直接产出的 indicators/flora/foods。
        """
        # 1. indicators：合并各指标分区（含主要消化道致病菌特殊键名）
        indicators = []
        for key, category in _INDICATOR_SECTION_CATEGORY:
            items = result.get(key)
            if not isinstance(items, list):
                continue
            for it in items:
                if not isinstance(it, dict) or not it.get('name'):
                    continue
                indicators.append({
                    'category': category,
                    'indicatorName': it['name'],
                    'indicatorValue': it.get('value', ''),
                    'unit': '',
                    'refRange': it.get('refRange', ''),
                    'status': it.get('status', ''),
                    'symptoms': '',
                })
        for it in result.get('主要消化道致病菌') or []:
            if not isinstance(it, dict):
                continue
            name = it.get('name') or it.get('致病菌')
            if name:
                indicators.append({
                    'category': '主要消化道致病菌',
                    'indicatorName': name,
                    'indicatorValue': it.get('value') or it.get('丰度', ''),
                    'unit': '',
                    'refRange': '',
                    'status': it.get('status') or it.get('评估', ''),
                    'symptoms': '',
                })
        if indicators:
            result['indicators'] = indicators

        # 2. 菌群分组 → snake_case 键（条目字段中文 → 英文）
        flora = result.get('菌群检出详细列表')
        if isinstance(flora, dict):
            for group, en_key in _FLORA_GROUP_TO_KEY.items():
                converted = []
                for it in flora.get(group) or []:
                    if not isinstance(it, dict) or not it.get('名称'):
                        continue
                    converted.append({
                        'name': it['名称'],
                        'value': it.get('丰度%', ''),
                        'normal_range': it.get('正常范围%', ''),
                        'population_level': it.get('人群水平%', ''),
                        'detection_rate': it.get('检出率%', ''),
                        'description': it.get('说明', ''),
                        'category': group,
                        'level': it.get('水平', ''),
                    })
                if converted:
                    result[en_key] = converted
            extra = []
            for group in _FLORA_EXTRA_GROUPS:
                for it in flora.get(group) or []:
                    if not isinstance(it, dict) or not it.get('名称'):
                        continue
                    extra.append({
                        'name': it['名称'],
                        'value': it.get('丰度%', ''),
                        'normal_range': it.get('正常范围%', ''),
                        'population_level': it.get('人群水平%', ''),
                        'detection_rate': it.get('检出率%', ''),
                        'description': it.get('说明', ''),
                        'category': group,
                        'level': it.get('水平', ''),
                    })
            if extra:
                result['gut_flora'] = (result.get('gut_flora') or []) + extra

        # 3. foods：个体化食物推荐表 → snake_case
        food_table = result.get('个体化食物推荐表')
        rows = food_table.get('数据') if isinstance(food_table, dict) else food_table
        foods = []
        if isinstance(rows, list):
            for it in rows:
                if not isinstance(it, dict) or not it.get('名称'):
                    continue
                foods.append({
                    'name': it['名称'],
                    'category': it.get('分类', ''),
                    'score': it.get('推荐指数'),
                    'energy_kj': it.get('能量KJ'),
                    'protein': it.get('蛋白g'),
                    'fat': it.get('脂肪g'),
                    'carbs': it.get('碳水化合物g'),
                    'starch': it.get('淀粉g'),
                    'fiber': it.get('总膳食纤维g'),
                    'cholesterol': it.get('胆固醇mg'),
                })
        if foods:
            result['foods'] = foods

    async def parse(self, file_path: str) -> dict:
        """解析 PDF 报告，算法解析 + LLM 兜底"""
        # 1. 算法解析
        result = parse_report_pdf_with_fallback(file_path)
        logger.info("算法解析完成: format=%s, overview_keys=%d",
                     result.get('format'), len(result.get('overview', {})))

        # 1.5 归一化 overview 键名：中文 → 英文（Java 消费端兼容）
        self._normalize_overview_keys(result)
        # 1.6 归一化指标/菌群/食物为 Java 消费端 snake_case 键
        self._normalize_for_java(result)

        # 2. 如果解析不完整，LLM 兜底
        if result.get('_parse_incomplete') or not result.get('disease_risks'):
            logger.info("算法解析不完整，尝试 LLM 兜底")
            llm_result = await self._parse_with_llm(file_path)
            if llm_result:
                # 合并 LLM 结果到算法结果上（LLM 覆盖缺失字段）
                for key in ['disease_risks', 'nutrition', 'amino_acids',
                            'vitamins', 'trace_elements', 'indicators']:
                    if key in llm_result and not result.get(key):
                        result[key] = llm_result[key]
                if llm_result.get('overview'):
                    for k, v in llm_result['overview'].items():
                        if k not in result.get('overview', {}):
                            result.setdefault('overview', {})[k] = v
                # 重新归一化：LLM 补齐的分区也要并入 indicators
                self._normalize_for_java(result)

        # 清理内部标记
        result.pop('_parse_incomplete', None)
        return result

    async def parse_generic(self, file_path: str, extra_context: Optional[dict] = None) -> dict:
        """通用报告 LLM 解析（不经过算法解析，直接走 LLM）"""
        try:
            from PyPDF2 import PdfReader
            reader = PdfReader(file_path)
            text = '\n'.join(page.extract_text() or '' for page in reader.pages)

            ctx_str = ""
            if extra_context:
                ctx_str = f"\n额外上下文：{json.dumps(extra_context, ensure_ascii=False)}"

            prompt = f"""你是一个通用报告解析专家。请从以下PDF文本中提取结构化数据，返回JSON格式。

报告文本内容：
{text[:12000]}{ctx_str}

请分析这份报告，推断它的类型和内容，然后按以下JSON Schema返回：
{{
    "reportType": "推断的报告类型名称",
    "reportTypeFamily": "报告家族分类（如: dan/cognitive/gut_flora/health_check/other）",
    "confidence": "high/medium/low",
    "summary": {{
        "personName": "姓名",
        "reportDate": "报告日期",
        "reportNumber": "报告编号",
        "overallScore": "总分（如果有）",
        "interpretation": "报告整体解读摘要"
    }},
    "indicators": [
        {{"name": "指标名称", "value": "数值", "category": "分类", "status": "状态"}}
    ],
    "sections": [
        {{"title": "段落标题", "content": "段落内容摘要", "items": [{{"name": "...", "value": "..."}}]}}
    ],
    "textFeatures": ["文本特征1", "文本特征2", ...]
}}

只返回JSON，不要其他文字。"""

            if self.llm_api_key:
                import httpx
                async with httpx.AsyncClient(timeout=120) as client:
                    resp = await client.post(
                        f"{settings.llm_base_url}/chat/completions",
                        json={
                            "model": settings.llm_model or "gpt-4o",
                            "messages": [{"role": "user", "content": prompt}],
                            "temperature": 0.1,
                        },
                        headers={"Authorization": f"Bearer {self.llm_api_key}"},
                    )
                    resp.raise_for_status()
                    data = resp.json()
                    content = data['choices'][0]['message']['content']
                    content = content.replace('```json', '').replace('```', '').strip()
                    return json.loads(content)
            else:
                logger.warning("LLM 未配置，返回空")
                return {"reportType": "unknown", "summary": {}, "indicators": [], "sections": []}
        except Exception as e:
            logger.error("通用LLM解析失败: %s", e)
            return {"reportType": "unknown", "error": str(e), "summary": {}, "indicators": [], "sections": []}

    async def _parse_with_llm(self, file_path: str) -> Optional[dict]:
        """LLM 兜底解析"""
        try:
            from PyPDF2 import PdfReader
            reader = PdfReader(file_path)
            text = '\n'.join(page.extract_text() or '' for page in reader.pages)

            # 构造 prompt
            prompt = f"""你是一个肠道菌群检测报告解析专家。请从以下PDF文本中提取结构化数据，返回JSON格式。

文本内容：
{text[:8000]}

请按以下JSON Schema返回：
{{
    "overview": {{ "person_name": "", "report_number": "", "age": 0, "gender": "male/female",
        "overallScore": 0, "gutHealthScore": 0, "chronicDiseaseScore": 0, "nutritionScore": 0,
        "gutAge": "", "gutType": "" }},
    "disease_risks": [{{"name": "", "value": "", "status": ""}}],
    "nutrition": [{{"name": "", "value": "", "status": ""}}],
    "amino_acids": [{{"name": "", "value": "", "status": ""}}],
    "vitamins": [{{"name": "", "value": "", "status": ""}}],
    "trace_elements": [{{"name": "", "value": "", "status": ""}}]
}}

只返回JSON，不要其他文字。"""

            if self.llm_api_key:
                # 调 OpenAI 兼容 API
                import httpx
                async with httpx.AsyncClient(timeout=60) as client:
                    resp = await client.post(
                        f"{settings.llm_base_url}/chat/completions",
                        json={
                            "model": settings.llm_model or "gpt-4o",
                            "messages": [{"role": "user", "content": prompt}],
                            "temperature": 0.1,
                        },
                        headers={"Authorization": f"Bearer {self.llm_api_key}"},
                    )
                    resp.raise_for_status()
                    data = resp.json()
                    content = data['choices'][0]['message']['content']
                    content = content.replace('```json', '').replace('```', '').strip()
                    return json.loads(content)
            else:
                logger.warning("LLM 未配置，跳过 LLM 兜底")
                return None
        except Exception as e:
            logger.warning("LLM 解析失败: %s", e)
            return None

    # ================================================================
    # 多类型报告解析
    # ================================================================

    async def parse_by_type(self, file_path: str, report_type: str,
                            extra_context: Optional[dict] = None) -> dict:
        """按报告类型路由到专用解析器。

        :param file_path: PDF 文件路径
        :param report_type: 报告类型标识:
            - "brain_status": 脑状态测量报告
            - "cognitive_aptitude": 先天智力潜能/皮纹学测评报告
            - "scanned_image": 扫描图片 PDF（无文字层，需多模态 LLM）
            - "auto": 自动检测类型
        :return: 结构化解析结果 dict
        """
        if report_type == "auto":
            report_type = self._detect_report_type(file_path)
            logger.info("自动检测报告类型: %s", report_type)

        if report_type == "brain_status":
            return await self._parse_brain_status(file_path)
        elif report_type == "cognitive_aptitude":
            return await self._parse_cognitive_aptitude(file_path)
        elif report_type == "scanned_image":
            return await self._parse_scanned_image(file_path)
        else:
            logger.info("未识别的报告类型 %s，走通用解析", report_type)
            return await self.parse_generic(file_path, extra_context)

    # ---- 格式检测 ----

    def _detect_report_type(self, file_path: str) -> str:
        """根据 PDF 文本内容自动检测报告类型。"""
        try:
            from PyPDF2 import PdfReader
            reader = PdfReader(file_path)

            # 检测是否有可提取文本
            total_text = ""
            for i in range(min(12, len(reader.pages))):
                t = reader.pages[i].extract_text() or ""
                total_text += t

            if len(total_text.strip()) < 20:
                # 前几页几乎无文字 → 可能是扫描图片 PDF
                return "scanned_image"

            # 脑状态测量报告
            if "脑状态测量报告" in total_text or "大脑综合状态" in total_text:
                return "brain_status"

            # 智力潜能 / 皮纹学报告
            if ("先天数据" in total_text or "智力潜能" in total_text
                    or "皮纹" in total_text or "ATD" in total_text
                    or "trc" in total_text.lower()):
                return "cognitive_aptitude"

            # 北京肠道菌群报告
            if ("肠道菌群检测" in total_text and "高通量测序" in total_text):
                return "gut_flora_beijing"

            return "generic"
        except Exception as e:
            logger.warning("报告类型检测失败: %s", e)
            return "generic"

    # ---- 通用 LLM 调用 ----

    async def _call_llm(self, prompt: str, timeout: int = 120) -> str:
        """调用 LLM 并返回文本响应。

        :raises RuntimeError: LLM 未配置或调用失败
        """
        if not self.llm_api_key:
            raise RuntimeError("LLM 未配置")
        import httpx
        async with httpx.AsyncClient(timeout=timeout) as client:
            resp = await client.post(
                f"{settings.llm_base_url}/chat/completions",
                json={
                    "model": settings.llm_model or "gpt-4o",
                    "messages": [{"role": "user", "content": prompt}],
                    "temperature": 0.1,
                },
                headers={"Authorization": f"Bearer {self.llm_api_key}"},
            )
            resp.raise_for_status()
            data = resp.json()
            content = data['choices'][0]['message']['content']
            return content.replace('```json', '').replace('```', '').strip()

    async def _call_llm_vision(self, prompt: str, image_base64: str,
                               timeout: int = 180) -> str:
        """调用多模态 LLM（vision），传入图片 + 文字提示。

        :raises RuntimeError: LLM 未配置或调用失败
        """
        if not self.llm_api_key:
            raise RuntimeError("LLM 未配置")
        import httpx
        async with httpx.AsyncClient(timeout=timeout) as client:
            resp = await client.post(
                f"{settings.llm_base_url}/chat/completions",
                json={
                    "model": settings.llm_model or "gpt-4o",
                    "messages": [
                        {
                            "role": "user",
                            "content": [
                                {"type": "text", "text": prompt},
                                {
                                    "type": "image_url",
                                    "image_url": {
                                        "url": f"data:image/png;base64,{image_base64}",
                                    },
                                },
                            ],
                        }
                    ],
                    "temperature": 0.1,
                },
                headers={"Authorization": f"Bearer {self.llm_api_key}"},
            )
            resp.raise_for_status()
            data = resp.json()
            content = data['choices'][0]['message']['content']
            return content.replace('```json', '').replace('```', '').strip()

    def _pdf_to_text(self, file_path: str, max_pages: int = 0) -> str:
        """用 PyPDF2 提取 PDF 全文。max_pages=0 表示全部。"""
        from PyPDF2 import PdfReader
        reader = PdfReader(file_path)
        n = len(reader.pages) if max_pages == 0 else min(max_pages, len(reader.pages))
        return '\n'.join(reader.pages[i].extract_text() or '' for i in range(n))

    # ---- 1. 脑状态测量报告 ----

    async def _parse_brain_status(self, file_path: str) -> dict:
        """解析脑状态测量报告。

        文本可提取，数据清晰。使用 LLM 提取结构化数据。

        返回结构:
        {
            "reportType": "brain_status",
            "reportTypeFamily": "cognitive",
            "summary": {
                "personName": "张文远",
                "age": 44,
                "gender": "male",
                "reportDate": "2026-05-03",
                "reportNumber": "CL79474",
                "overallScore": 82.1,
                "overallLevel": "良好",
            },
            "indicators": [
                {"name": "大脑综合状态得分", "value": "82.1", "category": "综合评分", "status": "良好"},
                {"name": "脑供血问题风险评估", "value": "低风险", "category": "风险评估", "status": "低风险"},
                ...
            ],
            "sections": [
                {"title": "疲劳评估", "content": "...", "items": [...]},
                {"title": "情绪评估", "content": "...", "items": [...]},
                {"title": "睡眠评估", "content": "...", "items": [...]},
            ]
        }
        """
        logger.info("开始解析脑状态测量报告: %s", file_path)
        text = self._pdf_to_text(file_path)

        prompt = f"""你是一个脑状态测量报告解析专家。请从以下文本中提取结构化数据，返回JSON格式。

报告文本内容：
{text}

请按以下JSON Schema返回：
{{
    "reportType": "brain_status",
    "reportTypeFamily": "cognitive",
    "summary": {{
        "personName": "姓名",
        "age": 年龄数字,
        "gender": "male/female",
        "reportDate": "报告日期 yyyy-MM-dd",
        "reportNumber": "报告编号",
        "overallScore": 综合状态得分数值,
        "overallLevel": "良好/一般/较差等文字描述"
    }},
    "indicators": [
        {{"name": "指标名称", "value": "数值或等级", "category": "分类", "status": "状态描述"}}
    ],
    "sections": [
        {{
            "title": "段落标题（如：健康风险评估/疲劳评估/情绪评估/睡眠评估等）",
            "content": "段落摘要",
            "items": [
                {{"name": "子项名称", "value": "数值", "status": "状态/等级"}}
            ]
        }}
    ]
}}

注意：
1. 尽量提取所有出现的评估指标，包括：大脑综合状态得分、大脑健康状态得分、大脑能力状态得分、
   脑供血问题风险、脑供氧问题风险、思维负荷、思维状态风险、大脑疲劳评估、用脑模式、
   焦虑情绪评估、抑郁情绪评估、抵触情绪评估、安全感评估、情绪管理评估、综合情绪评估、
   睡眠效果评估等
2. 数值尽量提取数字，状态文字原样保留
3. 只返回JSON，不要其他文字。"""

        try:
            content = await self._call_llm(prompt, timeout=120)
            result = json.loads(content)
            logger.info("脑状态报告解析完成: indicators=%d, sections=%d",
                        len(result.get('indicators', [])),
                        len(result.get('sections', [])))
            return result
        except Exception as e:
            logger.error("脑状态报告解析失败: %s", e)
            return {
                "reportType": "brain_status",
                "reportTypeFamily": "cognitive",
                "error": str(e),
                "summary": {},
                "indicators": [],
                "sections": [],
            }

    # ---- 2. 智力潜能 / 皮纹学测评报告 ----

    async def _parse_cognitive_aptitude(self, file_path: str) -> dict:
        """解析先天智力潜能/皮纹学测评报告。

        115页，文本碎片化严重。分块提取 + LLM 合并。

        返回结构:
        {
            "reportType": "cognitive_aptitude",
            "reportTypeFamily": "cognitive",
            "summary": {
                "personName": "张老师",
                "gender": "male",
                "region": "北京",
                "phone": "15901552192",
                "testDate": "2026-04-03",
                "birthday": "1982-02-06",
            },
            "indicators": [
                {"name": "TRC", "value": "107+X+M", "category": "先天数据", "status": ""},
                {"name": "ATD", "value": "35.5", "category": "思维敏捷性", "status": "超级敏感型"},
                ...
            ],
            "sections": [
                {"title": "智力潜能测评", "content": "...", "items": [...]},
                {"title": "学习风格测评", "content": "...", "items": [...]},
                {"title": "八大智能测试", "content": "...", "items": [...]},
                ...
            ]
        }
        """
        logger.info("开始解析智力潜能测评报告: %s", file_path)

        from PyPDF2 import PdfReader
        reader = PdfReader(file_path)
        total_pages = len(reader.pages)

        # 分块提取文本 — 每块约 8000 字符
        all_pages = []
        for i in range(total_pages):
            t = reader.pages[i].extract_text() or ''
            all_pages.append((i + 1, t))

        # 合并前 5 页（基本信息区）作为第一块
        first_chunk = '\n'.join(t for _, t in all_pages[:5])
        # 合并中间数据页（6-60页）作为第二块
        mid_chunk = '\n'.join(t for _, t in all_pages[5:min(30, len(all_pages))])
        # 合并后续页（30-62页有文字的）
        later_chunk = '\n'.join(t for _, t in all_pages[30:min(62, len(all_pages))])

        # 第一块: 基本信息 + 目录
        prompt1 = f"""你是一个先天智力潜能（皮纹学）测评报告解析专家。请从以下文本中提取基本信息和目录结构，返回JSON格式。

报告文本内容（前5页）：
{first_chunk}

请按以下JSON Schema返回：
{{
    "reportType": "cognitive_aptitude",
    "reportTypeFamily": "cognitive",
    "summary": {{
        "personName": "姓名",
        "gender": "male/female",
        "region": "地区",
        "phone": "电话",
        "testDate": "测评日期",
        "birthday": "出生日期",
        "trc": "TRC值（如107+X+M）",
        "atd": "ATD角度值",
        "learningType": "学习类型（听觉型/体觉型/视觉型）",
        "motivationType": "动机类型",
        "cognitiveType": "认知类型",
        "brainDominance": "左脑型/右脑型/全脑型"
    }},
    "toc": ["章节1标题", "章节2标题", ...]
}}

只返回JSON，不要其他文字。"""

        # 第二块: 智力潜能/学习风格/八大智能等核心数据
        prompt2 = f"""你是一个先天智力潜能（皮纹学）测评报告解析专家。请从以下文本中提取测评指标，返回JSON格式。

报告文本内容（数据页）：
{mid_chunk}

请按以下JSON Schema返回：
{{
    "indicators": [
        {{"name": "指标名称", "value": "数值", "category": "分类", "status": "状态/描述"}}
    ],
    "sections": [
        {{
            "title": "段落标题（如：智力潜能测评/学习风格测评/左右脑功能/先天性格/八大智能等）",
            "content": "段落摘要",
            "items": [{{"name": "子项名称", "value": "数值或描述", "status": "状态"}}]
        }}
    ]
}}

注意：
1. 指标包括但不限于：TRC(总脊纹数)、ATD(思维敏捷性角)、各脑区指标、
   八大智能（语言/逻辑数学/空间/身体动觉/音乐/人际/内省/自然观察）、
   先天学习潜能、先天行为导向、先天学习管道等
2. 如果某个指标的数值看起来是百分比或数值，直接提取
3. 只返回JSON，不要其他文字。"""

        # 第三块: 后续章节（学科建议/职业建议/心理学测试等）
        prompt3 = f"""你是一个先天智力潜能（皮纹学）测评报告解析专家。请从以下文本中提取测评建议和心理学测试结果，返回JSON格式。

报告文本内容（后续页面）：
{later_chunk}

请按以下JSON Schema返回：
{{
    "sections2": [
        {{
            "title": "段落标题（如：性格色彩/大五人格/MBTI/学科选择/职业能力/感觉统合等）",
            "content": "段落摘要",
            "items": [{{"name": "子项名称", "value": "数值或描述", "status": "状态"}}]
        }}
    ],
    "recommendations": [
        {{"category": "建议类别", "content": "建议内容"}}
    ]
}}

只返回JSON，不要其他文字。"""

        result = {
            "reportType": "cognitive_aptitude",
            "reportTypeFamily": "cognitive",
            "summary": {},
            "indicators": [],
            "sections": [],
        }

        try:
            # 并行请求三块
            import asyncio
            tasks = []
            if first_chunk.strip():
                tasks.append(self._call_llm(prompt1, timeout=120))
            if mid_chunk.strip():
                tasks.append(self._call_llm(prompt2, timeout=120))
            if later_chunk.strip():
                tasks.append(self._call_llm(prompt3, timeout=120))

            responses = await asyncio.gather(*tasks, return_exceptions=True)

            # 合并结果
            for i, resp in enumerate(responses):
                if isinstance(resp, Exception):
                    logger.warning("智力潜能报告第%d块解析失败: %s", i + 1, resp)
                    continue
                try:
                    chunk_result = json.loads(str(resp))
                    if i == 0:
                        # 第一块: summary + toc
                        result['summary'] = chunk_result.get('summary', {})
                        result['toc'] = chunk_result.get('toc', [])
                    elif i == 1:
                        # 第二块: indicators + sections
                        result['indicators'] = chunk_result.get('indicators', [])
                        result['sections'] = chunk_result.get('sections', [])
                    elif i == 2:
                        # 第三块: sections2 + recommendations
                        if chunk_result.get('sections2'):
                            result['sections'].extend(chunk_result['sections2'])
                        if chunk_result.get('recommendations'):
                            result['recommendations'] = chunk_result['recommendations']
                except (json.JSONDecodeError, KeyError) as e:
                    logger.warning("智力潜能报告第%d块JSON解析失败: %s", i + 1, e)

            logger.info("智力潜能报告解析完成: indicators=%d, sections=%d",
                        len(result.get('indicators', [])),
                        len(result.get('sections', [])))
            return result
        except Exception as e:
            logger.error("智力潜能报告解析失败: %s", e)
            return result

    # ---- 3. 扫描图片 PDF（多模态 LLM） ----

    async def _parse_scanned_image(self, file_path: str) -> dict:
        """解析扫描图片 PDF（无文字层）。

        流程:
        1. 用 PyPDF2 提取页面图片（base64 编码）
        2. 逐页发送给多模态 LLM 进行 OCR + 结构化提取
        3. 合并各页结果

        返回结构与 parse_generic 一致:
        {
            "reportType": "推断的类型",
            "reportTypeFamily": "报告家族",
            "summary": {...},
            "indicators": [...],
            "sections": [...]
        }
        """
        logger.info("开始解析扫描图片PDF: %s", file_path)

        from PyPDF2 import PdfReader
        reader = PdfReader(file_path)
        total_pages = len(reader.pages)

        all_indicators = []
        all_sections = []
        summary = {}
        detected_type = "unknown"

        for page_idx in range(total_pages):
            page = reader.pages[page_idx]
            image_b64 = self._extract_page_image_base64(page)
            if not image_b64:
                logger.warning("第%d页无图片可提取", page_idx + 1)
                continue

            prompt = f"""请分析这张报告图片（第{page_idx + 1}页，共{total_pages}页），提取其中的所有结构化数据。

请按以下JSON Schema返回：
{{
    "reportType": "推断的报告类型名称",
    "reportTypeFamily": "报告家族分类（如: gut_flora/health_check/cognitive/brain_status/other）",
    "summary": {{
        "personName": "姓名（如果能识别）",
        "reportDate": "报告日期（如果能识别）",
        "reportNumber": "报告编号（如果能识别）",
        "overallScore": "总分（如果可见）",
        "interpretation": "本页内容摘要"
    }},
    "indicators": [
        {{"name": "指标名称", "value": "数值", "category": "分类", "status": "状态"}}
    ],
    "sections": [
        {{"title": "段落标题", "content": "段落内容摘要", "items": [{{"name": "...", "value": "..."}}]}}
    ]
}}

注意：
1. 请仔细阅读图片中的所有文字，包括表格数据、数值、状态描述
2. 如果是菌群报告，提取菌属名称、丰度值、参考范围等
3. 如果是体检报告，提取各项检查指标、数值、参考范围、异常标记
4. 数值尽量提取精确数字，状态文字原样保留
5. 只返回JSON，不要其他文字。"""

            try:
                content = await self._call_llm_vision(prompt, image_b64, timeout=180)
                page_result = json.loads(content)

                # 合并指标
                if page_result.get('indicators'):
                    all_indicators.extend(page_result['indicators'])

                # 合并段落
                if page_result.get('sections'):
                    all_sections.extend(page_result['sections'])

                # 合并基本信息（第一页优先）
                if page_idx == 0 and page_result.get('summary'):
                    summary = page_result['summary']

                # 更新检测到的类型
                if page_result.get('reportType') and detected_type == "unknown":
                    detected_type = page_result['reportType']

                logger.info("扫描PDF第%d页解析完成: indicators=%d",
                            page_idx + 1, len(page_result.get('indicators', [])))
            except Exception as e:
                logger.warning("扫描PDF第%d页解析失败: %s", page_idx + 1, e)

        result = {
            "reportType": detected_type,
            "reportTypeFamily": "other",
            "summary": summary,
            "indicators": all_indicators,
            "sections": all_sections,
        }

        logger.info("扫描图片PDF解析完成: pages=%d, indicators=%d, sections=%d",
                    total_pages, len(all_indicators), len(all_sections))
        return result

    def _extract_page_image_base64(self, page) -> str:
        """从 PDF 页面对象中提取图片，返回 base64 编码字符串。

        支持以下情况：
        - 页面包含 /XObject 中的 /Image 类型对象
        - 页面是整页图片（常见于扫描件）

        :return: base64 编码的 PNG 图片，或空字符串（无图片）
        """
        try:
            from PyPDF2 import PdfReader
            import io

            # 获取页面资源
            resources = page.get('/Resources')
            if not resources:
                return ""

            x_objects = resources.get('/XObject')
            if not x_objects:
                return ""

            x_obj = x_objects.get_object()

            # 找最大的图片对象
            best_image = None
            best_size = 0

            for name in x_obj:
                obj = x_obj[name]
                obj_resolved = obj.get_object()

                subtype = obj_resolved.get('/Subtype')
                if subtype != '/Image':
                    continue

                width = int(obj_resolved.get('/Width', 0))
                height = int(obj_resolved.get('/Height', 0))
                size = width * height

                if size > best_size:
                    best_size = size
                    best_image = obj_resolved

            if not best_image:
                return ""

            # 提取图片数据
            color_space = best_image.get('/ColorSpace', '/DeviceRGB')
            bits_per_component = int(best_image.get('/BitsPerComponent', 8))
            width = int(best_image.get('/Width', 0))
            height = int(best_image.get('/Height', 0))

            raw_data = best_image.get_data()

            # 转换为 PIL Image → PNG → base64
            # 尝试安装 Pillow（如果没有）
            try:
                from PIL import Image
            except ImportError:
                logger.warning("Pillow 未安装，尝试基础 base64 编码")
                # 如果没有 Pillow，直接 base64 编码原始数据
                return base64.b64encode(raw_data).decode('utf-8')

            # 根据 ColorSpace 确定模式
            if isinstance(color_space, str):
                if 'RGB' in color_space or 'RGB' in str(color_space):
                    mode = 'RGB'
                elif 'Gray' in color_space or 'Gray' in str(color_space):
                    mode = 'L'
                elif 'CMYK' in color_space:
                    mode = 'CMYK'
                else:
                    mode = 'RGB'
            else:
                mode = 'RGB'

            if width > 0 and height > 0:
                img = Image.frombytes(mode, (width, height), raw_data)

                # CMYK → RGB
                if mode == 'CMYK':
                    img = img.convert('RGB')

                # 缩小图片以减少 token 消耗（最大 2000px 边）
                max_dim = 2000
                if max(width, height) > max_dim:
                    ratio = max_dim / max(width, height)
                    new_size = (int(width * ratio), int(height * ratio))
                    img = img.resize(new_size, Image.LANCZOS)

                # 转 PNG
                buf = io.BytesIO()
                img.save(buf, format='PNG', optimize=True)
                return base64.b64encode(buf.getvalue()).decode('utf-8')

            return ""
        except Exception as e:
            logger.warning("提取页面图片失败: %s", e)
            return ""