#!/usr/bin/env python3
"""
知识点匹配模块
连接 MySQL vala_test.vala_kp 表，实现两阶段匹配：粗召回 + LLM精筛。
"""

import os
import re
import json
import logging
import threading

import pymysql

CURRENT_PATH = os.path.dirname(os.path.abspath(__file__))
SKILL_ROOT = os.path.dirname(CURRENT_PATH)
WORKSPACE_ROOT = os.path.dirname(os.path.dirname(SKILL_ROOT))

logger = logging.getLogger("kp_matcher")
if not logger.handlers:
    handler = logging.StreamHandler()
    handler.setFormatter(logging.Formatter(
        "%(asctime)s - %(levelname)s - %(filename)s:%(lineno)d - %(message)s"
    ))
    logger.addHandler(handler)
    logger.setLevel(logging.INFO)

# ============ Secrets 加载 ============

def _load_mysql_password_from_secrets():
    """从 workspace secrets.md 读取测试 MySQL 密码"""
    secrets_path = os.path.join(WORKSPACE_ROOT, "secrets.md")
    if not os.path.exists(secrets_path):
        return None
    try:
        with open(secrets_path, "r", encoding="utf-8") as f:
            content = f.read()
        # 匹配 "### 测试 MySQL" 后面的 "read_only: xxx" 行
        match = re.search(r'###\s*测试\s*MySQL\s*\n\s*read_only:\s*(.+)', content)
        if match:
            return match.group(1).strip()
    except Exception:
        pass
    return None


def _get_mysql_password():
    """获取 MySQL 密码：环境变量 > secrets.md"""
    env_pw = os.getenv("MYSQL_PASSWORD")
    if env_pw:
        return env_pw
    pw = _load_mysql_password_from_secrets()
    if pw:
        return pw
    logger.warning("未找到 MySQL 密码（环境变量 MYSQL_PASSWORD 和 secrets.md 均不可用）")
    return ""


# ============ MySQL 配置 ============

MYSQL_CONFIG = {
    "host": os.getenv("MYSQL_HOST", "bj-cdb-8frbdwju.sql.tencentcdb.com"),
    "port": int(os.getenv("MYSQL_PORT", "25413")),
    "user": os.getenv("MYSQL_USER", "read_only"),
    "password": _get_mysql_password(),
    "database": os.getenv("MYSQL_DB", "vala_test"),
    "charset": "utf8mb4",
    "connect_timeout": 10,
    "read_timeout": 10,
}

# ============ kpSkill 默认值映射 ============

KP_SKILL_DEFAULTS = {
    "vocab": ("vocab_meaning", "词义"),
    "sentence": ("sentence_meaning", "语义"),
    "grammar": ("sentence_meaning", "语义"),
    "pron": ("vocab_pron", "发音"),
}

# ============ LLM 精筛 Prompt ============

KP_MATCH_SYSTEM_PROMPT = """你是一个知识点匹配专家。根据给定的知识点文本和候选列表，选出最匹配的知识点条目。

## 规则
1. 从候选列表中选出与"待匹配知识点"含义最接近的一条
2. 如果有 level 信息，优先选择同 level 的条目
3. 输出格式：仅输出被选中条目的 kp_id，不要包含其他内容
4. 如果没有任何候选匹配，输出 null"""


def _build_match_user_prompt(kp_text, candidates, level=None, context=""):
    """构建 LLM 精筛的 user prompt"""
    lines = [f"待匹配知识点: {kp_text}"]
    if level:
        lines.append(f"当前级别: {level}")
    if context:
        lines.append(f"组件上下文: {context}")
    lines.append("\n候选列表:")
    for c in candidates:
        lines.append(f"- kp_id={c['kp_id']}, title={c['title']}, type={c['type']}, "
                     f"level={c['vala_level']}, desc={c['en_desc'] or ''}")
    return "\n".join(lines)


# ============ MySQL 连接 ============

_thread_local = threading.local()


def _get_connection():
    """获取 MySQL 连接（线程本地存储，线程安全）"""
    conn = getattr(_thread_local, 'connection', None)
    if conn and conn.open:
        try:
            conn.ping(reconnect=True)
            return conn
        except Exception:
            pass

    conn = pymysql.connect(**MYSQL_CONFIG, cursorclass=pymysql.cursors.DictCursor)
    _thread_local.connection = conn
    return conn


def _close_connection():
    """关闭当前线程的连接"""
    conn = getattr(_thread_local, 'connection', None)
    if conn:
        try:
            conn.close()
        except Exception:
            pass
        _thread_local.connection = None


# ============ 粗召回 ============

def _rough_recall(title_text, level=None):
    """
    基于 title 字段的粗召回。

    策略：
    1. 精确匹配 title = text
    2. 如果无精确匹配，尝试 LIKE 模糊匹配
    3. 可选用 vala_level 过滤
    4. 最多返回 10 条候选
    """
    conn = _get_connection()
    results = []

    with conn.cursor() as cursor:
        # 精确匹配
        sql = "SELECT kp_id, type, title, vala_level, en_desc FROM vala_kp WHERE deleted_at IS NULL AND title = %s"
        params = [title_text]
        if level:
            sql += " AND vala_level = %s"
            params.append(level)
        sql += " LIMIT 10"
        cursor.execute(sql, params)
        results = cursor.fetchall()

        if results:
            return results

        # 精确匹配无 level 过滤
        if level:
            sql = "SELECT kp_id, type, title, vala_level, en_desc FROM vala_kp WHERE deleted_at IS NULL AND title = %s LIMIT 10"
            cursor.execute(sql, [title_text])
            results = cursor.fetchall()
            if results:
                return results

        # LIKE 模糊匹配
        like_pattern = f"%{title_text}%"
        sql = "SELECT kp_id, type, title, vala_level, en_desc FROM vala_kp WHERE deleted_at IS NULL AND title LIKE %s"
        params = [like_pattern]
        if level:
            sql += " AND vala_level = %s"
            params.append(level)
        sql += " LIMIT 10"
        cursor.execute(sql, params)
        results = cursor.fetchall()

        if not results and level:
            sql = "SELECT kp_id, type, title, vala_level, en_desc FROM vala_kp WHERE deleted_at IS NULL AND title LIKE %s LIMIT 10"
            cursor.execute(sql, [like_pattern])
            results = cursor.fetchall()

    return results


# ============ 精筛选 ============

def _precise_match(kp_text, candidates, level=None, context="", llm_client=None):
    """
    使用 LLM 从多个候选中精确选择最匹配的。

    Returns:
        dict or None: 选中的候选条目
    """
    if not candidates:
        return None
    if len(candidates) == 1:
        return candidates[0]

    # 优先用 level 过滤
    if level:
        level_matched = [c for c in candidates if c.get("vala_level") == level]
        if len(level_matched) == 1:
            return level_matched[0]
        if level_matched:
            candidates = level_matched

    if len(candidates) == 1:
        return candidates[0]

    # 多候选需要 LLM 精筛
    if llm_client is None:
        # 无 LLM 时取第一个（按 kp_id 排序）
        candidates.sort(key=lambda x: x.get("kp_id", ""))
        return candidates[0]

    user_prompt = _build_match_user_prompt(kp_text, candidates, level, context)
    try:
        response, usage = llm_client.call(KP_MATCH_SYSTEM_PROMPT, user_prompt, max_tokens=64, temperature=0.0)
        response = response.strip()
        if response == "null":
            return None
        # 从候选中查找返回的 kp_id
        for c in candidates:
            if c["kp_id"] == response:
                return c
        # LLM 可能返回带引号的 kp_id
        cleaned = response.strip('"').strip("'")
        for c in candidates:
            if c["kp_id"] == cleaned:
                return c
        # 兜底：取第一个
        logger.warning(f"LLM 返回的 kp_id '{response}' 不在候选列表中，使用第一个候选")
        return candidates[0]
    except Exception as e:
        logger.warning(f"LLM 精筛失败: {e}，使用第一个候选")
        return candidates[0]


# ============ 单条知识点匹配 ============

def _match_single_kp(kp_text, level=None, context="", llm_client=None):
    """
    匹配单个知识点文本到数据库记录。

    Returns:
        dict: {"kpId": str|None, "kpType": str, "kpTitle": str, "kpSkill": str, "kpSkillName": str, "candidates": list}
    """
    # 默认结果（未匹配）
    is_sentence = "..." in kp_text or len(kp_text.split()) > 3
    default_type = "sentence" if is_sentence else "vocab"
    skill, skill_name = KP_SKILL_DEFAULTS.get(default_type, ("vocab_meaning", "词义"))

    default_result = {
        "kpId": None,
        "kpType": default_type,
        "kpTitle": kp_text,
        "kpSkill": skill,
        "kpSkillName": skill_name,
        "candidates": [],
    }

    try:
        candidates = _rough_recall(kp_text, level)
    except Exception as e:
        logger.error(f"MySQL 粗召回失败 '{kp_text}': {e}", exc_info=True)
        return default_result

    if not candidates:
        logger.info(f"未找到匹配: '{kp_text}' (level={level})")
        return default_result

    # 保存候选列表供 HTML 下拉展示
    candidates_for_ui = [
        {"kp_id": c["kp_id"], "type": c.get("type", ""), "title": c.get("title", ""),
         "vala_level": c.get("vala_level", ""), "en_desc": c.get("en_desc") or ""}
        for c in candidates[:10]
    ]

    matched = _precise_match(kp_text, candidates, level, context, llm_client)
    if not matched:
        default_result["candidates"] = candidates_for_ui
        return default_result

    # 从匹配结果构建 kpInfo 条目
    kp_type = matched.get("type") or default_type
    skill, skill_name = KP_SKILL_DEFAULTS.get(kp_type, ("vocab_meaning", "词义"))

    return {
        "kpId": matched["kp_id"],
        "kpType": kp_type,
        "kpTitle": kp_text,
        "kpSkill": skill,
        "kpSkillName": skill_name,
        "candidates": candidates_for_ui,
    }


# ============ 主接口 ============

def match_knowledge_points(knowledge_text, cType, cId, level=None, llm_client=None):
    """
    知识点匹配主接口。解析知识点文本并逐条匹配到数据库。

    Args:
        knowledge_text: 知识点原始文本（可能多行，来自剧本 sheet）
        cType: 组件类型标识
        cId: 组件ID
        level: 剧本级别（如 "L1", "L2"）
        llm_client: LLMClient 实例（用于多候选精筛）

    Returns:
        dict or None: kpInfo 对象，无知识点时返回 None
    """
    if not knowledge_text or not knowledge_text.strip():
        return None

    # 清洗文本（复用现有逻辑）
    clean_text = re.sub(r'<text[^>]*>', '', knowledge_text)
    clean_text = re.sub(r'</text>', '', clean_text)
    clean_text = clean_text.strip()

    if not clean_text:
        return None

    lines = [l.strip() for l in clean_text.split("\n") if l.strip()]

    kp_list = []
    context = f"cType={cType}, cId={cId}"

    for line in lines:
        # 去除数字后缀（如 "school 1" → "school"）
        stripped = re.sub(r'\s+\d+$', '', line).strip()
        if not stripped:
            continue

        kp_entry = _match_single_kp(stripped, level, context, llm_client)
        kp_list.append(kp_entry)

    if not kp_list:
        return None

    logger.info(f"知识点匹配完成: cType={cType}, cId={cId}, "
                f"总数={len(kp_list)}, "
                f"已匹配={sum(1 for k in kp_list if k['kpId'] is not None)}")

    return {
        "pushType": "relationKp",
        "cType": cType,
        "cId": str(cId),
        "kpInfo": kp_list,
    }


# ============ CLI 测试 ============

if __name__ == "__main__":
    import sys
    sys.path.insert(0, CURRENT_PATH)

    print("=== 知识点匹配模块测试 ===\n")

    # 测试 MySQL 连接
    print("1. 测试 MySQL 连接...")
    try:
        conn = _get_connection()
        with conn.cursor() as cursor:
            cursor.execute("SELECT COUNT(*) as cnt FROM vala_kp WHERE deleted_at IS NULL")
            row = cursor.fetchone()
            print(f"   连接成功！有效记录数: {row['cnt']}")
    except Exception as e:
        print(f"   连接失败: {e}")
        sys.exit(1)

    # 测试单词匹配
    print("\n2. 测试单词匹配 'clean' (L2)...")
    result = _match_single_kp("clean", level="L2")
    print(f"   结果: kpId={result['kpId']}, kpType={result['kpType']}")

    # 测试句型匹配
    print("\n3. 测试句型匹配 \"Let's/Let me...\"...")
    result = _match_single_kp("Let's/Let me...", level="L2")
    print(f"   结果: kpId={result['kpId']}, kpType={result['kpType']}")

    # 测试完整接口
    print("\n4. 测试完整接口（多行知识点）...")
    test_text = "clean\nschool\nLet's go to school"
    result = match_knowledge_points(test_text, "mid_dialog_repeat", "0112001", level="L2")
    if result:
        print(f"   kpInfo 条数: {len(result['kpInfo'])}")
        for kp in result["kpInfo"]:
            print(f"   - {kp['kpTitle']}: kpId={kp['kpId']}, kpType={kp['kpType']}")
    else:
        print("   无结果")

    # 测试带 LLM 的精筛
    print("\n5. 测试带 LLM 精筛（多候选消歧）...")
    from llm_client import get_client
    llm = get_client()
    result = _match_single_kp("clean", level=None, context="cType=mid_dialog_repeat, 教学场景：打扫教室", llm_client=llm)
    print(f"   结果: kpId={result['kpId']}, kpType={result['kpType']}")

    _close_connection()
    print("\n=== 测试完成 ===")