#!/usr/bin/env python3
"""
虫群智能体系统 — 智能缓存层
缓存重复查询结果，减少API调用
LRU + TTL + 语义相似度匹配
"""

import hashlib
import json
import logging
import os
import threading
import time
from collections import OrderedDict
from typing import Dict, List, Optional, Tuple

logger = logging.getLogger(__name__)

DEFAULT_CACHE_DIR = "/home/admin/swarm/data/cache"


class CacheEntry:
    """缓存条目"""

    def __init__(self, query: str, response: str, model_id: str,
                 confidence: float, ttl: int = 3600):
        self.query = query
        self.response = response
        self.model_id = model_id
        self.confidence = confidence
        self.created_at = time.time()
        self.ttl = ttl  # 秒
        self.hit_count = 0
        self.last_hit_at = None

    @property
    def is_expired(self) -> bool:
        return time.time() - self.created_at > self.ttl

    @property
    def age_seconds(self) -> float:
        return time.time() - self.created_at

    def hit(self):
        """记录一次命中"""
        self.hit_count += 1
        self.last_hit_at = time.time()

    def to_dict(self) -> Dict:
        return {
            "query": self.query,
            "response": self.response,
            "model_id": self.model_id,
            "confidence": self.confidence,
            "created_at": self.created_at,
            "ttl": self.ttl,
            "hit_count": self.hit_count,
            "last_hit_at": self.last_hit_at,
        }

    @classmethod
    def from_dict(cls, d: Dict) -> "CacheEntry":
        entry = cls(d["query"], d["response"], d["model_id"],
                    d["confidence"], d.get("ttl", 3600))
        entry.created_at = d.get("created_at", time.time())
        entry.hit_count = d.get("hit_count", 0)
        entry.last_hit_at = d.get("last_hit_at")
        return entry


class SmartCache:
    """
    智能缓存 — 单例
    特性：
    - LRU淘汰（容量上限）
    - TTL过期（时间上限）
    - 语义相似度匹配（相似问题命中缓存）
    - 关键词去重（核心词相同视为同一查询）
    """

    _instance = None
    _lock = threading.Lock()

    # 缓存配置
    MAX_ENTRIES = 500        # 最大条目数
    DEFAULT_TTL = 3600       # 默认1小时过期
    SIMILARITY_THRESHOLD = 0.8  # 相似度阈值

    def __new__(cls):
        with cls._lock:
            if cls._instance is None:
                cls._instance = super().__new__(cls)
                cls._instance._cache = OrderedDict()  # key -> CacheEntry
                cls._instance._keyword_index = {}     # 关键词集合 -> cache_key
                cls._instance._stats = {
                    "hits": 0, "misses": 0, "evictions": 0, "expirations": 0
                }
                cls._instance._initialized = False
            return cls._instance

    def initialize(self):
        """初始化缓存"""
        # 尝试加载持久化缓存
        self._load()
        self._initialized = True
        logger.info(f"智能缓存初始化: {len(self._cache)}条缓存")

    # ----------------------------------------------------------
    # 核心：查询/存储
    # ----------------------------------------------------------

    def get(self, query: str) -> Optional[CacheEntry]:
        """
        查询缓存
        策略：精确匹配 → 关键词匹配 → 无命中
        """
        # 1. 精确匹配
        key = self._make_key(query)
        entry = self._cache.get(key)
        if entry and not entry.is_expired:
            entry.hit()
            self._stats["hits"] += 1
            # LRU：移到末尾
            self._cache.move_to_end(key)
            logger.debug(f"缓存命中(精确): {query[:30]}")
            return entry
        elif entry and entry.is_expired:
            # 过期，删除
            del self._cache[key]
            self._stats["expirations"] += 1

        # 2. 关键词相似度匹配
        similar = self._find_similar(query)
        if similar:
            similar.hit()
            self._stats["hits"] += 1
            logger.debug(f"缓存命中(相似): {query[:30]} → {similar.query[:30]}")
            return similar

        self._stats["misses"] += 1
        return None

    def put(self, query: str, response: str, model_id: str,
            confidence: float, ttl: int = None):
        """
        存储到缓存
        仅缓存有意义的查询（太短/太常见的跳过）
        """
        # 过滤不需要缓存的查询
        if self._should_skip(query):
            return

        key = self._make_key(query)
        entry = CacheEntry(query, response, model_id, confidence,
                           ttl or self.DEFAULT_TTL)

        # 容量检查：LRU淘汰
        while len(self._cache) >= self.MAX_ENTRIES:
            oldest_key = next(iter(self._cache))
            del self._cache[oldest_key]
            self._stats["evictions"] += 1

        self._cache[key] = entry
        # 更新关键词索引
        self._update_keyword_index(key, query)

        # 持久化（节流：每10次写入持久化一次）
        if self._stats["misses"] % 10 == 0:
            self._save()

    def invalidate(self, query: str):
        """使指定查询的缓存失效"""
        key = self._make_key(query)
        if key in self._cache:
            del self._cache[key]

    def clear(self):
        """清空缓存"""
        self._cache.clear()
        self._keyword_index.clear()
        self._save()
        logger.info("缓存已清空")

    # ----------------------------------------------------------
    # 相似度匹配
    # ----------------------------------------------------------

    def _find_similar(self, query: str) -> Optional[CacheEntry]:
        """基于关键词集合的相似度匹配"""
        query_keywords = self._extract_keywords(query)
        if not query_keywords:
            return None

        best_match = None
        best_score = 0.0

        for idx_key, cache_key in self._keyword_index.items():
            if cache_key not in self._cache:
                continue
            entry = self._cache[cache_key]
            if entry.is_expired:
                continue

            # Jaccard相似度
            stored_keywords = self._extract_keywords(entry.query)
            if not stored_keywords:
                continue

            intersection = len(query_keywords & stored_keywords)
            union = len(query_keywords | stored_keywords)
            if union == 0:
                continue

            score = intersection / union
            if score >= self.SIMILARITY_THRESHOLD and score > best_score:
                best_score = score
                best_match = entry

        return best_match

    def _extract_keywords(self, text: str) -> set:
        """提取关键词（简单的中文分词+英文分词）"""
        keywords = set()
        # 英文单词
        import re
        en_words = re.findall(r'[a-zA-Z]{2,}', text.lower())
        keywords.update(en_words)
        # 中文字符对（2-gram）
        cn_chars = re.findall(r'[\u4e00-\u9fff]+', text)
        for segment in cn_chars:
            if len(segment) >= 2:
                for i in range(len(segment) - 1):
                    keywords.add(segment[i:i+2])
            else:
                keywords.add(segment)
        # 去掉太常见的词
        stop_words = {"的是", "在了", "和有", "这不", "一我"}
        keywords -= stop_words
        return keywords

    def _update_keyword_index(self, cache_key: str, query: str):
        """更新关键词索引"""
        keywords = self._extract_keywords(query)
        if keywords:
            idx_key = frozenset(keywords)
            self._keyword_index[idx_key] = cache_key

    # ----------------------------------------------------------
    # 辅助
    # ----------------------------------------------------------

    def _make_key(self, query: str) -> str:
        """生成缓存key"""
        normalized = query.strip().lower()
        return hashlib.md5(normalized.encode()).hexdigest()

    def _should_skip(self, query: str) -> bool:
        """判断是否应跳过缓存"""
        q = query.strip()
        # 太短
        if len(q) < 3:
            return True
        # 问候语
        greetings = ["你好", "您好", "嗨", "hi", "hello", "早上好", "下午好", "晚上好"]
        if q.lower() in greetings:
            return True
        # 确认词
        confirms = ["好的", "明白", "收到", "ok", "谢谢", "感谢"]
        if q.lower() in confirms:
            return True
        return False

    # ----------------------------------------------------------
    # 统计与持久化
    # ----------------------------------------------------------

    def get_stats(self) -> Dict:
        """获取缓存统计"""
        total = self._stats["hits"] + self._stats["misses"]
        return {
            "total_entries": len(self._cache),
            "max_entries": self.MAX_ENTRIES,
            "hits": self._stats["hits"],
            "misses": self._stats["misses"],
            "hit_rate": round(self._stats["hits"] / max(1, total), 3),
            "evictions": self._stats["evictions"],
            "expirations": self._stats["expirations"],
        }

    def get_top_entries(self, top_k: int = 10) -> List[Dict]:
        """获取最热门缓存条目"""
        entries = sorted(
            self._cache.values(),
            key=lambda e: e.hit_count, reverse=True
        )
        return [
            {"query": e.query[:50], "hits": e.hit_count,
             "model": e.model_id, "age_s": round(e.age_seconds)}
            for e in entries[:top_k]
        ]

    def _save(self):
        """持久化缓存到磁盘"""
        try:
            os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
            data = {
                "entries": {k: v.to_dict() for k, v in self._cache.items()},
                "stats": self._stats,
            }
            path = os.path.join(DEFAULT_CACHE_DIR, "smart_cache.json")
            with open(path, "w", encoding="utf-8") as f:
                json.dump(data, f, ensure_ascii=False, indent=2)
        except Exception as e:
            logger.warning(f"缓存持久化失败: {e}")

    def _load(self):
        """从磁盘加载缓存"""
        path = os.path.join(DEFAULT_CACHE_DIR, "smart_cache.json")
        if not os.path.exists(path):
            return
        try:
            with open(path, "r", encoding="utf-8") as f:
                data = json.load(f)
            for k, v in data.get("entries", {}).items():
                entry = CacheEntry.from_dict(v)
                if not entry.is_expired:
                    self._cache[k] = entry
            self._stats.update(data.get("stats", {}))
            logger.info(f"加载缓存: {len(self._cache)}条")
        except Exception as e:
            logger.warning(f"缓存加载失败: {e}")


# ============================================================
# 便捷函数
# ============================================================

_cache = None


def get_cache() -> SmartCache:
    """获取全局缓存实例"""
    global _cache
    if _cache is None:
        _cache = SmartCache()
        if not _cache._initialized:
            _cache.initialize()
    return _cache