// =============================================================================
//  Reply Quality Utilities
//  共享工具：Bot 回复质量检查、反模式检测、个性化风味
// =============================================================================

import { tokenize } from "./chinese-text.mjs";

// 最小上下文长度（低于此长度不做关联度评分）
export const MIN_CONTEXT_LENGTH = 20;

// 检查回复与上下文的相关度
// 返回 { valid, score } – valid 为 true 表示关联度达标，score 为 0-100 的量化评分
export function validateContextRelevance(reply, context) {
  if (!context || context.length < MIN_CONTEXT_LENGTH) {
    return { valid: true, score: 70 };
  }

  const replyGrams = tokenize(reply);
  if (replyGrams.length === 0) {
    return { valid: false, score: 0 };
  }

  const contextSet = new Set(tokenize(context));
  const matched = replyGrams.filter(g => contextSet.has(g)).length;
  const relevance = matched / replyGrams.length;

  return {
    valid: relevance >= 0.05,
    score: Math.min(100, Math.round(relevance * 100)),
  };
}

// 反模式检测 – 识别过于通用/模板化的 AI 回复
export function detectBadPatterns(text) {
  const patterns = [
    /很高兴[能为可以]您/,
    /必须[地得]说/,
    /值得[一关]注/,
    /这是个好(问题|话题)/,
    /希望[能对]你[有们]帮助/,
    /感谢[您的你]分享/,
    /非常有[见解启]发/,
  ];

  const matches = patterns.filter(p => p.test(text));

  return {
    isGeneric: matches.length >= 3,
    matchCount: matches.length,
    patterns: patterns.filter(p => p.test(text)),
  };
}

// 人格风味前缀 – 给回复添加个性化开场白
const PERSONALITY_PREFIXES = {
  pragmatic: ["实际测试下来", "根据我的经验", "试过之后发现"],
  enthusiastic: ["太棒了！", "超赞！", "强烈推荐！"],
  analytical: ["从数据来看", "分析之后发现", "对比了一下数据"],
  cautious: ["客观来说", "理性分析一下", "需要说明的是"],
};

export function addPersonalityFlavor(text, personaType = "pragmatic") {
  const prefixes =
    PERSONALITY_PREFIXES[personaType] || PERSONALITY_PREFIXES.pragmatic;

  // 检查是否已包含任何前缀风味，避免重复添加
  const allPrefixes = Object.values(PERSONALITY_PREFIXES).flat();
  if (allPrefixes.some(p => text.startsWith(p))) {
    return text;
  }

  const prefix = prefixes[Math.floor(Math.random() * prefixes.length)];
  return `${prefix}，${text}`;
}

// 回复多样性指数 – 检测回复中是否包含重复短语
export function checkDiversityScore(text) {
  if (text.length < 50) return 0.8;

  // 将文本分成句子
  const sentences = text.split(/[。！？\n]/).filter(Boolean);
  if (sentences.length < 2) return 0.6;

  // 检查开头词的多样性
  const sentenceStarts = sentences
    .map(s => s.trim().substring(0, 2))
    .filter(Boolean);
  const uniqueStarts = new Set(sentenceStarts);
  const startVariety = uniqueStarts.size / sentenceStarts.length;

  // 检查句长分布
  const lengths = sentences.map(s => s.length);
  const avgLen = lengths.reduce((a, b) => a + b, 0) / lengths.length;
  const variance =
    lengths.reduce((sum, l) => sum + (l - avgLen) ** 2, 0) / lengths.length;

  // 句子开头变化率 + 句长变化率
  const lengthScore = Math.min(1, variance / 1000);
  return Math.round((startVariety * 0.6 + lengthScore * 0.4) * 100) / 100;
}

// 获取质量摘要统计
export function getQualityMetrics(reply, context, style) {
  const { score: relevance } = validateContextRelevance(reply, context);
  const { isGeneric, matchCount } = detectBadPatterns(reply);
  const diversity = checkDiversityScore(reply);

  return {
    relevance,
    isGeneric,
    patternMatches: matchCount,
    diversity,
    style,
  };
}