// Story Summary - Recall Engine
// L1 chunk + L2 event 召回
// - 全量向量打分
// - 指数衰减加权 Query Embedding
// - 实体/参与者加分
// - MMR 去重
// - floor 稀疏去重

import { getAllEventVectors, getAllChunkVectors, getChunksByFloors, getMeta } from './chunk-store.js';
import { embed, getEngineFingerprint } from './embedder.js';
import { xbLog } from '../../../core/debug-core.js';
import { getContext } from '../../../../../../extensions.js';
import { getSummaryStore } from '../data/store.js';

const MODULE_ID = 'recall';

const CONFIG = {
    QUERY_MSG_COUNT: 5,
    QUERY_DECAY_BETA: 0.7,
    QUERY_MAX_CHARS: 600,
    QUERY_CONTEXT_CHARS: 240,

    CANDIDATE_CHUNKS: 120,
    CANDIDATE_EVENTS: 100,

    TOP_K_CHUNKS: 40,
    TOP_K_EVENTS: 35,

    MIN_SIMILARITY: 0.35,
    MMR_LAMBDA: 0.72,

    BONUS_PARTICIPANT_HIT: 0.08,
    BONUS_TEXT_HIT: 0.05,
    BONUS_WORLD_TOPIC_HIT: 0.06,

    FLOOR_LIMIT: 1,
};

// ═══════════════════════════════════════════════════════════════════════════
// 工具函数
// ═══════════════════════════════════════════════════════════════════════════

function cosineSimilarity(a, b) {
    if (!a?.length || !b?.length || a.length !== b.length) return 0;
    let dot = 0, nA = 0, nB = 0;
    for (let i = 0; i < a.length; i++) {
        dot += a[i] * b[i];
        nA += a[i] * a[i];
        nB += b[i] * b[i];
    }
    return nA && nB ? dot / (Math.sqrt(nA) * Math.sqrt(nB)) : 0;
}

function normalizeVec(v) {
    let s = 0;
    for (let i = 0; i < v.length; i++) s += v[i] * v[i];
    s = Math.sqrt(s) || 1;
    return v.map(x => x / s);
}

function normalize(s) {
    return String(s || '').normalize('NFKC').replace(/[\u200B-\u200D\uFEFF]/g, '').trim();
}

function stripNoise(text) {
    return String(text || '')
        .replace(/<think>[\s\S]*?<\/think>/gi, '')
        .replace(/<thinking>[\s\S]*?<\/thinking>/gi, '')
        .replace(/\[tts:[^\]]*\]/gi, '')
        .trim();
}

function buildExpDecayWeights(n, beta) {
    const last = n - 1;
    const w = Array.from({ length: n }, (_, i) => Math.exp(beta * (i - last)));
    const sum = w.reduce((a, b) => a + b, 0) || 1;
    return w.map(x => x / sum);
}

// ═══════════════════════════════════════════════════════════════════════════
// Query 构建
// ═══════════════════════════════════════════════════════════════════════════

function buildQuerySegments(chat, count, excludeLastAi) {
    if (!chat?.length) return [];

    let messages = chat;
    if (excludeLastAi && messages.length > 0 && !messages[messages.length - 1]?.is_user) {
        messages = messages.slice(0, -1);
    }

    return messages.slice(-count).map((m, idx, arr) => {
        const speaker = m.name || (m.is_user ? '用户' : '角色');
        const clean = stripNoise(m.mes);
        if (!clean) return '';
        const limit = idx === arr.length - 1 ? CONFIG.QUERY_MAX_CHARS : CONFIG.QUERY_CONTEXT_CHARS;
        return `${speaker}: ${clean.slice(0, limit)}`;
    }).filter(Boolean);
}

async function embedWeightedQuery(segments, vectorConfig) {
    if (!segments?.length) return null;

    const weights = buildExpDecayWeights(segments.length, CONFIG.QUERY_DECAY_BETA);
    const vecs = await embed(segments, vectorConfig);
    const dims = vecs?.[0]?.length || 0;
    if (!dims) return null;

    const out = new Array(dims).fill(0);
    for (let i = 0; i < vecs.length; i++) {
        for (let j = 0; j < dims; j++) out[j] += (vecs[i][j] || 0) * weights[i];
    }

    return { vector: normalizeVec(out), weights };
}

// ═══════════════════════════════════════════════════════════════════════════
// 实体抽取
// ═══════════════════════════════════════════════════════════════════════════

function buildEntityLexicon(store, allEvents) {
    const { name1 } = getContext();
    const userName = normalize(name1);
    const set = new Set();

    for (const e of allEvents || []) {
        for (const p of e.participants || []) {
            const s = normalize(p);
            if (s) set.add(s);
        }
    }

    const json = store?.json || {};

    for (const m of json.characters?.main || []) {
        const s = normalize(typeof m === 'string' ? m : m?.name);
        if (s) set.add(s);
    }

    for (const a of json.arcs || []) {
        const s = normalize(a?.name);
        if (s) set.add(s);
    }

    for (const w of json.world || []) {
        const t = normalize(w?.topic);
        if (t && !t.includes('::')) set.add(t);
    }

    for (const r of json.characters?.relationships || []) {
        const from = normalize(r?.from);
        const to = normalize(r?.to);
        if (from) set.add(from);
        if (to) set.add(to);
    }

    const stop = new Set([userName, '我', '你', '他', '她', '它', '用户', '角色', 'assistant'].map(normalize).filter(Boolean));

    return Array.from(set)
        .filter(s => s.length >= 2 && !stop.has(s) && !/^[\s\p{P}\p{S}]+$/u.test(s) && !/<[^>]+>/.test(s))
        .slice(0, 5000);
}

function extractEntities(text, lexicon) {
    const t = normalize(text);
    if (!t || !lexicon?.length) return [];

    const sorted = [...lexicon].sort((a, b) => b.length - a.length);
    const hits = [];
    for (const e of sorted) {
        if (t.includes(e)) hits.push(e);
        if (hits.length >= 20) break;
    }
    return hits;
}

// ═══════════════════════════════════════════════════════════════════════════
// MMR
// ═══════════════════════════════════════════════════════════════════════════

function mmrSelect(candidates, k, lambda, getVector, getScore) {
    const selected = [];
    const ids = new Set();

    while (selected.length < k && candidates.length) {
        let best = null, bestScore = -Infinity;

        for (const c of candidates) {
            if (ids.has(c._id)) continue;

            const rel = getScore(c);
            let div = 0;

            if (selected.length) {
                const vC = getVector(c);
                if (vC?.length) {
                    for (const s of selected) {
                        const sim = cosineSimilarity(vC, getVector(s));
                        if (sim > div) div = sim;
                    }
                }
            }

            const score = lambda * rel - (1 - lambda) * div;
            if (score > bestScore) {
                bestScore = score;
                best = c;
            }
        }

        if (!best) break;
        selected.push(best);
        ids.add(best._id);
    }

    return selected;
}

// ═══════════════════════════════════════════════════════════════════════════
// L1 Chunks 检索
// ═══════════════════════════════════════════════════════════════════════════

async function searchChunks(queryVector, vectorConfig) {
    const { chatId } = getContext();
    if (!chatId || !queryVector?.length) return [];

    const meta = await getMeta(chatId);
    const fp = getEngineFingerprint(vectorConfig);
    if (meta.fingerprint && meta.fingerprint !== fp) return [];

    const chunkVectors = await getAllChunkVectors(chatId);
    if (!chunkVectors.length) return [];

    const scored = chunkVectors.map(cv => {
        const match = String(cv.chunkId).match(/c-(\d+)-(\d+)/);
        return {
            _id: cv.chunkId,
            chunkId: cv.chunkId,
            floor: match ? parseInt(match[1], 10) : 0,
            chunkIdx: match ? parseInt(match[2], 10) : 0,
            similarity: cosineSimilarity(queryVector, cv.vector),
            vector: cv.vector,
        };
    });

    const candidates = scored
        .filter(s => s.similarity >= CONFIG.MIN_SIMILARITY)
        .sort((a, b) => b.similarity - a.similarity)
        .slice(0, CONFIG.CANDIDATE_CHUNKS);

    const selected = mmrSelect(
        candidates,
        CONFIG.TOP_K_CHUNKS,
        CONFIG.MMR_LAMBDA,
        c => c.vector,
        c => c.similarity
    );

    // floor 稀疏去重
    const floorCount = new Map();
    const sparse = [];
    for (const s of selected.sort((a, b) => b.similarity - a.similarity)) {
        const cnt = floorCount.get(s.floor) || 0;
        if (cnt >= CONFIG.FLOOR_LIMIT) continue;
        floorCount.set(s.floor, cnt + 1);
        sparse.push(s);
    }

    const floors = [...new Set(sparse.map(c => c.floor))];
    const chunks = await getChunksByFloors(chatId, floors);
    const chunkMap = new Map(chunks.map(c => [c.chunkId, c]));

    return sparse.map(item => {
        const chunk = chunkMap.get(item.chunkId);
        if (!chunk) return null;
        return {
            chunkId: item.chunkId,
            floor: item.floor,
            chunkIdx: item.chunkIdx,
            speaker: chunk.speaker,
            isUser: chunk.isUser,
            text: chunk.text,
            similarity: item.similarity,
        };
    }).filter(Boolean);
}

// ═══════════════════════════════════════════════════════════════════════════
// L2 Events 检索
// ═══════════════════════════════════════════════════════════════════════════

async function searchEvents(queryVector, allEvents, vectorConfig, store, queryEntities) {
    const { chatId, name1 } = getContext();
    if (!chatId || !queryVector?.length) return [];

    const meta = await getMeta(chatId);
    const fp = getEngineFingerprint(vectorConfig);
    if (meta.fingerprint && meta.fingerprint !== fp) return [];

    const eventVectors = await getAllEventVectors(chatId);
    const vectorMap = new Map(eventVectors.map(v => [v.eventId, v.vector]));
    if (!vectorMap.size) return [];

    const userName = normalize(name1);
    const querySet = new Set((queryEntities || []).map(normalize));

    // 只取硬约束类的 world topic
    const worldTopics = (store?.json?.world || [])
        .filter(w => ['inventory', 'rule', 'knowledge'].includes(String(w.category).toLowerCase()))
        .map(w => normalize(w.topic))
        .filter(Boolean);

    const scored = (allEvents || []).map((event, idx) => {
        const v = vectorMap.get(event.id);
        const sim = v ? cosineSimilarity(queryVector, v) : 0;

        let bonus = 0;
        const reasons = [];

        // participants 命中
        const participants = (event.participants || []).map(normalize).filter(Boolean);
        if (participants.some(p => p !== userName && querySet.has(p))) {
            bonus += CONFIG.BONUS_PARTICIPANT_HIT;
            reasons.push('participant');
        }

        // text 命中
        const text = normalize(`${event.title || ''} ${event.summary || ''}`);
        if ((queryEntities || []).some(e => text.includes(normalize(e)))) {
            bonus += CONFIG.BONUS_TEXT_HIT;
            reasons.push('text');
        }

        // world topic 命中
        if (worldTopics.some(topic => querySet.has(topic) && text.includes(topic))) {
            bonus += CONFIG.BONUS_WORLD_TOPIC_HIT;
            reasons.push('world');
        }

        return {
            _id: event.id,
            _idx: idx,
            event,
            similarity: sim,
            bonus,
            finalScore: sim + bonus,
            reasons,
            isDirect: reasons.includes('participant'),
            vector: v,
        };
    });

    const candidates = scored
        .filter(s => s.similarity >= CONFIG.MIN_SIMILARITY)
        .sort((a, b) => b.finalScore - a.finalScore)
        .slice(0, CONFIG.CANDIDATE_EVENTS);

    const selected = mmrSelect(
        candidates,
        CONFIG.TOP_K_EVENTS,
        CONFIG.MMR_LAMBDA,
        c => c.vector,
        c => c.finalScore
    );

    return selected
        .sort((a, b) => b.finalScore - a.finalScore)
        .map(s => ({
            event: s.event,
            similarity: s.finalScore,
            _recallType: s.isDirect ? 'DIRECT' : 'SIMILAR',
            _recallReason: s.reasons.length ? s.reasons.join('+') : '相似',
        }));
}

// ═══════════════════════════════════════════════════════════════════════════
// 日志
// ═══════════════════════════════════════════════════════════════════════════

function formatRecallLog({ elapsed, segments, weights, chunkResults, eventResults, allEvents, queryEntities }) {
    const lines = [
        '╔══════════════════════════════════════════════════════════════╗',
        '║                    记忆召回报告                              ║',
        '╠══════════════════════════════════════════════════════════════╣',
        `║  耗时: ${elapsed}ms`,
        '╚══════════════════════════════════════════════════════════════╝',
        '',
        '┌─────────────────────────────────────────────────────────────┐',
        '│ 【查询构建】最近 5 条消息，指数衰减加权 (β=0.7)              │',
        '│ 权重越高 = 对召回方向影响越大                                │',
        '└─────────────────────────────────────────────────────────────┘',
    ];

    // 按权重从高到低排序显示
    const segmentsSorted = segments.map((s, i) => ({
        idx: i + 1,
        weight: weights?.[i] ?? 0,
        text: s,
    })).sort((a, b) => b.weight - a.weight);

    segmentsSorted.forEach((s, rank) => {
        const bar = '█'.repeat(Math.round(s.weight * 20));
        const preview = s.text.length > 60 ? s.text.slice(0, 60) + '...' : s.text;
        const marker = rank === 0 ? ' ◀ 主导' : '';
        lines.push(`  ${(s.weight * 100).toFixed(1).padStart(5)}% ${bar.padEnd(12)} ${preview}${marker}`);
    });

    lines.push('');
    lines.push('┌─────────────────────────────────────────────────────────────┐');
    lines.push('│ 【提取实体】用于判断"亲身经历"(DIRECT)                      │');
    lines.push('└─────────────────────────────────────────────────────────────┘');
    lines.push(`  ${queryEntities?.length ? queryEntities.join('、') : '(无)'}`);

    lines.push('');
    lines.push('┌─────────────────────────────────────────────────────────────┐');
    lines.push(`│ 【L1 原文片段】召回 ${chunkResults.length} 条`);
    lines.push('└─────────────────────────────────────────────────────────────┘');

    chunkResults.slice(0, 15).forEach((c, i) => {
        const preview = c.text.length > 50 ? c.text.slice(0, 50) + '...' : c.text;
        lines.push(`  ${String(i + 1).padStart(2)}. #${String(c.floor).padStart(3)} [${c.speaker}] ${preview}`);
        lines.push(`      相似度: ${c.similarity.toFixed(3)}`);
    });

    if (chunkResults.length > 15) {
        lines.push(`  ... 还有 ${chunkResults.length - 15} 条`);
    }

    lines.push('');
    lines.push('┌─────────────────────────────────────────────────────────────┐');
    lines.push(`│ 【L2 事件记忆】召回 ${eventResults.length} / ${allEvents.length} 条`);
    lines.push('│ DIRECT=亲身经历  SIMILAR=相关背景                           │');
    lines.push('└─────────────────────────────────────────────────────────────┘');

    eventResults.forEach((e, i) => {
        const type = e._recallType === 'DIRECT' ? '★ DIRECT ' : '  SIMILAR';
        const title = e.event.title || '(无标题)';
        lines.push(`  ${String(i + 1).padStart(2)}. ${type} ${title}`);
        lines.push(`      相似度: ${e.similarity.toFixed(3)} | 原因: ${e._recallReason}`);
    });

    // 统计
    const directCount = eventResults.filter(e => e._recallType === 'DIRECT').length;
    const similarCount = eventResults.filter(e => e._recallType === 'SIMILAR').length;

    lines.push('');
    lines.push('┌─────────────────────────────────────────────────────────────┐');
    lines.push('│ 【统计】                                                     │');
    lines.push('└─────────────────────────────────────────────────────────────┘');
    lines.push(`  L1 片段: ${chunkResults.length} 条`);
    lines.push(`  L2 事件: ${eventResults.length} 条 (DIRECT: ${directCount}, SIMILAR: ${similarCount})`);
    lines.push(`  实体命中: ${queryEntities?.length || 0} 个`);
    lines.push('');

    return lines.join('\n');
}

// ═══════════════════════════════════════════════════════════════════════════
// 主入口
// ═══════════════════════════════════════════════════════════════════════════

export async function recallMemory(queryText, allEvents, vectorConfig, options = {}) {
    const T0 = performance.now();
    const { chat } = getContext();
    const store = getSummaryStore();

    if (!allEvents?.length) {
        return { events: [], chunks: [], elapsed: 0, logText: 'No events.' };
    }

    const segments = buildQuerySegments(chat, CONFIG.QUERY_MSG_COUNT, !!options.excludeLastAi);

    let queryVector, weights;
    try {
        const result = await embedWeightedQuery(segments, vectorConfig);
        queryVector = result?.vector;
        weights = result?.weights;
    } catch (e) {
        xbLog.error(MODULE_ID, '查询向量生成失败', e);
        return { events: [], chunks: [], elapsed: Math.round(performance.now() - T0), logText: 'Query embedding failed.' };
    }

    if (!queryVector?.length) {
        return { events: [], chunks: [], elapsed: Math.round(performance.now() - T0), logText: 'Empty query vector.' };
    }

    const lexicon = buildEntityLexicon(store, allEvents);
    const queryEntities = extractEntities([queryText, ...segments].join('\n'), lexicon);

    const [chunkResults, eventResults] = await Promise.all([
        searchChunks(queryVector, vectorConfig),
        searchEvents(queryVector, allEvents, vectorConfig, store, queryEntities),
    ]);

    const elapsed = Math.round(performance.now() - T0);
    const logText = formatRecallLog({ elapsed, queryText, segments, weights, chunkResults, eventResults, allEvents, queryEntities });

    console.group('%c[Recall]', 'color: #7c3aed; font-weight: bold');
    console.log(`Elapsed: ${elapsed}ms | Entities: ${queryEntities.join(', ') || '(none)'}`);
    console.log(`L1: ${chunkResults.length} | L2: ${eventResults.length}/${allEvents.length}`);
    console.groupEnd();

    return { events: eventResults, chunks: chunkResults, elapsed, logText };
}

export function buildQueryText(chat, count = 2, excludeLastAi = false) {
    if (!chat?.length) return '';

    let messages = chat;
    if (excludeLastAi && messages.length > 0 && !messages[messages.length - 1]?.is_user) {
        messages = messages.slice(0, -1);
    }

    return messages.slice(-count).map(m => {
        const text = stripNoise(m.mes);
        const speaker = m.name || (m.is_user ? '用户' : '角色');
        return `${speaker}: ${text.slice(0, 500)}`;
    }).filter(Boolean).join('\n');
}