Zero-darkbox query updates and tokenizer improvements
This commit is contained in:
@@ -4,9 +4,10 @@
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// 职责:
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// 1. 对 L0 atoms + L1 chunks + L2 events 建立词法索引
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// 2. 提供词法检索接口(专名精确匹配兜底)
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// 3. 惰性构建 + 缓存失效机制
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// 3. 惰性构建 + 异步预热 + 缓存失效机制
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//
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// 索引存储:纯内存(不持久化)
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// 分词器:统一使用 tokenizer.js(结巴 + 实体保护 + 降级)
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// 重建时机:CHAT_CHANGED / L0提取完成 / L2总结完成
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// ═══════════════════════════════════════════════════════════════════════════
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@@ -16,6 +17,7 @@ import { getSummaryStore } from '../../data/store.js';
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import { getStateAtoms } from '../storage/state-store.js';
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import { getAllChunks } from '../storage/chunk-store.js';
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import { xbLog } from '../../../../core/debug-core.js';
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import { tokenizeForIndex } from '../utils/tokenizer.js';
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const MODULE_ID = 'lexical-index';
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@@ -23,9 +25,20 @@ const MODULE_ID = 'lexical-index';
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// 缓存
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// ─────────────────────────────────────────────────────────────────────────
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/** @type {MiniSearch|null} */
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let cachedIndex = null;
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/** @type {string|null} */
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let cachedChatId = null;
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let cachedFingerprint = null; // atoms.length + chunks.length + events.length 的简单指纹
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/** @type {string|null} 数据指纹(atoms + chunks + events 数量) */
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let cachedFingerprint = null;
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/** @type {boolean} 是否正在构建 */
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let building = false;
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/** @type {Promise<MiniSearch|null>|null} 当前构建 Promise(防重入) */
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let buildPromise = null;
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// ─────────────────────────────────────────────────────────────────────────
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// 工具函数
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@@ -43,7 +56,7 @@ function cleanSummary(summary) {
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}
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/**
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* 计算缓存指纹(用于判断是否需要重建)
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* 计算缓存指纹
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* @param {number} atomCount
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* @param {number} chunkCount
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* @param {number} eventCount
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@@ -53,39 +66,27 @@ function computeFingerprint(atomCount, chunkCount, eventCount) {
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return `${atomCount}:${chunkCount}:${eventCount}`;
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}
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/**
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* 让出主线程(避免长时间阻塞 UI)
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* @returns {Promise<void>}
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*/
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function yieldToMain() {
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return new Promise(resolve => setTimeout(resolve, 0));
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}
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// ─────────────────────────────────────────────────────────────────────────
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// 索引构建
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// 文档收集
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// ─────────────────────────────────────────────────────────────────────────
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/**
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* 构建 MiniSearch 索引
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*
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* 索引三类文档:
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* - L0 atoms: { id: atomId, type: 'atom', floor, text: semantic }
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* - L1 chunks: { id: chunkId, type: 'chunk', floor, text: chunk.text }
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* - L2 events: { id: eventId, type: 'event', floor: null, text: title + participants + summary }
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* 收集所有待索引文档
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*
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* @param {object[]} atoms - getStateAtoms() 返回值
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* @param {object[]} chunks - getAllChunks(chatId) 返回值
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* @param {object[]} events - store.json.events
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* @returns {MiniSearch}
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* @returns {object[]} 文档数组
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*/
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export function buildLexicalIndex(atoms, chunks, events) {
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const T0 = performance.now();
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const index = new MiniSearch({
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fields: ['text'],
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storeFields: ['type', 'floor'],
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idField: 'id',
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searchOptions: {
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boost: { text: 1 },
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fuzzy: 0.2,
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prefix: true,
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},
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// 中文友好的 tokenizer:按字符 bigram + 空格/标点分词
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tokenize: chineseTokenize,
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});
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function collectDocuments(atoms, chunks, events) {
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const docs = [];
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// L0 atoms
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@@ -129,72 +130,58 @@ export function buildLexicalIndex(atoms, chunks, events) {
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});
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}
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if (docs.length > 0) {
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index.addAll(docs);
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}
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const elapsed = Math.round(performance.now() - T0);
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xbLog.info(MODULE_ID, `索引构建完成: ${docs.length} 文档 (atoms=${atoms?.length || 0}, chunks=${chunks?.length || 0}, events=${events?.length || 0}) ${elapsed}ms`);
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return index;
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return docs;
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}
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// ─────────────────────────────────────────────────────────────────────────
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// 中文 Tokenizer
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// 索引构建(分片,不阻塞主线程)
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// ─────────────────────────────────────────────────────────────────────────
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/** 每批添加的文档数 */
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const BUILD_BATCH_SIZE = 500;
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/**
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* 中文友好的分词器
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* 构建 MiniSearch 索引(分片异步)
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*
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* 策略:
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* 1. 连续中文字符 → 滑动 bigram("黄英梅" → "黄英", "英梅")
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* 2. 连续非中文字符 → 按空格/标点分割
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* 3. 保留完整中文词(2-4字)作为额外 token
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*
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* @param {string} text
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* @returns {string[]}
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* @param {object[]} docs - 文档数组
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* @returns {Promise<MiniSearch>}
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*/
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function chineseTokenize(text) {
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if (!text) return [];
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async function buildIndexAsync(docs) {
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const T0 = performance.now();
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const tokens = [];
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const s = String(text).toLowerCase();
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const index = new MiniSearch({
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fields: ['text'],
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storeFields: ['type', 'floor'],
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idField: 'id',
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searchOptions: {
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boost: { text: 1 },
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fuzzy: 0.2,
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prefix: true,
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},
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tokenize: tokenizeForIndex,
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});
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// 分离中文段和非中文段
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const segments = s.split(/([\u4e00-\u9fff]+)/g);
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if (!docs.length) {
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return index;
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}
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for (const seg of segments) {
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if (!seg) continue;
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// 分片添加,每批 BUILD_BATCH_SIZE 条后让出主线程
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for (let i = 0; i < docs.length; i += BUILD_BATCH_SIZE) {
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const batch = docs.slice(i, i + BUILD_BATCH_SIZE);
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index.addAll(batch);
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// 中文段:bigram + 完整段(如果 2-6 字)
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if (/^[\u4e00-\u9fff]+$/.test(seg)) {
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// 完整段作为一个 token(如果长度合适)
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if (seg.length >= 2 && seg.length <= 6) {
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tokens.push(seg);
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}
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// bigram
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for (let i = 0; i < seg.length - 1; i++) {
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tokens.push(seg.slice(i, i + 2));
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}
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// trigram(对 3+ 字的段)
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for (let i = 0; i < seg.length - 2; i++) {
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tokens.push(seg.slice(i, i + 3));
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}
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} else {
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// 非中文段:按空格/标点分割
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const words = seg.split(/[\s\-_.,;:!?'"()[\]{}<>/\\|@#$%^&*+=~`]+/);
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for (const w of words) {
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const trimmed = w.trim();
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if (trimmed.length >= 2) {
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tokens.push(trimmed);
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}
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}
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// 非最后一批时让出主线程
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if (i + BUILD_BATCH_SIZE < docs.length) {
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await yieldToMain();
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}
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}
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return tokens;
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const elapsed = Math.round(performance.now() - T0);
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xbLog.info(MODULE_ID,
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`索引构建完成: ${docs.length} 文档 (${elapsed}ms)`
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);
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return index;
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}
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// ─────────────────────────────────────────────────────────────────────────
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@@ -247,6 +234,8 @@ export function searchLexicalIndex(index, terms) {
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fuzzy: 0.2,
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prefix: true,
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combineWith: 'OR',
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// 使用与索引相同的分词器
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tokenize: tokenizeForIndex,
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});
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} catch (e) {
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xbLog.warn(MODULE_ID, '检索失败', e);
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@@ -305,22 +294,17 @@ export function searchLexicalIndex(index, terms) {
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}
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// ─────────────────────────────────────────────────────────────────────────
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// 惰性缓存管理
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// 内部构建流程(收集数据 + 构建索引)
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// ─────────────────────────────────────────────────────────────────────────
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/**
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* 获取词法索引(惰性构建 + 缓存)
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* 收集数据并构建索引
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*
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* 如果缓存有效则直接返回;否则自动构建。
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* 缓存失效条件:chatId 变化 / 数据指纹变化 / 手动 invalidate
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*
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* @returns {Promise<MiniSearch>}
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* @param {string} chatId
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* @returns {Promise<{index: MiniSearch, fingerprint: string}>}
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*/
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export async function getLexicalIndex() {
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const { chatId } = getContext();
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if (!chatId) return null;
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// 收集当前数据
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async function collectAndBuild(chatId) {
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// 收集数据
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const atoms = getStateAtoms() || [];
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const store = getSummaryStore();
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const events = store?.json?.events || [];
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@@ -334,30 +318,118 @@ export async function getLexicalIndex() {
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const fp = computeFingerprint(atoms.length, chunks.length, events.length);
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// 缓存命中
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// 检查是否在收集过程中缓存已被其他调用更新
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if (cachedIndex && cachedChatId === chatId && cachedFingerprint === fp) {
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return { index: cachedIndex, fingerprint: fp };
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}
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// 收集文档
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const docs = collectDocuments(atoms, chunks, events);
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// 异步分片构建
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const index = await buildIndexAsync(docs);
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return { index, fingerprint: fp };
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}
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// ─────────────────────────────────────────────────────────────────────────
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// 公开接口:getLexicalIndex(惰性获取)
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// ─────────────────────────────────────────────────────────────────────────
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/**
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* 获取词法索引(惰性构建 + 缓存)
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*
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* 如果缓存有效则直接返回;否则自动构建。
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* 如果正在构建中,等待构建完成。
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*
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* @returns {Promise<MiniSearch|null>}
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*/
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export async function getLexicalIndex() {
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const { chatId } = getContext();
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if (!chatId) return null;
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// 快速路径:如果缓存存在且 chatId 未变,则直接命中
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// 指纹校验放到构建流程中完成,避免为指纹而额外读一次 IndexedDB
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if (cachedIndex && cachedChatId === chatId && cachedFingerprint) {
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return cachedIndex;
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}
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// 重建
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xbLog.info(MODULE_ID, `缓存失效,重建索引 (chatId=${chatId.slice(0, 8)}, fp=${fp})`);
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// 正在构建中,等待结果
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if (building && buildPromise) {
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try {
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await buildPromise;
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if (cachedIndex && cachedChatId === chatId && cachedFingerprint) {
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return cachedIndex;
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}
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} catch {
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// 构建失败,继续往下重建
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}
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}
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const index = buildLexicalIndex(atoms, chunks, events);
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// 需要重建(指纹将在 collectAndBuild 内部计算并写入缓存)
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xbLog.info(MODULE_ID, `缓存失效,重建索引 (chatId=${chatId.slice(0, 8)})`);
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cachedIndex = index;
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cachedChatId = chatId;
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cachedFingerprint = fp;
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building = true;
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buildPromise = collectAndBuild(chatId);
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return index;
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try {
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const { index, fingerprint } = await buildPromise;
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// 原子替换缓存
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cachedIndex = index;
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cachedChatId = chatId;
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cachedFingerprint = fingerprint;
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return index;
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} catch (e) {
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xbLog.error(MODULE_ID, '索引构建失败', e);
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return null;
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} finally {
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building = false;
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buildPromise = null;
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}
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}
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// ─────────────────────────────────────────────────────────────────────────
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// 公开接口:warmupIndex(异步预建)
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// ─────────────────────────────────────────────────────────────────────────
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/**
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* 使缓存失效(下次 getLexicalIndex 时自动重建)
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* 异步预建索引
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*
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* 在 CHAT_CHANGED 时调用,后台构建索引。
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* 不阻塞调用方,不返回结果。
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* 构建完成后缓存自动更新,后续 getLexicalIndex() 直接命中。
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*
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* 调用时机:
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* - handleChatChanged(实体注入后)
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* - L0 提取完成
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* - L2 总结完成
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*/
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export function warmupIndex() {
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const { chatId } = getContext();
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if (!chatId) return;
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// 已在构建中,不重复触发
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if (building) return;
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// fire-and-forget
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getLexicalIndex().catch(e => {
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xbLog.warn(MODULE_ID, '预热索引失败', e);
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});
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}
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// ─────────────────────────────────────────────────────────────────────────
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// 公开接口:invalidateLexicalIndex(缓存失效)
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// ─────────────────────────────────────────────────────────────────────────
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/**
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* 使缓存失效(下次 getLexicalIndex / warmupIndex 时自动重建)
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*
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* 调用时机:
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* - CHAT_CHANGED
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* - L0 提取完成(handleAnchorGenerate 完成后)
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* - L2 总结完成(onComplete 回调中)
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* - L0 提取完成
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* - L2 总结完成
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*/
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export function invalidateLexicalIndex() {
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if (cachedIndex) {
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@@ -16,6 +16,11 @@ export function createMetrics() {
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query: {
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buildTime: 0,
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refineTime: 0,
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lengths: {
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v0Chars: 0,
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v1Chars: null, // null = NA
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rerankChars: 0,
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},
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},
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// Anchor (L0 StateAtoms) - 语义锚点
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@@ -177,6 +182,13 @@ export function formatMetricsLog(metrics) {
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lines.push('════════════════════════════════════════');
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lines.push('');
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// Query Length
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lines.push('[Query Length] 查询长度');
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lines.push(`├─ query_v0_chars: ${m.query?.lengths?.v0Chars ?? 0}`);
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lines.push(`├─ query_v1_chars: ${m.query?.lengths?.v1Chars == null ? 'NA' : m.query.lengths.v1Chars}`);
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lines.push(`└─ rerank_query_chars: ${m.query?.lengths?.rerankChars ?? 0}`);
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lines.push('');
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// Query Build
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lines.push('[Query] 查询构建');
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lines.push(`├─ build_time: ${m.query.buildTime}ms`);
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@@ -12,36 +12,18 @@ import { getContext } from '../../../../../../../extensions.js';
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import { buildEntityLexicon, buildDisplayNameMap, extractEntitiesFromText } from './entity-lexicon.js';
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import { getSummaryStore } from '../../data/store.js';
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import { filterText } from '../utils/text-filter.js';
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import { tokenizeForIndex as tokenizerTokenizeForIndex } from '../utils/tokenizer.js';
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// ─────────────────────────────────────────────────────────────────────────
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// 常量
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// ─────────────────────────────────────────────────────────────────────────
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const DIALOGUE_MAX_CHARS = 400;
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const PENDING_MAX_CHARS = 400;
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const MEMORY_HINT_MAX_CHARS = 100;
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// Zero-darkbox policy:
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// - No internal truncation. We rely on model-side truncation / provider limits.
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// - If provider rejects due to length, we fail loudly and degrade explicitly.
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const MEMORY_HINT_ATOMS_MAX = 5;
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const MEMORY_HINT_EVENTS_MAX = 3;
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const RERANK_QUERY_MAX_CHARS = 500;
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const RERANK_SNIPPET_CHARS = 150;
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const LEXICAL_TERMS_MAX = 10;
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const LEXICAL_TERM_MIN_LEN = 2;
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const LEXICAL_TERM_MAX_LEN = 6;
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// 中文停用词(高频无意义词)
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const STOP_WORDS = new Set([
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'的', '了', '在', '是', '我', '有', '和', '就', '不', '人',
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||||
'都', '一', '一个', '上', '也', '很', '到', '说', '要', '去',
|
||||
'你', '会', '着', '没有', '看', '好', '自己', '这', '他', '她',
|
||||
'它', '吗', '什么', '那', '里', '来', '吧', '呢', '啊', '哦',
|
||||
'嗯', '呀', '哈', '嘿', '喂', '哎', '唉', '哇', '呃', '嘛',
|
||||
'把', '被', '让', '给', '从', '向', '对', '跟', '比', '但',
|
||||
'而', '或', '如果', '因为', '所以', '虽然', '但是', '然后',
|
||||
'可以', '这样', '那样', '怎么', '为什么', '什么样', '哪里',
|
||||
'时候', '现在', '已经', '还是', '只是', '可能', '应该', '知道',
|
||||
'觉得', '开始', '一下', '一些', '这个', '那个', '他们', '我们',
|
||||
'你们', '自己', '起来', '出来', '进去', '回来', '过来', '下去',
|
||||
]);
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────────────
|
||||
// 工具函数
|
||||
@@ -65,10 +47,7 @@ function cleanMessageText(text) {
|
||||
* @param {number} maxLen
|
||||
* @returns {string}
|
||||
*/
|
||||
function truncate(text, maxLen) {
|
||||
if (!text || text.length <= maxLen) return text || '';
|
||||
return text.slice(0, maxLen) + '…';
|
||||
}
|
||||
// truncate removed by design (zero-darkbox)
|
||||
|
||||
/**
|
||||
* 清理事件摘要(移除楼层标记)
|
||||
@@ -84,8 +63,7 @@ function cleanSummary(summary) {
|
||||
/**
|
||||
* 从文本中提取高频实词(用于词法检索)
|
||||
*
|
||||
* 策略:按中文字符边界 + 空格/标点分词,取长度 2-6 的片段
|
||||
* 过滤停用词,按频率排序
|
||||
* 使用统一分词器(结巴 + 实体保护 + 停用词过滤),按频率排序
|
||||
*
|
||||
* @param {string} text - 清洗后的文本
|
||||
* @param {number} maxTerms - 最大词数
|
||||
@@ -94,15 +72,15 @@ function cleanSummary(summary) {
|
||||
function extractKeyTerms(text, maxTerms = LEXICAL_TERMS_MAX) {
|
||||
if (!text) return [];
|
||||
|
||||
// 提取连续中文片段 + 英文单词
|
||||
const segments = text.match(/[\u4e00-\u9fff]{2,6}|[a-zA-Z]{3,}/g) || [];
|
||||
// 使用统一分词器(索引用,不去重,保留词频)
|
||||
const tokens = tokenizerTokenizeForIndex(text);
|
||||
|
||||
// 统计词频
|
||||
const freq = new Map();
|
||||
for (const seg of segments) {
|
||||
const s = seg.toLowerCase();
|
||||
if (s.length < LEXICAL_TERM_MIN_LEN || s.length > LEXICAL_TERM_MAX_LEN) continue;
|
||||
if (STOP_WORDS.has(s)) continue;
|
||||
freq.set(s, (freq.get(s) || 0) + 1);
|
||||
for (const token of tokens) {
|
||||
const key = String(token || '').toLowerCase();
|
||||
if (!key) continue;
|
||||
freq.set(key, (freq.get(key) || 0) + 1);
|
||||
}
|
||||
|
||||
return Array.from(freq.entries())
|
||||
@@ -160,8 +138,9 @@ export function buildQueryBundle(lastMessages, pendingUserMessage, store = null,
|
||||
const clean = cleanMessageText(m.mes || '');
|
||||
|
||||
if (clean) {
|
||||
// ★ 修复 A:不使用楼层号,embedding 模型不需要
|
||||
dialogueLines.push(`${speaker}: ${truncate(clean, DIALOGUE_MAX_CHARS)}`);
|
||||
// 不使用楼层号,embedding 模型不需要
|
||||
// 不截断,零暗箱
|
||||
dialogueLines.push(`${speaker}: ${clean}`);
|
||||
allCleanText.push(clean);
|
||||
}
|
||||
}
|
||||
@@ -191,30 +170,15 @@ export function buildQueryBundle(lastMessages, pendingUserMessage, store = null,
|
||||
}
|
||||
|
||||
if (pendingClean) {
|
||||
queryParts.push(`[PENDING_USER]\n${truncate(pendingClean, PENDING_MAX_CHARS)}`);
|
||||
// 不截断,零暗箱
|
||||
queryParts.push(`[PENDING_USER]\n${pendingClean}`);
|
||||
}
|
||||
|
||||
const queryText_v0 = queryParts.join('\n\n');
|
||||
|
||||
// 6. 构建 rerankQuery(短版)
|
||||
const rerankParts = [];
|
||||
|
||||
if (focusEntities.length > 0) {
|
||||
rerankParts.push(focusEntities.join(' '));
|
||||
}
|
||||
|
||||
for (const m of (lastMessages || [])) {
|
||||
const clean = cleanMessageText(m.mes || '');
|
||||
if (clean) {
|
||||
rerankParts.push(truncate(clean, RERANK_SNIPPET_CHARS));
|
||||
}
|
||||
}
|
||||
|
||||
if (pendingClean) {
|
||||
rerankParts.push(truncate(pendingClean, RERANK_SNIPPET_CHARS));
|
||||
}
|
||||
|
||||
const rerankQuery = truncate(rerankParts.join('\n'), RERANK_QUERY_MAX_CHARS);
|
||||
// 6. rerankQuery 与 embedding query 同源(零暗箱)
|
||||
// 后续 refine 会把它升级为与 queryText_v1 同源。
|
||||
const rerankQuery = queryText_v0;
|
||||
|
||||
// 7. 构建 lexicalTerms
|
||||
const entityTerms = focusEntities.map(e => e.toLowerCase());
|
||||
@@ -265,7 +229,8 @@ export function refineQueryBundle(bundle, anchorHits, eventHits) {
|
||||
for (const hit of topAnchors) {
|
||||
const semantic = hit.atom?.semantic || '';
|
||||
if (semantic) {
|
||||
hints.push(truncate(semantic, MEMORY_HINT_MAX_CHARS));
|
||||
// 不截断,零暗箱
|
||||
hints.push(semantic);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -279,13 +244,15 @@ export function refineQueryBundle(bundle, anchorHits, eventHits) {
|
||||
? `${title}: ${summary}`
|
||||
: title || summary;
|
||||
if (line) {
|
||||
hints.push(truncate(line, MEMORY_HINT_MAX_CHARS));
|
||||
// 不截断,零暗箱
|
||||
hints.push(line);
|
||||
}
|
||||
}
|
||||
|
||||
// 3. 构建 queryText_v1
|
||||
// 3. 构建 queryText_v1(Hints 前置,最优先)
|
||||
if (hints.length > 0) {
|
||||
bundle.queryText_v1 = bundle.queryText_v0 + `\n\n[MEMORY_HINTS]\n${hints.join('\n')}`;
|
||||
const hintText = `[MEMORY_HINTS]\n${hints.join('\n')}`;
|
||||
bundle.queryText_v1 = hintText + `\n\n` + bundle.queryText_v0;
|
||||
} else {
|
||||
bundle.queryText_v1 = bundle.queryText_v0;
|
||||
}
|
||||
@@ -314,17 +281,8 @@ export function refineQueryBundle(bundle, anchorHits, eventHits) {
|
||||
}
|
||||
}
|
||||
|
||||
// 5. 增强 rerankQuery
|
||||
if (hints.length > 0) {
|
||||
const hintKeywords = extractKeyTerms(hints.join(' '), 5);
|
||||
if (hintKeywords.length > 0) {
|
||||
const addition = hintKeywords.join(' ');
|
||||
bundle.rerankQuery = truncate(
|
||||
bundle.rerankQuery + '\n' + addition,
|
||||
RERANK_QUERY_MAX_CHARS
|
||||
);
|
||||
}
|
||||
}
|
||||
// 5. rerankQuery 与最终 query 同源(零暗箱)
|
||||
bundle.rerankQuery = bundle.queryText_v1 || bundle.queryText_v0;
|
||||
|
||||
// 6. 增强 lexicalTerms
|
||||
if (hints.length > 0) {
|
||||
|
||||
@@ -782,6 +782,14 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
|
||||
metrics.query.buildTime = Math.round(performance.now() - T_Build_Start);
|
||||
metrics.anchor.focusEntities = bundle.focusEntities;
|
||||
|
||||
// Query lengths (v0 available here)
|
||||
if (metrics.query?.lengths) {
|
||||
metrics.query.lengths.v0Chars = String(bundle.queryText_v0 || '').length;
|
||||
// v1 not built yet
|
||||
metrics.query.lengths.v1Chars = null;
|
||||
metrics.query.lengths.rerankChars = String(bundle.rerankQuery || bundle.queryText_v0 || '').length;
|
||||
}
|
||||
|
||||
xbLog.info(MODULE_ID,
|
||||
`Query Build: focus=[${bundle.focusEntities.join(',')}] lexTerms=[${bundle.lexicalTerms.slice(0, 5).join(',')}]`
|
||||
);
|
||||
@@ -841,6 +849,12 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
|
||||
// 更新 focusEntities(refinement 可能扩展了)
|
||||
metrics.anchor.focusEntities = bundle.focusEntities;
|
||||
|
||||
// Query lengths (v1/rerank updated here)
|
||||
if (metrics.query?.lengths) {
|
||||
metrics.query.lengths.v1Chars = bundle.queryText_v1 == null ? null : String(bundle.queryText_v1).length;
|
||||
metrics.query.lengths.rerankChars = String(bundle.rerankQuery || bundle.queryText_v1 || bundle.queryText_v0 || '').length;
|
||||
}
|
||||
|
||||
xbLog.info(MODULE_ID,
|
||||
`Refinement: focus=[${bundle.focusEntities.join(',')}] hasV1=${!!bundle.queryText_v1} (${metrics.query.refineTime}ms)`
|
||||
);
|
||||
|
||||
Reference in New Issue
Block a user