342 lines
13 KiB
JavaScript
342 lines
13 KiB
JavaScript
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// ═══════════════════════════════════════════════════════════════════════════
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// query-builder.js - 确定性查询构建器（无 LLM）
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//
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// 职责：
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// 1. 从最近消息 + 实体词典构建 QueryBundle_v0
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// 2. 用第一轮召回结果增强为 QueryBundle_v1
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//
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// 不负责：向量化、检索、rerank
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// ═══════════════════════════════════════════════════════════════════════════
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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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// ─────────────────────────────────────────────────────────────────────────
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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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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',
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'你', '会', '着', '没有', '看', '好', '自己', '这', '他', '她',
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'它', '吗', '什么', '那', '里', '来', '吧', '呢', '啊', '哦',
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'嗯', '呀', '哈', '嘿', '喂', '哎', '唉', '哇', '呃', '嘛',
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'把', '被', '让', '给', '从', '向', '对', '跟', '比', '但',
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'而', '或', '如果', '因为', '所以', '虽然', '但是', '然后',
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'可以', '这样', '那样', '怎么', '为什么', '什么样', '哪里',
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'时候', '现在', '已经', '还是', '只是', '可能', '应该', '知道',
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'觉得', '开始', '一下', '一些', '这个', '那个', '他们', '我们',
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'你们', '自己', '起来', '出来', '进去', '回来', '过来', '下去',
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]);
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// ─────────────────────────────────────────────────────────────────────────
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// 工具函数
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// ─────────────────────────────────────────────────────────────────────────
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/**
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* 清洗消息文本（与 chunk-builder / recall 保持一致）
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* @param {string} text
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* @returns {string}
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*/
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function cleanMessageText(text) {
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return filterText(text)
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.replace(/\[tts:[^\]]*\]/gi, '')
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.replace(/<state>[\s\S]*?<\/state>/gi, '')
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.trim();
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}
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/**
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* 截断文本到指定长度
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* @param {string} text
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* @param {number} maxLen
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* @returns {string}
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*/
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function truncate(text, maxLen) {
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if (!text || text.length <= maxLen) return text || '';
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return text.slice(0, maxLen) + '…';
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}
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/**
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* 清理事件摘要（移除楼层标记）
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* @param {string} summary
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* @returns {string}
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*/
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function cleanSummary(summary) {
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return String(summary || '')
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.replace(/\s*\(#\d+(?:-\d+)?\)\s*$/, '')
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.trim();
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}
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/**
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* 从文本中提取高频实词（用于词法检索）
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*
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* 策略：按中文字符边界 + 空格/标点分词，取长度 2-6 的片段
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* 过滤停用词，按频率排序
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*
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* @param {string} text - 清洗后的文本
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* @param {number} maxTerms - 最大词数
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* @returns {string[]}
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*/
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function extractKeyTerms(text, maxTerms = LEXICAL_TERMS_MAX) {
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if (!text) return [];
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// 提取连续中文片段 + 英文单词
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const segments = text.match(/[\u4e00-\u9fff]{2,6}|[a-zA-Z]{3,}/g) || [];
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const freq = new Map();
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for (const seg of segments) {
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const s = seg.toLowerCase();
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if (s.length < LEXICAL_TERM_MIN_LEN || s.length > LEXICAL_TERM_MAX_LEN) continue;
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if (STOP_WORDS.has(s)) continue;
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freq.set(s, (freq.get(s) || 0) + 1);
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}
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return Array.from(freq.entries())
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.sort((a, b) => b[1] - a[1])
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.slice(0, maxTerms)
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.map(([term]) => term);
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}
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// ─────────────────────────────────────────────────────────────────────────
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// QueryBundle 类型定义（JSDoc）
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// ─────────────────────────────────────────────────────────────────────────
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/**
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* @typedef {object} QueryBundle
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* @property {string[]} focusEntities - 焦点实体（原词形，已排除 name1）
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* @property {string} queryText_v0 - 第一轮查询文本
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* @property {string|null} queryText_v1 - 第二轮查询文本（refinement 后填充）
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* @property {string} rerankQuery - rerank 用的短查询
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* @property {string[]} lexicalTerms - MiniSearch 查询词
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* @property {Set<string>} _lexicon - 实体词典（内部使用）
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* @property {Map<string, string>} _displayMap - 标准化→原词形映射（内部使用）
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*/
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// ─────────────────────────────────────────────────────────────────────────
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// 阶段 1：构建 QueryBundle_v0
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// ─────────────────────────────────────────────────────────────────────────
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/**
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* 构建初始查询包
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*
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* @param {object[]} lastMessages - 最近 K=2 条消息
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* @param {string|null} pendingUserMessage - 用户刚输入但未进 chat 的消息
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* @param {object|null} store - getSummaryStore() 返回值（可选，内部会自动获取）
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* @param {object|null} context - { name1, name2 }（可选，内部会自动获取）
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* @returns {QueryBundle}
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*/
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export function buildQueryBundle(lastMessages, pendingUserMessage, store = null, context = null) {
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// 自动获取 store 和 context
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if (!store) store = getSummaryStore();
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if (!context) {
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const ctx = getContext();
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context = { name1: ctx.name1, name2: ctx.name2 };
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}
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// 1. 构建实体词典
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const lexicon = buildEntityLexicon(store, context);
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const displayMap = buildDisplayNameMap(store, context);
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// 2. 清洗消息文本
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const dialogueLines = [];
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const allCleanText = [];
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for (const m of (lastMessages || [])) {
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const speaker = m.is_user ? (context.name1 || '用户') : (m.name || context.name2 || '角色');
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const clean = cleanMessageText(m.mes || '');
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if (clean) {
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// ★ 修复 A：不使用楼层号，embedding 模型不需要
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dialogueLines.push(`${speaker}: ${truncate(clean, DIALOGUE_MAX_CHARS)}`);
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allCleanText.push(clean);
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}
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}
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// 3. 处理 pendingUserMessage
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let pendingClean = '';
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if (pendingUserMessage) {
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pendingClean = cleanMessageText(pendingUserMessage);
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if (pendingClean) {
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allCleanText.push(pendingClean);
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}
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}
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// 4. 提取焦点实体
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const combinedText = allCleanText.join(' ');
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const focusEntities = extractEntitiesFromText(combinedText, lexicon, displayMap);
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// 5. 构建 queryText_v0
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const queryParts = [];
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if (focusEntities.length > 0) {
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queryParts.push(`[ENTITIES]\n${focusEntities.join('\n')}`);
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}
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if (dialogueLines.length > 0) {
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queryParts.push(`[DIALOGUE]\n${dialogueLines.join('\n')}`);
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}
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if (pendingClean) {
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queryParts.push(`[PENDING_USER]\n${truncate(pendingClean, PENDING_MAX_CHARS)}`);
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}
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const queryText_v0 = queryParts.join('\n\n');
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// 6. 构建 rerankQuery（短版）
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const rerankParts = [];
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if (focusEntities.length > 0) {
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rerankParts.push(focusEntities.join(' '));
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}
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for (const m of (lastMessages || [])) {
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const clean = cleanMessageText(m.mes || '');
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if (clean) {
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rerankParts.push(truncate(clean, RERANK_SNIPPET_CHARS));
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}
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}
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if (pendingClean) {
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rerankParts.push(truncate(pendingClean, RERANK_SNIPPET_CHARS));
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}
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const rerankQuery = truncate(rerankParts.join('\n'), RERANK_QUERY_MAX_CHARS);
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// 7. 构建 lexicalTerms
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const entityTerms = focusEntities.map(e => e.toLowerCase());
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const textTerms = extractKeyTerms(combinedText);
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// 合并去重：实体优先
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const termSet = new Set(entityTerms);
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for (const t of textTerms) {
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if (termSet.size >= LEXICAL_TERMS_MAX) break;
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termSet.add(t);
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}
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const lexicalTerms = Array.from(termSet);
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return {
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focusEntities,
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queryText_v0,
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queryText_v1: null,
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rerankQuery,
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lexicalTerms,
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_lexicon: lexicon,
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_displayMap: displayMap,
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};
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}
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// ─────────────────────────────────────────────────────────────────────────
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// 阶段 3：Query Refinement（用第一轮召回结果增强）
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// ─────────────────────────────────────────────────────────────────────────
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/**
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* 用第一轮召回结果增强 QueryBundle
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*
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* 原地修改 bundle：
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* - queryText_v1 = queryText_v0 + [MEMORY_HINTS]
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* - focusEntities 可能扩展（从 anchorHits 的 subject/object 中补充）
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* - rerankQuery 追加 memory hints 关键词
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* - lexicalTerms 追加 memory hints 关键词
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*
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* @param {QueryBundle} bundle - 原始查询包
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* @param {object[]} anchorHits - 第一轮 L0 命中（按相似度降序）
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* @param {object[]} eventHits - 第一轮 L2 命中（按相似度降序）
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*/
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export function refineQueryBundle(bundle, anchorHits, eventHits) {
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const hints = [];
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// 1. 从 top anchorHits 提取 memory hints
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const topAnchors = (anchorHits || []).slice(0, MEMORY_HINT_ATOMS_MAX);
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for (const hit of topAnchors) {
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const semantic = hit.atom?.semantic || '';
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if (semantic) {
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hints.push(truncate(semantic, MEMORY_HINT_MAX_CHARS));
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}
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}
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// 2. 从 top eventHits 提取 memory hints
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const topEvents = (eventHits || []).slice(0, MEMORY_HINT_EVENTS_MAX);
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for (const hit of topEvents) {
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const ev = hit.event || {};
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const title = String(ev.title || '').trim();
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const summary = cleanSummary(ev.summary);
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const line = title && summary
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? `${title}: ${summary}`
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: title || summary;
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if (line) {
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hints.push(truncate(line, MEMORY_HINT_MAX_CHARS));
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}
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}
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// 3. 构建 queryText_v1
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if (hints.length > 0) {
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bundle.queryText_v1 = bundle.queryText_v0 + `\n\n[MEMORY_HINTS]\n${hints.join('\n')}`;
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} else {
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bundle.queryText_v1 = bundle.queryText_v0;
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}
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// 4. 从 anchorHits 补充 focusEntities
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const lexicon = bundle._lexicon;
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const displayMap = bundle._displayMap;
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if (lexicon && topAnchors.length > 0) {
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const existingSet = new Set(bundle.focusEntities.map(e => e.toLowerCase()));
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for (const hit of topAnchors) {
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const atom = hit.atom;
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if (!atom) continue;
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// 检查 subject 和 object
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for (const field of [atom.subject, atom.object]) {
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if (!field) continue;
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const norm = String(field).normalize('NFKC').replace(/[\u200B-\u200D\uFEFF]/g, '').trim().toLowerCase();
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if (norm.length >= 2 && lexicon.has(norm) && !existingSet.has(norm)) {
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existingSet.add(norm);
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const display = displayMap?.get(norm) || field;
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bundle.focusEntities.push(display);
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}
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}
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}
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}
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// 5. 增强 rerankQuery
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if (hints.length > 0) {
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|||
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|
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
|
|||
|
|
);
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// 6. 增强 lexicalTerms
|
|||
|
|
if (hints.length > 0) {
|
|||
|
|
const hintTerms = extractKeyTerms(hints.join(' '), 5);
|
|||
|
|
const termSet = new Set(bundle.lexicalTerms);
|
|||
|
|
for (const t of hintTerms) {
|
|||
|
|
if (termSet.size >= LEXICAL_TERMS_MAX) break;
|
|||
|
|
if (!termSet.has(t)) {
|
|||
|
|
termSet.add(t);
|
|||
|
|
bundle.lexicalTerms.push(t);
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|