- Bridge server with LLM agent tool-use (search_kb, search_products, search_orders, search_inventory, escalate_human) - pgvector RAG knowledge base (95 FAQ chunks) - Auto opportunity creation in Twenty CRM - Auto follow-up task workflow - Chatwoot AgentBot integration (HMAC, webhook) - Docker compose infrastructure (PG18, Redis 8.8, node24-alpine) - Configuration templates (example files) - real secrets excluded via .gitignore
207 lines
8.4 KiB
JavaScript
207 lines
8.4 KiB
JavaScript
// 客服知识库向量化:读取 customer-service-kb/*.md,按 "### Q:" 问答块切分,
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// 用 SiliconFlow Qwen3-Embedding-0.6B(1024维) 生成向量,存入共享 postgres
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// products 库的 kb_docs 表,供 bridge 做 RAG 检索回答客户政策类问题。
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//
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// 切块策略(遵循知识库 README 建议):
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// - 每个文件先解析 YAML frontmatter(title/category/keywords/source)
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// - 每个 "### Q: ..." 到下一个 "### Q:" 之间为一个 chunk(含问题+答案)
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// - embedding 文本 = keywords + Q + A,提升召回
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// - 额外保留 title/category 便于过滤/引用
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//
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// 运行: node index_kb.js (PG_URL 默认 postgres://postgres:postgres@localhost:5432/products)
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const fs = require('fs');
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const path = require('path');
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const { Pool } = require('pg');
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const EMB = JSON.parse(fs.readFileSync(path.join(__dirname, 'kb.config.json'), 'utf8'));
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const MODEL = EMB.model || 'text-embedding-v4';
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const DIM = EMB.dim || 1024;
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const PG_URL = process.env.PG_URL || 'postgres://postgres:postgres@localhost:5432/products';
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const pool = new Pool({ connectionString: PG_URL });
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const KB_DIR = path.join(__dirname, '..', 'customer-service-kb');
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// ---------- 解析 frontmatter(支持多行 YAML 数组,如 source_url) ----------
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function parseFrontmatter(text) {
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const m = text.match(/^---\n([\s\S]*?)\n---\n([\s\S]*)$/);
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if (!m) return { meta: {}, body: text };
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const meta = {};
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const fm = m[1].split('\n');
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for (let i = 0; i < fm.length; i++) {
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const kv = fm[i].match(/^(\w+):\s*(.*)$/);
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if (!kv) continue;
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const key = kv[1];
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let v = kv[2].trim();
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if (v.startsWith('[')) {
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// 单行数组 [a, b, c]
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v = v.replace(/[\[\]]/g, '').split(',').map((s) => s.trim().replace(/^["']|["']$/g, '')).filter(Boolean);
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} else if (v === '') {
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// 多行数组:后续缩进的 - 开头行
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const arr = [];
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while (i + 1 < fm.length && /^\s+-\s+/.test(fm[i + 1])) {
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i++;
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let item = fm[i].replace(/^\s+-\s+/, '').trim().replace(/^["']|["']$/g, '');
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if (item) arr.push(item);
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}
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v = arr;
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} else {
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v = v.replace(/^["']|["']$/g, '');
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}
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meta[key] = v;
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}
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return { meta, body: m[2] };
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}
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// ---------- 按 Q: 切块 ----------
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function splitChunks(body) {
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// 去掉文件顶部的 "> 引用块"(章节说明),但保留答案里的内容
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const lines = body.split('\n');
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const chunks = [];
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let cur = null;
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let heading = null; // 文件内 "## " 一级标题(章节名)
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for (const line of lines) {
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const h2 = line.match(/^##\s+(.*)$/);
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if (h2) { heading = h2[1].trim(); continue; }
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const q = line.match(/^###\s+Q:\s*(.*)$/);
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if (q) {
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if (cur) chunks.push(cur);
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cur = { question: q[1].trim(), answer: '', section: heading || '' };
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} else if (cur) {
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cur.answer += line + '\n';
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}
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}
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if (cur) chunks.push(cur);
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// 清理答案首尾空白
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return chunks.map((c) => ({ ...c, answer: c.answer.trim() }));
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}
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// 章节 → 来源 URL 映射:dropshipping KB 各章节对应不同 faq 子页,
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// 避免"物流"问题附上 dropshipping 首页/about-us 等无关链接。
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const SECTION_URL_MAP = {
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'Value proposition': ['https://dropshipping.yehwang.com/', 'https://dropshipping.yehwang.com/about-us'],
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'Joining & requirements': ['https://dropshipping.yehwang.com/faq/register'],
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'How it works (4 steps)': ['https://dropshipping.yehwang.com/'],
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'Brands & catalog': ['https://dropshipping.yehwang.com/'],
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'Pricing & margins': ['https://dropshipping.yehwang.com/'],
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'Packaging & branding': ['https://dropshipping.yehwang.com/'],
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'Orders, minimum & products': ['https://dropshipping.yehwang.com/faq/order'],
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'Payment & VAT': ['https://dropshipping.yehwang.com/faq/payment'],
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'Shipping & delivery': ['https://dropshipping.yehwang.com/faq/shipment'],
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'Returns': ['https://dropshipping.yehwang.com/faq/returns'],
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};
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// ---------- embedding ----------
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async function embedText(text) {
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const resp = await fetch(`${EMB.baseURL}/embeddings`, {
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method: 'POST',
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headers: { Authorization: `Bearer ${EMB.apiKey}`, 'Content-Type': 'application/json' },
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body: JSON.stringify({ model: MODEL, input: text, encoding_format: 'float', dimensions: DIM }),
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});
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const d = await resp.json();
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if (!d.data || !d.data[0] || !d.data[0].embedding) {
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throw new Error('embedText err: ' + JSON.stringify(d).slice(0, 200));
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}
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return d.data[0].embedding;
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}
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const vecStr = (v) => `[${v.join(',')}]`;
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async function main() {
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// 1. 建表
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await pool.query(`CREATE EXTENSION IF NOT EXISTS vector`);
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await pool.query(`
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CREATE TABLE IF NOT EXISTS kb_docs (
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id SERIAL PRIMARY KEY,
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source_file TEXT,
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title TEXT,
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category TEXT,
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section TEXT,
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question TEXT,
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answer TEXT,
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keywords TEXT[],
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source_urls TEXT[],
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text_vec vector(${DIM})
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)
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`);
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// 重建表:模型/维度/向量空间变了(0.6B -> v4),旧向量不可复用,直接 DROP 重建
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await pool.query('DROP TABLE IF EXISTS kb_docs');
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await pool.query(`
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CREATE TABLE kb_docs (
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id SERIAL PRIMARY KEY,
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source_file TEXT,
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title TEXT,
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category TEXT,
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section TEXT,
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question TEXT,
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answer TEXT,
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keywords TEXT[],
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source_urls TEXT[],
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text_vec vector(${DIM})
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)
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`);
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// HNSW 索引:小数据集召回质量优于 ivfflat,且无需调 lists 参数。
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// 检索用余弦距离(<=>),故显式指定 vector_cosine_ops。
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await pool.query(`CREATE INDEX kb_docs_vec_idx ON kb_docs USING hnsw (text_vec vector_cosine_ops)`);
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console.log(`建表完成, model=${MODEL}, dim=${DIM}`);
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// 2. 收集文件(排除 README/index/_source)
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const files = fs.readdirSync(KB_DIR)
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.filter((f) => f.endsWith('.md') && /^\d+/.test(f))
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.sort();
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console.log(`发现 ${files.length} 个知识库文件: ${files.join(', ')}`);
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let total = 0;
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for (const file of files) {
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const raw = fs.readFileSync(path.join(KB_DIR, file), 'utf8');
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const { meta, body } = parseFrontmatter(raw);
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const chunks = splitChunks(body);
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console.log(`\n[${file}] title="${meta.title}" category="${meta.category}" -> ${chunks.length} chunks`);
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for (const ch of chunks) {
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const keywords = Array.isArray(meta.keywords) ? meta.keywords : [];
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// 来源 URL:优先用章节级映射(如 dropshipping 各章节对应不同 faq 子页),再兜底文件级
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let sourceUrls = [];
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if (ch.section && SECTION_URL_MAP[ch.section]) {
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sourceUrls = SECTION_URL_MAP[ch.section];
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} else if (Array.isArray(meta.source_url)) {
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sourceUrls = meta.source_url.filter((u) => /^https?:\/\//.test(u));
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}
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// embedding 文本:keywords + 问题 + 答案,最大化召回
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const embText = [
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keywords.length ? keywords.join(' ') : '',
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ch.question,
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ch.answer,
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].filter(Boolean).join('\n');
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const vec = await embedText(embText);
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await pool.query(
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`INSERT INTO kb_docs (source_file, title, category, section, question, answer, keywords, source_urls, text_vec)
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VALUES ($1,$2,$3,$4,$5,$6,$7,$8,$9::vector)`,
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[file, meta.title || '', meta.category || '', ch.section, ch.question, ch.answer, keywords, sourceUrls, vecStr(vec)]
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);
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total++;
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process.stdout.write(` [${total}] Q: ${ch.question.slice(0, 60)}${sourceUrls.length ? ' src=' + sourceUrls.length : ''}\n`);
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}
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}
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console.log(`\n上传完成,共 ${total} 个 chunk`);
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// 3. 测试检索
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const tq = await embedText('What is the minimum order value? Can I pay by bank transfer?');
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const ts = await pool.query(
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`SELECT question, category, 1 - (text_vec <=> $1::vector) AS score
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FROM kb_docs ORDER BY text_vec <=> $1::vector LIMIT 5`, [vecStr(tq)]
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);
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console.log('\n检索 "What is the minimum order value? bank transfer":');
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ts.rows.forEach((r, i) => console.log(` ${i + 1}. [${r.category}] score=${Number(r.score).toFixed(3)} Q: ${r.question}`));
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const tq2 = await embedText('meine Bestellung stornieren Rückgabe kaputt'); // 德语
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const ts2 = await pool.query(
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`SELECT question, category, 1 - (text_vec <=> $1::vector) AS score
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FROM kb_docs ORDER BY text_vec <=> $1::vector LIMIT 3`, [vecStr(tq2)]
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);
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console.log('\n检索(德语) "meine Bestellung stornieren Rückgabe kaputt":');
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ts2.rows.forEach((r, i) => console.log(` ${i + 1}. [${r.category}] score=${Number(r.score).toFixed(3)} Q: ${r.question}`));
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await pool.end();
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}
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main().catch((e) => { console.error('ERR', e.message); process.exit(1); });
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