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- // 为雅思英语单词批量补充例句(写入 Words.ExampleSentence)
- // 策略:
- // 1) 复用:97% 的雅思词在现有词典(BookID=110, 47401词)或其它词书中已有例句,
- // 直接复用其 ExampleSentence,并按难度筛选为 2简单(A2)+2中等(B1/B2)+1难(C1)。
- // 2) 生成:仅对完全没例句的少量词,用本机 Ollama(qwen3) 本地生成,不依赖外网。
- // 输出格式对齐六级:{"word":..,"CEFR_Level":..,"Sentences":[{"Sentence":..,"Translate":..,"Level":..}]}
- //
- // 用法:
- // node generate_examples.js # 复用+本地生成
- // REUSE_ONLY=1 node generate_examples.js # 只做复用(跳过 Ollama),可先跑这步看覆盖
- // LIMIT=50 node generate_examples.js # 只处理前 N 个未生成词(验证)
- // OLLAMA_MODEL=qwen3.8:27b-mlx BATCH=8 node generate_examples.js
- import { query } from '../../../src/util/db.js';
- import axios from 'axios';
- import fs from 'fs';
- import path from 'path';
- import { fileURLToPath } from 'url';
- const __dirname = path.dirname(fileURLToPath(import.meta.url));
- const PROGRESS_FILE = path.join(__dirname, '.ielts_examples_progress.json');
- const BOOK_IDS = [212, 213, 214, 215, 216, 217];
- const OLLAMA_URL = process.env.OLLAMA_URL || 'http://localhost:11434/api/generate';
- const OLLAMA_MODEL = process.env.OLLAMA_MODEL || 'qwen3.8:27b-mlx';
- const BATCH = Number(process.env.BATCH || 8); // Ollama 每批词数
- const LIMIT = Number(process.env.LIMIT || 0); // >0 只处理前 N 个
- const REUSE_ONLY = process.env.REUSE_ONLY === '1';
- const sleep = (ms) => new Promise((r) => setTimeout(r, ms));
- // ---------- 难度筛选:2 A2 + 2 B1/B2 + 1 C1,不足则就近补 ----------
- function pickSentences(sentences) {
- const valid = (sentences || []).filter((s) => s && s.Sentence);
- const byLevel = {};
- for (const s of valid) {
- const l = (s.Level || 'B1').toUpperCase();
- (byLevel[l] = byLevel[l] || []).push(s);
- }
- const chosen = [];
- const take = (levels, n) => {
- for (const lv of levels) {
- while (n > 0 && byLevel[lv] && byLevel[lv].length) {
- chosen.push(byLevel[lv].shift());
- n--;
- }
- if (n <= 0) break;
- }
- return n;
- };
- take(['A2', 'A1'], 2); // 简单
- take(['B1', 'B2'], 2); // 中等
- take(['C1', 'C2'], 1); // 难
- const order = ['A2', 'A1', 'B1', 'B2', 'C1', 'C2'];
- const rem = [];
- for (const lv of order) if (byLevel[lv]) rem.push(...byLevel[lv]);
- while (chosen.length < 5 && rem.length) chosen.push(rem.shift());
- return chosen.slice(0, 5).map((s) => ({
- Sentence: s.Sentence,
- Translate: s.Translate || '',
- Level: (s.Level || 'B1').toUpperCase(),
- }));
- }
- function buildExampleSentence(word, cefr, sentences) {
- return JSON.stringify({
- word,
- CEFR_Level: (cefr || 'B1').toUpperCase(),
- Sentences: sentences,
- });
- }
- // ---------- 复用源:优先 BookID=110 大词典 ----------
- async function buildSourceMap() {
- const rows = await query(
- `select Word, ExampleSentence, BookID from kylx365_db.Words where ExampleSentence is not null and ExampleSentence!=''`
- );
- const map = new Map();
- for (const r of rows) {
- const key = r.Word.toLowerCase();
- const cur = map.get(key);
- if (!cur) map.set(key, r);
- else if (cur.BookID !== 110 && r.BookID === 110) map.set(key, r); // 大词典优先
- }
- return map;
- }
- function parseSourceExample(raw) {
- try {
- const o = JSON.parse(raw);
- const sents = o.Sentences || o.sentences || [];
- if (!sents.length) return null;
- return { cefr: o.CEFR_Level || o.cefr, sentences: sents };
- } catch {
- return null;
- }
- }
- // ---------- 本地 Ollama 生成 ----------
- async function callOllama(userPrompt) {
- const resp = await axios.post(
- OLLAMA_URL,
- { model: OLLAMA_MODEL, format: 'json', stream: false, think: false, prompt: userPrompt },
- { timeout: 300000 }
- );
- const data = resp.data || {};
- let text = data.response || '';
- if (!text && data.thinking) text = data.thinking; // 思考模式兜底
- text = (text || '').replace(/^```(?:json)?\s*\n/i, '').replace(/\n```\s*$/i, '');
- return text;
- }
- function buildGenPrompt(words) {
- const lines = words.map((w, i) => `${i + 1}. ${w.Word} | ${w.Translate || ''}`);
- return `Generate example sentences for each English word (with Chinese meaning).
- Per word: exactly 5 sentences — 2 at CEFR A2 (simple), 2 at B1-B2 (medium), 1 at C1 (relatively hard).
- Each sentence must use the word naturally and correctly per its meaning. Add a concise Chinese translation. Also give the word's approximate CEFR level.
- Return JSON only: {"items":[{"word":"emperor","cefr":"B1","sentences":[{"sentence":"...","translate":"...","level":"A2"}, ...5]}]}
- The "word" field must exactly match the input word. Include every input word once.
- Input words (word | meaning):
- ${lines.join('\n')}`;
- }
- function loadProgress() {
- try {
- return JSON.parse(fs.readFileSync(PROGRESS_FILE, 'utf8'));
- } catch {
- return { done: [] };
- }
- }
- function saveProgress(p) {
- fs.writeFileSync(PROGRESS_FILE, JSON.stringify(p));
- }
- async function main() {
- let words = await query(
- `select ID, Word, Translate from kylx365_db.Words where BookID in (${BOOK_IDS.join(',')}) and (ExampleSentence is null or ExampleSentence='') order by BookID, ID`
- );
- if (LIMIT > 0) words = words.slice(0, LIMIT);
- console.log(`待处理雅思词行数: ${words.length}`);
- const progress = loadProgress();
- const doneIds = new Set(progress.done);
- words = words.filter((w) => !doneIds.has(w.ID));
- console.log(`本次新增处理: ${words.length}(已跳过 ${doneIds.size})`);
- const srcMap = await buildSourceMap();
- console.log(`复用源词条数: ${srcMap.size}`);
- let reuseOk = 0,
- genOk = 0,
- fail = 0;
- const genQueue = [];
- // 第一步:尽量复用
- for (const w of words) {
- const src = srcMap.get(w.Word.toLowerCase());
- const parsed = src ? parseSourceExample(src.ExampleSentence) : null;
- if (parsed && parsed.sentences.length) {
- const picked = pickSentences(parsed.sentences);
- if (picked.length >= 1) {
- const json = buildExampleSentence(w.Word, parsed.cefr, picked);
- await query('update kylx365_db.Words set ExampleSentence=? where ID=?', [json, w.ID]);
- reuseOk++;
- progress.done.push(w.ID);
- continue;
- }
- }
- if (!REUSE_ONLY) genQueue.push(w);
- else {
- fail++;
- console.warn(` ✗ 无复用源: ${w.Word}`);
- }
- }
- saveProgress(progress);
- console.log(`复用完成: ${reuseOk},待生成: ${genQueue.length}`);
- // 第二步:本地 Ollama 生成剩余
- if (genQueue.length) {
- const batches = [];
- for (let i = 0; i < genQueue.length; i += BATCH) batches.push(genQueue.slice(i, i + BATCH));
- console.log(`Ollama 生成批次数: ${batches.length}`);
- for (let bi = 0; bi < batches.length; bi++) {
- const b = batches[bi];
- const label = `gen batch ${bi + 1}/${batches.length} (${b.map((w) => w.Word).join(',')})`;
- let ok = false;
- for (let attempt = 1; attempt <= 3 && !ok; attempt++) {
- try {
- const raw = await callOllama(buildGenPrompt(b));
- let parsed = JSON.parse(raw);
- const items = parsed.items || parsed;
- const m = new Map();
- for (const it of items) if (it && it.word) m.set(it.word.toLowerCase(), it);
- for (const w of b) {
- const it = m.get(w.Word.toLowerCase());
- if (it && it.sentences && it.sentences.length >= 5) {
- const sents = it.sentences.slice(0, 5).map((s) => ({
- Sentence: s.sentence,
- Translate: s.translate || '',
- Level: (s.level || 'B1').toUpperCase(),
- }));
- const json = buildExampleSentence(w.Word, it.cefr, sents);
- await query('update kylx365_db.Words set ExampleSentence=? where ID=?', [json, w.ID]);
- progress.done.push(w.ID);
- genOk++;
- } else {
- console.warn(` ✗ 生成缺句: ${w.Word} (got ${(it?.sentences || []).length})`);
- }
- }
- saveProgress(progress);
- ok = true;
- console.log(`✓ ${label} (genOk=${genOk})`);
- } catch (err) {
- console.error(`✗ ${label} 尝试${attempt}失败: ${err?.message || err}`);
- await sleep(3000 * attempt);
- }
- }
- if (!ok) fail += b.length;
- await sleep(500);
- }
- }
- console.log(`\n完成。reuseOk=${reuseOk}, genOk=${genOk}, fail=${fail}, 累计已生成=${progress.done.length}`);
- }
- main()
- .then(() => process.exit(0))
- .catch((e) => {
- console.error(e);
- process.exit(1);
- });
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