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91111e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 | #!/usr/bin/env node
// ============================================================
// scripts/preprocess.js — Tiền xử lý dữ liệu cho text similarity
//
// Chạy: node scripts/preprocess.js
// Output: data/preprocessed.json
//
// Script này đọc QA dataset + specific responses + brain .rive files, thực hiện:
// 1. Tokenize (tách từ)
// 2. Normalize (lowercase, bỏ dấu tiếng Việt, bỏ punctuation)
// 3. Tính TF vector cho mỗi câu hỏi
// 4. Tính synonym group indices cho mỗi từ
// 5. Tạo IDF (inverse document frequency) cho toàn bộ corpus
// 6. Tính TF-IDF vector cho mỗi câu hỏi
// 7. Lưu tất cả vào data/preprocessed.json
//
// Khi dữ liệu thay đổi (qa-dataset.json, specific-responses.json, brain/*.rive),
// chạy lại script này để tạo preprocessed.json mới.
// ============================================================
var fs = require('fs');
var path = require('path');
var ROOT = path.join(__dirname, '..');
var QA_DATASET = JSON.parse(fs.readFileSync(path.join(ROOT, 'data', 'qa-dataset.json'), 'utf8'));
var SPECIFIC_RESPONSES = JSON.parse(fs.readFileSync(path.join(ROOT, 'data', 'specific-responses.json'), 'utf8'));
// --- Parse brain .rive files to extract trigger-response pairs ---
var BRAIN_DIR = path.join(ROOT, 'brain');
/**
* Parse file .rive và trích xuất các cặp trigger-response.
* Bỏ qua: trigger wildcard mặc định (*), trigger chứa <call>, trigger chỉ có wildcard.
*/
function parseBrainFile(filePath) {
var content = fs.readFileSync(filePath, 'utf8');
var lines = content.split('\n');
var pairs = [];
var currentTrigger = null;
for (var i = 0; i < lines.length; i++) {
var line = lines[i].trim();
// Skip comments and empty lines
if (line.indexOf('//') === 0 || line.length === 0) continue;
// Skip directives (! version, ! var)
if (line.indexOf('!') === 0) continue;
if (line.indexOf('+ ') === 0) {
currentTrigger = line.substring(2).trim();
} else if (line.indexOf('- ') === 0 && currentTrigger) {
var response = line.substring(2).trim();
// Bỏ qua trigger wildcard mặc định
if (currentTrigger === '*') { currentTrigger = null; continue; }
// Bỏ qua response chứa <call> (adapter calls, không phải text response)
if (response.indexOf('<call>') !== -1) { currentTrigger = null; continue; }
// Bỏ qua trigger chỉ chứa wildcard (ví dụ: "* la ai")
var nonWild = currentTrigger.replace(/\*/g, '').trim();
if (nonWild.length < 2) { currentTrigger = null; continue; }
pairs.push({ trigger: currentTrigger, response: response });
currentTrigger = null;
}
}
return pairs;
}
// --- Vietnamese diacritics removal (copy from app.js) ---
var VIETNAMESE_DIACRITICS_MAP = {
'à':'a','á':'a','ả':'a','ã':'a','ạ':'a',
'ă':'a','ằ':'a','ắ':'a','ẳ':'a','ẵ':'a','ặ':'a',
'â':'a','ầ':'a','ấ':'a','ẩ':'a','ẫ':'a','ậ':'a',
'đ':'d',
'è':'e','é':'e','ẻ':'e','ẽ':'e','ẹ':'e',
'ê':'e','ề':'e','ế':'e','ể':'e','ễ':'e','ệ':'e',
'ì':'i','í':'i','ỉ':'i','ĩ':'i','ị':'i',
'ò':'o','ó':'o','ỏ':'o','õ':'o','ọ':'o',
'ô':'o','ồ':'o','ố':'o','ổ':'o','ỗ':'o','ộ':'o',
'ơ':'o','ờ':'o','ớ':'o','ở':'o','ỡ':'o','ợ':'o',
'ù':'u','ú':'u','ủ':'u','ũ':'u','ụ':'u',
'ư':'u','ừ':'u','ứ':'u','ử':'u','ữ':'u','ự':'u',
'ỳ':'y','ý':'y','ỷ':'y','ỹ':'y','ỵ':'y'
};
function removeDiacritics(str) {
var result = '';
for (var i = 0; i < str.length; i++) {
var ch = str[i];
result += VIETNAMESE_DIACRITICS_MAP[ch] || ch;
}
return result;
}
// --- Synonym groups (copy from text-similarity.js) ---
var SYNONYM_GROUPS = [
['xin chao','chao','hi','hello','hey','yo'],
['tam biet','bye','goodbye','hen gap lai','tot lanh'],
['cam on','thanks','thank','thank you'],
['ten','name','ai','who','la ai'],
['lam gi','lam duoc','co the','giup','help','what can'],
['may gio','gio','time','clock','bao gio'],
['ngay','hom nay','date','today','ngay may'],
['thu','thu may','day','what day'],
['tinh','calculate','math','cong','tru','nhan','chia','plus','minus'],
['doi','convert','sang','to','chuyen doi'],
['chatbot','bot','ai','robot','may','machine'],
['la gi','what is','what','gi','mean'],
['khoe','vui','happy','fine','good','ok'],
['tuoi','age','old','bao nhieu tuoi'],
['o dau','where','dau','location'],
['thich','like','love','yeu'],
['huong dan','cach','how','guide','help','su dung','use']
];
var synonymLookup = {};
for (var g = 0; g < SYNONYM_GROUPS.length; g++) {
for (var w = 0; w < SYNONYM_GROUPS[g].length; w++) {
var word = SYNONYM_GROUPS[g][w].toLowerCase();
if (!synonymLookup[word]) synonymLookup[word] = [];
synonymLookup[word].push(g);
}
}
// --- Preprocessing functions ---
function tokenize(text, lang) {
var s = text.toLowerCase();
if (lang === 'vi') s = removeDiacritics(s);
s = s.replace(/[?!.,;:"""''`~()[\]{}\\|@#$%^&]/g, ' ');
return s.split(/\s+/).filter(function(w) { return w.length > 0; });
}
function buildTF(tokens) {
var tf = {};
for (var i = 0; i < tokens.length; i++) {
tf[tokens[i]] = (tf[tokens[i]] || 0) + 1;
}
return tf;
}
function getSynonymGroupIndices(tokens) {
var indices = {};
for (var i = 0; i < tokens.length; i++) {
var groups = synonymLookup[tokens[i]];
if (groups) {
for (var j = 0; j < groups.length; j++) {
indices[groups[j]] = true;
}
}
}
return Object.keys(indices).map(Number);
}
// --- Build IDF from entire corpus ---
function buildCorpusIDF(allDocs) {
var docCount = allDocs.length;
var df = {}; // document frequency: word → number of docs containing it
for (var d = 0; d < allDocs.length; d++) {
var seen = {};
for (var t = 0; t < allDocs[d].length; t++) {
var word = allDocs[d][t];
if (!seen[word]) {
df[word] = (df[word] || 0) + 1;
seen[word] = true;
}
}
}
// IDF = log(N / df) + 1 (smoothed)
var idf = {};
for (var w in df) {
idf[w] = Math.log(docCount / df[w]) + 1;
}
return idf;
}
function buildTFIDF(tf, idf) {
var tfidf = {};
for (var word in tf) {
tfidf[word] = tf[word] * (idf[word] || 1);
}
return tfidf;
}
function vectorMagnitude(vec) {
var sum = 0;
for (var k in vec) sum += vec[k] * vec[k];
return Math.sqrt(sum);
}
// --- Main preprocessing ---
console.log('Preprocessing data...');
var output = { version: Date.now(), langs: {} };
var LANGS = ['vi', 'en', 'ja'];
for (var li = 0; li < LANGS.length; li++) {
var lang = LANGS[li];
console.log(' Processing language:', lang);
// Collect all statements (QA questions + specific response keys + brain triggers)
var statements = [];
// From QA dataset
var qaData = QA_DATASET[lang] || [];
for (var qi = 0; qi < qaData.length; qi++) {
statements.push({ source: 'qa', index: qi, text: qaData[qi].q, answer: qaData[qi].a });
}
// From specific responses
var specData = SPECIFIC_RESPONSES[lang] || {};
var specKeys = Object.keys(specData);
for (var si = 0; si < specKeys.length; si++) {
statements.push({ source: 'specific', index: si, text: specKeys[si], answer: specData[specKeys[si]] });
}
// From brain .rive files (trigger-response pairs)
var brainFile = path.join(BRAIN_DIR, lang + '.rive');
if (fs.existsSync(brainFile)) {
var brainPairs = parseBrainFile(brainFile);
var seenTriggers = {};
for (var bi = 0; bi < brainPairs.length; bi++) {
var trigger = brainPairs[bi].trigger;
// Bỏ wildcard suffix/prefix để lấy phần text chính (ví dụ: "xin chao *" → "xin chao")
var cleanTrigger = trigger.replace(/\*/g, '').trim();
if (cleanTrigger.length < 2) continue;
// Dedup: chỉ giữ trigger đầu tiên nếu trùng text
if (seenTriggers[cleanTrigger]) continue;
seenTriggers[cleanTrigger] = true;
statements.push({ source: 'brain', index: bi, text: cleanTrigger, answer: brainPairs[bi].response });
}
console.log(' Brain triggers:', Object.keys(seenTriggers).length);
}
// Tokenize all statements
var allTokens = [];
var processed = [];
for (var i = 0; i < statements.length; i++) {
var stmt = statements[i];
var tokens = tokenize(stmt.text, lang);
var tf = buildTF(tokens);
var synGroups = getSynonymGroupIndices(tokens);
allTokens.push(tokens);
processed.push({
source: stmt.source,
originalText: stmt.text,
answer: stmt.answer,
tokens: tokens,
tf: tf,
synGroups: synGroups
// tfidf and magnitude will be added after IDF is computed
});
}
// Build IDF from all documents in this language
var idf = buildCorpusIDF(allTokens);
// Compute TF-IDF vectors and magnitudes
for (var j = 0; j < processed.length; j++) {
processed[j].tfidf = buildTFIDF(processed[j].tf, idf);
processed[j].magnitude = vectorMagnitude(processed[j].tfidf);
}
output.langs[lang] = {
idf: idf,
statements: processed
};
console.log(' Statements:', processed.length, '| Vocab size:', Object.keys(idf).length);
}
// Write output
var outputPath = path.join(ROOT, 'data', 'preprocessed.json');
fs.writeFileSync(outputPath, JSON.stringify(output, null, 2), 'utf8');
console.log('Done! Output:', outputPath);
console.log('Version:', output.version);
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