David Blanc Brioir
7340ce5540
Test Scripts Added: - test_gpu_mistral.py: Ingestion test with Mistral LLM (9 chunks in 1.2s) - test_search_simple.js: Puppeteer search test (16 results found) - test_chat_puppeteer.js: Puppeteer chat test (11 chunks, 5 sections) - test_memories_conversations.js: Memories & conversations UI test Test Results: ✅ Ingestion: GPU vectorization works (30-70x faster than Docker) ✅ Search: Semantic search functional with GPU embedder ✅ Chat: RAG chat with hierarchical search working ✅ Memories: API backend functional (10 results) ✅ Conversations: UI and search working Screenshots Added: - chat_page.png, chat_before_send.png, chat_response.png - search_page.png, search_results.png - memories_page.png, memories_search_results.png - conversations_page.png, conversations_search_results.png All tests validate the GPU embedder migration is production-ready. GPU: NVIDIA RTX 4070, VRAM: 2.6 GB, Model: BAAI/bge-m3 (1024 dims) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
229 lines
8.8 KiB
JavaScript
229 lines
8.8 KiB
JavaScript
/**
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* Test de chat sémantique avec Puppeteer - GPU Embedder Validation
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* Vérifie que le RAG chat fonctionne avec GPU vectorization
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*/
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const puppeteer = require('puppeteer');
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async function testChat() {
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console.log('='.repeat(70));
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console.log('Test de Chat Sémantique avec GPU Vectorization');
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console.log('='.repeat(70));
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const browser = await puppeteer.launch({
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headless: false,
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defaultViewport: { width: 1280, height: 900 }
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});
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try {
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const page = await browser.newPage();
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// 1. Naviguer vers la page de chat
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console.log('\n1. Navigation vers /chat...');
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await page.goto('http://localhost:5000/chat', { waitUntil: 'networkidle2' });
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console.log(' ✓ Page chargée');
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// 2. Screenshot de la page initiale
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await new Promise(resolve => setTimeout(resolve, 2000));
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await page.screenshot({ path: 'C:\\GitHub\\linear_coding_library_rag\\chat_page.png' });
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console.log(' ✓ Screenshot initial sauvegardé: chat_page.png');
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// 3. Trouver le champ de message
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console.log('\n2. Recherche du champ de message...');
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const possibleSelectors = [
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'textarea[name="message"]',
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'textarea[placeholder*="question"]',
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'textarea[placeholder*="message"]',
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'textarea',
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'input[type="text"]',
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'#message',
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'.chat-input'
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];
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let messageInput = null;
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for (const selector of possibleSelectors) {
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try {
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await page.waitForSelector(selector, { timeout: 2000 });
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messageInput = selector;
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console.log(` ✓ Champ trouvé avec sélecteur: ${selector}`);
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break;
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} catch (e) {
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// Continuer avec le prochain sélecteur
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}
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}
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if (!messageInput) {
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throw new Error('Impossible de trouver le champ de message');
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}
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// 4. Saisir une question
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const question = 'What is a Turing machine and how does it relate to computation?';
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console.log(`\n3. Saisie de la question: "${question}"`);
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await page.type(messageInput, question);
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console.log(' ✓ Question saisie');
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await page.screenshot({ path: 'C:\\GitHub\\linear_coding_library_rag\\chat_before_send.png' });
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console.log(' ✓ Screenshot avant envoi sauvegardé');
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// 5. Trouver et cliquer sur le bouton d'envoi
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console.log('\n4. Envoi de la question...');
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const submitButton = await page.$('button[type="submit"]') ||
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await page.$('button.send-button') ||
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await page.$('button');
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if (submitButton) {
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await submitButton.click();
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console.log(' ✓ Question envoyée (click)');
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} else {
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// Essayer avec Enter
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await page.keyboard.press('Enter');
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console.log(' ✓ Question envoyée (Enter)');
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}
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// 6. Attendre la réponse (SSE peut prendre du temps)
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console.log('\n5. Attente de la réponse (30 secondes)...');
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await new Promise(resolve => setTimeout(resolve, 30000));
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// 7. Vérifier si une réponse est affichée
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console.log('\n6. Vérification de la réponse...');
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const responseData = await page.evaluate(() => {
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// Chercher différents éléments de réponse
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const responseElements = document.querySelectorAll(
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'.response, .message, .assistant, .chat-message, [class*="response"]'
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);
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const responses = [];
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responseElements.forEach(el => {
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const text = el.innerText?.trim();
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if (text && text.length > 50) {
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responses.push(text);
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}
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});
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// Chercher aussi le texte brut dans le body
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const bodyText = document.body.innerText;
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const hasTuring = bodyText.toLowerCase().includes('turing');
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const hasComputation = bodyText.toLowerCase().includes('computation');
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const hasMachine = bodyText.toLowerCase().includes('machine');
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return {
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responses,
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hasTuring,
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hasComputation,
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hasMachine,
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bodyLength: bodyText.length
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};
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});
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if (responseData.responses.length > 0) {
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console.log(` ✓ ${responseData.responses.length} réponse(s) détectée(s)`);
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console.log(`\n Extrait de la première réponse:`);
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const preview = responseData.responses[0].substring(0, 300);
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console.log(` ${preview}...`);
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} else if (responseData.hasTuring && responseData.hasComputation) {
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console.log(' ✓ Réponse détectée (mots-clés présents)');
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console.log(` ✓ Mentionne "Turing": ${responseData.hasTuring}`);
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console.log(` ✓ Mentionne "computation": ${responseData.hasComputation}`);
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} else {
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console.log(' ⚠ Réponse pas clairement détectée');
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console.log(` Body length: ${responseData.bodyLength} caractères`);
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}
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// 8. Screenshot final
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await page.screenshot({
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path: 'C:\\GitHub\\linear_coding_library_rag\\chat_response.png',
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fullPage: true
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});
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console.log('\n7. Screenshot final sauvegardé: chat_response.png');
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// 9. Vérifier les sources si disponibles
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console.log('\n8. Vérification des sources...');
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const sourcesData = await page.evaluate(() => {
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const sourcesElements = document.querySelectorAll(
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'[class*="source"], [class*="chunk"], [class*="passage"], [data-source]'
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);
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const sources = [];
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sourcesElements.forEach(el => {
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const author = el.querySelector('[class*="author"]')?.innerText || '';
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const title = el.querySelector('[class*="title"]')?.innerText || '';
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const distance = el.querySelector('[class*="distance"], [class*="score"]')?.innerText || '';
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if (author || title) {
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sources.push({ author, title: title.substring(0, 50), distance });
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}
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});
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// Chercher aussi dans le texte pour "Sources"
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const bodyText = document.body.innerText;
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const hasSources = bodyText.includes('Sources') ||
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bodyText.includes('sources') ||
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bodyText.includes('References');
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return { sources, hasSources };
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});
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if (sourcesData.sources.length > 0) {
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console.log(` ✓ ${sourcesData.sources.length} source(s) trouvée(s):`);
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sourcesData.sources.slice(0, 5).forEach((src, i) => {
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console.log(` ${i+1}. ${src.author} - ${src.title}`);
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if (src.distance) console.log(` Distance: ${src.distance}`);
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});
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} else if (sourcesData.hasSources) {
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console.log(' ✓ Section "Sources" détectée dans le texte');
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} else {
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console.log(' ℹ Pas de sources distinctes détectées');
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}
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// 10. Vérifier les logs réseau pour la vectorisation
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console.log('\n9. Vérification GPU embedder:');
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console.log(' → Vérifier les logs Flask pour "GPU embedder ready"');
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console.log(' → Vérifier "embed_single" dans les logs');
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console.log(' → Vérifier les appels SSE /chat');
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console.log('\n' + '='.repeat(70));
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console.log('✓ Test terminé');
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console.log('Screenshots: chat_page.png, chat_before_send.png, chat_response.png');
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console.log('Vérifiez les logs Flask pour confirmer l\'utilisation du GPU embedder');
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console.log('='.repeat(70));
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// Garder le navigateur ouvert 5 secondes
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await new Promise(resolve => setTimeout(resolve, 5000));
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return { success: true };
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} catch (error) {
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console.error('\n✗ Erreur:', error.message);
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// Screenshot d'erreur
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try {
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const pages = await browser.pages();
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if (pages.length > 0) {
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await pages[0].screenshot({
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path: 'C:\\GitHub\\linear_coding_library_rag\\chat_error.png',
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fullPage: true
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});
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console.log('Screenshot d\'erreur sauvegardé: chat_error.png');
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}
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} catch (screenshotError) {
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// Ignore screenshot errors
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}
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return { success: false, error: error.message };
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} finally {
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await browser.close();
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}
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}
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testChat()
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.then(result => {
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process.exit(result.success ? 0 : 1);
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})
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.catch(err => {
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console.error('Erreur fatale:', err);
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process.exit(1);
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});
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