perf: réécriture nox-memory.js avec node fetch natif (sans Playwright)
This commit is contained in:
Nox
+89
-131
@@ -1,19 +1,18 @@
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#!/usr/bin/env node
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/**
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* nox-memory.js — Mémoire vectorielle de Nox via Qdrant
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*
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* Utilise node fetch (Node 22+) + OpenAI embeddings
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*
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* Usage:
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* node nox-memory.js add "texte à mémoriser" --type fact --tags "ha,lumiere" --importance 3
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* node nox-memory.js search "question en langage naturel" --limit 5
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* node nox-memory.js add "texte" --type fact --tags "ha,lumiere" --importance 3
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* node nox-memory.js search "question naturelle" [--limit 5] [--type fact]
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* node nox-memory.js list [--type fact] [--limit 20]
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* node nox-memory.js delete <id>
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* node nox-memory.js import-md <fichier.md> ← importe MEMORY.md
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* node nox-memory.js import-md <fichier.md>
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* node nox-memory.js stats
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*/
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const { chromium } = require('./node_modules/playwright');
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const QDRANT_URL = 'http://192.168.1.150:6333';
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const QDRANT_URL = 'http://192.168.1.150:6333';
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const COLLECTION = 'nox-memory';
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const OPENAI_URL = 'https://api.openai.com/v1/embeddings';
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const EMBED_MODEL = 'text-embedding-3-small';
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@@ -21,17 +20,18 @@ const EMBED_DIM = 1536;
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// ─── Helpers ──────────────────────────────────────────────────────────────────
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async function getRequestContext() {
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const browser = await chromium.launch({
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executablePath: '/home/node/.cache/ms-playwright/chromium-1208/chrome-linux64/chrome',
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args: ['--no-sandbox', '--disable-gpu', '--disable-dev-shm-usage']
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});
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const context = await browser.newContext();
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return { browser, req: context.request };
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async function qdrant(method, path, body) {
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const opts = {
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method,
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headers: { 'Content-Type': 'application/json' }
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};
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if (body) opts.body = JSON.stringify(body);
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const r = await fetch(`${QDRANT_URL}${path}`, opts);
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return r.json();
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}
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async function embed(text) {
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const res = await fetch(OPENAI_URL, {
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const r = await fetch(OPENAI_URL, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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@@ -39,13 +39,12 @@ async function embed(text) {
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},
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body: JSON.stringify({ model: EMBED_MODEL, input: text })
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});
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const json = await res.json();
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const json = await r.json();
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if (!json.data) throw new Error('Embedding failed: ' + JSON.stringify(json));
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return json.data[0].embedding;
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}
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function genId() {
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// UUID v4 simplifié
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return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, c => {
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const r = Math.random() * 16 | 0;
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return (c === 'x' ? r : (r & 0x3 | 0x8)).toString(16);
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@@ -55,125 +54,91 @@ function genId() {
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// ─── Commandes ────────────────────────────────────────────────────────────────
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async function cmdAdd(text, opts) {
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const { browser, req } = await getRequestContext();
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try {
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const vector = await embed(text);
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const id = genId();
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const payload = {
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text,
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type: opts.type || 'fact',
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tags: opts.tags ? opts.tags.split(',').map(t => t.trim()) : [],
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importance: parseInt(opts.importance || '3'),
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timestamp: Date.now(),
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date: new Date().toISOString()
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};
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const r = await req.put(`${QDRANT_URL}/collections/${COLLECTION}/points`, {
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data: { points: [{ id, vector, payload }] }
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});
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const body = await r.json();
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if (body.status === 'ok') {
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console.log(`✅ Mémorisé [${id}]`);
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console.log(` type: ${payload.type} | importance: ${payload.importance} | tags: ${payload.tags.join(', ') || 'aucun'}`);
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console.log(` "${text.slice(0, 80)}${text.length > 80 ? '...' : ''}"`);
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} else {
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console.error('❌ Erreur:', JSON.stringify(body));
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}
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} finally {
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await browser.close();
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const vector = await embed(text);
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const id = genId();
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const payload = {
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text,
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type: opts.type || 'fact',
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tags: opts.tags ? opts.tags.split(',').map(t => t.trim()) : [],
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importance: parseInt(opts.importance || '3'),
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timestamp: Date.now(),
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date: new Date().toISOString()
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};
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const res = await qdrant('PUT', `/collections/${COLLECTION}/points`, {
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points: [{ id, vector, payload }]
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});
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if (res.status === 'ok') {
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console.log(`✅ Mémorisé [${id}]`);
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console.log(` type: ${payload.type} | importance: ${payload.importance} | tags: ${payload.tags.join(', ') || 'aucun'}`);
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console.log(` "${text.slice(0, 80)}${text.length > 80 ? '...' : ''}"`);
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} else {
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console.error('❌ Erreur:', JSON.stringify(res));
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}
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}
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async function cmdSearch(query, opts) {
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const { browser, req } = await getRequestContext();
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try {
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const vector = await embed(query);
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const limit = parseInt(opts.limit || '5');
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const body = {
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vector,
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limit,
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with_payload: true,
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score_threshold: 0.3
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};
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if (opts.type) {
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body.filter = { must: [{ key: 'type', match: { value: opts.type } }] };
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}
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const r = await req.post(`${QDRANT_URL}/collections/${COLLECTION}/points/search`, { data: body });
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const res = await r.json();
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const results = res.result || [];
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if (results.length === 0) {
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console.log('🔍 Aucun résultat pertinent trouvé.');
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return;
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}
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console.log(`🔍 ${results.length} résultat(s) pour : "${query}"\n`);
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results.forEach((p, i) => {
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const pl = p.payload;
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const score = (p.score * 100).toFixed(1);
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console.log(`[${i+1}] ${score}% — ${pl.type} | imp:${pl.importance} | ${pl.date?.slice(0,10) || '?'}`);
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if (pl.tags?.length) console.log(` Tags: ${pl.tags.join(', ')}`);
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console.log(` ${pl.text}`);
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console.log(` ID: ${p.id}\n`);
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});
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} finally {
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await browser.close();
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const vector = await embed(query);
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const limit = parseInt(opts.limit || '5');
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const body = {
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vector,
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limit,
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with_payload: true,
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score_threshold: 0.3
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};
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if (opts.type) {
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body.filter = { must: [{ key: 'type', match: { value: opts.type } }] };
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}
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const res = await qdrant('POST', `/collections/${COLLECTION}/points/search`, body);
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const results = res.result || [];
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if (results.length === 0) {
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console.log('🔍 Aucun résultat pertinent trouvé.');
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return;
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}
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console.log(`🔍 ${results.length} résultat(s) pour : "${query}"\n`);
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results.forEach((p, i) => {
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const pl = p.payload;
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const score = (p.score * 100).toFixed(1);
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console.log(`[${i+1}] ${score}% — ${pl.type} | imp:${pl.importance} | ${pl.date?.slice(0,10) || '?'}`);
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if (pl.tags?.length) console.log(` Tags: ${pl.tags.join(', ')}`);
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console.log(` ${pl.text}`);
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console.log(` ID: ${p.id}\n`);
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});
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}
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async function cmdList(opts) {
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const { browser, req } = await getRequestContext();
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try {
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const limit = parseInt(opts.limit || '20');
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const body = { limit, with_payload: true };
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if (opts.type) {
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body.filter = { must: [{ key: 'type', match: { value: opts.type } }] };
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}
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const r = await req.post(`${QDRANT_URL}/collections/${COLLECTION}/points/scroll`, { data: body });
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const res = await r.json();
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const points = res.result?.points || [];
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if (points.length === 0) { console.log('📭 Collection vide.'); return; }
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console.log(`📋 ${points.length} souvenir(s) :\n`);
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points.forEach(p => {
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const pl = p.payload;
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console.log(`• [${pl.type}|${pl.importance}] ${pl.date?.slice(0,10)} — ${pl.text?.slice(0,100)}${pl.text?.length>100?'...':''}`);
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});
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} finally {
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await browser.close();
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const limit = parseInt(opts.limit || '20');
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const body = { limit, with_payload: true };
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if (opts.type) {
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body.filter = { must: [{ key: 'type', match: { value: opts.type } }] };
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}
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const res = await qdrant('POST', `/collections/${COLLECTION}/points/scroll`, body);
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const points = res.result?.points || [];
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if (points.length === 0) { console.log('📭 Collection vide.'); return; }
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console.log(`📋 ${points.length} souvenir(s) :\n`);
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points.forEach(p => {
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const pl = p.payload;
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console.log(`• [${pl.type}|${pl.importance}] ${pl.date?.slice(0,10)} — ${pl.text?.slice(0,100)}${pl.text?.length>100?'...':''}`);
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});
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}
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async function cmdDelete(id) {
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const { browser, req } = await getRequestContext();
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try {
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const r = await req.post(`${QDRANT_URL}/collections/${COLLECTION}/points/delete`, {
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data: { points: [id] }
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});
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const body = await r.json();
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console.log(body.status === 'ok' ? `🗑️ Point ${id} supprimé.` : `❌ Erreur: ${JSON.stringify(body)}`);
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} finally {
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await browser.close();
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}
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const res = await qdrant('POST', `/collections/${COLLECTION}/points/delete`, { points: [id] });
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console.log(res.status === 'ok' ? `🗑️ Point ${id} supprimé.` : `❌ Erreur: ${JSON.stringify(res)}`);
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}
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async function cmdStats() {
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const { browser, req } = await getRequestContext();
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try {
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const r = await req.get(`${QDRANT_URL}/collections/${COLLECTION}`);
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const body = await r.json();
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const info = body.result;
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console.log('📊 Collection nox-memory :');
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console.log(` Points : ${info.points_count}`);
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console.log(` Vecteurs : ${info.vectors_count}`);
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console.log(` Segments : ${info.segments_count}`);
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console.log(` Statut : ${info.status}`);
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console.log(` Dim. : ${EMBED_DIM} (${EMBED_MODEL})`);
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} finally {
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await browser.close();
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}
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const res = await qdrant('GET', `/collections/${COLLECTION}`);
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const info = res.result;
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console.log('📊 Collection nox-memory :');
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console.log(` Points : ${info.points_count}`);
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console.log(` Segments : ${info.segments_count}`);
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console.log(` Statut : ${info.status}`);
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console.log(` Modèle : ${EMBED_MODEL} (${EMBED_DIM} dims)`);
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}
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async function cmdImportMd(file) {
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const fs = require('fs');
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const text = fs.readFileSync(file, 'utf8');
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// Découper en sections par ## ou ### ou lignes non vides significatives
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const chunks = [];
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let current = '';
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for (const line of text.split('\n')) {
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@@ -185,19 +150,15 @@ async function cmdImportMd(file) {
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}
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}
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if (current.trim()) chunks.push(current.trim());
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const valid = chunks.filter(c => c.length > 20);
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console.log(`📥 Import de ${valid.length} chunks depuis ${file}...`);
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for (let i = 0; i < valid.length; i++) {
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const chunk = valid[i];
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process.stdout.write(` [${i+1}/${valid.length}] `);
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try {
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await cmdAdd(chunk, { type: 'semantic', importance: '4', tags: 'memory.md,import' });
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await cmdAdd(valid[i], { type: 'semantic', importance: '4', tags: 'memory.md,import' });
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} catch(e) {
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console.error('Erreur:', e.message);
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}
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// Pause pour éviter rate limit OpenAI
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await new Promise(r => setTimeout(r, 300));
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}
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console.log('\n✅ Import terminé !');
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@@ -208,8 +169,6 @@ async function cmdImportMd(file) {
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async function main() {
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const args = process.argv.slice(2);
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const cmd = args[0];
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// Parser les options --key value
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const opts = {};
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const positional = [];
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for (let i = 1; i < args.length; i++) {
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@@ -248,21 +207,20 @@ async function main() {
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console.log(`
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nox-memory — Mémoire vectorielle (Qdrant + OpenAI embeddings)
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Commandes :
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add "texte" Ajouter un souvenir
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--type fact | semantic | preference | episodic (défaut: fact)
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--type fact | semantic | preference | episodic (défaut: fact)
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--tags "tag1,tag2"
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--importance 1-5 (défaut: 3)
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--importance 1-5 (défaut: 3)
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search "query" Recherche sémantique
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--limit Nombre de résultats (défaut: 5)
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--limit Résultats (défaut: 5)
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--type Filtrer par type
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list Lister les souvenirs
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--limit Nombre (défaut: 20)
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--type Filtrer par type
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delete <id> Supprimer un point par ID
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delete <id> Supprimer un point
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stats Stats de la collection
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import-md <file> Importer un fichier Markdown
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`);
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