M.I.M.I.R - Multi-agent Intelligent Memory & Insight Repository

#!/usr/bin/env node import { createGraphManager } from '../build/managers/index.js'; import { EmbeddingsService } from '../build/indexing/EmbeddingsService.js'; async function regenerateEmbeddings() { console.log('🔄 Starting embedding regeneration...\n'); const graphManager = await createGraphManager(); const driver = graphManager.getDriver(); const embeddingsService = new EmbeddingsService(); await embeddingsService.initialize(); if (!embeddingsService.isEnabled()) { console.error('❌ Embeddings service is not enabled. Check your LLM configuration.'); process.exit(1); } const session = driver.session(); try { // Find all nodes marked for embedding regeneration const result = await session.run(` MATCH (n:Node) WHERE n.needs_embedding = true OR n.embedding_model = 'nomic-embed-text' RETURN n.id as id, n.type as type, n.title as title, n.content as content, n.text as text `); const nodes = result.records.map(r => ({ id: r.get('id'), type: r.get('type'), title: r.get('title'), content: r.get('content'), text: r.get('text') })); console.log(`📊 Found ${nodes.length} nodes needing embedding regeneration\n`); if (nodes.length === 0) { console.log('✅ No nodes need embedding regeneration'); return; } for (const node of nodes) { console.log(`\n🔨 Processing: ${node.type} - ${node.id}`); if (node.title) console.log(` Title: ${node.title.substring(0, 60)}`); // Get text content for embedding let textContent = node.content || node.text || node.title || ''; if (!textContent) { console.log(' ⚠️ No text content found, skipping'); continue; } try { // Generate new embedding with mxbai-embed-large const embeddingResult = await embeddingsService.generateEmbedding(textContent); console.log(` ✅ Generated embedding (${embeddingResult.dimensions} dims, ${embeddingResult.model})`); // Update node with new embedding await session.run(` MATCH (n:Node {id: $id}) SET n.embedding = $embedding, n.embedding_model = $model, n.embedding_dimensions = $dimensions, n.has_embedding = true REMOVE n.needs_embedding `, { id: node.id, embedding: embeddingResult.embedding, model: embeddingResult.model, dimensions: embeddingResult.dimensions }); console.log(` 💾 Updated node in database`); } catch (error) { console.error(` ❌ Failed to regenerate embedding: ${error.message}`); } } console.log('\n\n✅ Embedding regeneration complete!'); // Show final statistics const statsResult = await session.run(` MATCH (n:Node) WHERE n.embedding_model IS NOT NULL RETURN n.embedding_model as model, count(n) as count ORDER BY model `); console.log('\n📊 Final embedding statistics:'); statsResult.records.forEach(r => { console.log(` ${r.get('model')}: ${r.get('count')} nodes`); }); } finally { await session.close(); await driver.close(); } } regenerateEmbeddings().catch(error => { console.error('❌ Fatal error:', error); process.exit(1); });