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

#!/usr/bin/env node import neo4j from 'neo4j-driver'; import fetch from 'node-fetch'; const QUERY = "how do i integrate ngx-cmk-translate with ps-custom-radio-group in angular?"; async function generateEmbedding(text) { console.log(`\n🔮 Generating embedding for: "${text.substring(0, 80)}..."`); const response = await fetch('http://localhost:11434/api/embeddings', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ model: 'mxbai-embed-large', prompt: text }) }); const data = await response.json(); console.log(`✅ Generated embedding with ${data.embedding.length} dimensions`); return data.embedding; } async function testQuery() { console.log('🔍 Testing RAG Query\n'); console.log(`Query: "${QUERY}"\n`); // Generate embedding const embedding = await generateEmbedding(QUERY); // Extract keywords const keywords = QUERY.toLowerCase().split(' ').filter(w => w.length > 3); console.log(`🔑 Keywords: ${keywords.join(', ')}\n`); // Connect to Neo4j const driver = neo4j.driver( 'neo4j://localhost:7687', neo4j.auth.basic('neo4j', 'password') ); const session = driver.session(); try { // Test with multiple thresholds const thresholds = [0.55, 0.4, 0.3, 0.2, 0.1]; for (const threshold of thresholds) { console.log(`\n${'='.repeat(70)}`); console.log(`Testing with threshold: ${threshold}`); console.log('='.repeat(70)); const query = ` CALL { // Search file chunks MATCH (file:File)-[:HAS_CHUNK]->(chunk:FileChunk) WHERE chunk.embedding IS NOT NULL WITH file, chunk, reduce(dot = 0.0, i IN range(0, size(chunk.embedding)-1) | dot + chunk.embedding[i] * $embedding[i]) AS dotProduct, sqrt(reduce(sum = 0.0, x IN chunk.embedding | sum + x * x)) AS normA, sqrt(reduce(sum = 0.0, x IN $embedding | sum + x * x)) AS normB, split(coalesce(file.absolute_path, file.path), '/') AS pathParts WITH coalesce(file.absolute_path, file.path) AS source_path, file.name AS source_name, chunk.text AS content, chunk.start_offset AS start_offset, dotProduct / (normA * normB) AS similarity, 'file_chunk' AS source_type, pathParts, CASE WHEN size(pathParts) > 2 THEN pathParts[2] ELSE 'unknown' END AS project_name WHERE similarity >= $minThreshold RETURN content, start_offset, source_path, source_name, similarity, source_type, project_name, pathParts UNION ALL // Search small files MATCH (file:File) WHERE file.embedding IS NOT NULL AND file.has_chunks = false WITH file, reduce(dot = 0.0, i IN range(0, size(file.embedding)-1) | dot + file.embedding[i] * $embedding[i]) AS dotProduct, sqrt(reduce(sum = 0.0, x IN file.embedding | sum + x * x)) AS normA, sqrt(reduce(sum = 0.0, x IN $embedding | sum + x * x)) AS normB, split(coalesce(file.absolute_path, file.path), '/') AS pathParts WITH coalesce(file.absolute_path, file.path) AS source_path, file.name AS source_name, file.content AS content, 0 AS start_offset, dotProduct / (normA * normB) AS similarity, 'file' AS source_type, pathParts, CASE WHEN size(pathParts) > 2 THEN pathParts[2] ELSE 'unknown' END AS project_name WHERE similarity >= $minThreshold RETURN content, start_offset, source_path, source_name, similarity, source_type, project_name, pathParts } WITH content, start_offset, source_path, source_name, similarity, source_type, project_name, pathParts // Hybrid search: boost keyword matches WITH content, start_offset, source_path, source_name, similarity, source_type, project_name, CASE WHEN $enableHybrid AND size($keywords) > 0 THEN reduce(matches = 0, kw IN $keywords | matches + CASE WHEN toLower(coalesce(content, '')) CONTAINS kw THEN 1 ELSE 0 END + CASE WHEN toLower(source_path) CONTAINS kw THEN 1 ELSE 0 END ) * 0.02 ELSE 0.0 END AS keyword_boost WITH content, start_offset, source_path, source_name, (similarity + keyword_boost) AS boosted_similarity, similarity AS original_similarity, source_type, project_name ORDER BY boosted_similarity DESC LIMIT 20 RETURN source_path, source_name, original_similarity, boosted_similarity, source_type, project_name, content[0..100] as preview `; const result = await session.run(query, { embedding, minThreshold: threshold, enableHybrid: true, keywords }); console.log(`\n📊 Found ${result.records.length} results`); if (result.records.length > 0) { const projects = new Set(); console.log('\n🎯 Top Results:'); result.records.slice(0, 10).forEach((record, idx) => { const path = record.get('source_path'); const name = record.get('source_name'); const origSim = record.get('original_similarity'); const boostedSim = record.get('boosted_similarity'); const project = record.get('project_name'); const preview = record.get('preview') || ''; projects.add(project); console.log(`\n${idx + 1}. [${project}] ${name}`); console.log(` Path: ${path.substring(0, 80)}...`); console.log(` Similarity: ${origSim.toFixed(4)} → Boosted: ${boostedSim.toFixed(4)}`); if (preview) { console.log(` Preview: ${String(preview).substring(0, 100)}...`); } }); console.log(`\n📁 Projects found: ${[...projects].join(', ')}`); if (projects.has('ngx-cmk-translate') && projects.has('digital-pulse-web')) { console.log('✅ CROSS-PROJECT MATCH FOUND!'); } else { console.log(`⚠️ Missing projects - found: ${[...projects].join(', ')}`); } break; // Stop at first successful threshold } else { console.log(`❌ No results at threshold ${threshold}`); } } } finally { await session.close(); await driver.close(); } } testQuery().catch(error => { console.error('❌ Error:', error); process.exit(1); });