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

#!/usr/bin/env node import neo4j from 'neo4j-driver'; const driver = neo4j.driver( 'bolt://localhost:7687', neo4j.auth.basic('neo4j', 'password') ); async function testSearchAndRelationships() { const session = driver.session(); try { console.log('\n📊 === INDEXING STATUS ===\n'); // Check indexed files const filesResult = await session.run(` MATCH (f:File) RETURN f.path as path, f.name as name, f.language as language ORDER BY f.indexed_date DESC LIMIT 10 `); console.log(`Found ${filesResult.records.length} indexed files:`); filesResult.records.forEach((r, i) => { console.log(` ${i + 1}. ${r.get('name')} (${r.get('language')}) - ${r.get('path')}`); }); // Check file chunks with embeddings console.log('\n📦 === FILE CHUNKS ===\n'); const chunksResult = await session.run(` MATCH (f:File)-[:HAS_CHUNK]->(c:FileChunk) RETURN f.name as file, count(c) as chunk_count ORDER BY chunk_count DESC LIMIT 5 `); console.log('Files with most chunks:'); chunksResult.records.forEach((r, i) => { console.log(` ${i + 1}. ${r.get('file')}: ${r.get('chunk_count')} chunks`); }); // Check chunk IDs console.log('\n🔗 === CHUNK IDS & RELATIONSHIPS ===\n'); const chunkIdResult = await session.run(` MATCH (c:FileChunk) WHERE c.id IS NOT NULL RETURN c.id as chunk_id, c.filePath as file, c.chunk_index as idx ORDER BY c.filePath, c.chunk_index LIMIT 5 `); console.log('Sample chunk IDs:'); chunkIdResult.records.forEach((r, i) => { console.log(` ${i + 1}. ${r.get('chunk_id')} (index: ${r.get('idx')})`); }); // Check NEXT_CHUNK relationships const nextChunkResult = await session.run(` MATCH (c1:FileChunk)-[r:NEXT_CHUNK]->(c2:FileChunk) RETURN c1.filePath as file, count(r) as relationship_count LIMIT 5 `); console.log('\nNEXT_CHUNK relationships per file:'); nextChunkResult.records.forEach((r, i) => { console.log(` ${i + 1}. ${r.get('file')}: ${r.get('relationship_count')} links`); }); // Test semantic search with embeddings console.log('\n🔍 === TESTING SEMANTIC SEARCH ===\n'); // Get a sample text from a chunk to search for const sampleResult = await session.run(` MATCH (c:FileChunk) WHERE c.text IS NOT NULL AND size(c.text) > 50 RETURN c.text as text, c.filePath as file LIMIT 1 `); if (sampleResult.records.length > 0) { const sampleText = sampleResult.records[0].get('text').substring(0, 100); const sampleFile = sampleResult.records[0].get('file'); console.log(`Searching for text from: ${sampleFile}`); console.log(`Sample text: "${sampleText}..."`); // Search using vector similarity (if embeddings exist) const searchResult = await session.run(` MATCH (c:FileChunk) WHERE c.embedding IS NOT NULL RETURN c.filePath as file, c.chunk_index as idx, size(c.embedding) as embedding_dims LIMIT 3 `); console.log(`\nChunks with embeddings (${searchResult.records.length} found):`); searchResult.records.forEach((r, i) => { console.log(` ${i + 1}. ${r.get('file')} [chunk ${r.get('idx')}] - ${r.get('embedding_dims')} dimensions`); }); } else { console.log('No chunks with text content found yet'); } // Check aggregation - chunks grouped by file console.log('\n📑 === CHUNK AGGREGATION ===\n'); const aggResult = await session.run(` MATCH (f:File)-[:HAS_CHUNK]->(c:FileChunk) WHERE c.embedding IS NOT NULL WITH f.path as file, count(c) as total_chunks, avg(c.chunk_index) as avg_index RETURN file, total_chunks, avg_index ORDER BY total_chunks DESC LIMIT 5 `); console.log('File chunk aggregation:'); aggResult.records.forEach((r, i) => { console.log(` ${i + 1}. ${r.get('file')}`); console.log(` Total chunks: ${r.get('total_chunks')}`); console.log(` Avg index: ${r.get('avg_index').toFixed(1)}`); }); // Check vector index console.log('\n⚡ === VECTOR INDEX STATUS ===\n'); try { const indexResult = await session.run(` SHOW INDEXES YIELD name, type, labelsOrTypes, properties, options WHERE type = 'VECTOR' RETURN name, labelsOrTypes, options `); if (indexResult.records.length > 0) { indexResult.records.forEach(r => { const options = r.get('options'); console.log(`Vector index: ${r.get('name')}`); console.log(` Labels: ${r.get('labelsOrTypes')}`); if (options && options.indexConfig) { console.log(` Dimensions: ${options.indexConfig['vector.dimensions']}`); console.log(` Similarity: ${options.indexConfig['vector.similarity_function']}`); } }); } else { console.log('No vector indexes found'); } } catch (err) { console.log('Could not query indexes (requires Neo4j 5.0+)'); } console.log('\n✅ === TEST COMPLETE ===\n'); } catch (error) { console.error('❌ Error:', error.message); throw error; } finally { await session.close(); await driver.close(); } } testSearchAndRelationships() .then(() => process.exit(0)) .catch((error) => { console.error('Failed:', error); process.exit(1); });