#!/usr/bin/env tsx
/**
* Test automatic embedding triggers
*/
import { initDatabaseFromEnv } from './src/database/index.js';
import { MemoryCore } from './src/core/index.js';
async function testEmbeddingTriggers() {
console.log('š§Ŗ Testing Automatic Embedding Triggers\n');
// Initialize with OpenAI key from environment
const db = initDatabaseFromEnv();
const memoryCore = new MemoryCore(
db,
process.env.OPENAI_API_KEY,
{ autoUpdateEmbeddings: true }
);
await memoryCore.initialize();
const userId = 'bob@matsuoka.com';
console.log('API Key Status:', process.env.OPENAI_API_KEY ? 'ā
Configured' : 'ā Missing');
// Test 1: Create memory and check if embedding is generated
console.log('\n1ļøā£ TEST: Create memory with auto-embedding');
const testMem = await memoryCore.addMemory(
'AI Research Paper',
'Deep learning with transformers for natural language processing',
'semantic' as any,
{
userId,
tags: ['ai', 'transformers', 'nlp'],
metadata: { category: 'research' },
generateEmbedding: false, // Deliberately set to false to test auto-update
}
);
if (testMem.status === 'success' && testMem.data) {
const memId = (testMem.data as any).id;
console.log(` Memory created: ${memId}`);
// Wait for async embedding update
console.log(' Waiting for automatic embedding generation...');
await new Promise(resolve => setTimeout(resolve, 3000));
// Check if embedding was created
const checkResult = await db.execute(
`SELECT
embedding IS NOT NULL as has_embedding,
json_array_length(embedding) as dimensions
FROM memories
WHERE id = ?`,
[memId]
);
const row = checkResult.rows[0] as any;
if (row?.has_embedding && row?.dimensions > 0) {
console.log(` ā
Embedding auto-generated: ${row.dimensions} dimensions`);
} else {
console.log(' ā Embedding not generated automatically');
}
// Test 2: Update memory and check if embedding is regenerated
console.log('\n2ļøā£ TEST: Update memory triggers embedding regeneration');
const updateResult = await memoryCore.updateMemory(
memId,
{
content: 'Advanced deep learning with transformers and attention mechanisms for NLP tasks',
tags: ['ai', 'transformers', 'attention', 'deep-learning'],
},
{ userId }
);
if (updateResult.status === 'success') {
console.log(' Memory updated');
// Wait for async embedding update
console.log(' Waiting for embedding regeneration...');
await new Promise(resolve => setTimeout(resolve, 3000));
// Get updated memory
const updatedCheck = await db.execute(
`SELECT
embedding IS NOT NULL as has_embedding,
json_array_length(embedding) as dimensions,
updated_at
FROM memories
WHERE id = ?`,
[memId]
);
const updatedRow = updatedCheck.rows[0] as any;
console.log(` ā
Embedding status: ${updatedRow?.has_embedding ? 'Present' : 'Missing'}`);
console.log(` Last updated: ${updatedRow?.updated_at}`);
}
// Clean up
await memoryCore.deleteMemory(memId, { userId });
}
// Test 3: Batch update of missing embeddings
console.log('\n3ļøā£ TEST: Manual batch update of missing embeddings');
// Create memories without embeddings
const mem1 = await memoryCore.addMemory(
'Test Memory 1',
'Content without embedding',
'semantic' as any,
{ userId, generateEmbedding: false }
);
const mem2 = await memoryCore.addMemory(
'Test Memory 2',
'Another content without embedding',
'semantic' as any,
{ userId, generateEmbedding: false }
);
const memIds = [];
if (mem1.status === 'success') memIds.push((mem1.data as any).id);
if (mem2.status === 'success') memIds.push((mem2.data as any).id);
console.log(` Created ${memIds.length} memories without embeddings`);
// Trigger manual update
const updateResult = await memoryCore.updateMissingEmbeddings();
if (updateResult.status === 'success' && updateResult.data) {
const stats = updateResult.data as any;
console.log(` ā
Batch update completed: ${stats.updated} updated, ${stats.failed} failed`);
} else {
console.log(` ā Batch update failed: ${updateResult.error}`);
}
// Clean up
for (const id of memIds) {
await memoryCore.deleteMemory(id, { userId });
}
// Test 4: Statistics
console.log('\n4ļøā£ EMBEDDING STATISTICS:');
const stats = await memoryCore.getStatistics(userId);
if (stats.status === 'success' && stats.data) {
const data = stats.data as any;
const percentage = data.totalMemories > 0
? ((data.memoriesWithEmbeddings / data.totalMemories) * 100).toFixed(1)
: '0';
console.log(` Total memories: ${data.totalMemories}`);
console.log(` With embeddings: ${data.memoriesWithEmbeddings}`);
console.log(` Coverage: ${percentage}%`);
}
console.log('\nā
Test complete!');
await db.disconnect();
process.exit(0);
}
testEmbeddingTriggers().catch(console.error);
