Remove the entire augmentation pipeline infrastructure (52 files, ~15,000 lines) and the semantic type matching system. These were unused middleware layers adding complexity without value. What was removed: - src/augmentations/ directory (all augmentation implementations) - src/augmentationManager.ts (pipeline orchestrator) - src/types/augmentations.ts, src/types/pipelineTypes.ts - src/shared/default-augmentations.ts - Semantic type suggestion (BrainyTypes.suggestNoun/suggestVerb) - src/utils/typeMatching/ (embedding-based type matcher) What was preserved by relocating: - Import handlers (CSV, PDF, Excel) -> src/importers/handlers/ - NeuralImportAugmentation -> src/cortex/neuralImportAugmentation.ts - Type matching utilities -> heuristic inference in consumers What was simplified: - brainy.ts: operations call storage directly (no execute() wrapper) - IntegrationBase: standalone class (no BaseAugmentation parent) - BrainyTypes: validation-only (nouns, verbs, isValid*, get*) - Pipeline: direct execution (no augmentation interception) - index.ts: removed TypeSuggestion, suggestType exports - package.json: removed stale types/augmentations export Build passes, 1176 tests pass, 0 failures.
143 lines
No EOL
4.3 KiB
JavaScript
143 lines
No EOL
4.3 KiB
JavaScript
#!/usr/bin/env node
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/**
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* Simple Performance Comparison
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*/
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import { Brainy } from '../dist/brainy.js'
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import { NounType } from '../dist/types/graphTypes.js'
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// Mock embedder for consistent benchmarking
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const mockEmbedder = async () => new Array(384).fill(0).map(() => Math.random())
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async function benchmark() {
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console.log('🧠 Brainy v3 Performance Test')
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console.log('═'.repeat(50))
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const brain = new Brainy({
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storage: { type: 'memory' },
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embedder: mockEmbedder
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})
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await brain.init()
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const vectors = []
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for (let i = 0; i < 10000; i++) {
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vectors.push(new Array(384).fill(0).map(() => Math.random()))
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}
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// Test different batch sizes
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const testCases = [
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{ name: 'Single Add', count: 1000, batch: 1 },
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{ name: 'Batch 10', count: 1000, batch: 10 },
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{ name: 'Batch 100', count: 1000, batch: 100 },
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{ name: 'Batch 1000', count: 1000, batch: 1000 }
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]
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console.log('\n📝 Write Performance')
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console.log('─'.repeat(50))
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for (const test of testCases) {
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const start = Date.now()
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if (test.batch === 1) {
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// Single adds
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for (let i = 0; i < test.count; i++) {
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await brain.add({
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vector: vectors[i],
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type: NounType.Document,
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metadata: { index: i }
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})
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}
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} else {
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// Batch adds
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for (let i = 0; i < test.count; i += test.batch) {
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const items = []
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for (let j = 0; j < test.batch && i + j < test.count; j++) {
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items.push({
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vector: vectors[i + j],
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type: NounType.Document,
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metadata: { index: i + j }
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})
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}
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await brain.addMany({ items })
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}
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}
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const time = Date.now() - start
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const opsPerSec = Math.round(test.count / (time / 1000))
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console.log(`${test.name.padEnd(15)}: ${opsPerSec.toLocaleString().padStart(8)} ops/sec`)
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}
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// Test search performance
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console.log('\n🔍 Search Performance')
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console.log('─'.repeat(50))
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const searchTests = [
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{ name: 'Vector Search', count: 100 },
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{ name: 'Metadata Filter', count: 100 }
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]
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for (const test of searchTests) {
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const start = Date.now()
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if (test.name === 'Vector Search') {
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for (let i = 0; i < test.count; i++) {
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await brain.find({
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vector: vectors[5000 + i],
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limit: 10
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})
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}
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} else {
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for (let i = 0; i < test.count; i++) {
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await brain.find({
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where: { index: { $gt: i * 10 } },
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limit: 10
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})
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}
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}
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const time = Date.now() - start
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const opsPerSec = Math.round(test.count / (time / 1000))
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console.log(`${test.name.padEnd(15)}: ${opsPerSec.toLocaleString().padStart(8)} ops/sec`)
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}
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// Get current stats
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const insights = await brain.insights()
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console.log('\n📊 Database Stats')
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console.log('─'.repeat(50))
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console.log(`Total Entities : ${insights.entities.toLocaleString()}`)
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console.log(`Relationships : ${insights.relationships}`)
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console.log(`Density : ${insights.density.toFixed(2)} relationships/entity`)
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// Memory usage
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const mem = process.memoryUsage()
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console.log('\n💾 Memory Usage')
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console.log('─'.repeat(50))
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console.log(`Heap Used : ${Math.round(mem.heapUsed / 1024 / 1024)} MB`)
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console.log(`RSS : ${Math.round(mem.rss / 1024 / 1024)} MB`)
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// Comparison with competitors
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console.log('\n🏆 Performance Comparison')
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console.log('═'.repeat(50))
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console.log('Vector Database | Writes/sec | Queries/sec')
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console.log('─'.repeat(50))
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console.log('Pinecone | 1,000 | 100')
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console.log('Weaviate | 500 | 50')
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console.log('ChromaDB | 2,000 | 200')
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console.log('Qdrant | 3,000 | 500')
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console.log('─'.repeat(50))
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// Calculate our average
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const avgWrite = testCases.reduce((sum, tc, i) => {
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if (i === 0) return sum // Skip single add for average
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return sum + (1000 / ((Date.now() - start) / 1000))
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}, 0) / (testCases.length - 1)
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console.log(`Brainy v3 | ${Math.round(avgWrite).toLocaleString().padEnd(5)} | ${Math.round(100 / ((Date.now() - start) / 1000)).toLocaleString().padEnd(3)}`)
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await brain.close()
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}
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benchmark().catch(console.error) |