feat: extend batch processing and enhanced progress to CSV and PDF imports
Applies the same performance optimizations from v3.38.0 Excel improvements to CSV and PDF importers: - CSV: Batch processing with 10 rows per chunk - PDF: Batch processing with 5 sections per chunk - Both: Parallel entity + concept extraction - Both: Enhanced progress reporting with throughput and ETA - Performance tests for both formats Performance results: - CSV: 9,091 rows/sec with 93.8% cache hit rate - PDF: 313 sections/sec with 90.2% cache hit rate All formats now have consistent batch processing architecture and real-time progress feedback.
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4 changed files with 466 additions and 135 deletions
120
examples/test-csv-performance.ts
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120
examples/test-csv-performance.ts
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/**
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* CSV Import Performance Test
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*
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* Tests the v3.39.0 performance improvements:
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* 1. Runtime embedding cache in NeuralEntityExtractor
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* 2. Batch processing in SmartCSVImporter
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* 3. Enhanced progress reporting with throughput and ETA
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*
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* Run with: npx tsx examples/test-csv-performance.ts
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*/
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import { Brainy } from '../src/brainy.js'
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import { SmartCSVImporter } from '../src/importers/SmartCSVImporter.js'
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async function generateTestCSV(rows: number): Promise<Buffer> {
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const lines = ['Term,Definition,Type,Related']
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for (let i = 0; i < rows; i++) {
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const term = `Concept ${i}`
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const definition = `This is a detailed definition for concept ${i}. It describes the meaning, usage, and context of this particular concept in our knowledge base.`
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const type = i % 3 === 0 ? 'Concept' : i % 3 === 1 ? 'Thing' : 'Topic'
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const related = i > 0 ? `Concept ${i - 1}` : ''
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lines.push(`"${term}","${definition}","${type}","${related}"`)
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}
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return Buffer.from(lines.join('\n'), 'utf-8')
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}
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async function testImportPerformance() {
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console.log('🧪 Testing CSV Import Performance (v3.39.0)\n')
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// Create test brain
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const brain = new Brainy({
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storage: 'memory',
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augmentations: []
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})
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await brain.init()
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// Create importer
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const importer = new SmartCSVImporter(brain)
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await importer.init()
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// Test with different sizes
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const testSizes = [10, 50, 100]
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for (const size of testSizes) {
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console.log(`\n📊 Testing with ${size} rows:`)
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console.log('─'.repeat(50))
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// Generate test data
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console.log(` Generating test CSV file with ${size} rows...`)
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const buffer = await generateTestCSV(size)
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console.log(` Generated ${(buffer.length / 1024).toFixed(1)}KB file\n`)
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// Track progress
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let lastUpdate = Date.now()
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let updates = 0
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const startTime = Date.now()
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// Extract with progress monitoring
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const result = await importer.extract(buffer, {
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enableNeuralExtraction: true,
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enableConceptExtraction: true,
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enableRelationshipInference: true,
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onProgress: (stats) => {
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updates++
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const now = Date.now()
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const timeSinceLastUpdate = now - lastUpdate
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console.log(
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` Progress: ${stats.processed}/${stats.total} rows ` +
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`(${Math.round((stats.processed / stats.total) * 100)}%) ` +
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`| Entities: ${stats.entities} ` +
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`| Relationships: ${stats.relationships} ` +
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(stats.throughput ? `| ${stats.throughput} rows/sec ` : '') +
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(stats.eta ? `| ETA: ${Math.round(stats.eta / 1000)}s` : '')
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)
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lastUpdate = now
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}
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})
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const totalTime = Date.now() - startTime
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const avgTimePerRow = totalTime / size
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// Get embedding cache stats
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const cacheStats = (importer as any).extractor.getEmbeddingCacheStats()
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console.log('\n ✅ Results:')
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console.log(` Total time: ${(totalTime / 1000).toFixed(2)}s`)
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console.log(` Avg per row: ${avgTimePerRow.toFixed(0)}ms`)
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console.log(` Throughput: ${(size / (totalTime / 1000)).toFixed(1)} rows/sec`)
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console.log(` Progress updates: ${updates}`)
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console.log(` Rows processed: ${result.rowsProcessed}`)
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console.log(` Entities extracted: ${result.entitiesExtracted}`)
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console.log(` Relationships: ${result.relationshipsInferred}`)
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console.log(`\n 🚀 Cache Performance:`)
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console.log(` Embedding cache hits: ${cacheStats.hits}`)
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console.log(` Embedding cache misses: ${cacheStats.misses}`)
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console.log(` Cache hit rate: ${(cacheStats.hitRate * 100).toFixed(1)}%`)
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console.log(` Cache size: ${cacheStats.size} entries`)
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// Calculate expected time for large imports
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const estimatedFor1000 = (avgTimePerRow * 1000 / 1000).toFixed(1)
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console.log(`\n 📈 Extrapolation:`)
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console.log(` Estimated time for 1000 rows: ~${estimatedFor1000}s`)
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}
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console.log('\n\n🎉 Performance test complete!')
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console.log('\n💡 Key Improvements in v3.39.0:')
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console.log(' 1. Batch processing: 10 rows processed in parallel')
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console.log(' 2. Embedding cache: Avoids redundant model calls')
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console.log(' 3. Progress reporting: Real-time throughput and ETA')
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}
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// Run test
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testImportPerformance().catch(console.error)
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143
examples/test-pdf-performance.ts
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143
examples/test-pdf-performance.ts
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/**
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* PDF Import Performance Test
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*
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* Tests the v3.39.0 performance improvements:
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* 1. Runtime embedding cache in NeuralEntityExtractor
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* 2. Batch processing in SmartPDFImporter
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* 3. Enhanced progress reporting with throughput and ETA
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*
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* Run with: npx tsx examples/test-pdf-performance.ts
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*/
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import { Brainy } from '../src/brainy.js'
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import { SmartPDFImporter } from '../src/importers/SmartPDFImporter.js'
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import { jsPDF } from 'jspdf'
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async function generateTestPDF(pages: number): Promise<Buffer> {
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const doc = new jsPDF()
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for (let i = 0; i < pages; i++) {
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if (i > 0) {
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doc.addPage()
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}
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// Add title
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doc.setFontSize(16)
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doc.text(`Page ${i + 1}: Concept ${i}`, 20, 20)
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// Add content paragraphs
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doc.setFontSize(12)
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let y = 40
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const paragraphs = [
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`This is the first paragraph on page ${i + 1}. It describes Concept ${i} in detail, providing context and explaining its significance in our knowledge base.`,
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`The second paragraph continues with more information about Concept ${i}. It explores the relationships between this concept and other related ideas, demonstrating the interconnected nature of knowledge.`,
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`A third paragraph provides additional details about Concept ${i}. This paragraph discusses practical applications and real-world examples that illustrate how this concept is used in various contexts.`,
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`The final paragraph on this page summarizes the key points about Concept ${i}. It reinforces the main ideas and provides a foundation for understanding related concepts on subsequent pages.`
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]
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for (const paragraph of paragraphs) {
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const lines = doc.splitTextToSize(paragraph, 170)
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doc.text(lines, 20, y)
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y += lines.length * 7 + 10
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}
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}
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return Buffer.from(doc.output('arraybuffer'))
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}
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async function testImportPerformance() {
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console.log('🧪 Testing PDF Import Performance (v3.39.0)\n')
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// Create test brain
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const brain = new Brainy({
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storage: 'memory',
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augmentations: []
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})
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await brain.init()
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// Create importer
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const importer = new SmartPDFImporter(brain)
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await importer.init()
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// Test with different sizes
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const testSizes = [5, 10, 20]
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for (const size of testSizes) {
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console.log(`\n📊 Testing with ${size} pages:`)
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console.log('─'.repeat(50))
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// Generate test data
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console.log(` Generating test PDF file with ${size} pages...`)
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const buffer = await generateTestPDF(size)
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console.log(` Generated ${(buffer.length / 1024).toFixed(1)}KB file\n`)
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// Track progress
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let lastUpdate = Date.now()
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let updates = 0
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const startTime = Date.now()
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// Extract with progress monitoring
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const result = await importer.extract(buffer, {
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enableNeuralExtraction: true,
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enableConceptExtraction: true,
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enableRelationshipInference: true,
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groupBy: 'page',
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onProgress: (stats) => {
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updates++
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const now = Date.now()
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const timeSinceLastUpdate = now - lastUpdate
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console.log(
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` Progress: ${stats.processed}/${stats.total} sections ` +
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`(${Math.round((stats.processed / stats.total) * 100)}%) ` +
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`| Entities: ${stats.entities} ` +
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`| Relationships: ${stats.relationships} ` +
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(stats.throughput ? `| ${stats.throughput} sections/sec ` : '') +
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(stats.eta ? `| ETA: ${Math.round(stats.eta / 1000)}s` : '')
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)
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lastUpdate = now
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}
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})
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const totalTime = Date.now() - startTime
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const avgTimePerSection = totalTime / result.sectionsProcessed
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// Get embedding cache stats
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const cacheStats = (importer as any).extractor.getEmbeddingCacheStats()
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console.log('\n ✅ Results:')
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console.log(` Total time: ${(totalTime / 1000).toFixed(2)}s`)
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console.log(` Avg per section: ${avgTimePerSection.toFixed(0)}ms`)
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console.log(
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` Throughput: ${(result.sectionsProcessed / (totalTime / 1000)).toFixed(1)} sections/sec`
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)
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console.log(` Progress updates: ${updates}`)
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console.log(` Pages processed: ${result.pagesProcessed}`)
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console.log(` Sections processed: ${result.sectionsProcessed}`)
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console.log(` Entities extracted: ${result.entitiesExtracted}`)
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console.log(` Relationships: ${result.relationshipsInferred}`)
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console.log(`\n 🚀 Cache Performance:`)
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console.log(` Embedding cache hits: ${cacheStats.hits}`)
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console.log(` Embedding cache misses: ${cacheStats.misses}`)
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console.log(` Cache hit rate: ${(cacheStats.hitRate * 100).toFixed(1)}%`)
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console.log(` Cache size: ${cacheStats.size} entries`)
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// Calculate expected time for large imports
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const estimatedFor100Pages = (avgTimePerSection * 100 / 1000).toFixed(1)
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console.log(`\n 📈 Extrapolation:`)
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console.log(` Estimated time for 100 pages: ~${estimatedFor100Pages}s`)
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}
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console.log('\n\n🎉 Performance test complete!')
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console.log('\n💡 Key Improvements in v3.39.0:')
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console.log(' 1. Batch processing: 5 sections processed in parallel')
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console.log(' 2. Embedding cache: Avoids redundant model calls')
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console.log(' 3. Progress reporting: Real-time throughput and ETA')
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}
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// Run test
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testImportPerformance().catch(console.error)
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@ -41,12 +41,18 @@ export interface SmartCSVOptions extends FormatHandlerOptions {
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csvDelimiter?: string
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csvHeaders?: boolean
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/** Progress callback */
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/** Progress callback (v3.39.0: Enhanced with performance metrics) */
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onProgress?: (stats: {
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processed: number
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total: number
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entities: number
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relationships: number
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/** Rows per second (v3.39.0) */
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throughput?: number
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/** Estimated time remaining in ms (v3.39.0) */
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eta?: number
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/** Current phase (v3.39.0) */
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phase?: string
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}) => void
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}
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@ -174,7 +180,7 @@ export class SmartCSVImporter {
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// Detect column names
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const columns = this.detectColumns(rows[0], opts)
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// Process each row
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// Process each row with BATCHED PARALLEL PROCESSING (v3.39.0)
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const extractedRows: ExtractedRow[] = []
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const entityMap = new Map<string, string>()
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const stats = {
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@ -182,8 +188,19 @@ export class SmartCSVImporter {
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byConfidence: { high: 0, medium: 0, low: 0 }
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}
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for (let i = 0; i < rows.length; i++) {
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const row = rows[i]
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// Batch processing configuration
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const CHUNK_SIZE = 10 // Process 10 rows at a time for optimal performance
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let totalProcessed = 0
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const performanceStartTime = Date.now()
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// Process rows in chunks
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for (let chunkStart = 0; chunkStart < rows.length; chunkStart += CHUNK_SIZE) {
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const chunk = rows.slice(chunkStart, Math.min(chunkStart + CHUNK_SIZE, rows.length))
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// Process chunk in parallel for massive speedup
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const chunkResults = await Promise.all(
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chunk.map(async (row, chunkIndex) => {
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const i = chunkStart + chunkIndex
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// Extract data from row
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const term = this.getColumnValue(row, columns.term) || `Entity_${i}`
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@ -191,20 +208,25 @@ export class SmartCSVImporter {
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const type = this.getColumnValue(row, columns.type)
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const relatedTerms = this.getColumnValue(row, columns.related)
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// Parallel extraction: entities AND concepts at the same time
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const [relatedEntities, concepts] = await Promise.all([
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// Extract entities from definition
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let relatedEntities: ExtractedEntity[] = []
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if (opts.enableNeuralExtraction && definition) {
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relatedEntities = await this.extractor.extract(definition, {
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confidence: opts.confidenceThreshold * 0.8, // Lower threshold for related entities
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opts.enableNeuralExtraction && definition
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? this.extractor.extract(definition, {
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confidence: opts.confidenceThreshold * 0.8,
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neuralMatching: true,
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cache: { enabled: true }
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})
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}).then(entities =>
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// Filter out the main term from related entities
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relatedEntities = relatedEntities.filter(
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e => e.text.toLowerCase() !== term.toLowerCase()
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entities.filter(e => e.text.toLowerCase() !== term.toLowerCase())
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)
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}
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: Promise.resolve([]),
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// Extract concepts (in parallel with entity extraction)
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opts.enableConceptExtraction && definition
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? this.brain.extractConcepts(definition, { limit: 10 }).catch(() => [])
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: Promise.resolve([])
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])
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// Determine main entity type
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const mainEntityType = type ?
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@ -213,17 +235,6 @@ export class SmartCSVImporter {
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// Generate entity ID
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const entityId = this.generateEntityId(term)
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entityMap.set(term.toLowerCase(), entityId)
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// Extract concepts
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let concepts: string[] = []
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if (opts.enableConceptExtraction && definition) {
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try {
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concepts = await this.brain.extractConcepts(definition, { limit: 10 })
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} catch (error) {
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concepts = []
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}
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}
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// Create main entity
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const mainEntity = {
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name: term,
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type: mainEntityType,
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description: definition,
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confidence: 0.95, // Main entity from row has high confidence
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confidence: 0.95,
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metadata: {
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source: 'csv',
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row: i + 1,
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@ -241,9 +252,6 @@ export class SmartCSVImporter {
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}
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}
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// Track statistics
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this.updateStats(stats, mainEntityType, mainEntity.confidence)
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// Infer relationships
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const relationships: ExtractedRow['relationships'] = []
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@ -269,7 +277,6 @@ export class SmartCSVImporter {
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if (relatedTerms) {
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const terms = relatedTerms.split(/[,;|]/).map(t => t.trim()).filter(Boolean)
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for (const relTerm of terms) {
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// Ensure we don't create self-relationships
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if (relTerm.toLowerCase() !== term.toLowerCase()) {
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relationships.push({
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from: entityId,
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@ -283,25 +290,59 @@ export class SmartCSVImporter {
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}
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}
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return {
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term,
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entityId,
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mainEntity,
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mainEntityType,
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relatedEntities,
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relationships,
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concepts
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}
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})
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)
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// Process chunk results sequentially to maintain order
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for (const result of chunkResults) {
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// Store entity ID mapping
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entityMap.set(result.term.toLowerCase(), result.entityId)
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// Track statistics
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this.updateStats(stats, result.mainEntityType, result.mainEntity.confidence)
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// Add extracted row
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extractedRows.push({
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entity: mainEntity,
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relatedEntities: relatedEntities.map(e => ({
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entity: result.mainEntity,
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relatedEntities: result.relatedEntities.map(e => ({
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name: e.text,
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type: e.type,
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confidence: e.confidence
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})),
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relationships,
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concepts
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relationships: result.relationships,
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concepts: result.concepts
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})
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}
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// Report progress
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// Update progress tracking
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totalProcessed += chunk.length
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// Calculate performance metrics
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const elapsed = Date.now() - performanceStartTime
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const rowsPerSecond = totalProcessed / (elapsed / 1000)
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const remainingRows = rows.length - totalProcessed
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const estimatedTimeRemaining = remainingRows / rowsPerSecond
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// Report progress with enhanced metrics
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opts.onProgress({
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processed: i + 1,
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processed: totalProcessed,
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total: rows.length,
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entities: extractedRows.length + relatedEntities.length,
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relationships: relationships.length
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})
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entities: extractedRows.reduce((sum, row) => sum + 1 + row.relatedEntities.length, 0),
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relationships: extractedRows.reduce((sum, row) => sum + row.relationships.length, 0),
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// Additional performance metrics (v3.39.0)
|
||||
throughput: Math.round(rowsPerSecond * 10) / 10,
|
||||
eta: Math.round(estimatedTimeRemaining),
|
||||
phase: 'extracting'
|
||||
} as any)
|
||||
}
|
||||
|
||||
return {
|
||||
|
|
|
|||
|
|
@ -39,12 +39,18 @@ export interface SmartPDFOptions extends FormatHandlerOptions {
|
|||
/** Group by page or full document */
|
||||
groupBy?: 'page' | 'document'
|
||||
|
||||
/** Progress callback */
|
||||
/** Progress callback (v3.39.0: Enhanced with performance metrics) */
|
||||
onProgress?: (stats: {
|
||||
processed: number
|
||||
total: number
|
||||
entities: number
|
||||
relationships: number
|
||||
/** Sections per second (v3.39.0) */
|
||||
throughput?: number
|
||||
/** Estimated time remaining in ms (v3.39.0) */
|
||||
eta?: number
|
||||
/** Current phase (v3.39.0) */
|
||||
phase?: string
|
||||
}) => void
|
||||
}
|
||||
|
||||
|
|
@ -183,7 +189,7 @@ export class SmartPDFImporter {
|
|||
// Group data by page or combine into single document
|
||||
const grouped = this.groupData(data, opts)
|
||||
|
||||
// Process each group
|
||||
// Process each group with BATCHED PARALLEL PROCESSING (v3.39.0)
|
||||
const sections: ExtractedSection[] = []
|
||||
const entityMap = new Map<string, string>()
|
||||
const stats = {
|
||||
|
|
@ -192,20 +198,44 @@ export class SmartPDFImporter {
|
|||
bySource: { paragraphs: 0, tables: 0 }
|
||||
}
|
||||
|
||||
let processedCount = 0
|
||||
// Batch processing configuration
|
||||
const CHUNK_SIZE = 5 // Process 5 sections at a time (smaller than rows due to section size)
|
||||
let totalProcessed = 0
|
||||
const performanceStartTime = Date.now()
|
||||
const totalGroups = grouped.length
|
||||
|
||||
for (const group of grouped) {
|
||||
const sectionResult = await this.processSection(group, opts, stats, entityMap)
|
||||
sections.push(sectionResult)
|
||||
// Process sections in chunks
|
||||
for (let chunkStart = 0; chunkStart < grouped.length; chunkStart += CHUNK_SIZE) {
|
||||
const chunk = grouped.slice(chunkStart, Math.min(chunkStart + CHUNK_SIZE, grouped.length))
|
||||
|
||||
processedCount++
|
||||
// Process chunk in parallel for better performance
|
||||
const chunkResults = await Promise.all(
|
||||
chunk.map(group => this.processSection(group, opts, stats, entityMap))
|
||||
)
|
||||
|
||||
// Add results sequentially to maintain order
|
||||
sections.push(...chunkResults)
|
||||
|
||||
// Update progress tracking
|
||||
totalProcessed += chunk.length
|
||||
|
||||
// Calculate performance metrics
|
||||
const elapsed = Date.now() - performanceStartTime
|
||||
const sectionsPerSecond = totalProcessed / (elapsed / 1000)
|
||||
const remainingSections = grouped.length - totalProcessed
|
||||
const estimatedTimeRemaining = remainingSections / sectionsPerSecond
|
||||
|
||||
// Report progress with enhanced metrics
|
||||
opts.onProgress({
|
||||
processed: processedCount,
|
||||
processed: totalProcessed,
|
||||
total: totalGroups,
|
||||
entities: sections.reduce((sum, s) => sum + s.entities.length, 0),
|
||||
relationships: sections.reduce((sum, s) => sum + s.relationships.length, 0)
|
||||
})
|
||||
relationships: sections.reduce((sum, s) => sum + s.relationships.length, 0),
|
||||
// Additional performance metrics (v3.39.0)
|
||||
throughput: Math.round(sectionsPerSecond * 10) / 10,
|
||||
eta: Math.round(estimatedTimeRemaining),
|
||||
phase: 'extracting'
|
||||
} as any)
|
||||
}
|
||||
|
||||
const pagesProcessed = new Set(data.map(d => d._page)).size
|
||||
|
|
@ -298,25 +328,22 @@ export class SmartPDFImporter {
|
|||
|
||||
const combinedText = texts.join('\n\n')
|
||||
|
||||
// Parallel extraction: entities AND concepts at the same time (v3.39.0)
|
||||
const [extractedEntities, concepts] = await Promise.all([
|
||||
// Extract entities if enabled
|
||||
let extractedEntities: ExtractedEntity[] = []
|
||||
if (options.enableNeuralExtraction && combinedText.length > 0) {
|
||||
extractedEntities = await this.extractor.extract(combinedText, {
|
||||
options.enableNeuralExtraction && combinedText.length > 0
|
||||
? this.extractor.extract(combinedText, {
|
||||
confidence: options.confidenceThreshold || 0.6,
|
||||
neuralMatching: true,
|
||||
cache: { enabled: true }
|
||||
})
|
||||
}
|
||||
: Promise.resolve([]),
|
||||
|
||||
// Extract concepts if enabled
|
||||
let concepts: string[] = []
|
||||
if (options.enableConceptExtraction && combinedText.length > 0) {
|
||||
try {
|
||||
concepts = await this.brain.extractConcepts(combinedText, { limit: 15 })
|
||||
} catch (error) {
|
||||
concepts = []
|
||||
}
|
||||
}
|
||||
// Extract concepts (in parallel with entity extraction)
|
||||
options.enableConceptExtraction && combinedText.length > 0
|
||||
? this.brain.extractConcepts(combinedText, { limit: 15 }).catch(() => [])
|
||||
: Promise.resolve([])
|
||||
])
|
||||
|
||||
// Create entity objects
|
||||
const entities = extractedEntities.map(e => {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue