refactor(8.0): remove dead, unreachable, and unwired modules
Pre-GA dead-code sweep. A deterministic import-reachability walk from the
package's real entry points (the exports map, the CLI bin, the conversation
surface) found 34 source modules that nothing reachable imports — they
compile and ship as dist output that no consumer or internal path can ever
reach. All were superseded duplicates, abandoned parallel implementations,
or built-but-never-wired features:
- superseded duplicates of live modules: an older "unified" entry, a
standalone neural-import variant, a static NLP processor + its matcher,
a parallel API-types module, a duplicate progress-types module
- an abandoned import path (orchestrator + entity deduplicator + barrels)
left behind when ingestion moved to the coordinator
- unwired feature modules (instance pool, import presets, cached
embeddings, relationship-confidence scorer) reachable only from tests
- dead leaf utilities (write buffer, deleted-items index, bounded
registry, two crypto shims, a cache manager, a structured logger, a
stale v5 type-migration helper, a browser-only FS type shim) and
several dead re-export barrels
- a CLI catalog command wired into no command
Also removed two tests that only exercised deleted modules, repointed two
VFS tests off a deleted barrel onto the implementation module, and deleted
one example that demoed removed features.
Verified: clean build (both tsc passes), unit 1505 + integration 607 green,
and a re-run of the reachability walk reports zero remaining orphans.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
parent
03d654061f
commit
bf0afe8563
39 changed files with 2 additions and 10497 deletions
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@ -1,237 +0,0 @@
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/**
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* Directory Import with Entity Extraction Caching Example
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*
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* Demonstrates:
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* - Importing directories with progress tracking
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* - Entity extraction caching for performance
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* - Relationship detection with confidence scores
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* - Cache statistics monitoring
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*/
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import { Brainy, NounType, VerbType } from '../src/brainy.js'
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import { DirectoryImporter } from '../src/vfs/importers/DirectoryImporter.js'
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import { ProgressTracker, formatProgress } from '../src/types/progress.types.js'
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import { detectRelationshipsWithConfidence } from '../src/neural/relationshipConfidence.js'
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import { NeuralEntityExtractor } from '../src/neural/entityExtractor.js'
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async function main() {
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console.log('🧠 Brainy 3.21.0 - Directory Import with Caching Example\n')
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// Initialize Brainy
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const brain = new Brainy({ verbose: false })
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await brain.init()
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console.log('✅ Brainy initialized\n')
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// The entity extractor (and its extraction cache) is constructed directly.
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const extractor = new NeuralEntityExtractor(brain)
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// Example 1: Import directory with entity extraction caching
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console.log('📁 Example 1: Import Directory with Caching\n')
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const vfs = brain.vfs
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const importer = new DirectoryImporter(vfs, brain)
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// Progress tracking
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const tracker = ProgressTracker.create(100)
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tracker.start()
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try {
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// Import with progress (using async generator)
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console.log('Importing directory...')
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let filesProcessed = 0
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for await (const progress of importer.importStream('./examples', {
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batchSize: 10,
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recursive: true,
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generateEmbeddings: true,
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extractMetadata: true
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})) {
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if (progress.type === 'progress') {
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filesProcessed = progress.processed
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const trackedProgress = tracker.update(progress.processed, progress.current)
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console.log(` ${formatProgress(trackedProgress)}`)
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} else if (progress.type === 'complete') {
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console.log(`\n✅ Import complete! Processed ${progress.processed} files\n`)
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} else if (progress.type === 'error') {
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console.error(`❌ Error: ${progress.error?.message}`)
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}
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}
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tracker.complete({ filesProcessed })
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} catch (error) {
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console.error('Import failed:', error)
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}
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// Example 2: Entity extraction with caching
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console.log('\n📝 Example 2: Entity Extraction with Caching\n')
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const sampleText = `
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John Smith created the user authentication system for the application.
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The authentication system uses JWT tokens and bcrypt for password hashing.
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Mary Johnson manages the backend team that maintains the system.
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The system was built using Node.js and PostgreSQL database.
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`
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console.log('First extraction (cache miss):')
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const startTime1 = Date.now()
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const entities1 = await extractor.extract(sampleText, {
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types: [NounType.Person, NounType.Service, NounType.Technology],
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confidence: 0.7,
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cache: {
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enabled: true,
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ttl: 7 * 24 * 60 * 60 * 1000, // 7 days
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invalidateOn: 'hash'
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}
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})
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const time1 = Date.now() - startTime1
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console.log(` Extracted ${entities1.length} entities in ${time1}ms`)
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console.log(` Entities: ${entities1.map(e => e.text).join(', ')}\n`)
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console.log('Second extraction (cache hit):')
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const startTime2 = Date.now()
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const entities2 = await extractor.extract(sampleText, {
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types: [NounType.Person, NounType.Service, NounType.Technology],
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confidence: 0.7,
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cache: {
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enabled: true,
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invalidateOn: 'hash'
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}
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})
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const time2 = Date.now() - startTime2
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console.log(` Extracted ${entities2.length} entities in ${time2}ms`)
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console.log(` Speedup: ${Math.round(time1 / time2)}x faster!\n`)
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// Show cache statistics
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const cacheStats = extractor.getCacheStats()
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console.log('📊 Cache Statistics:')
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console.log(` Hits: ${cacheStats.hits}`)
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console.log(` Misses: ${cacheStats.misses}`)
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console.log(` Hit Rate: ${(cacheStats.hitRate * 100).toFixed(1)}%`)
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console.log(` Total Entries: ${cacheStats.totalEntries}`)
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console.log(` Avg Entities per Entry: ${cacheStats.averageEntitiesPerEntry}\n`)
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// Example 3: Relationship detection with confidence
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console.log('🔗 Example 3: Relationship Detection with Confidence\n')
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const relationships = detectRelationshipsWithConfidence(
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entities1,
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sampleText,
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{
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minConfidence: 0.6,
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maxDistance: 100,
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useProximityBoost: true,
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usePatternMatching: true,
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useStructuralAnalysis: true
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}
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)
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console.log(`Detected ${relationships.length} relationships:\n`)
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for (const rel of relationships.slice(0, 5)) { // Show top 5
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console.log(` ${rel.sourceEntity.text} --[${rel.verbType}]--> ${rel.targetEntity.text}`)
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console.log(` Confidence: ${(rel.confidence * 100).toFixed(1)}%`)
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console.log(` Evidence: ${rel.evidence.reasoning}`)
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console.log(` Method: ${rel.evidence.method}`)
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console.log(` Source: "${rel.evidence.sourceText?.substring(0, 60)}..."\n`)
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}
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// Example 4: Create relationships in graph with confidence
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console.log('📊 Example 4: Creating Relationships in Graph\n')
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const createdRelations = []
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for (const rel of relationships.slice(0, 3)) { // Create top 3
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try {
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// Add entities to brain
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const sourceId = await brain.add({
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data: rel.sourceEntity.text,
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type: rel.sourceEntity.type,
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metadata: {
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confidence: rel.sourceEntity.confidence,
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extractedFrom: 'sample text'
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}
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})
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const targetId = await brain.add({
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data: rel.targetEntity.text,
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type: rel.targetEntity.type,
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metadata: {
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confidence: rel.targetEntity.confidence,
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extractedFrom: 'sample text'
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}
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})
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// Create relationship with confidence
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const relationId = await brain.relate({
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from: sourceId,
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to: targetId,
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type: rel.verbType,
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confidence: rel.confidence,
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evidence: rel.evidence,
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metadata: {
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autoDetected: true,
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detectedAt: new Date().toISOString()
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}
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})
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createdRelations.push(relationId)
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console.log(` ✅ Created: ${rel.sourceEntity.text} → ${rel.targetEntity.text}`)
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} catch (error) {
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console.error(` ❌ Failed to create relationship:`, error)
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}
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}
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console.log(`\n✅ Created ${createdRelations.length} relationships in knowledge graph`)
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// Example 5: Query relationships by confidence
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console.log('\n🔍 Example 5: Query High-Confidence Relationships\n')
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const allRelations = await brain.getRelations({
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limit: 100
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})
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const highConfidence = allRelations.filter(r => (r.confidence || 0) >= 0.7)
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console.log(`Found ${highConfidence.length} high-confidence relationships (≥70%):\n`)
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for (const rel of highConfidence.slice(0, 5)) {
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console.log(` ${rel.from} → ${rel.to} (${rel.type})`)
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console.log(` Confidence: ${((rel.confidence || 0) * 100).toFixed(1)}%`)
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if (rel.evidence) {
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console.log(` Method: ${rel.evidence.method}`)
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console.log(` Reasoning: ${rel.evidence.reasoning}\n`)
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}
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}
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// Example 6: Cache management
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console.log('🧹 Example 6: Cache Management\n')
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console.log('Cache operations:')
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// Cleanup expired entries
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const cleaned = extractor.cleanupCache()
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console.log(` Cleaned ${cleaned} expired entries`)
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// Invalidate specific cache entry
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const invalidated = extractor.invalidateCache('hash:abc123')
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console.log(` Invalidated entry: ${invalidated}`)
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// Get final stats
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const finalStats = extractor.getCacheStats()
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console.log(` Final cache size: ${finalStats.totalEntries} entries`)
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console.log(` Memory used: ~${Math.round(finalStats.cacheSize / 1024)}KB`)
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// Clear all cache (optional)
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// extractor.clearCache()
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// console.log(' Cleared entire cache')
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console.log('\n✨ Example complete!')
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console.log('\n📚 Key Takeaways:')
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console.log(' • Entity extraction caching provides 10-100x speedup on repeated content')
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console.log(' • Progress tracking gives real-time feedback for long operations')
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console.log(' • Relationship confidence helps filter low-quality connections')
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console.log(' • Evidence tracking makes relationships explainable and debuggable')
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console.log(' • All features are opt-in and backward compatible')
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}
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// Run example
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main().catch(console.error)
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@ -1,440 +0,0 @@
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/**
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* Augmentation Catalog for CLI
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*
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* Displays available augmentations catalog
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* Local catalog with caching support
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*/
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import chalk from 'chalk'
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import { readFileSync, writeFileSync, existsSync, mkdirSync } from 'node:fs'
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import { join } from 'node:path'
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import { homedir } from 'node:os'
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const CATALOG_API = process.env.BRAINY_CATALOG_URL || null
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const CACHE_PATH = join(homedir(), '.brainy', 'catalog-cache.json')
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const CACHE_TTL = 24 * 60 * 60 * 1000 // 24 hours
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interface Augmentation {
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id: string
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name: string
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description: string
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category: string
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status: 'available' | 'coming_soon' | 'deprecated'
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popular?: boolean
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eta?: string
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}
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interface Category {
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id: string
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name: string
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icon: string
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description: string
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}
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interface Catalog {
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version: string
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categories: Category[]
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augmentations: Augmentation[]
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}
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/**
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* Fetch catalog from API with caching
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*/
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export async function fetchCatalog(): Promise<Catalog | null> {
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try {
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// Check cache first
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const cached = loadCache()
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if (cached) return cached
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// If external catalog API is configured, try to fetch
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if (CATALOG_API) {
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const response = await fetch(`${CATALOG_API}/api/catalog/cli`)
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if (!response.ok) throw new Error('API unavailable')
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const catalog = await response.json()
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// Save to cache
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saveCache(catalog)
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return catalog
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}
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// Fall back to local catalog
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return getDefaultCatalog()
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} catch (error) {
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// Try loading from cache even if expired
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const cached = loadCache(true)
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if (cached) {
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console.log(chalk.yellow('📡 Using cached catalog'))
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return cached
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}
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// Fall back to hardcoded catalog
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return getDefaultCatalog()
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}
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}
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/**
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* Display catalog in CLI
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*/
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export async function showCatalog(options: {
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category?: string
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search?: string
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detailed?: boolean
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}) {
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const catalog = await fetchCatalog()
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if (!catalog) {
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console.log(chalk.red('❌ Could not load augmentation catalog'))
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return
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}
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console.log(chalk.cyan.bold('🧠 Brainy Augmentation Catalog'))
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console.log(chalk.gray(`Version ${catalog.version}`))
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console.log('')
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// Filter augmentations
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let augmentations = catalog.augmentations
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if (options.category) {
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augmentations = augmentations.filter(a => a.category === options.category)
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}
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if (options.search) {
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const query = options.search.toLowerCase()
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augmentations = augmentations.filter(a =>
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a.name.toLowerCase().includes(query) ||
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a.description.toLowerCase().includes(query)
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)
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}
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// Group by category
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const grouped = groupByCategory(augmentations, catalog.categories)
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// Display
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for (const [category, augs] of Object.entries(grouped)) {
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if (augs.length === 0) continue
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const cat = catalog.categories.find(c => c.id === category)
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console.log(chalk.bold(`${cat?.icon || '📦'} ${cat?.name || category}`))
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for (const aug of augs) {
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const status = getStatusIcon(aug.status)
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const popular = aug.popular ? chalk.yellow(' ⭐') : ''
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const eta = aug.eta ? chalk.gray(` (${aug.eta})`) : ''
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console.log(` ${status} ${aug.name}${popular}${eta}`)
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if (options.detailed) {
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console.log(chalk.gray(` ${aug.description}`))
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}
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}
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console.log('')
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}
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// Show summary
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const available = augmentations.filter(a => a.status === 'available').length
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const coming = augmentations.filter(a => a.status === 'coming_soon').length
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console.log(chalk.gray('─'.repeat(50)))
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console.log(chalk.green(`✅ ${available} available`) + chalk.gray(` • `) +
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chalk.yellow(`🔜 ${coming} coming soon`))
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console.log('')
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console.log(chalk.dim('Configure augmentations with "brainy augment"'))
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console.log(chalk.dim('Run "brainy augment info <name>" for details'))
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}
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/**
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* Show detailed info about an augmentation
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*/
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export async function showAugmentationInfo(id: string) {
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const catalog = await fetchCatalog()
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if (!catalog) {
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console.log(chalk.red('❌ Could not load augmentation catalog'))
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return
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}
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const aug = catalog.augmentations.find(a => a.id === id)
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if (!aug) {
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console.log(chalk.red(`❌ Augmentation not found: ${id}`))
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console.log('')
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console.log('Available augmentations:')
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catalog.augmentations.forEach(a => {
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console.log(` • ${a.id}`)
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})
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return
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}
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// Fetch full details from API if available
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try {
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if (!CATALOG_API) throw new Error('No external catalog configured')
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const response = await fetch(`${CATALOG_API}/api/catalog/augmentation/${id}`)
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const details = await response.json()
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console.log(chalk.cyan.bold(`📦 ${details.name}`))
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if (details.popular) console.log(chalk.yellow('⭐ Popular'))
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console.log('')
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console.log(chalk.bold('Category:'), getCategoryName(details.category, catalog.categories))
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console.log(chalk.bold('Status:'), getStatusText(details.status))
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if (details.eta) console.log(chalk.bold('Expected:'), details.eta)
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console.log('')
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|
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console.log(chalk.bold('Description:'))
|
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console.log(details.longDescription || details.description)
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console.log('')
|
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|
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if (details.features) {
|
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console.log(chalk.bold('Features:'))
|
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details.features.forEach((f: string) => console.log(` ✓ ${f}`))
|
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console.log('')
|
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}
|
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|
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if (details.example) {
|
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console.log(chalk.bold('Example:'))
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console.log(chalk.gray('─'.repeat(50)))
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console.log(details.example.code)
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console.log(chalk.gray('─'.repeat(50)))
|
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console.log('')
|
||||
}
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|
||||
if (details.requirements?.config) {
|
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console.log(chalk.bold('Required Configuration:'))
|
||||
details.requirements.config.forEach((c: string) => console.log(` • ${c}`))
|
||||
console.log('')
|
||||
}
|
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|
||||
if (details.pricing) {
|
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console.log(chalk.bold('Available in:'))
|
||||
details.pricing.tiers.forEach((t: string) => console.log(` • ${t}`))
|
||||
console.log('')
|
||||
}
|
||||
|
||||
console.log(chalk.dim('To activate: brainy augment activate'))
|
||||
} catch (error) {
|
||||
// Show basic info if API fails
|
||||
console.log(chalk.cyan.bold(`📦 ${aug.name}`))
|
||||
console.log(aug.description)
|
||||
console.log('')
|
||||
console.log(chalk.dim('Full details unavailable (no external catalog configured)'))
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Show user's available augmentations
|
||||
*/
|
||||
export async function showAvailable(licenseKey?: string) {
|
||||
// Show local catalog as default
|
||||
const catalog = await fetchCatalog()
|
||||
if (!catalog) {
|
||||
console.log(chalk.red('❌ Could not load augmentation catalog'))
|
||||
return
|
||||
}
|
||||
|
||||
console.log(chalk.cyan.bold('🧠 Available Augmentations'))
|
||||
console.log('')
|
||||
|
||||
const available = catalog.augmentations.filter(a => a.status === 'available')
|
||||
const grouped = groupByCategory(available, catalog.categories)
|
||||
|
||||
for (const [category, augs] of Object.entries(grouped)) {
|
||||
if (augs.length === 0) continue
|
||||
|
||||
const cat = catalog.categories.find(c => c.id === category)
|
||||
console.log(chalk.bold(`${cat?.icon || '📦'} ${cat?.name || category}`))
|
||||
augs.forEach(aug => {
|
||||
console.log(` ✅ ${aug.name}`)
|
||||
console.log(chalk.gray(` ${aug.description}`))
|
||||
})
|
||||
console.log('')
|
||||
}
|
||||
|
||||
console.log(chalk.green(`✅ ${available.length} augmentations available`))
|
||||
|
||||
// If external API is configured and license key provided, try to fetch personalized data
|
||||
if (CATALOG_API && licenseKey) {
|
||||
try {
|
||||
const response = await fetch(`${CATALOG_API}/api/catalog/available`, {
|
||||
headers: { 'x-license-key': licenseKey }
|
||||
})
|
||||
|
||||
if (response.ok) {
|
||||
const data = await response.json()
|
||||
console.log(chalk.gray(`Plan: ${data.plan || 'Standard'}`))
|
||||
|
||||
if (data.operations) {
|
||||
const used = data.operations.used || 0
|
||||
const limit = data.operations.limit
|
||||
const percent = limit === 'unlimited' ? 0 : Math.round((used / limit) * 100)
|
||||
|
||||
console.log(chalk.bold('Usage:'))
|
||||
if (limit === 'unlimited') {
|
||||
console.log(` Unlimited operations`)
|
||||
} else {
|
||||
console.log(` ${used.toLocaleString()} / ${limit.toLocaleString()} operations (${percent}%)`)
|
||||
}
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
// Ignore external API errors - local catalog is sufficient
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Helper functions
|
||||
|
||||
function loadCache(ignoreExpiry = false): Catalog | null {
|
||||
try {
|
||||
if (!existsSync(CACHE_PATH)) return null
|
||||
|
||||
const data = JSON.parse(readFileSync(CACHE_PATH, 'utf8'))
|
||||
|
||||
if (!ignoreExpiry && Date.now() - data.timestamp > CACHE_TTL) {
|
||||
return null
|
||||
}
|
||||
|
||||
return data.catalog
|
||||
} catch {
|
||||
return null
|
||||
}
|
||||
}
|
||||
|
||||
function saveCache(catalog: Catalog): void {
|
||||
try {
|
||||
const dir = join(homedir(), '.brainy')
|
||||
if (!existsSync(dir)) {
|
||||
mkdirSync(dir, { recursive: true })
|
||||
}
|
||||
|
||||
writeFileSync(CACHE_PATH, JSON.stringify({
|
||||
catalog,
|
||||
timestamp: Date.now()
|
||||
}))
|
||||
} catch {
|
||||
// Ignore cache save errors
|
||||
}
|
||||
}
|
||||
|
||||
function groupByCategory(augmentations: Augmentation[], categories: Category[]) {
|
||||
const grouped: Record<string, Augmentation[]> = {}
|
||||
|
||||
for (const aug of augmentations) {
|
||||
if (!grouped[aug.category]) {
|
||||
grouped[aug.category] = []
|
||||
}
|
||||
grouped[aug.category].push(aug)
|
||||
}
|
||||
|
||||
// Sort by category order
|
||||
const ordered: Record<string, Augmentation[]> = {}
|
||||
const categoryOrder = ['memory', 'coordination', 'enterprise', 'perception', 'dialog', 'activation', 'cognition', 'websocket']
|
||||
|
||||
for (const cat of categoryOrder) {
|
||||
if (grouped[cat]) {
|
||||
ordered[cat] = grouped[cat]
|
||||
}
|
||||
}
|
||||
|
||||
return ordered
|
||||
}
|
||||
|
||||
function getStatusIcon(status: string): string {
|
||||
switch (status) {
|
||||
case 'available': return chalk.green('✅')
|
||||
case 'coming_soon': return chalk.yellow('🔜')
|
||||
case 'deprecated': return chalk.red('⚠️')
|
||||
default: return '❓'
|
||||
}
|
||||
}
|
||||
|
||||
function getStatusText(status: string): string {
|
||||
switch (status) {
|
||||
case 'available': return chalk.green('Available')
|
||||
case 'coming_soon': return chalk.yellow('Coming Soon')
|
||||
case 'deprecated': return chalk.red('Deprecated')
|
||||
default: return 'Unknown'
|
||||
}
|
||||
}
|
||||
|
||||
function getCategoryName(categoryId: string, categories: Category[]): string {
|
||||
const cat = categories.find(c => c.id === categoryId)
|
||||
return cat ? `${cat.icon} ${cat.name}` : categoryId
|
||||
}
|
||||
|
||||
function readLicenseFile(): string | null {
|
||||
try {
|
||||
const licensePath = join(homedir(), '.brainy', 'license')
|
||||
if (existsSync(licensePath)) {
|
||||
return readFileSync(licensePath, 'utf8').trim()
|
||||
}
|
||||
} catch (error) {
|
||||
// License file read failed, return null
|
||||
console.debug('Failed to read license file:', error)
|
||||
}
|
||||
return null
|
||||
}
|
||||
|
||||
function getDefaultCatalog(): Catalog {
|
||||
// Local catalog with current features
|
||||
return {
|
||||
version: '1.5.0',
|
||||
categories: [
|
||||
{ id: 'core', name: 'Core Features', icon: '🧠', description: 'Essential brainy functionality' },
|
||||
{ id: 'neural', name: 'Neural API', icon: '🔗', description: 'Semantic similarity and clustering' },
|
||||
{ id: 'enterprise', name: 'Enterprise', icon: '🏢', description: 'Business integrations' },
|
||||
{ id: 'storage', name: 'Storage', icon: '💾', description: 'Data persistence and caching' }
|
||||
],
|
||||
augmentations: [
|
||||
{
|
||||
id: 'vector-search',
|
||||
name: 'Vector Search',
|
||||
category: 'core',
|
||||
description: 'High-performance semantic search with HNSW indexing',
|
||||
status: 'available',
|
||||
popular: true
|
||||
},
|
||||
{
|
||||
id: 'neural-similarity',
|
||||
name: 'Neural Similarity API',
|
||||
category: 'neural',
|
||||
description: 'Advanced semantic similarity, clustering, and hierarchy detection',
|
||||
status: 'available',
|
||||
popular: true
|
||||
},
|
||||
{
|
||||
id: 'intelligent-verb-scoring',
|
||||
name: 'Intelligent Verb Scoring',
|
||||
category: 'neural',
|
||||
description: 'Smart relationship scoring with taxonomy understanding',
|
||||
status: 'available'
|
||||
},
|
||||
{
|
||||
id: 'connection-pooling',
|
||||
name: 'Connection Pooling',
|
||||
category: 'enterprise',
|
||||
description: 'Efficient database connection management',
|
||||
status: 'available'
|
||||
},
|
||||
{
|
||||
id: 'batch-processing',
|
||||
name: 'Batch Processing',
|
||||
category: 'enterprise',
|
||||
description: 'High-throughput batch operations with deduplication',
|
||||
status: 'available'
|
||||
},
|
||||
{
|
||||
id: 's3-storage',
|
||||
name: 'S3 Compatible Storage',
|
||||
category: 'storage',
|
||||
description: 'Cloud storage with optimized batch operations',
|
||||
status: 'available'
|
||||
},
|
||||
{
|
||||
id: 'opfs-storage',
|
||||
name: 'OPFS Storage',
|
||||
category: 'storage',
|
||||
description: 'Browser-based persistent storage',
|
||||
status: 'available'
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
|
@ -1,163 +0,0 @@
|
|||
/**
|
||||
* Cached Embeddings - Performance Optimization Layer
|
||||
*
|
||||
* Provides pre-computed embeddings for common terms to avoid
|
||||
* unnecessary model calls. Falls back to EmbeddingManager for
|
||||
* unknown terms.
|
||||
*
|
||||
* This is purely a performance optimization - it doesn't affect
|
||||
* the consistency or accuracy of embeddings.
|
||||
*/
|
||||
|
||||
import { Vector } from '../coreTypes.js'
|
||||
import { embeddingManager } from './EmbeddingManager.js'
|
||||
|
||||
// Pre-computed embeddings for top common terms
|
||||
// In production, this could be loaded from a file or expanded significantly
|
||||
const PRECOMPUTED_EMBEDDINGS: Record<string, Vector> = {
|
||||
// Programming languages
|
||||
'javascript': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.1)),
|
||||
'python': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.1)),
|
||||
'typescript': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.15)),
|
||||
'java': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.15)),
|
||||
'rust': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.2)),
|
||||
'go': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.2)),
|
||||
'c++': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.22)),
|
||||
'c#': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.22)),
|
||||
|
||||
// Web frameworks
|
||||
'react': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.25)),
|
||||
'vue': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.25)),
|
||||
'angular': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.3)),
|
||||
'svelte': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.3)),
|
||||
'nextjs': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.32)),
|
||||
'nuxt': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.32)),
|
||||
|
||||
// Databases
|
||||
'postgresql': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.35)),
|
||||
'mysql': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.35)),
|
||||
'mongodb': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.4)),
|
||||
'redis': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.4)),
|
||||
'elasticsearch': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.42)),
|
||||
|
||||
// Common tech terms
|
||||
'database': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.45)),
|
||||
'api': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.45)),
|
||||
'server': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.5)),
|
||||
'client': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.5)),
|
||||
'frontend': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.55)),
|
||||
'backend': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.55)),
|
||||
'fullstack': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.57)),
|
||||
'devops': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.57)),
|
||||
'cloud': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.6)),
|
||||
'docker': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.6)),
|
||||
'kubernetes': new Array(384).fill(0).map((_, i) => Math.sin(i * 0.62)),
|
||||
'microservices': new Array(384).fill(0).map((_, i) => Math.cos(i * 0.62)),
|
||||
}
|
||||
|
||||
/**
|
||||
* Simple character n-gram based embedding for short text
|
||||
* This is much faster than using the model for simple terms
|
||||
*/
|
||||
function computeSimpleEmbedding(text: string): Vector {
|
||||
const normalized = text.toLowerCase().trim()
|
||||
const vector = new Array(384).fill(0)
|
||||
|
||||
// Character trigrams for simple semantic similarity
|
||||
for (let i = 0; i < normalized.length - 2; i++) {
|
||||
const trigram = normalized.slice(i, i + 3)
|
||||
const hash = trigram.charCodeAt(0) * 31 +
|
||||
trigram.charCodeAt(1) * 7 +
|
||||
trigram.charCodeAt(2)
|
||||
const index = Math.abs(hash) % 384
|
||||
vector[index] += 1 / (normalized.length - 2)
|
||||
}
|
||||
|
||||
// Normalize vector
|
||||
const magnitude = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0))
|
||||
if (magnitude > 0) {
|
||||
for (let i = 0; i < vector.length; i++) {
|
||||
vector[i] /= magnitude
|
||||
}
|
||||
}
|
||||
|
||||
return vector
|
||||
}
|
||||
|
||||
/**
|
||||
* Cached Embeddings with fallback to EmbeddingManager
|
||||
*/
|
||||
export class CachedEmbeddings {
|
||||
private stats = {
|
||||
cacheHits: 0,
|
||||
simpleComputes: 0,
|
||||
modelCalls: 0
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate embedding with caching
|
||||
*/
|
||||
async embed(text: string | string[]): Promise<Vector | Vector[]> {
|
||||
if (Array.isArray(text)) {
|
||||
return Promise.all(text.map(t => this.embedSingle(t)))
|
||||
}
|
||||
return this.embedSingle(text)
|
||||
}
|
||||
|
||||
/**
|
||||
* Embed single text with cache lookup
|
||||
*/
|
||||
private async embedSingle(text: string): Promise<Vector> {
|
||||
const normalized = text.toLowerCase().trim()
|
||||
|
||||
// 1. Check pre-computed cache (instant, zero cost)
|
||||
if (PRECOMPUTED_EMBEDDINGS[normalized]) {
|
||||
this.stats.cacheHits++
|
||||
return PRECOMPUTED_EMBEDDINGS[normalized]
|
||||
}
|
||||
|
||||
// 2. Check for partial matches in cache
|
||||
for (const [term, embedding] of Object.entries(PRECOMPUTED_EMBEDDINGS)) {
|
||||
if (normalized.includes(term) || term.includes(normalized)) {
|
||||
this.stats.cacheHits++
|
||||
// Return slightly modified version to maintain uniqueness
|
||||
return embedding.map(v => v * 0.95)
|
||||
}
|
||||
}
|
||||
|
||||
// 3. For short text, use simple embedding (fast, low cost)
|
||||
if (normalized.length < 50 && normalized.split(' ').length < 5) {
|
||||
this.stats.simpleComputes++
|
||||
return computeSimpleEmbedding(normalized)
|
||||
}
|
||||
|
||||
// 4. Fall back to EmbeddingManager for complex text
|
||||
this.stats.modelCalls++
|
||||
return await embeddingManager.embed(text)
|
||||
}
|
||||
|
||||
/**
|
||||
* Get cache statistics
|
||||
*/
|
||||
getStats() {
|
||||
return {
|
||||
...this.stats,
|
||||
totalEmbeddings: this.stats.cacheHits + this.stats.simpleComputes + this.stats.modelCalls,
|
||||
cacheHitRate: this.stats.cacheHits /
|
||||
(this.stats.cacheHits + this.stats.simpleComputes + this.stats.modelCalls) || 0
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Add custom pre-computed embeddings
|
||||
*/
|
||||
addPrecomputed(term: string, embedding: Vector) {
|
||||
if (embedding.length !== 384) {
|
||||
throw new Error('Embedding must have 384 dimensions')
|
||||
}
|
||||
PRECOMPUTED_EMBEDDINGS[term.toLowerCase()] = embedding
|
||||
}
|
||||
}
|
||||
|
||||
// Export singleton instance
|
||||
export const cachedEmbeddings = new CachedEmbeddings()
|
||||
|
|
@ -1,28 +0,0 @@
|
|||
/**
|
||||
* Embeddings Module - Clean, Unified Architecture
|
||||
*
|
||||
* This module provides all embedding functionality for Brainy.
|
||||
*
|
||||
* Main Components:
|
||||
* - EmbeddingManager: Core embedding generation with Q8/FP32 support
|
||||
* - CachedEmbeddings: Performance optimization layer with pre-computed embeddings
|
||||
*/
|
||||
|
||||
// Core embedding functionality
|
||||
export {
|
||||
EmbeddingManager,
|
||||
embeddingManager,
|
||||
embed,
|
||||
getEmbeddingFunction,
|
||||
getEmbeddingStats,
|
||||
type ModelPrecision
|
||||
} from './EmbeddingManager.js'
|
||||
|
||||
// Cached embeddings for performance
|
||||
export {
|
||||
CachedEmbeddings,
|
||||
cachedEmbeddings
|
||||
} from './CachedEmbeddings.js'
|
||||
|
||||
// Default export is the singleton manager
|
||||
export { embeddingManager as default } from './EmbeddingManager.js'
|
||||
|
|
@ -1,523 +0,0 @@
|
|||
/**
|
||||
* Advanced Graph Pathfinding Algorithms
|
||||
* Provides shortest path, multi-hop traversal, and path ranking
|
||||
*/
|
||||
|
||||
// Graph pathfinding doesn't need to import from coreTypes
|
||||
|
||||
export interface GraphNode {
|
||||
id: string
|
||||
[key: string]: any
|
||||
}
|
||||
|
||||
export interface GraphEdge {
|
||||
source: string
|
||||
target: string
|
||||
type: string
|
||||
weight: number
|
||||
metadata?: any
|
||||
}
|
||||
|
||||
export interface Path {
|
||||
nodes: string[]
|
||||
edges: GraphEdge[]
|
||||
totalWeight: number
|
||||
length: number
|
||||
}
|
||||
|
||||
export interface PathfindingOptions {
|
||||
maxDepth?: number
|
||||
maxPaths?: number
|
||||
bidirectional?: boolean
|
||||
weightField?: string
|
||||
relationshipTypes?: string[]
|
||||
nodeFilter?: (node: GraphNode) => boolean
|
||||
edgeFilter?: (edge: GraphEdge) => boolean
|
||||
}
|
||||
|
||||
export class GraphPathfinding {
|
||||
private adjacencyList: Map<string, Map<string, GraphEdge[]>> = new Map()
|
||||
private nodes: Map<string, GraphNode> = new Map()
|
||||
|
||||
/**
|
||||
* Add a node to the graph
|
||||
*/
|
||||
public addNode(node: GraphNode): void {
|
||||
this.nodes.set(node.id, node)
|
||||
if (!this.adjacencyList.has(node.id)) {
|
||||
this.adjacencyList.set(node.id, new Map())
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Add an edge to the graph
|
||||
*/
|
||||
public addEdge(edge: GraphEdge): void {
|
||||
// Ensure nodes exist
|
||||
if (!this.adjacencyList.has(edge.source)) {
|
||||
this.adjacencyList.set(edge.source, new Map())
|
||||
}
|
||||
if (!this.adjacencyList.has(edge.target)) {
|
||||
this.adjacencyList.set(edge.target, new Map())
|
||||
}
|
||||
|
||||
// Add edge to adjacency list
|
||||
const sourceEdges = this.adjacencyList.get(edge.source)!
|
||||
if (!sourceEdges.has(edge.target)) {
|
||||
sourceEdges.set(edge.target, [])
|
||||
}
|
||||
sourceEdges.get(edge.target)!.push(edge)
|
||||
}
|
||||
|
||||
/**
|
||||
* Find shortest path using Dijkstra's algorithm
|
||||
* O((V + E) log V) with binary heap
|
||||
*/
|
||||
public shortestPath(
|
||||
start: string,
|
||||
end: string,
|
||||
options: PathfindingOptions = {}
|
||||
): Path | null {
|
||||
const {
|
||||
maxDepth = Infinity,
|
||||
relationshipTypes,
|
||||
edgeFilter
|
||||
} = options
|
||||
|
||||
// Priority queue: [nodeId, distance, path]
|
||||
const pq: Array<[string, number, string[], GraphEdge[]]> = [[start, 0, [start], []]]
|
||||
const visited = new Set<string>()
|
||||
const distances = new Map<string, number>([[start, 0]])
|
||||
|
||||
while (pq.length > 0) {
|
||||
// Sort by distance (simple array, could optimize with heap)
|
||||
pq.sort((a, b) => a[1] - b[1])
|
||||
const [current, distance, path, edges] = pq.shift()!
|
||||
|
||||
if (visited.has(current)) continue
|
||||
visited.add(current)
|
||||
|
||||
// Found target
|
||||
if (current === end) {
|
||||
return {
|
||||
nodes: path,
|
||||
edges,
|
||||
totalWeight: distance,
|
||||
length: path.length - 1
|
||||
}
|
||||
}
|
||||
|
||||
// Max depth reached
|
||||
if (path.length > maxDepth) continue
|
||||
|
||||
// Explore neighbors
|
||||
const neighbors = this.adjacencyList.get(current)
|
||||
if (!neighbors) continue
|
||||
|
||||
for (const [neighbor, edgeList] of neighbors) {
|
||||
if (visited.has(neighbor)) continue
|
||||
|
||||
// Find best edge to neighbor
|
||||
let bestEdge: GraphEdge | null = null
|
||||
let bestWeight = Infinity
|
||||
|
||||
for (const edge of edgeList) {
|
||||
// Apply filters
|
||||
if (relationshipTypes && !relationshipTypes.includes(edge.type)) continue
|
||||
if (edgeFilter && !edgeFilter(edge)) continue
|
||||
|
||||
if (edge.weight < bestWeight) {
|
||||
bestWeight = edge.weight
|
||||
bestEdge = edge
|
||||
}
|
||||
}
|
||||
|
||||
if (!bestEdge) continue
|
||||
|
||||
const newDistance = distance + bestWeight
|
||||
const currentBest = distances.get(neighbor) ?? Infinity
|
||||
|
||||
if (newDistance < currentBest) {
|
||||
distances.set(neighbor, newDistance)
|
||||
pq.push([
|
||||
neighbor,
|
||||
newDistance,
|
||||
[...path, neighbor],
|
||||
[...edges, bestEdge]
|
||||
])
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return null // No path found
|
||||
}
|
||||
|
||||
/**
|
||||
* Find all paths between two nodes
|
||||
* Uses DFS with cycle detection
|
||||
*/
|
||||
public allPaths(
|
||||
start: string,
|
||||
end: string,
|
||||
options: PathfindingOptions = {}
|
||||
): Path[] {
|
||||
const {
|
||||
maxDepth = 10,
|
||||
maxPaths = 100,
|
||||
relationshipTypes,
|
||||
edgeFilter
|
||||
} = options
|
||||
|
||||
const paths: Path[] = []
|
||||
const visited = new Set<string>()
|
||||
|
||||
const dfs = (
|
||||
current: string,
|
||||
path: string[],
|
||||
edges: GraphEdge[],
|
||||
weight: number
|
||||
): void => {
|
||||
if (paths.length >= maxPaths) return
|
||||
if (path.length > maxDepth) return
|
||||
|
||||
if (current === end && path.length > 1) {
|
||||
paths.push({
|
||||
nodes: [...path],
|
||||
edges: [...edges],
|
||||
totalWeight: weight,
|
||||
length: path.length - 1
|
||||
})
|
||||
return
|
||||
}
|
||||
|
||||
visited.add(current)
|
||||
|
||||
const neighbors = this.adjacencyList.get(current)
|
||||
if (neighbors) {
|
||||
for (const [neighbor, edgeList] of neighbors) {
|
||||
if (visited.has(neighbor)) continue
|
||||
|
||||
for (const edge of edgeList) {
|
||||
// Apply filters
|
||||
if (relationshipTypes && !relationshipTypes.includes(edge.type)) continue
|
||||
if (edgeFilter && !edgeFilter(edge)) continue
|
||||
|
||||
dfs(
|
||||
neighbor,
|
||||
[...path, neighbor],
|
||||
[...edges, edge],
|
||||
weight + edge.weight
|
||||
)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
visited.delete(current)
|
||||
}
|
||||
|
||||
dfs(start, [start], [], 0)
|
||||
|
||||
// Sort paths by weight
|
||||
paths.sort((a, b) => a.totalWeight - b.totalWeight)
|
||||
|
||||
return paths
|
||||
}
|
||||
|
||||
/**
|
||||
* Bidirectional search for faster pathfinding
|
||||
* Searches from both start and end simultaneously
|
||||
*/
|
||||
public bidirectionalSearch(
|
||||
start: string,
|
||||
end: string,
|
||||
options: PathfindingOptions = {}
|
||||
): Path | null {
|
||||
const { maxDepth = 10 } = options
|
||||
|
||||
// Two search frontiers
|
||||
const forwardVisited = new Map<string, { path: string[], edges: GraphEdge[], weight: number }>()
|
||||
const backwardVisited = new Map<string, { path: string[], edges: GraphEdge[], weight: number }>()
|
||||
|
||||
forwardVisited.set(start, { path: [start], edges: [], weight: 0 })
|
||||
backwardVisited.set(end, { path: [end], edges: [], weight: 0 })
|
||||
|
||||
const forwardQueue = [start]
|
||||
const backwardQueue = [end]
|
||||
|
||||
let depth = 0
|
||||
|
||||
while (
|
||||
(forwardQueue.length > 0 || backwardQueue.length > 0) &&
|
||||
depth < maxDepth
|
||||
) {
|
||||
// Expand forward frontier
|
||||
const forwardNext: string[] = []
|
||||
for (const current of forwardQueue) {
|
||||
const currentData = forwardVisited.get(current)!
|
||||
const neighbors = this.adjacencyList.get(current)
|
||||
|
||||
if (neighbors) {
|
||||
for (const [neighbor, edges] of neighbors) {
|
||||
if (forwardVisited.has(neighbor)) continue
|
||||
|
||||
// Select edge with lowest weight for optimal path
|
||||
const bestEdge = edges.reduce((best, edge) =>
|
||||
edge.weight < best.weight ? edge : best, edges[0])
|
||||
forwardVisited.set(neighbor, {
|
||||
path: [...currentData.path, neighbor],
|
||||
edges: [...currentData.edges, bestEdge],
|
||||
weight: currentData.weight + bestEdge.weight
|
||||
})
|
||||
|
||||
// Check if we met the backward search
|
||||
if (backwardVisited.has(neighbor)) {
|
||||
const forward = forwardVisited.get(neighbor)!
|
||||
const backward = backwardVisited.get(neighbor)!
|
||||
|
||||
// Combine paths
|
||||
const fullPath = [
|
||||
...forward.path,
|
||||
...backward.path.slice(1).reverse()
|
||||
]
|
||||
|
||||
// Reverse backward edges and combine
|
||||
const backwardEdgesReversed = backward.edges
|
||||
.map(e => ({
|
||||
...e,
|
||||
source: e.target,
|
||||
target: e.source
|
||||
}))
|
||||
.reverse()
|
||||
|
||||
return {
|
||||
nodes: fullPath,
|
||||
edges: [...forward.edges, ...backwardEdgesReversed],
|
||||
totalWeight: forward.weight + backward.weight,
|
||||
length: fullPath.length - 1
|
||||
}
|
||||
}
|
||||
|
||||
forwardNext.push(neighbor)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Expand backward frontier
|
||||
const backwardNext: string[] = []
|
||||
for (const current of backwardQueue) {
|
||||
const currentData = backwardVisited.get(current)!
|
||||
|
||||
// For backward search, we need to look at incoming edges
|
||||
for (const [nodeId, neighbors] of this.adjacencyList) {
|
||||
const edges = neighbors.get(current)
|
||||
if (!edges) continue
|
||||
|
||||
if (backwardVisited.has(nodeId)) continue
|
||||
|
||||
// Select edge with lowest weight for optimal path
|
||||
const bestEdge = edges.reduce((best, edge) =>
|
||||
edge.weight < best.weight ? edge : best, edges[0])
|
||||
backwardVisited.set(nodeId, {
|
||||
path: [...currentData.path, nodeId],
|
||||
edges: [...currentData.edges, bestEdge],
|
||||
weight: currentData.weight + bestEdge.weight
|
||||
})
|
||||
|
||||
// Check if we met the forward search
|
||||
if (forwardVisited.has(nodeId)) {
|
||||
const forward = forwardVisited.get(nodeId)!
|
||||
const backward = backwardVisited.get(nodeId)!
|
||||
|
||||
// Combine paths
|
||||
const fullPath = [
|
||||
...forward.path,
|
||||
...backward.path.slice(1).reverse()
|
||||
]
|
||||
|
||||
// Reverse backward edges and combine
|
||||
const backwardEdgesReversed = backward.edges
|
||||
.map(e => ({
|
||||
...e,
|
||||
source: e.target,
|
||||
target: e.source
|
||||
}))
|
||||
.reverse()
|
||||
|
||||
return {
|
||||
nodes: fullPath,
|
||||
edges: [...forward.edges, ...backwardEdgesReversed],
|
||||
totalWeight: forward.weight + backward.weight,
|
||||
length: fullPath.length - 1
|
||||
}
|
||||
}
|
||||
|
||||
backwardNext.push(nodeId)
|
||||
}
|
||||
}
|
||||
|
||||
forwardQueue.splice(0, forwardQueue.length, ...forwardNext)
|
||||
backwardQueue.splice(0, backwardQueue.length, ...backwardNext)
|
||||
depth++
|
||||
}
|
||||
|
||||
return null
|
||||
}
|
||||
|
||||
/**
|
||||
* Multi-hop traversal (e.g., friends of friends)
|
||||
* Returns all nodes within N hops
|
||||
*/
|
||||
public multiHopTraversal(
|
||||
start: string,
|
||||
hops: number,
|
||||
options: PathfindingOptions = {}
|
||||
): Map<string, { distance: number, paths: Path[] }> {
|
||||
const { relationshipTypes, nodeFilter, edgeFilter } = options
|
||||
|
||||
const results = new Map<string, { distance: number, paths: Path[] }>()
|
||||
const visited = new Set<string>()
|
||||
const queue: Array<{ node: string, distance: number, path: string[], edges: GraphEdge[] }> = [
|
||||
{ node: start, distance: 0, path: [start], edges: [] }
|
||||
]
|
||||
|
||||
while (queue.length > 0) {
|
||||
const { node, distance, path, edges } = queue.shift()!
|
||||
|
||||
if (distance > hops) continue
|
||||
|
||||
// Record this node
|
||||
if (!results.has(node)) {
|
||||
results.set(node, { distance, paths: [] })
|
||||
}
|
||||
results.get(node)!.paths.push({
|
||||
nodes: path,
|
||||
edges,
|
||||
totalWeight: edges.reduce((sum, e) => sum + e.weight, 0),
|
||||
length: path.length - 1
|
||||
})
|
||||
|
||||
if (distance === hops) continue
|
||||
|
||||
// Explore neighbors
|
||||
const neighbors = this.adjacencyList.get(node)
|
||||
if (neighbors) {
|
||||
for (const [neighbor, edgeList] of neighbors) {
|
||||
// Apply node filter
|
||||
if (nodeFilter) {
|
||||
const neighborNode = this.nodes.get(neighbor)
|
||||
if (neighborNode && !nodeFilter(neighborNode)) continue
|
||||
}
|
||||
|
||||
for (const edge of edgeList) {
|
||||
// Apply filters
|
||||
if (relationshipTypes && !relationshipTypes.includes(edge.type)) continue
|
||||
if (edgeFilter && !edgeFilter(edge)) continue
|
||||
|
||||
queue.push({
|
||||
node: neighbor,
|
||||
distance: distance + 1,
|
||||
path: [...path, neighbor],
|
||||
edges: [...edges, edge]
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return results
|
||||
}
|
||||
|
||||
/**
|
||||
* Find connected components using DFS
|
||||
*/
|
||||
public connectedComponents(): Array<Set<string>> {
|
||||
const visited = new Set<string>()
|
||||
const components: Array<Set<string>> = []
|
||||
|
||||
const dfs = (node: string, component: Set<string>): void => {
|
||||
visited.add(node)
|
||||
component.add(node)
|
||||
|
||||
const neighbors = this.adjacencyList.get(node)
|
||||
if (neighbors) {
|
||||
for (const neighbor of neighbors.keys()) {
|
||||
if (!visited.has(neighbor)) {
|
||||
dfs(neighbor, component)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (const node of this.adjacencyList.keys()) {
|
||||
if (!visited.has(node)) {
|
||||
const component = new Set<string>()
|
||||
dfs(node, component)
|
||||
components.push(component)
|
||||
}
|
||||
}
|
||||
|
||||
return components
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculate PageRank for all nodes
|
||||
* Useful for ranking importance in the graph
|
||||
*/
|
||||
public pageRank(iterations: number = 100, damping: number = 0.85): Map<string, number> {
|
||||
const nodes = Array.from(this.adjacencyList.keys())
|
||||
const n = nodes.length
|
||||
|
||||
if (n === 0) return new Map()
|
||||
|
||||
// Initialize ranks
|
||||
const ranks = new Map<string, number>()
|
||||
for (const node of nodes) {
|
||||
ranks.set(node, 1 / n)
|
||||
}
|
||||
|
||||
// Calculate outgoing edge counts
|
||||
const outDegree = new Map<string, number>()
|
||||
for (const [node, neighbors] of this.adjacencyList) {
|
||||
let count = 0
|
||||
for (const edges of neighbors.values()) {
|
||||
count += edges.length
|
||||
}
|
||||
outDegree.set(node, count)
|
||||
}
|
||||
|
||||
// Iterate PageRank algorithm
|
||||
for (let i = 0; i < iterations; i++) {
|
||||
const newRanks = new Map<string, number>()
|
||||
|
||||
for (const node of nodes) {
|
||||
let rank = (1 - damping) / n
|
||||
|
||||
// Sum contributions from incoming edges
|
||||
for (const [source, neighbors] of this.adjacencyList) {
|
||||
if (neighbors.has(node)) {
|
||||
const sourceRank = ranks.get(source) ?? 0
|
||||
const sourceOutDegree = outDegree.get(source) ?? 1
|
||||
rank += damping * (sourceRank / sourceOutDegree)
|
||||
}
|
||||
}
|
||||
|
||||
newRanks.set(node, rank)
|
||||
}
|
||||
|
||||
// Update ranks
|
||||
for (const [node, rank] of newRanks) {
|
||||
ranks.set(node, rank)
|
||||
}
|
||||
}
|
||||
|
||||
return ranks
|
||||
}
|
||||
|
||||
/**
|
||||
* Clear the graph
|
||||
*/
|
||||
public clear(): void {
|
||||
this.adjacencyList.clear()
|
||||
this.nodes.clear()
|
||||
}
|
||||
}
|
||||
|
|
@ -1,354 +0,0 @@
|
|||
/**
|
||||
* Entity Deduplicator
|
||||
*
|
||||
* Finds and merges duplicate entities across imports using:
|
||||
* - Embedding-based similarity matching
|
||||
* - Type-aware comparison
|
||||
* - Confidence-weighted merging
|
||||
* - Provenance tracking
|
||||
*
|
||||
* NO MOCKS - Production-ready implementation
|
||||
*/
|
||||
|
||||
import { Brainy } from '../brainy.js'
|
||||
import { NounType } from '../types/graphTypes.js'
|
||||
|
||||
export interface EntityCandidate {
|
||||
id?: string
|
||||
name: string
|
||||
type: NounType
|
||||
description: string
|
||||
confidence: number
|
||||
metadata: Record<string, any>
|
||||
}
|
||||
|
||||
export interface DuplicateMatch {
|
||||
existingId: string
|
||||
existingName: string
|
||||
similarity: number
|
||||
shouldMerge: boolean
|
||||
reason: string
|
||||
}
|
||||
|
||||
export interface EntityDeduplicationOptions {
|
||||
/** Similarity threshold for considering entities as duplicates (0-1) */
|
||||
similarityThreshold?: number
|
||||
|
||||
/** Only match entities of the same type */
|
||||
strictTypeMatching?: boolean
|
||||
|
||||
/** Enable fuzzy name matching */
|
||||
enableFuzzyMatching?: boolean
|
||||
|
||||
/** Minimum confidence to consider for merging */
|
||||
minConfidence?: number
|
||||
}
|
||||
|
||||
export interface MergeResult {
|
||||
mergedEntityId: string
|
||||
wasMerged: boolean
|
||||
mergedWith?: string
|
||||
confidence: number
|
||||
provenance: string[]
|
||||
}
|
||||
|
||||
/**
|
||||
* EntityDeduplicator - Prevents duplicate entities across imports
|
||||
*/
|
||||
export class EntityDeduplicator {
|
||||
private brain: Brainy
|
||||
|
||||
constructor(brain: Brainy) {
|
||||
this.brain = brain
|
||||
}
|
||||
|
||||
/**
|
||||
* Find duplicate entities in the knowledge graph
|
||||
*/
|
||||
async findDuplicates(
|
||||
candidate: EntityCandidate,
|
||||
options: EntityDeduplicationOptions = {}
|
||||
): Promise<DuplicateMatch | null> {
|
||||
const opts = {
|
||||
similarityThreshold: options.similarityThreshold || 0.85,
|
||||
strictTypeMatching: options.strictTypeMatching !== false,
|
||||
enableFuzzyMatching: options.enableFuzzyMatching !== false,
|
||||
minConfidence: options.minConfidence || 0.6
|
||||
}
|
||||
|
||||
// Skip low-confidence candidates
|
||||
if (candidate.confidence < opts.minConfidence) {
|
||||
return null
|
||||
}
|
||||
|
||||
// Search for similar entities by name and description
|
||||
const searchText = `${candidate.name} ${candidate.description}`.trim()
|
||||
|
||||
try {
|
||||
const results = await this.brain.find({
|
||||
query: searchText,
|
||||
limit: 5,
|
||||
where: opts.strictTypeMatching ? { type: candidate.type } : undefined
|
||||
})
|
||||
|
||||
// Check each result for potential duplicates
|
||||
for (const result of results) {
|
||||
const similarity = result.score || 0
|
||||
const existingName = result.entity.metadata?.name || result.id
|
||||
const existingType = result.entity.metadata?.type || result.entity.metadata?.nounType || result.entity.type
|
||||
|
||||
// Skip if below similarity threshold
|
||||
if (similarity < opts.similarityThreshold) {
|
||||
continue
|
||||
}
|
||||
|
||||
// Type matching check
|
||||
if (opts.strictTypeMatching && existingType !== candidate.type) {
|
||||
continue
|
||||
}
|
||||
|
||||
// Exact name match (case-insensitive)
|
||||
if (this.normalizeString(candidate.name) === this.normalizeString(existingName)) {
|
||||
return {
|
||||
existingId: result.id,
|
||||
existingName,
|
||||
similarity: 1.0,
|
||||
shouldMerge: true,
|
||||
reason: 'Exact name match'
|
||||
}
|
||||
}
|
||||
|
||||
// High similarity match
|
||||
if (similarity >= opts.similarityThreshold) {
|
||||
// Additional validation for fuzzy matching
|
||||
if (opts.enableFuzzyMatching && this.areSimilarNames(candidate.name, existingName)) {
|
||||
return {
|
||||
existingId: result.id,
|
||||
existingName,
|
||||
similarity,
|
||||
shouldMerge: true,
|
||||
reason: `High similarity (${(similarity * 100).toFixed(1)}%)`
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
// If search fails, assume no duplicates
|
||||
return null
|
||||
}
|
||||
|
||||
return null
|
||||
}
|
||||
|
||||
/**
|
||||
* Merge entity data with existing entity
|
||||
*/
|
||||
async mergeEntity(
|
||||
existingId: string,
|
||||
candidate: EntityCandidate,
|
||||
importSource: string
|
||||
): Promise<MergeResult> {
|
||||
try {
|
||||
// Get existing entity
|
||||
const existing = await this.brain.get(existingId)
|
||||
if (!existing) {
|
||||
throw new Error(`Entity ${existingId} not found`)
|
||||
}
|
||||
|
||||
// Update confidence (weighted average) — `confidence` is a reserved
|
||||
// top-level field, so it travels via the dedicated update() param, not
|
||||
// the metadata bag.
|
||||
const mergedConfidence = this.mergeConfidence(
|
||||
existing.confidence ?? 0.5,
|
||||
candidate.confidence
|
||||
)
|
||||
|
||||
// Merge metadata (custom fields only — reserved fields are top-level)
|
||||
const mergedMetadata = {
|
||||
...existing.metadata,
|
||||
// Track provenance
|
||||
imports: [
|
||||
...(existing.metadata?.imports || []),
|
||||
importSource
|
||||
],
|
||||
// Merge VFS paths
|
||||
vfsPaths: [
|
||||
...(existing.metadata?.vfsPaths || [existing.metadata?.vfsPath]).filter(Boolean),
|
||||
candidate.metadata?.vfsPath
|
||||
].filter(Boolean),
|
||||
// Merge other metadata
|
||||
...this.mergeMetadataFields(existing.metadata, candidate.metadata),
|
||||
// Track last update
|
||||
lastUpdated: Date.now(),
|
||||
mergeCount: (existing.metadata?.mergeCount || 0) + 1
|
||||
}
|
||||
|
||||
// Update entity
|
||||
await this.brain.update({
|
||||
id: existingId,
|
||||
confidence: mergedConfidence,
|
||||
metadata: mergedMetadata,
|
||||
merge: true
|
||||
})
|
||||
|
||||
return {
|
||||
mergedEntityId: existingId,
|
||||
wasMerged: true,
|
||||
mergedWith: existing.metadata?.name || existingId,
|
||||
confidence: mergedConfidence,
|
||||
provenance: mergedMetadata.imports
|
||||
}
|
||||
} catch (error) {
|
||||
throw new Error(`Failed to merge entity: ${error instanceof Error ? error.message : String(error)}`)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create or merge entity with deduplication
|
||||
*/
|
||||
async createOrMerge(
|
||||
candidate: EntityCandidate,
|
||||
importSource: string,
|
||||
options: EntityDeduplicationOptions = {}
|
||||
): Promise<MergeResult> {
|
||||
// Check for duplicates
|
||||
const duplicate = await this.findDuplicates(candidate, options)
|
||||
|
||||
if (duplicate && duplicate.shouldMerge) {
|
||||
// Merge with existing entity
|
||||
return await this.mergeEntity(duplicate.existingId, candidate, importSource)
|
||||
}
|
||||
|
||||
// No duplicate found, create new entity. Preserve any subtype the candidate
|
||||
// carried (set by the extractor or upstream importer), else fall back to
|
||||
// `'imported'` so enforcement doesn't fire (added 7.30.1).
|
||||
// `confidence` is a reserved top-level field (dedicated add() param);
|
||||
// creation time is system-managed — neither belongs in the metadata bag.
|
||||
const entityId = await this.brain.add({
|
||||
data: candidate.description || candidate.name,
|
||||
type: candidate.type,
|
||||
subtype:
|
||||
(candidate as EntityCandidate & { subtype?: string }).subtype ??
|
||||
'imported',
|
||||
confidence: candidate.confidence,
|
||||
metadata: {
|
||||
...candidate.metadata,
|
||||
name: candidate.name,
|
||||
imports: [importSource],
|
||||
vfsPaths: [candidate.metadata?.vfsPath].filter(Boolean),
|
||||
mergeCount: 0
|
||||
}
|
||||
})
|
||||
|
||||
// Update candidate with new ID
|
||||
candidate.id = entityId
|
||||
|
||||
return {
|
||||
mergedEntityId: entityId,
|
||||
wasMerged: false,
|
||||
confidence: candidate.confidence,
|
||||
provenance: [importSource]
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Normalize string for comparison
|
||||
*/
|
||||
private normalizeString(str: string): string {
|
||||
return str
|
||||
.toLowerCase()
|
||||
.trim()
|
||||
.replace(/[^a-z0-9]/g, '')
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if two names are similar (fuzzy matching)
|
||||
*/
|
||||
private areSimilarNames(name1: string, name2: string): boolean {
|
||||
const n1 = this.normalizeString(name1)
|
||||
const n2 = this.normalizeString(name2)
|
||||
|
||||
// Exact match
|
||||
if (n1 === n2) return true
|
||||
|
||||
// Length difference check
|
||||
const lengthDiff = Math.abs(n1.length - n2.length)
|
||||
if (lengthDiff > 3) return false
|
||||
|
||||
// Levenshtein distance
|
||||
const distance = this.levenshteinDistance(n1, n2)
|
||||
const maxLength = Math.max(n1.length, n2.length)
|
||||
const similarity = 1 - (distance / maxLength)
|
||||
|
||||
return similarity >= 0.85
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculate Levenshtein distance between two strings
|
||||
*/
|
||||
private levenshteinDistance(str1: string, str2: string): number {
|
||||
const m = str1.length
|
||||
const n = str2.length
|
||||
const dp: number[][] = Array(m + 1).fill(null).map(() => Array(n + 1).fill(0))
|
||||
|
||||
for (let i = 0; i <= m; i++) dp[i][0] = i
|
||||
for (let j = 0; j <= n; j++) dp[0][j] = j
|
||||
|
||||
for (let i = 1; i <= m; i++) {
|
||||
for (let j = 1; j <= n; j++) {
|
||||
if (str1[i - 1] === str2[j - 1]) {
|
||||
dp[i][j] = dp[i - 1][j - 1]
|
||||
} else {
|
||||
dp[i][j] = Math.min(
|
||||
dp[i - 1][j] + 1, // deletion
|
||||
dp[i][j - 1] + 1, // insertion
|
||||
dp[i - 1][j - 1] + 1 // substitution
|
||||
)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return dp[m][n]
|
||||
}
|
||||
|
||||
/**
|
||||
* Merge confidence scores (weighted average favoring higher confidence)
|
||||
*/
|
||||
private mergeConfidence(existing: number, incoming: number): number {
|
||||
// Weight higher confidence more heavily
|
||||
const weights = existing > incoming ? [0.6, 0.4] : [0.4, 0.6]
|
||||
return existing * weights[0] + incoming * weights[1]
|
||||
}
|
||||
|
||||
/**
|
||||
* Merge metadata fields intelligently
|
||||
*/
|
||||
private mergeMetadataFields(
|
||||
existing: Record<string, any>,
|
||||
incoming: Record<string, any>
|
||||
): Record<string, any> {
|
||||
const merged: Record<string, any> = {}
|
||||
|
||||
// Merge arrays
|
||||
const arrayFields = ['concepts', 'tags', 'categories']
|
||||
for (const field of arrayFields) {
|
||||
if (existing[field] || incoming[field]) {
|
||||
const combined = [
|
||||
...(existing[field] || []),
|
||||
...(incoming[field] || [])
|
||||
]
|
||||
// Deduplicate
|
||||
merged[field] = [...new Set(combined)]
|
||||
}
|
||||
}
|
||||
|
||||
// Prefer longer descriptions
|
||||
if (existing.description || incoming.description) {
|
||||
merged.description = (existing.description || '').length > (incoming.description || '').length
|
||||
? existing.description
|
||||
: incoming.description
|
||||
}
|
||||
|
||||
return merged
|
||||
}
|
||||
}
|
||||
|
|
@ -1,269 +0,0 @@
|
|||
/**
|
||||
* InstancePool - Shared instance management for memory efficiency
|
||||
*
|
||||
* Production-grade instance pooling to prevent memory leaks during imports.
|
||||
* Critical for scaling to billions of entities.
|
||||
*
|
||||
* Problem: Creating new NLP/Extractor instances in loops → memory leak
|
||||
* Solution: Reuse shared instances across entire import session
|
||||
*
|
||||
* Memory savings:
|
||||
* - Without pooling: 100K rows × 50MB per instance = 5TB RAM (OOM!)
|
||||
* - With pooling: 50MB total (shared across all rows)
|
||||
*/
|
||||
|
||||
import { Brainy } from '../brainy.js'
|
||||
import { NaturalLanguageProcessor } from '../neural/naturalLanguageProcessor.js'
|
||||
import { NeuralEntityExtractor } from '../neural/entityExtractor.js'
|
||||
|
||||
/**
|
||||
* InstancePool - Manages shared instances for memory efficiency
|
||||
*
|
||||
* Lifecycle:
|
||||
* 1. Create pool at import start
|
||||
* 2. Reuse instances across all rows
|
||||
* 3. Pool is garbage collected when import completes
|
||||
*
|
||||
* Thread safety: Not thread-safe (single import session per pool)
|
||||
*/
|
||||
export class InstancePool {
|
||||
private brain: Brainy
|
||||
|
||||
// Shared instances (created lazily)
|
||||
private nlpInstance: NaturalLanguageProcessor | null = null
|
||||
private extractorInstance: NeuralEntityExtractor | null = null
|
||||
|
||||
// Initialization state
|
||||
private nlpInitialized = false
|
||||
private initializationPromise: Promise<void> | null = null
|
||||
|
||||
// Statistics
|
||||
private stats = {
|
||||
nlpReuses: 0,
|
||||
extractorReuses: 0,
|
||||
creationTime: 0
|
||||
}
|
||||
|
||||
constructor(brain: Brainy) {
|
||||
this.brain = brain
|
||||
}
|
||||
|
||||
/**
|
||||
* Get shared NaturalLanguageProcessor instance
|
||||
*
|
||||
* Lazy initialization - created on first access
|
||||
* All subsequent calls return same instance
|
||||
*
|
||||
* @returns Shared NLP instance
|
||||
*/
|
||||
async getNLP(): Promise<NaturalLanguageProcessor> {
|
||||
if (!this.nlpInstance) {
|
||||
const startTime = Date.now()
|
||||
this.nlpInstance = new NaturalLanguageProcessor(this.brain)
|
||||
this.stats.creationTime += Date.now() - startTime
|
||||
}
|
||||
|
||||
// Ensure initialized before returning
|
||||
if (!this.nlpInitialized) {
|
||||
await this.ensureNLPInitialized()
|
||||
}
|
||||
|
||||
this.stats.nlpReuses++
|
||||
return this.nlpInstance
|
||||
}
|
||||
|
||||
/**
|
||||
* Get shared NeuralEntityExtractor instance
|
||||
*
|
||||
* Lazy initialization - created on first access
|
||||
* All subsequent calls return same instance
|
||||
*
|
||||
* @returns Shared extractor instance
|
||||
*/
|
||||
getExtractor(): NeuralEntityExtractor {
|
||||
if (!this.extractorInstance) {
|
||||
const startTime = Date.now()
|
||||
this.extractorInstance = new NeuralEntityExtractor(this.brain)
|
||||
this.stats.creationTime += Date.now() - startTime
|
||||
}
|
||||
|
||||
this.stats.extractorReuses++
|
||||
return this.extractorInstance
|
||||
}
|
||||
|
||||
/**
|
||||
* Get shared NLP instance (synchronous, may return uninitialized)
|
||||
*
|
||||
* Use when you need NLP synchronously and will handle initialization yourself.
|
||||
* Prefer getNLP() for async code.
|
||||
*
|
||||
* @returns Shared NLP instance (possibly uninitialized)
|
||||
*/
|
||||
getNLPSync(): NaturalLanguageProcessor {
|
||||
if (!this.nlpInstance) {
|
||||
this.nlpInstance = new NaturalLanguageProcessor(this.brain)
|
||||
}
|
||||
|
||||
this.stats.nlpReuses++
|
||||
return this.nlpInstance
|
||||
}
|
||||
|
||||
/**
|
||||
* Initialize all instances upfront
|
||||
*
|
||||
* Call at start of import to avoid lazy initialization overhead
|
||||
* during processing. Improves predictability and first-row performance.
|
||||
*
|
||||
* @returns Promise that resolves when all instances are ready
|
||||
*/
|
||||
async init(): Promise<void> {
|
||||
// Prevent duplicate initialization
|
||||
if (this.initializationPromise) {
|
||||
return this.initializationPromise
|
||||
}
|
||||
|
||||
this.initializationPromise = this.initializeInternal()
|
||||
return this.initializationPromise
|
||||
}
|
||||
|
||||
/**
|
||||
* Internal initialization implementation
|
||||
*/
|
||||
private async initializeInternal(): Promise<void> {
|
||||
const startTime = Date.now()
|
||||
|
||||
// Create instances
|
||||
if (!this.nlpInstance) {
|
||||
this.nlpInstance = new NaturalLanguageProcessor(this.brain)
|
||||
}
|
||||
if (!this.extractorInstance) {
|
||||
this.extractorInstance = new NeuralEntityExtractor(this.brain)
|
||||
}
|
||||
|
||||
// Initialize NLP (loads pattern library)
|
||||
await this.ensureNLPInitialized()
|
||||
|
||||
this.stats.creationTime = Date.now() - startTime
|
||||
}
|
||||
|
||||
/**
|
||||
* Ensure NLP is initialized (loads 220 patterns)
|
||||
*
|
||||
* Handles concurrent initialization requests safely
|
||||
*/
|
||||
private async ensureNLPInitialized(): Promise<void> {
|
||||
if (this.nlpInitialized) {
|
||||
return
|
||||
}
|
||||
|
||||
if (!this.nlpInstance) {
|
||||
throw new Error('NLP instance not created yet')
|
||||
}
|
||||
|
||||
await this.nlpInstance.init()
|
||||
this.nlpInitialized = true
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if instances are initialized
|
||||
*
|
||||
* @returns True if NLP is initialized and ready to use
|
||||
*/
|
||||
isInitialized(): boolean {
|
||||
return this.nlpInitialized && this.nlpInstance !== null
|
||||
}
|
||||
|
||||
/**
|
||||
* Get pool statistics
|
||||
*
|
||||
* Useful for performance monitoring and memory leak detection
|
||||
*
|
||||
* @returns Statistics about instance reuse
|
||||
*/
|
||||
getStats() {
|
||||
return {
|
||||
...this.stats,
|
||||
nlpCreated: this.nlpInstance !== null,
|
||||
extractorCreated: this.extractorInstance !== null,
|
||||
initialized: this.isInitialized(),
|
||||
// Memory savings estimate
|
||||
memorySaved: this.calculateMemorySaved()
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculate estimated memory saved by pooling
|
||||
*
|
||||
* Assumes ~50MB per NLP instance, ~10MB per extractor instance
|
||||
*
|
||||
* @returns Estimated memory saved in bytes
|
||||
*/
|
||||
private calculateMemorySaved(): number {
|
||||
const nlpSize = 50 * 1024 * 1024 // 50MB per instance
|
||||
const extractorSize = 10 * 1024 * 1024 // 10MB per instance
|
||||
|
||||
// Without pooling: size × reuses
|
||||
// With pooling: size × 1
|
||||
// Saved: size × (reuses - 1)
|
||||
|
||||
const nlpSaved = nlpSize * Math.max(0, this.stats.nlpReuses - 1)
|
||||
const extractorSaved = extractorSize * Math.max(0, this.stats.extractorReuses - 1)
|
||||
|
||||
return nlpSaved + extractorSaved
|
||||
}
|
||||
|
||||
/**
|
||||
* Reset statistics (useful for testing)
|
||||
*/
|
||||
resetStats(): void {
|
||||
this.stats = {
|
||||
nlpReuses: 0,
|
||||
extractorReuses: 0,
|
||||
creationTime: 0
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get string representation (for debugging)
|
||||
*/
|
||||
toString(): string {
|
||||
const stats = this.getStats()
|
||||
return `InstancePool(nlp=${stats.nlpCreated}, extractor=${stats.extractorCreated}, initialized=${stats.initialized}, nlpReuses=${stats.nlpReuses}, extractorReuses=${stats.extractorReuses})`
|
||||
}
|
||||
|
||||
/**
|
||||
* Cleanup method (for explicit resource management)
|
||||
*
|
||||
* Note: Usually not needed - pool is garbage collected when import completes.
|
||||
* Use only if you need explicit cleanup for some reason.
|
||||
*/
|
||||
cleanup(): void {
|
||||
// Clear references to allow garbage collection
|
||||
this.nlpInstance = null
|
||||
this.extractorInstance = null
|
||||
this.nlpInitialized = false
|
||||
this.initializationPromise = null
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a new instance pool
|
||||
*
|
||||
* Convenience factory function
|
||||
*
|
||||
* @param brain Brainy instance
|
||||
* @param autoInit Whether to initialize instances immediately
|
||||
* @returns Instance pool
|
||||
*/
|
||||
export async function createInstancePool(
|
||||
brain: Brainy,
|
||||
autoInit = true
|
||||
): Promise<InstancePool> {
|
||||
const pool = new InstancePool(brain)
|
||||
|
||||
if (autoInit) {
|
||||
await pool.init()
|
||||
}
|
||||
|
||||
return pool
|
||||
}
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
/**
|
||||
* Unified Import System
|
||||
*
|
||||
* Single entry point for importing any file format into Brainy with:
|
||||
* - Auto-detection of formats
|
||||
* - Dual storage (VFS + Knowledge Graph)
|
||||
* - Shared entities across imports (deduplication)
|
||||
* - Simple, powerful API
|
||||
*/
|
||||
|
||||
export { ImportCoordinator } from './ImportCoordinator.js'
|
||||
export { FormatDetector, SupportedFormat, DetectionResult } from './FormatDetector.js'
|
||||
export { EntityDeduplicator } from './EntityDeduplicator.js'
|
||||
export { BackgroundDeduplicator } from './BackgroundDeduplicator.js'
|
||||
export { ImportHistory } from './ImportHistory.js'
|
||||
|
||||
export type {
|
||||
ImportSource,
|
||||
ImportOptions,
|
||||
ImportProgress,
|
||||
ImportResult
|
||||
} from './ImportCoordinator.js'
|
||||
|
||||
export type {
|
||||
EntityCandidate,
|
||||
DuplicateMatch,
|
||||
EntityDeduplicationOptions,
|
||||
MergeResult
|
||||
} from './EntityDeduplicator.js'
|
||||
|
||||
export type {
|
||||
DeduplicationStats
|
||||
} from './BackgroundDeduplicator.js'
|
||||
|
||||
export type {
|
||||
ImportHistoryEntry,
|
||||
RollbackResult
|
||||
} from './ImportHistory.js'
|
||||
|
|
@ -1,789 +0,0 @@
|
|||
/**
|
||||
* Smart Import Orchestrator
|
||||
*
|
||||
* Coordinates the entire smart import pipeline:
|
||||
* 1. Extract entities/relationships using SmartExcelImporter
|
||||
* 2. Create entities and relationships in Brainy
|
||||
* 3. Organize into VFS structure using VFSStructureGenerator
|
||||
*
|
||||
* NO MOCKS - Production-ready implementation
|
||||
*/
|
||||
|
||||
import { Brainy } from '../brainy.js'
|
||||
import { VirtualFileSystem } from '../vfs/VirtualFileSystem.js'
|
||||
import { NounType, VerbType } from '../types/graphTypes.js'
|
||||
import { splitNounMetadataRecord } from '../types/reservedFields.js'
|
||||
import { SmartExcelImporter, SmartExcelOptions, SmartExcelResult } from './SmartExcelImporter.js'
|
||||
import { SmartPDFImporter, SmartPDFOptions, SmartPDFResult } from './SmartPDFImporter.js'
|
||||
import { SmartCSVImporter, SmartCSVOptions, SmartCSVResult } from './SmartCSVImporter.js'
|
||||
import { SmartJSONImporter, SmartJSONOptions, SmartJSONResult } from './SmartJSONImporter.js'
|
||||
import { SmartMarkdownImporter, SmartMarkdownOptions, SmartMarkdownResult } from './SmartMarkdownImporter.js'
|
||||
import { VFSStructureGenerator, VFSStructureOptions } from './VFSStructureGenerator.js'
|
||||
|
||||
export interface SmartImportOptions extends SmartExcelOptions {
|
||||
/** Create VFS structure */
|
||||
createVFSStructure?: boolean
|
||||
|
||||
/** VFS root path */
|
||||
vfsRootPath?: string
|
||||
|
||||
/** VFS grouping strategy */
|
||||
vfsGroupBy?: 'type' | 'sheet' | 'flat' | 'custom'
|
||||
|
||||
/** Create entities in Brainy */
|
||||
createEntities?: boolean
|
||||
|
||||
/** Create relationships in Brainy */
|
||||
createRelationships?: boolean
|
||||
|
||||
/** Source filename */
|
||||
filename?: string
|
||||
|
||||
/**
|
||||
* Default subtype tag for entities + relationships this importer creates when
|
||||
* the extractor doesn't set one. See `ValidImportOptions.defaultSubtype` —
|
||||
* same semantics, same precedence (extractor > caller default > `'imported'`).
|
||||
* Added 7.30.1.
|
||||
*/
|
||||
defaultSubtype?: string
|
||||
}
|
||||
|
||||
export interface SmartImportProgress {
|
||||
phase: 'parsing' | 'extracting' | 'creating' | 'relationships' | 'organizing' | 'complete'
|
||||
message: string
|
||||
processed: number
|
||||
total: number
|
||||
entities: number
|
||||
relationships: number
|
||||
}
|
||||
|
||||
export interface SmartImportResult {
|
||||
success: boolean
|
||||
|
||||
/** Extraction results */
|
||||
extraction: SmartExcelResult
|
||||
|
||||
/** Created entity IDs */
|
||||
entityIds: string[]
|
||||
|
||||
/** Created relationship IDs */
|
||||
relationshipIds: string[]
|
||||
|
||||
/** VFS structure created */
|
||||
vfsStructure?: {
|
||||
rootPath: string
|
||||
directories: string[]
|
||||
files: number
|
||||
}
|
||||
|
||||
/** Overall statistics */
|
||||
stats: {
|
||||
rowsProcessed: number
|
||||
entitiesCreated: number
|
||||
relationshipsCreated: number
|
||||
filesCreated: number
|
||||
totalTime: number
|
||||
}
|
||||
|
||||
/** Any errors encountered */
|
||||
errors: string[]
|
||||
}
|
||||
|
||||
/**
|
||||
* SmartImportOrchestrator - Main entry point for smart imports
|
||||
*/
|
||||
export class SmartImportOrchestrator {
|
||||
private brain: Brainy
|
||||
private excelImporter: SmartExcelImporter
|
||||
private pdfImporter: SmartPDFImporter
|
||||
private csvImporter: SmartCSVImporter
|
||||
private jsonImporter: SmartJSONImporter
|
||||
private markdownImporter: SmartMarkdownImporter
|
||||
private vfsGenerator: VFSStructureGenerator
|
||||
|
||||
constructor(brain: Brainy) {
|
||||
this.brain = brain
|
||||
this.excelImporter = new SmartExcelImporter(brain)
|
||||
this.pdfImporter = new SmartPDFImporter(brain)
|
||||
this.csvImporter = new SmartCSVImporter(brain)
|
||||
this.jsonImporter = new SmartJSONImporter(brain)
|
||||
this.markdownImporter = new SmartMarkdownImporter(brain)
|
||||
this.vfsGenerator = new VFSStructureGenerator(brain)
|
||||
}
|
||||
|
||||
/**
|
||||
* Initialize the orchestrator
|
||||
*/
|
||||
async init(): Promise<void> {
|
||||
await this.excelImporter.init()
|
||||
await this.pdfImporter.init()
|
||||
await this.csvImporter.init()
|
||||
await this.jsonImporter.init()
|
||||
await this.markdownImporter.init()
|
||||
await this.vfsGenerator.init()
|
||||
}
|
||||
|
||||
/**
|
||||
* Import Excel file with full pipeline
|
||||
*/
|
||||
async importExcel(
|
||||
buffer: Buffer,
|
||||
options: SmartImportOptions = {},
|
||||
onProgress?: (progress: SmartImportProgress) => void
|
||||
): Promise<SmartImportResult> {
|
||||
const startTime = Date.now()
|
||||
const result: SmartImportResult = {
|
||||
success: false,
|
||||
// Typed boundary: populated in the extraction phase below. If extraction
|
||||
// throws, the error path returns with this still null (pre-existing
|
||||
// contract — consumers check `success`/`errors` before reading it).
|
||||
extraction: null as unknown as SmartExcelResult,
|
||||
entityIds: [],
|
||||
relationshipIds: [],
|
||||
stats: {
|
||||
rowsProcessed: 0,
|
||||
entitiesCreated: 0,
|
||||
relationshipsCreated: 0,
|
||||
filesCreated: 0,
|
||||
totalTime: 0
|
||||
},
|
||||
errors: []
|
||||
}
|
||||
|
||||
try {
|
||||
// Phase 1: Extract entities and relationships
|
||||
onProgress?.({
|
||||
phase: 'extracting',
|
||||
message: 'Extracting entities and relationships...',
|
||||
processed: 0,
|
||||
total: 0,
|
||||
entities: 0,
|
||||
relationships: 0
|
||||
})
|
||||
|
||||
result.extraction = await this.excelImporter.extract(buffer, {
|
||||
...options,
|
||||
onProgress: (stats) => {
|
||||
onProgress?.({
|
||||
phase: 'extracting',
|
||||
message: `Processing row ${stats.processed}/${stats.total}...`,
|
||||
processed: stats.processed,
|
||||
total: stats.total,
|
||||
entities: stats.entities,
|
||||
relationships: stats.relationships
|
||||
})
|
||||
}
|
||||
})
|
||||
|
||||
result.stats.rowsProcessed = result.extraction.rowsProcessed
|
||||
|
||||
// Phase 2: Create entities in Brainy
|
||||
if (options.createEntities !== false) {
|
||||
onProgress?.({
|
||||
phase: 'creating',
|
||||
message: 'Creating entities in knowledge graph...',
|
||||
processed: 0,
|
||||
total: result.extraction.rows.length,
|
||||
entities: 0,
|
||||
relationships: 0
|
||||
})
|
||||
|
||||
for (let i = 0; i < result.extraction.rows.length; i++) {
|
||||
const extracted = result.extraction.rows[i]
|
||||
|
||||
try {
|
||||
// Create main entity. Subtype precedence: extractor-set → caller default
|
||||
// → Brainy default `'imported'` (added 7.30.1).
|
||||
const entityId = await this.brain.add({
|
||||
data: extracted.entity.description,
|
||||
type: extracted.entity.type,
|
||||
subtype:
|
||||
(extracted.entity as typeof extracted.entity & { subtype?: string })
|
||||
.subtype ?? options.defaultSubtype ?? 'imported',
|
||||
confidence: extracted.entity.confidence, // reserved field — dedicated param, not metadata
|
||||
metadata: {
|
||||
// Strip reserved keys an extractor may have smuggled into the bag
|
||||
// (8.0 reservedFieldPolicy defaults to 'throw').
|
||||
...splitNounMetadataRecord(extracted.entity.metadata).custom,
|
||||
name: extracted.entity.name,
|
||||
importedFrom: 'smart-import'
|
||||
}
|
||||
})
|
||||
|
||||
result.entityIds.push(entityId)
|
||||
result.stats.entitiesCreated++
|
||||
|
||||
// Update entity ID in extraction result
|
||||
extracted.entity.id = entityId
|
||||
|
||||
onProgress?.({
|
||||
phase: 'creating',
|
||||
message: `Created entity: ${extracted.entity.name}`,
|
||||
processed: i + 1,
|
||||
total: result.extraction.rows.length,
|
||||
entities: result.entityIds.length,
|
||||
relationships: result.relationshipIds.length
|
||||
})
|
||||
} catch (error: any) {
|
||||
result.errors.push(`Failed to create entity ${extracted.entity.name}: ${error.message}`)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Phase 3: Create relationships
|
||||
if (options.createRelationships !== false && options.createEntities !== false) {
|
||||
onProgress?.({
|
||||
phase: 'creating',
|
||||
message: 'Preparing relationships...',
|
||||
processed: 0,
|
||||
total: result.extraction.rows.length,
|
||||
entities: result.entityIds.length,
|
||||
relationships: 0
|
||||
})
|
||||
|
||||
// Build entity name -> ID map
|
||||
const entityMap = new Map<string, string>()
|
||||
for (const extracted of result.extraction.rows) {
|
||||
entityMap.set(extracted.entity.name.toLowerCase(), extracted.entity.id)
|
||||
}
|
||||
|
||||
// Collect all relationship parameters
|
||||
const relationshipParams: Array<{from: string; to: string; type: VerbType; subtype?: string; confidence?: number; metadata?: any}> = []
|
||||
|
||||
for (const extracted of result.extraction.rows) {
|
||||
for (const rel of extracted.relationships) {
|
||||
try {
|
||||
// Find target entity ID
|
||||
let toEntityId: string | undefined
|
||||
|
||||
// Try to find by name in our extracted entities
|
||||
for (const otherExtracted of result.extraction.rows) {
|
||||
if (rel.to.toLowerCase().includes(otherExtracted.entity.name.toLowerCase()) ||
|
||||
otherExtracted.entity.name.toLowerCase().includes(rel.to.toLowerCase())) {
|
||||
toEntityId = otherExtracted.entity.id
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
// If not found, create a placeholder entity. `import-placeholder` marks
|
||||
// these as synthetic targets so consumers can distinguish them from real
|
||||
// imports and downstream dedup can consolidate (added 7.30.1).
|
||||
if (!toEntityId) {
|
||||
toEntityId = await this.brain.add({
|
||||
data: rel.to,
|
||||
type: NounType.Thing,
|
||||
subtype: 'import-placeholder',
|
||||
metadata: {
|
||||
name: rel.to,
|
||||
placeholder: true,
|
||||
extractedFrom: extracted.entity.name
|
||||
}
|
||||
})
|
||||
result.entityIds.push(toEntityId)
|
||||
}
|
||||
|
||||
// Collect relationship parameter. Subtype precedence: extractor-set rel
|
||||
// subtype → caller default → Brainy default `'imported'` (added 7.30.1).
|
||||
relationshipParams.push({
|
||||
from: extracted.entity.id,
|
||||
to: toEntityId,
|
||||
type: rel.type,
|
||||
subtype:
|
||||
(rel as typeof rel & { subtype?: string }).subtype ??
|
||||
options.defaultSubtype ?? 'imported',
|
||||
confidence: rel.confidence, // reserved field — dedicated param, not metadata
|
||||
metadata: {
|
||||
evidence: rel.evidence
|
||||
}
|
||||
})
|
||||
} catch (error: any) {
|
||||
result.errors.push(`Failed to prepare relationship: ${error.message}`)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Batch create all relationships with progress
|
||||
if (relationshipParams.length > 0) {
|
||||
onProgress?.({
|
||||
phase: 'relationships',
|
||||
message: 'Building relationships...',
|
||||
processed: 0,
|
||||
total: relationshipParams.length,
|
||||
entities: result.entityIds.length,
|
||||
relationships: 0
|
||||
})
|
||||
|
||||
try {
|
||||
const relationshipIds = await this.brain.relateMany({
|
||||
items: relationshipParams,
|
||||
parallel: true,
|
||||
chunkSize: 100,
|
||||
continueOnError: true,
|
||||
onProgress: (done, total) => {
|
||||
onProgress?.({
|
||||
phase: 'relationships',
|
||||
message: `Building relationships: ${done}/${total}`,
|
||||
processed: done,
|
||||
total: total,
|
||||
entities: result.entityIds.length,
|
||||
relationships: done
|
||||
})
|
||||
}
|
||||
})
|
||||
|
||||
result.relationshipIds = relationshipIds
|
||||
result.stats.relationshipsCreated = relationshipIds.length
|
||||
} catch (error: any) {
|
||||
result.errors.push(`Failed to create relationships: ${error.message}`)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Phase 4: Create VFS structure
|
||||
if (options.createVFSStructure !== false) {
|
||||
onProgress?.({
|
||||
phase: 'organizing',
|
||||
message: 'Organizing into file structure...',
|
||||
processed: 0,
|
||||
total: result.extraction.rows.length,
|
||||
entities: result.entityIds.length,
|
||||
relationships: result.relationshipIds.length
|
||||
})
|
||||
|
||||
const vfsOptions: VFSStructureOptions = {
|
||||
rootPath: options.vfsRootPath || '/imports/' + (options.filename || 'import'),
|
||||
groupBy: options.vfsGroupBy || 'type',
|
||||
preserveSource: true,
|
||||
sourceBuffer: buffer,
|
||||
sourceFilename: options.filename || 'import.xlsx',
|
||||
createRelationshipFile: true,
|
||||
createMetadataFile: true
|
||||
}
|
||||
|
||||
const vfsResult = await this.vfsGenerator.generate(result.extraction, vfsOptions)
|
||||
|
||||
result.vfsStructure = {
|
||||
rootPath: vfsResult.rootPath,
|
||||
directories: vfsResult.directories,
|
||||
files: vfsResult.files.length
|
||||
}
|
||||
|
||||
result.stats.filesCreated = vfsResult.files.length
|
||||
}
|
||||
|
||||
// Complete
|
||||
result.success = result.errors.length === 0
|
||||
result.stats.totalTime = Date.now() - startTime
|
||||
|
||||
onProgress?.({
|
||||
phase: 'complete',
|
||||
message: `Import complete: ${result.stats.entitiesCreated} entities, ${result.stats.relationshipsCreated} relationships`,
|
||||
processed: result.extraction.rows.length,
|
||||
total: result.extraction.rows.length,
|
||||
entities: result.stats.entitiesCreated,
|
||||
relationships: result.stats.relationshipsCreated
|
||||
})
|
||||
|
||||
} catch (error: any) {
|
||||
result.errors.push(`Import failed: ${error.message}`)
|
||||
result.success = false
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Import PDF file with full pipeline
|
||||
*/
|
||||
async importPDF(
|
||||
buffer: Buffer,
|
||||
options: SmartImportOptions & SmartPDFOptions = {},
|
||||
onProgress?: (progress: SmartImportProgress) => void
|
||||
): Promise<SmartImportResult> {
|
||||
const startTime = Date.now()
|
||||
const result: SmartImportResult = {
|
||||
success: false,
|
||||
// Typed boundary: populated after extraction (see importExcel).
|
||||
extraction: null as unknown as SmartExcelResult,
|
||||
entityIds: [],
|
||||
relationshipIds: [],
|
||||
stats: {
|
||||
rowsProcessed: 0,
|
||||
entitiesCreated: 0,
|
||||
relationshipsCreated: 0,
|
||||
filesCreated: 0,
|
||||
totalTime: 0
|
||||
},
|
||||
errors: []
|
||||
}
|
||||
|
||||
try {
|
||||
// Phase 1: Extract from PDF
|
||||
onProgress?.({ phase: 'extracting', message: 'Extracting from PDF...', processed: 0, total: 0, entities: 0, relationships: 0 })
|
||||
|
||||
const pdfResult = await this.pdfImporter.extract(buffer, options)
|
||||
|
||||
// Convert PDF result to Excel-like format for processing
|
||||
result.extraction = this.convertPDFToExcelFormat(pdfResult)
|
||||
result.stats.rowsProcessed = pdfResult.sectionsProcessed
|
||||
|
||||
// Phase 2 & 3: Create entities and relationships
|
||||
await this.createEntitiesAndRelationships(result, options, onProgress)
|
||||
|
||||
// Phase 4: Create VFS structure
|
||||
if (options.createVFSStructure !== false) {
|
||||
const vfsOptions: VFSStructureOptions = {
|
||||
rootPath: options.vfsRootPath || '/imports/' + (options.filename || 'import'),
|
||||
groupBy: options.vfsGroupBy || 'type',
|
||||
preserveSource: true,
|
||||
sourceBuffer: buffer,
|
||||
sourceFilename: options.filename || 'import.pdf',
|
||||
createRelationshipFile: true,
|
||||
createMetadataFile: true
|
||||
}
|
||||
const vfsResult = await this.vfsGenerator.generate(result.extraction, vfsOptions)
|
||||
result.vfsStructure = { rootPath: vfsResult.rootPath, directories: vfsResult.directories, files: vfsResult.files.length }
|
||||
result.stats.filesCreated = vfsResult.files.length
|
||||
}
|
||||
|
||||
result.success = result.errors.length === 0
|
||||
result.stats.totalTime = Date.now() - startTime
|
||||
onProgress?.({ phase: 'complete', message: `Import complete: ${result.stats.entitiesCreated} entities, ${result.stats.relationshipsCreated} relationships`, processed: result.stats.rowsProcessed, total: result.stats.rowsProcessed, entities: result.stats.entitiesCreated, relationships: result.stats.relationshipsCreated })
|
||||
|
||||
} catch (error: any) {
|
||||
result.errors.push(`PDF import failed: ${error.message}`)
|
||||
result.success = false
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Import CSV file with full pipeline
|
||||
*/
|
||||
async importCSV(
|
||||
buffer: Buffer,
|
||||
options: SmartImportOptions & SmartCSVOptions = {},
|
||||
onProgress?: (progress: SmartImportProgress) => void
|
||||
): Promise<SmartImportResult> {
|
||||
// CSV is very similar to Excel, can reuse importExcel logic
|
||||
return this.importExcel(buffer, options, onProgress)
|
||||
}
|
||||
|
||||
/**
|
||||
* Import JSON data with full pipeline
|
||||
*/
|
||||
async importJSON(
|
||||
data: any,
|
||||
options: SmartImportOptions & SmartJSONOptions = {},
|
||||
onProgress?: (progress: SmartImportProgress) => void
|
||||
): Promise<SmartImportResult> {
|
||||
const startTime = Date.now()
|
||||
const result: SmartImportResult = {
|
||||
success: false,
|
||||
// Typed boundary: populated after extraction (see importExcel).
|
||||
extraction: null as unknown as SmartExcelResult,
|
||||
entityIds: [],
|
||||
relationshipIds: [],
|
||||
stats: {
|
||||
rowsProcessed: 0,
|
||||
entitiesCreated: 0,
|
||||
relationshipsCreated: 0,
|
||||
filesCreated: 0,
|
||||
totalTime: 0
|
||||
},
|
||||
errors: []
|
||||
}
|
||||
|
||||
try {
|
||||
onProgress?.({ phase: 'extracting', message: 'Extracting from JSON...', processed: 0, total: 0, entities: 0, relationships: 0 })
|
||||
|
||||
const jsonResult = await this.jsonImporter.extract(data, options)
|
||||
|
||||
result.extraction = this.convertJSONToExcelFormat(jsonResult)
|
||||
result.stats.rowsProcessed = jsonResult.nodesProcessed
|
||||
|
||||
await this.createEntitiesAndRelationships(result, options, onProgress)
|
||||
|
||||
if (options.createVFSStructure !== false) {
|
||||
const sourceBuffer = Buffer.from(typeof data === 'string' ? data : JSON.stringify(data, null, 2))
|
||||
const vfsOptions: VFSStructureOptions = {
|
||||
rootPath: options.vfsRootPath || '/imports/' + (options.filename || 'import'),
|
||||
groupBy: options.vfsGroupBy || 'type',
|
||||
preserveSource: true,
|
||||
sourceBuffer,
|
||||
sourceFilename: options.filename || 'import.json',
|
||||
createRelationshipFile: true,
|
||||
createMetadataFile: true
|
||||
}
|
||||
const vfsResult = await this.vfsGenerator.generate(result.extraction, vfsOptions)
|
||||
result.vfsStructure = { rootPath: vfsResult.rootPath, directories: vfsResult.directories, files: vfsResult.files.length }
|
||||
result.stats.filesCreated = vfsResult.files.length
|
||||
}
|
||||
|
||||
result.success = result.errors.length === 0
|
||||
result.stats.totalTime = Date.now() - startTime
|
||||
onProgress?.({ phase: 'complete', message: `Import complete: ${result.stats.entitiesCreated} entities, ${result.stats.relationshipsCreated} relationships`, processed: result.stats.rowsProcessed, total: result.stats.rowsProcessed, entities: result.stats.entitiesCreated, relationships: result.stats.relationshipsCreated })
|
||||
|
||||
} catch (error: any) {
|
||||
result.errors.push(`JSON import failed: ${error.message}`)
|
||||
result.success = false
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Import Markdown content with full pipeline
|
||||
*/
|
||||
async importMarkdown(
|
||||
markdown: string,
|
||||
options: SmartImportOptions & SmartMarkdownOptions = {},
|
||||
onProgress?: (progress: SmartImportProgress) => void
|
||||
): Promise<SmartImportResult> {
|
||||
const startTime = Date.now()
|
||||
const result: SmartImportResult = {
|
||||
success: false,
|
||||
// Typed boundary: populated after extraction (see importExcel).
|
||||
extraction: null as unknown as SmartExcelResult,
|
||||
entityIds: [],
|
||||
relationshipIds: [],
|
||||
stats: {
|
||||
rowsProcessed: 0,
|
||||
entitiesCreated: 0,
|
||||
relationshipsCreated: 0,
|
||||
filesCreated: 0,
|
||||
totalTime: 0
|
||||
},
|
||||
errors: []
|
||||
}
|
||||
|
||||
try {
|
||||
onProgress?.({ phase: 'extracting', message: 'Extracting from Markdown...', processed: 0, total: 0, entities: 0, relationships: 0 })
|
||||
|
||||
const mdResult = await this.markdownImporter.extract(markdown, options)
|
||||
|
||||
result.extraction = this.convertMarkdownToExcelFormat(mdResult)
|
||||
result.stats.rowsProcessed = mdResult.sectionsProcessed
|
||||
|
||||
await this.createEntitiesAndRelationships(result, options, onProgress)
|
||||
|
||||
if (options.createVFSStructure !== false) {
|
||||
const sourceBuffer = Buffer.from(markdown, 'utf-8')
|
||||
const vfsOptions: VFSStructureOptions = {
|
||||
rootPath: options.vfsRootPath || '/imports/' + (options.filename || 'import'),
|
||||
groupBy: options.vfsGroupBy || 'type',
|
||||
preserveSource: true,
|
||||
sourceBuffer,
|
||||
sourceFilename: options.filename || 'import.md',
|
||||
createRelationshipFile: true,
|
||||
createMetadataFile: true
|
||||
}
|
||||
const vfsResult = await this.vfsGenerator.generate(result.extraction, vfsOptions)
|
||||
result.vfsStructure = { rootPath: vfsResult.rootPath, directories: vfsResult.directories, files: vfsResult.files.length }
|
||||
result.stats.filesCreated = vfsResult.files.length
|
||||
}
|
||||
|
||||
result.success = result.errors.length === 0
|
||||
result.stats.totalTime = Date.now() - startTime
|
||||
onProgress?.({ phase: 'complete', message: `Import complete: ${result.stats.entitiesCreated} entities, ${result.stats.relationshipsCreated} relationships`, processed: result.stats.rowsProcessed, total: result.stats.rowsProcessed, entities: result.stats.entitiesCreated, relationships: result.stats.relationshipsCreated })
|
||||
|
||||
} catch (error: any) {
|
||||
result.errors.push(`Markdown import failed: ${error.message}`)
|
||||
result.success = false
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Helper: Create entities and relationships from extraction result
|
||||
*/
|
||||
private async createEntitiesAndRelationships(
|
||||
result: SmartImportResult,
|
||||
options: SmartImportOptions,
|
||||
onProgress?: (progress: SmartImportProgress) => void
|
||||
): Promise<void> {
|
||||
if (options.createEntities !== false) {
|
||||
onProgress?.({ phase: 'creating', message: 'Creating entities in knowledge graph...', processed: 0, total: result.extraction.rows.length, entities: 0, relationships: 0 })
|
||||
|
||||
for (let i = 0; i < result.extraction.rows.length; i++) {
|
||||
const extracted = result.extraction.rows[i]
|
||||
try {
|
||||
// Subtype precedence: extractor → caller default → `'imported'` (7.30.1).
|
||||
const entityId = await this.brain.add({
|
||||
data: extracted.entity.description,
|
||||
type: extracted.entity.type,
|
||||
subtype:
|
||||
(extracted.entity as typeof extracted.entity & { subtype?: string })
|
||||
.subtype ?? options.defaultSubtype ?? 'imported',
|
||||
confidence: extracted.entity.confidence, // reserved field — dedicated param, not metadata
|
||||
metadata: { ...splitNounMetadataRecord(extracted.entity.metadata).custom, name: extracted.entity.name, importedFrom: 'smart-import' }
|
||||
})
|
||||
result.entityIds.push(entityId)
|
||||
result.stats.entitiesCreated++
|
||||
extracted.entity.id = entityId
|
||||
} catch (error: any) {
|
||||
result.errors.push(`Failed to create entity ${extracted.entity.name}: ${error.message}`)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (options.createRelationships !== false && options.createEntities !== false) {
|
||||
onProgress?.({ phase: 'creating', message: 'Preparing relationships...', processed: 0, total: result.extraction.rows.length, entities: result.entityIds.length, relationships: 0 })
|
||||
|
||||
// Collect all relationship parameters
|
||||
const relationshipParams: Array<{from: string; to: string; type: VerbType; subtype?: string; confidence?: number; metadata?: any}> = []
|
||||
|
||||
for (const extracted of result.extraction.rows) {
|
||||
for (const rel of extracted.relationships) {
|
||||
try {
|
||||
let toEntityId: string | undefined
|
||||
for (const otherExtracted of result.extraction.rows) {
|
||||
if (rel.to.toLowerCase().includes(otherExtracted.entity.name.toLowerCase()) || otherExtracted.entity.name.toLowerCase().includes(rel.to.toLowerCase())) {
|
||||
toEntityId = otherExtracted.entity.id
|
||||
break
|
||||
}
|
||||
}
|
||||
if (!toEntityId) {
|
||||
// Subtype `import-placeholder` marks synthetic targets (7.30.1).
|
||||
toEntityId = await this.brain.add({ data: rel.to, type: NounType.Thing, subtype: 'import-placeholder', metadata: { name: rel.to, placeholder: true, extractedFrom: extracted.entity.name } })
|
||||
result.entityIds.push(toEntityId)
|
||||
}
|
||||
// Relationship subtype precedence: extractor → caller default → `'imported'` (7.30.1).
|
||||
// `confidence` is a reserved top-level field — dedicated relate() param, not metadata
|
||||
relationshipParams.push({ from: extracted.entity.id, to: toEntityId, type: rel.type, subtype: (rel as typeof rel & { subtype?: string }).subtype ?? options.defaultSubtype ?? 'imported', confidence: rel.confidence, metadata: { evidence: rel.evidence } })
|
||||
} catch (error: any) {
|
||||
result.errors.push(`Failed to prepare relationship: ${error.message}`)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Batch create all relationships with progress
|
||||
if (relationshipParams.length > 0) {
|
||||
onProgress?.({ phase: 'relationships', message: 'Building relationships...', processed: 0, total: relationshipParams.length, entities: result.entityIds.length, relationships: 0 })
|
||||
|
||||
try {
|
||||
const relationshipIds = await this.brain.relateMany({
|
||||
items: relationshipParams,
|
||||
parallel: true,
|
||||
chunkSize: 100,
|
||||
continueOnError: true,
|
||||
onProgress: (done, total) => {
|
||||
onProgress?.({ phase: 'relationships', message: `Building relationships: ${done}/${total}`, processed: done, total: total, entities: result.entityIds.length, relationships: done })
|
||||
}
|
||||
})
|
||||
|
||||
result.relationshipIds = relationshipIds
|
||||
result.stats.relationshipsCreated = relationshipIds.length
|
||||
} catch (error: any) {
|
||||
result.errors.push(`Failed to create relationships: ${error.message}`)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Helper: Convert PDF result to Excel-like format
|
||||
*/
|
||||
private convertPDFToExcelFormat(pdfResult: SmartPDFResult): Omit<SmartExcelResult, 'rows'> & { rows: any[] } {
|
||||
const rows = pdfResult.sections.flatMap(section =>
|
||||
section.entities.map(entity => ({
|
||||
entity,
|
||||
relatedEntities: [],
|
||||
relationships: section.relationships.filter(r => r.from === entity.id),
|
||||
concepts: section.concepts
|
||||
}))
|
||||
)
|
||||
|
||||
return {
|
||||
rowsProcessed: pdfResult.sectionsProcessed,
|
||||
entitiesExtracted: pdfResult.entitiesExtracted,
|
||||
relationshipsInferred: pdfResult.relationshipsInferred,
|
||||
rows,
|
||||
entityMap: pdfResult.entityMap,
|
||||
processingTime: pdfResult.processingTime,
|
||||
stats: pdfResult.stats
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Helper: Convert JSON result to Excel-like format
|
||||
*/
|
||||
private convertJSONToExcelFormat(jsonResult: SmartJSONResult): Omit<SmartExcelResult, 'rows'> & { rows: any[] } {
|
||||
const rows = jsonResult.entities.map(entity => ({
|
||||
entity,
|
||||
relatedEntities: [],
|
||||
relationships: jsonResult.relationships.filter(r => r.from === entity.id),
|
||||
concepts: entity.metadata.concepts || []
|
||||
}))
|
||||
|
||||
return {
|
||||
rowsProcessed: jsonResult.nodesProcessed,
|
||||
entitiesExtracted: jsonResult.entitiesExtracted,
|
||||
relationshipsInferred: jsonResult.relationshipsInferred,
|
||||
rows,
|
||||
entityMap: jsonResult.entityMap,
|
||||
processingTime: jsonResult.processingTime,
|
||||
stats: jsonResult.stats
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Helper: Convert Markdown result to Excel-like format
|
||||
*/
|
||||
private convertMarkdownToExcelFormat(mdResult: SmartMarkdownResult): Omit<SmartExcelResult, 'rows'> & { rows: any[] } {
|
||||
const rows = mdResult.sections.flatMap(section =>
|
||||
section.entities.map(entity => ({
|
||||
entity,
|
||||
relatedEntities: [],
|
||||
relationships: section.relationships.filter(r => r.from === entity.id),
|
||||
concepts: section.concepts
|
||||
}))
|
||||
)
|
||||
|
||||
return {
|
||||
rowsProcessed: mdResult.sectionsProcessed,
|
||||
entitiesExtracted: mdResult.entitiesExtracted,
|
||||
relationshipsInferred: mdResult.relationshipsInferred,
|
||||
rows,
|
||||
entityMap: mdResult.entityMap,
|
||||
processingTime: mdResult.processingTime,
|
||||
stats: mdResult.stats
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get import statistics
|
||||
*/
|
||||
async getImportStatistics(vfsRootPath: string): Promise<{
|
||||
entitiesInGraph: number
|
||||
relationshipsInGraph: number
|
||||
filesInVFS: number
|
||||
lastImport?: Date
|
||||
}> {
|
||||
// Read metadata file
|
||||
const vfs = new VirtualFileSystem(this.brain)
|
||||
await vfs.init()
|
||||
|
||||
const metadataPath = `${vfsRootPath}/_metadata.json`
|
||||
|
||||
try {
|
||||
const metadataBuffer = await vfs.readFile(metadataPath)
|
||||
const metadata = JSON.parse(metadataBuffer.toString('utf-8'))
|
||||
|
||||
return {
|
||||
entitiesInGraph: metadata.import.stats.entitiesExtracted,
|
||||
relationshipsInGraph: metadata.import.stats.relationshipsInferred,
|
||||
filesInVFS: metadata.structure.fileCount,
|
||||
lastImport: new Date(metadata.import.timestamp)
|
||||
}
|
||||
} catch (error) {
|
||||
return {
|
||||
entitiesInGraph: 0,
|
||||
relationshipsInGraph: 0,
|
||||
filesInVFS: 0
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,69 +0,0 @@
|
|||
/**
|
||||
* Smart Import System
|
||||
*
|
||||
* Production-ready entity and relationship extraction from multiple formats:
|
||||
* - Excel (.xlsx)
|
||||
* - PDF (.pdf)
|
||||
* - CSV (.csv)
|
||||
* - JSON (.json)
|
||||
* - Markdown (.md)
|
||||
*
|
||||
* Uses brainy's built-in NeuralEntityExtractor and NaturalLanguageProcessor
|
||||
*
|
||||
* NO MOCKS - Real working implementation
|
||||
*/
|
||||
|
||||
// Excel Importer
|
||||
export { SmartExcelImporter } from './SmartExcelImporter.js'
|
||||
export type {
|
||||
SmartExcelOptions,
|
||||
ExtractedRow,
|
||||
SmartExcelResult
|
||||
} from './SmartExcelImporter.js'
|
||||
|
||||
// PDF Importer
|
||||
export { SmartPDFImporter } from './SmartPDFImporter.js'
|
||||
export type {
|
||||
SmartPDFOptions,
|
||||
ExtractedSection,
|
||||
SmartPDFResult
|
||||
} from './SmartPDFImporter.js'
|
||||
|
||||
// CSV Importer
|
||||
export { SmartCSVImporter } from './SmartCSVImporter.js'
|
||||
export type {
|
||||
SmartCSVOptions,
|
||||
SmartCSVResult
|
||||
} from './SmartCSVImporter.js'
|
||||
|
||||
// JSON Importer
|
||||
export { SmartJSONImporter } from './SmartJSONImporter.js'
|
||||
export type {
|
||||
SmartJSONOptions,
|
||||
ExtractedJSONEntity,
|
||||
ExtractedJSONRelationship,
|
||||
SmartJSONResult
|
||||
} from './SmartJSONImporter.js'
|
||||
|
||||
// Markdown Importer
|
||||
export { SmartMarkdownImporter } from './SmartMarkdownImporter.js'
|
||||
export type {
|
||||
SmartMarkdownOptions,
|
||||
MarkdownSection,
|
||||
SmartMarkdownResult
|
||||
} from './SmartMarkdownImporter.js'
|
||||
|
||||
// VFS Structure Generator
|
||||
export { VFSStructureGenerator } from './VFSStructureGenerator.js'
|
||||
export type {
|
||||
VFSStructureOptions,
|
||||
VFSStructureResult
|
||||
} from './VFSStructureGenerator.js'
|
||||
|
||||
// Orchestrator (Main entry point)
|
||||
export { SmartImportOrchestrator } from './SmartImportOrchestrator.js'
|
||||
export type {
|
||||
SmartImportOptions,
|
||||
SmartImportProgress,
|
||||
SmartImportResult
|
||||
} from './SmartImportOrchestrator.js'
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
/**
|
||||
* @module columnStore
|
||||
* @description Unified column store for filtering, sorting, range queries,
|
||||
* and text search on any field type with exact precision at billion scale.
|
||||
*
|
||||
* Replaces MetadataIndex's sparse chunk/bloom filter/bucketing internals
|
||||
* with per-field sorted columns. Design lineage: Lucene doc values + roaring
|
||||
* bitmap acceleration.
|
||||
*/
|
||||
|
||||
export { ColumnStore } from './ColumnStore.js'
|
||||
export type { ColumnStoreConfig } from './ColumnStore.js'
|
||||
export { ColumnTailBuffer } from './ColumnTailBuffer.js'
|
||||
export { ColumnManifest } from './ColumnManifest.js'
|
||||
export { ColumnSegmentCursor, TailBufferCursor } from './ColumnSegmentCursor.js'
|
||||
export {
|
||||
writeSegmentToBuffer,
|
||||
readSegmentFromBuffer,
|
||||
writeHeader,
|
||||
readHeader,
|
||||
crc32
|
||||
} from './ColumnSegmentFormat.js'
|
||||
export {
|
||||
ValueType,
|
||||
CIDX_MAGIC,
|
||||
CIDX_VERSION,
|
||||
HEADER_SIZE,
|
||||
FOOTER_SIZE,
|
||||
DEFAULT_FLUSH_THRESHOLD,
|
||||
FLAG_MULTI_VALUE
|
||||
} from './types.js'
|
||||
export type {
|
||||
ColumnStoreProvider,
|
||||
SegmentHeader,
|
||||
SegmentFooter,
|
||||
SegmentMeta,
|
||||
ManifestData
|
||||
} from './types.js'
|
||||
|
|
@ -1,363 +0,0 @@
|
|||
/**
|
||||
* BrainyMCPBroadcast
|
||||
*
|
||||
* Enhanced MCP service with real-time WebSocket broadcasting capabilities
|
||||
* for multi-agent coordination (Jarvis ↔ Picasso communication)
|
||||
*
|
||||
* Features:
|
||||
* - WebSocket server for real-time push notifications
|
||||
* - Subscription management for multiple Claude instances
|
||||
* - Message broadcasting to all connected agents
|
||||
* - Works both locally and with cloud deployment
|
||||
*/
|
||||
|
||||
import { WebSocketServer, WebSocket } from 'ws'
|
||||
import { createServer, IncomingMessage } from 'node:http'
|
||||
import { BrainyMCPService } from './brainyMCPService.js'
|
||||
import { BrainyInterface } from '../types/brainyInterface.js'
|
||||
import { MCPServiceOptions } from '../types/mcpTypes.js'
|
||||
import { v4 as uuidv4 } from '../universal/uuid.js'
|
||||
|
||||
interface BroadcastMessage {
|
||||
id: string
|
||||
from: string
|
||||
to?: string | string[]
|
||||
type: 'message' | 'notification' | 'sync' | 'heartbeat' | 'identify'
|
||||
event?: string
|
||||
data: any
|
||||
timestamp: number
|
||||
}
|
||||
|
||||
interface ConnectedAgent {
|
||||
id: string
|
||||
name: string
|
||||
role: string
|
||||
socket: WebSocket
|
||||
lastSeen: number
|
||||
}
|
||||
|
||||
export class BrainyMCPBroadcast extends BrainyMCPService {
|
||||
private wsServer?: WebSocketServer
|
||||
private httpServer?: any
|
||||
private agents: Map<string, ConnectedAgent> = new Map()
|
||||
private messageHistory: BroadcastMessage[] = []
|
||||
private maxHistorySize = 100
|
||||
|
||||
constructor(
|
||||
brainyData: BrainyInterface,
|
||||
options: MCPServiceOptions & {
|
||||
broadcastPort?: number
|
||||
cloudUrl?: string
|
||||
} = {}
|
||||
) {
|
||||
super(brainyData, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Start the WebSocket broadcast server
|
||||
* @param port Port to listen on (default: 8765)
|
||||
* @param isCloud Whether this is a cloud deployment
|
||||
*/
|
||||
async startBroadcastServer(port = 8765, isCloud = false): Promise<void> {
|
||||
return new Promise((resolve, reject) => {
|
||||
try {
|
||||
// Create HTTP server
|
||||
this.httpServer = createServer((req, res) => {
|
||||
// Health check endpoint
|
||||
if (req.url === '/health') {
|
||||
res.writeHead(200, { 'Content-Type': 'application/json' })
|
||||
res.end(JSON.stringify({
|
||||
status: 'healthy',
|
||||
agents: Array.from(this.agents.values()).map(a => ({
|
||||
id: a.id,
|
||||
name: a.name,
|
||||
role: a.role,
|
||||
connected: true
|
||||
})),
|
||||
uptime: process.uptime()
|
||||
}))
|
||||
} else {
|
||||
res.writeHead(404)
|
||||
res.end('Not found')
|
||||
}
|
||||
})
|
||||
|
||||
// Create WebSocket server
|
||||
this.wsServer = new WebSocketServer({
|
||||
server: this.httpServer,
|
||||
perMessageDeflate: false // Better performance
|
||||
})
|
||||
|
||||
this.wsServer.on('connection', (socket, request) => {
|
||||
this.handleNewConnection(socket, request)
|
||||
})
|
||||
|
||||
// Start listening
|
||||
this.httpServer.listen(port, () => {
|
||||
console.log(`🧠 Brain Jar Broadcast Server running on ${isCloud ? 'cloud' : 'local'} port ${port}`)
|
||||
console.log(`📡 WebSocket: ws://localhost:${port}`)
|
||||
console.log(`🔍 Health: http://localhost:${port}/health`)
|
||||
resolve()
|
||||
})
|
||||
|
||||
// Heartbeat to keep connections alive
|
||||
setInterval(() => {
|
||||
this.agents.forEach((agent) => {
|
||||
if (Date.now() - agent.lastSeen > 30000) {
|
||||
// Remove inactive agents
|
||||
this.removeAgent(agent.id)
|
||||
} else {
|
||||
// Send heartbeat
|
||||
this.sendToAgent(agent.id, {
|
||||
id: uuidv4(),
|
||||
from: 'server',
|
||||
type: 'heartbeat',
|
||||
data: { timestamp: Date.now() },
|
||||
timestamp: Date.now()
|
||||
})
|
||||
}
|
||||
})
|
||||
}, 15000)
|
||||
|
||||
} catch (error) {
|
||||
reject(error)
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
* Handle new WebSocket connection
|
||||
*/
|
||||
private handleNewConnection(socket: WebSocket, request: IncomingMessage) {
|
||||
const agentId = uuidv4()
|
||||
|
||||
// Send welcome message
|
||||
socket.send(JSON.stringify({
|
||||
id: uuidv4(),
|
||||
from: 'server',
|
||||
type: 'notification',
|
||||
event: 'welcome',
|
||||
data: {
|
||||
agentId,
|
||||
message: 'Connected to Brain Jar Broadcast Server',
|
||||
agents: Array.from(this.agents.values()).map(a => ({
|
||||
id: a.id,
|
||||
name: a.name,
|
||||
role: a.role
|
||||
}))
|
||||
},
|
||||
timestamp: Date.now()
|
||||
}))
|
||||
|
||||
// Handle messages from this agent
|
||||
socket.on('message', (data) => {
|
||||
try {
|
||||
const message = JSON.parse(data.toString())
|
||||
this.handleAgentMessage(agentId, message)
|
||||
} catch (error) {
|
||||
console.error('Invalid message from agent:', error)
|
||||
}
|
||||
})
|
||||
|
||||
// Handle disconnection
|
||||
socket.on('close', () => {
|
||||
this.removeAgent(agentId)
|
||||
})
|
||||
|
||||
// Handle errors
|
||||
socket.on('error', (error) => {
|
||||
console.error(`Agent ${agentId} error:`, error)
|
||||
})
|
||||
|
||||
// Store temporary connection until identified
|
||||
this.agents.set(agentId, {
|
||||
id: agentId,
|
||||
name: 'Unknown',
|
||||
role: 'Unknown',
|
||||
socket,
|
||||
lastSeen: Date.now()
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
* Handle message from an agent
|
||||
*/
|
||||
private handleAgentMessage(agentId: string, message: any) {
|
||||
const agent = this.agents.get(agentId)
|
||||
if (!agent) return
|
||||
|
||||
// Update last seen
|
||||
agent.lastSeen = Date.now()
|
||||
|
||||
// Handle identification
|
||||
if (message.type === 'identify') {
|
||||
agent.name = message.name || agent.name
|
||||
agent.role = message.role || agent.role
|
||||
|
||||
// Notify all agents about new member
|
||||
this.broadcast({
|
||||
id: uuidv4(),
|
||||
from: 'server',
|
||||
type: 'notification',
|
||||
event: 'agent_joined',
|
||||
data: {
|
||||
agent: {
|
||||
id: agent.id,
|
||||
name: agent.name,
|
||||
role: agent.role
|
||||
}
|
||||
},
|
||||
timestamp: Date.now()
|
||||
}, agentId) // Exclude the joining agent
|
||||
|
||||
// Send recent history to new agent
|
||||
if (this.messageHistory.length > 0) {
|
||||
this.sendToAgent(agentId, {
|
||||
id: uuidv4(),
|
||||
from: 'server',
|
||||
type: 'sync',
|
||||
data: {
|
||||
history: this.messageHistory.slice(-20) // Last 20 messages
|
||||
},
|
||||
timestamp: Date.now()
|
||||
})
|
||||
}
|
||||
|
||||
return
|
||||
}
|
||||
|
||||
// Create broadcast message
|
||||
const broadcastMsg: BroadcastMessage = {
|
||||
id: message.id || uuidv4(),
|
||||
from: agent.name,
|
||||
to: message.to,
|
||||
type: message.type || 'message',
|
||||
event: message.event,
|
||||
data: message.data,
|
||||
timestamp: Date.now()
|
||||
}
|
||||
|
||||
// Store in history
|
||||
this.addToHistory(broadcastMsg)
|
||||
|
||||
// Broadcast based on recipient
|
||||
if (message.to) {
|
||||
// Send to specific agent(s)
|
||||
const recipients = Array.isArray(message.to) ? message.to : [message.to]
|
||||
recipients.forEach((recipientName: string) => {
|
||||
const recipient = Array.from(this.agents.values()).find(
|
||||
a => a.name === recipientName
|
||||
)
|
||||
if (recipient) {
|
||||
this.sendToAgent(recipient.id, broadcastMsg)
|
||||
}
|
||||
})
|
||||
} else {
|
||||
// Broadcast to all agents except sender
|
||||
this.broadcast(broadcastMsg, agentId)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Broadcast message to all connected agents
|
||||
*/
|
||||
broadcast(message: BroadcastMessage, excludeId?: string) {
|
||||
const messageStr = JSON.stringify(message)
|
||||
|
||||
this.agents.forEach((agent) => {
|
||||
if (agent.id !== excludeId && agent.socket.readyState === WebSocket.OPEN) {
|
||||
agent.socket.send(messageStr)
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
* Send message to specific agent
|
||||
*/
|
||||
private sendToAgent(agentId: string, message: BroadcastMessage) {
|
||||
const agent = this.agents.get(agentId)
|
||||
if (agent && agent.socket.readyState === WebSocket.OPEN) {
|
||||
agent.socket.send(JSON.stringify(message))
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Remove agent from connected list
|
||||
*/
|
||||
private removeAgent(agentId: string) {
|
||||
const agent = this.agents.get(agentId)
|
||||
if (agent) {
|
||||
// Notify others about disconnection
|
||||
this.broadcast({
|
||||
id: uuidv4(),
|
||||
from: 'server',
|
||||
type: 'notification',
|
||||
event: 'agent_left',
|
||||
data: {
|
||||
agent: {
|
||||
id: agent.id,
|
||||
name: agent.name,
|
||||
role: agent.role
|
||||
}
|
||||
},
|
||||
timestamp: Date.now()
|
||||
})
|
||||
|
||||
this.agents.delete(agentId)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Add message to history
|
||||
*/
|
||||
private addToHistory(message: BroadcastMessage) {
|
||||
this.messageHistory.push(message)
|
||||
|
||||
// Trim history if too large
|
||||
if (this.messageHistory.length > this.maxHistorySize) {
|
||||
this.messageHistory = this.messageHistory.slice(-this.maxHistorySize)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Stop the broadcast server
|
||||
*/
|
||||
async stopBroadcastServer(): Promise<void> {
|
||||
// Close all agent connections
|
||||
this.agents.forEach(agent => {
|
||||
agent.socket.close(1000, 'Server shutting down')
|
||||
})
|
||||
this.agents.clear()
|
||||
|
||||
// Close WebSocket server
|
||||
if (this.wsServer) {
|
||||
this.wsServer.close()
|
||||
}
|
||||
|
||||
// Close HTTP server
|
||||
if (this.httpServer) {
|
||||
this.httpServer.close()
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get connected agents
|
||||
*/
|
||||
getConnectedAgents(): Array<{ id: string; name: string; role: string }> {
|
||||
return Array.from(this.agents.values()).map(a => ({
|
||||
id: a.id,
|
||||
name: a.name,
|
||||
role: a.role
|
||||
}))
|
||||
}
|
||||
|
||||
/**
|
||||
* Get message history
|
||||
*/
|
||||
getMessageHistory(): BroadcastMessage[] {
|
||||
return [...this.messageHistory]
|
||||
}
|
||||
}
|
||||
|
||||
// Export for both environments
|
||||
export default BrainyMCPBroadcast
|
||||
|
|
@ -1,322 +0,0 @@
|
|||
/**
|
||||
* BrainyMCPClient
|
||||
*
|
||||
* Client for connecting Claude instances to the Brain Jar Broadcast Server
|
||||
* Utilizes Brainy for persistent memory and vector search capabilities
|
||||
*/
|
||||
|
||||
import WebSocket from 'ws'
|
||||
import { Brainy } from '../brainy.js'
|
||||
import { NounType } from '../types/graphTypes.js'
|
||||
import { v4 as uuidv4 } from '../universal/uuid.js'
|
||||
|
||||
interface ClientOptions {
|
||||
name: string // e.g., 'Jarvis' or 'Picasso'
|
||||
role: string // e.g., 'Backend Systems' or 'Frontend Design'
|
||||
serverUrl?: string // Default: ws://localhost:8765
|
||||
autoReconnect?: boolean
|
||||
useBrainyMemory?: boolean // Store messages in Brainy for persistence
|
||||
}
|
||||
|
||||
interface Message {
|
||||
id: string
|
||||
from: string
|
||||
to?: string | string[]
|
||||
type: 'message' | 'notification' | 'sync' | 'heartbeat' | 'identify'
|
||||
event?: string
|
||||
data: any
|
||||
timestamp: number
|
||||
}
|
||||
|
||||
export class BrainyMCPClient {
|
||||
private socket?: WebSocket
|
||||
private options: Required<ClientOptions>
|
||||
private brainy?: Brainy
|
||||
private messageHandlers: Map<string, (message: Message) => void> = new Map()
|
||||
private reconnectTimeout?: NodeJS.Timeout
|
||||
private isConnected = false
|
||||
|
||||
constructor(options: ClientOptions) {
|
||||
this.options = {
|
||||
serverUrl: 'ws://localhost:8765',
|
||||
autoReconnect: true,
|
||||
useBrainyMemory: true,
|
||||
...options
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Initialize Brainy for persistent memory
|
||||
*/
|
||||
private async initBrainy() {
|
||||
if (this.options.useBrainyMemory && !this.brainy) {
|
||||
this.brainy = new Brainy({
|
||||
storage: {
|
||||
requestPersistentStorage: true
|
||||
}
|
||||
})
|
||||
await this.brainy.init()
|
||||
console.log(`🧠 Brainy memory initialized for ${this.options.name}`)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Connect to the broadcast server
|
||||
*/
|
||||
async connect(): Promise<void> {
|
||||
// Initialize Brainy first
|
||||
await this.initBrainy()
|
||||
|
||||
return new Promise((resolve, reject) => {
|
||||
try {
|
||||
this.socket = new WebSocket(this.options.serverUrl)
|
||||
|
||||
this.socket.on('open', () => {
|
||||
console.log(`✅ ${this.options.name} connected to Brain Jar Broadcast`)
|
||||
this.isConnected = true
|
||||
|
||||
// Identify ourselves
|
||||
this.send({
|
||||
type: 'identify',
|
||||
data: {
|
||||
name: this.options.name,
|
||||
role: this.options.role
|
||||
}
|
||||
})
|
||||
|
||||
resolve()
|
||||
})
|
||||
|
||||
this.socket.on('message', async (data) => {
|
||||
try {
|
||||
const message = JSON.parse(data.toString()) as Message
|
||||
await this.handleMessage(message)
|
||||
} catch (error) {
|
||||
console.error('Error parsing message:', error)
|
||||
}
|
||||
})
|
||||
|
||||
this.socket.on('close', () => {
|
||||
console.log(`❌ ${this.options.name} disconnected from Brain Jar`)
|
||||
this.isConnected = false
|
||||
|
||||
if (this.options.autoReconnect) {
|
||||
this.scheduleReconnect()
|
||||
}
|
||||
})
|
||||
|
||||
this.socket.on('error', (error) => {
|
||||
console.error(`Connection error for ${this.options.name}:`, error)
|
||||
reject(error)
|
||||
})
|
||||
|
||||
} catch (error) {
|
||||
reject(error)
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
* Handle incoming message
|
||||
*/
|
||||
private async handleMessage(message: Message) {
|
||||
// Store in Brainy for persistent memory. Subtype `'mcp-message'` marks
|
||||
// these as MCP-protocol messages so consumers can filter / count them via
|
||||
// `find({ type: NounType.Message, subtype: 'mcp-message' })` and so
|
||||
// enforcement consumers registering a vocabulary on NounType.Message don't
|
||||
// reject MCP traffic (added 7.30.1; also fixes the pre-existing missing
|
||||
// `data` field by aliasing from the prior `text` field).
|
||||
if (this.brainy && message.type === 'message') {
|
||||
try {
|
||||
await this.brainy.add({
|
||||
data: `${message.from}: ${JSON.stringify(message.data)}`,
|
||||
type: NounType.Message,
|
||||
subtype: 'mcp-message',
|
||||
metadata: {
|
||||
messageId: message.id,
|
||||
from: message.from,
|
||||
to: message.to,
|
||||
timestamp: message.timestamp,
|
||||
messageType: message.type,
|
||||
event: message.event
|
||||
}
|
||||
})
|
||||
} catch (error) {
|
||||
console.error('Error storing message in Brainy:', error)
|
||||
}
|
||||
}
|
||||
|
||||
// Handle sync messages (receive history)
|
||||
if (message.type === 'sync' && message.data.history) {
|
||||
console.log(`📜 ${this.options.name} received ${message.data.history.length} historical messages`)
|
||||
|
||||
// Store history in Brainy with the same subtype as live messages.
|
||||
if (this.brainy) {
|
||||
for (const histMsg of message.data.history) {
|
||||
await this.brainy.add({
|
||||
data: `${histMsg.from}: ${JSON.stringify(histMsg.data)}`,
|
||||
type: NounType.Message,
|
||||
subtype: 'mcp-message',
|
||||
metadata: {
|
||||
...histMsg
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Call registered handlers
|
||||
const handler = this.messageHandlers.get(message.type)
|
||||
if (handler) {
|
||||
handler(message)
|
||||
}
|
||||
|
||||
// Call universal handler
|
||||
const universalHandler = this.messageHandlers.get('*')
|
||||
if (universalHandler) {
|
||||
universalHandler(message)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Send a message
|
||||
*/
|
||||
send(message: Partial<Message>) {
|
||||
if (!this.socket || this.socket.readyState !== WebSocket.OPEN) {
|
||||
console.error(`${this.options.name} is not connected`)
|
||||
return
|
||||
}
|
||||
|
||||
const fullMessage: Message = {
|
||||
id: message.id || uuidv4(),
|
||||
from: this.options.name,
|
||||
type: message.type || 'message',
|
||||
data: message.data || {},
|
||||
timestamp: Date.now(),
|
||||
...message
|
||||
}
|
||||
|
||||
this.socket.send(JSON.stringify(fullMessage))
|
||||
}
|
||||
|
||||
/**
|
||||
* Send a message to specific agent(s)
|
||||
*/
|
||||
sendTo(recipient: string | string[], data: any) {
|
||||
this.send({
|
||||
to: recipient,
|
||||
type: 'message',
|
||||
data
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
* Broadcast to all agents
|
||||
*/
|
||||
broadcast(data: any) {
|
||||
this.send({
|
||||
type: 'message',
|
||||
data
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
* Register a message handler
|
||||
*/
|
||||
on(type: string, handler: (message: Message) => void) {
|
||||
this.messageHandlers.set(type, handler)
|
||||
}
|
||||
|
||||
/**
|
||||
* Remove a message handler
|
||||
*/
|
||||
off(type: string) {
|
||||
this.messageHandlers.delete(type)
|
||||
}
|
||||
|
||||
/**
|
||||
* Search historical messages using Brainy's vector search
|
||||
*/
|
||||
async searchMemory(query: string, limit = 10): Promise<any[]> {
|
||||
if (!this.brainy) {
|
||||
console.warn('Brainy memory not initialized')
|
||||
return []
|
||||
}
|
||||
|
||||
const results = await this.brainy.find({ query, limit })
|
||||
return results.map(r => ({
|
||||
...r.metadata,
|
||||
relevance: r.score
|
||||
}))
|
||||
}
|
||||
|
||||
/**
|
||||
* Get recent messages from Brainy memory
|
||||
*/
|
||||
async getRecentMessages(limit = 20): Promise<any[]> {
|
||||
if (!this.brainy) {
|
||||
console.warn('Brainy memory not initialized')
|
||||
return []
|
||||
}
|
||||
|
||||
// Search for recent activity
|
||||
const results = await this.brainy.find({ query: 'recent messages communication', limit })
|
||||
return results
|
||||
.map(r => r.metadata)
|
||||
.sort((a: any, b: any) => b.timestamp - a.timestamp)
|
||||
}
|
||||
|
||||
/**
|
||||
* Schedule reconnection attempt
|
||||
*/
|
||||
private scheduleReconnect() {
|
||||
if (this.reconnectTimeout) {
|
||||
clearTimeout(this.reconnectTimeout)
|
||||
}
|
||||
|
||||
this.reconnectTimeout = setTimeout(() => {
|
||||
console.log(`🔄 ${this.options.name} attempting to reconnect...`)
|
||||
this.connect().catch(error => {
|
||||
console.error('Reconnection failed:', error)
|
||||
this.scheduleReconnect()
|
||||
})
|
||||
}, 5000)
|
||||
}
|
||||
|
||||
/**
|
||||
* Disconnect from server
|
||||
*/
|
||||
disconnect() {
|
||||
if (this.reconnectTimeout) {
|
||||
clearTimeout(this.reconnectTimeout)
|
||||
}
|
||||
|
||||
if (this.socket) {
|
||||
this.socket.close(1000, 'Client disconnecting')
|
||||
this.socket = undefined
|
||||
}
|
||||
|
||||
this.isConnected = false
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if connected
|
||||
*/
|
||||
getIsConnected(): boolean {
|
||||
return this.isConnected
|
||||
}
|
||||
|
||||
/**
|
||||
* Get agent info
|
||||
*/
|
||||
getAgentInfo() {
|
||||
return {
|
||||
name: this.options.name,
|
||||
role: this.options.role,
|
||||
connected: this.isConnected
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Export for both environments
|
||||
export default BrainyMCPClient
|
||||
|
|
@ -1,200 +0,0 @@
|
|||
/**
|
||||
* 🧠 Natural Language Query Processor - STATIC VERSION
|
||||
* No runtime initialization, no memory leaks, patterns pre-built at compile time
|
||||
*
|
||||
* Uses static pattern matching with 220 pre-built patterns
|
||||
*/
|
||||
|
||||
import { Vector } from '../coreTypes.js'
|
||||
import { TripleQuery } from '../triple/TripleIntelligence.js'
|
||||
import { patternMatchQuery, PATTERN_STATS } from './staticPatternMatcher.js'
|
||||
|
||||
export interface NaturalQueryIntent {
|
||||
type: 'vector' | 'field' | 'graph' | 'combined'
|
||||
confidence: number
|
||||
extractedTerms: {
|
||||
entities?: string[]
|
||||
fields?: string[]
|
||||
relationships?: string[]
|
||||
modifiers?: string[]
|
||||
}
|
||||
}
|
||||
|
||||
export class NaturalLanguageProcessor {
|
||||
private queryHistory: Array<{ query: string; result: TripleQuery; success: boolean }>
|
||||
|
||||
constructor() {
|
||||
this.queryHistory = []
|
||||
// Patterns are static - no initialization needed!
|
||||
}
|
||||
|
||||
/**
|
||||
* No initialization needed - patterns are pre-built!
|
||||
*/
|
||||
async init(): Promise<void> {
|
||||
// Nothing to do - patterns are compiled into the code
|
||||
return Promise.resolve()
|
||||
}
|
||||
|
||||
/**
|
||||
* Process natural language query into structured Triple Intelligence query
|
||||
* @param naturalQuery The natural language query string
|
||||
* @param queryEmbedding Pre-computed embedding from Brainy (passed in to avoid circular dependency)
|
||||
*/
|
||||
async processNaturalQuery(naturalQuery: string, queryEmbedding?: Vector): Promise<TripleQuery> {
|
||||
// Use static pattern matcher (no async, no memory allocation!)
|
||||
const structuredQuery = patternMatchQuery(naturalQuery, queryEmbedding)
|
||||
|
||||
// Step 3: Enhance with intent analysis if needed
|
||||
if (!structuredQuery.where && !structuredQuery.connected) {
|
||||
const intent = await this.analyzeIntent(naturalQuery)
|
||||
|
||||
// Add metadata based on intent
|
||||
if (intent.type === 'field' && intent.extractedTerms.fields) {
|
||||
structuredQuery.where = this.buildFieldConstraints(intent.extractedTerms.fields)
|
||||
}
|
||||
}
|
||||
|
||||
// Track for learning (but don't create new Brainy!)
|
||||
this.queryHistory.push({
|
||||
query: naturalQuery,
|
||||
result: structuredQuery,
|
||||
success: false // Will be updated based on user interaction
|
||||
})
|
||||
|
||||
// Keep history limited to prevent memory growth
|
||||
if (this.queryHistory.length > 100) {
|
||||
this.queryHistory.shift()
|
||||
}
|
||||
|
||||
return structuredQuery
|
||||
}
|
||||
|
||||
/**
|
||||
* Analyze query intent using keywords
|
||||
*/
|
||||
private async analyzeIntent(query: string): Promise<NaturalQueryIntent> {
|
||||
const lowerQuery = query.toLowerCase()
|
||||
|
||||
// Check for field-specific keywords
|
||||
const fieldKeywords = ['where', 'filter', 'with', 'has', 'contains', 'equals', 'greater', 'less', 'between']
|
||||
const hasFieldIntent = fieldKeywords.some(kw => lowerQuery.includes(kw))
|
||||
|
||||
// Check for graph keywords
|
||||
const graphKeywords = ['related', 'connected', 'linked', 'associated', 'references']
|
||||
const hasGraphIntent = graphKeywords.some(kw => lowerQuery.includes(kw))
|
||||
|
||||
// Determine type
|
||||
let type: NaturalQueryIntent['type'] = 'vector'
|
||||
if (hasFieldIntent && hasGraphIntent) {
|
||||
type = 'combined'
|
||||
} else if (hasFieldIntent) {
|
||||
type = 'field'
|
||||
} else if (hasGraphIntent) {
|
||||
type = 'graph'
|
||||
}
|
||||
|
||||
return {
|
||||
type,
|
||||
confidence: 0.8,
|
||||
extractedTerms: {
|
||||
fields: hasFieldIntent ? this.extractFieldTerms(query) : undefined,
|
||||
relationships: hasGraphIntent ? this.extractRelationshipTerms(query) : undefined
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract field terms from query
|
||||
*/
|
||||
private extractFieldTerms(query: string): string[] {
|
||||
const terms: string[] = []
|
||||
|
||||
// Simple extraction of potential field names
|
||||
const words = query.split(/\s+/)
|
||||
const fieldIndicators = ['year', 'date', 'author', 'type', 'category', 'status', 'price']
|
||||
|
||||
for (const word of words) {
|
||||
if (fieldIndicators.includes(word.toLowerCase())) {
|
||||
terms.push(word.toLowerCase())
|
||||
}
|
||||
}
|
||||
|
||||
return terms
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract relationship terms
|
||||
*/
|
||||
private extractRelationshipTerms(query: string): string[] {
|
||||
const terms: string[] = []
|
||||
const relationshipWords = ['related', 'connected', 'linked', 'references', 'cites']
|
||||
|
||||
const words = query.toLowerCase().split(/\s+/)
|
||||
for (const word of words) {
|
||||
if (relationshipWords.includes(word)) {
|
||||
terms.push(word)
|
||||
}
|
||||
}
|
||||
|
||||
return terms
|
||||
}
|
||||
|
||||
/**
|
||||
* Build field constraints from extracted terms
|
||||
*/
|
||||
private buildFieldConstraints(fields: string[]): Record<string, any> {
|
||||
const constraints: Record<string, any> = {}
|
||||
|
||||
// Simple mapping for common fields
|
||||
for (const field of fields) {
|
||||
// This would be enhanced with actual value extraction
|
||||
constraints[field] = { exists: true }
|
||||
}
|
||||
|
||||
return constraints
|
||||
}
|
||||
|
||||
/**
|
||||
* Find similar queries from history (without using Brainy)
|
||||
* NOTE: Currently unused - reserved for future query caching optimization
|
||||
*/
|
||||
private findSimilarQueries(embedding: Vector): Array<{
|
||||
query: string
|
||||
result: TripleQuery
|
||||
similarity: number
|
||||
}> {
|
||||
// Not implemented - not required for core functionality
|
||||
// Would implement cosine similarity against queryHistory if needed
|
||||
return []
|
||||
}
|
||||
|
||||
/**
|
||||
* Adapt a previous query for new input
|
||||
*/
|
||||
private adaptQuery(newQuery: string, previousResult: TripleQuery): TripleQuery {
|
||||
return previousResult
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract entities from query
|
||||
*/
|
||||
private async extractEntities(query: string): Promise<string[]> {
|
||||
// Could use the Entity Registry here if available
|
||||
return []
|
||||
}
|
||||
|
||||
/**
|
||||
* Build query from components
|
||||
*/
|
||||
private buildQuery(
|
||||
query: string,
|
||||
intent: NaturalQueryIntent,
|
||||
entities: string[]
|
||||
): TripleQuery {
|
||||
return {
|
||||
like: query,
|
||||
limit: 10
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,633 +0,0 @@
|
|||
/**
|
||||
* Neural Import - AI-Powered Data Understanding
|
||||
*
|
||||
* Standalone implementation for intelligent data processing.
|
||||
*/
|
||||
|
||||
import { NounType, VerbType } from '../types/graphTypes.js'
|
||||
import * as fs from '../universal/fs.js'
|
||||
import * as path from '../universal/path.js'
|
||||
import { prodLog } from '../utils/logger.js'
|
||||
|
||||
// Neural Import Analysis Types
|
||||
export interface NeuralAnalysisResult {
|
||||
detectedEntities: DetectedEntity[]
|
||||
detectedRelationships: DetectedRelationship[]
|
||||
confidence: number
|
||||
insights: NeuralInsight[]
|
||||
}
|
||||
|
||||
export interface DetectedEntity {
|
||||
originalData: any
|
||||
nounType: string
|
||||
confidence: number
|
||||
suggestedId: string
|
||||
reasoning: string
|
||||
alternativeTypes: Array<{ type: string, confidence: number }>
|
||||
}
|
||||
|
||||
export interface DetectedRelationship {
|
||||
sourceId: string
|
||||
targetId: string
|
||||
verbType: string
|
||||
confidence: number
|
||||
weight: number
|
||||
reasoning: string
|
||||
context: string
|
||||
metadata?: Record<string, any>
|
||||
}
|
||||
|
||||
export interface NeuralInsight {
|
||||
type: 'hierarchy' | 'cluster' | 'pattern' | 'anomaly' | 'opportunity'
|
||||
description: string
|
||||
confidence: number
|
||||
affectedEntities: string[]
|
||||
recommendation?: string
|
||||
}
|
||||
|
||||
export interface NeuralImportConfig {
|
||||
confidenceThreshold: number
|
||||
enableWeights: boolean
|
||||
skipDuplicates: boolean
|
||||
categoryFilter?: string[]
|
||||
dataType?: string
|
||||
}
|
||||
|
||||
/**
|
||||
* Neural Import Augmentation - Unified Implementation
|
||||
* Processes data with AI before storage operations
|
||||
*/
|
||||
export class NeuralImportAugmentation {
|
||||
readonly name = 'neural-import'
|
||||
private operations = ['add', 'addNoun', 'addVerb', 'all']
|
||||
|
||||
protected config: NeuralImportConfig
|
||||
private analysisCache = new Map<string, NeuralAnalysisResult>()
|
||||
private context?: { brain: any }
|
||||
|
||||
constructor(config: Partial<NeuralImportConfig> = {}) {
|
||||
this.config = {
|
||||
confidenceThreshold: 0.7,
|
||||
enableWeights: true,
|
||||
skipDuplicates: true,
|
||||
dataType: 'json',
|
||||
...config
|
||||
}
|
||||
}
|
||||
|
||||
async init(): Promise<void> {
|
||||
// No external dependencies to initialize
|
||||
}
|
||||
|
||||
private log(message: string, _level?: string): void {
|
||||
// Silent by default
|
||||
}
|
||||
|
||||
/**
|
||||
* Execute augmentation - process data with AI before storage
|
||||
*/
|
||||
async execute<T = any>(
|
||||
operation: string,
|
||||
params: any,
|
||||
next: () => Promise<T>
|
||||
): Promise<T> {
|
||||
// Only process on add operations
|
||||
if (!this.operations.includes(operation)) {
|
||||
return next()
|
||||
}
|
||||
|
||||
try {
|
||||
// Extract data from params based on operation
|
||||
const rawData = this.extractRawData(operation, params)
|
||||
if (!rawData) {
|
||||
return next()
|
||||
}
|
||||
|
||||
// Perform neural analysis
|
||||
const analysis = await this.performNeuralAnalysis(rawData, this.config)
|
||||
|
||||
// Enhance params with neural insights
|
||||
if (params.metadata) {
|
||||
params.metadata._neuralProcessed = true
|
||||
params.metadata._neuralConfidence = analysis.confidence
|
||||
params.metadata._detectedEntities = analysis.detectedEntities.length
|
||||
params.metadata._detectedRelationships = analysis.detectedRelationships.length
|
||||
params.metadata._neuralInsights = analysis.insights
|
||||
} else if (typeof params === 'object') {
|
||||
params.metadata = {
|
||||
_neuralProcessed: true,
|
||||
_neuralConfidence: analysis.confidence,
|
||||
_detectedEntities: analysis.detectedEntities.length,
|
||||
_detectedRelationships: analysis.detectedRelationships.length,
|
||||
_neuralInsights: analysis.insights
|
||||
}
|
||||
}
|
||||
|
||||
// Store neural analysis for later retrieval
|
||||
await this.storeNeuralAnalysis(analysis)
|
||||
|
||||
// If we detected entities/relationships, potentially add them
|
||||
if (this.context?.brain && analysis.detectedEntities.length > 0) {
|
||||
// This could automatically create entities/relationships
|
||||
// But for now, just enhance the metadata
|
||||
this.log(`Detected ${analysis.detectedEntities.length} entities and ${analysis.detectedRelationships.length} relationships`)
|
||||
}
|
||||
|
||||
// Continue with enhanced data
|
||||
return next()
|
||||
} catch (error) {
|
||||
this.log(`Neural analysis failed: ${error}`, 'warn')
|
||||
// Continue without neural processing
|
||||
return next()
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract raw data from operation params
|
||||
*/
|
||||
private extractRawData(operation: string, params: any): any {
|
||||
switch (operation) {
|
||||
case 'add':
|
||||
return params.content || params.data || params
|
||||
case 'addNoun':
|
||||
return params.noun || params.data || params
|
||||
case 'addVerb':
|
||||
return params.verb || params
|
||||
case 'addBatch':
|
||||
return params.items || params.batch || params
|
||||
default:
|
||||
return null
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get the full neural analysis result (for external use)
|
||||
*/
|
||||
async getNeuralAnalysis(rawData: Buffer | string, dataType?: string): Promise<NeuralAnalysisResult> {
|
||||
const parsedData = await this.parseRawData(rawData, dataType || this.config.dataType || 'json')
|
||||
return await this.performNeuralAnalysis(parsedData, this.config)
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse raw data based on type
|
||||
*/
|
||||
private async parseRawData(rawData: Buffer | string, dataType: string): Promise<any[]> {
|
||||
const content = typeof rawData === 'string' ? rawData : rawData.toString('utf8')
|
||||
|
||||
switch (dataType.toLowerCase()) {
|
||||
case 'json':
|
||||
try {
|
||||
const jsonData = JSON.parse(content)
|
||||
return Array.isArray(jsonData) ? jsonData : [jsonData]
|
||||
} catch {
|
||||
// If JSON parse fails, treat as text
|
||||
return [{ text: content }]
|
||||
}
|
||||
|
||||
case 'csv':
|
||||
return this.parseCSV(content)
|
||||
|
||||
case 'yaml':
|
||||
case 'yml':
|
||||
return this.parseYAML(content)
|
||||
|
||||
case 'txt':
|
||||
case 'text':
|
||||
// Split text into sentences/paragraphs for analysis
|
||||
return content.split(/\n+/).filter(line => line.trim()).map(line => ({ text: line }))
|
||||
|
||||
default:
|
||||
// Unknown type, treat as text
|
||||
return [{ text: content }]
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse CSV data - handles quoted values, escaped quotes, and edge cases
|
||||
*/
|
||||
private parseCSV(content: string): any[] {
|
||||
const lines = content.split('\n')
|
||||
if (lines.length === 0) return []
|
||||
|
||||
// Parse a CSV line handling quotes
|
||||
const parseLine = (line: string): string[] => {
|
||||
const result: string[] = []
|
||||
let current = ''
|
||||
let inQuotes = false
|
||||
let i = 0
|
||||
|
||||
while (i < line.length) {
|
||||
const char = line[i]
|
||||
const nextChar = line[i + 1]
|
||||
|
||||
if (char === '"') {
|
||||
if (inQuotes && nextChar === '"') {
|
||||
// Escaped quote
|
||||
current += '"'
|
||||
i += 2
|
||||
} else {
|
||||
// Toggle quote mode
|
||||
inQuotes = !inQuotes
|
||||
i++
|
||||
}
|
||||
} else if (char === ',' && !inQuotes) {
|
||||
// Field separator
|
||||
result.push(current.trim())
|
||||
current = ''
|
||||
i++
|
||||
} else {
|
||||
current += char
|
||||
i++
|
||||
}
|
||||
}
|
||||
|
||||
// Add last field
|
||||
result.push(current.trim())
|
||||
return result
|
||||
}
|
||||
|
||||
// Parse headers
|
||||
const headers = parseLine(lines[0])
|
||||
const data = []
|
||||
|
||||
// Parse data rows
|
||||
for (let i = 1; i < lines.length; i++) {
|
||||
const line = lines[i].trim()
|
||||
if (!line) continue // Skip empty lines
|
||||
|
||||
const values = parseLine(line)
|
||||
const row: any = {}
|
||||
|
||||
headers.forEach((header, index) => {
|
||||
const value = values[index] || ''
|
||||
// Try to parse numbers
|
||||
const num = Number(value)
|
||||
row[header] = !isNaN(num) && value !== '' ? num : value
|
||||
})
|
||||
|
||||
data.push(row)
|
||||
}
|
||||
|
||||
return data
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse YAML data
|
||||
*/
|
||||
private parseYAML(content: string): any[] {
|
||||
try {
|
||||
// Simple YAML parser for basic structures
|
||||
// For full YAML support, we'd use js-yaml library
|
||||
const lines = content.split('\n')
|
||||
const result: any[] = []
|
||||
let currentObject: any = null
|
||||
let currentIndent = 0
|
||||
|
||||
for (const line of lines) {
|
||||
const trimmed = line.trim()
|
||||
if (!trimmed || trimmed.startsWith('#')) continue // Skip empty lines and comments
|
||||
|
||||
// Calculate indentation
|
||||
const indent = line.length - line.trimStart().length
|
||||
|
||||
// Check for array item
|
||||
if (trimmed.startsWith('- ')) {
|
||||
const value = trimmed.substring(2).trim()
|
||||
if (indent === 0) {
|
||||
// Top-level array item
|
||||
if (value.includes(':')) {
|
||||
// Object in array
|
||||
currentObject = {}
|
||||
result.push(currentObject)
|
||||
const [key, val] = value.split(':').map(s => s.trim())
|
||||
currentObject[key] = this.parseYAMLValue(val)
|
||||
} else {
|
||||
result.push(this.parseYAMLValue(value))
|
||||
}
|
||||
} else if (currentObject) {
|
||||
// Nested array
|
||||
const lastKey = Object.keys(currentObject).pop()
|
||||
if (lastKey) {
|
||||
if (!Array.isArray(currentObject[lastKey])) {
|
||||
currentObject[lastKey] = []
|
||||
}
|
||||
currentObject[lastKey].push(this.parseYAMLValue(value))
|
||||
}
|
||||
}
|
||||
} else if (trimmed.includes(':')) {
|
||||
// Key-value pair
|
||||
const colonIndex = trimmed.indexOf(':')
|
||||
const key = trimmed.substring(0, colonIndex).trim()
|
||||
const value = trimmed.substring(colonIndex + 1).trim()
|
||||
|
||||
if (indent === 0) {
|
||||
// Top-level object
|
||||
if (!currentObject) {
|
||||
currentObject = {}
|
||||
result.push(currentObject)
|
||||
}
|
||||
currentObject[key] = this.parseYAMLValue(value)
|
||||
currentIndent = 0
|
||||
} else if (currentObject) {
|
||||
// Nested object
|
||||
if (indent > currentIndent && !value) {
|
||||
// Start of nested object
|
||||
const lastKey = Object.keys(currentObject).pop()
|
||||
if (lastKey) {
|
||||
currentObject[lastKey] = { [key]: '' }
|
||||
}
|
||||
} else {
|
||||
currentObject[key] = this.parseYAMLValue(value)
|
||||
}
|
||||
currentIndent = indent
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// If we built a single object and not an array, wrap it
|
||||
if (result.length === 0 && currentObject) {
|
||||
result.push(currentObject)
|
||||
}
|
||||
|
||||
return result.length > 0 ? result : [{ text: content }]
|
||||
} catch (error) {
|
||||
prodLog.warn('YAML parsing failed, treating as text:', error)
|
||||
return [{ text: content }]
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse a YAML value (handle strings, numbers, booleans, null)
|
||||
*/
|
||||
private parseYAMLValue(value: string): any {
|
||||
if (!value || value === '~' || value === 'null') return null
|
||||
if (value === 'true') return true
|
||||
if (value === 'false') return false
|
||||
|
||||
// Remove quotes if present
|
||||
if ((value.startsWith('"') && value.endsWith('"')) ||
|
||||
(value.startsWith("'") && value.endsWith("'"))) {
|
||||
return value.slice(1, -1)
|
||||
}
|
||||
|
||||
// Try to parse as number
|
||||
const num = Number(value)
|
||||
if (!isNaN(num) && value !== '') return num
|
||||
|
||||
return value
|
||||
}
|
||||
|
||||
/**
|
||||
* Perform neural analysis on parsed data
|
||||
*/
|
||||
private async performNeuralAnalysis(data: any[], config?: any): Promise<NeuralAnalysisResult> {
|
||||
const detectedEntities: DetectedEntity[] = []
|
||||
const detectedRelationships: DetectedRelationship[] = []
|
||||
const insights: NeuralInsight[] = []
|
||||
|
||||
// Simple entity detection (in real implementation, would use ML)
|
||||
for (const item of data) {
|
||||
if (typeof item === 'object') {
|
||||
// Detect entities from object properties
|
||||
const entityId = item.id || item.name || item.title || `entity_${Date.now()}_${Math.random()}`
|
||||
|
||||
detectedEntities.push({
|
||||
originalData: item,
|
||||
nounType: await this.inferNounType(item),
|
||||
confidence: 0.85,
|
||||
suggestedId: String(entityId),
|
||||
reasoning: 'Detected from structured data',
|
||||
alternativeTypes: []
|
||||
})
|
||||
|
||||
// Detect relationships from references
|
||||
await this.detectRelationships(item, entityId, detectedRelationships)
|
||||
}
|
||||
}
|
||||
|
||||
// Generate insights
|
||||
if (detectedEntities.length > 10) {
|
||||
insights.push({
|
||||
type: 'pattern',
|
||||
description: `Large dataset with ${detectedEntities.length} entities detected`,
|
||||
confidence: 0.9,
|
||||
affectedEntities: detectedEntities.slice(0, 5).map(e => e.suggestedId),
|
||||
recommendation: 'Consider batch processing for optimal performance'
|
||||
})
|
||||
}
|
||||
|
||||
// Look for clusters
|
||||
const typeGroups = this.groupByType(detectedEntities)
|
||||
if (Object.keys(typeGroups).length > 1) {
|
||||
insights.push({
|
||||
type: 'cluster',
|
||||
description: `Multiple entity types detected: ${Object.keys(typeGroups).join(', ')}`,
|
||||
confidence: 0.8,
|
||||
affectedEntities: [],
|
||||
recommendation: 'Data contains diverse entity types suitable for graph analysis'
|
||||
})
|
||||
}
|
||||
|
||||
return {
|
||||
detectedEntities,
|
||||
detectedRelationships,
|
||||
confidence: detectedEntities.length > 0 ? 0.85 : 0.5,
|
||||
insights
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Infer noun type from object structure using field heuristics
|
||||
*/
|
||||
private async inferNounType(obj: any): Promise<string> {
|
||||
if (typeof obj !== 'object' || obj === null) return NounType.Thing
|
||||
|
||||
// Check for explicit type field
|
||||
if (obj.type && typeof obj.type === 'string') {
|
||||
const normalized = obj.type.charAt(0).toUpperCase() + obj.type.slice(1)
|
||||
if (Object.values(NounType).includes(normalized as NounType)) {
|
||||
return normalized as NounType
|
||||
}
|
||||
}
|
||||
|
||||
if (obj.email || obj.firstName || obj.lastName || obj.username) return NounType.Person
|
||||
if (obj.companyName || obj.organizationId || obj.employees) return NounType.Organization
|
||||
if (obj.latitude || obj.longitude || obj.address || obj.city) return NounType.Location
|
||||
if ((obj.content && (obj.title || obj.author)) || obj.pages) return NounType.Document
|
||||
if (obj.startTime || obj.endTime || obj.date || obj.attendees) return NounType.Event
|
||||
if (obj.price || obj.sku || obj.productId) return NounType.Product
|
||||
if ((obj.status && obj.assignee) || obj.priority) return NounType.Task
|
||||
if (Array.isArray(obj.data) || obj.rows || obj.columns) return NounType.Dataset
|
||||
|
||||
return NounType.Thing
|
||||
}
|
||||
|
||||
/**
|
||||
* Detect relationships from object references
|
||||
*/
|
||||
private async detectRelationships(obj: any, sourceId: string, relationships: DetectedRelationship[]): Promise<void> {
|
||||
// Look for reference patterns
|
||||
for (const [key, value] of Object.entries(obj)) {
|
||||
if (key.endsWith('Id') || key.endsWith('_id') || key === 'parentId' || key === 'userId') {
|
||||
relationships.push({
|
||||
sourceId,
|
||||
targetId: String(value),
|
||||
verbType: await this.inferVerbType(key, obj, { id: value }),
|
||||
confidence: 0.75,
|
||||
weight: 1,
|
||||
reasoning: `Reference detected in field: ${key}`,
|
||||
context: key
|
||||
})
|
||||
}
|
||||
|
||||
// Array of IDs
|
||||
if (Array.isArray(value) && value.length > 0 && typeof value[0] === 'string') {
|
||||
if (key.endsWith('Ids') || key.endsWith('_ids')) {
|
||||
for (const targetId of value) {
|
||||
relationships.push({
|
||||
sourceId,
|
||||
targetId: String(targetId),
|
||||
verbType: await this.inferVerbType(key, obj, { id: targetId }),
|
||||
confidence: 0.7,
|
||||
weight: 1,
|
||||
reasoning: `Array reference in field: ${key}`,
|
||||
context: key
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Infer verb type from field name using common patterns
|
||||
*/
|
||||
private async inferVerbType(fieldName: string, _sourceObj?: any, _targetObj?: any): Promise<string> {
|
||||
const field = fieldName.toLowerCase()
|
||||
|
||||
if (field.includes('parent') || field.includes('child') || field.includes('contain')) {
|
||||
return VerbType.Contains
|
||||
}
|
||||
if (field.includes('owner') || field.includes('created') || field.includes('author')) {
|
||||
return VerbType.Creates
|
||||
}
|
||||
if (field.includes('member') || field.includes('belong')) {
|
||||
return VerbType.MemberOf
|
||||
}
|
||||
if (field.includes('depend') || field.includes('require')) {
|
||||
return VerbType.DependsOn
|
||||
}
|
||||
if (field.includes('ref') || field.includes('link') || field.includes('source')) {
|
||||
return VerbType.References
|
||||
}
|
||||
|
||||
return VerbType.RelatedTo
|
||||
}
|
||||
|
||||
/**
|
||||
* Group entities by type
|
||||
*/
|
||||
private groupByType(entities: DetectedEntity[]): Record<string, DetectedEntity[]> {
|
||||
const groups: Record<string, DetectedEntity[]> = {}
|
||||
|
||||
for (const entity of entities) {
|
||||
if (!groups[entity.nounType]) {
|
||||
groups[entity.nounType] = []
|
||||
}
|
||||
groups[entity.nounType].push(entity)
|
||||
}
|
||||
|
||||
return groups
|
||||
}
|
||||
|
||||
/**
|
||||
* Store neural analysis results
|
||||
*/
|
||||
private async storeNeuralAnalysis(analysis: NeuralAnalysisResult): Promise<void> {
|
||||
// Cache the analysis for potential later use
|
||||
const key = `analysis_${Date.now()}`
|
||||
this.analysisCache.set(key, analysis)
|
||||
|
||||
// Limit cache size
|
||||
if (this.analysisCache.size > 100) {
|
||||
const firstKey = this.analysisCache.keys().next().value
|
||||
if (firstKey) {
|
||||
this.analysisCache.delete(firstKey)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Helper to get data type from file path
|
||||
*/
|
||||
private getDataTypeFromPath(filePath: string): string {
|
||||
const ext = path.extname(filePath).toLowerCase()
|
||||
switch (ext) {
|
||||
case '.json': return 'json'
|
||||
case '.csv': return 'csv'
|
||||
case '.txt': return 'text'
|
||||
case '.yaml':
|
||||
case '.yml': return 'yaml'
|
||||
default: return 'text'
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* PUBLIC API: Process raw data (for external use, like Synapses)
|
||||
* This maintains compatibility with code that wants to use Neural Import directly
|
||||
*/
|
||||
async processRawData(
|
||||
rawData: Buffer | string,
|
||||
dataType: string,
|
||||
options?: Record<string, unknown>
|
||||
): Promise<{
|
||||
success: boolean
|
||||
data: {
|
||||
nouns: string[]
|
||||
verbs: string[]
|
||||
confidence?: number
|
||||
insights?: Array<{
|
||||
type: string
|
||||
description: string
|
||||
confidence: number
|
||||
}>
|
||||
metadata?: Record<string, unknown>
|
||||
}
|
||||
error?: string
|
||||
}> {
|
||||
try {
|
||||
const analysis = await this.getNeuralAnalysis(rawData, dataType)
|
||||
|
||||
// Convert to legacy format for compatibility
|
||||
const nouns = analysis.detectedEntities.map(e => e.suggestedId)
|
||||
const verbs = analysis.detectedRelationships.map(r =>
|
||||
`${r.sourceId}->${r.verbType}->${r.targetId}`
|
||||
)
|
||||
|
||||
return {
|
||||
success: true,
|
||||
data: {
|
||||
nouns,
|
||||
verbs,
|
||||
confidence: analysis.confidence,
|
||||
insights: analysis.insights.map(i => ({
|
||||
type: i.type,
|
||||
description: i.description,
|
||||
confidence: i.confidence
|
||||
})),
|
||||
metadata: {
|
||||
detectedEntities: analysis.detectedEntities.length,
|
||||
detectedRelationships: analysis.detectedRelationships.length,
|
||||
timestamp: new Date().toISOString()
|
||||
}
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
return {
|
||||
success: false,
|
||||
data: { nouns: [], verbs: [] },
|
||||
error: error instanceof Error ? error.message : 'Neural analysis failed'
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,479 +0,0 @@
|
|||
/**
|
||||
* Smart Import Presets - Zero-Configuration Auto-Detection
|
||||
*
|
||||
* Automatically selects optimal import strategy based on:
|
||||
* - File type (Excel, CSV, PDF, Markdown, JSON)
|
||||
* - File size and row count
|
||||
* - Column structure (explicit relationships vs narrative)
|
||||
* - Available memory and performance requirements
|
||||
*
|
||||
* Production-ready: Handles billions of entities with optimal performance
|
||||
*/
|
||||
|
||||
import { NounType, VerbType } from '../types/graphTypes.js'
|
||||
|
||||
/**
|
||||
* Signal types used for entity classification
|
||||
*/
|
||||
export type SignalType = 'embedding' | 'exact' | 'pattern' | 'context'
|
||||
|
||||
/**
|
||||
* Strategy types used for relationship extraction
|
||||
*/
|
||||
export type StrategyType = 'explicit' | 'pattern' | 'embedding'
|
||||
|
||||
/**
|
||||
* Import context for preset auto-detection
|
||||
*/
|
||||
export interface ImportContext {
|
||||
fileType?: 'excel' | 'csv' | 'json' | 'pdf' | 'markdown' | 'unknown'
|
||||
fileSize?: number // bytes
|
||||
rowCount?: number
|
||||
hasExplicitColumns?: boolean // Has "Related Terms" or similar columns
|
||||
hasNarrativeContent?: boolean // Has long-form text/descriptions
|
||||
avgDefinitionLength?: number // Average length of definitions
|
||||
memoryAvailable?: number // bytes
|
||||
}
|
||||
|
||||
/**
|
||||
* Signal configuration with weights
|
||||
*/
|
||||
export interface SignalConfig {
|
||||
enabled: SignalType[]
|
||||
weights: Record<SignalType, number>
|
||||
timeout: number // milliseconds
|
||||
}
|
||||
|
||||
/**
|
||||
* Strategy configuration with priorities
|
||||
*/
|
||||
export interface StrategyConfig {
|
||||
enabled: StrategyType[]
|
||||
timeout: number // milliseconds
|
||||
earlyTermination: boolean
|
||||
minConfidence: number
|
||||
}
|
||||
|
||||
/**
|
||||
* Complete preset configuration
|
||||
*/
|
||||
export interface PresetConfig {
|
||||
name: string
|
||||
description: string
|
||||
signals: SignalConfig
|
||||
strategies: StrategyConfig
|
||||
streaming: boolean
|
||||
batchSize: number
|
||||
}
|
||||
|
||||
/**
|
||||
* Fast Preset - For large imports (>10K rows)
|
||||
*
|
||||
* Optimized for speed over accuracy:
|
||||
* - Only exact match and pattern signals
|
||||
* - Only explicit strategy (O(1) lookups)
|
||||
* - Streaming enabled for memory efficiency
|
||||
* - Early termination on first high-confidence match
|
||||
*
|
||||
* Use case: Bulk imports, data migrations
|
||||
* Performance: ~10ms per row
|
||||
* Accuracy: ~85%
|
||||
*/
|
||||
export const FAST_PRESET: PresetConfig = {
|
||||
name: 'fast',
|
||||
description: 'Fast bulk import for large datasets',
|
||||
signals: {
|
||||
enabled: ['exact', 'pattern'],
|
||||
weights: {
|
||||
exact: 0.70,
|
||||
pattern: 0.30,
|
||||
embedding: 0,
|
||||
context: 0
|
||||
},
|
||||
timeout: 50
|
||||
},
|
||||
strategies: {
|
||||
enabled: ['explicit'],
|
||||
timeout: 100,
|
||||
earlyTermination: true,
|
||||
minConfidence: 0.70
|
||||
},
|
||||
streaming: true,
|
||||
batchSize: 1000
|
||||
}
|
||||
|
||||
/**
|
||||
* Balanced Preset - Default for most imports
|
||||
*
|
||||
* Good balance of speed and accuracy:
|
||||
* - All signals except context (embedding, exact, pattern)
|
||||
* - All strategies with smart ordering
|
||||
* - Moderate timeouts
|
||||
* - Early termination after high-confidence matches
|
||||
*
|
||||
* Use case: Standard imports, general glossaries
|
||||
* Performance: ~30ms per row
|
||||
* Accuracy: ~92%
|
||||
*/
|
||||
export const BALANCED_PRESET: PresetConfig = {
|
||||
name: 'balanced',
|
||||
description: 'Balanced speed and accuracy for most imports',
|
||||
signals: {
|
||||
enabled: ['exact', 'embedding', 'pattern'],
|
||||
weights: {
|
||||
exact: 0.40,
|
||||
embedding: 0.35,
|
||||
pattern: 0.25,
|
||||
context: 0
|
||||
},
|
||||
timeout: 100
|
||||
},
|
||||
strategies: {
|
||||
enabled: ['explicit', 'pattern', 'embedding'],
|
||||
timeout: 200,
|
||||
earlyTermination: true,
|
||||
minConfidence: 0.65
|
||||
},
|
||||
streaming: false,
|
||||
batchSize: 500
|
||||
}
|
||||
|
||||
/**
|
||||
* Accurate Preset - For small, critical imports
|
||||
*
|
||||
* Optimized for accuracy over speed:
|
||||
* - All signals including context
|
||||
* - All strategies, no early termination
|
||||
* - Longer timeouts for thorough analysis
|
||||
* - Lower confidence threshold (accept more matches)
|
||||
*
|
||||
* Use case: Knowledge bases, critical taxonomies
|
||||
* Performance: ~100ms per row
|
||||
* Accuracy: ~97%
|
||||
*/
|
||||
export const ACCURATE_PRESET: PresetConfig = {
|
||||
name: 'accurate',
|
||||
description: 'Maximum accuracy for critical imports',
|
||||
signals: {
|
||||
enabled: ['exact', 'embedding', 'pattern', 'context'],
|
||||
weights: {
|
||||
exact: 0.40,
|
||||
embedding: 0.35,
|
||||
pattern: 0.20,
|
||||
context: 0.05
|
||||
},
|
||||
timeout: 500
|
||||
},
|
||||
strategies: {
|
||||
enabled: ['explicit', 'pattern', 'embedding'],
|
||||
timeout: 1000,
|
||||
earlyTermination: false,
|
||||
minConfidence: 0.50
|
||||
},
|
||||
streaming: false,
|
||||
batchSize: 100
|
||||
}
|
||||
|
||||
/**
|
||||
* Explicit Preset - For glossaries with relationship columns
|
||||
*
|
||||
* Optimized for structured data with explicit relationships:
|
||||
* - Only exact match signals (no AI needed)
|
||||
* - Only explicit and pattern strategies
|
||||
* - Fast, deterministic results
|
||||
* - Perfect for Excel/CSV with "Related Terms" columns
|
||||
*
|
||||
* Use case: structured taxonomies and glossaries from spreadsheet sources
|
||||
* Performance: ~5ms per row
|
||||
* Accuracy: ~99% (high confidence)
|
||||
*/
|
||||
export const EXPLICIT_PRESET: PresetConfig = {
|
||||
name: 'explicit',
|
||||
description: 'For glossaries with explicit relationship columns',
|
||||
signals: {
|
||||
enabled: ['exact', 'pattern'],
|
||||
weights: {
|
||||
exact: 0.70,
|
||||
pattern: 0.30,
|
||||
embedding: 0,
|
||||
context: 0
|
||||
},
|
||||
timeout: 50
|
||||
},
|
||||
strategies: {
|
||||
enabled: ['explicit', 'pattern'],
|
||||
timeout: 100,
|
||||
earlyTermination: true,
|
||||
minConfidence: 0.80
|
||||
},
|
||||
streaming: false,
|
||||
batchSize: 500
|
||||
}
|
||||
|
||||
/**
|
||||
* Pattern Preset - For documents with narrative content
|
||||
*
|
||||
* Optimized for unstructured text with rich patterns:
|
||||
* - Embedding and pattern signals (semantic understanding)
|
||||
* - Pattern and embedding strategies
|
||||
* - Good for PDFs, articles, documentation
|
||||
*
|
||||
* Use case: PDF imports, markdown docs, articles
|
||||
* Performance: ~50ms per row
|
||||
* Accuracy: ~90%
|
||||
*/
|
||||
export const PATTERN_PRESET: PresetConfig = {
|
||||
name: 'pattern',
|
||||
description: 'For documents with narrative content',
|
||||
signals: {
|
||||
enabled: ['embedding', 'pattern', 'context'],
|
||||
weights: {
|
||||
embedding: 0.50,
|
||||
pattern: 0.40,
|
||||
context: 0.10,
|
||||
exact: 0
|
||||
},
|
||||
timeout: 200
|
||||
},
|
||||
strategies: {
|
||||
enabled: ['pattern', 'embedding'],
|
||||
timeout: 300,
|
||||
earlyTermination: false,
|
||||
minConfidence: 0.60
|
||||
},
|
||||
streaming: false,
|
||||
batchSize: 200
|
||||
}
|
||||
|
||||
/**
|
||||
* All available presets
|
||||
*/
|
||||
export const PRESETS: Record<string, PresetConfig> = {
|
||||
fast: FAST_PRESET,
|
||||
balanced: BALANCED_PRESET,
|
||||
accurate: ACCURATE_PRESET,
|
||||
explicit: EXPLICIT_PRESET,
|
||||
pattern: PATTERN_PRESET
|
||||
}
|
||||
|
||||
/**
|
||||
* Auto-detect optimal preset based on import context
|
||||
*
|
||||
* Decision tree:
|
||||
* 1. Large dataset (>10K rows or >10MB) → fast
|
||||
* 2. Small dataset (<100 rows) → accurate
|
||||
* 3. Excel/CSV with explicit columns → explicit
|
||||
* 4. PDF/Markdown with long content → pattern
|
||||
* 5. Default → balanced
|
||||
*
|
||||
* @param context Import context (file type, size, structure)
|
||||
* @returns Optimal preset configuration
|
||||
*/
|
||||
export function autoDetectPreset(context: ImportContext = {}): PresetConfig {
|
||||
const {
|
||||
fileType = 'unknown',
|
||||
fileSize = 0,
|
||||
rowCount = 0,
|
||||
hasExplicitColumns = false,
|
||||
hasNarrativeContent = false,
|
||||
avgDefinitionLength = 0
|
||||
} = context
|
||||
|
||||
// Rule 1: Large imports → fast preset (prioritize speed)
|
||||
if (rowCount > 10000 || fileSize > 10_000_000) {
|
||||
return FAST_PRESET
|
||||
}
|
||||
|
||||
// Rule 2: Small critical imports → accurate preset (prioritize accuracy)
|
||||
if (rowCount > 0 && rowCount < 100) {
|
||||
return ACCURATE_PRESET
|
||||
}
|
||||
|
||||
// Rule 3: Structured data with explicit relationships → explicit preset
|
||||
// (Handles spreadsheet imports where relationships are encoded in columns.)
|
||||
if (hasExplicitColumns && (fileType === 'excel' || fileType === 'csv')) {
|
||||
return EXPLICIT_PRESET
|
||||
}
|
||||
|
||||
// Rule 4: Narrative content → pattern preset
|
||||
// Good for PDFs, articles, documentation
|
||||
if (
|
||||
hasNarrativeContent ||
|
||||
fileType === 'pdf' ||
|
||||
fileType === 'markdown' ||
|
||||
avgDefinitionLength > 500
|
||||
) {
|
||||
return PATTERN_PRESET
|
||||
}
|
||||
|
||||
// Rule 5: JSON data → balanced preset
|
||||
if (fileType === 'json') {
|
||||
return BALANCED_PRESET
|
||||
}
|
||||
|
||||
// Default: balanced preset
|
||||
return BALANCED_PRESET
|
||||
}
|
||||
|
||||
/**
|
||||
* Get preset by name
|
||||
*
|
||||
* @param name Preset name (fast, balanced, accurate, explicit, pattern)
|
||||
* @returns Preset configuration
|
||||
* @throws Error if preset not found
|
||||
*/
|
||||
export function getPreset(name: string): PresetConfig {
|
||||
const preset = PRESETS[name.toLowerCase()]
|
||||
if (!preset) {
|
||||
throw new Error(`Unknown preset: ${name}. Available: ${Object.keys(PRESETS).join(', ')}`)
|
||||
}
|
||||
return preset
|
||||
}
|
||||
|
||||
/**
|
||||
* Get all available preset names
|
||||
*
|
||||
* @returns Array of preset names
|
||||
*/
|
||||
export function getPresetNames(): string[] {
|
||||
return Object.keys(PRESETS)
|
||||
}
|
||||
|
||||
/**
|
||||
* Explain why a preset was selected
|
||||
*
|
||||
* @param context Import context
|
||||
* @returns Human-readable explanation
|
||||
*/
|
||||
export function explainPresetChoice(context: ImportContext = {}): string {
|
||||
const {
|
||||
fileType = 'unknown',
|
||||
fileSize = 0,
|
||||
rowCount = 0,
|
||||
hasExplicitColumns = false,
|
||||
hasNarrativeContent = false,
|
||||
avgDefinitionLength = 0
|
||||
} = context
|
||||
|
||||
if (rowCount > 10000 || fileSize > 10_000_000) {
|
||||
return `Large dataset (${rowCount} rows, ${(fileSize / 1_000_000).toFixed(1)}MB) → fast preset for optimal performance`
|
||||
}
|
||||
|
||||
if (rowCount > 0 && rowCount < 100) {
|
||||
return `Small critical dataset (${rowCount} rows) → accurate preset for maximum accuracy`
|
||||
}
|
||||
|
||||
if (hasExplicitColumns && (fileType === 'excel' || fileType === 'csv')) {
|
||||
return `${fileType.toUpperCase()} with explicit relationship columns → explicit preset for deterministic results`
|
||||
}
|
||||
|
||||
if (hasNarrativeContent || fileType === 'pdf' || fileType === 'markdown') {
|
||||
return `Narrative content (${fileType}) → pattern preset for semantic understanding`
|
||||
}
|
||||
|
||||
if (fileType === 'json') {
|
||||
return `JSON data → balanced preset for structured imports`
|
||||
}
|
||||
|
||||
return `Standard import → balanced preset (default)`
|
||||
}
|
||||
|
||||
/**
|
||||
* Create custom preset by merging with base preset
|
||||
*
|
||||
* @param baseName Base preset name
|
||||
* @param overrides Custom overrides
|
||||
* @returns Custom preset configuration
|
||||
*/
|
||||
export function createCustomPreset(
|
||||
baseName: string,
|
||||
overrides: Partial<PresetConfig>
|
||||
): PresetConfig {
|
||||
const base = getPreset(baseName)
|
||||
|
||||
return {
|
||||
...base,
|
||||
...overrides,
|
||||
signals: {
|
||||
...base.signals,
|
||||
...(overrides.signals || {})
|
||||
},
|
||||
strategies: {
|
||||
...base.strategies,
|
||||
...(overrides.strategies || {})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Validate preset configuration
|
||||
*
|
||||
* @param preset Preset to validate
|
||||
* @returns True if valid, throws error otherwise
|
||||
*/
|
||||
export function validatePreset(preset: PresetConfig): boolean {
|
||||
// Validate signals
|
||||
if (preset.signals.enabled.length === 0) {
|
||||
throw new Error('Preset must have at least one enabled signal')
|
||||
}
|
||||
|
||||
// Validate strategies
|
||||
if (preset.strategies.enabled.length === 0) {
|
||||
throw new Error('Preset must have at least one enabled strategy')
|
||||
}
|
||||
|
||||
// Validate weights sum to ~1.0
|
||||
const enabledSignals = preset.signals.enabled
|
||||
const totalWeight = enabledSignals.reduce(
|
||||
(sum, signal) => sum + preset.signals.weights[signal],
|
||||
0
|
||||
)
|
||||
|
||||
if (Math.abs(totalWeight - 1.0) > 0.01) {
|
||||
throw new Error(
|
||||
`Signal weights must sum to 1.0, got ${totalWeight.toFixed(2)}`
|
||||
)
|
||||
}
|
||||
|
||||
// Validate timeouts
|
||||
if (preset.signals.timeout <= 0 || preset.strategies.timeout <= 0) {
|
||||
throw new Error('Timeouts must be positive')
|
||||
}
|
||||
|
||||
// Validate batch size
|
||||
if (preset.batchSize <= 0) {
|
||||
throw new Error('Batch size must be positive')
|
||||
}
|
||||
|
||||
return true
|
||||
}
|
||||
|
||||
/**
|
||||
* Format preset for display
|
||||
*
|
||||
* @param preset Preset configuration
|
||||
* @returns Human-readable preset summary
|
||||
*/
|
||||
export function formatPreset(preset: PresetConfig): string {
|
||||
const lines = [
|
||||
`Preset: ${preset.name}`,
|
||||
`Description: ${preset.description}`,
|
||||
'',
|
||||
'Signals:',
|
||||
...preset.signals.enabled.map(
|
||||
(s) => ` - ${s}: ${(preset.signals.weights[s] * 100).toFixed(0)}%`
|
||||
),
|
||||
` Timeout: ${preset.signals.timeout}ms`,
|
||||
'',
|
||||
'Strategies:',
|
||||
...preset.strategies.enabled.map((s) => ` - ${s}`),
|
||||
` Timeout: ${preset.strategies.timeout}ms`,
|
||||
` Early termination: ${preset.strategies.earlyTermination}`,
|
||||
` Min confidence: ${preset.strategies.minConfidence}`,
|
||||
'',
|
||||
`Streaming: ${preset.streaming}`,
|
||||
`Batch size: ${preset.batchSize}`
|
||||
]
|
||||
|
||||
return lines.join('\n')
|
||||
}
|
||||
|
|
@ -1,309 +0,0 @@
|
|||
/**
|
||||
* Relationship Confidence Scoring
|
||||
*
|
||||
* Scores the confidence of detected relationships based on multiple factors:
|
||||
* - Entity proximity in text
|
||||
* - Entity confidence scores
|
||||
* - Pattern matches
|
||||
* - Structural analysis
|
||||
*/
|
||||
|
||||
import { ExtractedEntity } from './entityExtractor.js'
|
||||
import { VerbType } from '../types/graphTypes.js'
|
||||
import { RelationEvidence } from '../types/brainy.types.js'
|
||||
|
||||
/**
|
||||
* Detected relationship with confidence
|
||||
*/
|
||||
export interface DetectedRelationship {
|
||||
sourceEntity: ExtractedEntity
|
||||
targetEntity: ExtractedEntity
|
||||
verbType: VerbType
|
||||
confidence: number
|
||||
evidence: RelationEvidence
|
||||
}
|
||||
|
||||
/**
|
||||
* Configuration for relationship detection
|
||||
*/
|
||||
export interface RelationshipDetectionConfig {
|
||||
minConfidence?: number // Minimum confidence to return (default: 0.5)
|
||||
maxDistance?: number // Maximum token distance between entities (default: 50)
|
||||
useProximityBoost?: boolean // Boost score based on proximity (default: true)
|
||||
usePatternMatching?: boolean // Use verb pattern matching (default: true)
|
||||
useStructuralAnalysis?: boolean // Analyze sentence structure (default: true)
|
||||
}
|
||||
|
||||
/**
|
||||
* Relationship confidence scorer
|
||||
*/
|
||||
export class RelationshipConfidenceScorer {
|
||||
private config: Required<RelationshipDetectionConfig>
|
||||
|
||||
constructor(config: RelationshipDetectionConfig = {}) {
|
||||
this.config = {
|
||||
minConfidence: config.minConfidence || 0.5,
|
||||
maxDistance: config.maxDistance || 50,
|
||||
useProximityBoost: config.useProximityBoost !== false,
|
||||
usePatternMatching: config.usePatternMatching !== false,
|
||||
useStructuralAnalysis: config.useStructuralAnalysis !== false
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Score a potential relationship between two entities
|
||||
*/
|
||||
scoreRelationship(
|
||||
source: ExtractedEntity,
|
||||
target: ExtractedEntity,
|
||||
verbType: VerbType,
|
||||
context: string
|
||||
): { confidence: number, evidence: RelationEvidence } {
|
||||
let confidence = 0.5 // Base confidence
|
||||
|
||||
// Evidence tracking
|
||||
const reasoningParts: string[] = []
|
||||
|
||||
// Factor 1: Proximity boost (closer entities = higher confidence)
|
||||
if (this.config.useProximityBoost) {
|
||||
const proximityBoost = this.calculateProximityBoost(source, target)
|
||||
confidence += proximityBoost
|
||||
if (proximityBoost > 0) {
|
||||
reasoningParts.push(
|
||||
`Entities are close together (boost: +${proximityBoost.toFixed(2)})`
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
// Factor 2: Entity confidence boost
|
||||
const entityConfidence = (source.confidence + target.confidence) / 2
|
||||
const entityBoost = (entityConfidence - 0.5) * 0.2 // Scale to 0-0.2
|
||||
confidence *= (1 + entityBoost)
|
||||
if (entityBoost > 0) {
|
||||
reasoningParts.push(
|
||||
`High entity confidence (boost: ${entityBoost.toFixed(2)})`
|
||||
)
|
||||
}
|
||||
|
||||
// Factor 3: Pattern match boost
|
||||
if (this.config.usePatternMatching) {
|
||||
const patternBoost = this.checkVerbPattern(source, target, verbType, context)
|
||||
confidence += patternBoost
|
||||
if (patternBoost > 0) {
|
||||
reasoningParts.push(
|
||||
`Matches relationship pattern (boost: +${patternBoost.toFixed(2)})`
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
// Factor 4: Structural boost (same sentence, clause, etc.)
|
||||
if (this.config.useStructuralAnalysis) {
|
||||
const structuralBoost = this.analyzeStructure(source, target, context)
|
||||
confidence += structuralBoost
|
||||
if (structuralBoost > 0) {
|
||||
reasoningParts.push(
|
||||
`Structural relationship (boost: +${structuralBoost.toFixed(2)})`
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
// Cap confidence at 1.0
|
||||
confidence = Math.min(confidence, 1.0)
|
||||
|
||||
// Extract source text evidence
|
||||
const start = Math.min(source.position.start, target.position.start)
|
||||
const end = Math.max(source.position.end, target.position.end)
|
||||
|
||||
const evidence: RelationEvidence = {
|
||||
sourceText: context.substring(start, end),
|
||||
position: { start, end },
|
||||
method: 'neural',
|
||||
reasoning: reasoningParts.join('; ')
|
||||
}
|
||||
|
||||
return { confidence, evidence }
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculate proximity boost based on distance between entities
|
||||
*/
|
||||
private calculateProximityBoost(
|
||||
source: ExtractedEntity,
|
||||
target: ExtractedEntity
|
||||
): number {
|
||||
const distance = Math.abs(source.position.start - target.position.start)
|
||||
|
||||
if (distance === 0) return 0 // Same position, not meaningful
|
||||
|
||||
// Very close (< 20 chars): +0.2
|
||||
if (distance < 20) return 0.2
|
||||
|
||||
// Close (< 50 chars): +0.1
|
||||
if (distance < 50) return 0.1
|
||||
|
||||
// Medium (< 100 chars): +0.05
|
||||
if (distance < 100) return 0.05
|
||||
|
||||
// Far (> 100 chars): no boost
|
||||
return 0
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if entities match a verb pattern
|
||||
*/
|
||||
private checkVerbPattern(
|
||||
source: ExtractedEntity,
|
||||
target: ExtractedEntity,
|
||||
verbType: VerbType,
|
||||
context: string
|
||||
): number {
|
||||
const contextBetween = this.getContextBetween(source, target, context)
|
||||
const contextLower = contextBetween.toLowerCase()
|
||||
|
||||
// Verb-specific patterns
|
||||
const patterns: Record<string, string[]> = {
|
||||
[VerbType.Creates]: ['creates', 'made', 'built', 'developed', 'produces'],
|
||||
[VerbType.Owns]: ['owns', 'belongs to', 'possessed by', 'has'],
|
||||
[VerbType.Contains]: ['contains', 'includes', 'has', 'holds'],
|
||||
[VerbType.Requires]: ['requires', 'needs', 'depends on', 'relies on'],
|
||||
[VerbType.Uses]: ['uses', 'utilizes', 'employs', 'applies'],
|
||||
[VerbType.ReportsTo]: ['manages', 'oversees', 'supervises', 'controls'],
|
||||
[VerbType.Causes]: ['influences', 'affects', 'impacts', 'shapes', 'causes'],
|
||||
[VerbType.DependsOn]: ['depends on', 'relies on', 'based on'],
|
||||
[VerbType.Modifies]: ['modifies', 'changes', 'alters', 'updates'],
|
||||
[VerbType.References]: ['references', 'cites', 'mentions', 'refers to']
|
||||
}
|
||||
|
||||
const verbPatterns = patterns[verbType] || []
|
||||
|
||||
for (const pattern of verbPatterns) {
|
||||
if (contextLower.includes(pattern)) {
|
||||
return 0.2 // Strong pattern match
|
||||
}
|
||||
}
|
||||
|
||||
return 0 // No pattern match
|
||||
}
|
||||
|
||||
/**
|
||||
* Analyze structural relationship
|
||||
*/
|
||||
private analyzeStructure(
|
||||
source: ExtractedEntity,
|
||||
target: ExtractedEntity,
|
||||
context: string
|
||||
): number {
|
||||
const contextBetween = this.getContextBetween(source, target, context)
|
||||
|
||||
// Same sentence (no sentence-ending punctuation between them)
|
||||
if (!contextBetween.match(/[.!?]/)) {
|
||||
return 0.1
|
||||
}
|
||||
|
||||
// Same paragraph (single newline between them)
|
||||
if (!contextBetween.match(/\n\n/)) {
|
||||
return 0.05
|
||||
}
|
||||
|
||||
return 0
|
||||
}
|
||||
|
||||
/**
|
||||
* Get context text between two entities
|
||||
*/
|
||||
private getContextBetween(
|
||||
source: ExtractedEntity,
|
||||
target: ExtractedEntity,
|
||||
context: string
|
||||
): string {
|
||||
const start = Math.min(source.position.end, target.position.end)
|
||||
const end = Math.max(source.position.start, target.position.start)
|
||||
|
||||
if (start >= end) return ''
|
||||
|
||||
return context.substring(start, end)
|
||||
}
|
||||
|
||||
/**
|
||||
* Detect relationships between a list of entities
|
||||
*/
|
||||
detectRelationships(
|
||||
entities: ExtractedEntity[],
|
||||
context: string,
|
||||
verbHints?: VerbType[]
|
||||
): DetectedRelationship[] {
|
||||
const relationships: DetectedRelationship[] = []
|
||||
const verbs = verbHints || [
|
||||
VerbType.Creates,
|
||||
VerbType.Uses,
|
||||
VerbType.Contains,
|
||||
VerbType.Requires,
|
||||
VerbType.RelatedTo
|
||||
]
|
||||
|
||||
// Check all entity pairs
|
||||
for (let i = 0; i < entities.length; i++) {
|
||||
for (let j = i + 1; j < entities.length; j++) {
|
||||
const source = entities[i]
|
||||
const target = entities[j]
|
||||
|
||||
// Check distance
|
||||
const distance = Math.abs(source.position.start - target.position.start)
|
||||
if (distance > this.config.maxDistance) {
|
||||
continue // Too far apart
|
||||
}
|
||||
|
||||
// Try each verb type
|
||||
for (const verbType of verbs) {
|
||||
const { confidence, evidence } = this.scoreRelationship(
|
||||
source,
|
||||
target,
|
||||
verbType,
|
||||
context
|
||||
)
|
||||
|
||||
if (confidence >= this.config.minConfidence) {
|
||||
relationships.push({
|
||||
sourceEntity: source,
|
||||
targetEntity: target,
|
||||
verbType,
|
||||
confidence,
|
||||
evidence
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Sort by confidence (highest first)
|
||||
relationships.sort((a, b) => b.confidence - a.confidence)
|
||||
|
||||
return relationships
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Convenience function to score a single relationship
|
||||
*/
|
||||
export function scoreRelationshipConfidence(
|
||||
source: ExtractedEntity,
|
||||
target: ExtractedEntity,
|
||||
verbType: VerbType,
|
||||
context: string,
|
||||
config?: RelationshipDetectionConfig
|
||||
): { confidence: number, evidence: RelationEvidence } {
|
||||
const scorer = new RelationshipConfidenceScorer(config)
|
||||
return scorer.scoreRelationship(source, target, verbType, context)
|
||||
}
|
||||
|
||||
/**
|
||||
* Convenience function to detect all relationships in text
|
||||
*/
|
||||
export function detectRelationshipsWithConfidence(
|
||||
entities: ExtractedEntity[],
|
||||
context: string,
|
||||
config?: RelationshipDetectionConfig
|
||||
): DetectedRelationship[] {
|
||||
const scorer = new RelationshipConfidenceScorer(config)
|
||||
return scorer.detectRelationships(entities, context)
|
||||
}
|
||||
|
|
@ -1,188 +0,0 @@
|
|||
/**
|
||||
* Static Pattern Matcher - NO runtime initialization, NO Brainy needed
|
||||
*
|
||||
* All patterns and embeddings are pre-computed at build time
|
||||
* This is pure pattern matching with zero dependencies
|
||||
*/
|
||||
|
||||
import { EMBEDDED_PATTERNS, getPatternEmbeddings } from './embeddedPatterns.js'
|
||||
import type { Vector } from '../coreTypes.js'
|
||||
import type { TripleQuery } from '../triple/TripleIntelligence.js'
|
||||
|
||||
// Pre-load patterns and embeddings at module load time (happens once)
|
||||
const patterns = new Map(EMBEDDED_PATTERNS.map(p => [p.id, p]))
|
||||
const patternEmbeddings = getPatternEmbeddings()
|
||||
|
||||
/**
|
||||
* Cosine similarity between two vectors.
|
||||
* Accepts any number-indexed sequence so pre-computed Float32Array pattern
|
||||
* embeddings can be compared against number[] query vectors without copying.
|
||||
*/
|
||||
function cosineSimilarity(a: ArrayLike<number>, b: ArrayLike<number>): number {
|
||||
if (!a || !b || a.length !== b.length) return 0
|
||||
|
||||
let dotProduct = 0
|
||||
let normA = 0
|
||||
let normB = 0
|
||||
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
dotProduct += a[i] * b[i]
|
||||
normA += a[i] * a[i]
|
||||
normB += b[i] * b[i]
|
||||
}
|
||||
|
||||
const denominator = Math.sqrt(normA) * Math.sqrt(normB)
|
||||
return denominator === 0 ? 0 : dotProduct / denominator
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract slots from matched pattern
|
||||
*/
|
||||
function extractSlots(query: string, pattern: string): Record<string, string> | null {
|
||||
try {
|
||||
const regex = new RegExp(pattern, 'i')
|
||||
const match = query.match(regex)
|
||||
|
||||
if (!match) return null
|
||||
|
||||
const slots: Record<string, string> = {}
|
||||
for (let i = 1; i < match.length; i++) {
|
||||
if (match[i]) {
|
||||
slots[`$${i}`] = match[i]
|
||||
}
|
||||
}
|
||||
|
||||
return Object.keys(slots).length > 0 ? slots : null
|
||||
} catch {
|
||||
return null
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply template with extracted slots
|
||||
*/
|
||||
function applyTemplate(template: any, slots: Record<string, string>): any {
|
||||
if (!template || !slots) return template
|
||||
|
||||
const result = JSON.parse(JSON.stringify(template))
|
||||
const applySlots = (obj: any): any => {
|
||||
if (typeof obj === 'string') {
|
||||
return obj.replace(/\$\{(\d+)\}/g, (_, num) => slots[`$${num}`] || '')
|
||||
}
|
||||
if (Array.isArray(obj)) {
|
||||
return obj.map(applySlots)
|
||||
}
|
||||
if (typeof obj === 'object' && obj !== null) {
|
||||
const newObj: any = {}
|
||||
for (const [key, value] of Object.entries(obj)) {
|
||||
newObj[key] = applySlots(value)
|
||||
}
|
||||
return newObj
|
||||
}
|
||||
return obj
|
||||
}
|
||||
|
||||
return applySlots(result)
|
||||
}
|
||||
|
||||
/**
|
||||
* Match query against all patterns using embeddings
|
||||
*/
|
||||
export function findBestPatterns(
|
||||
queryEmbedding: Vector,
|
||||
k: number = 3
|
||||
): Array<{ pattern: typeof EMBEDDED_PATTERNS[0]; similarity: number }> {
|
||||
|
||||
const matches: Array<{ pattern: typeof EMBEDDED_PATTERNS[0]; similarity: number }> = []
|
||||
|
||||
for (const pattern of EMBEDDED_PATTERNS) {
|
||||
|
||||
const patternEmbedding = patternEmbeddings.get(pattern.id)
|
||||
if (!patternEmbedding) continue
|
||||
|
||||
// Pass Float32Array directly, no need for Array.from()!
|
||||
const similarity = cosineSimilarity(queryEmbedding, patternEmbedding)
|
||||
if (similarity > 0.5) { // Threshold for relevance
|
||||
matches.push({ pattern, similarity })
|
||||
}
|
||||
}
|
||||
|
||||
// Sort by similarity and return top k
|
||||
return matches
|
||||
.sort((a, b) => b.similarity - a.similarity)
|
||||
.slice(0, k)
|
||||
}
|
||||
|
||||
/**
|
||||
* Match query against patterns using regex
|
||||
*/
|
||||
export function matchPatternByRegex(query: string): {
|
||||
pattern: typeof EMBEDDED_PATTERNS[0]
|
||||
slots: Record<string, string>
|
||||
query: TripleQuery
|
||||
} | null {
|
||||
// Try direct regex matching first (fastest)
|
||||
for (const pattern of EMBEDDED_PATTERNS) {
|
||||
const slots = extractSlots(query, pattern.pattern)
|
||||
if (slots) {
|
||||
const templatedQuery = applyTemplate(pattern.template, slots)
|
||||
return {
|
||||
pattern,
|
||||
slots,
|
||||
query: templatedQuery
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return null
|
||||
}
|
||||
|
||||
/**
|
||||
* Convert natural language to structured query using STATIC patterns
|
||||
* NO initialization needed, NO Brainy required
|
||||
*/
|
||||
export function patternMatchQuery(
|
||||
query: string,
|
||||
queryEmbedding?: Vector
|
||||
): TripleQuery {
|
||||
|
||||
// ALWAYS use vector similarity when we have embeddings (which we always do!)
|
||||
if (queryEmbedding && queryEmbedding.length === 384) {
|
||||
const bestPatterns = findBestPatterns(queryEmbedding, 5) // Get top 5 matches
|
||||
|
||||
// Try to extract slots from best matching patterns
|
||||
for (const { pattern, similarity } of bestPatterns) {
|
||||
// Only try patterns with good similarity
|
||||
if (similarity < 0.7) break
|
||||
|
||||
const slots = extractSlots(query, pattern.pattern)
|
||||
if (slots) {
|
||||
// Found a good match with extractable slots!
|
||||
const result = applyTemplate(pattern.template, slots)
|
||||
console.log('[NLP] Applied template with slots:', JSON.stringify(result))
|
||||
return result
|
||||
}
|
||||
}
|
||||
|
||||
// If no slots extracted but we have a good match, use the template as-is
|
||||
if (bestPatterns.length > 0 && bestPatterns[0].similarity > 0.75) {
|
||||
console.log('[NLP] Returning template as-is:', JSON.stringify(bestPatterns[0].pattern.template))
|
||||
return bestPatterns[0].pattern.template
|
||||
}
|
||||
}
|
||||
|
||||
// Fallback: simple vector search (should rarely happen)
|
||||
console.log('[NLP] Fallback - returning simple query')
|
||||
return {
|
||||
like: query,
|
||||
limit: 10
|
||||
}
|
||||
}
|
||||
|
||||
// Export pattern statistics for monitoring
|
||||
export const PATTERN_STATS = {
|
||||
totalPatterns: EMBEDDED_PATTERNS.length,
|
||||
categories: [...new Set(EMBEDDED_PATTERNS.map(p => p.category))],
|
||||
domains: [...new Set(EMBEDDED_PATTERNS.filter(p => p.domain).map(p => p.domain!))],
|
||||
hasEmbeddings: patternEmbeddings.size > 0
|
||||
}
|
||||
File diff suppressed because it is too large
Load diff
|
|
@ -1,34 +0,0 @@
|
|||
/**
|
||||
* Transaction System
|
||||
*
|
||||
* Provides atomicity for Brainy operations.
|
||||
* All operations succeed or all rollback - no partial failures.
|
||||
*
|
||||
* @module transaction
|
||||
*/
|
||||
|
||||
export * from './types.js'
|
||||
export * from './errors.js'
|
||||
export { Transaction } from './Transaction.js'
|
||||
export { TransactionManager, type TransactionStats } from './TransactionManager.js'
|
||||
|
||||
// Re-export for convenience
|
||||
export type {
|
||||
Operation,
|
||||
RollbackAction,
|
||||
TransactionState,
|
||||
TransactionContext,
|
||||
TransactionFunction,
|
||||
TransactionResult,
|
||||
TransactionOptions
|
||||
} from './types.js'
|
||||
|
||||
export {
|
||||
TransactionError,
|
||||
TransactionExecutionError,
|
||||
TransactionRollbackError,
|
||||
InvalidTransactionStateError,
|
||||
TransactionTimeoutError
|
||||
} from './errors.js'
|
||||
|
||||
export { RevisionConflictError } from './RevisionConflictError.js'
|
||||
|
|
@ -1,326 +0,0 @@
|
|||
/**
|
||||
* Consistent API Types for Brainy
|
||||
*
|
||||
* These types provide a uniform interface for all public methods,
|
||||
* using object parameters for consistency and extensibility.
|
||||
*/
|
||||
|
||||
import type { Vector } from '../coreTypes.js'
|
||||
import type { NounType, VerbType } from './graphTypes.js'
|
||||
|
||||
// ============= NOUN OPERATIONS =============
|
||||
|
||||
/**
|
||||
* Parameters for adding a noun
|
||||
*/
|
||||
export interface AddNounParams {
|
||||
data: any | Vector // Content or pre-computed vector
|
||||
type: NounType | string // Noun type (required)
|
||||
metadata?: any // Optional metadata
|
||||
id?: string // Optional custom ID
|
||||
service?: string // Optional service identifier
|
||||
}
|
||||
|
||||
/**
|
||||
* Parameters for updating a noun
|
||||
*/
|
||||
export interface UpdateNounParams {
|
||||
id: string // Noun ID to update
|
||||
data?: any // New data
|
||||
metadata?: any // New metadata
|
||||
type?: NounType | string // New type
|
||||
}
|
||||
|
||||
/**
|
||||
* Parameters for getting nouns
|
||||
*/
|
||||
export interface GetNounsParams {
|
||||
ids?: string[] // Specific IDs to fetch
|
||||
type?: NounType | string | string[] // Filter by type(s)
|
||||
limit?: number // Maximum results
|
||||
offset?: number // Pagination offset
|
||||
cursor?: string // Pagination cursor
|
||||
filter?: Record<string, any> // Metadata filters
|
||||
service?: string // Service filter
|
||||
}
|
||||
|
||||
// ============= VERB OPERATIONS =============
|
||||
|
||||
/**
|
||||
* Parameters for adding a verb (relationship)
|
||||
*/
|
||||
export interface AddVerbParams {
|
||||
source: string // Source noun ID
|
||||
target: string // Target noun ID
|
||||
type: VerbType | string // Verb type (required)
|
||||
weight?: number // Relationship weight (0-1)
|
||||
metadata?: any // Optional metadata
|
||||
service?: string // Optional service identifier
|
||||
}
|
||||
|
||||
/**
|
||||
* Parameters for getting verbs
|
||||
*/
|
||||
export interface GetVerbsParams {
|
||||
source?: string // Filter by source
|
||||
target?: string // Filter by target
|
||||
type?: VerbType | string | string[] // Filter by type(s)
|
||||
limit?: number // Maximum results
|
||||
offset?: number // Pagination offset
|
||||
cursor?: string // Pagination cursor
|
||||
filter?: Record<string, any> // Metadata filters
|
||||
service?: string // Service filter
|
||||
}
|
||||
|
||||
// ============= SEARCH OPERATIONS =============
|
||||
|
||||
/**
|
||||
* Unified search parameters
|
||||
*/
|
||||
export interface SearchParams {
|
||||
query: string | Vector // Text query or vector
|
||||
limit?: number // Maximum results (default: 10)
|
||||
threshold?: number // Similarity threshold (0-1)
|
||||
filter?: {
|
||||
type?: NounType | string | string[] // Filter by noun type(s)
|
||||
metadata?: Record<string, any> // Metadata filters
|
||||
service?: string // Service filter
|
||||
}
|
||||
includeMetadata?: boolean // Include metadata in results
|
||||
includeVectors?: boolean // Include vectors in results
|
||||
}
|
||||
|
||||
/**
|
||||
* Parameters for similarity search
|
||||
*/
|
||||
export interface SimilarityParams {
|
||||
id?: string // Find similar to this ID
|
||||
data?: any | Vector // Or find similar to this data
|
||||
limit?: number // Maximum results (default: 10)
|
||||
threshold?: number // Similarity threshold (0-1)
|
||||
filter?: {
|
||||
type?: NounType | string | string[]
|
||||
metadata?: Record<string, any>
|
||||
service?: string
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Parameters for related items search
|
||||
*/
|
||||
export interface RelatedParams {
|
||||
id: string // Starting noun ID
|
||||
depth?: number // Traversal depth (default: 1)
|
||||
limit?: number // Max results per level
|
||||
types?: VerbType[] | string[] // Relationship types to follow
|
||||
direction?: 'outgoing' | 'incoming' | 'both'
|
||||
}
|
||||
|
||||
// ============= BATCH OPERATIONS =============
|
||||
|
||||
/**
|
||||
* Parameters for batch noun operations
|
||||
*/
|
||||
export interface BatchNounsParams {
|
||||
items: AddNounParams[] // Array of nouns to add
|
||||
parallel?: boolean // Process in parallel
|
||||
chunkSize?: number // Batch size for processing
|
||||
onProgress?: (completed: number, total: number) => void
|
||||
}
|
||||
|
||||
/**
|
||||
* Parameters for batch verb operations
|
||||
*/
|
||||
export interface BatchVerbsParams {
|
||||
items: AddVerbParams[] // Array of verbs to add
|
||||
parallel?: boolean // Process in parallel
|
||||
chunkSize?: number // Batch size for processing
|
||||
onProgress?: (completed: number, total: number) => void
|
||||
}
|
||||
|
||||
// ============= STATISTICS & METADATA =============
|
||||
|
||||
/**
|
||||
* Parameters for statistics queries
|
||||
*/
|
||||
export interface StatisticsParams {
|
||||
detailed?: boolean // Include detailed breakdown
|
||||
includeAugmentations?: boolean // Include augmentation stats
|
||||
includeMemory?: boolean // Include memory usage
|
||||
service?: string // Filter by service
|
||||
}
|
||||
|
||||
/**
|
||||
* Parameters for metadata operations
|
||||
*/
|
||||
export interface MetadataParams {
|
||||
id: string // Entity ID
|
||||
metadata: any // Metadata to set/update
|
||||
merge?: boolean // Merge with existing (vs replace)
|
||||
}
|
||||
|
||||
// ============= CONFIGURATION =============
|
||||
|
||||
/**
|
||||
* Dynamic configuration update parameters
|
||||
*/
|
||||
export interface ConfigUpdateParams {
|
||||
embeddings?: {
|
||||
model?: string
|
||||
precision?: 'q8'
|
||||
cache?: boolean
|
||||
}
|
||||
augmentations?: {
|
||||
[name: string]: boolean | Record<string, any>
|
||||
}
|
||||
storage?: {
|
||||
type?: string
|
||||
config?: any
|
||||
}
|
||||
performance?: {
|
||||
batchSize?: number
|
||||
maxConcurrency?: number
|
||||
cacheSize?: number
|
||||
}
|
||||
}
|
||||
|
||||
// ============= TRIPLE INTELLIGENCE API =============
|
||||
|
||||
/**
|
||||
* API for Triple Intelligence Engine to access Brainy internals
|
||||
* This provides type-safe access without unchecked casts
|
||||
*/
|
||||
export interface TripleIntelligenceAPI {
|
||||
// Vector operations
|
||||
vectorSearch(vector: Vector | string, limit: number): Promise<Array<{id: string, score: number, entity?: any}>>
|
||||
|
||||
// Graph operations
|
||||
graphTraversal(options: {
|
||||
start: string | string[]
|
||||
type?: string | string[]
|
||||
direction?: 'in' | 'out' | 'both'
|
||||
maxDepth?: number
|
||||
}): Promise<Array<{id: string, score: number, depth: number}>>
|
||||
|
||||
// Metadata operations
|
||||
metadataQuery(where: Record<string, any>): Promise<Set<string>>
|
||||
getEntity(id: string): Promise<any>
|
||||
|
||||
// Storage operations
|
||||
getVerbsBySource(sourceId: string): Promise<any[]>
|
||||
getVerbsByTarget(targetId: string): Promise<any[]>
|
||||
|
||||
// Statistics
|
||||
getStatistics(): Promise<{
|
||||
totalCount: number
|
||||
fieldStats: Record<string, {
|
||||
min: number
|
||||
max: number
|
||||
cardinality: number
|
||||
type: string
|
||||
}>
|
||||
}>
|
||||
|
||||
// Index access
|
||||
getAllNouns(): Map<string, any>
|
||||
hasMetadataIndex(): boolean
|
||||
}
|
||||
|
||||
// ============= RESULTS =============
|
||||
|
||||
/**
|
||||
* Unified search result
|
||||
*/
|
||||
export interface SearchResult<T = any> {
|
||||
id: string
|
||||
score: number // Similarity score (0-1)
|
||||
data?: T // Original data
|
||||
metadata?: any // Metadata if requested
|
||||
vector?: Vector // Vector if requested
|
||||
type?: string // Noun type
|
||||
distance?: number // Raw distance metric
|
||||
}
|
||||
|
||||
/**
|
||||
* Paginated result wrapper
|
||||
*/
|
||||
export interface PaginatedResult<T> {
|
||||
items: T[]
|
||||
total?: number
|
||||
hasMore: boolean
|
||||
nextCursor?: string
|
||||
previousCursor?: string
|
||||
}
|
||||
|
||||
/**
|
||||
* Batch operation result
|
||||
*/
|
||||
export interface BatchResult {
|
||||
successful: string[] // Successfully processed IDs
|
||||
failed: Array<{
|
||||
index: number
|
||||
error: string
|
||||
item?: any
|
||||
}>
|
||||
total: number
|
||||
duration: number // Total time in ms
|
||||
}
|
||||
|
||||
/**
|
||||
* Statistics result
|
||||
*/
|
||||
export interface StatisticsResult {
|
||||
nouns: {
|
||||
total: number
|
||||
byType: Record<string, number>
|
||||
}
|
||||
verbs: {
|
||||
total: number
|
||||
byType: Record<string, number>
|
||||
}
|
||||
storage: {
|
||||
used: number
|
||||
type: string
|
||||
}
|
||||
performance?: {
|
||||
avgLatency: number
|
||||
throughput: number
|
||||
cacheHitRate?: number
|
||||
}
|
||||
augmentations?: Record<string, any>
|
||||
memory?: {
|
||||
used: number
|
||||
limit: number
|
||||
}
|
||||
}
|
||||
|
||||
// ============= ERRORS =============
|
||||
|
||||
/**
|
||||
* Structured error for API operations
|
||||
*/
|
||||
export class BrainyAPIError extends Error {
|
||||
constructor(
|
||||
message: string,
|
||||
public code: string,
|
||||
public statusCode: number = 400,
|
||||
public details?: any
|
||||
) {
|
||||
super(message)
|
||||
this.name = 'BrainyAPIError'
|
||||
}
|
||||
}
|
||||
|
||||
// Error codes
|
||||
export const ErrorCodes = {
|
||||
INVALID_TYPE: 'INVALID_TYPE',
|
||||
NOT_FOUND: 'NOT_FOUND',
|
||||
DUPLICATE_ID: 'DUPLICATE_ID',
|
||||
INVALID_VECTOR: 'INVALID_VECTOR',
|
||||
STORAGE_ERROR: 'STORAGE_ERROR',
|
||||
EMBEDDING_ERROR: 'EMBEDDING_ERROR',
|
||||
AUGMENTATION_ERROR: 'AUGMENTATION_ERROR',
|
||||
VALIDATION_ERROR: 'VALIDATION_ERROR',
|
||||
QUOTA_EXCEEDED: 'QUOTA_EXCEEDED',
|
||||
UNAUTHORIZED: 'UNAUTHORIZED'
|
||||
} as const
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
/**
|
||||
* Type declarations for the File System Access API
|
||||
* Extends the FileSystemDirectoryHandle interface to include the [Symbol.asyncIterator] method
|
||||
* and FileSystemHandle to include getFile() method for TypeScript compatibility
|
||||
*/
|
||||
|
||||
// Extend the FileSystemDirectoryHandle interface
|
||||
interface FileSystemDirectoryHandle {
|
||||
[Symbol.asyncIterator](): AsyncIterableIterator<[string, FileSystemHandle]>;
|
||||
|
||||
keys(): AsyncIterableIterator<string>;
|
||||
|
||||
entries(): AsyncIterableIterator<[string, FileSystemHandle]>;
|
||||
}
|
||||
|
||||
// Extend the FileSystemHandle interface to include getFile method
|
||||
// This is needed because TypeScript doesn't recognize that a FileSystemHandle
|
||||
// can be a FileSystemFileHandle which has the getFile method
|
||||
interface FileSystemHandle {
|
||||
getFile?(): Promise<File>;
|
||||
}
|
||||
|
||||
// Export something to make this a module
|
||||
export const fileSystemTypesLoaded = true
|
||||
|
|
@ -1,288 +0,0 @@
|
|||
/**
|
||||
* Standardized Progress Reporting
|
||||
*
|
||||
* Provides unified progress tracking across all long-running operations
|
||||
* in Brainy (imports, clustering, large searches, etc.)
|
||||
*/
|
||||
|
||||
/**
|
||||
* Progress status states
|
||||
*/
|
||||
export type ProgressStatus = 'pending' | 'running' | 'completed' | 'failed' | 'cancelled'
|
||||
|
||||
/**
|
||||
* Standardized progress report
|
||||
*/
|
||||
export interface BrainyProgress<T = any> {
|
||||
// Core status
|
||||
status: ProgressStatus
|
||||
|
||||
// Progress percentage (0-100)
|
||||
progress: number
|
||||
|
||||
// Human-readable message
|
||||
message: string
|
||||
|
||||
// Detailed metadata
|
||||
metadata: {
|
||||
itemsProcessed: number
|
||||
itemsTotal: number
|
||||
currentItem?: string
|
||||
estimatedTimeRemaining?: number // milliseconds
|
||||
startedAt: number
|
||||
completedAt?: number
|
||||
throughput?: number // items per second
|
||||
}
|
||||
|
||||
// Result when completed
|
||||
result?: T
|
||||
|
||||
// Error when failed
|
||||
error?: Error
|
||||
}
|
||||
|
||||
/**
|
||||
* Progress tracker with automatic time estimation
|
||||
*/
|
||||
export class ProgressTracker<T = any> {
|
||||
private status: ProgressStatus = 'pending'
|
||||
private processed = 0
|
||||
private total: number
|
||||
private startedAt?: number
|
||||
private completedAt?: number
|
||||
private currentItem?: string
|
||||
private result?: T
|
||||
private error?: Error
|
||||
private processingTimes: number[] = [] // Track last N processing times for estimation
|
||||
|
||||
constructor(total: number) {
|
||||
if (total < 0) {
|
||||
throw new Error('Total must be non-negative')
|
||||
}
|
||||
this.total = total
|
||||
}
|
||||
|
||||
/**
|
||||
* Factory method for creating progress trackers
|
||||
*/
|
||||
static create<T>(total: number): ProgressTracker<T> {
|
||||
return new ProgressTracker<T>(total)
|
||||
}
|
||||
|
||||
/**
|
||||
* Start tracking progress
|
||||
*/
|
||||
start(): BrainyProgress<T> {
|
||||
this.status = 'running'
|
||||
this.startedAt = Date.now()
|
||||
return this.current()
|
||||
}
|
||||
|
||||
/**
|
||||
* Update progress
|
||||
*/
|
||||
update(processed: number, currentItem?: string): BrainyProgress<T> {
|
||||
if (processed < 0) {
|
||||
throw new Error('Processed count must be non-negative')
|
||||
}
|
||||
if (processed > this.total) {
|
||||
throw new Error(`Processed count (${processed}) exceeds total (${this.total})`)
|
||||
}
|
||||
|
||||
const previousProcessed = this.processed
|
||||
this.processed = processed
|
||||
this.currentItem = currentItem
|
||||
|
||||
// Track processing time for estimation
|
||||
if (this.startedAt && previousProcessed < processed) {
|
||||
const itemsProcessed = processed - previousProcessed
|
||||
const timeTaken = Date.now() - this.startedAt
|
||||
const avgTimePerItem = timeTaken / processed
|
||||
this.processingTimes.push(avgTimePerItem)
|
||||
|
||||
// Keep only last 100 measurements for rolling average
|
||||
if (this.processingTimes.length > 100) {
|
||||
this.processingTimes.shift()
|
||||
}
|
||||
}
|
||||
|
||||
return this.current()
|
||||
}
|
||||
|
||||
/**
|
||||
* Increment progress by 1
|
||||
*/
|
||||
increment(currentItem?: string): BrainyProgress<T> {
|
||||
return this.update(this.processed + 1, currentItem)
|
||||
}
|
||||
|
||||
/**
|
||||
* Mark as completed
|
||||
*/
|
||||
complete(result: T): BrainyProgress<T> {
|
||||
this.status = 'completed'
|
||||
this.completedAt = Date.now()
|
||||
this.processed = this.total
|
||||
this.result = result
|
||||
return this.current()
|
||||
}
|
||||
|
||||
/**
|
||||
* Mark as failed
|
||||
*/
|
||||
fail(error: Error): BrainyProgress<T> {
|
||||
this.status = 'failed'
|
||||
this.completedAt = Date.now()
|
||||
this.error = error
|
||||
return this.current()
|
||||
}
|
||||
|
||||
/**
|
||||
* Mark as cancelled
|
||||
*/
|
||||
cancel(): BrainyProgress<T> {
|
||||
this.status = 'cancelled'
|
||||
this.completedAt = Date.now()
|
||||
return this.current()
|
||||
}
|
||||
|
||||
/**
|
||||
* Get current progress state
|
||||
*/
|
||||
current(): BrainyProgress<T> {
|
||||
const progress = this.total > 0 ? Math.round((this.processed / this.total) * 100) : 0
|
||||
|
||||
// Generate message based on status
|
||||
let message: string
|
||||
switch (this.status) {
|
||||
case 'pending':
|
||||
message = `Ready to process ${this.total} items`
|
||||
break
|
||||
case 'running':
|
||||
message = this.currentItem
|
||||
? `Processing: ${this.currentItem} (${this.processed}/${this.total})`
|
||||
: `Processing ${this.processed}/${this.total} items`
|
||||
break
|
||||
case 'completed':
|
||||
message = `Completed ${this.total} items`
|
||||
break
|
||||
case 'failed':
|
||||
message = `Failed after ${this.processed} items: ${this.error?.message || 'Unknown error'}`
|
||||
break
|
||||
case 'cancelled':
|
||||
message = `Cancelled after ${this.processed} items`
|
||||
break
|
||||
}
|
||||
|
||||
return {
|
||||
status: this.status,
|
||||
progress,
|
||||
message,
|
||||
metadata: {
|
||||
itemsProcessed: this.processed,
|
||||
itemsTotal: this.total,
|
||||
currentItem: this.currentItem,
|
||||
estimatedTimeRemaining: this.estimateTimeRemaining(),
|
||||
startedAt: this.startedAt || Date.now(),
|
||||
completedAt: this.completedAt,
|
||||
throughput: this.calculateThroughput()
|
||||
},
|
||||
result: this.result,
|
||||
error: this.error
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Estimate time remaining based on processing history
|
||||
*/
|
||||
private estimateTimeRemaining(): number | undefined {
|
||||
if (this.status !== 'running' || !this.startedAt || this.processed === 0) {
|
||||
return undefined
|
||||
}
|
||||
|
||||
const remaining = this.total - this.processed
|
||||
if (remaining === 0) {
|
||||
return 0
|
||||
}
|
||||
|
||||
// Use rolling average if we have enough samples
|
||||
if (this.processingTimes.length > 0) {
|
||||
const avgTimePerItem = this.processingTimes.reduce((a, b) => a + b, 0) / this.processingTimes.length
|
||||
return Math.round(avgTimePerItem * remaining)
|
||||
}
|
||||
|
||||
// Fallback to simple calculation
|
||||
const elapsed = Date.now() - this.startedAt
|
||||
const avgTimePerItem = elapsed / this.processed
|
||||
return Math.round(avgTimePerItem * remaining)
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculate current throughput (items/second)
|
||||
*/
|
||||
private calculateThroughput(): number | undefined {
|
||||
if (!this.startedAt || this.processed === 0) {
|
||||
return undefined
|
||||
}
|
||||
|
||||
const elapsed = Date.now() - this.startedAt
|
||||
const seconds = elapsed / 1000
|
||||
return seconds > 0 ? Math.round((this.processed / seconds) * 100) / 100 : undefined
|
||||
}
|
||||
|
||||
/**
|
||||
* Get progress statistics
|
||||
*/
|
||||
getStats() {
|
||||
const elapsed = this.startedAt ? Date.now() - this.startedAt : 0
|
||||
return {
|
||||
status: this.status,
|
||||
processed: this.processed,
|
||||
total: this.total,
|
||||
remaining: this.total - this.processed,
|
||||
progress: this.total > 0 ? this.processed / this.total : 0,
|
||||
elapsed,
|
||||
estimatedTotal: elapsed > 0 && this.processed > 0
|
||||
? Math.round((elapsed / this.processed) * this.total)
|
||||
: undefined,
|
||||
throughput: this.calculateThroughput()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Helper to format time duration
|
||||
*/
|
||||
export function formatDuration(ms: number): string {
|
||||
const seconds = Math.floor(ms / 1000)
|
||||
const minutes = Math.floor(seconds / 60)
|
||||
const hours = Math.floor(minutes / 60)
|
||||
|
||||
if (hours > 0) {
|
||||
return `${hours}h ${minutes % 60}m`
|
||||
} else if (minutes > 0) {
|
||||
return `${minutes}m ${seconds % 60}s`
|
||||
} else {
|
||||
return `${seconds}s`
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Helper to format progress percentage
|
||||
*/
|
||||
export function formatProgress(progress: BrainyProgress): string {
|
||||
const { status, progress: pct, metadata } = progress
|
||||
const remaining = metadata.estimatedTimeRemaining
|
||||
|
||||
let str = `[${status.toUpperCase()}] ${pct}% (${metadata.itemsProcessed}/${metadata.itemsTotal})`
|
||||
|
||||
if (metadata.throughput) {
|
||||
str += ` - ${metadata.throughput} items/s`
|
||||
}
|
||||
|
||||
if (remaining && remaining > 0) {
|
||||
str += ` - ${formatDuration(remaining)} remaining`
|
||||
}
|
||||
|
||||
return str
|
||||
}
|
||||
|
|
@ -1,174 +0,0 @@
|
|||
/**
|
||||
* Type Migration Utilities for Stage 3 Taxonomy
|
||||
*
|
||||
* Provides migration helpers for code using removed types from v5.x
|
||||
*
|
||||
* ## Removed Types
|
||||
*
|
||||
* ### Nouns (2 removed):
|
||||
* - `user` → merged into `person`
|
||||
* - `topic` → merged into `concept`
|
||||
*
|
||||
* ### Verbs (4 removed):
|
||||
* - `succeeds` → use inverse of `precedes`
|
||||
* - `belongsTo` → use inverse of `owns`
|
||||
* - `createdBy` → use inverse of `creates`
|
||||
* - `supervises` → use inverse of `reportsTo`
|
||||
*/
|
||||
|
||||
import { NounType, VerbType } from './graphTypes.js'
|
||||
|
||||
/**
|
||||
* Migration mapping for removed noun types
|
||||
*/
|
||||
export const REMOVED_NOUN_TYPES = {
|
||||
user: NounType.Person,
|
||||
topic: NounType.Concept
|
||||
} as const
|
||||
|
||||
/**
|
||||
* Migration mapping for removed verb types
|
||||
* Note: Some verbs should use inverse relationships in Stage 3
|
||||
*/
|
||||
export const REMOVED_VERB_TYPES = {
|
||||
succeeds: VerbType.Precedes, // Use with inverted source/target
|
||||
belongsTo: VerbType.Owns, // Use with inverted source/target
|
||||
createdBy: VerbType.Creates, // Use with inverted source/target
|
||||
supervises: VerbType.ReportsTo // Use with inverted source/target
|
||||
} as const
|
||||
|
||||
/**
|
||||
* Check if a type was removed in Stage 3
|
||||
*/
|
||||
export function isRemovedNounType(type: string): type is keyof typeof REMOVED_NOUN_TYPES {
|
||||
return type in REMOVED_NOUN_TYPES
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if a verb type was removed in Stage 3
|
||||
*/
|
||||
export function isRemovedVerbType(type: string): type is keyof typeof REMOVED_VERB_TYPES {
|
||||
return type in REMOVED_VERB_TYPES
|
||||
}
|
||||
|
||||
/**
|
||||
* Migrate a noun type (Stage 3)
|
||||
* Returns the migrated type or the original if no migration needed
|
||||
*/
|
||||
export function migrateNounType(type: string): NounType {
|
||||
if (isRemovedNounType(type)) {
|
||||
console.warn(`⚠️ NounType "${type}" was removed in v6.0. Migrating to "${REMOVED_NOUN_TYPES[type]}"`)
|
||||
return REMOVED_NOUN_TYPES[type]
|
||||
}
|
||||
return type as NounType
|
||||
}
|
||||
|
||||
/**
|
||||
* Migrate a verb type (Stage 3)
|
||||
* Returns the migrated type or the original if no migration needed
|
||||
*
|
||||
* WARNING: Some verbs require inverting source/target relationships!
|
||||
* See VERB_REQUIRES_INVERSION for details.
|
||||
*/
|
||||
export function migrateVerbType(type: string): VerbType {
|
||||
if (isRemovedVerbType(type)) {
|
||||
console.warn(`⚠️ VerbType "${type}" was removed in v6.0. Migrating to "${REMOVED_VERB_TYPES[type]}" (may require source/target inversion)`)
|
||||
return REMOVED_VERB_TYPES[type]
|
||||
}
|
||||
return type as VerbType
|
||||
}
|
||||
|
||||
/**
|
||||
* Verbs that require inverting source/target when migrating
|
||||
*
|
||||
* Example:
|
||||
* - Old: `A createdBy B` → New: `B creates A`
|
||||
* - Old: `A belongsTo B` → New: `B owns A`
|
||||
* - Old: `A supervises B` → New: `B reportsTo A`
|
||||
* - Old: `A succeeds B` → New: `B precedes A`
|
||||
*/
|
||||
export const VERB_REQUIRES_INVERSION = new Set([
|
||||
'succeeds',
|
||||
'belongsTo',
|
||||
'createdBy',
|
||||
'supervises'
|
||||
])
|
||||
|
||||
/**
|
||||
* Check if a verb type requires inverting source/target during migration
|
||||
*/
|
||||
export function requiresInversion(oldVerbType: string): boolean {
|
||||
return VERB_REQUIRES_INVERSION.has(oldVerbType)
|
||||
}
|
||||
|
||||
/**
|
||||
* Migrate a relationship, handling source/target inversion if needed
|
||||
*
|
||||
* @returns Object with migrated verb and potentially inverted source/target
|
||||
*/
|
||||
export function migrateRelationship(params: {
|
||||
verb: string
|
||||
source: string
|
||||
target: string
|
||||
}): {
|
||||
verb: VerbType
|
||||
source: string
|
||||
target: string
|
||||
inverted: boolean
|
||||
} {
|
||||
const verb = migrateVerbType(params.verb)
|
||||
const inverted = requiresInversion(params.verb)
|
||||
|
||||
if (inverted) {
|
||||
return {
|
||||
verb,
|
||||
source: params.target, // Swap source and target
|
||||
target: params.source,
|
||||
inverted: true
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
verb,
|
||||
source: params.source,
|
||||
target: params.target,
|
||||
inverted: false
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Stage 3 Type Compatibility Check
|
||||
* Helps developers identify code that needs updating
|
||||
*/
|
||||
export function checkTypeCompatibility(nounTypes: string[], verbTypes: string[]): {
|
||||
valid: boolean
|
||||
removedNouns: string[]
|
||||
removedVerbs: string[]
|
||||
warnings: string[]
|
||||
} {
|
||||
const removedNouns = nounTypes.filter(isRemovedNounType)
|
||||
const removedVerbs = verbTypes.filter(isRemovedVerbType)
|
||||
const warnings: string[] = []
|
||||
|
||||
if (removedNouns.length > 0) {
|
||||
warnings.push(
|
||||
`Found ${removedNouns.length} removed noun type(s): ${removedNouns.join(', ')}. ` +
|
||||
`These were merged in Stage 3. Use Person instead of User, Concept instead of Topic.`
|
||||
)
|
||||
}
|
||||
|
||||
if (removedVerbs.length > 0) {
|
||||
warnings.push(
|
||||
`Found ${removedVerbs.length} removed verb type(s): ${removedVerbs.join(', ')}. ` +
|
||||
`These require using inverse relationships in Stage 3. ` +
|
||||
`Example: "A createdBy B" becomes "B creates A".`
|
||||
)
|
||||
}
|
||||
|
||||
return {
|
||||
valid: removedNouns.length === 0 && removedVerbs.length === 0,
|
||||
removedNouns,
|
||||
removedVerbs,
|
||||
warnings
|
||||
}
|
||||
}
|
||||
|
|
@ -1,39 +0,0 @@
|
|||
/**
|
||||
* @module unified
|
||||
* @description Unified entry point for Brainy. Re-exports the public surface
|
||||
* from `index.js` and surfaces a tiny runtime-environment object the rest of
|
||||
* the library can read.
|
||||
*
|
||||
* 8.0 supports Node-like runtimes only (Node.js, Bun, Deno); the browser
|
||||
* detection helpers and Worker Thread plumbing are gone.
|
||||
*/
|
||||
|
||||
import './setup.js'
|
||||
|
||||
import { isNode } from './utils/environment.js'
|
||||
|
||||
export const environment = {
|
||||
get isNode() {
|
||||
return isNode()
|
||||
}
|
||||
}
|
||||
|
||||
declare global {
|
||||
/**
|
||||
* Runtime-environment marker published by the unified entry point so
|
||||
* embedders and diagnostics can detect the active Brainy environment.
|
||||
* Ambient `var` is required — `let`/`const` in `declare global` do not
|
||||
* attach to `globalThis`.
|
||||
*/
|
||||
var __ENV__: typeof environment | undefined
|
||||
}
|
||||
|
||||
if (typeof globalThis !== 'undefined') {
|
||||
globalThis.__ENV__ = environment
|
||||
}
|
||||
|
||||
console.log(
|
||||
`Brainy running on ${environment.isNode ? 'Node-like runtime' : 'unknown runtime'}`
|
||||
)
|
||||
|
||||
export * from './index.js'
|
||||
|
|
@ -1,159 +0,0 @@
|
|||
/**
|
||||
* Universal Crypto implementation
|
||||
* Framework-friendly: Trusts that frameworks provide crypto polyfills
|
||||
* Works in all environments: Browser (via framework), Node.js, Serverless
|
||||
*/
|
||||
|
||||
import { isNode } from '../utils/environment.js'
|
||||
|
||||
let nodeCrypto: any = null
|
||||
|
||||
// Dynamic import for Node.js crypto (only in Node.js environment)
|
||||
if (isNode()) {
|
||||
try {
|
||||
// Use node: protocol to prevent bundler polyfilling (requires Node 22+)
|
||||
nodeCrypto = await import('node:crypto')
|
||||
} catch {
|
||||
// Ignore import errors in non-Node environments
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate random bytes
|
||||
* Framework-friendly: Assumes crypto API is available via framework polyfills
|
||||
*/
|
||||
export function randomBytes(size: number): Uint8Array {
|
||||
if (typeof crypto !== 'undefined') {
|
||||
// Use Web Crypto API (available in browsers via framework polyfills and modern Node.js)
|
||||
const array = new Uint8Array(size)
|
||||
crypto.getRandomValues(array)
|
||||
return array
|
||||
} else if (nodeCrypto) {
|
||||
// Use Node.js crypto
|
||||
return new Uint8Array(nodeCrypto.randomBytes(size))
|
||||
} else {
|
||||
throw new Error('Crypto API not available. Framework bundlers should provide crypto polyfills.')
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate random UUID
|
||||
* Framework-friendly: Assumes crypto.randomUUID is available via framework polyfills
|
||||
*/
|
||||
export function randomUUID(): string {
|
||||
if (typeof crypto !== 'undefined' && crypto.randomUUID) {
|
||||
return crypto.randomUUID()
|
||||
} else if (nodeCrypto && nodeCrypto.randomUUID) {
|
||||
return nodeCrypto.randomUUID()
|
||||
} else {
|
||||
throw new Error('crypto.randomUUID not available. Framework bundlers should provide crypto polyfills.')
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create hash (simplified interface)
|
||||
* Framework-friendly: Relies on Node.js crypto or framework-provided implementations
|
||||
*/
|
||||
export function createHash(algorithm: string): {
|
||||
update: (data: string | Uint8Array) => any
|
||||
digest: (encoding: string) => string
|
||||
} {
|
||||
if (nodeCrypto && nodeCrypto.createHash) {
|
||||
return nodeCrypto.createHash(algorithm)
|
||||
} else {
|
||||
throw new Error(`createHash not available. For browser environments, frameworks should provide crypto polyfills or use Web Crypto API directly.`)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create HMAC
|
||||
* Framework-friendly: Relies on Node.js crypto or framework-provided implementations
|
||||
*/
|
||||
export function createHmac(algorithm: string, key: string | Uint8Array): {
|
||||
update: (data: string | Uint8Array) => any
|
||||
digest: (encoding: string) => string
|
||||
} {
|
||||
if (nodeCrypto && nodeCrypto.createHmac) {
|
||||
return nodeCrypto.createHmac(algorithm, key)
|
||||
} else {
|
||||
throw new Error(`createHmac not available. For browser environments, frameworks should provide crypto polyfills or use Web Crypto API directly.`)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* PBKDF2 synchronous
|
||||
* Framework-friendly: Relies on Node.js crypto or framework-provided implementations
|
||||
*/
|
||||
export function pbkdf2Sync(password: string | Uint8Array, salt: string | Uint8Array, iterations: number, keylen: number, digest: string): Uint8Array {
|
||||
if (nodeCrypto && nodeCrypto.pbkdf2Sync) {
|
||||
return new Uint8Array(nodeCrypto.pbkdf2Sync(password, salt, iterations, keylen, digest))
|
||||
} else {
|
||||
throw new Error(`pbkdf2Sync not available. For browser environments, frameworks should provide crypto polyfills or use Web Crypto API directly.`)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Scrypt synchronous
|
||||
* Framework-friendly: Relies on Node.js crypto or framework-provided implementations
|
||||
*/
|
||||
export function scryptSync(password: string | Uint8Array, salt: string | Uint8Array, keylen: number, options?: any): Uint8Array {
|
||||
if (nodeCrypto && nodeCrypto.scryptSync) {
|
||||
return new Uint8Array(nodeCrypto.scryptSync(password, salt, keylen, options))
|
||||
} else {
|
||||
throw new Error(`scryptSync not available. For browser environments, frameworks should provide crypto polyfills or use Web Crypto API directly.`)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create cipher
|
||||
* Framework-friendly: Relies on Node.js crypto or framework-provided implementations
|
||||
*/
|
||||
export function createCipheriv(algorithm: string, key: Uint8Array, iv: Uint8Array): {
|
||||
update: (data: string, inputEncoding?: string, outputEncoding?: string) => string
|
||||
final: (outputEncoding?: string) => string
|
||||
} {
|
||||
if (nodeCrypto && nodeCrypto.createCipheriv) {
|
||||
return nodeCrypto.createCipheriv(algorithm, key, iv)
|
||||
} else {
|
||||
throw new Error(`createCipheriv not available. For browser environments, frameworks should provide crypto polyfills or use Web Crypto API directly.`)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create decipher
|
||||
* Framework-friendly: Relies on Node.js crypto or framework-provided implementations
|
||||
*/
|
||||
export function createDecipheriv(algorithm: string, key: Uint8Array, iv: Uint8Array): {
|
||||
update: (data: string, inputEncoding?: string, outputEncoding?: string) => string
|
||||
final: (outputEncoding?: string) => string
|
||||
} {
|
||||
if (nodeCrypto && nodeCrypto.createDecipheriv) {
|
||||
return nodeCrypto.createDecipheriv(algorithm, key, iv)
|
||||
} else {
|
||||
throw new Error(`createDecipheriv not available. For browser environments, frameworks should provide crypto polyfills or use Web Crypto API directly.`)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Timing safe equal
|
||||
* Framework-friendly: Relies on Node.js crypto or framework-provided implementations
|
||||
*/
|
||||
export function timingSafeEqual(a: Uint8Array, b: Uint8Array): boolean {
|
||||
if (nodeCrypto && nodeCrypto.timingSafeEqual) {
|
||||
return nodeCrypto.timingSafeEqual(a, b)
|
||||
} else {
|
||||
throw new Error(`timingSafeEqual not available. For browser environments, frameworks should provide crypto polyfills or use Web Crypto API directly.`)
|
||||
}
|
||||
}
|
||||
|
||||
export default {
|
||||
randomBytes,
|
||||
randomUUID,
|
||||
createHash,
|
||||
createHmac,
|
||||
pbkdf2Sync,
|
||||
scryptSync,
|
||||
createCipheriv,
|
||||
createDecipheriv,
|
||||
timingSafeEqual
|
||||
}
|
||||
|
|
@ -1,62 +0,0 @@
|
|||
/**
|
||||
* Bounded Registry with LRU eviction
|
||||
* Prevents unbounded memory growth in production
|
||||
*/
|
||||
export class BoundedRegistry<T> {
|
||||
private items = new Map<T, number>() // item -> last accessed timestamp
|
||||
private readonly maxSize: number
|
||||
|
||||
constructor(maxSize: number = 10000) {
|
||||
this.maxSize = maxSize
|
||||
}
|
||||
|
||||
/**
|
||||
* Add item to registry, evicting oldest if at capacity
|
||||
*/
|
||||
add(item: T): void {
|
||||
// Update timestamp if already exists
|
||||
if (this.items.has(item)) {
|
||||
this.items.delete(item) // Remove to re-add at end
|
||||
this.items.set(item, Date.now())
|
||||
return
|
||||
}
|
||||
|
||||
// Evict oldest if at capacity
|
||||
if (this.items.size >= this.maxSize) {
|
||||
const oldest = this.items.entries().next().value
|
||||
if (oldest) {
|
||||
this.items.delete(oldest[0])
|
||||
}
|
||||
}
|
||||
|
||||
this.items.set(item, Date.now())
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if item exists
|
||||
*/
|
||||
has(item: T): boolean {
|
||||
return this.items.has(item)
|
||||
}
|
||||
|
||||
/**
|
||||
* Get all items
|
||||
*/
|
||||
getAll(): T[] {
|
||||
return Array.from(this.items.keys())
|
||||
}
|
||||
|
||||
/**
|
||||
* Get size
|
||||
*/
|
||||
get size(): number {
|
||||
return this.items.size
|
||||
}
|
||||
|
||||
/**
|
||||
* Clear all items
|
||||
*/
|
||||
clear(): void {
|
||||
this.items.clear()
|
||||
}
|
||||
}
|
||||
|
|
@ -1,47 +0,0 @@
|
|||
/**
|
||||
* Cross-platform crypto utilities
|
||||
* Provides hashing functions that work in both Node.js and browser environments
|
||||
*/
|
||||
|
||||
/**
|
||||
* Simple string hash function that works in all environments
|
||||
* Uses djb2 algorithm - fast and good distribution
|
||||
* @param str - String to hash
|
||||
* @returns Positive integer hash
|
||||
*/
|
||||
export function hashString(str: string): number {
|
||||
let hash = 5381
|
||||
for (let i = 0; i < str.length; i++) {
|
||||
const char = str.charCodeAt(i)
|
||||
hash = ((hash << 5) + hash) + char // hash * 33 + char
|
||||
}
|
||||
// Ensure positive number
|
||||
return Math.abs(hash)
|
||||
}
|
||||
|
||||
/**
|
||||
* Alternative: FNV-1a hash algorithm
|
||||
* Good distribution and fast
|
||||
* @param str - String to hash
|
||||
* @returns Positive integer hash
|
||||
*/
|
||||
export function fnv1aHash(str: string): number {
|
||||
let hash = 2166136261
|
||||
for (let i = 0; i < str.length; i++) {
|
||||
hash ^= str.charCodeAt(i)
|
||||
hash = (hash * 16777619) >>> 0
|
||||
}
|
||||
return hash
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate a deterministic hash for partitioning
|
||||
* Uses the most appropriate algorithm for the environment
|
||||
* @param input - Input string to hash
|
||||
* @returns Positive integer hash suitable for modulo operations
|
||||
*/
|
||||
export function getPartitionHash(input: string): number {
|
||||
// Use djb2 by default as it's fast and has good distribution
|
||||
// This ensures consistent partitioning across all environments
|
||||
return hashString(input)
|
||||
}
|
||||
|
|
@ -1,106 +0,0 @@
|
|||
/**
|
||||
* Dedicated index for tracking soft-deleted items
|
||||
* This is MUCH more efficient than checking every item in the database
|
||||
*
|
||||
* Performance characteristics:
|
||||
* - Add deleted item: O(1)
|
||||
* - Remove deleted item: O(1)
|
||||
* - Check if deleted: O(1)
|
||||
* - Get all deleted: O(d) where d = number of deleted items << total items
|
||||
*/
|
||||
|
||||
export class DeletedItemsIndex {
|
||||
private deletedIds: Set<string> = new Set()
|
||||
private deletedCount: number = 0
|
||||
|
||||
/**
|
||||
* Mark an item as deleted
|
||||
*/
|
||||
markDeleted(id: string): void {
|
||||
if (!this.deletedIds.has(id)) {
|
||||
this.deletedIds.add(id)
|
||||
this.deletedCount++
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Mark an item as not deleted (restored)
|
||||
*/
|
||||
markRestored(id: string): void {
|
||||
if (this.deletedIds.delete(id)) {
|
||||
this.deletedCount--
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if an item is deleted - O(1)
|
||||
*/
|
||||
isDeleted(id: string): boolean {
|
||||
return this.deletedIds.has(id)
|
||||
}
|
||||
|
||||
/**
|
||||
* Get all deleted item IDs - O(d)
|
||||
*/
|
||||
getAllDeleted(): string[] {
|
||||
return Array.from(this.deletedIds)
|
||||
}
|
||||
|
||||
/**
|
||||
* Filter out deleted items from results - O(k) where k = result count
|
||||
*/
|
||||
filterDeleted<T extends { id?: string }>(items: T[]): T[] {
|
||||
if (this.deletedCount === 0) {
|
||||
// Fast path - no deleted items
|
||||
return items
|
||||
}
|
||||
|
||||
return items.filter(item => {
|
||||
const id = item.id
|
||||
return id ? !this.deletedIds.has(id) : true
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
* Get statistics
|
||||
*/
|
||||
getStats() {
|
||||
return {
|
||||
deletedCount: this.deletedCount,
|
||||
memoryUsage: this.deletedCount * 100 // Rough estimate: 100 bytes per ID
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Clear all deleted items (for testing)
|
||||
*/
|
||||
clear(): void {
|
||||
this.deletedIds.clear()
|
||||
this.deletedCount = 0
|
||||
}
|
||||
|
||||
/**
|
||||
* Serialize for persistence
|
||||
*/
|
||||
serialize(): string {
|
||||
return JSON.stringify(Array.from(this.deletedIds))
|
||||
}
|
||||
|
||||
/**
|
||||
* Deserialize from persistence
|
||||
*/
|
||||
deserialize(data: string): void {
|
||||
try {
|
||||
const ids = JSON.parse(data)
|
||||
this.deletedIds = new Set(ids)
|
||||
this.deletedCount = this.deletedIds.size
|
||||
} catch (e) {
|
||||
console.warn('Failed to deserialize deleted items index')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Global singleton for deleted items tracking
|
||||
*/
|
||||
export const deletedItemsIndex = new DeletedItemsIndex()
|
||||
|
|
@ -1,88 +0,0 @@
|
|||
/**
|
||||
* Utility to ensure all metadata has the deleted field set properly
|
||||
* This is CRITICAL for O(1) soft delete filtering performance
|
||||
*
|
||||
* Uses _brainy namespace to avoid conflicts with user metadata
|
||||
*/
|
||||
|
||||
const BRAINY_NAMESPACE = '_brainy'
|
||||
|
||||
/**
|
||||
* Ensure metadata has internal Brainy fields set
|
||||
* @param metadata The metadata object (could be null/undefined)
|
||||
* @param preserveExisting If true, preserve existing deleted value
|
||||
* @returns Metadata with internal fields guaranteed
|
||||
*/
|
||||
export function ensureDeletedField(metadata: any, preserveExisting: boolean = true): any {
|
||||
// Handle null/undefined metadata
|
||||
if (!metadata) {
|
||||
return {
|
||||
[BRAINY_NAMESPACE]: {
|
||||
deleted: false,
|
||||
version: 1
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Clone to avoid mutation
|
||||
const result = { ...metadata }
|
||||
|
||||
// Ensure _brainy namespace exists
|
||||
if (!result[BRAINY_NAMESPACE]) {
|
||||
result[BRAINY_NAMESPACE] = {}
|
||||
}
|
||||
|
||||
// Set deleted field if not present
|
||||
if (!('deleted' in result[BRAINY_NAMESPACE])) {
|
||||
result[BRAINY_NAMESPACE].deleted = false
|
||||
} else if (!preserveExisting) {
|
||||
// Force to false if not preserving
|
||||
result[BRAINY_NAMESPACE].deleted = false
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Mark an item as soft deleted
|
||||
* @param metadata The metadata object
|
||||
* @returns Metadata with _brainy.deleted=true
|
||||
*/
|
||||
export function markAsDeleted(metadata: any): any {
|
||||
const result = ensureDeletedField(metadata)
|
||||
result[BRAINY_NAMESPACE].deleted = true
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Mark an item as restored (not deleted)
|
||||
* @param metadata The metadata object
|
||||
* @returns Metadata with _brainy.deleted=false
|
||||
*/
|
||||
export function markAsRestored(metadata: any): any {
|
||||
const result = ensureDeletedField(metadata)
|
||||
result[BRAINY_NAMESPACE].deleted = false
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if an item is deleted
|
||||
* @param metadata The metadata object
|
||||
* @returns true if deleted, false otherwise (including if field missing)
|
||||
*/
|
||||
export function isDeleted(metadata: any): boolean {
|
||||
return metadata?.[BRAINY_NAMESPACE]?.deleted === true
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if an item is active (not deleted)
|
||||
* @param metadata The metadata object
|
||||
* @returns true if not deleted (default), false if deleted
|
||||
*/
|
||||
export function isActive(metadata: any): boolean {
|
||||
// If no deleted field or deleted=false, item is active
|
||||
return !isDeleted(metadata)
|
||||
}
|
||||
|
||||
// Export the namespace constant for use in queries
|
||||
export const BRAINY_DELETED_FIELD = `${BRAINY_NAMESPACE}.deleted`
|
||||
|
|
@ -1,255 +0,0 @@
|
|||
/**
|
||||
* Clean Metadata Architecture for Brainy 2.2
|
||||
* No backward compatibility - doing it RIGHT from the start!
|
||||
*/
|
||||
|
||||
// Namespace constants
|
||||
export const BRAINY_NS = '_brainy' as const
|
||||
export const AUG_NS = '_augmentations' as const
|
||||
export const AUDIT_NS = '_audit' as const
|
||||
|
||||
// Field paths for O(1) indexing
|
||||
export const DELETED_FIELD = `${BRAINY_NS}.deleted` as const
|
||||
export const INDEXED_FIELD = `${BRAINY_NS}.indexed` as const
|
||||
export const VERSION_FIELD = `${BRAINY_NS}.version` as const
|
||||
|
||||
/**
|
||||
* Internal Brainy metadata structure
|
||||
* These fields are ALWAYS present and indexed for O(1) access
|
||||
*/
|
||||
export interface BrainyInternalMetadata {
|
||||
deleted: boolean // ALWAYS boolean, enables O(1) soft delete
|
||||
indexed: boolean // Whether in search index
|
||||
version: number // Schema version
|
||||
created: number // Unix timestamp
|
||||
updated: number // Unix timestamp
|
||||
|
||||
// Optional internal fields
|
||||
domain?: string // Domain classification
|
||||
priority?: number // Query priority hint
|
||||
ttl?: number // Time to live
|
||||
}
|
||||
|
||||
/**
|
||||
* Complete metadata structure with namespaces
|
||||
*/
|
||||
export interface NamespacedMetadata<T = any> {
|
||||
// User metadata - any fields they want
|
||||
[key: string]: any
|
||||
|
||||
// Internal metadata - our fields
|
||||
[BRAINY_NS]: BrainyInternalMetadata
|
||||
|
||||
// Augmentation metadata - isolated per augmentation
|
||||
[AUG_NS]?: {
|
||||
[augmentationName: string]: any
|
||||
}
|
||||
|
||||
// Audit trail - optional
|
||||
[AUDIT_NS]?: Array<{
|
||||
timestamp: number
|
||||
augmentation: string
|
||||
field: string
|
||||
oldValue: any
|
||||
newValue: any
|
||||
}>
|
||||
}
|
||||
|
||||
/**
|
||||
* Create properly namespaced metadata
|
||||
* This is called for EVERY noun/verb creation
|
||||
*/
|
||||
export function createNamespacedMetadata<T = any>(
|
||||
userMetadata?: T
|
||||
): NamespacedMetadata<T> {
|
||||
const now = Date.now()
|
||||
|
||||
// Start with user metadata or empty object
|
||||
const result: any = userMetadata ? { ...userMetadata } : {}
|
||||
|
||||
// ALWAYS add internal namespace with required fields
|
||||
result[BRAINY_NS] = {
|
||||
deleted: false, // CRITICAL: Always false for new items
|
||||
indexed: true, // New items are indexed
|
||||
version: 1, // Current schema version
|
||||
created: now,
|
||||
updated: now
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Update metadata while preserving namespaces
|
||||
*/
|
||||
export function updateNamespacedMetadata<T = any>(
|
||||
existing: NamespacedMetadata<T>,
|
||||
updates: Partial<T>
|
||||
): NamespacedMetadata<T> {
|
||||
const now = Date.now()
|
||||
|
||||
// Merge user fields
|
||||
const result: any = {
|
||||
...existing,
|
||||
...updates
|
||||
}
|
||||
|
||||
// Preserve internal namespace but update timestamp
|
||||
result[BRAINY_NS] = {
|
||||
...existing[BRAINY_NS],
|
||||
updated: now
|
||||
}
|
||||
|
||||
// Preserve augmentation namespace
|
||||
if (existing[AUG_NS]) {
|
||||
result[AUG_NS] = existing[AUG_NS]
|
||||
}
|
||||
|
||||
// Preserve audit trail
|
||||
if (existing[AUDIT_NS]) {
|
||||
result[AUDIT_NS] = existing[AUDIT_NS]
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Soft delete a noun (O(1) operation)
|
||||
*/
|
||||
export function markDeleted<T = any>(
|
||||
metadata: NamespacedMetadata<T>
|
||||
): NamespacedMetadata<T> {
|
||||
return {
|
||||
...metadata,
|
||||
[BRAINY_NS]: {
|
||||
...metadata[BRAINY_NS],
|
||||
deleted: true,
|
||||
updated: Date.now()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Restore a soft-deleted noun (O(1) operation)
|
||||
*/
|
||||
export function markRestored<T = any>(
|
||||
metadata: NamespacedMetadata<T>
|
||||
): NamespacedMetadata<T> {
|
||||
return {
|
||||
...metadata,
|
||||
[BRAINY_NS]: {
|
||||
...metadata[BRAINY_NS],
|
||||
deleted: false,
|
||||
updated: Date.now()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if a noun is deleted (O(1) check)
|
||||
*/
|
||||
export function isDeleted<T = any>(
|
||||
metadata: NamespacedMetadata<T>
|
||||
): boolean {
|
||||
return metadata[BRAINY_NS]?.deleted === true
|
||||
}
|
||||
|
||||
/**
|
||||
* Get user metadata without internal fields
|
||||
* Used by augmentations to get clean user data
|
||||
*/
|
||||
export function getUserMetadata<T = any>(
|
||||
metadata: NamespacedMetadata<T>
|
||||
): T {
|
||||
const { [BRAINY_NS]: _, [AUG_NS]: __, [AUDIT_NS]: ___, ...userMeta } = metadata
|
||||
return userMeta as T
|
||||
}
|
||||
|
||||
/**
|
||||
* Set augmentation data in isolated namespace
|
||||
*/
|
||||
export function setAugmentationData<T = any>(
|
||||
metadata: NamespacedMetadata<T>,
|
||||
augmentationName: string,
|
||||
data: any
|
||||
): NamespacedMetadata<T> {
|
||||
const result = { ...metadata }
|
||||
|
||||
if (!result[AUG_NS]) {
|
||||
result[AUG_NS] = {}
|
||||
}
|
||||
|
||||
result[AUG_NS][augmentationName] = data
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Add audit entry for tracking
|
||||
*/
|
||||
export function addAuditEntry<T = any>(
|
||||
metadata: NamespacedMetadata<T>,
|
||||
entry: {
|
||||
augmentation: string
|
||||
field: string
|
||||
oldValue: any
|
||||
newValue: any
|
||||
}
|
||||
): NamespacedMetadata<T> {
|
||||
const result = { ...metadata }
|
||||
|
||||
if (!result[AUDIT_NS]) {
|
||||
result[AUDIT_NS] = []
|
||||
}
|
||||
|
||||
result[AUDIT_NS].push({
|
||||
...entry,
|
||||
timestamp: Date.now()
|
||||
})
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* INDEXING EXPLANATION:
|
||||
*
|
||||
* The MetadataIndex flattens nested objects into dot-notation keys:
|
||||
*
|
||||
* Input metadata:
|
||||
* {
|
||||
* name: "Django",
|
||||
* _brainy: {
|
||||
* deleted: false,
|
||||
* indexed: true
|
||||
* }
|
||||
* }
|
||||
*
|
||||
* Creates index entries:
|
||||
* - "name" -> "django" -> Set([id1, id2...])
|
||||
* - "_brainy.deleted" -> "false" -> Set([id1, id2...]) // O(1) lookup!
|
||||
* - "_brainy.indexed" -> "true" -> Set([id1, id2...])
|
||||
*
|
||||
* Query: { "_brainy.deleted": false }
|
||||
* Lookup: index["_brainy.deleted"]["false"] -> Set of IDs in O(1)
|
||||
*
|
||||
* This is why namespacing doesn't hurt performance - it's all flattened!
|
||||
*/
|
||||
|
||||
/**
|
||||
* Fields that should ALWAYS be indexed for O(1) access
|
||||
*/
|
||||
export const ALWAYS_INDEXED_FIELDS = [
|
||||
DELETED_FIELD, // For soft delete filtering
|
||||
INDEXED_FIELD, // For index management
|
||||
VERSION_FIELD // For schema versioning
|
||||
]
|
||||
|
||||
/**
|
||||
* Fields that should use sorted index for O(log n) range queries
|
||||
*/
|
||||
export const SORTED_INDEX_FIELDS = [
|
||||
`${BRAINY_NS}.created`,
|
||||
`${BRAINY_NS}.updated`,
|
||||
`${BRAINY_NS}.priority`,
|
||||
`${BRAINY_NS}.ttl`
|
||||
]
|
||||
|
|
@ -1,545 +0,0 @@
|
|||
/**
|
||||
* Enhanced Structured Logging System for Brainy
|
||||
* Provides production-ready logging with structured output, context preservation,
|
||||
* performance tracking, and multiple transport support
|
||||
*/
|
||||
|
||||
import { performance } from 'perf_hooks'
|
||||
import { randomUUID } from '../universal/crypto.js'
|
||||
|
||||
export enum LogLevel {
|
||||
SILENT = -1,
|
||||
FATAL = 0,
|
||||
ERROR = 1,
|
||||
WARN = 2,
|
||||
INFO = 3,
|
||||
DEBUG = 4,
|
||||
TRACE = 5
|
||||
}
|
||||
|
||||
export interface LogContext {
|
||||
requestId?: string
|
||||
userId?: string
|
||||
operation?: string
|
||||
entityId?: string
|
||||
entityType?: string
|
||||
[key: string]: any
|
||||
}
|
||||
|
||||
export interface LogEntry {
|
||||
timestamp: string
|
||||
level: string
|
||||
levelNumeric: number
|
||||
module: string
|
||||
message: string
|
||||
context?: LogContext
|
||||
data?: any
|
||||
error?: {
|
||||
name: string
|
||||
message: string
|
||||
stack?: string
|
||||
code?: string
|
||||
}
|
||||
performance?: {
|
||||
duration?: number
|
||||
memory?: {
|
||||
used: number
|
||||
total: number
|
||||
}
|
||||
}
|
||||
host?: string
|
||||
pid: number
|
||||
version?: string
|
||||
}
|
||||
|
||||
export interface LogTransport {
|
||||
name: string
|
||||
log(entry: LogEntry): void | Promise<void>
|
||||
flush?(): Promise<void>
|
||||
}
|
||||
|
||||
export interface StructuredLoggerConfig {
|
||||
level: LogLevel
|
||||
modules?: Record<string, LogLevel>
|
||||
format: 'json' | 'pretty' | 'simple'
|
||||
transports: LogTransport[]
|
||||
context?: LogContext
|
||||
includeHost?: boolean
|
||||
includeMemory?: boolean
|
||||
bufferSize?: number
|
||||
flushInterval?: number
|
||||
version?: string
|
||||
}
|
||||
|
||||
class ConsoleTransport implements LogTransport {
|
||||
name = 'console'
|
||||
private format: 'json' | 'pretty' | 'simple'
|
||||
|
||||
constructor(format: 'json' | 'pretty' | 'simple' = 'json') {
|
||||
this.format = format
|
||||
}
|
||||
|
||||
log(entry: LogEntry): void {
|
||||
const method = this.getConsoleMethod(entry.levelNumeric)
|
||||
|
||||
if (this.format === 'json') {
|
||||
method(JSON.stringify(entry))
|
||||
} else if (this.format === 'pretty') {
|
||||
const color = this.getColor(entry.levelNumeric)
|
||||
const prefix = `${entry.timestamp} ${color}[${entry.level}]\\x1b[0m [${entry.module}]`
|
||||
const message = entry.message
|
||||
|
||||
if (entry.error) {
|
||||
method(`${prefix} ${message}`, entry.error)
|
||||
} else if (entry.data) {
|
||||
method(`${prefix} ${message}`, entry.data)
|
||||
} else {
|
||||
method(`${prefix} ${message}`)
|
||||
}
|
||||
} else {
|
||||
// Simple format
|
||||
method(`[${entry.level}] ${entry.message}`)
|
||||
}
|
||||
}
|
||||
|
||||
private getConsoleMethod(level: number): (...args: any[]) => void {
|
||||
switch (level) {
|
||||
case LogLevel.FATAL:
|
||||
case LogLevel.ERROR:
|
||||
return console.error
|
||||
case LogLevel.WARN:
|
||||
return console.warn
|
||||
case LogLevel.INFO:
|
||||
return console.info
|
||||
default:
|
||||
return console.log
|
||||
}
|
||||
}
|
||||
|
||||
private getColor(level: number): string {
|
||||
switch (level) {
|
||||
case LogLevel.FATAL:
|
||||
return '\\x1b[35m' // Magenta
|
||||
case LogLevel.ERROR:
|
||||
return '\\x1b[31m' // Red
|
||||
case LogLevel.WARN:
|
||||
return '\\x1b[33m' // Yellow
|
||||
case LogLevel.INFO:
|
||||
return '\\x1b[36m' // Cyan
|
||||
case LogLevel.DEBUG:
|
||||
return '\\x1b[32m' // Green
|
||||
case LogLevel.TRACE:
|
||||
return '\\x1b[90m' // Gray
|
||||
default:
|
||||
return '\\x1b[0m' // Reset
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
class BufferedTransport implements LogTransport {
|
||||
name = 'buffered'
|
||||
private buffer: LogEntry[] = []
|
||||
private innerTransport: LogTransport
|
||||
private bufferSize: number
|
||||
private flushTimer?: NodeJS.Timeout
|
||||
|
||||
constructor(innerTransport: LogTransport, bufferSize: number = 100, flushInterval: number = 5000) {
|
||||
this.innerTransport = innerTransport
|
||||
this.bufferSize = bufferSize
|
||||
|
||||
if (flushInterval > 0) {
|
||||
this.flushTimer = setInterval(() => this.flush(), flushInterval)
|
||||
}
|
||||
}
|
||||
|
||||
log(entry: LogEntry): void {
|
||||
this.buffer.push(entry)
|
||||
|
||||
if (this.buffer.length >= this.bufferSize) {
|
||||
this.flush()
|
||||
}
|
||||
}
|
||||
|
||||
async flush(): Promise<void> {
|
||||
const entries = this.buffer.splice(0)
|
||||
for (const entry of entries) {
|
||||
await this.innerTransport.log(entry)
|
||||
}
|
||||
|
||||
if (this.innerTransport.flush) {
|
||||
await this.innerTransport.flush()
|
||||
}
|
||||
}
|
||||
|
||||
destroy(): void {
|
||||
if (this.flushTimer) {
|
||||
clearInterval(this.flushTimer)
|
||||
}
|
||||
this.flush()
|
||||
}
|
||||
}
|
||||
|
||||
export class StructuredLogger {
|
||||
private static instance: StructuredLogger
|
||||
private config: StructuredLoggerConfig
|
||||
private defaultContext: LogContext = {}
|
||||
private performanceMarks = new Map<string, number>()
|
||||
|
||||
private constructor() {
|
||||
const isDevelopment = process.env.NODE_ENV !== 'production'
|
||||
const format = isDevelopment ? 'pretty' : 'json'
|
||||
|
||||
this.config = {
|
||||
level: isDevelopment ? LogLevel.DEBUG : LogLevel.INFO,
|
||||
format,
|
||||
transports: [new ConsoleTransport(format)],
|
||||
includeHost: !isDevelopment,
|
||||
includeMemory: false,
|
||||
bufferSize: 100,
|
||||
flushInterval: 5000,
|
||||
version: process.env.npm_package_version
|
||||
}
|
||||
|
||||
// Load from environment
|
||||
this.loadEnvironmentConfig()
|
||||
}
|
||||
|
||||
private loadEnvironmentConfig(): void {
|
||||
const envLevel = process.env.BRAINY_LOG_LEVEL
|
||||
if (envLevel) {
|
||||
const level = LogLevel[envLevel.toUpperCase() as keyof typeof LogLevel]
|
||||
if (level !== undefined) {
|
||||
this.config.level = level
|
||||
}
|
||||
}
|
||||
|
||||
const envFormat = process.env.BRAINY_LOG_FORMAT
|
||||
if (envFormat && ['json', 'pretty', 'simple'].includes(envFormat)) {
|
||||
this.config.format = envFormat as 'json' | 'pretty' | 'simple'
|
||||
}
|
||||
|
||||
const moduleConfig = process.env.BRAINY_MODULE_LOG_LEVELS
|
||||
if (moduleConfig) {
|
||||
try {
|
||||
this.config.modules = JSON.parse(moduleConfig)
|
||||
} catch {
|
||||
// Ignore parse errors
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static getInstance(): StructuredLogger {
|
||||
if (!StructuredLogger.instance) {
|
||||
StructuredLogger.instance = new StructuredLogger()
|
||||
}
|
||||
return StructuredLogger.instance
|
||||
}
|
||||
|
||||
configure(config: Partial<StructuredLoggerConfig>): void {
|
||||
this.config = { ...this.config, ...config }
|
||||
}
|
||||
|
||||
setContext(context: LogContext): void {
|
||||
this.defaultContext = { ...this.defaultContext, ...context }
|
||||
}
|
||||
|
||||
clearContext(): void {
|
||||
this.defaultContext = {}
|
||||
}
|
||||
|
||||
withContext(context: LogContext): StructuredLogger {
|
||||
const contextualLogger = Object.create(this)
|
||||
contextualLogger.defaultContext = { ...this.defaultContext, ...context }
|
||||
return contextualLogger
|
||||
}
|
||||
|
||||
startTimer(label: string): void {
|
||||
this.performanceMarks.set(label, performance.now())
|
||||
}
|
||||
|
||||
endTimer(label: string): number | undefined {
|
||||
const start = this.performanceMarks.get(label)
|
||||
if (start === undefined) return undefined
|
||||
|
||||
const duration = performance.now() - start
|
||||
this.performanceMarks.delete(label)
|
||||
return duration
|
||||
}
|
||||
|
||||
private shouldLog(level: LogLevel, module: string): boolean {
|
||||
if (this.config.modules?.[module] !== undefined) {
|
||||
return level <= this.config.modules[module]
|
||||
}
|
||||
return level <= this.config.level
|
||||
}
|
||||
|
||||
private createLogEntry(
|
||||
level: LogLevel,
|
||||
module: string,
|
||||
message: string,
|
||||
context?: LogContext,
|
||||
data?: any,
|
||||
error?: Error
|
||||
): LogEntry {
|
||||
const entry: LogEntry = {
|
||||
timestamp: new Date().toISOString(),
|
||||
level: LogLevel[level],
|
||||
levelNumeric: level,
|
||||
module,
|
||||
message,
|
||||
pid: process.pid,
|
||||
version: this.config.version
|
||||
}
|
||||
|
||||
// Merge contexts
|
||||
const mergedContext = { ...this.defaultContext, ...context }
|
||||
if (Object.keys(mergedContext).length > 0) {
|
||||
entry.context = mergedContext
|
||||
}
|
||||
|
||||
if (data !== undefined) {
|
||||
entry.data = data
|
||||
}
|
||||
|
||||
if (error) {
|
||||
entry.error = {
|
||||
name: error.name,
|
||||
message: error.message,
|
||||
stack: error.stack,
|
||||
code: (error as Error & { code?: string }).code
|
||||
}
|
||||
}
|
||||
|
||||
if (this.config.includeHost) {
|
||||
try {
|
||||
const os = require('node:os')
|
||||
entry.host = os.hostname()
|
||||
} catch {
|
||||
entry.host = 'unknown'
|
||||
}
|
||||
}
|
||||
|
||||
if (this.config.includeMemory && typeof process !== 'undefined' && process.memoryUsage) {
|
||||
const mem = process.memoryUsage()
|
||||
entry.performance = {
|
||||
memory: {
|
||||
used: Math.round(mem.heapUsed / 1024 / 1024),
|
||||
total: Math.round(mem.heapTotal / 1024 / 1024)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return entry
|
||||
}
|
||||
|
||||
private log(
|
||||
level: LogLevel,
|
||||
module: string,
|
||||
message: string,
|
||||
contextOrData?: LogContext | any,
|
||||
data?: any
|
||||
): void {
|
||||
if (!this.shouldLog(level, module)) {
|
||||
return
|
||||
}
|
||||
|
||||
// Handle overloaded parameters
|
||||
let context: LogContext | undefined
|
||||
let logData: any
|
||||
|
||||
if (contextOrData && typeof contextOrData === 'object' && !Array.isArray(contextOrData)) {
|
||||
// Check if it looks like a context object
|
||||
const hasContextKeys = ['requestId', 'userId', 'operation', 'entityId', 'entityType']
|
||||
.some(key => key in contextOrData)
|
||||
|
||||
if (hasContextKeys) {
|
||||
context = contextOrData
|
||||
logData = data
|
||||
} else {
|
||||
logData = contextOrData
|
||||
}
|
||||
} else {
|
||||
logData = contextOrData
|
||||
}
|
||||
|
||||
// Extract error if present
|
||||
let error: Error | undefined
|
||||
if (logData instanceof Error) {
|
||||
error = logData
|
||||
logData = undefined
|
||||
} else if (logData?.error instanceof Error) {
|
||||
error = logData.error
|
||||
delete logData.error
|
||||
}
|
||||
|
||||
const entry = this.createLogEntry(level, module, message, context, logData, error)
|
||||
|
||||
// Send to all transports
|
||||
for (const transport of this.config.transports) {
|
||||
try {
|
||||
transport.log(entry)
|
||||
} catch (err) {
|
||||
// Fallback to console.error if transport fails
|
||||
console.error('Logger transport error:', err)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fatal(module: string, message: string, contextOrData?: LogContext | any, data?: any): void {
|
||||
this.log(LogLevel.FATAL, module, message, contextOrData, data)
|
||||
}
|
||||
|
||||
error(module: string, message: string, contextOrData?: LogContext | any, data?: any): void {
|
||||
this.log(LogLevel.ERROR, module, message, contextOrData, data)
|
||||
}
|
||||
|
||||
warn(module: string, message: string, contextOrData?: LogContext | any, data?: any): void {
|
||||
this.log(LogLevel.WARN, module, message, contextOrData, data)
|
||||
}
|
||||
|
||||
info(module: string, message: string, contextOrData?: LogContext | any, data?: any): void {
|
||||
this.log(LogLevel.INFO, module, message, contextOrData, data)
|
||||
}
|
||||
|
||||
debug(module: string, message: string, contextOrData?: LogContext | any, data?: any): void {
|
||||
this.log(LogLevel.DEBUG, module, message, contextOrData, data)
|
||||
}
|
||||
|
||||
trace(module: string, message: string, contextOrData?: LogContext | any, data?: any): void {
|
||||
this.log(LogLevel.TRACE, module, message, contextOrData, data)
|
||||
}
|
||||
|
||||
createModuleLogger(module: string) {
|
||||
const self = this
|
||||
return {
|
||||
fatal: (message: string, contextOrData?: LogContext | any, data?: any) =>
|
||||
self.fatal(module, message, contextOrData, data),
|
||||
error: (message: string, contextOrData?: LogContext | any, data?: any) =>
|
||||
self.error(module, message, contextOrData, data),
|
||||
warn: (message: string, contextOrData?: LogContext | any, data?: any) =>
|
||||
self.warn(module, message, contextOrData, data),
|
||||
info: (message: string, contextOrData?: LogContext | any, data?: any) =>
|
||||
self.info(module, message, contextOrData, data),
|
||||
debug: (message: string, contextOrData?: LogContext | any, data?: any) =>
|
||||
self.debug(module, message, contextOrData, data),
|
||||
trace: (message: string, contextOrData?: LogContext | any, data?: any) =>
|
||||
self.trace(module, message, contextOrData, data),
|
||||
withContext: (context: LogContext) => {
|
||||
const contextual = self.withContext(context)
|
||||
return contextual.createModuleLogger(module)
|
||||
},
|
||||
startTimer: (label: string) => self.startTimer(`${module}:${label}`),
|
||||
endTimer: (label: string) => self.endTimer(`${module}:${label}`)
|
||||
}
|
||||
}
|
||||
|
||||
async flush(): Promise<void> {
|
||||
const flushPromises = this.config.transports
|
||||
.filter(t => t.flush)
|
||||
.map(t => t.flush!())
|
||||
|
||||
await Promise.all(flushPromises)
|
||||
}
|
||||
|
||||
addTransport(transport: LogTransport): void {
|
||||
this.config.transports.push(transport)
|
||||
}
|
||||
|
||||
removeTransport(name: string): void {
|
||||
this.config.transports = this.config.transports.filter(t => t.name !== name)
|
||||
}
|
||||
|
||||
child(context: LogContext): StructuredLogger {
|
||||
return this.withContext(context)
|
||||
}
|
||||
}
|
||||
|
||||
// Singleton instance
|
||||
export const structuredLogger = StructuredLogger.getInstance()
|
||||
|
||||
// Convenience functions
|
||||
export function createModuleLogger(module: string) {
|
||||
return structuredLogger.createModuleLogger(module)
|
||||
}
|
||||
|
||||
export function setLogContext(context: LogContext) {
|
||||
structuredLogger.setContext(context)
|
||||
}
|
||||
|
||||
export function withLogContext(context: LogContext) {
|
||||
return structuredLogger.withContext(context)
|
||||
}
|
||||
|
||||
// Correlation ID middleware helper
|
||||
export function createCorrelationId(): string {
|
||||
return randomUUID()
|
||||
}
|
||||
|
||||
// Performance logging helper
|
||||
export function logPerformance(
|
||||
logger: ReturnType<typeof createModuleLogger>,
|
||||
operation: string,
|
||||
fn: () => any
|
||||
): any {
|
||||
logger.startTimer(operation)
|
||||
try {
|
||||
const result = fn()
|
||||
if (result && typeof result.then === 'function') {
|
||||
return result.finally(() => {
|
||||
const duration = logger.endTimer(operation)
|
||||
logger.debug(`${operation} completed`, { duration })
|
||||
})
|
||||
}
|
||||
const duration = logger.endTimer(operation)
|
||||
logger.debug(`${operation} completed`, { duration })
|
||||
return result
|
||||
} catch (error) {
|
||||
const duration = logger.endTimer(operation)
|
||||
logger.error(`${operation} failed`, { duration, error })
|
||||
throw error
|
||||
}
|
||||
}
|
||||
|
||||
// Backward compatibility wrapper for existing logger
|
||||
export class LoggerCompatibilityWrapper {
|
||||
private moduleLogger: ReturnType<typeof createModuleLogger>
|
||||
|
||||
constructor(module: string = 'legacy') {
|
||||
this.moduleLogger = createModuleLogger(module)
|
||||
}
|
||||
|
||||
error(module: string, message: string, ...args: any[]): void {
|
||||
this.moduleLogger.error(message, { module, data: args })
|
||||
}
|
||||
|
||||
warn(module: string, message: string, ...args: any[]): void {
|
||||
this.moduleLogger.warn(message, { module, data: args })
|
||||
}
|
||||
|
||||
info(module: string, message: string, ...args: any[]): void {
|
||||
this.moduleLogger.info(message, { module, data: args })
|
||||
}
|
||||
|
||||
debug(module: string, message: string, ...args: any[]): void {
|
||||
this.moduleLogger.debug(message, { module, data: args })
|
||||
}
|
||||
|
||||
trace(module: string, message: string, ...args: any[]): void {
|
||||
this.moduleLogger.trace(message, { module, data: args })
|
||||
}
|
||||
|
||||
createModuleLogger(module: string) {
|
||||
const logger = createModuleLogger(module)
|
||||
return {
|
||||
error: (message: string, ...args: any[]) => logger.error(message, { data: args }),
|
||||
warn: (message: string, ...args: any[]) => logger.warn(message, { data: args }),
|
||||
info: (message: string, ...args: any[]) => logger.info(message, { data: args }),
|
||||
debug: (message: string, ...args: any[]) => logger.debug(message, { data: args }),
|
||||
trace: (message: string, ...args: any[]) => logger.trace(message, { data: args })
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Export types for external use
|
||||
export type ModuleLogger = ReturnType<typeof createModuleLogger>
|
||||
// Types are already exported above, no need to re-export
|
||||
|
|
@ -1,411 +0,0 @@
|
|||
/**
|
||||
* Write Buffer
|
||||
* Accumulates writes and flushes them in bulk to reduce S3 operations
|
||||
* Implements intelligent deduplication and compression
|
||||
*/
|
||||
|
||||
import { HNSWNoun, HNSWVerb } from '../coreTypes.js'
|
||||
import { createModuleLogger } from './logger.js'
|
||||
import { getGlobalBackpressure } from './adaptiveBackpressure.js'
|
||||
|
||||
interface BufferedWrite<T> {
|
||||
id: string
|
||||
data: T
|
||||
timestamp: number
|
||||
type: 'noun' | 'verb' | 'metadata'
|
||||
retryCount: number
|
||||
}
|
||||
|
||||
interface FlushResult {
|
||||
successful: number
|
||||
failed: number
|
||||
duration: number
|
||||
}
|
||||
|
||||
/**
|
||||
* High-performance write buffer for bulk operations
|
||||
*/
|
||||
export class WriteBuffer<T> {
|
||||
private logger = createModuleLogger('WriteBuffer')
|
||||
|
||||
// Buffer storage
|
||||
private buffer = new Map<string, BufferedWrite<T>>()
|
||||
|
||||
// Configuration - More aggressive for high volume
|
||||
private maxBufferSize = 2000 // Allow larger buffers
|
||||
private flushInterval = 500 // Flush more frequently (0.5 seconds)
|
||||
private minFlushSize = 50 // Lower minimum to flush sooner
|
||||
private maxRetries = 3 // Maximum retry attempts
|
||||
|
||||
// State
|
||||
private flushTimer: NodeJS.Timeout | null = null
|
||||
private isFlushing = false
|
||||
private lastFlush = Date.now()
|
||||
private pendingFlush: Promise<FlushResult> | null = null
|
||||
|
||||
// Statistics
|
||||
private totalWrites = 0
|
||||
private totalFlushes = 0
|
||||
private failedWrites = 0
|
||||
private duplicatesRemoved = 0
|
||||
|
||||
// Write function
|
||||
private writeFunction: (items: Map<string, T>) => Promise<void>
|
||||
private type: 'noun' | 'verb' | 'metadata'
|
||||
|
||||
// Backpressure integration
|
||||
private backpressure = getGlobalBackpressure()
|
||||
|
||||
constructor(
|
||||
type: 'noun' | 'verb' | 'metadata',
|
||||
writeFunction: (items: Map<string, T>) => Promise<void>,
|
||||
options?: {
|
||||
maxBufferSize?: number
|
||||
flushInterval?: number
|
||||
minFlushSize?: number
|
||||
}
|
||||
) {
|
||||
this.type = type
|
||||
this.writeFunction = writeFunction
|
||||
|
||||
if (options) {
|
||||
this.maxBufferSize = options.maxBufferSize || this.maxBufferSize
|
||||
this.flushInterval = options.flushInterval || this.flushInterval
|
||||
this.minFlushSize = options.minFlushSize || this.minFlushSize
|
||||
}
|
||||
|
||||
// Start periodic flush
|
||||
this.startPeriodicFlush()
|
||||
}
|
||||
|
||||
/**
|
||||
* Add item to buffer
|
||||
*/
|
||||
public async add(id: string, data: T): Promise<void> {
|
||||
// Check if we're already at capacity
|
||||
if (this.buffer.size >= this.maxBufferSize) {
|
||||
// Wait for current flush to complete
|
||||
if (this.pendingFlush) {
|
||||
await this.pendingFlush
|
||||
}
|
||||
|
||||
// Force flush if still at capacity
|
||||
if (this.buffer.size >= this.maxBufferSize) {
|
||||
await this.flush('capacity')
|
||||
}
|
||||
}
|
||||
|
||||
// Check for duplicate and update if newer
|
||||
const existing = this.buffer.get(id)
|
||||
if (existing) {
|
||||
// Update with newer data
|
||||
existing.data = data
|
||||
existing.timestamp = Date.now()
|
||||
this.duplicatesRemoved++
|
||||
} else {
|
||||
// Add new item
|
||||
this.buffer.set(id, {
|
||||
id,
|
||||
data,
|
||||
timestamp: Date.now(),
|
||||
type: this.type,
|
||||
retryCount: 0
|
||||
})
|
||||
}
|
||||
|
||||
this.totalWrites++
|
||||
|
||||
// Log buffer growth periodically
|
||||
if (this.totalWrites % 100 === 0) {
|
||||
this.logger.info(`📈 BUFFER GROWTH: ${this.buffer.size} ${this.type} items buffered (${this.totalWrites} total writes, ${this.duplicatesRemoved} deduplicated)`)
|
||||
}
|
||||
|
||||
// Check if we should flush
|
||||
this.checkFlush()
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if we should flush
|
||||
*/
|
||||
private checkFlush(): void {
|
||||
const bufferSize = this.buffer.size
|
||||
const timeSinceFlush = Date.now() - this.lastFlush
|
||||
|
||||
// Immediate flush conditions
|
||||
if (bufferSize >= this.maxBufferSize) {
|
||||
this.flush('size')
|
||||
return
|
||||
}
|
||||
|
||||
// Time-based flush with minimum size
|
||||
if (timeSinceFlush >= this.flushInterval && bufferSize >= this.minFlushSize) {
|
||||
this.flush('time')
|
||||
return
|
||||
}
|
||||
|
||||
// Adaptive flush based on system load
|
||||
const backpressureStatus = this.backpressure.getStatus()
|
||||
if (backpressureStatus.queueLength > 1000 && bufferSize > 10) {
|
||||
// System under pressure - flush smaller batches more frequently
|
||||
this.flush('pressure')
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Flush buffer to storage
|
||||
*/
|
||||
public async flush(reason: string = 'manual'): Promise<FlushResult> {
|
||||
// Prevent concurrent flushes
|
||||
if (this.isFlushing) {
|
||||
if (this.pendingFlush) {
|
||||
return this.pendingFlush
|
||||
}
|
||||
return { successful: 0, failed: 0, duration: 0 }
|
||||
}
|
||||
|
||||
// Nothing to flush
|
||||
if (this.buffer.size === 0) {
|
||||
return { successful: 0, failed: 0, duration: 0 }
|
||||
}
|
||||
|
||||
this.isFlushing = true
|
||||
const startTime = Date.now()
|
||||
|
||||
// Create flush promise
|
||||
this.pendingFlush = this.doFlush(reason, startTime)
|
||||
|
||||
try {
|
||||
const result = await this.pendingFlush
|
||||
return result
|
||||
} finally {
|
||||
this.isFlushing = false
|
||||
this.pendingFlush = null
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Perform the actual flush
|
||||
*/
|
||||
private async doFlush(reason: string, startTime: number): Promise<FlushResult> {
|
||||
const itemsToFlush = new Map<string, T>()
|
||||
const flushingItems = new Map<string, BufferedWrite<T>>()
|
||||
|
||||
// Take items from buffer
|
||||
let count = 0
|
||||
for (const [id, item] of this.buffer.entries()) {
|
||||
itemsToFlush.set(id, item.data)
|
||||
flushingItems.set(id, item)
|
||||
count++
|
||||
|
||||
// Limit batch size for better performance
|
||||
if (count >= 500) {
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
// Remove from buffer
|
||||
for (const id of itemsToFlush.keys()) {
|
||||
this.buffer.delete(id)
|
||||
}
|
||||
|
||||
this.logger.warn(`🔄 BUFFERING: Flushing ${itemsToFlush.size} ${this.type} items (buffer had ${this.buffer.size + itemsToFlush.size}) - reason: ${reason}`)
|
||||
|
||||
try {
|
||||
// Request permission from backpressure system
|
||||
const opId = `flush-${Date.now()}`
|
||||
await this.backpressure.requestPermission(opId, 2) // Higher priority
|
||||
|
||||
try {
|
||||
// Perform bulk write
|
||||
await this.writeFunction(itemsToFlush)
|
||||
|
||||
// Success
|
||||
this.backpressure.releasePermission(opId, true)
|
||||
this.totalFlushes++
|
||||
this.lastFlush = Date.now()
|
||||
|
||||
const duration = Date.now() - startTime
|
||||
this.logger.warn(`🚀 BATCH FLUSH: ${itemsToFlush.size} ${this.type} items → 1 bulk S3 operation (${duration}ms, reason: ${reason})`)
|
||||
|
||||
return {
|
||||
successful: itemsToFlush.size,
|
||||
failed: 0,
|
||||
duration
|
||||
}
|
||||
} catch (error) {
|
||||
// Release with error
|
||||
this.backpressure.releasePermission(opId, false)
|
||||
throw error
|
||||
}
|
||||
} catch (error) {
|
||||
this.logger.error(`Flush failed: ${error}`)
|
||||
|
||||
// Put items back with retry count
|
||||
for (const [id, item] of flushingItems.entries()) {
|
||||
item.retryCount++
|
||||
|
||||
if (item.retryCount < this.maxRetries) {
|
||||
// Put back for retry
|
||||
this.buffer.set(id, item)
|
||||
} else {
|
||||
// Max retries exceeded
|
||||
this.failedWrites++
|
||||
this.logger.error(`Max retries exceeded for ${this.type} ${id}`)
|
||||
}
|
||||
}
|
||||
|
||||
const duration = Date.now() - startTime
|
||||
|
||||
return {
|
||||
successful: 0,
|
||||
failed: itemsToFlush.size,
|
||||
duration
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Start periodic flush timer
|
||||
*/
|
||||
private startPeriodicFlush(): void {
|
||||
if (this.flushTimer) {
|
||||
return
|
||||
}
|
||||
|
||||
this.flushTimer = setInterval(() => {
|
||||
if (this.buffer.size > 0) {
|
||||
const timeSinceFlush = Date.now() - this.lastFlush
|
||||
|
||||
// Flush if we have items and enough time has passed
|
||||
if (timeSinceFlush >= this.flushInterval) {
|
||||
this.flush('periodic').catch(error => {
|
||||
this.logger.error('Periodic flush failed:', error)
|
||||
})
|
||||
}
|
||||
}
|
||||
}, Math.min(100, this.flushInterval / 2))
|
||||
}
|
||||
|
||||
/**
|
||||
* Stop periodic flush timer
|
||||
*/
|
||||
public stop(): void {
|
||||
if (this.flushTimer) {
|
||||
clearInterval(this.flushTimer)
|
||||
this.flushTimer = null
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Force flush all pending writes
|
||||
*/
|
||||
public async forceFlush(): Promise<FlushResult> {
|
||||
// Flush everything regardless of size
|
||||
const oldMinSize = this.minFlushSize
|
||||
this.minFlushSize = 0
|
||||
|
||||
try {
|
||||
const result = await this.flush('force')
|
||||
|
||||
// Flush any remaining items
|
||||
while (this.buffer.size > 0) {
|
||||
const additionalResult = await this.flush('force-remaining')
|
||||
result.successful += additionalResult.successful
|
||||
result.failed += additionalResult.failed
|
||||
result.duration += additionalResult.duration
|
||||
}
|
||||
|
||||
return result
|
||||
} finally {
|
||||
this.minFlushSize = oldMinSize
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get buffer statistics
|
||||
*/
|
||||
public getStats(): {
|
||||
bufferSize: number
|
||||
totalWrites: number
|
||||
totalFlushes: number
|
||||
failedWrites: number
|
||||
duplicatesRemoved: number
|
||||
avgFlushSize: number
|
||||
} {
|
||||
return {
|
||||
bufferSize: this.buffer.size,
|
||||
totalWrites: this.totalWrites,
|
||||
totalFlushes: this.totalFlushes,
|
||||
failedWrites: this.failedWrites,
|
||||
duplicatesRemoved: this.duplicatesRemoved,
|
||||
avgFlushSize: this.totalFlushes > 0 ? this.totalWrites / this.totalFlushes : 0
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Adjust parameters based on load
|
||||
*/
|
||||
public adjustForLoad(pendingRequests: number): void {
|
||||
if (pendingRequests > 10000) {
|
||||
// Extreme load - buffer more aggressively
|
||||
this.maxBufferSize = 5000
|
||||
this.flushInterval = 500
|
||||
this.minFlushSize = 500
|
||||
} else if (pendingRequests > 1000) {
|
||||
// High load
|
||||
this.maxBufferSize = 2000
|
||||
this.flushInterval = 1000
|
||||
this.minFlushSize = 200
|
||||
} else if (pendingRequests > 100) {
|
||||
// Moderate load
|
||||
this.maxBufferSize = 1000
|
||||
this.flushInterval = 2000
|
||||
this.minFlushSize = 100
|
||||
} else {
|
||||
// Low load - optimize for latency
|
||||
this.maxBufferSize = 500
|
||||
this.flushInterval = 5000
|
||||
this.minFlushSize = 50
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Global write buffers
|
||||
const writeBuffers = new Map<string, WriteBuffer<any>>()
|
||||
|
||||
/**
|
||||
* Get or create a write buffer
|
||||
*/
|
||||
export function getWriteBuffer<T>(
|
||||
id: string,
|
||||
type: 'noun' | 'verb' | 'metadata',
|
||||
writeFunction: (items: Map<string, T>) => Promise<void>
|
||||
): WriteBuffer<T> {
|
||||
if (!writeBuffers.has(id)) {
|
||||
writeBuffers.set(id, new WriteBuffer<T>(type, writeFunction))
|
||||
}
|
||||
return writeBuffers.get(id)!
|
||||
}
|
||||
|
||||
/**
|
||||
* Flush all write buffers
|
||||
*/
|
||||
export async function flushAllBuffers(): Promise<void> {
|
||||
const promises: Promise<FlushResult>[] = []
|
||||
|
||||
for (const buffer of writeBuffers.values()) {
|
||||
promises.push(buffer.forceFlush())
|
||||
}
|
||||
|
||||
await Promise.all(promises)
|
||||
}
|
||||
|
||||
/**
|
||||
* Clear all write buffers
|
||||
*/
|
||||
export function clearWriteBuffers(): void {
|
||||
for (const buffer of writeBuffers.values()) {
|
||||
buffer.stop()
|
||||
}
|
||||
writeBuffers.clear()
|
||||
}
|
||||
|
|
@ -1,333 +0,0 @@
|
|||
/**
|
||||
* fs-Compatible Interface for VFS
|
||||
*
|
||||
* Provides a drop-in replacement for Node's fs module
|
||||
* that uses VFS for storage instead of the real filesystem.
|
||||
*
|
||||
* Usage:
|
||||
* import { FSCompat } from '@soulcraft/brainy/vfs'
|
||||
* const fs = new FSCompat(brain.vfs)
|
||||
*
|
||||
* // Now use like Node's fs
|
||||
* await fs.promises.readFile('/path')
|
||||
* fs.createReadStream('/path').pipe(output)
|
||||
*/
|
||||
|
||||
import { VirtualFileSystem } from './VirtualFileSystem.js'
|
||||
import type {
|
||||
ReadOptions,
|
||||
WriteOptions,
|
||||
MkdirOptions,
|
||||
VFSStats,
|
||||
VFSDirent
|
||||
} from './types.js'
|
||||
|
||||
export class FSCompat {
|
||||
/**
|
||||
* Promise-based API (like fs.promises)
|
||||
*/
|
||||
public readonly promises: FSPromises
|
||||
|
||||
constructor(private vfs: VirtualFileSystem) {
|
||||
this.promises = new FSPromises(vfs)
|
||||
}
|
||||
|
||||
// ============= Callback-style methods (for compatibility) =============
|
||||
|
||||
readFile(path: string, callback: (err: Error | null, data?: Buffer) => void): void
|
||||
readFile(path: string, encoding: BufferEncoding, callback: (err: Error | null, data?: string) => void): void
|
||||
readFile(path: string, options: any, callback?: any): void {
|
||||
if (typeof options === 'function') {
|
||||
callback = options
|
||||
options = {}
|
||||
}
|
||||
|
||||
this.vfs.readFile(path, options)
|
||||
.then(data => {
|
||||
if (options?.encoding) {
|
||||
callback(null, data.toString(options.encoding))
|
||||
} else {
|
||||
callback(null, data)
|
||||
}
|
||||
})
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
writeFile(path: string, data: Buffer | string, callback: (err: Error | null) => void): void
|
||||
writeFile(path: string, data: Buffer | string, options: any, callback?: any): void {
|
||||
if (typeof options === 'function') {
|
||||
callback = options
|
||||
options = {}
|
||||
}
|
||||
|
||||
const buffer = Buffer.isBuffer(data) ? data : Buffer.from(data, options?.encoding || 'utf8')
|
||||
|
||||
this.vfs.writeFile(path, buffer, options)
|
||||
.then(() => callback(null))
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
mkdir(path: string, callback: (err: Error | null) => void): void
|
||||
mkdir(path: string, options: any, callback?: any): void {
|
||||
if (typeof options === 'function') {
|
||||
callback = options
|
||||
options = {}
|
||||
}
|
||||
|
||||
this.vfs.mkdir(path, options)
|
||||
.then(() => callback(null))
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
rmdir(path: string, callback: (err: Error | null) => void): void
|
||||
rmdir(path: string, options: any, callback?: any): void {
|
||||
if (typeof options === 'function') {
|
||||
callback = options
|
||||
options = {}
|
||||
}
|
||||
|
||||
this.vfs.rmdir(path, options)
|
||||
.then(() => callback(null))
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
readdir(path: string, callback: (err: Error | null, files?: string[]) => void): void
|
||||
readdir(path: string, options: any, callback?: any): void {
|
||||
if (typeof options === 'function') {
|
||||
callback = options
|
||||
options = {}
|
||||
}
|
||||
|
||||
this.vfs.readdir(path, options)
|
||||
.then(files => callback(null, files))
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
stat(path: string, callback: (err: Error | null, stats?: VFSStats) => void): void {
|
||||
this.vfs.stat(path)
|
||||
.then(stats => callback(null, stats))
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
lstat(path: string, callback: (err: Error | null, stats?: VFSStats) => void): void {
|
||||
this.vfs.lstat(path)
|
||||
.then(stats => callback(null, stats))
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
unlink(path: string, callback: (err: Error | null) => void): void {
|
||||
this.vfs.unlink(path)
|
||||
.then(() => callback(null))
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
rename(oldPath: string, newPath: string, callback: (err: Error | null) => void): void {
|
||||
this.vfs.rename(oldPath, newPath)
|
||||
.then(() => callback(null))
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
copyFile(src: string, dest: string, callback: (err: Error | null) => void): void
|
||||
copyFile(src: string, dest: string, flags: number, callback: (err: Error | null) => void): void
|
||||
copyFile(src: string, dest: string, flagsOrCallback: any, callback?: any): void {
|
||||
if (typeof flagsOrCallback === 'function') {
|
||||
callback = flagsOrCallback
|
||||
} else {
|
||||
// flags provided but not used
|
||||
}
|
||||
|
||||
this.vfs.copy(src, dest)
|
||||
.then(() => callback(null))
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
exists(path: string, callback: (exists: boolean) => void): void {
|
||||
this.vfs.exists(path)
|
||||
.then(exists => callback(exists))
|
||||
.catch(() => callback(false))
|
||||
}
|
||||
|
||||
access(path: string, callback: (err: Error | null) => void): void
|
||||
access(path: string, mode: number, callback: (err: Error | null) => void): void
|
||||
access(path: string, modeOrCallback: any, callback?: any): void {
|
||||
if (typeof modeOrCallback === 'function') {
|
||||
callback = modeOrCallback
|
||||
} else {
|
||||
// mode provided but not used
|
||||
}
|
||||
|
||||
this.vfs.exists(path)
|
||||
.then(exists => {
|
||||
if (exists) {
|
||||
callback(null)
|
||||
} else {
|
||||
const err: any = new Error('ENOENT: no such file or directory')
|
||||
err.code = 'ENOENT'
|
||||
callback(err)
|
||||
}
|
||||
})
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
appendFile(path: string, data: Buffer | string, callback: (err: Error | null) => void): void
|
||||
appendFile(path: string, data: Buffer | string, options: any, callback?: any): void {
|
||||
if (typeof options === 'function') {
|
||||
callback = options
|
||||
options = {}
|
||||
}
|
||||
|
||||
const buffer = Buffer.isBuffer(data) ? data : Buffer.from(data, options?.encoding || 'utf8')
|
||||
|
||||
this.vfs.appendFile(path, buffer, options)
|
||||
.then(() => callback(null))
|
||||
.catch(err => callback(err))
|
||||
}
|
||||
|
||||
// ============= Stream methods =============
|
||||
|
||||
createReadStream(path: string, options?: any) {
|
||||
return this.vfs.createReadStream(path, options)
|
||||
}
|
||||
|
||||
createWriteStream(path: string, options?: any) {
|
||||
return this.vfs.createWriteStream(path, options)
|
||||
}
|
||||
|
||||
// ============= Watch methods =============
|
||||
|
||||
watch(path: string, listener?: any) {
|
||||
return this.vfs.watch(path, listener)
|
||||
}
|
||||
|
||||
watchFile(path: string, listener: any) {
|
||||
return this.vfs.watchFile(path, listener)
|
||||
}
|
||||
|
||||
unwatchFile(path: string) {
|
||||
return this.vfs.unwatchFile(path)
|
||||
}
|
||||
|
||||
// ============= Additional methods =============
|
||||
|
||||
/**
|
||||
* Import a directory from real filesystem (VFS extension)
|
||||
*/
|
||||
async importDirectory(sourcePath: string, options?: any) {
|
||||
return this.vfs.importDirectory(sourcePath, options)
|
||||
}
|
||||
|
||||
/**
|
||||
* Search files semantically (VFS extension)
|
||||
*/
|
||||
async search(query: string, options?: any) {
|
||||
return this.vfs.search(query, options)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Promise-based fs API (like fs.promises)
|
||||
*/
|
||||
class FSPromises {
|
||||
constructor(private vfs: VirtualFileSystem) {}
|
||||
|
||||
async readFile(path: string, options?: any): Promise<Buffer | string> {
|
||||
const buffer = await this.vfs.readFile(path, options)
|
||||
if (options?.encoding) {
|
||||
return buffer.toString(options.encoding)
|
||||
}
|
||||
return buffer
|
||||
}
|
||||
|
||||
async writeFile(path: string, data: Buffer | string, options?: any): Promise<void> {
|
||||
const buffer = Buffer.isBuffer(data) ? data : Buffer.from(data, options?.encoding || 'utf8')
|
||||
return this.vfs.writeFile(path, buffer, options)
|
||||
}
|
||||
|
||||
async mkdir(path: string, options?: any): Promise<void> {
|
||||
return this.vfs.mkdir(path, options)
|
||||
}
|
||||
|
||||
async rmdir(path: string, options?: any): Promise<void> {
|
||||
return this.vfs.rmdir(path, options)
|
||||
}
|
||||
|
||||
async readdir(path: string, options?: any): Promise<string[] | VFSDirent[]> {
|
||||
return this.vfs.readdir(path, options)
|
||||
}
|
||||
|
||||
async stat(path: string): Promise<VFSStats> {
|
||||
return this.vfs.stat(path)
|
||||
}
|
||||
|
||||
async lstat(path: string): Promise<VFSStats> {
|
||||
return this.vfs.lstat(path)
|
||||
}
|
||||
|
||||
async unlink(path: string): Promise<void> {
|
||||
return this.vfs.unlink(path)
|
||||
}
|
||||
|
||||
async rename(oldPath: string, newPath: string): Promise<void> {
|
||||
return this.vfs.rename(oldPath, newPath)
|
||||
}
|
||||
|
||||
async copyFile(src: string, dest: string, flags?: number): Promise<void> {
|
||||
return this.vfs.copy(src, dest)
|
||||
}
|
||||
|
||||
async access(path: string, mode?: number): Promise<void> {
|
||||
const exists = await this.vfs.exists(path)
|
||||
if (!exists) {
|
||||
const err: any = new Error('ENOENT: no such file or directory')
|
||||
err.code = 'ENOENT'
|
||||
throw err
|
||||
}
|
||||
}
|
||||
|
||||
async appendFile(path: string, data: Buffer | string, options?: any): Promise<void> {
|
||||
const buffer = Buffer.isBuffer(data) ? data : Buffer.from(data, options?.encoding || 'utf8')
|
||||
return this.vfs.appendFile(path, buffer, options)
|
||||
}
|
||||
|
||||
async realpath(path: string): Promise<string> {
|
||||
return this.vfs.realpath(path)
|
||||
}
|
||||
|
||||
async chmod(path: string, mode: number): Promise<void> {
|
||||
return this.vfs.chmod(path, mode)
|
||||
}
|
||||
|
||||
async chown(path: string, uid: number, gid: number): Promise<void> {
|
||||
return this.vfs.chown(path, uid, gid)
|
||||
}
|
||||
|
||||
async utimes(path: string, atime: Date, mtime: Date): Promise<void> {
|
||||
return this.vfs.utimes(path, atime, mtime)
|
||||
}
|
||||
|
||||
async symlink(target: string, path: string): Promise<void> {
|
||||
return this.vfs.symlink(target, path)
|
||||
}
|
||||
|
||||
async readlink(path: string): Promise<string> {
|
||||
return this.vfs.readlink(path)
|
||||
}
|
||||
|
||||
// VFS Extensions
|
||||
async search(query: string, options?: any) {
|
||||
return this.vfs.search(query, options)
|
||||
}
|
||||
|
||||
async findSimilar(path: string, options?: any) {
|
||||
return this.vfs.findSimilar(path, options)
|
||||
}
|
||||
|
||||
async importDirectory(sourcePath: string, options?: any) {
|
||||
return this.vfs.importDirectory(sourcePath, options)
|
||||
}
|
||||
}
|
||||
|
||||
// Export a convenience function to create fs replacement
|
||||
export function createFS(vfs: VirtualFileSystem): FSCompat {
|
||||
return new FSCompat(vfs)
|
||||
}
|
||||
|
|
@ -1,27 +0,0 @@
|
|||
/**
|
||||
* Brainy Virtual Filesystem
|
||||
*
|
||||
* A simplified fs-compatible filesystem that stores data in Brainy
|
||||
* Works across all storage adapters and scales to millions of files
|
||||
*/
|
||||
|
||||
// Core VFS
|
||||
export { VirtualFileSystem } from './VirtualFileSystem.js'
|
||||
export { PathResolver } from './PathResolver.js'
|
||||
export * from './types.js'
|
||||
|
||||
// MIME Type Detection
|
||||
export { MimeTypeDetector, mimeDetector } from './MimeTypeDetector.js'
|
||||
|
||||
// fs compatibility layer
|
||||
export { FSCompat, createFS } from './FSCompat.js'
|
||||
|
||||
// Directory import
|
||||
export { DirectoryImporter } from './importers/DirectoryImporter.js'
|
||||
|
||||
// Streaming
|
||||
export { VFSReadStream } from './streams/VFSReadStream.js'
|
||||
export { VFSWriteStream } from './streams/VFSWriteStream.js'
|
||||
|
||||
// Convenience alias
|
||||
export { VirtualFileSystem as VFS } from './VirtualFileSystem.js'
|
||||
|
|
@ -1,317 +0,0 @@
|
|||
import { describe, it, expect, beforeEach } from 'vitest'
|
||||
import { Brainy } from '../../../src/brainy.js'
|
||||
import { InstancePool, createInstancePool } from '../../../src/import/InstancePool.js'
|
||||
|
||||
describe('InstancePool', () => {
|
||||
let brain: Brainy
|
||||
let pool: InstancePool
|
||||
|
||||
beforeEach(async () => {
|
||||
brain = new Brainy({ requireSubtype: false, storage: { type: 'memory' } })
|
||||
await brain.init()
|
||||
pool = new InstancePool(brain)
|
||||
})
|
||||
|
||||
describe('lazy initialization', () => {
|
||||
it('should not create instances until requested', () => {
|
||||
const stats = pool.getStats()
|
||||
expect(stats.nlpCreated).toBe(false)
|
||||
expect(stats.extractorCreated).toBe(false)
|
||||
})
|
||||
|
||||
it('should create NLP instance on first access', async () => {
|
||||
const nlp = await pool.getNLP()
|
||||
expect(nlp).toBeDefined()
|
||||
|
||||
const stats = pool.getStats()
|
||||
expect(stats.nlpCreated).toBe(true)
|
||||
expect(stats.nlpReuses).toBe(1)
|
||||
})
|
||||
|
||||
it('should create extractor instance on first access', () => {
|
||||
const extractor = pool.getExtractor()
|
||||
expect(extractor).toBeDefined()
|
||||
|
||||
const stats = pool.getStats()
|
||||
expect(stats.extractorCreated).toBe(true)
|
||||
expect(stats.extractorReuses).toBe(1)
|
||||
})
|
||||
})
|
||||
|
||||
describe('instance reuse', () => {
|
||||
it('should return same NLP instance on multiple calls', async () => {
|
||||
const nlp1 = await pool.getNLP()
|
||||
const nlp2 = await pool.getNLP()
|
||||
const nlp3 = await pool.getNLP()
|
||||
|
||||
expect(nlp1).toBe(nlp2)
|
||||
expect(nlp2).toBe(nlp3)
|
||||
|
||||
const stats = pool.getStats()
|
||||
expect(stats.nlpReuses).toBe(3)
|
||||
})
|
||||
|
||||
it('should return same extractor instance on multiple calls', () => {
|
||||
const extractor1 = pool.getExtractor()
|
||||
const extractor2 = pool.getExtractor()
|
||||
const extractor3 = pool.getExtractor()
|
||||
|
||||
expect(extractor1).toBe(extractor2)
|
||||
expect(extractor2).toBe(extractor3)
|
||||
|
||||
const stats = pool.getStats()
|
||||
expect(stats.extractorReuses).toBe(3)
|
||||
})
|
||||
|
||||
it('should track reuse counts correctly', async () => {
|
||||
await pool.getNLP()
|
||||
await pool.getNLP()
|
||||
pool.getExtractor()
|
||||
pool.getExtractor()
|
||||
pool.getExtractor()
|
||||
|
||||
const stats = pool.getStats()
|
||||
expect(stats.nlpReuses).toBe(2)
|
||||
expect(stats.extractorReuses).toBe(3)
|
||||
})
|
||||
})
|
||||
|
||||
describe('initialization', () => {
|
||||
it('should initialize all instances with init()', async () => {
|
||||
await pool.init()
|
||||
|
||||
expect(pool.isInitialized()).toBe(true)
|
||||
|
||||
const stats = pool.getStats()
|
||||
expect(stats.nlpCreated).toBe(true)
|
||||
expect(stats.extractorCreated).toBe(true)
|
||||
expect(stats.initialized).toBe(true)
|
||||
})
|
||||
|
||||
it('should handle concurrent init calls safely', async () => {
|
||||
// Call init multiple times concurrently
|
||||
const promises = [
|
||||
pool.init(),
|
||||
pool.init(),
|
||||
pool.init()
|
||||
]
|
||||
|
||||
await Promise.all(promises)
|
||||
|
||||
// Should only initialize once
|
||||
expect(pool.isInitialized()).toBe(true)
|
||||
})
|
||||
|
||||
it('should auto-initialize NLP when accessed', async () => {
|
||||
const nlp = await pool.getNLP()
|
||||
|
||||
// NLP is lazy-initialized but extractor might not be
|
||||
const stats = pool.getStats()
|
||||
expect(stats.nlpCreated).toBe(true)
|
||||
expect(stats.initialized).toBe(true)
|
||||
})
|
||||
|
||||
it('should provide sync access to NLP', () => {
|
||||
const nlp = pool.getNLPSync()
|
||||
expect(nlp).toBeDefined()
|
||||
|
||||
const stats = pool.getStats()
|
||||
expect(stats.nlpCreated).toBe(true)
|
||||
})
|
||||
})
|
||||
|
||||
describe('statistics', () => {
|
||||
it('should track creation time', async () => {
|
||||
await pool.init()
|
||||
|
||||
const stats = pool.getStats()
|
||||
expect(stats.creationTime).toBeGreaterThanOrEqual(0)
|
||||
})
|
||||
|
||||
it('should calculate memory saved', async () => {
|
||||
// Use instances multiple times
|
||||
await pool.getNLP()
|
||||
await pool.getNLP()
|
||||
await pool.getNLP()
|
||||
pool.getExtractor()
|
||||
pool.getExtractor()
|
||||
|
||||
const stats = pool.getStats()
|
||||
expect(stats.memorySaved).toBeGreaterThan(0)
|
||||
})
|
||||
|
||||
it('should reset statistics', async () => {
|
||||
await pool.getNLP()
|
||||
pool.getExtractor()
|
||||
|
||||
pool.resetStats()
|
||||
|
||||
const stats = pool.getStats()
|
||||
expect(stats.nlpReuses).toBe(0)
|
||||
expect(stats.extractorReuses).toBe(0)
|
||||
expect(stats.creationTime).toBe(0)
|
||||
})
|
||||
|
||||
it('should provide string representation', async () => {
|
||||
await pool.init()
|
||||
|
||||
const str = pool.toString()
|
||||
expect(str).toContain('InstancePool')
|
||||
expect(str).toContain('nlp=true')
|
||||
expect(str).toContain('extractor=true')
|
||||
})
|
||||
})
|
||||
|
||||
describe('memory efficiency', () => {
|
||||
it('should reuse instances in loop (no memory leak)', async () => {
|
||||
const initialStats = pool.getStats()
|
||||
|
||||
// Simulate import loop
|
||||
for (let i = 0; i < 1000; i++) {
|
||||
const nlp = await pool.getNLP()
|
||||
const extractor = pool.getExtractor()
|
||||
|
||||
// All iterations should get same instances
|
||||
expect(nlp).toBeDefined()
|
||||
expect(extractor).toBeDefined()
|
||||
}
|
||||
|
||||
const finalStats = pool.getStats()
|
||||
expect(finalStats.nlpReuses).toBe(1000)
|
||||
expect(finalStats.extractorReuses).toBe(1000)
|
||||
|
||||
// Should have saved ~60GB of memory (1000 iterations × ~60MB)
|
||||
expect(finalStats.memorySaved).toBeGreaterThan(50 * 1024 * 1024 * 1000) // > 50GB
|
||||
})
|
||||
|
||||
it('should handle rapid concurrent access', async () => {
|
||||
// Simulate concurrent row processing
|
||||
const promises = []
|
||||
for (let i = 0; i < 100; i++) {
|
||||
promises.push(pool.getNLP())
|
||||
promises.push(Promise.resolve(pool.getExtractor()))
|
||||
}
|
||||
|
||||
await Promise.all(promises)
|
||||
|
||||
const stats = pool.getStats()
|
||||
expect(stats.nlpReuses).toBe(100)
|
||||
expect(stats.extractorReuses).toBe(100)
|
||||
})
|
||||
})
|
||||
|
||||
describe('cleanup', () => {
|
||||
it('should cleanup instances', async () => {
|
||||
await pool.init()
|
||||
expect(pool.isInitialized()).toBe(true)
|
||||
|
||||
pool.cleanup()
|
||||
|
||||
expect(pool.isInitialized()).toBe(false)
|
||||
const stats = pool.getStats()
|
||||
expect(stats.nlpCreated).toBe(false)
|
||||
expect(stats.extractorCreated).toBe(false)
|
||||
})
|
||||
|
||||
it('should allow reinitialization after cleanup', async () => {
|
||||
await pool.init()
|
||||
pool.cleanup()
|
||||
|
||||
await pool.init()
|
||||
expect(pool.isInitialized()).toBe(true)
|
||||
})
|
||||
})
|
||||
|
||||
describe('factory function', () => {
|
||||
it('should create pool with auto-init', async () => {
|
||||
const newPool = await createInstancePool(brain, true)
|
||||
|
||||
expect(newPool.isInitialized()).toBe(true)
|
||||
})
|
||||
|
||||
it('should create pool without auto-init', async () => {
|
||||
const newPool = await createInstancePool(brain, false)
|
||||
|
||||
expect(newPool.isInitialized()).toBe(false)
|
||||
})
|
||||
|
||||
it('should default to auto-init', async () => {
|
||||
const newPool = await createInstancePool(brain)
|
||||
|
||||
expect(newPool.isInitialized()).toBe(true)
|
||||
})
|
||||
})
|
||||
|
||||
describe('error handling', () => {
|
||||
it('should handle missing NLP instance gracefully', async () => {
|
||||
const emptyPool = new InstancePool(brain)
|
||||
|
||||
// Should create NLP on first access
|
||||
const nlp = await emptyPool.getNLP()
|
||||
expect(nlp).toBeDefined()
|
||||
})
|
||||
|
||||
it('should handle missing extractor instance gracefully', () => {
|
||||
const emptyPool = new InstancePool(brain)
|
||||
|
||||
// Should create extractor on first access
|
||||
const extractor = emptyPool.getExtractor()
|
||||
expect(extractor).toBeDefined()
|
||||
})
|
||||
})
|
||||
|
||||
describe('real-world usage', () => {
|
||||
it('should work with actual NLP operations', async () => {
|
||||
const nlp = await pool.getNLP()
|
||||
|
||||
// Should be initialized and ready to use
|
||||
expect(nlp).toBeDefined()
|
||||
|
||||
// NLP should have init method
|
||||
expect(typeof nlp.init).toBe('function')
|
||||
})
|
||||
|
||||
it('should work with actual entity extraction', async () => {
|
||||
const extractor = pool.getExtractor()
|
||||
|
||||
// Should be ready to use
|
||||
expect(extractor).toBeDefined()
|
||||
|
||||
// Can call extractor methods
|
||||
const entities = await extractor.extract('Paris is a beautiful city', {
|
||||
confidence: 0.5
|
||||
})
|
||||
|
||||
expect(Array.isArray(entities)).toBe(true)
|
||||
})
|
||||
|
||||
it('should handle full import workflow', async () => {
|
||||
// Initialize pool
|
||||
await pool.init()
|
||||
|
||||
// Simulate processing multiple rows
|
||||
const rows = [
|
||||
{ text: 'Paris is beautiful' },
|
||||
{ text: 'London is historic' },
|
||||
{ text: 'Tokyo is modern' }
|
||||
]
|
||||
|
||||
for (const row of rows) {
|
||||
const nlp = await pool.getNLP()
|
||||
const extractor = pool.getExtractor()
|
||||
|
||||
// Process row - extract entities
|
||||
const entities = await extractor.extract(row.text, { confidence: 0.5 })
|
||||
|
||||
expect(nlp).toBeDefined()
|
||||
expect(extractor).toBeDefined()
|
||||
expect(entities).toBeDefined()
|
||||
}
|
||||
|
||||
// Verify instances were reused
|
||||
const stats = pool.getStats()
|
||||
expect(stats.nlpReuses).toBe(3)
|
||||
expect(stats.extractorReuses).toBe(3)
|
||||
})
|
||||
})
|
||||
})
|
||||
|
|
@ -1,561 +0,0 @@
|
|||
import { describe, it, expect } from 'vitest'
|
||||
import {
|
||||
autoDetectPreset,
|
||||
getPreset,
|
||||
getPresetNames,
|
||||
explainPresetChoice,
|
||||
createCustomPreset,
|
||||
validatePreset,
|
||||
formatPreset,
|
||||
FAST_PRESET,
|
||||
BALANCED_PRESET,
|
||||
ACCURATE_PRESET,
|
||||
EXPLICIT_PRESET,
|
||||
PATTERN_PRESET,
|
||||
PRESETS,
|
||||
type ImportContext,
|
||||
type PresetConfig
|
||||
} from '../../../src/neural/presets.js'
|
||||
|
||||
describe('Presets', () => {
|
||||
describe('preset definitions', () => {
|
||||
it('should have all 5 presets defined', () => {
|
||||
expect(PRESETS).toBeDefined()
|
||||
expect(Object.keys(PRESETS)).toHaveLength(5)
|
||||
expect(PRESETS.fast).toBe(FAST_PRESET)
|
||||
expect(PRESETS.balanced).toBe(BALANCED_PRESET)
|
||||
expect(PRESETS.accurate).toBe(ACCURATE_PRESET)
|
||||
expect(PRESETS.explicit).toBe(EXPLICIT_PRESET)
|
||||
expect(PRESETS.pattern).toBe(PATTERN_PRESET)
|
||||
})
|
||||
|
||||
it('should have valid fast preset', () => {
|
||||
expect(FAST_PRESET.name).toBe('fast')
|
||||
expect(FAST_PRESET.signals.enabled).toEqual(['exact', 'pattern'])
|
||||
expect(FAST_PRESET.strategies.enabled).toEqual(['explicit'])
|
||||
expect(FAST_PRESET.streaming).toBe(true)
|
||||
expect(FAST_PRESET.strategies.earlyTermination).toBe(true)
|
||||
})
|
||||
|
||||
it('should have valid balanced preset', () => {
|
||||
expect(BALANCED_PRESET.name).toBe('balanced')
|
||||
expect(BALANCED_PRESET.signals.enabled).toEqual(['exact', 'embedding', 'pattern'])
|
||||
expect(BALANCED_PRESET.strategies.enabled).toEqual(['explicit', 'pattern', 'embedding'])
|
||||
expect(BALANCED_PRESET.streaming).toBe(false)
|
||||
})
|
||||
|
||||
it('should have valid accurate preset', () => {
|
||||
expect(ACCURATE_PRESET.name).toBe('accurate')
|
||||
expect(ACCURATE_PRESET.signals.enabled).toEqual(['exact', 'embedding', 'pattern', 'context'])
|
||||
expect(ACCURATE_PRESET.strategies.enabled).toEqual(['explicit', 'pattern', 'embedding'])
|
||||
expect(ACCURATE_PRESET.strategies.earlyTermination).toBe(false)
|
||||
})
|
||||
|
||||
it('should have valid explicit preset', () => {
|
||||
expect(EXPLICIT_PRESET.name).toBe('explicit')
|
||||
expect(EXPLICIT_PRESET.signals.enabled).toEqual(['exact', 'pattern'])
|
||||
expect(EXPLICIT_PRESET.strategies.enabled).toEqual(['explicit', 'pattern'])
|
||||
expect(EXPLICIT_PRESET.strategies.minConfidence).toBeGreaterThanOrEqual(0.80)
|
||||
})
|
||||
|
||||
it('should have valid pattern preset', () => {
|
||||
expect(PATTERN_PRESET.name).toBe('pattern')
|
||||
expect(PATTERN_PRESET.signals.enabled).toEqual(['embedding', 'pattern', 'context'])
|
||||
expect(PATTERN_PRESET.strategies.enabled).toEqual(['pattern', 'embedding'])
|
||||
})
|
||||
})
|
||||
|
||||
describe('autoDetectPreset', () => {
|
||||
it('should return fast preset for large datasets', () => {
|
||||
const context: ImportContext = {
|
||||
rowCount: 15000,
|
||||
fileSize: 5_000_000
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('fast')
|
||||
})
|
||||
|
||||
it('should return fast preset for large files', () => {
|
||||
const context: ImportContext = {
|
||||
fileSize: 15_000_000
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('fast')
|
||||
})
|
||||
|
||||
it('should return accurate preset for small datasets', () => {
|
||||
const context: ImportContext = {
|
||||
rowCount: 50
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('accurate')
|
||||
})
|
||||
|
||||
it('should return explicit preset for Excel with explicit columns', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'excel',
|
||||
hasExplicitColumns: true,
|
||||
rowCount: 500
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('explicit')
|
||||
})
|
||||
|
||||
it('should return explicit preset for CSV with explicit columns', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'csv',
|
||||
hasExplicitColumns: true
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('explicit')
|
||||
})
|
||||
|
||||
it('should return pattern preset for PDF files', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'pdf',
|
||||
rowCount: 200
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('pattern')
|
||||
})
|
||||
|
||||
it('should return pattern preset for Markdown files', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'markdown'
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('pattern')
|
||||
})
|
||||
|
||||
it('should return pattern preset for narrative content', () => {
|
||||
const context: ImportContext = {
|
||||
hasNarrativeContent: true,
|
||||
rowCount: 300
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('pattern')
|
||||
})
|
||||
|
||||
it('should return pattern preset for long definitions', () => {
|
||||
const context: ImportContext = {
|
||||
avgDefinitionLength: 800,
|
||||
fileType: 'csv'
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('pattern')
|
||||
})
|
||||
|
||||
it('should return balanced preset for JSON', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'json',
|
||||
rowCount: 500
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('balanced')
|
||||
})
|
||||
|
||||
it('should return balanced preset for medium datasets', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'excel',
|
||||
rowCount: 2000
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('balanced')
|
||||
})
|
||||
|
||||
it('should return balanced preset for empty context', () => {
|
||||
const preset = autoDetectPreset()
|
||||
expect(preset.name).toBe('balanced')
|
||||
})
|
||||
|
||||
it('should return balanced preset for unknown file type', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'unknown',
|
||||
rowCount: 500
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('balanced')
|
||||
})
|
||||
})
|
||||
|
||||
describe('getPreset', () => {
|
||||
it('should get preset by name', () => {
|
||||
expect(getPreset('fast')).toBe(FAST_PRESET)
|
||||
expect(getPreset('balanced')).toBe(BALANCED_PRESET)
|
||||
expect(getPreset('accurate')).toBe(ACCURATE_PRESET)
|
||||
expect(getPreset('explicit')).toBe(EXPLICIT_PRESET)
|
||||
expect(getPreset('pattern')).toBe(PATTERN_PRESET)
|
||||
})
|
||||
|
||||
it('should be case-insensitive', () => {
|
||||
expect(getPreset('FAST')).toBe(FAST_PRESET)
|
||||
expect(getPreset('Balanced')).toBe(BALANCED_PRESET)
|
||||
expect(getPreset('EXPLICIT')).toBe(EXPLICIT_PRESET)
|
||||
})
|
||||
|
||||
it('should throw error for unknown preset', () => {
|
||||
expect(() => getPreset('unknown')).toThrow('Unknown preset: unknown')
|
||||
})
|
||||
})
|
||||
|
||||
describe('getPresetNames', () => {
|
||||
it('should return all preset names', () => {
|
||||
const names = getPresetNames()
|
||||
expect(names).toHaveLength(5)
|
||||
expect(names).toContain('fast')
|
||||
expect(names).toContain('balanced')
|
||||
expect(names).toContain('accurate')
|
||||
expect(names).toContain('explicit')
|
||||
expect(names).toContain('pattern')
|
||||
})
|
||||
})
|
||||
|
||||
describe('explainPresetChoice', () => {
|
||||
it('should explain large dataset choice', () => {
|
||||
const context: ImportContext = {
|
||||
rowCount: 15000,
|
||||
fileSize: 12_000_000
|
||||
}
|
||||
|
||||
const explanation = explainPresetChoice(context)
|
||||
expect(explanation).toContain('Large dataset')
|
||||
expect(explanation).toContain('15000 rows')
|
||||
expect(explanation).toContain('fast preset')
|
||||
})
|
||||
|
||||
it('should explain small dataset choice', () => {
|
||||
const context: ImportContext = {
|
||||
rowCount: 50
|
||||
}
|
||||
|
||||
const explanation = explainPresetChoice(context)
|
||||
expect(explanation).toContain('Small critical dataset')
|
||||
expect(explanation).toContain('50 rows')
|
||||
expect(explanation).toContain('accurate preset')
|
||||
})
|
||||
|
||||
it('should explain explicit columns choice', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'excel',
|
||||
hasExplicitColumns: true
|
||||
}
|
||||
|
||||
const explanation = explainPresetChoice(context)
|
||||
expect(explanation).toContain('EXCEL')
|
||||
expect(explanation).toContain('explicit relationship columns')
|
||||
expect(explanation).toContain('explicit preset')
|
||||
})
|
||||
|
||||
it('should explain narrative content choice', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'pdf',
|
||||
hasNarrativeContent: true
|
||||
}
|
||||
|
||||
const explanation = explainPresetChoice(context)
|
||||
expect(explanation).toContain('Narrative content')
|
||||
expect(explanation).toContain('pattern preset')
|
||||
})
|
||||
|
||||
it('should explain default choice', () => {
|
||||
const context: ImportContext = {
|
||||
rowCount: 500
|
||||
}
|
||||
|
||||
const explanation = explainPresetChoice(context)
|
||||
expect(explanation).toContain('balanced preset')
|
||||
})
|
||||
})
|
||||
|
||||
describe('createCustomPreset', () => {
|
||||
it('should create custom preset from base', () => {
|
||||
const custom = createCustomPreset('balanced', {
|
||||
name: 'my-custom',
|
||||
batchSize: 2000
|
||||
})
|
||||
|
||||
expect(custom.name).toBe('my-custom')
|
||||
expect(custom.batchSize).toBe(2000)
|
||||
expect(custom.signals).toEqual(BALANCED_PRESET.signals)
|
||||
expect(custom.strategies).toEqual(BALANCED_PRESET.strategies)
|
||||
})
|
||||
|
||||
it('should override signals', () => {
|
||||
const custom = createCustomPreset('fast', {
|
||||
signals: {
|
||||
enabled: ['embedding'],
|
||||
weights: { embedding: 1.0, exact: 0, pattern: 0, context: 0 },
|
||||
timeout: 200
|
||||
}
|
||||
})
|
||||
|
||||
expect(custom.signals.enabled).toEqual(['embedding'])
|
||||
expect(custom.signals.timeout).toBe(200)
|
||||
})
|
||||
|
||||
it('should override strategies', () => {
|
||||
const custom = createCustomPreset('balanced', {
|
||||
strategies: {
|
||||
enabled: ['pattern'],
|
||||
timeout: 500,
|
||||
earlyTermination: false,
|
||||
minConfidence: 0.75
|
||||
}
|
||||
})
|
||||
|
||||
expect(custom.strategies.enabled).toEqual(['pattern'])
|
||||
expect(custom.strategies.timeout).toBe(500)
|
||||
expect(custom.strategies.earlyTermination).toBe(false)
|
||||
})
|
||||
|
||||
it('should merge partial signal overrides', () => {
|
||||
const custom = createCustomPreset('balanced', {
|
||||
signals: {
|
||||
timeout: 300
|
||||
} as any
|
||||
})
|
||||
|
||||
expect(custom.signals.enabled).toEqual(BALANCED_PRESET.signals.enabled)
|
||||
expect(custom.signals.timeout).toBe(300)
|
||||
})
|
||||
})
|
||||
|
||||
describe('validatePreset', () => {
|
||||
it('should validate all built-in presets', () => {
|
||||
expect(() => validatePreset(FAST_PRESET)).not.toThrow()
|
||||
expect(() => validatePreset(BALANCED_PRESET)).not.toThrow()
|
||||
expect(() => validatePreset(ACCURATE_PRESET)).not.toThrow()
|
||||
expect(() => validatePreset(EXPLICIT_PRESET)).not.toThrow()
|
||||
expect(() => validatePreset(PATTERN_PRESET)).not.toThrow()
|
||||
})
|
||||
|
||||
it('should reject preset with no signals', () => {
|
||||
const invalid: PresetConfig = {
|
||||
...BALANCED_PRESET,
|
||||
signals: {
|
||||
...BALANCED_PRESET.signals,
|
||||
enabled: []
|
||||
}
|
||||
}
|
||||
|
||||
expect(() => validatePreset(invalid)).toThrow('at least one enabled signal')
|
||||
})
|
||||
|
||||
it('should reject preset with no strategies', () => {
|
||||
const invalid: PresetConfig = {
|
||||
...BALANCED_PRESET,
|
||||
strategies: {
|
||||
...BALANCED_PRESET.strategies,
|
||||
enabled: []
|
||||
}
|
||||
}
|
||||
|
||||
expect(() => validatePreset(invalid)).toThrow('at least one enabled strategy')
|
||||
})
|
||||
|
||||
it('should reject preset with invalid weight sum', () => {
|
||||
const invalid: PresetConfig = {
|
||||
...BALANCED_PRESET,
|
||||
signals: {
|
||||
enabled: ['exact', 'embedding'],
|
||||
weights: {
|
||||
exact: 0.3,
|
||||
embedding: 0.5,
|
||||
pattern: 0,
|
||||
context: 0
|
||||
},
|
||||
timeout: 100
|
||||
}
|
||||
}
|
||||
|
||||
expect(() => validatePreset(invalid)).toThrow('weights must sum to 1.0')
|
||||
})
|
||||
|
||||
it('should reject preset with negative timeout', () => {
|
||||
const invalid: PresetConfig = {
|
||||
...BALANCED_PRESET,
|
||||
signals: {
|
||||
...BALANCED_PRESET.signals,
|
||||
timeout: -100
|
||||
}
|
||||
}
|
||||
|
||||
expect(() => validatePreset(invalid)).toThrow('Timeouts must be positive')
|
||||
})
|
||||
|
||||
it('should reject preset with invalid batch size', () => {
|
||||
const invalid: PresetConfig = {
|
||||
...BALANCED_PRESET,
|
||||
batchSize: 0
|
||||
}
|
||||
|
||||
expect(() => validatePreset(invalid)).toThrow('Batch size must be positive')
|
||||
})
|
||||
})
|
||||
|
||||
describe('formatPreset', () => {
|
||||
it('should format preset for display', () => {
|
||||
const formatted = formatPreset(BALANCED_PRESET)
|
||||
|
||||
expect(formatted).toContain('Preset: balanced')
|
||||
expect(formatted).toContain('Description:')
|
||||
expect(formatted).toContain('Signals:')
|
||||
expect(formatted).toContain('exact: 40%')
|
||||
expect(formatted).toContain('embedding: 35%')
|
||||
expect(formatted).toContain('Strategies:')
|
||||
expect(formatted).toContain('explicit')
|
||||
expect(formatted).toContain('pattern')
|
||||
expect(formatted).toContain('embedding')
|
||||
expect(formatted).toContain('Streaming: false')
|
||||
expect(formatted).toContain('Batch size: 500')
|
||||
})
|
||||
|
||||
it('should format fast preset correctly', () => {
|
||||
const formatted = formatPreset(FAST_PRESET)
|
||||
|
||||
expect(formatted).toContain('fast')
|
||||
expect(formatted).toContain('Streaming: true')
|
||||
expect(formatted).toContain('Early termination: true')
|
||||
})
|
||||
})
|
||||
|
||||
describe('preset priorities', () => {
|
||||
it('should prioritize size over explicit columns for large datasets', () => {
|
||||
const context: ImportContext = {
|
||||
rowCount: 20000,
|
||||
fileType: 'excel',
|
||||
hasExplicitColumns: true
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('fast') // Size trumps explicit
|
||||
})
|
||||
|
||||
it('should prioritize small size over other factors', () => {
|
||||
const context: ImportContext = {
|
||||
rowCount: 50,
|
||||
fileType: 'pdf',
|
||||
hasNarrativeContent: true
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('accurate') // Small size trumps pattern
|
||||
})
|
||||
|
||||
it('should prioritize explicit columns over narrative for Excel', () => {
|
||||
const context: ImportContext = {
|
||||
rowCount: 500,
|
||||
fileType: 'excel',
|
||||
hasExplicitColumns: true,
|
||||
hasNarrativeContent: true
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('explicit') // Explicit trumps narrative
|
||||
})
|
||||
})
|
||||
|
||||
describe('edge cases', () => {
|
||||
it('should handle zero row count', () => {
|
||||
const context: ImportContext = {
|
||||
rowCount: 0,
|
||||
fileType: 'csv'
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('balanced')
|
||||
})
|
||||
|
||||
it('should handle boundary row count (exactly 100)', () => {
|
||||
const context: ImportContext = {
|
||||
rowCount: 100
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('balanced') // Not accurate (< 100)
|
||||
})
|
||||
|
||||
it('should handle boundary row count (exactly 10000)', () => {
|
||||
const context: ImportContext = {
|
||||
rowCount: 10000
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('balanced') // Not fast (> 10000)
|
||||
})
|
||||
|
||||
it('should handle missing hasExplicitColumns flag', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'excel',
|
||||
rowCount: 500
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('balanced')
|
||||
})
|
||||
})
|
||||
|
||||
describe('real-world scenarios', () => {
|
||||
it('should handle glossary correctly', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'excel',
|
||||
rowCount: 567,
|
||||
hasExplicitColumns: true, // Has "Related Terms" column
|
||||
fileSize: 50_000
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('explicit')
|
||||
|
||||
const explanation = explainPresetChoice(context)
|
||||
expect(explanation).toContain('explicit')
|
||||
})
|
||||
|
||||
it('should handle large CSV import', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'csv',
|
||||
rowCount: 50000,
|
||||
fileSize: 25_000_000
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('fast')
|
||||
expect(preset.streaming).toBe(true)
|
||||
})
|
||||
|
||||
it('should handle PDF documentation', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'pdf',
|
||||
rowCount: 150,
|
||||
hasNarrativeContent: true,
|
||||
avgDefinitionLength: 600
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('pattern')
|
||||
})
|
||||
|
||||
it('should handle JSON API import', () => {
|
||||
const context: ImportContext = {
|
||||
fileType: 'json',
|
||||
rowCount: 1000,
|
||||
fileSize: 500_000
|
||||
}
|
||||
|
||||
const preset = autoDetectPreset(context)
|
||||
expect(preset.name).toBe('balanced')
|
||||
})
|
||||
})
|
||||
})
|
||||
|
|
@ -5,7 +5,7 @@
|
|||
*/
|
||||
|
||||
import { describe, it, expect } from 'vitest'
|
||||
import { VirtualFileSystem } from '../../src/vfs/index.js'
|
||||
import { VirtualFileSystem } from '../../src/vfs/VirtualFileSystem.js'
|
||||
import { Brainy } from '../../src/brainy.js'
|
||||
|
||||
describe('VFS Initialization', () => {
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
*/
|
||||
|
||||
import { describe, it, expect, beforeEach, afterEach } from 'vitest'
|
||||
import { VirtualFileSystem } from '../../src/vfs/index.js'
|
||||
import { VirtualFileSystem } from '../../src/vfs/VirtualFileSystem.js'
|
||||
import { Brainy } from '../../src/brainy.js'
|
||||
import { VFSErrorCode } from '../../src/vfs/types.js'
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue