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refactor(8.0): remove dead, unreachable, and unwired modules

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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 All were superseded duplicates, abandoned parallel implementations,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  - superseded duplicates of live modules: an older "unified" entry, a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:
David Snelling 2026-06-24 15:18:40 -07:00
parent 03d654061f
commit bf0afe8563
39 changed files with 2 additions and 10497 deletions
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237
examples/directory-import-with-caching.ts
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/**
* Directory Import with Entity Extraction Caching Example
*
* Demonstrates:
* - Importing directories with progress tracking
* - Entity extraction caching for performance
* - Relationship detection with confidence scores
* - Cache statistics monitoring
*/
import { Brainy, NounType, VerbType } from '../src/brainy.js'
import { DirectoryImporter } from '../src/vfs/importers/DirectoryImporter.js'
import { ProgressTracker, formatProgress } from '../src/types/progress.types.js'
import { detectRelationshipsWithConfidence } from '../src/neural/relationshipConfidence.js'
import { NeuralEntityExtractor } from '../src/neural/entityExtractor.js'
async function main() {
console.log('🧠 Brainy 3.21.0 - Directory Import with Caching Example\n')
// Initialize Brainy
const brain = new Brainy({ verbose: false })
await brain.init()
console.log('✅ Brainy initialized\n')
// The entity extractor (and its extraction cache) is constructed directly.
const extractor = new NeuralEntityExtractor(brain)
// Example 1: Import directory with entity extraction caching
console.log('📁 Example 1: Import Directory with Caching\n')
const vfs = brain.vfs
const importer = new DirectoryImporter(vfs, brain)
// Progress tracking
const tracker = ProgressTracker.create(100)
tracker.start()
try {
// Import with progress (using async generator)
console.log('Importing directory...')
let filesProcessed = 0
for await (const progress of importer.importStream('./examples', {
batchSize: 10,
recursive: true,
generateEmbeddings: true,
extractMetadata: true
})) {
if (progress.type === 'progress') {
filesProcessed = progress.processed
const trackedProgress = tracker.update(progress.processed, progress.current)
console.log(` ${formatProgress(trackedProgress)}`)
} else if (progress.type === 'complete') {
console.log(`\n✅ Import complete! Processed ${progress.processed} files\n`)
} else if (progress.type === 'error') {
console.error(`❌ Error: ${progress.error?.message}`)
}
}
tracker.complete({ filesProcessed })
} catch (error) {
console.error('Import failed:', error)
}
// Example 2: Entity extraction with caching
console.log('\n📝 Example 2: Entity Extraction with Caching\n')
const sampleText = `
John Smith created the user authentication system for the application.
The authentication system uses JWT tokens and bcrypt for password hashing.
Mary Johnson manages the backend team that maintains the system.
The system was built using Node.js and PostgreSQL database.
`
console.log('First extraction (cache miss):')
const startTime1 = Date.now()
const entities1 = await extractor.extract(sampleText, {
types: [NounType.Person, NounType.Service, NounType.Technology],
confidence: 0.7,
cache: {
enabled: true,
ttl: 7 * 24 * 60 * 60 * 1000, // 7 days
invalidateOn: 'hash'
}
})
const time1 = Date.now() - startTime1
console.log(` Extracted ${entities1.length} entities in ${time1}ms`)
console.log(` Entities: ${entities1.map(e => e.text).join(', ')}\n`)
console.log('Second extraction (cache hit):')
const startTime2 = Date.now()
const entities2 = await extractor.extract(sampleText, {
types: [NounType.Person, NounType.Service, NounType.Technology],
confidence: 0.7,
cache: {
enabled: true,
invalidateOn: 'hash'
}
})
const time2 = Date.now() - startTime2
console.log(` Extracted ${entities2.length} entities in ${time2}ms`)
console.log(` Speedup: ${Math.round(time1 / time2)}x faster!\n`)
// Show cache statistics
const cacheStats = extractor.getCacheStats()
console.log('📊 Cache Statistics:')
console.log(` Hits: ${cacheStats.hits}`)
console.log(` Misses: ${cacheStats.misses}`)
console.log(` Hit Rate: ${(cacheStats.hitRate * 100).toFixed(1)}%`)
console.log(` Total Entries: ${cacheStats.totalEntries}`)
console.log(` Avg Entities per Entry: ${cacheStats.averageEntitiesPerEntry}\n`)
// Example 3: Relationship detection with confidence
console.log('🔗 Example 3: Relationship Detection with Confidence\n')
const relationships = detectRelationshipsWithConfidence(
entities1,
sampleText,
{
minConfidence: 0.6,
maxDistance: 100,
useProximityBoost: true,
usePatternMatching: true,
useStructuralAnalysis: true
}
)
console.log(`Detected ${relationships.length} relationships:\n`)
for (const rel of relationships.slice(0, 5)) { // Show top 5
console.log(` ${rel.sourceEntity.text} --[${rel.verbType}]--> ${rel.targetEntity.text}`)
console.log(` Confidence: ${(rel.confidence * 100).toFixed(1)}%`)
console.log(` Evidence: ${rel.evidence.reasoning}`)
console.log(` Method: ${rel.evidence.method}`)
console.log(` Source: "${rel.evidence.sourceText?.substring(0, 60)}..."\n`)
}
// Example 4: Create relationships in graph with confidence
console.log('📊 Example 4: Creating Relationships in Graph\n')
const createdRelations = []
for (const rel of relationships.slice(0, 3)) { // Create top 3
try {
// Add entities to brain
const sourceId = await brain.add({
data: rel.sourceEntity.text,
type: rel.sourceEntity.type,
metadata: {
confidence: rel.sourceEntity.confidence,
extractedFrom: 'sample text'
}
})
const targetId = await brain.add({
data: rel.targetEntity.text,
type: rel.targetEntity.type,
metadata: {
confidence: rel.targetEntity.confidence,
extractedFrom: 'sample text'
}
})
// Create relationship with confidence
const relationId = await brain.relate({
from: sourceId,
to: targetId,
type: rel.verbType,
confidence: rel.confidence,
evidence: rel.evidence,
metadata: {
autoDetected: true,
detectedAt: new Date().toISOString()
}
})
createdRelations.push(relationId)
console.log(` ✅ Created: ${rel.sourceEntity.text} → ${rel.targetEntity.text}`)
} catch (error) {
console.error(` ❌ Failed to create relationship:`, error)
}
}
console.log(`\n✅ Created ${createdRelations.length} relationships in knowledge graph`)
// Example 5: Query relationships by confidence
console.log('\n🔍 Example 5: Query High-Confidence Relationships\n')
const allRelations = await brain.getRelations({
limit: 100
})
const highConfidence = allRelations.filter(r => (r.confidence || 0) >= 0.7)
console.log(`Found ${highConfidence.length} high-confidence relationships (≥70%):\n`)
for (const rel of highConfidence.slice(0, 5)) {
console.log(` ${rel.from} → ${rel.to} (${rel.type})`)
console.log(` Confidence: ${((rel.confidence || 0) * 100).toFixed(1)}%`)
if (rel.evidence) {
console.log(` Method: ${rel.evidence.method}`)
console.log(` Reasoning: ${rel.evidence.reasoning}\n`)
}
}
// Example 6: Cache management
console.log('🧹 Example 6: Cache Management\n')
console.log('Cache operations:')
// Cleanup expired entries
const cleaned = extractor.cleanupCache()
console.log(` Cleaned ${cleaned} expired entries`)
// Invalidate specific cache entry
const invalidated = extractor.invalidateCache('hash:abc123')
console.log(` Invalidated entry: ${invalidated}`)
// Get final stats
const finalStats = extractor.getCacheStats()
console.log(` Final cache size: ${finalStats.totalEntries} entries`)
console.log(` Memory used: ~${Math.round(finalStats.cacheSize / 1024)}KB`)
// Clear all cache (optional)
// extractor.clearCache()
// console.log(' Cleared entire cache')
console.log('\n✨ Example complete!')
console.log('\n📚 Key Takeaways:')
console.log(' • Entity extraction caching provides 10-100x speedup on repeated content')
console.log(' • Progress tracking gives real-time feedback for long operations')
console.log(' • Relationship confidence helps filter low-quality connections')
console.log(' • Evidence tracking makes relationships explainable and debuggable')
console.log(' • All features are opt-in and backward compatible')
}
// Run example
main().catch(console.error)