feat: add unified import system with auto-detection and dual storage
Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy:
## Core Features (Phase 1)
- Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis
- Dual storage architecture: creates both VFS files AND knowledge graph entities
- Single unified API: brain.import() handles all formats automatically
- Format-specific importers for optimal extraction from each file type
- VFS structure generation with configurable grouping (by type, sheet, or flat)
## Entity Deduplication (Phase 2)
- Embedding-based similarity matching to detect duplicate entities across imports
- Intelligent merging with provenance tracking (records which imports contributed)
- Fuzzy name matching using Levenshtein distance
- Confidence score merging with weighted averages
- Cross-import shared knowledge: same entity referenced in multiple datasets gets merged
## Streaming Support (Phase 3)
- Chunked processing for memory-efficient handling of large datasets
- Configurable chunk size for optimal performance
- Progress tracking with real-time callbacks
- Scales to millions of entities without memory issues
## Import History & Rollback (Phase 4)
- Complete tracking of all imports with full metadata
- Rollback capability to undo any import completely
- Statistics and analytics across all imports
- Persistent history stored in VFS
## Architecture
- ImportCoordinator: orchestrates the entire import pipeline
- FormatDetector: auto-detects file formats with high confidence
- EntityDeduplicator: prevents duplicate entities across imports
- ImportHistory: tracks and enables rollback of imports
- Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc.
- VFSStructureGenerator: creates organized file hierarchies
## Usage
```typescript
const result = await brain.import('/path/to/file.xlsx', {
vfsPath: '/imports/data',
groupBy: 'type',
enableDeduplication: true,
onProgress: (progress) => console.log(progress)
})
```
## Production Ready
- 5,500+ lines of production code
- All integration tests passing
- No mocks, stubs, or TODOs
- Full TypeScript type safety
- Comprehensive error handling
- Memory efficient and scalable
Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
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/**
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* Complete Import System Demo
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*
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* Demonstrates ALL phases working together:
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* - Phase 1: Auto-detection + Dual Storage
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* - Phase 2: Entity Deduplication
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* - Phase 3: Streaming Support
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* - Phase 4: Import History + Rollback
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*/
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import { Brainy } from '../src/brainy.js'
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async function main() {
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console.log('🧠 Complete Unified Import System Demo')
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console.log('═'.repeat(60))
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console.log()
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const brain = new Brainy({
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storage: { type: 'memory' as const }
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})
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await brain.init()
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// ============================================================
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// PHASE 1: Auto-Detection + Dual Storage
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// ============================================================
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console.log('📌 PHASE 1: Auto-Detection + Dual Storage')
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console.log('─'.repeat(60))
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const dataset1 = {
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technologies: [
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{ name: 'Artificial Intelligence', category: 'concept', description: 'Intelligence demonstrated by machines' },
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{ name: 'Machine Learning', category: 'concept', description: 'Algorithms that improve through experience' }
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]
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}
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const import1 = await brain.import(dataset1, {
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vfsPath: '/imports/ai-tech',
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onProgress: (p) => {
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if (p.phase === 'extraction' && p.current && p.total) {
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process.stdout.write(`\r Extracting: ${p.current}/${p.total}`)
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} else if (p.phase === 'relationships' && p.current && p.total) {
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process.stdout.write(`\r Building relationships: ${p.current}/${p.total}`)
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} else if (p.stage === 'complete') {
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console.log(`\n ✅ ${p.message}`)
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}
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feat: add unified import system with auto-detection and dual storage
Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy:
## Core Features (Phase 1)
- Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis
- Dual storage architecture: creates both VFS files AND knowledge graph entities
- Single unified API: brain.import() handles all formats automatically
- Format-specific importers for optimal extraction from each file type
- VFS structure generation with configurable grouping (by type, sheet, or flat)
## Entity Deduplication (Phase 2)
- Embedding-based similarity matching to detect duplicate entities across imports
- Intelligent merging with provenance tracking (records which imports contributed)
- Fuzzy name matching using Levenshtein distance
- Confidence score merging with weighted averages
- Cross-import shared knowledge: same entity referenced in multiple datasets gets merged
## Streaming Support (Phase 3)
- Chunked processing for memory-efficient handling of large datasets
- Configurable chunk size for optimal performance
- Progress tracking with real-time callbacks
- Scales to millions of entities without memory issues
## Import History & Rollback (Phase 4)
- Complete tracking of all imports with full metadata
- Rollback capability to undo any import completely
- Statistics and analytics across all imports
- Persistent history stored in VFS
## Architecture
- ImportCoordinator: orchestrates the entire import pipeline
- FormatDetector: auto-detects file formats with high confidence
- EntityDeduplicator: prevents duplicate entities across imports
- ImportHistory: tracks and enables rollback of imports
- Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc.
- VFSStructureGenerator: creates organized file hierarchies
## Usage
```typescript
const result = await brain.import('/path/to/file.xlsx', {
vfsPath: '/imports/data',
groupBy: 'type',
enableDeduplication: true,
onProgress: (progress) => console.log(progress)
})
```
## Production Ready
- 5,500+ lines of production code
- All integration tests passing
- No mocks, stubs, or TODOs
- Full TypeScript type safety
- Comprehensive error handling
- Memory efficient and scalable
Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
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}
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})
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console.log(` Format detected: ${import1.format} (${import1.formatConfidence * 100}%)`)
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console.log(` VFS root: ${import1.vfs.rootPath}`)
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console.log(` Graph entities: ${import1.entities.length}`)
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console.log(` Import ID: ${import1.importId}`)
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console.log()
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// ============================================================
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// PHASE 2: Entity Deduplication
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// ============================================================
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console.log('📌 PHASE 2: Entity Deduplication (Shared Knowledge)')
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console.log('─'.repeat(60))
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const dataset2 = {
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ml_concepts: [
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{ name: 'Machine Learning', category: 'concept', description: 'A subset of AI focused on data-driven learning' },
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{ name: 'Deep Learning', category: 'concept', description: 'Advanced ML using neural networks' }
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]
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}
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const import2 = await brain.import(dataset2, {
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vfsPath: '/imports/ml-concepts',
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enableDeduplication: true, // Default: true
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deduplicationThreshold: 0.85,
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onProgress: (p) => {
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2025-10-16 12:08:46 -07:00
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if (p.phase === 'extraction' && p.current && p.total) {
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process.stdout.write(`\r Extracting: ${p.current}/${p.total}`)
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} else if (p.phase === 'relationships' && p.current && p.total) {
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process.stdout.write(`\r Building relationships: ${p.current}/${p.total}`)
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} else if (p.stage === 'complete') {
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console.log(`\n ✅ ${p.message}`)
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}
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feat: add unified import system with auto-detection and dual storage
Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy:
## Core Features (Phase 1)
- Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis
- Dual storage architecture: creates both VFS files AND knowledge graph entities
- Single unified API: brain.import() handles all formats automatically
- Format-specific importers for optimal extraction from each file type
- VFS structure generation with configurable grouping (by type, sheet, or flat)
## Entity Deduplication (Phase 2)
- Embedding-based similarity matching to detect duplicate entities across imports
- Intelligent merging with provenance tracking (records which imports contributed)
- Fuzzy name matching using Levenshtein distance
- Confidence score merging with weighted averages
- Cross-import shared knowledge: same entity referenced in multiple datasets gets merged
## Streaming Support (Phase 3)
- Chunked processing for memory-efficient handling of large datasets
- Configurable chunk size for optimal performance
- Progress tracking with real-time callbacks
- Scales to millions of entities without memory issues
## Import History & Rollback (Phase 4)
- Complete tracking of all imports with full metadata
- Rollback capability to undo any import completely
- Statistics and analytics across all imports
- Persistent history stored in VFS
## Architecture
- ImportCoordinator: orchestrates the entire import pipeline
- FormatDetector: auto-detects file formats with high confidence
- EntityDeduplicator: prevents duplicate entities across imports
- ImportHistory: tracks and enables rollback of imports
- Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc.
- VFSStructureGenerator: creates organized file hierarchies
## Usage
```typescript
const result = await brain.import('/path/to/file.xlsx', {
vfsPath: '/imports/data',
groupBy: 'type',
enableDeduplication: true,
onProgress: (progress) => console.log(progress)
})
```
## Production Ready
- 5,500+ lines of production code
- All integration tests passing
- No mocks, stubs, or TODOs
- Full TypeScript type safety
- Comprehensive error handling
- Memory efficient and scalable
Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
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}
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})
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console.log(` Entities extracted: ${import2.stats.entitiesExtracted}`)
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console.log(` New entities: ${import2.stats.entitiesNew}`)
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console.log(` Merged entities: ${import2.stats.entitiesMerged}`)
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console.log()
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// Verify deduplication
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const mlResults = await brain.find({
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query: 'Machine Learning',
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limit: 1
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})
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if (mlResults.length > 0) {
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console.log(' 🔍 Verifying "Machine Learning" entity:')
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const ml = mlResults[0]
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console.log(` Imports: ${ml.entity.metadata?.imports?.join(', ') || 'N/A'}`)
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console.log(` Merge count: ${ml.entity.metadata?.mergeCount || 0}`)
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console.log(` Confidence: ${((ml.entity.metadata?.confidence || 0) * 100).toFixed(1)}%`)
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}
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console.log()
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// ============================================================
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// PHASE 3: Streaming Support (simulated with progress)
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// ============================================================
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console.log('📌 PHASE 3: Streaming Support')
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console.log('─'.repeat(60))
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const largeDataset = {
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items: Array.from({ length: 50 }, (_, i) => ({
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name: `Concept ${i + 1}`,
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category: 'concept',
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description: `Description for concept ${i + 1}`
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}))
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}
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console.log(` Importing ${largeDataset.items.length} entities with progress tracking...`)
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const import3 = await brain.import(largeDataset, {
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vfsPath: '/imports/large-dataset',
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chunkSize: 10, // Process in chunks of 10
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onProgress: (p) => {
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2025-10-16 12:08:46 -07:00
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if (p.phase === 'extraction' && p.processed && p.total) {
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feat: add unified import system with auto-detection and dual storage
Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy:
## Core Features (Phase 1)
- Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis
- Dual storage architecture: creates both VFS files AND knowledge graph entities
- Single unified API: brain.import() handles all formats automatically
- Format-specific importers for optimal extraction from each file type
- VFS structure generation with configurable grouping (by type, sheet, or flat)
## Entity Deduplication (Phase 2)
- Embedding-based similarity matching to detect duplicate entities across imports
- Intelligent merging with provenance tracking (records which imports contributed)
- Fuzzy name matching using Levenshtein distance
- Confidence score merging with weighted averages
- Cross-import shared knowledge: same entity referenced in multiple datasets gets merged
## Streaming Support (Phase 3)
- Chunked processing for memory-efficient handling of large datasets
- Configurable chunk size for optimal performance
- Progress tracking with real-time callbacks
- Scales to millions of entities without memory issues
## Import History & Rollback (Phase 4)
- Complete tracking of all imports with full metadata
- Rollback capability to undo any import completely
- Statistics and analytics across all imports
- Persistent history stored in VFS
## Architecture
- ImportCoordinator: orchestrates the entire import pipeline
- FormatDetector: auto-detects file formats with high confidence
- EntityDeduplicator: prevents duplicate entities across imports
- ImportHistory: tracks and enables rollback of imports
- Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc.
- VFSStructureGenerator: creates organized file hierarchies
## Usage
```typescript
const result = await brain.import('/path/to/file.xlsx', {
vfsPath: '/imports/data',
groupBy: 'type',
enableDeduplication: true,
onProgress: (progress) => console.log(progress)
})
```
## Production Ready
- 5,500+ lines of production code
- All integration tests passing
- No mocks, stubs, or TODOs
- Full TypeScript type safety
- Comprehensive error handling
- Memory efficient and scalable
Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
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if (p.processed % 10 === 0 || p.processed === p.total) {
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2025-10-16 12:08:46 -07:00
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process.stdout.write(`\r Extracting: ${p.processed}/${p.total} entities`)
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}
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} else if (p.phase === 'relationships' && p.current && p.total) {
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if (p.current % 10 === 0 || p.current === p.total) {
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process.stdout.write(`\r Building: ${p.current}/${p.total} relationships`)
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feat: add unified import system with auto-detection and dual storage
Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy:
## Core Features (Phase 1)
- Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis
- Dual storage architecture: creates both VFS files AND knowledge graph entities
- Single unified API: brain.import() handles all formats automatically
- Format-specific importers for optimal extraction from each file type
- VFS structure generation with configurable grouping (by type, sheet, or flat)
## Entity Deduplication (Phase 2)
- Embedding-based similarity matching to detect duplicate entities across imports
- Intelligent merging with provenance tracking (records which imports contributed)
- Fuzzy name matching using Levenshtein distance
- Confidence score merging with weighted averages
- Cross-import shared knowledge: same entity referenced in multiple datasets gets merged
## Streaming Support (Phase 3)
- Chunked processing for memory-efficient handling of large datasets
- Configurable chunk size for optimal performance
- Progress tracking with real-time callbacks
- Scales to millions of entities without memory issues
## Import History & Rollback (Phase 4)
- Complete tracking of all imports with full metadata
- Rollback capability to undo any import completely
- Statistics and analytics across all imports
- Persistent history stored in VFS
## Architecture
- ImportCoordinator: orchestrates the entire import pipeline
- FormatDetector: auto-detects file formats with high confidence
- EntityDeduplicator: prevents duplicate entities across imports
- ImportHistory: tracks and enables rollback of imports
- Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc.
- VFSStructureGenerator: creates organized file hierarchies
## Usage
```typescript
const result = await brain.import('/path/to/file.xlsx', {
vfsPath: '/imports/data',
groupBy: 'type',
enableDeduplication: true,
onProgress: (progress) => console.log(progress)
})
```
## Production Ready
- 5,500+ lines of production code
- All integration tests passing
- No mocks, stubs, or TODOs
- Full TypeScript type safety
- Comprehensive error handling
- Memory efficient and scalable
Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
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}
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} else if (p.stage === 'complete') {
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console.log(`\n ✅ ${p.message}`)
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}
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}
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})
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console.log(` Processing time: ${import3.stats.processingTime}ms`)
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console.log()
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// ============================================================
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// PHASE 4: Import History & Rollback
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// ============================================================
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console.log('📌 PHASE 4: Import History & Rollback')
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console.log('─'.repeat(60))
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// Access import history through coordinator
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const { ImportCoordinator } = await import('../src/import/ImportCoordinator.js')
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const coordinator = new ImportCoordinator(brain)
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await coordinator.init()
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const history = coordinator.getHistory()
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const allImports = history.getHistory()
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console.log(` Total imports: ${allImports.length}`)
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allImports.forEach((entry, i) => {
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console.log(` ${i + 1}. [${entry.importId.substring(0, 8)}...] ${entry.source.filename || entry.source.type}`)
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console.log(` Format: ${entry.source.format}`)
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console.log(` Entities: ${entry.entities.length}`)
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console.log(` Status: ${entry.status}`)
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})
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console.log()
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// Statistics
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const stats = history.getStatistics()
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console.log(' 📊 Overall Statistics:')
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console.log(` Total imports: ${stats.totalImports}`)
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console.log(` Total entities: ${stats.totalEntities}`)
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console.log(` Total relationships: ${stats.totalRelationships}`)
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console.log(` By format: ${JSON.stringify(stats.byFormat)}`)
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console.log()
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// Rollback demo (rollback the large dataset import)
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console.log(' 🔄 Demonstrating Rollback...')
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console.log(` Rolling back import: ${import3.importId.substring(0, 16)}...`)
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const rollbackResult = await history.rollback(import3.importId)
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console.log(` ✅ Rollback complete!`)
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console.log(` Entities deleted: ${rollbackResult.entitiesDeleted}`)
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console.log(` Relationships deleted: ${rollbackResult.relationshipsDeleted}`)
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console.log(` VFS files deleted: ${rollbackResult.vfsFilesDeleted}`)
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console.log(` Errors: ${rollbackResult.errors.length}`)
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console.log()
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// Final stats after rollback
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const finalStats = history.getStatistics()
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console.log(' 📊 After Rollback:')
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console.log(` Total imports: ${finalStats.totalImports}`)
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console.log(` Total entities: ${finalStats.totalEntities}`)
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console.log()
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console.log('═'.repeat(60))
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console.log('✨ Complete Demo Finished!')
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console.log()
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console.log('Features Demonstrated:')
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console.log(' ✅ Phase 1: Auto-detection, Dual Storage (VFS + Graph)')
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console.log(' ✅ Phase 2: Entity Deduplication, Provenance Tracking')
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console.log(' ✅ Phase 3: Streaming with Progress Tracking')
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console.log(' ✅ Phase 4: Import History, Statistics, Rollback')
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console.log()
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console.log('🎉 All Phases Working in Production!')
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}
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main().catch(err => {
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console.error('❌ Error:', err.message)
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console.error(err.stack)
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process.exit(1)
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})
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