soulcraft/brainy
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Author SHA1 Message Date
David Snelling
1874b77896 feat: add ImageHandler with EXIF extraction and comprehensive MIME detection (v5.2.0) 
Implements Phase 1.5 (Comprehensive MIME Type Detection) and adds built-in image processing support to IntelligentImportAugmentation.

**New Features:**
- ImageHandler: Extracts image metadata (dimensions, format, color space) using sharp
- EXIF extraction: Camera data, GPS, timestamps using exifr library
- Support for JPEG, PNG, WebP, GIF, TIFF, BMP, SVG, HEIC, AVIF formats
- MimeTypeDetector: Unified MIME type detection with magic byte support
- FormatDetector: Enhanced with image format detection via MIME + magic bytes

**Architecture Fixes:**
- Fixed brain.import() augmentation pipeline integration (src/brainy.ts:3140-3154)
- Added parameter spreading for ImportSource objects to enable augmentation access
- Fixed metadata propagation through ImportCoordinator to final results
- Added augmentation data check in ImportCoordinator.extract()

**Integration:**
- ImageHandler registered as built-in handler alongside CSV, Excel, PDF
- Images import as 'media' entities with 'image' subtype
- Full metadata preserved in knowledge graph entities
- Configuration options: enableImage, extractEXIF, imageDefaults

**Test Coverage:**
- 15 integration tests (image-import.test.ts) - 100% passing
- 27 unit tests (image-handler.test.ts) - 100% passing
- Format detection tests for all supported image types
- Error handling and resilience tests

**Breaking Changes:** None - backward compatible

Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-11-03 14:06:17 -08:00
David Snelling
0bcf50a442 fix: resolve HNSW concurrency race condition across all storage adapters 
Fixes critical P0 bug causing data corruption during bulk imports with 50+ concurrent operations. The non-atomic read-modify-write pattern in saveHNSWData() combined with fire-and-forget neighbor updates was causing 16-32 concurrent writes per entity, resulting in lost HNSW connections and corrupted graph structure.

**Root Cause:**
- saveHNSWData() used non-atomic read-modify-write
- HNSW neighbor updates fired without await (16-32 concurrent writes/entity)
- Popular nodes became hotspots (100 concurrent imports = 3,400 concurrent saveHNSWData calls)
- Result: Lost neighbor connections, 0 search results

**Atomic Write Strategies by Adapter:**

FileSystemStorage:
- Atomic rename with temp files
- Write to {file}.tmp.{timestamp}.{random}
- POSIX-guaranteed atomic rename(temp, final)

GCSStorage:
- Optimistic locking with generation numbers
- preconditionOpts: { ifGenerationMatch }
- 5 retries with exponential backoff (50ms→800ms)

S3/R2/AzureStorage:
- ETag-based optimistic locking
- IfMatch/conditions preconditions
- 5 retries with exponential backoff

MemoryStorage + OPFSStorage:
- Mutex locks per entity path
- Serializes async operations even in single-threaded environments

HNSW Index:
- Changed fire-and-forget .catch() to await
- Serializes 16-32 neighbor updates per entity
- Trade-off: 20-30% slower bulk import vs 100% data integrity

**Sharding Compatibility:**
- ✅ Works with deterministic UUID sharding (256 shards, always on)
- ✅ Works with distributed multi-node sharding (optional)
- ✅ All atomic strategies work in both single-node and distributed deployments

**Index Impact:**
- Only HNSW index modified (saveHNSWData, saveHNSWSystem)
- Other 4 indexes unaffected (Metadata, Graph Adjacency, Deleted Items, Entity ID Mapper)
- No regression risk - isolated code paths

**Testing:**
- 8/8 unit tests passing (real concurrent operations, no mocks)
- Tests verify data integrity after 20 concurrent updates
- Tests verify temp file cleanup and mutex serialization

**Files Modified:**
- All 8 storage adapters (FileSystem, GCS, S3, R2, Azure, Memory, OPFS)
- HNSW Index (neighbor update serialization)
- New test: tests/unit/storage/hnswConcurrency.test.ts (8 passing tests)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2025-10-29 15:24:20 -07:00
David Snelling
b3c4f348ab feat: implement complete VFS with Knowledge Layer integration 
Add production-ready Virtual File System with intelligent Knowledge Layer:

Core VFS Features:
- Complete file system operations (read, write, mkdir, etc.)
- Intelligent PathResolver with 4-layer caching system
- Chunked storage for large files with real compression
- Embedding generation for semantic operations
- File relationships and metadata tracking
- Import functionality from local filesystem

Knowledge Layer Integration:
- EventRecorder for complete file history and temporal coupling
- SemanticVersioning with content-based change detection
- PersistentEntitySystem for character/entity tracking across files
- ConceptSystem for universal concept mapping and graphs
- GitBridge for import/export between VFS and Git repositories

Architecture:
- KnowledgeAugmentation properly integrated into Brainy augmentation system
- KnowledgeLayer wrapper provides real-time VFS operation interception
- Background processing ensures VFS operations remain fast
- All components use real Brainy embed() method for embeddings
- Support for creative writing, coding projects, and project management

Technical Implementation:
- Fixed all stub/mock implementations with real working code
- TypeScript compilation passes without errors
- Comprehensive test suite demonstrating all features
- Documentation covering architecture and usage patterns
- Backwards compatible with existing Brainy functionality

This enables scenarios like writing books with persistent characters,
managing coding projects with concept tracking, and complete project
coordination with intelligent file relationships.
 2025-09-24 17:31:48 -07:00