Commit graph

14 commits

Author SHA1 Message Date
d938a6bd4f feat: progressive init and readiness API for cloud storage
- Add `initMode` option to all cloud storage adapters (GCS, S3, Azure)
  - 'auto' (default): progressive in cloud, strict locally
  - 'progressive': <200ms cold starts, lazy bucket validation
  - 'strict': blocking validation (current behavior)

- Add cloud environment auto-detection for:
  - Cloud Run (K_SERVICE, K_REVISION)
  - AWS Lambda (AWS_LAMBDA_FUNCTION_NAME)
  - Cloud Functions (FUNCTIONS_TARGET)
  - Azure Functions (AZURE_FUNCTIONS_ENVIRONMENT)

- Add readiness API to Brainy class:
  - `brain.ready`: Promise that resolves when init() completes
  - `brain.isFullyInitialized()`: checks if background tasks done
  - `brain.awaitBackgroundInit()`: waits for background tasks

- Lazy bucket validation on first write (not during init)
- Background count synchronization
- Update AWS/GCP deployment docs with readiness patterns

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-07 12:51:05 -08:00
14231554e1 fix: prevent circuit breaker activation and data loss during bulk imports
Storage-aware batching system prevents rate limiting issues on cloud storage (GCS, S3, R2, Azure). Replaces entity-by-entity creation with addMany()/relateMany() batch operations in ImportCoordinator. Separate read/write circuit breakers prevent read lockouts during write throttling. Each storage adapter auto-configures optimal batch sizes and delays. Fixes silent data loss and 30+ second lockouts on 1000+ row imports.

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

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-30 08:54:04 -07:00
798a6946d6 fix(storage): resolve count synchronization race condition across all storage adapters
Fixed critical bug where entity and relationship counts were not being tracked correctly
during add(), relate(), and import() operations. The root cause was a race condition where
count increment code tried to read metadata before it was saved to storage.

Core Fixes:
- Modified baseStorage.saveNounMetadata_internal to increment counts AFTER metadata is saved
- Modified baseStorage.saveVerbMetadata_internal to increment verb counts AFTER metadata is saved
- Added verb type to VerbMetadata to avoid circular dependency during count tracking
- Refactored verb count methods to prevent mutex deadlocks (synchronous base + async Safe wrapper)

Storage Adapter Cleanup:
- Removed broken count increment code from FileSystemStorage, GcsStorage, R2Storage, AzureBlobStorage
- Updated MemoryStorage comments to reflect centralized fix
- All count tracking now centralized in baseStorage (fixes ALL adapters automatically)

New Utilities:
- Added rebuildCounts utility to repair corrupted counts.json from actual storage data
- Added comprehensive integration tests for count synchronization across all operations

Verification:
- All 8 storage adapters verified (FileSystem, GCS, Memory, S3Compatible, R2, Azure, OPFS, TypeAware)
- All code paths verified (add, relate, import, batch, update, delete)
- 599 tests passing (no regressions)
- No deadlocks (tests complete in 6s vs 150s+)

Fixes #1 and #2 reported by Workshop team
2025-10-21 11:19:08 -07:00
92c96246fb feat(v4.0.0): Complete metadata/vector separation architecture with Azure support
This commit completes the core v4.0.0 architecture changes for billion-scale
performance with metadata/vector separation. NO RELEASE YET - remaining optimizations
and testing required before production release.

## Core v4.0.0 Architecture Changes

### Type System Updates
- Fixed all TypeScript compilation errors (zero errors achieved)
- Updated HNSWNoun/HNSWVerb to separate core fields from metadata
- Implemented HNSWNounWithMetadata/HNSWVerbWithMetadata for API boundaries
- Added required 'noun' field to NounMetadata for semantic structure
- Renamed verb.type to verb.verb for consistency

### Storage Adapter Updates
**All adapters updated for v4.0.0 two-file storage pattern:**
- memoryStorage: Proper metadata/vector separation
- fileSystemStorage: Two-file pattern with sharding
- opfsStorage: Browser persistent storage updated
- s3CompatibleStorage: AWS/MinIO/DigitalOcean support
- r2Storage: Cloudflare R2 optimization
- gcsStorage: Google Cloud with ADC support
- **azureBlobStorage: NEW - Full Azure Blob Storage support**

### Storage Features
- BaseStorage: Internal vs public method separation (_getNoun vs getNoun)
- Two-file storage: Vectors in one file, metadata in another
- Change tracking: getChangesSince return type updated
- Pagination: getNounsWithPagination returns WithMetadata types

### Azure Blob Storage Integration (NEW)
- Native @azure/storage-blob SDK integration
- Four authentication methods:
  * DefaultAzureCredential (Managed Identity) - recommended
  * Connection String - simplest setup
  * Account Name + Key - traditional auth
  * SAS Token - delegated access
- High-volume mode with write buffering
- Adaptive backpressure for throttling
- UUID-based sharding for billion-scale
- Full HNSW support with graph persistence

### Utility Updates
- EmbeddingManager: Updated to accept Record<string, unknown>
- LSMTree: Wrapped data in NounMetadata structure with 'noun' field
- EntityIdMapper: Fixed nested metadata.data structure access
- MetadataIndex: Fixed field type inference integration
- PeriodicCleanup: Updated for new metadata structure

### Core API Updates
- Brainy: Updated verb property access from v.type to v.verb
- ConfigAPI: Fixed NounMetadata access patterns
- DataAPI: Updated metadata handling

### Documentation Updates
- CREATING-AUGMENTATIONS.md: v4.0.0 breaking changes guide
- DEVELOPER-GUIDE.md: Migration checklist and examples
- COMPLETE-REFERENCE.md: v4.0.0 architecture improvements
- **finite-type-system.md: NEW - Revolutionary type system benefits**

### Build & Dependencies
- Zero TypeScript compilation errors
- Added @azure/storage-blob and @azure/identity
- 591 tests passing (23 timeout in long-running neural tests)

## What's NOT in This Release
This is a work-in-progress commit. Before v4.0.0 release we need:
- Storage adapter optimizations (batch operations, compression)
- Azure blob tier management (Hot/Cool/Archive)
- Cost optimization implementations
- Additional performance testing at billion-scale
- Migration guides for v3.x users

## Testing
- Clean build: 
- Type checking:  (zero errors)
- Test suite:  (591/614 passing, timeouts in neural tests only)

🔐 Generated with Claude Code
https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-17 12:29:27 -07:00
e600865d96 fix: metadata explosion bug - 69K files reduced to ~1K
Critical fix for metadata indexing that was creating 60+ chunk files per entity.

Root cause: Vector embeddings (384-dimensional arrays) were being indexed in
metadata, causing each dimension to create a separate chunk file with numeric
field names ("0", "1", "2", etc.).

Changes:
- Modified extractIndexableFields() to exclude vector/embedding fields
- Added NEVER_INDEX set: ['vector', 'embedding', 'embeddings', 'connections']
- Added safety check to skip arrays > 10 elements
- Preserves small array indexing (tags, categories, roles)

Impact:
- Reduces metadata files from 69,429 → ~1,200 (58x reduction)
- Fixes server initialization hangs
- Fixes metadata batch loading stalling at batch 23
- Fixes VFS getDescendants() hanging with large datasets
- Fixes Graph View UI not loading

Test Results:
- 7/7 integration tests passing
- Verified: 6 chunk files for 10 entities (was 7,210 before fix)
- 611/622 unit tests passing

Files Modified:
- src/utils/metadataIndex.ts - Core fix
- src/coreTypes.ts - HNSWVerb type enforcement with VerbType enum
- src/storage/adapters/* - Include core relational fields in HNSWVerb
- src/storage/adapters/baseStorageAdapter.ts - Type enforcement (HNSWNoun, GraphVerb)
- tests/integration/metadata-vector-exclusion.test.ts - Comprehensive test coverage

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

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-16 16:10:31 -07:00
6a4d1aeb2b feat: implement HNSW index rebuild and unified index interface
Fixes critical bugs causing data loss after container restarts:
- Bug #1: GraphAdjacencyIndex rebuild now properly called
- Bug #2: Improved early return logic (checks actual storage data)
- Bug #4: HNSW index now has production-grade rebuild mechanism

New features:
- Production-grade HNSW rebuild() with O(N) restoration algorithm
- Unified IIndex interface for consistent lifecycle management
- Parallel index rebuilds (HNSW, Graph, Metadata in parallel)
- HNSW persistence methods across all 5 storage adapters
- Comprehensive integration tests with 9 test scenarios

Performance improvements:
- 20 entities: 8ms rebuild time
- Handles millions of entities via cursor-based pagination
- O(N) restoration vs O(N log N) rebuilding from scratch

All changes are production-ready with no mocks, stubs, or TODOs.
2025-10-10 11:15:17 -07:00
e52bcaf294 perf: implement smart count batching for 10x faster bulk operations
Add storage-type aware count batching that maintains reliability while
dramatically improving bulk operation performance (v3.32.3).

**Performance Impact:**
- Cloud storage: 1000 entities = 100 writes (was 1000) = 10x faster
- Local storage: Immediate persist (no batching needed)
- API use case: 2-10x faster for small batches

**How It Works:**
- Cloud storage (GCS, S3, R2): Batches 10 ops OR 5 seconds
- Local storage (File, Memory): Persists immediately
- Graceful shutdown: SIGTERM/SIGINT hooks flush pending counts

**Reliability:**
- Container restart: Same reliability as v3.32.2
- Graceful shutdown: Zero data loss
- Production ready: Backward compatible, zero config

**Changes:**
- baseStorageAdapter.ts: Smart batching with scheduleCountPersist()
- gcsStorage.ts: Cloud storage detection (isCloudStorage = true)
- s3CompatibleStorage.ts: Cloud storage detection
- brainy.ts: Graceful shutdown hooks (SIGTERM/SIGINT/beforeExit)
- package.json: Bump version to 3.32.3
- CHANGELOG.md: Document performance optimization

Fixes container restart bugs while making bulk imports production-scale ready.
No breaking changes, no migration required.
2025-10-09 17:35:01 -07:00
39525aff26 fix: resolve critical count persistence bugs affecting container restarts
Fixes two critical production bugs in GCS storage adapter:

1. brain.find({ where: {...} }) returned empty array after restart
   - Root cause: Counts only persisted every 10 operations
   - If <10 entities added before restart, counts were lost
   - After restart: totalNounCount = 0, causing empty results

2. brain.init() returned 0 entities after container restart
   - Same root cause as bug #1
   - Counts file never written for small datasets
   - getStats() returned 0 despite data in GCS bucket

Changes:
- baseStorageAdapter.ts: Persist counts on EVERY operation (not every 10)
  - incrementEntityCountSafe(): Now persists immediately
  - decrementEntityCountSafe(): Now persists immediately
  - incrementVerbCount(): Now persists immediately
  - decrementVerbCount(): Now persists immediately

- gcsStorage.ts: Better error handling for count initialization
  - initializeCounts(): Fail loudly on network/permission errors
  - initializeCountsFromScan(): Throw on scan failures instead of silent fail
  - Added recovery logic with bucket scan fallback

Impact: Critical for serverless/containerized deployments (Cloud Run, Fargate, Lambda)
where containers restart frequently. The basic write→restart→read scenario now works.
2025-10-09 17:12:55 -07:00
32f5ac6fee fix: metadata batch reading from correct directory
Fixed bug where getMetadataBatch() was reading from wrong directory:
- FileSystemStorage: Changed to use getNounMetadata() instead of getMetadata()
- OPFSStorage: Changed to use getNounMetadata() instead of getMetadata()
- MetadataIndex fallback: Fixed to use getNounMetadata()
- Added getNounMetadata() to StorageAdapter interface

This resolves 0% success rate during metadata index rebuild.

Also added comprehensive API documentation for return values and data field behavior.
2025-10-06 15:43:45 -07:00
ed64c266ec feat: add distributed architecture with sharding and coordination
- Wire up distributed components (Coordinator, ShardManager, CacheSync)
- Implement automatic sharding for S3 storage (256 shards)
- Add read/write separation for operational modes
- Zero-config automatic detection for distributed mode
- Add mutex implementation for thread safety
- Fix metadata filtering in find operations
- Fix neural API vector similarity calculations
- Improve batch operations performance
- Add Bluesky distributed setup example

BREAKING CHANGE: None - backward compatible
2025-09-22 15:45:35 -07:00
f463ee3fe2 fix: remove dangerous count methods from interface
- Removed countNouns() and countVerbs() from BaseStorageAdapter
- These methods would be dangerous with millions of entries
- We already have incremental statistics tracking via incrementStatistic()
- Statistics are maintained in cache and updated as items are added/removed
- Much more scalable than iterating through all items

The existing statistics system is the proper way to get counts:
- Uses incrementStatistic('noun'/'verb', service) on add
- Uses decrementStatistic() on delete
- Access via getStatistics() which returns cached counts
- No iteration through millions of items needed
2025-09-02 15:02:50 -07:00
7c46879334 chore(release): 2.14.1
- Fix verb retrieval in FileSystemStorage adapter
- Add safety warnings for large datasets
- Document performance considerations for count methods
2025-09-02 14:56:16 -07:00
e5c6b0afe7 fix: implement getVerbsWithPagination in FileSystemStorage adapter
- Add missing getVerbsWithPagination() method to FileSystemStorage
- Fixes verb retrieval returning empty arrays
- Add pagination method declarations to BaseStorageAdapter interface
- Support filtering by sourceId, targetId, verbType, and service
- Include metadata retrieval for each verb

Resolves issue where brain.getVerbs() returned empty array even after
successfully adding verbs with FileSystemStorage adapter.
2025-09-02 14:55:15 -07:00
9c87982a7d 🧠 Brainy 2.0.0 - Zero-Configuration AI Database with Triple Intelligence™
MAJOR RELEASE: Complete evolution of Brainy with groundbreaking features and performance.

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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 Triple Intelligence™ Engine
  - Unified Vector + Metadata + Graph search
  - O(log n) performance on all operations
  - 3ms average search latency at any scale

 API Consolidation
  - 15+ search methods → 2 clean APIs
  - search() for vector similarity
  - find() for natural language queries

 Natural Language Processing
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  - Instant context understanding
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  - Node.js, Browser, Edge Workers
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• Initialize: 450ms (24MB memory)
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• Professional README with examples
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Built with ❤️ by the Brainy community.
Visit https://soulcraft.com for Brain Cloud integration.
2025-08-26 12:32:21 -07:00