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>
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Creating Augmentations for Brainy
Updated for v4.0.0 - Includes metadata structure changes and type system improvements
The BrainyAugmentation Interface
Every augmentation implements this simple yet powerful interface:
interface BrainyAugmentation {
// Identification
name: string // Unique name for your augmentation
// Execution control
timing: 'before' | 'after' | 'around' | 'replace' // When to execute
operations: string[] // Which operations to intercept
priority: number // Execution order (higher = first)
// Lifecycle methods
initialize(context: AugmentationContext): Promise<void>
execute<T>(operation: string, params: any, next: () => Promise<T>): Promise<T>
shutdown?(): Promise<void> // Optional cleanup
}
v4.0.0 Breaking Changes for Augmentation Developers
1. Metadata Structure Separation
v4.0.0 introduces strict metadata/vector separation for billion-scale performance:
// ✅ v4.0.0: Metadata has required type field
interface NounMetadata {
noun: NounType // Required! Must be a valid noun type
[key: string]: any // Your custom metadata
}
interface VerbMetadata {
verb: VerbType // Required! Must be a valid verb type
sourceId: string
targetId: string
[key: string]: any
}
2. Storage Adapter Return Types
Storage adapters now return different types at different boundaries:
// Internal methods: Pure structures (no metadata)
abstract _getNoun(id: string): Promise<HNSWNoun | null>
// Public API: WithMetadata structures
abstract getNoun(id: string): Promise<HNSWNounWithMetadata | null>
3. Verb Property Renamed
The verb relationship field changed from type to verb:
// ❌ v3.x
verb.type === 'relatedTo'
// ✅ v4.0.0
verb.verb === 'relatedTo'
Creating a Storage Augmentation
Storage augmentations are special - they provide the storage backend for Brainy.
Important: v4.0.0 Storage Requirements
Your storage adapter MUST:
- Wrap metadata with required
noun/verbfields - Return pure structures from internal
_methods - Return WithMetadata types from public methods
import { StorageAugmentation } from 'brainy/augmentations'
import { BaseStorageAdapter, HNSWNoun, HNSWNounWithMetadata, NounMetadata } from 'brainy'
export class MyCustomStorage extends BaseStorageAdapter {
// Internal method: Returns pure structure
async _getNoun(id: string): Promise<HNSWNoun | null> {
const data = await this.fetchFromDatabase(id)
return data ? {
id: data.id,
vector: data.vector,
nounType: data.type
} : null
}
// Public method: Returns WithMetadata structure
async getNoun(id: string): Promise<HNSWNounWithMetadata | null> {
const noun = await this._getNoun(id)
if (!noun) return null
// Fetch metadata separately (v4.0.0 pattern)
const metadata = await this.getNounMetadata(id)
return {
...noun,
metadata: metadata || { noun: noun.nounType || 'thing' }
}
}
// CRITICAL: Always save with proper metadata structure
async saveNoun(noun: HNSWNoun, metadata?: NounMetadata): Promise<void> {
// Validate metadata has required 'noun' field
if (!metadata?.noun) {
throw new Error('v4.0.0: NounMetadata requires "noun" field')
}
await this.database.save({
id: noun.id,
vector: noun.vector,
nounType: noun.nounType,
metadata: metadata // Stored separately in v4.0.0
})
}
}
export class MyStorageAugmentation extends StorageAugmentation {
private config: MyStorageConfig
constructor(config: MyStorageConfig) {
super()
this.name = 'my-custom-storage'
this.config = config
}
// Called during storage resolution phase
async provideStorage(): Promise<StorageAdapter> {
const storage = new MyCustomStorage(this.config)
this.storageAdapter = storage
return storage
}
// Called during augmentation initialization
protected async onInitialize(): Promise<void> {
await this.storageAdapter!.init()
this.log(`Custom storage initialized`)
}
}
Using Your Storage Augmentation
// Register before brain.init()
const brain = new Brainy()
brain.augmentations.register(new MyStorageAugmentation({
connectionString: 'redis://localhost:6379'
}))
await brain.init() // Will use your storage!
Creating a Feature Augmentation
Here's a complete example of a caching augmentation:
import { BaseAugmentation, BrainyAugmentation } from 'brainy/augmentations'
export class CachingAugmentation extends BaseAugmentation {
private cache = new Map<string, any>()
constructor() {
super()
this.name = 'smart-cache'
this.timing = 'around' // Wrap operations
this.operations = ['search'] // Only cache searches
this.priority = 50 // Mid-priority
}
async execute<T>(operation: string, params: any, next: () => Promise<T>): Promise<T> {
if (operation === 'search') {
// Check cache
const cacheKey = JSON.stringify(params)
if (this.cache.has(cacheKey)) {
this.log('Cache hit!')
return this.cache.get(cacheKey)
}
// Execute and cache
const result = await next()
this.cache.set(cacheKey, result)
return result
}
// Pass through other operations
return next()
}
protected async onInitialize(): Promise<void> {
this.log('Cache initialized')
}
async shutdown(): Promise<void> {
this.cache.clear()
await super.shutdown()
}
}
The Four Timing Modes
1. before - Pre-processing
timing = 'before'
async execute(op, params, next) {
// Validate/transform input
const validated = await validate(params)
return next(validated) // Pass modified params
}
2. after - Post-processing
timing = 'after'
async execute(op, params, next) {
const result = await next()
// Log, analyze, or modify result
console.log(`Operation ${op} returned:`, result)
return result
}
3. around - Wrapping (middleware)
timing = 'around'
async execute(op, params, next) {
console.log('Starting', op)
try {
const result = await next()
console.log('Success', op)
return result
} catch (error) {
console.log('Failed', op, error)
throw error
}
}
4. replace - Complete replacement
timing = 'replace'
async execute(op, params, next) {
// Don't call next() - replace entirely!
return myCustomImplementation(params)
}
Operations You Can Intercept
Common operations in Brainy:
'storage'- Storage resolution (special)'add'- Adding data'search','similar'- Searching'update','delete'- Modifications'saveNoun','saveVerb'- Storage operations'all'- Intercept everything
Context Available to Augmentations
interface AugmentationContext {
brain: Brainy // The brain instance
storage: StorageAdapter // Storage backend
config: BrainyConfig // Configuration
log: (message: string, level?: 'info' | 'warn' | 'error') => void
}
Real-World Examples
1. Redis Storage Augmentation
export class RedisStorageAugmentation extends StorageAugmentation {
async provideStorage(): Promise<StorageAdapter> {
return new RedisAdapter({
host: 'localhost',
port: 6379,
// Implement full StorageAdapter interface
})
}
}
2. Audit Trail Augmentation
export class AuditAugmentation extends BaseAugmentation {
timing = 'after'
operations = ['add', 'update', 'delete']
async execute(op, params, next) {
const result = await next()
// Log to audit trail
await this.logAudit({
operation: op,
params,
result,
timestamp: new Date(),
user: this.context.config.currentUser
})
return result
}
}
3. Rate Limiting Augmentation
export class RateLimitAugmentation extends BaseAugmentation {
timing = 'before'
operations = ['search']
private limiter = new RateLimiter({ rps: 100 })
async execute(op, params, next) {
await this.limiter.acquire() // Wait if rate limited
return next()
}
}
Publishing to Brain Cloud Marketplace
Future capability for premium augmentations:
// package.json
{
"name": "@brain-cloud/redis-storage",
"brainy": {
"type": "augmentation",
"category": "storage",
"premium": true
}
}
// Users can install via:
// brainy augment install redis-storage
Best Practices
General Practices
- Use BaseAugmentation - Provides common functionality
- Set appropriate priority - Storage (100), System (80-99), Features (10-50)
- Be selective with operations - Don't use 'all' unless necessary
- Handle errors gracefully - Don't break the chain
- Clean up in shutdown() - Release resources
- Log appropriately - Use context.log() for consistent output
- Document your augmentation - Include examples
v4.0.0 Specific Best Practices
-
Always include
nounfield when creating/modifying NounMetadata:const metadata: NounMetadata = { noun: 'thing', // REQUIRED! yourField: 'value' } -
Use
verbproperty nottypewhen working with relationships:// ✅ Correct if (verb.verb === 'relatedTo') { ... } // ❌ Wrong (v3.x pattern) if (verb.type === 'relatedTo') { ... } -
Access metadata correctly from storage:
// ✅ Correct - metadata is already structured const nounType = noun.metadata.noun // ⚠️ Fallback pattern for robustness const nounType = noun.metadata?.noun || 'thing' -
Respect the two-file storage pattern - Don't mix vector and metadata operations:
// ✅ Good - Separate concerns await storage.saveNoun(noun) await storage.saveMetadata(noun.id, metadata) // ❌ Bad - Mixing concerns await storage.saveNounWithEverything(combinedData)
Testing Your Augmentation
import { Brainy } from 'brainy'
import { MyAugmentation } from './my-augmentation'
describe('MyAugmentation', () => {
let brain: Brainy
beforeEach(async () => {
brain = new Brainy()
brain.augmentations.register(new MyAugmentation())
await brain.init()
})
afterEach(async () => {
await brain.destroy()
})
it('should enhance searches', async () => {
// Test your augmentation's effect
const results = await brain.search('test')
expect(results).toHaveProperty('enhanced', true)
})
})
Summary
Augmentations are Brainy's extension system. They can:
- Replace storage backends
- Add caching layers
- Implement audit trails
- Add rate limiting
- Sync with external systems
- Transform data
- And much more!
The unified BrainyAugmentation interface makes it easy to create powerful extensions while maintaining consistency across the entire system.