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>

src/embeddings/EmbeddingManager.ts

@@ -184,7 +184,7 @@ export class EmbeddingManager {
   /**
    * Generate embeddings
    */
-  async embed(text: string | string[]): Promise<Vector> {
+  async embed(text: string | string[] | Record<string, unknown>): Promise<Vector> {
     // Check for unit test environment - use mocks to prevent ONNX conflicts
     const isTestMode = process.env.BRAINY_UNIT_TEST === 'true' || (globalThis as any).__BRAINY_UNIT_TEST__
 
@@ -210,9 +210,12 @@ export class EmbeddingManager {
       input = text.map(t => typeof t === 'string' ? t : String(t)).join(' ')
     } else if (typeof text === 'string') {
       input = text
+    } else if (typeof text === 'object') {
+      // Convert object to string representation
+      input = JSON.stringify(text)
     } else {
       // This shouldn't happen but let's be defensive
-      console.warn('EmbeddingManager.embed received non-string input:', typeof text)
+      console.warn('EmbeddingManager.embed received unexpected input type:', typeof text)
       input = String(text)
     }
     
@@ -243,22 +246,22 @@ export class EmbeddingManager {
   /**
    * Generate mock embeddings for unit tests
    */
-  private getMockEmbedding(text: string | string[]): Vector {
+  private getMockEmbedding(text: string | string[] | Record<string, unknown>): Vector {
     // Use the same mock logic as setup-unit.ts for consistency
     const input = Array.isArray(text) ? text.join(' ') : text
     const str = typeof input === 'string' ? input : JSON.stringify(input)
     const vector = new Array(384).fill(0)
-    
+
     // Create semi-realistic embeddings based on text content
     for (let i = 0; i < Math.min(str.length, 384); i++) {
       vector[i] = (str.charCodeAt(i % str.length) % 256) / 256
     }
-    
+
     // Add position-based variation
     for (let i = 0; i < 384; i++) {
       vector[i] += Math.sin(i * 0.1 + str.length) * 0.1
     }
-    
+
     // Track mock embedding count
     this.embedCount++
     return vector
@@ -268,7 +271,7 @@ export class EmbeddingManager {
    * Get embedding function for compatibility
    */
   getEmbeddingFunction(): EmbeddingFunction {
-    return async (data: string | string[]): Promise<Vector> => {
+    return async (data: string | string[] | Record<string, unknown>): Promise<Vector> => {
       return await this.embed(data)
     }
   }
@@ -390,7 +393,7 @@ export const embeddingManager = EmbeddingManager.getInstance()
 /**
  * Direct embed function
  */
-export async function embed(text: string | string[]): Promise<Vector> {
+export async function embed(text: string | string[] | Record<string, unknown>): Promise<Vector> {
   return await embeddingManager.embed(text)
 }