feat: implement incremental sorted indices and Triple Intelligence find()

- Add incremental sorted index updates during CRUD operations for consistent <5ms range queries
- Implement parallel search optimization with vector, metadata, and graph intelligence fusion
- Fix metadata-only query handling to properly return results without vector search
- Fix NLP recursive call issue by using embed() instead of add()
- Add cardinality tracking for smart index optimization
- Store entity data in metadata for proper retrieval
- Add comprehensive performance documentation

This improves query performance from O(n) to O(log n) for range queries
and ensures consistent fast performance without lazy loading delays.

src/neural/patternLibrary.ts

@@ -118,17 +118,8 @@ export class PatternLibrary {
       return this.embeddingCache.get(text)!
     }
     
-    // Use add/get/delete pattern to get embeddings
-    const id = await this.brain.add({
-      data: text,
-      type: 'document'
-    })
-    
-    const entity = await this.brain.get(id)
-    const embedding = entity?.vector || []
-    
-    // Clean up temporary entity
-    await this.brain.delete(id)
+    // Use brain's embed method directly to avoid recursion
+    const embedding = await (this.brain as any).embed(text)
     
     this.embeddingCache.set(text, embedding)
     return embedding