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.

scripts/buildEmbeddedPatterns.ts

@@ -45,20 +45,11 @@ async function buildEmbeddedPatterns() {
       
       for (const example of pattern.examples || []) {
         try {
-          // Add the example temporarily to get its embedding
-          const id = await brain.add({
-            data: example,
-            type: 'document' // Use document type for text
-          })
-          
-          // Get the entity with its embedding
-          const entity = await brain.get(id)
-          if (entity?.vector && Array.isArray(entity.vector)) {
-            embeddings.push(entity.vector)
+          // Use brain's embed method directly - no add/delete needed!
+          const embedding = await (brain as any).embed(example)
+          if (embedding && Array.isArray(embedding)) {
+            embeddings.push(embedding)
           }
-          
-          // Remove the temporary entity
-          await brain.delete(id)
         } catch (error) {
           console.warn(`  ⚠️ Failed to embed example: "${example}"`)
         }