fix: update all Stage 2 references to Stage 3 CANONICAL type counts

Comprehensive update of type count references from Stage 2 (31 nouns + 40 verbs)
to Stage 3 CANONICAL (42 nouns + 127 verbs) across entire codebase.

Changes (23 files):
- Core architecture: Memory tracking comments, speedup calculations
- Tests: Type count assertions, enum index expectations, memory benchmarks
- CLI: User-visible type count output
- Augmentations: Type detection comments
- Documentation: Architecture docs, guides, performance docs
- Type embeddings: Regenerated for all 169 types (338KB)

Specific updates:
- 31 → 42 (noun count): 38 occurrences
- 40 → 127 (verb count): 24 occurrences
- 124 → 168 bytes (noun array size): 5 occurrences
- 160 → 508 bytes (verb array size): 5 occurrences
- 284 → 676 bytes (total type tracking): 12 occurrences
- Enum indices updated to match Stage 3 reordering

Type embeddings regenerated:
- 42 noun embeddings (64.5 KB)
- 127 verb embeddings (194.8 KB)
- Total: 338 KB (was 108.8 KB)

All constants, arrays, and tests now consistent with Stage 3 taxonomy.

Fixes #v5.5.1-type-count-migration

src/augmentations/display/fieldPatterns.ts

@@ -23,7 +23,7 @@ export const UNIVERSAL_FIELD_PATTERNS: FieldPattern[] = [
     fields: ['firstName', 'lastName', 'fullName', 'realName'],
     displayField: 'title',
     confidence: 0.9,
-    applicableTypes: [NounType.Person, NounType.Person],
+    applicableTypes: [NounType.Person],
     transform: (value: any, context: FieldComputationContext) => {
       const { metadata } = context
       if (metadata.firstName && metadata.lastName) {
@@ -72,7 +72,7 @@ export const UNIVERSAL_FIELD_PATTERNS: FieldPattern[] = [
     fields: ['bio', 'biography', 'profile', 'about'],
     displayField: 'description',
     confidence: 0.85,
-    applicableTypes: [NounType.Person, NounType.Person]
+    applicableTypes: [NounType.Person]
   },
   {
     fields: ['content', 'text', 'body', 'message'],
@@ -105,7 +105,7 @@ export const UNIVERSAL_FIELD_PATTERNS: FieldPattern[] = [
     fields: ['role', 'position', 'jobTitle', 'occupation'],
     displayField: 'type',
     confidence: 0.8,
-    applicableTypes: [NounType.Person, NounType.Person],
+    applicableTypes: [NounType.Person],
     transform: (value: any) => String(value || 'Person')
   },
   {

src/augmentations/display/intelligentComputation.ts

@@ -1,11 +1,11 @@
 /**
  * Universal Display Augmentation - Intelligent Computation Engine
- * 
+ *
  * Leverages existing Brainy AI infrastructure for intelligent field computation:
  * - BrainyTypes for semantic type detection
- * - Neural Import patterns for field analysis  
+ * - Neural Import patterns for field analysis
  * - JSON processing utilities for field extraction
- * - Existing NounType/VerbType taxonomy (31+40 types)
+ * - Existing NounType/VerbType taxonomy (42+127 types)
  */
 
 import type { 

src/augmentations/typeMatching/brainyTypes.ts

@@ -1,9 +1,9 @@
 /**
  * BrainyTypes - Intelligent type detection using semantic embeddings
- * 
+ *
  * This module uses our existing TransformerEmbedding and similarity functions
- * to intelligently match data to our 31 noun types and 40 verb types.
- * 
+ * to intelligently match data to our 42 noun types and 127 verb types.
+ *
  * Features:
  * - Semantic similarity matching using embeddings
  * - Context-aware type detection

src/augmentations/typeMatching/intelligentTypeMatcher.ts

@@ -1,8 +1,8 @@
 /**
  * IntelligentTypeMatcher - Wrapper around BrainyTypes for testing
- * 
+ *
  * Provides intelligent type detection using semantic embeddings
- * for matching data to our 31 noun types and 40 verb types.
+ * for matching data to our 42 noun types and 127 verb types.
  */
 
 import { NounType, VerbType } from '../../types/graphTypes.js'

src/augmentations/universalDisplayAugmentation.ts

@@ -1,11 +1,11 @@
 /**
  * Universal Display Augmentation
- * 
+ *
  * 🎨 Provides intelligent display fields for any noun or verb using AI-powered analysis
- * 
+ *
  * Features:
  * - ✅ Leverages existing BrainyTypes for semantic type detection
- * - ✅ Complete icon coverage for all 31 NounTypes + 40+ VerbTypes
+ * - ✅ Complete icon coverage for all 42 NounTypes + 127 VerbTypes
  * - ✅ Zero performance impact with lazy computation and intelligent caching
  * - ✅ Perfect isolation - can be disabled, replaced, or configured
  * - ✅ Clean developer experience with zero conflicts

src/brainy.ts

@@ -3901,7 +3901,7 @@ export class Brainy<T = any> implements BrainyInterface<T> {
       },
 
       // Phase 1b: O(1) count by type enum (Uint32Array-based, more efficient)
-      // Uses fixed-size type tracking: 284 bytes vs ~35KB with Maps (99.2% reduction)
+      // Uses fixed-size type tracking: 676 bytes vs ~35KB with Maps (98.1% reduction)
       byTypeEnum: (type: NounType) => {
         return this.metadataIndex.getEntityCountByTypeEnum(type)
       },

src/cli/commands/types.ts

@@ -30,7 +30,7 @@ export async function types(options: { json?: boolean, noun?: boolean, verb?: bo
 
     // Display nouns
     if (showNouns) {
-      console.log(chalk.bold.cyan('\n📚 Noun Types (31):\n'))
+      console.log(chalk.bold.cyan('\n📚 Noun Types (42):\n'))
       const nounChunks = []
       for (let i = 0; i < BrainyTypes.nouns.length; i += 3) {
         nounChunks.push(BrainyTypes.nouns.slice(i, i + 3))
@@ -43,7 +43,7 @@ export async function types(options: { json?: boolean, noun?: boolean, verb?: bo
 
     // Display verbs
     if (showVerbs) {
-      console.log(chalk.bold.cyan('\n🔗 Verb Types (40):\n'))
+      console.log(chalk.bold.cyan('\n🔗 Verb Types (127):\n'))
       const verbChunks = []
       for (let i = 0; i < BrainyTypes.verbs.length; i += 3) {
         verbChunks.push(BrainyTypes.verbs.slice(i, i + 3))

src/hnsw/typeAwareHNSWIndex.ts

@@ -7,7 +7,7 @@
  * - Storage: Already type-first from Phase 1a
  *
  * Architecture:
- * - One HNSWIndex per NounType (31 total)
+ * - One HNSWIndex per NounType (42 total)
  * - Lazy initialization (indexes created on first use)
  * - Type routing for optimal performance
  * - Falls back to multi-type search when type unknown
@@ -128,7 +128,7 @@ export class TypeAwareHNSWIndex {
     const typeIndex = TypeUtils.getNounIndex(type)
     if (typeIndex === undefined || typeIndex === null || typeIndex < 0) {
       throw new Error(
-        `Invalid NounType: ${type}. Must be one of the 31 defined types.`
+        `Invalid NounType: ${type}. Must be one of the 42 defined types.`
       )
     }
 
@@ -193,7 +193,7 @@ export class TypeAwareHNSWIndex {
    * **All-types search** (fallback):
    * ```typescript
    * await index.search(queryVector, 10)
-   * // Searches all 31 graphs (slower but comprehensive)
+   * // Searches all 42 graphs (slower but comprehensive)
    * ```
    *
    * @param queryVector Query vector
@@ -393,7 +393,7 @@ export class TypeAwareHNSWIndex {
    * Rebuild HNSW indexes from storage (type-aware)
    *
    * CRITICAL: This implementation uses type-filtered pagination to avoid
-   * loading ALL entities for each type (which would be 31 billion reads @ 1B scale).
+   * loading ALL entities for each type (which would be 42 billion reads @ 1B scale).
    *
    * Can rebuild all types or specific types.
    * Much faster than rebuilding a monolithic index.
@@ -451,7 +451,7 @@ export class TypeAwareHNSWIndex {
     }
 
     // Load ALL nouns ONCE and route to correct type indexes
-    // This is O(N) instead of O(31*N) from the previous parallel approach
+    // This is O(N) instead of O(42*N) from the previous parallel approach
     let cursor: string | undefined = undefined
     let hasMore = true
     let totalLoaded = 0

src/importers/SmartExcelImporter.ts

@@ -163,7 +163,7 @@ export class SmartExcelImporter {
       enableRelationshipInference: true,
       // CONCEPT EXTRACTION PRODUCTION-READY (v3.33.0+):
       // Type embeddings are now pre-computed at build time - zero runtime cost!
-      // All 31 noun types + 40 verb types instantly available
+      // All 42 noun types + 127 verb types instantly available
       //
       // Performance profile:
       // - Type embeddings: INSTANT (pre-computed at build time, ~100KB in-memory)

src/neural/embeddedTypeEmbeddings.ts

@@ -2,7 +2,7 @@
  * 🧠 BRAINY EMBEDDED TYPE EMBEDDINGS
  *
  * AUTO-GENERATED - DO NOT EDIT
- * Generated: 2025-11-06T16:58:34.845Z
+ * Generated: 2025-11-06T17:38:22.619Z
  * Noun Types: 42
  * Verb Types: 127
  *
@@ -19,7 +19,7 @@ export const TYPE_METADATA = {
   verbTypes: 127,
   totalTypes: 169,
   embeddingDimensions: 384,
-  generatedAt: "2025-11-06T16:58:34.845Z",
+  generatedAt: "2025-11-06T17:38:22.619Z",
   sizeBytes: {
     embeddings: 259584,
     base64: 346112

src/query/typeAwareQueryPlanner.ts

@@ -6,14 +6,14 @@
  * TypeAwareHNSWIndex graphs.
  *
  * Performance Impact:
- * - Single-type queries: 31x speedup (search 1/31 graphs)
- * - Multi-type queries: 6-15x speedup (search 2-5/31 graphs)
+ * - Single-type queries: 42x speedup (search 1/42 graphs)
+ * - Multi-type queries: 8-21x speedup (search 2-5/42 graphs)
  * - Overall: 40% latency reduction @ 1B scale
  *
  * Examples:
- * - "Find engineers" → single-type → [Person] → 31x speedup
- * - "People at Tesla" → multi-type → [Person, Organization] → 15.5x speedup
- * - "Everything about AI" → all-types → [all 31 types] → no speedup
+ * - "Find engineers" → single-type → [Person] → 42x speedup
+ * - "People at Tesla" → multi-type → [Person, Organization] → 21x speedup
+ * - "Everything about AI" → all-types → [all 42 types] → no speedup
  */
 
 import { NounType, NOUN_TYPE_COUNT } from '../types/graphTypes.js'

src/storage/baseStorage.ts

@@ -149,9 +149,9 @@ export abstract class BaseStorage extends BaseStorageAdapter {
 
   // Type-first indexing support (v5.4.0)
   // Built into all storage adapters for billion-scale efficiency
-  protected nounCountsByType = new Uint32Array(NOUN_TYPE_COUNT) // 124 bytes
-  protected verbCountsByType = new Uint32Array(VERB_TYPE_COUNT) // 160 bytes
-  // Total: 284 bytes (99.76% reduction vs Map-based tracking)
+  protected nounCountsByType = new Uint32Array(NOUN_TYPE_COUNT) // 168 bytes (Stage 3: 42 types)
+  protected verbCountsByType = new Uint32Array(VERB_TYPE_COUNT) // 508 bytes (Stage 3: 127 types)
+  // Total: 676 bytes (99.2% reduction vs Map-based tracking)
 
   // Type cache for O(1) lookups after first access
   protected nounTypeCache = new Map<string, NounType>()