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

docs/FIND_SYSTEM.md

@@ -245,7 +245,7 @@ return results.slice(offset, offset + limit)
 ## Type-Aware NLP Features
 
 ### 1. Dynamic Field Discovery
-- **No Hardcoded Fields**: Only NounType/VerbType taxonomies are fixed (30+ noun, 40+ verb types)
+- **No Hardcoded Fields**: Only NounType/VerbType taxonomies are fixed (42 noun, 127 verb types)
 - **Real Data Learning**: Field affinity learned from actual indexed entities
 - **Semantic Matching**: "by" → "author" via embedding similarity (87% confidence)
 - **Type Context**: Documents have different fields than Persons or Organizations
@@ -287,7 +287,7 @@ return results.slice(offset, offset + limit)
 
 Where:
 - n = number of entities in database
-- t = number of types (70 total: 30 noun + 40 verb)  
+- t = number of types (169 total: 42 noun + 127 verb)  
 - f = number of fields for detected entity type (typically 5-15)
 
 ### Scalability

docs/PERFORMANCE.md

@@ -22,7 +22,7 @@ Where:
 - `k` = number of results returned
 - `m` = number of patterns to check
 - `f` = number of fields for entity type
-- `t` = number of types (30+ nouns, 40+ verbs)
+- `t` = number of types (42 nouns, 127 verbs)
 
 ## Architecture Deep Dive
 
@@ -140,7 +140,7 @@ The NLP processor uses **zero hardcoded fields** - everything is discovered dyna
 
 ```typescript
 class NaturalLanguageProcessor {
-  // Pre-embedded NounTypes (30+) and VerbTypes (40+) - ONLY hardcoded vocabularies
+  // Pre-embedded NounTypes (42) and VerbTypes (127) - ONLY hardcoded vocabularies
   private nounTypeEmbeddings = new Map<string, Vector>()
   private verbTypeEmbeddings = new Map<string, Vector>()
   
@@ -162,7 +162,7 @@ class NaturalLanguageProcessor {
 5. **Query Optimization**: Process low-cardinality type-specific fields first
 
 **Performance Characteristics:**
-- Type detection: O(t) where t = 70 total types (30 noun + 40 verb)
+- Type detection: O(t) where t = 169 total types (42 noun + 127 verb)
 - Field matching: O(f) where f = fields for detected type (typically 5-15)
 - Validation: O(1) lookup in type-field affinity map
 - No hardcoded assumptions - learns from actual data patterns

docs/architecture/noun-verb-taxonomy.md

@@ -8,7 +8,7 @@
 
 Brainy's **Noun-Verb Taxonomy** achieves **universal coverage** of all human knowledge through **infinite expressiveness**:
 
-- **31 Noun Types × 40 Verb Types = 1,240 Base Combinations**
+- **42 Noun Types × 127 Verb Types = 5,334 Base Combinations**
 - **Unlimited Metadata Fields = ∞ Domain Specificity**  
 - **Multi-hop Graph Traversals = ∞ Relationship Complexity**
 - **Result: Can Model ANY Data in ANY Industry**
@@ -95,7 +95,7 @@ await brain.sync.jira({
 
 Like **HTTP** became the protocol for the web and **TCP/IP** for the internet, Brainy's noun-verb taxonomy is becoming the **Universal Knowledge Protocol**:
 
-- **Learn Once**: Developers learn 31 nouns + 40 verbs, not 1000s of schemas
+- **Learn Once**: Developers learn 42 nouns + 127 verbs, not 1000s of schemas
 - **Build Anywhere**: Tools built for one domain work in others
 - **Share Everything**: Knowledge graphs are universally shareable
 - **Compose Freely**: Augmentations compose without conflicts
@@ -1159,7 +1159,7 @@ We intentionally keep the type system minimal because:
 
 ## Industry-Specific Coverage Analysis
 
-### Why 31 Nouns + 40 Verbs = Universal Coverage
+### Why 42 Nouns + 127 Verbs = Universal Coverage
 
 The combination of **31 noun types** and **40 verb types** creates **1,240 basic combinations**, but with metadata and multi-hop relationships, this expands to **infinite expressiveness**. Here's how it covers every industry:
 

docs/guides/import-anything.md

@@ -185,7 +185,7 @@ Brainy automatically detects what TYPE of data you're importing:
 { latitude: 37.7, longitude: -122.4, city: 'SF' }
 ```
 
-**31 noun types** and **40 verb types** cover EVERYTHING!
+**42 noun types and 127 verb types** cover EVERYTHING!
 
 ## Relationship Detection
 
@@ -382,7 +382,7 @@ await brain.find('posts by users following Alice with >10 comments')
 
 **Zero Configuration**: Works perfectly out of the box
 **Maximum Intelligence**: AI understands your data's meaning
-**Universal Protocol**: 31 nouns × 40 verbs = ANY data model
+**Universal Protocol**: 42 nouns × 127 verbs = ANY data model
 **Delightful DX**: Simple, clean, modern API
 
 ## The ONE Method Philosophy

docs/vfs/NEURAL_EXTRACTION.md

@@ -35,7 +35,7 @@ Brainy's Neural Extraction system uses embeddings and a sophisticated NounType t
 
 ## NounType Taxonomy
 
-Brainy uses a 30+ type taxonomy for entity classification:
+Brainy uses a 42-noun + 127-verb type taxonomy for entity classification:
 
 ### Core Types
 - **Person** - Individual humans