fix(04-GC-01): test-personality-learner-init
Verify PersonalityLearner instantiation works correctly after AdaptationRate import fix. Tests confirm no NameError occurs. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
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Mai Development
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# Phase 4 Gap Closure Summary
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**Date:** 2026-01-28
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**Status:** Planning Complete - Ready for Execution
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**Critical Gaps Identified:** 2
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**Plans Created:** 2
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## Gap Analysis
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### Gap 1: Missing AdaptationRate Import (BLOCKING)
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**Severity:** CRITICAL - Blocks PersonalityLearner instantiation
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**Location:** src/memory/__init__.py, line 56
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**Problem:**
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PersonalityLearner.__init__() uses `AdaptationRate` enum to configure learning rates, but this enum is not imported in the module, causing a NameError when creating any PersonalityLearner instance.
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**Impact Chain:**
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- PersonalityLearner cannot be instantiated
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- MemoryManager.initialize() fails when trying to initialize PersonalityLearner
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- Entire personality learning system is broken
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- Verification requirement "Personality layers learn from conversation patterns" FAILS
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**Solution:**
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Add `AdaptationRate` to imports from `src.memory.personality.adaptation` in src/memory/__init__.py
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---
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### Gap 2: Missing SQLiteManager Methods (BLOCKING)
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**Severity:** CRITICAL - Breaks personality learning pipeline
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**Location:** src/memory/storage/sqlite_manager.py
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**Problem:**
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PersonalityLearner.learn_from_conversations() calls two methods that don't exist:
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- `get_conversations_by_date_range(start_date, end_date)` - line 85
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- `get_conversation_messages(conversation_id)` - line 99
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These methods are essential for fetching conversations and their messages to extract personality patterns.
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**Impact Chain:**
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- learn_from_conversations() raises AttributeError on line 85
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- Cannot retrieve conversations within date range
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- Cannot access messages for pattern extraction
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- Pattern extraction pipeline fails
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- Personality learning system cannot extract patterns from history
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- Verification requirement "Personality layers learn from conversation patterns" FAILS
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**Solution:**
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Implement two new methods in SQLiteManager to support date-range queries and message retrieval.
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---
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## Gap Closure Plans
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### 04-GC-01-PLAN.md: Fix PersonalityLearner Initialization
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**Wave:** 1
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**Dependencies:** None
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**Files Modified:** src/memory/__init__.py
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**Scope:**
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- Add AdaptationRate import
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- Verify export in __all__
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- Test initialization with different configs
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**Verification Points:**
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- AdaptationRate can be imported from memory module
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- PersonalityLearner(config={'learning_rate': 'medium'}) works without error
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- All AdaptationRate enum values (SLOW, MEDIUM, FAST) are accessible
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---
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### 04-GC-02-PLAN.md: Implement Missing SQLiteManager Methods
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**Wave:** 1 (depends on 04-GC-01 for full pipeline testing)
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**Dependencies:** 04-GC-01-PLAN.md (soft dependency - methods are independent but testing together is recommended)
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**Files Modified:**
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- src/memory/storage/sqlite_manager.py
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- tests/test_personality_learning.py (new)
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**Scope:**
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- Implement get_conversations_by_date_range() method
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- Implement get_conversation_messages() method
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- Create comprehensive integration tests for personality learning pipeline
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**Verification Points:**
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- get_conversations_by_date_range() returns conversations created within date range
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- get_conversation_messages() returns all messages for a conversation in chronological order
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- learn_from_conversations() executes successfully with sample data
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- Personality patterns are extracted from message content
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- Personality layers are created from extracted patterns
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- End-to-end integration test passes
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---
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## Execution Order
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**Phase 1 - Foundation (Parallel Execution Possible):**
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1. Execute 04-GC-01-PLAN.md → Fix AdaptationRate import
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2. Execute 04-GC-02-PLAN.md → Implement missing SQLiteManager methods
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**Phase 2 - Verification:**
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3. Run integration tests to verify complete personality learning pipeline
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4. Verify both gap closure plans have all must-haves checked
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**Expected Outcome:**
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- PersonalityLearner can be instantiated and configured
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- Personality learning pipeline executes end-to-end without errors
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- Patterns are extracted from conversations and messages
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- Personality layers are created from learned patterns
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- Verification requirement "Personality layers learn from conversation patterns" is VERIFIED
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---
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## Must-Haves Checklist
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### 04-GC-01-PLAN.md Completion Criteria
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- [ ] AdaptationRate import added to src/memory/__init__.py
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- [ ] AdaptationRate appears in __all__ export list
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- [ ] PersonalityLearner instantiation test passes
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- [ ] All learning_rate config values (slow, medium, fast) work correctly
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- [ ] No NameError when using AdaptationRate in PersonalityLearner
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### 04-GC-02-PLAN.md Completion Criteria
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- [ ] get_conversations_by_date_range() implemented in SQLiteManager
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- [ ] get_conversation_messages() implemented in SQLiteManager
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- [ ] Both methods handle edge cases (no results, errors)
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- [ ] Integration test created in tests/test_personality_learning.py
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- [ ] learn_from_conversations() executes without errors
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- [ ] Pattern extraction completes successfully
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- [ ] Personality layers are created from patterns
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---
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## Traceability
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**Requirements Being Closed:**
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- MEMORY-04: "Distill patterns into personality layers" → Currently BLOCKED, will be VERIFIED
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- MEMORY-05: "Proactively surface relevant context" → Dependent on MEMORY-04
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**Related Completed Work:**
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- PersonalityAdaptation class: 701 lines (COMPLETED)
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- PersonalityLearner properly exported: (COMPLETED)
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- src/personality.py created with memory integration: 483 lines (COMPLETED)
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- Pattern extraction methods implemented: (COMPLETED - except integration)
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- Layer management system: (COMPLETED)
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**Integration Points:**
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- MemoryManager.personality_learner property
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- PersonalitySystem integration (src/personality.py)
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- VectorStore and SemanticSearch for context retrieval
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- Archival and compression systems
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---
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## Risk Assessment
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**Risk Level:** LOW
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- Both gaps are straightforward implementations
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- Methods follow existing patterns in codebase
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- No database schema changes needed
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- Import is simple add-to-list operation
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**Mitigation:**
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- Comprehensive unit tests for new methods
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- Integration test verifying entire pipeline
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- Edge case handling (no data, date boundaries)
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- Error logging for debugging
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---
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## Notes
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- Extract_conversation_patterns method DOES exist and works correctly
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- Method signature is compatible with how it's being called
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- Issue was with PersonalityLearner not being able to instantiate, not with the method itself
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- Both gaps must be closed for personality learning to function
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- No other blockers identified in personality learning system
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