docs(04): create phase plan
Phase 04: Memory & Context Management - 4 plan(s) in 3 wave(s) - 2 parallel, 2 sequential - Ready for execution
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Mai Development
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.planning/phases/04-memory-context-management/04-04-PLAN.md
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.planning/phases/04-memory-context-management/04-04-PLAN.md
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---
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phase: 04-memory-context-management
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plan: 04
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type: execute
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wave: 3
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depends_on: ["04-01", "04-02", "04-03"]
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files_modified: ["src/memory/personality/__init__.py", "src/memory/personality/pattern_extractor.py", "src/memory/personality/layer_manager.py", "src/memory/personality/adaptation.py", "src/memory/__init__.py", "src/personality.py"]
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autonomous: true
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must_haves:
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truths:
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- "Personality layers learn from conversation patterns"
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- "Multi-dimensional learning covers topics, sentiment, interaction patterns"
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- "Personality overlays enhance rather than replace core values"
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- "Learning algorithms prevent overfitting to recent conversations"
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- "Personality system integrates with existing personality.py"
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artifacts:
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- path: "src/memory/personality/pattern_extractor.py"
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provides: "Pattern extraction from conversations"
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min_lines: 80
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- path: "src/memory/personality/layer_manager.py"
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provides: "Personality overlay system"
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min_lines: 60
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- path: "src/memory/personality/adaptation.py"
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provides: "Dynamic personality updates"
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min_lines: 50
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- path: "src/memory/__init__.py"
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provides: "Complete MemoryManager with personality learning"
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exports: ["MemoryManager", "PersonalityLearner"]
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- path: "src/personality.py"
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provides: "Updated personality system with memory integration"
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min_lines: 20
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key_links:
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- from: "src/memory/personality/pattern_extractor.py"
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to: "src/memory/storage/sqlite_manager.py"
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via: "conversation data for pattern analysis"
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pattern: "sqlite_manager\\.get_conversations_for_analysis"
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- from: "src/memory/personality/layer_manager.py"
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to: "src/memory/personality/pattern_extractor.py"
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via: "pattern data for layer creation"
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pattern: "pattern_extractor\\.extract_patterns"
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- from: "src/personality.py"
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to: "src/memory/personality/layer_manager.py"
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via: "personality overlay application"
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pattern: "layer_manager\\.get_active_layers"
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---
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<objective>
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Implement personality learning system that extracts patterns from conversations and creates adaptive personality layers. This enables Mai to learn and adapt communication patterns while maintaining core personality values.
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Purpose: Enable Mai to learn from user interactions and adapt personality while preserving core values
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Output: Working personality learning system with pattern extraction, layer management, and dynamic adaptation
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</objective>
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<execution_context>
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@~/.opencode/get-shit-done/workflows/execute-plan.md
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@~/.opencode/get-shit-done/templates/summary.md
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</execution_context>
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<context>
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@.planning/phases/04-memory-context-management/04-CONTEXT.md
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@.planning/phases/04-memory-context-management/04-RESEARCH.md
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@.planning/PROJECT.md
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@.planning/ROADMAP.md
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@.planning/STATE.md
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# Reference existing personality system
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@src/personality.py
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@src/resource/personality.py
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# Reference memory components
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@.planning/phases/04-memory-context-management/04-01-SUMMARY.md
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@.planning/phases/04-memory-context-management/04-02-SUMMARY.md
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@.planning/phases/04-memory-context-management/04-03-SUMMARY.md
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</context>
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<tasks>
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<task type="auto">
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<name>Task 1: Create pattern extraction system</name>
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<files>src/memory/personality/__init__.py, src/memory/personality/pattern_extractor.py</files>
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<action>
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Create src/memory/personality/pattern_extractor.py with PatternExtractor class:
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1. Implement multi-dimensional pattern extraction following research:
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- Topics: Track frequently discussed subjects and user interests
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- Sentiment: Analyze emotional tone and sentiment patterns
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- Interaction patterns: Response times, question asking, information sharing
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- Time-based preferences: Communication style by time of day/week
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- Response styles: Formality level, verbosity, use of emojis/humor
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2. Pattern extraction methods:
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- extract_topic_patterns(conversations: List[Conversation]) -> TopicPatterns
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- extract_sentiment_patterns(conversations: List[Conversation]) -> SentimentPatterns
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- extract_interaction_patterns(conversations: List[Conversation]) -> InteractionPatterns
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- extract_temporal_patterns(conversations: List[Conversation]) -> TemporalPatterns
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- extract_response_style_patterns(conversations: List[Conversation]) -> ResponseStylePatterns
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3. Analysis techniques:
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- Simple frequency analysis for topics
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- Basic sentiment analysis using keyword lists or simple models
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- Statistical analysis for interaction patterns
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- Time series analysis for temporal patterns
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- Linguistic analysis for response styles
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4. Pattern validation:
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- Confidence scoring for extracted patterns
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- Pattern stability tracking over time
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- Outlier detection for unusual patterns
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Follow existing error handling patterns. Keep analysis lightweight to avoid heavy computational overhead.
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</action>
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<verify>python -c "from src.memory.personality.pattern_extractor import PatternExtractor; pe = PatternExtractor(); print('Pattern extractor created successfully')"</verify>
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<done>Pattern extractor can analyze conversations and extract patterns</done>
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</task>
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<task type="auto">
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<name>Task 2: Implement personality layer management and adaptation</name>
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<files>src/memory/personality/layer_manager.py, src/memory/personality/adaptation.py, src/memory/__init__.py, src/personality.py</files>
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<action>
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Create personality management system:
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1. Create src/memory/personality/layer_manager.py with LayerManager:
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- PersonalityLayer dataclass with weights and application rules
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- Layer creation from extracted patterns
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- Layer conflict resolution (when patterns contradict)
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- Layer activation based on conversation context
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- Methods: create_layer_from_patterns(), get_active_layers(), apply_layers()
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2. Create src/memory/personality/adaptation.py with PersonalityAdaptation:
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- Time-weighted learning (recent patterns have less influence)
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- Gradual adaptation with stability controls
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- Feedback integration for user preferences
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- Adaptation rate limiting to prevent rapid changes
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- Methods: update_personality_layer(), calculate_adaptation_rate(), apply_stability_controls()
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3. Update src/memory/__init__.py to integrate personality learning:
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- Add PersonalityLearner to MemoryManager
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- Implement learning triggers (after conversations, periodically)
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- Add personality data persistence
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- Provide learning controls and configuration
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4. Update src/personality.py to integrate with memory:
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- Import and use PersonalityLearner from memory system
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- Apply personality layers during conversation responses
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- Maintain separation between core personality and learned layers
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- Add configuration for learning enable/disable
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5. Personality layer application:
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- Hybrid system prompt + behavior configuration
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- Context-aware layer activation
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- Core value enforcement (learned layers cannot override core values)
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- Layer priority and conflict resolution
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Follow existing patterns from src/resource/personality.py for personality management. Ensure core personality values remain protected from learned modifications.
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</action>
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<verify>python -c "from src.memory.personality.layer_manager import LayerManager; lm = LayerManager(); print('Layer manager created successfully')"</verify>
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<done>Personality system can learn patterns and apply adaptive layers</done>
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</task>
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</tasks>
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<verification>
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After completion, verify:
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1. Pattern extractor analyzes conversations across multiple dimensions
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2. Layer manager creates personality overlays from patterns
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3. Adaptation system prevents overfitting and maintains stability
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4. Personality learning integrates with existing personality.py
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5. Core personality values are protected from learned modifications
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6. Learning system can be enabled/disabled through configuration
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</verification>
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<success_criteria>
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- Pattern extraction covers topics, sentiment, interaction, temporal, and style patterns
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- Personality layers work as adaptive overlays that enhance core personality
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- Time-weighted learning prevents overfitting to recent conversations
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- Stability controls maintain personality consistency
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- Integration with existing personality system preserves core values
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- Learning system is configurable and can be controlled by user
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</success_criteria>
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<output>
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After completion, create `.planning/phases/04-memory-context-management/04-04-SUMMARY.md`
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</output>
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