From 9cdb1e7f6cac8a0a31f1cac2116e82cba4867978 Mon Sep 17 00:00:00 2001 From: Mai Development Date: Tue, 27 Jan 2026 21:53:07 -0500 Subject: [PATCH] docs(04): create phase plan Phase 04: Memory & Context Management - 4 plan(s) in 3 wave(s) - 2 parallel, 2 sequential - Ready for execution --- .planning/ROADMAP.md | 6 + .../04-01-PLAN.md | 140 +++++++++++++ .../04-02-PLAN.md | 161 +++++++++++++++ .../04-03-PLAN.md | 172 ++++++++++++++++ .../04-04-PLAN.md | 184 ++++++++++++++++++ 5 files changed, 663 insertions(+) create mode 100644 .planning/phases/04-memory-context-management/04-01-PLAN.md create mode 100644 .planning/phases/04-memory-context-management/04-02-PLAN.md create mode 100644 .planning/phases/04-memory-context-management/04-03-PLAN.md create mode 100644 .planning/phases/04-memory-context-management/04-04-PLAN.md diff --git a/.planning/ROADMAP.md b/.planning/ROADMAP.md index ad241cd..570a7cd 100644 --- a/.planning/ROADMAP.md +++ b/.planning/ROADMAP.md @@ -51,6 +51,12 @@ Mai's development is organized into three major milestones, each delivering dist - Distill long-term patterns into personality layers - Proactively surface relevant context from memory +**Plans:** 4 plans in 3 waves +- [ ] 04-01-PLAN.md — Storage foundation with SQLite and sqlite-vec +- [ ] 04-02-PLAN.md — Semantic search and context-aware retrieval +- [ ] 04-03-PLAN.md — Progressive compression and JSON archival +- [ ] 04-04-PLAN.md — Personality learning and adaptive layers + ### Phase 5: Conversation Engine - Multi-turn context preservation - Reasoning transparency and clarifying questions diff --git a/.planning/phases/04-memory-context-management/04-01-PLAN.md b/.planning/phases/04-memory-context-management/04-01-PLAN.md new file mode 100644 index 0000000..9d98f84 --- /dev/null +++ b/.planning/phases/04-memory-context-management/04-01-PLAN.md @@ -0,0 +1,140 @@ +--- +phase: 04-memory-context-management +plan: 01 +type: execute +wave: 1 +depends_on: [] +files_modified: ["src/memory/__init__.py", "src/memory/storage/sqlite_manager.py", "src/memory/storage/vector_store.py", "src/memory/storage/__init__.py", "requirements.txt"] +autonomous: true + +must_haves: + truths: + - "Conversations are stored locally in SQLite database" + - "Vector embeddings are stored using sqlite-vec extension" + - "Database schema supports conversations, messages, and embeddings" + - "Memory system persists across application restarts" + artifacts: + - path: "src/memory/storage/sqlite_manager.py" + provides: "SQLite database operations and schema management" + min_lines: 80 + - path: "src/memory/storage/vector_store.py" + provides: "Vector storage and retrieval with sqlite-vec" + min_lines: 60 + - path: "src/memory/__init__.py" + provides: "Memory module entry point" + exports: ["MemoryManager"] + key_links: + - from: "src/memory/storage/sqlite_manager.py" + to: "sqlite-vec extension" + via: "extension loading and virtual table creation" + pattern: "load_extension.*vec0" + - from: "src/memory/storage/vector_store.py" + to: "src/memory/storage/sqlite_manager.py" + via: "database connection for vector operations" + pattern: "sqlite_manager\\.db" +--- + + +Create the foundational storage layer for conversation memory using SQLite with sqlite-vec extension. This establishes the hybrid storage architecture where recent conversations are kept in SQLite for fast access, with vector capabilities for semantic search. + +Purpose: Provide persistent, reliable storage that serves as the foundation for all memory operations +Output: Working SQLite database with vector support and basic conversation/message storage + + + +@~/.opencode/get-shit-done/workflows/execute-plan.md +@~/.opencode/get-shit-done/templates/summary.md + + + +@.planning/phases/04-memory-context-management/04-CONTEXT.md +@.planning/phases/04-memory-context-management/04-RESEARCH.md +@.planning/PROJECT.md +@.planning/ROADMAP.md +@.planning/STATE.md + +# Reference existing models structure +@src/models/context_manager.py +@src/models/conversation.py + + + + + + Task 1: Create memory module structure and SQLite manager + src/memory/__init__.py, src/memory/storage/__init__.py, src/memory/storage/sqlite_manager.py + +Create the memory module structure following the research pattern: + +1. Create src/memory/__init__.py with MemoryManager class stub +2. Create src/memory/storage/__init__.py +3. Create src/memory/storage/sqlite_manager.py with: + - SQLiteManager class with connection management + - Database schema for conversations, messages, metadata + - Table creation with proper indexing + - Connection pooling and thread safety + - Database migration support + +Use the schema from research with conversations table (id, title, created_at, updated_at, metadata) and messages table (id, conversation_id, role, content, timestamp, embedding_id). + +Include proper error handling, connection management, and follow existing code patterns from src/models/ modules. + + python -c "from src.memory.storage.sqlite_manager import SQLiteManager; db = SQLiteManager(':memory:'); print('SQLite manager created successfully')" + SQLite manager can create and connect to database with proper schema + + + + Task 2: Implement vector store with sqlite-vec integration + src/memory/storage/vector_store.py, requirements.txt + +Create src/memory/storage/vector_store.py with VectorStore class: + +1. Add sqlite-vec to requirements.txt +2. Implement VectorStore with: + - sqlite-vec extension loading + - Virtual table creation for embeddings (using vec0) + - Vector insertion and retrieval methods + - Support for different embedding dimensions (start with 384 for all-MiniLM-L6-v2) + - Integration with SQLiteManager for database connection + +Follow the research pattern for sqlite-vec setup: +```python +db.enable_load_extension(True) +db.load_extension("vec0") +CREATE VIRTUAL TABLE IF NOT EXISTS vec_memory USING vec0(embedding float[384], content text, message_id integer) +``` + +Include methods to: +- Store embeddings with message references +- Search by vector similarity +- Batch operations for multiple embeddings +- Handle embedding model version tracking + +Use existing error handling patterns from src/models/ modules. + + python -c "from src.memory.storage.vector_store import VectorStore; import numpy as np; vs = VectorStore(':memory:'); test_vec = np.random.rand(384).astype(np.float32); print('Vector store created successfully')" + Vector store can create tables and handle basic vector operations + + + + + +After completion, verify: +1. SQLite database can be created with proper schema +2. Vector extension loads correctly +3. Basic conversation and message storage works +4. Vector embeddings can be stored and retrieved +5. Integration with existing model system works + + + +- Memory module structure created following research recommendations +- SQLite manager handles database operations with proper schema +- Vector store integrates sqlite-vec for embedding storage and search +- Error handling and connection management follow existing patterns +- Database persists data correctly across restarts + + + +After completion, create `.planning/phases/04-memory-context-management/04-01-SUMMARY.md` + \ No newline at end of file diff --git a/.planning/phases/04-memory-context-management/04-02-PLAN.md b/.planning/phases/04-memory-context-management/04-02-PLAN.md new file mode 100644 index 0000000..1503a98 --- /dev/null +++ b/.planning/phases/04-memory-context-management/04-02-PLAN.md @@ -0,0 +1,161 @@ +--- +phase: 04-memory-context-management +plan: 02 +type: execute +wave: 2 +depends_on: ["04-01"] +files_modified: ["src/memory/retrieval/__init__.py", "src/memory/retrieval/semantic_search.py", "src/memory/retrieval/context_aware.py", "src/memory/retrieval/timeline_search.py", "src/memory/__init__.py"] +autonomous: true + +must_haves: + truths: + - "User can search conversations by semantic meaning" + - "Search results are ranked by relevance to query" + - "Context-aware search prioritizes current topic discussions" + - "Timeline search allows filtering by date ranges" + - "Hybrid search combines semantic and keyword matching" + artifacts: + - path: "src/memory/retrieval/semantic_search.py" + provides: "Semantic search with embedding-based similarity" + min_lines: 70 + - path: "src/memory/retrieval/context_aware.py" + provides: "Topic-based search prioritization" + min_lines: 50 + - path: "src/memory/retrieval/timeline_search.py" + provides: "Date-range filtering and temporal search" + min_lines: 40 + - path: "src/memory/__init__.py" + provides: "Updated MemoryManager with search capabilities" + exports: ["MemoryManager", "SemanticSearch"] + key_links: + - from: "src/memory/retrieval/semantic_search.py" + to: "src/memory/storage/vector_store.py" + via: "vector similarity search operations" + pattern: "vector_store\\.search_similar" + - from: "src/memory/retrieval/context_aware.py" + to: "src/memory/storage/sqlite_manager.py" + via: "conversation metadata for topic analysis" + pattern: "sqlite_manager\\.get_conversation_metadata" + - from: "src/memory/__init__.py" + to: "src/memory/retrieval/" + via: "search method delegation" + pattern: "semantic_search\\.find" +--- + + +Implement the memory retrieval system with semantic search, context-aware prioritization, and timeline filtering. This enables intelligent recall of past conversations using multiple search strategies. + +Purpose: Allow users and the system to find relevant conversations quickly using semantic meaning, context awareness, and temporal filters +Output: Working search system that can retrieve conversations by meaning, topic, and time range + + + +@~/.opencode/get-shit-done/workflows/execute-plan.md +@~/.opencode/get-shit-done/templates/summary.md + + + +@.planning/phases/04-memory-context-management/04-CONTEXT.md +@.planning/phases/04-memory-context-management/04-RESEARCH.md +@.planning/PROJECT.md +@.planning/ROADMAP.md +@.planning/STATE.md + +# Reference storage foundation +@.planning/phases/04-memory-context-management/04-01-SUMMARY.md + +# Reference existing conversation handling +@src/models/conversation.py +@src/models/context_manager.py + + + + + + Task 1: Create semantic search with embedding-based retrieval + src/memory/retrieval/__init__.py, src/memory/retrieval/semantic_search.py + +Create src/memory/retrieval/semantic_search.py with SemanticSearch class: + +1. Add sentence-transformers to requirements.txt (use all-MiniLM-L6-v2 for efficiency) +2. Implement SemanticSearch with: + - Embedding model loading (lazy loading for performance) + - Query embedding generation + - Vector similarity search using VectorStore from plan 04-01 + - Hybrid search combining semantic and keyword matching + - Result ranking and relevance scoring + - Conversation snippet generation for context + +Follow research pattern for hybrid search: +- Generate query embedding +- Search vector store for similar conversations +- Fallback to keyword search if no semantic results +- Combine and rank results with weighted scoring + +Include methods to: +- search(query: str, limit: int = 5) -> List[SearchResult] +- search_by_embedding(embedding: np.ndarray, limit: int = 5) -> List[SearchResult] +- keyword_search(query: str, limit: int = 5) -> List[SearchResult] + +Use existing error handling patterns and type hints from src/models/ modules. + + python -c "from src.memory.retrieval.semantic_search import SemanticSearch; search = SemanticSearch(':memory:'); print('Semantic search created successfully')" + Semantic search can generate embeddings and perform basic search operations + + + + Task 2: Implement context-aware and timeline search capabilities + src/memory/retrieval/context_aware.py, src/memory/retrieval/timeline_search.py, src/memory/__init__.py + +Create context-aware and timeline search components: + +1. Create src/memory/retrieval/context_aware.py with ContextAwareSearch: + - Topic extraction from current conversation context + - Conversation topic classification using simple heuristics + - Topic-based result prioritization + - Current conversation context tracking + - Methods: prioritize_by_topic(results: List[SearchResult], current_topic: str) -> List[SearchResult] + +2. Create src/memory/retrieval/timeline_search.py with TimelineSearch: + - Date range filtering for conversations + - Temporal proximity search (find conversations near specific dates) + - Recency-based result weighting + - Conversation age calculation and compression level awareness + - Methods: search_by_date_range(start: datetime, end: datetime, limit: int = 5) -> List[SearchResult] + +3. Update src/memory/__init__.py to integrate search capabilities: + - Import all search classes + - Add search methods to MemoryManager + - Provide unified search interface combining semantic, context-aware, and timeline search + - Add search result dataclasses with relevance scores and conversation snippets + +Follow existing patterns from src/models/ for data structures and error handling. Ensure search results include conversation metadata for context. + + python -c "from src.memory import MemoryManager; mm = MemoryManager(':memory:'); print('Memory manager with search created successfully')" + Memory manager provides unified search interface with all search modes + + + + + +After completion, verify: +1. Semantic search can find conversations by meaning +2. Context-aware search prioritizes relevant topics +3. Timeline search filters by date ranges correctly +4. Hybrid search combines semantic and keyword results +5. Search results include proper relevance scoring and conversation snippets +6. Integration with storage layer works correctly + + + +- Semantic search uses sentence-transformers for embedding generation +- Context-aware search prioritizes topics relevant to current discussion +- Timeline search enables date-range filtering and temporal search +- Hybrid search combines multiple search strategies with proper ranking +- Memory manager provides unified search interface +- Search results include conversation context and relevance scoring + + + +After completion, create `.planning/phases/04-memory-context-management/04-02-SUMMARY.md` + \ No newline at end of file diff --git a/.planning/phases/04-memory-context-management/04-03-PLAN.md b/.planning/phases/04-memory-context-management/04-03-PLAN.md new file mode 100644 index 0000000..d5a916e --- /dev/null +++ b/.planning/phases/04-memory-context-management/04-03-PLAN.md @@ -0,0 +1,172 @@ +--- +phase: 04-memory-context-management +plan: 03 +type: execute +wave: 2 +depends_on: ["04-01"] +files_modified: ["src/memory/backup/__init__.py", "src/memory/backup/archival.py", "src/memory/backup/retention.py", "src/memory/storage/compression.py", "src/memory/__init__.py"] +autonomous: true + +must_haves: + truths: + - "Old conversations are automatically compressed to save space" + - "Compression preserves important information while reducing size" + - "JSON archival system stores compressed conversations" + - "Smart retention keeps important conversations longer" + - "7/30/90 day compression tiers are implemented" + artifacts: + - path: "src/memory/storage/compression.py" + provides: "Progressive conversation compression" + min_lines: 80 + - path: "src/memory/backup/archival.py" + provides: "JSON export/import for long-term storage" + min_lines: 60 + - path: "src/memory/backup/retention.py" + provides: "Smart retention policies based on conversation importance" + min_lines: 50 + - path: "src/memory/__init__.py" + provides: "MemoryManager with archival capabilities" + exports: ["MemoryManager", "CompressionEngine"] + key_links: + - from: "src/memory/storage/compression.py" + to: "src/memory/storage/sqlite_manager.py" + via: "conversation data retrieval for compression" + pattern: "sqlite_manager\\.get_conversation" + - from: "src/memory/backup/archival.py" + to: "src/memory/storage/compression.py" + via: "compressed conversation data" + pattern: "compression_engine\\.compress" + - from: "src/memory/backup/retention.py" + to: "src/memory/storage/sqlite_manager.py" + via: "conversation importance analysis" + pattern: "sqlite_manager\\.update_importance_score" +--- + + +Implement progressive compression and archival system to manage memory growth efficiently. This ensures the memory system can scale without indefinite growth while preserving important information. + +Purpose: Automatically compress and archive old conversations to maintain performance and storage efficiency +Output: Working compression engine with JSON archival and smart retention policies + + + +@~/.opencode/get-shit-done/workflows/execute-plan.md +@~/.opencode/get-shit-done/templates/summary.md + + + +@.planning/phases/04-memory-context-management/04-CONTEXT.md +@.planning/phases/04-memory-context-management/04-RESEARCH.md +@.planning/PROJECT.md +@.planning/ROADMAP.md +@.planning/STATE.md + +# Reference storage foundation +@.planning/phases/04-memory-context-management/04-01-SUMMARY.md + +# Reference compression research patterns +@.planning/phases/04-memory-context-management/04-RESEARCH.md + + + + + + Task 1: Implement progressive compression engine + src/memory/storage/compression.py + +Create src/memory/storage/compression.py with CompressionEngine class: + +1. Implement progressive compression following research pattern: + - 7 days: Full content (no compression) + - 30 days: Key points extraction (70% retention) + - 90 days: Brief summary (40% retention) + - 365+ days: Metadata only + +2. Add transformers to requirements.txt for summarization +3. Implement compression methods: + - extract_key_points(conversation: Conversation) -> str + - generate_summary(conversation: Conversation, target_ratio: float = 0.4) -> str + - extract_metadata_only(conversation: Conversation) -> dict + +4. Use hybrid extractive-abstractive approach: + - Extract key sentences using NLTK or simple heuristics + - Generate abstractive summary using transformers pipeline + - Preserve important quotes, facts, and decision points + +5. Include compression quality metrics: + - Information retention scoring + - Compression ratio calculation + - Quality validation checks + +6. Add methods: + - compress_by_age(conversation: Conversation) -> CompressedConversation + - get_compression_level(age_days: int) -> CompressionLevel + - decompress(compressed: CompressedConversation) -> ConversationSummary + +Follow existing error handling patterns from src/models/ modules. + + python -c "from src.memory.storage.compression import CompressionEngine; ce = CompressionEngine(); print('Compression engine created successfully')" + Compression engine can compress conversations at different levels + + + + Task 2: Create JSON archival and smart retention systems + src/memory/backup/__init__.py, src/memory/backup/archival.py, src/memory/backup/retention.py, src/memory/__init__.py + +Create archival and retention components: + +1. Create src/memory/backup/archival.py with ArchivalManager: + - JSON export/import for compressed conversations + - Archival directory structure by year/month + - Batch archival operations + - Import capabilities for restoring conversations + - Methods: archive_conversations(), restore_conversation(), list_archived() + +2. Create src/memory/backup/retention.py with RetentionPolicy: + - Value-based retention scoring + - User-marked important conversations + - High engagement detection (length, back-and-forth) + - Smart retention overrides compression rules + - Methods: calculate_importance_score(), should_retain_full(), update_retention_policy() + +3. Update src/memory/__init__.py to integrate archival: + - Add archival methods to MemoryManager + - Implement automatic compression triggering + - Add archival scheduling capabilities + - Provide manual archival controls + +4. Include backup integration: + - Integrate with existing system backup processes + - Ensure archival data is included in regular backups + - Provide restore verification and validation + +Follow existing patterns for data management and error handling. Ensure archival JSON structure is human-readable and versioned for future compatibility. + + python -c "from src.memory import MemoryManager; mm = MemoryManager(':memory:'); print('Memory manager with archival created successfully')" + Memory manager can compress and archive conversations automatically + + + + + +After completion, verify: +1. Compression engine works at all 4 levels (7/30/90/365+ days) +2. JSON archival stores compressed conversations correctly +3. Smart retention keeps important conversations from over-compression +4. Archival directory structure is organized and navigable +5. Integration with storage layer works for compression triggers +6. Restore functionality brings back conversations correctly + + + +- Progressive compression reduces storage usage while preserving information +- JSON archival provides human-readable long-term storage +- Smart retention policies preserve important conversations +- Compression ratios meet research recommendations (70%/40%/metadata) +- Archival system integrates with existing backup processes +- Memory manager provides unified interface for compression and archival + + + +After completion, create `.planning/phases/04-memory-context-management/04-03-SUMMARY.md` + \ No newline at end of file diff --git a/.planning/phases/04-memory-context-management/04-04-PLAN.md b/.planning/phases/04-memory-context-management/04-04-PLAN.md new file mode 100644 index 0000000..63d2b0e --- /dev/null +++ b/.planning/phases/04-memory-context-management/04-04-PLAN.md @@ -0,0 +1,184 @@ +--- +phase: 04-memory-context-management +plan: 04 +type: execute +wave: 3 +depends_on: ["04-01", "04-02", "04-03"] +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"] +autonomous: true + +must_haves: + truths: + - "Personality layers learn from conversation patterns" + - "Multi-dimensional learning covers topics, sentiment, interaction patterns" + - "Personality overlays enhance rather than replace core values" + - "Learning algorithms prevent overfitting to recent conversations" + - "Personality system integrates with existing personality.py" + artifacts: + - path: "src/memory/personality/pattern_extractor.py" + provides: "Pattern extraction from conversations" + min_lines: 80 + - path: "src/memory/personality/layer_manager.py" + provides: "Personality overlay system" + min_lines: 60 + - path: "src/memory/personality/adaptation.py" + provides: "Dynamic personality updates" + min_lines: 50 + - path: "src/memory/__init__.py" + provides: "Complete MemoryManager with personality learning" + exports: ["MemoryManager", "PersonalityLearner"] + - path: "src/personality.py" + provides: "Updated personality system with memory integration" + min_lines: 20 + key_links: + - from: "src/memory/personality/pattern_extractor.py" + to: "src/memory/storage/sqlite_manager.py" + via: "conversation data for pattern analysis" + pattern: "sqlite_manager\\.get_conversations_for_analysis" + - from: "src/memory/personality/layer_manager.py" + to: "src/memory/personality/pattern_extractor.py" + via: "pattern data for layer creation" + pattern: "pattern_extractor\\.extract_patterns" + - from: "src/personality.py" + to: "src/memory/personality/layer_manager.py" + via: "personality overlay application" + pattern: "layer_manager\\.get_active_layers" +--- + + +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. + +Purpose: Enable Mai to learn from user interactions and adapt personality while preserving core values +Output: Working personality learning system with pattern extraction, layer management, and dynamic adaptation + + + +@~/.opencode/get-shit-done/workflows/execute-plan.md +@~/.opencode/get-shit-done/templates/summary.md + + + +@.planning/phases/04-memory-context-management/04-CONTEXT.md +@.planning/phases/04-memory-context-management/04-RESEARCH.md +@.planning/PROJECT.md +@.planning/ROADMAP.md +@.planning/STATE.md + +# Reference existing personality system +@src/personality.py +@src/resource/personality.py + +# Reference memory components +@.planning/phases/04-memory-context-management/04-01-SUMMARY.md +@.planning/phases/04-memory-context-management/04-02-SUMMARY.md +@.planning/phases/04-memory-context-management/04-03-SUMMARY.md + + + + + + Task 1: Create pattern extraction system + src/memory/personality/__init__.py, src/memory/personality/pattern_extractor.py + +Create src/memory/personality/pattern_extractor.py with PatternExtractor class: + +1. Implement multi-dimensional pattern extraction following research: + - Topics: Track frequently discussed subjects and user interests + - Sentiment: Analyze emotional tone and sentiment patterns + - Interaction patterns: Response times, question asking, information sharing + - Time-based preferences: Communication style by time of day/week + - Response styles: Formality level, verbosity, use of emojis/humor + +2. Pattern extraction methods: + - extract_topic_patterns(conversations: List[Conversation]) -> TopicPatterns + - extract_sentiment_patterns(conversations: List[Conversation]) -> SentimentPatterns + - extract_interaction_patterns(conversations: List[Conversation]) -> InteractionPatterns + - extract_temporal_patterns(conversations: List[Conversation]) -> TemporalPatterns + - extract_response_style_patterns(conversations: List[Conversation]) -> ResponseStylePatterns + +3. Analysis techniques: + - Simple frequency analysis for topics + - Basic sentiment analysis using keyword lists or simple models + - Statistical analysis for interaction patterns + - Time series analysis for temporal patterns + - Linguistic analysis for response styles + +4. Pattern validation: + - Confidence scoring for extracted patterns + - Pattern stability tracking over time + - Outlier detection for unusual patterns + +Follow existing error handling patterns. Keep analysis lightweight to avoid heavy computational overhead. + + python -c "from src.memory.personality.pattern_extractor import PatternExtractor; pe = PatternExtractor(); print('Pattern extractor created successfully')" + Pattern extractor can analyze conversations and extract patterns + + + + Task 2: Implement personality layer management and adaptation + src/memory/personality/layer_manager.py, src/memory/personality/adaptation.py, src/memory/__init__.py, src/personality.py + +Create personality management system: + +1. Create src/memory/personality/layer_manager.py with LayerManager: + - PersonalityLayer dataclass with weights and application rules + - Layer creation from extracted patterns + - Layer conflict resolution (when patterns contradict) + - Layer activation based on conversation context + - Methods: create_layer_from_patterns(), get_active_layers(), apply_layers() + +2. Create src/memory/personality/adaptation.py with PersonalityAdaptation: + - Time-weighted learning (recent patterns have less influence) + - Gradual adaptation with stability controls + - Feedback integration for user preferences + - Adaptation rate limiting to prevent rapid changes + - Methods: update_personality_layer(), calculate_adaptation_rate(), apply_stability_controls() + +3. Update src/memory/__init__.py to integrate personality learning: + - Add PersonalityLearner to MemoryManager + - Implement learning triggers (after conversations, periodically) + - Add personality data persistence + - Provide learning controls and configuration + +4. Update src/personality.py to integrate with memory: + - Import and use PersonalityLearner from memory system + - Apply personality layers during conversation responses + - Maintain separation between core personality and learned layers + - Add configuration for learning enable/disable + +5. Personality layer application: + - Hybrid system prompt + behavior configuration + - Context-aware layer activation + - Core value enforcement (learned layers cannot override core values) + - Layer priority and conflict resolution + +Follow existing patterns from src/resource/personality.py for personality management. Ensure core personality values remain protected from learned modifications. + + python -c "from src.memory.personality.layer_manager import LayerManager; lm = LayerManager(); print('Layer manager created successfully')" + Personality system can learn patterns and apply adaptive layers + + + + + +After completion, verify: +1. Pattern extractor analyzes conversations across multiple dimensions +2. Layer manager creates personality overlays from patterns +3. Adaptation system prevents overfitting and maintains stability +4. Personality learning integrates with existing personality.py +5. Core personality values are protected from learned modifications +6. Learning system can be enabled/disabled through configuration + + + +- Pattern extraction covers topics, sentiment, interaction, temporal, and style patterns +- Personality layers work as adaptive overlays that enhance core personality +- Time-weighted learning prevents overfitting to recent conversations +- Stability controls maintain personality consistency +- Integration with existing personality system preserves core values +- Learning system is configurable and can be controlled by user + + + +After completion, create `.planning/phases/04-memory-context-management/04-04-SUMMARY.md` + \ No newline at end of file