- Created src/memory/personality/__init__.py module structure
- Implemented PatternExtractor class with multi-dimensional analysis:
- Topics: Track frequently discussed subjects and user interests
- Sentiment: Analyze emotional tone and sentiment patterns
- Interaction: Response times, question asking, information sharing
- Temporal: Communication style by time of day/week
- Response styles: Formality level, verbosity, emoji/humor use
- Pattern extraction methods for all dimensions with confidence scoring
- Lightweight analysis techniques to avoid computational overhead
- Pattern validation with stability tracking and outlier detection
- Completed Task 2: Context-aware and timeline search
- ContextAwareSearch class with topic classification and result prioritization
- TimelineSearch class with date-range filtering and temporal proximity
- Enhanced MemoryManager with unified search interface
- Supports semantic, keyword, context-aware, timeline, and hybrid search
- Added search result dataclasses with relevance scoring
- Integrated all search strategies into MemoryManager.search() method
All search modes operational:
- Semantic search with sentence-transformers embeddings
- Context-aware search with topic-based prioritization
- Timeline search with date filtering and recency weighting
- Hybrid search combining multiple strategies
Search results include conversation context and relevance scoring as required.
- Added sentence-transformers to requirements.txt for semantic embeddings
- Created src/memory/retrieval/ module with search capabilities
- Implemented SemanticSearch class with embedding generation and vector similarity
- Added SearchResult and SearchQuery dataclasses for structured search results
- Included hybrid search combining semantic and keyword matching
- Added conversation indexing for semantic search
- Followed lazy loading pattern for embedding model performance
Files created:
- src/memory/retrieval/__init__.py
- src/memory/retrieval/search_types.py
- src/memory/retrieval/semantic_search.py
- Updated src/memory/__init__.py with enhanced MemoryManager
Note: sentence-transformers installation requires proper venv setup in production
- Added ResourcePersonality import and initialization to ModelManager
- Created personality_aware_model_switch() method for graceful degradation notifications
- Only notifies users about capability downgrades, not upgrades (per requirements)
- Includes optional technical tips for resource optimization
- Updated proactive scaling callbacks to use personality-aware switching
- Enhanced failure handling with personality-driven resource requests
- Added _is_capability_downgrade() helper for capability comparison
- Created ResourcePersonality class with Drowsy Dere-Tsun Onee-san Hex-Mentor Gremlin personality
- Includes mood system with sleepy, grumpy, helpful, gremlin, and mentor states
- Personality-specific vocabularies for different emotional responses
- Optional technical tips with hexadecimal/coding references
- generate_resource_message() for contextual resource communications
- Support for resource requests, degradation notices, system status, and scaling recommendations
- Added ProactiveScaler integration with HardwareTierDetector
- Implemented pre-flight resource checks before model inference
- Enhanced model selection with scaling recommendations
- Added graceful degradation handling for resource constraints
- Integrated performance metrics tracking for scaling decisions
- Added proactive upgrade execution with stabilization periods
- Enhanced status reporting with scaling information
- Maintained silent switching behavior per Phase 1 decisions
- Created ProactiveScaler class for proactive resource management
- Implemented continuous background monitoring with configurable intervals
- Added pre-flight resource checks before operations
- Implemented graceful degradation cascades with stabilization periods
- Added trend analysis for predictive scaling decisions
- Included hysteresis to prevent model switching thrashing
- Provided callbacks for integration with ModelManager
- Thread-safe implementation with proper shutdown handling
- Created comprehensive hardware tier detection system
- Loads configurable tier definitions from YAML
- Classifies systems based on RAM, CPU cores, and GPU capabilities
- Provides model recommendations and performance characteristics
- Includes caching for performance and error handling
- Integrates with ResourceMonitor for real-time data
- Added comprehensive tier definitions for low_end, mid_range, high_end
- Configurable thresholds for RAM, CPU cores, GPU requirements
- Model size recommendations per tier (1B-70B parameter range)
- Performance characteristics and scaling thresholds
- Global settings for model selection and scaling behavior
- Created src/resource/__init__.py with module docstring
- Exported HardwareTierDetector (to be implemented)
- Established resource management module foundation
- Added caching for GPU info to avoid repeated pynvml initialization
- Added pynvml failure tracking to skip repeated failed attempts
- Optimized CPU measurement interval from 1.0s to 0.05s
- Reduced monitoring overhead from ~1000ms to ~50ms per call
- Maintained accuracy while significantly improving performance
- Added pynvml import with graceful fallback handling
- Enhanced _get_gpu_info() method using pynvml for NVIDIA GPUs
- Added detailed GPU metrics: total/used/free VRAM, utilization, temperature
- Updated get_current_resources() to include comprehensive GPU info
- Maintained backward compatibility with existing gpu_vram_gb field
- Added gpu-tracker fallback for AMD/Intel GPUs
- Proper error handling for pynvml initialization failures
- Ensured pynvmlShutdown() always called in finally-style logic
- Implement __main__.py with argparse command-line interface
- Add interactive chat loop for testing model switching
- Include status commands to show current model and resources
- Support models listing and manual model switching
- Add proper signal handling for graceful shutdown
- Include help text and usage examples
- Fix import issues for relative imports in package
- Initialize ModelManager, ContextManager, and subsystems
- Provide main conversation interface with process_message
- Support both synchronous and async operations
- Add system status monitoring and conversation history
- Include graceful shutdown with signal handlers
- Background resource monitoring and maintenance tasks
- Model switching commands and information methods
- Load model configuration from config/models.yaml
- Intelligent model selection based on system resources and context
- Dynamic model switching with silent behavior (no user notifications)
- Fallback chains for model failures
- Proper resource cleanup and error handling
- Background preloading capability
- Auto-retry on model failures with graceful degradation
- Add ConversationMetadata to imports
- Fix metadata initialization in create_conversation()
- Resolve type error for conversation metadata
File: src/models/context_manager.py
- Define Message, Conversation, ContextBudget, and ContextWindow classes
- Implement MessageRole and MessageType enums for classification
- Add Pydantic models for validation and serialization
- Include importance scoring and token estimation utilities
- Support system, user, assistant, and tool message types
File: src/models/conversation.py (147 lines)
- Created ResourceMonitor class with psutil integration
- Monitor CPU usage, memory availability, and GPU VRAM
- Added resource trend analysis for load prediction
- Implemented should_switch_model() logic based on thresholds
- Added can_load_model() method with safety margins
- Follow Pattern 2 from research: Resource-Aware Model Selection
- Graceful handling of missing gpu-tracker dependency
- Created LMStudioAdapter class using lmstudio-python SDK
- Added context manager get_client() for safe client handling
- Implemented list_available_models() with size estimation
- Added load_model(), unload_model(), get_model_info() methods
- Created mock_lmstudio.py for graceful fallback when lmstudio not installed
- Included error handling for LM Studio not running and model loading failures
- Implemented Pattern 1 from research: Model Client Factory
- Created pyproject.toml with lmstudio, psutil, pydantic dependencies
- Created requirements.txt as fallback for pip install
- Created src/models/__init__.py with proper imports
- Set up PEP 518 compliant package structure
- Fixed .gitignore to allow src/models/ directory
