- Create headroom/models/config.py as single source of truth for all ML model defaults
- Support environment variable overrides (HEADROOM_SENTENCE_TRANSFORMER, etc.)
- Update all components to use ML_MODEL_DEFAULTS instead of hardcoded values
- Switch LLMLingua default to smaller bert-base model (~350MB vs 1GB)
- Total memory footprint reduced from ~1.6GB to ~980MB
Updated files:
- MLModelRegistry now resolves defaults from config
- EmbeddingScorer, LocalEmbedder, TrainedRouter use config
- All dataclass configs use field(default_factory=...) for consistency
- Tests updated to handle auto-selected vector backends
Implement comprehensive memory system supporting:
- Local backend (SQLite + FTS5 + HNSW) for zero-dependency operation
- Mem0 backends (Neo4j + Qdrant) for production graph memory
- DirectMem0Adapter for optimized pre-extracted data (bypasses LLM)
- Memory extraction with facts, entities, and relationships
- Proxy integration with --memory flag for automatic memory injection
Key components:
- headroom/memory/backends/: LocalBackend, Mem0Backend, DirectMem0Adapter
- headroom/memory/system.py: MemorySystem with tool-based interface
- headroom/memory/extraction.py: Entity and relationship extraction
- headroom/proxy/memory_handler.py: Proxy integration layer
- headroom/prediction/feature_extractor.py: Content analysis features
Testing:
- 217 new memory system tests covering all backends
- LoCoMo evaluation framework for memory quality assessment
- Integration tests for proxy memory functionality
Also removes deprecated example files in favor of focused test coverage.
