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97 lines
3.4 KiB
Markdown
97 lines
3.4 KiB
Markdown
# Changelog
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All notable changes to Vestige will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [1.1.2] - 2025-01-27
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### Fixed
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- Embedding model cache now uses platform-appropriate directories instead of polluting project folders
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- macOS: `~/Library/Caches/com.vestige.core/fastembed`
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- Linux: `~/.cache/vestige/fastembed`
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- Windows: `%LOCALAPPDATA%\vestige\cache\fastembed`
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- Can still override with `FASTEMBED_CACHE_PATH` environment variable
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---
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## [1.1.1] - 2025-01-27
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### Fixed
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- UTF-8 string slicing issues in keyword search and prospective memory
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- Silent error handling in MCP stdio protocol
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- Feature flag forwarding between crates
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- All GitHub issues resolved (#1, #3, #4)
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### Added
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- Pre-built binaries for Linux, Windows, and macOS (Intel & ARM)
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- GitHub Actions CI/CD for automated releases
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---
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## [1.1.0] - 2025-01-26
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### Changed
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- **Tool Consolidation**: 29 tools → 8 cognitive primitives
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- `recall`, `semantic_search`, `hybrid_search` → `search`
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- `get_knowledge`, `delete_knowledge`, `get_memory_state` → `memory`
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- `remember_pattern`, `remember_decision`, `get_codebase_context` → `codebase`
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- 5 intention tools → `intention`
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- Stats and maintenance moved from MCP to CLI (`vestige stats`, `vestige health`, etc.)
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### Added
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- CLI admin commands: `vestige stats`, `vestige health`, `vestige consolidate`, `vestige restore`
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- Feedback tools: `promote_memory`, `demote_memory`
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- 30+ FAQ entries with verified neuroscience claims
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- Storage modes documentation: Global, per-project, multi-Claude household
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- CLAUDE.md templates for proactive memory use
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- Version pinning via git tags
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### Deprecated
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- Old tool names (still work with warnings, removed in v2.0)
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---
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## [1.0.0] - 2025-01-25
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### Added
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- FSRS-6 spaced repetition algorithm with 21 parameters
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- Bjork & Bjork dual-strength memory model (storage + retrieval strength)
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- Local semantic embeddings with fastembed v5 (BGE-base-en-v1.5, 768 dimensions)
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- HNSW vector search with USearch (20x faster than FAISS)
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- Hybrid search combining BM25 keyword + semantic + RRF fusion
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- Two-stage retrieval with reranking (+15-20% precision)
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- MCP server for Claude Desktop integration
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- Tauri desktop application
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- Codebase memory module for AI code understanding
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- Neuroscience-inspired memory mechanisms:
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- Synaptic Tagging and Capture (retroactive importance)
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- Context-Dependent Memory (Tulving encoding specificity)
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- Spreading Activation Networks
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- Memory States (Active/Dormant/Silent/Unavailable)
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- Multi-channel Importance Signals (Novelty/Arousal/Reward/Attention)
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- Hippocampal Indexing (Teyler & Rudy 2007)
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- Prospective memory (intentions and reminders)
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- Sleep consolidation with 5-stage processing
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- Memory compression for long-term storage
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- Cross-project learning for universal patterns
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### Changed
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- Upgraded embedding model from all-MiniLM-L6-v2 (384d) to BGE-base-en-v1.5 (768d)
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- Upgraded fastembed from v4 to v5
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### Fixed
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- SQL injection protection in FTS5 queries
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- Infinite loop prevention in file watcher
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- SIGSEGV crash in vector index (reserve before add)
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- Memory safety with Mutex wrapper for embedding model
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## [0.1.0] - 2026-01-24
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### Added
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- Initial release
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- Core memory storage with SQLite + FTS5
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- Basic FSRS scheduling
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- MCP protocol support
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- Desktop app skeleton
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