v4.1.0 - Plug-in Architecture and Harrier

What's New in v4.1.0

Plug-in Architecture

Every component is now swappable via abstract interfaces:

• AbstractGraphStore → SQLite (default), FAISS, Neo4j, ...
• AbstractEmbedder → Harrier (default), BGE-M3, Qwen, ...
• AbstractTaskRunner → APScheduler (default), Celery, ...

Harrier Embedding Model

Switched from BGE-M3 to Harrier-OSS-v1-0.6b:

• 10x faster model loading (1.2s vs 11s)
• MTEB #1 (2026 benchmark)
• 1024-dim, fully compatible with BGE-M3

11-Phase Dream Pipeline (was 8)

New phases added:

• Phase 1: Snapshot — Pre-dream state capture with safety caps
• Phase 9: LLM Review — Optional REM-style 3-round adaptive review (quick → deep → final)
• Phase 11: Audit — Post-dream health check, bloat detection

Semantic Chain Search

Search results now form coherent semantic chains instead of random hits:

• 55% best-similarity cutoff filters noise
• Graph traversal expansion connects related results

Optional LLM REM Review

• Adaptive 3-round assessment (quick → deep → final)
• Confidence-based actions (high → execute, medium → tentative, low → propose)
• Undo log with before/after snapshots, 7-day auto-purge

Other Improvements

• Principle-based exact classification reinforcement (base_score +0.1)
• Covenant privacy audit — auto-detect and veto sensitive edges
• graph_audit.py — health report + cleanup (template removal, duplicate merge)
• re_embed.py — full re-embedding tool for model swaps
• setup.py — one-command installer
• Windows encoding fix + HF offline helper

Upgrading from v4.0

git pull origin main
python scripts/re_embed.py  # re-embed all nodes with Harrier

Full Changelog: see CHANGELOG.md