v2.1.0 - Quality-First Extraction, Taxonomy Normalization & Graph Pruning

What's Changed in v2.1.0

• 🎯 Quality-First Extraction & Strict Learnings Filtering:

• Overhauled the memory extraction prompt (sync_turn): strictly excludes one-time bugfixes, debugging sessions, codebase internals, temporary configs, and UI layout tweaks.
• Implemented the Litmus test rule: "Would this insight help handle a SIMILAR situation in the future?"
• Re-focused experiential learnings exclusively on reusable heuristics, communication styles, and personal behavioral rules.

• 📐 Canonical Taxonomies & Runtime Normalization:

• Established a closed taxonomy for Entity Links with exactly 26 canonical relation types (hosted_on, runs_on, depends_on, monitors, part_of, etc.).
• Enforced strict category taxonomies across all 4 layers (20 Fact categories, 12 Learning categories, 13 Episode topics).
• Integrated runtime normalization (_normalize_category) and relation fallback mapping directly into the extraction pipeline.

• 🕸️ Full Entity Graph Pruning:

• Enhanced orphan link pruning (prune_orphan_links) to remove links whenever either endpoint (source or target) is missing or deleted.
• Cleaned up dangling graph pointers resulting from fact consolidation and entity merges.

• 🧹 Maintenance, Web Dashboard & DevSecOps:

• Integrated full taxonomy normalization and graph pruning into optimize_db and the dashboard endpoint POST /api/optimize.
• Rebuilt SQLite FTS5 indices and ran database VACUUM.
• Comprehensive DevSecOps pipeline validation: 0 secrets detected via Gitleaks, 45 of 45 unit tests passing.