v2.2.0 - Semantic Recall & In-Process Hybrid Search

Highlights of v2.2.0

• In-Process Vector Extension via sqlite-vec: Native C-extension loaded directly into SQLite with zero daemon overhead or RAM bloat.
• Dense Multilingual Embeddings via FastEmbed: ONNX-based inference using sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 (384 dimensions, ~9ms latency).
• Reciprocal Rank Fusion (RRF): Combines BM25 lexical precision with dense semantic cosine similarity in search_memory and the real-time debug web dashboard.
• Zero Latency Impact on CLI Prompts: Pre-invocation prefetch remains strictly under 2ms using FTS5 + Trigram subtoken matching.
• Background Vector Lifecycle: Auto-indexes new facts, episodes, and learnings via Calm Memory Queue Worker; automatic SQLite triggers handle cascade deletion.
• Reindexing Utility: Included standalone scripts/reindex_vectors.py for database vector re-generation.
• Testing: 49/49 unit and integration tests passing.