Add Projects, Milestones, RAG auto-inject, push notifications, PWA, tag normalisation

## Projects & Milestones (Phases A + G)
- New models: Project, Milestone (Project → Milestone → Task hierarchy)
- notes table: project_id + milestone_id FKs; parent_id FK constraint activated
- Migrations: 0017 (projects), 0018 (push_subscriptions), 0019 (events), 0020 (milestones)
- Services: projects.py, milestones.py (CRUD + progress tracking)
- Routes: /api/projects + /api/projects/<id>/milestones
- LLM tools: create/list/get/update project; create/list milestone; project + milestone + parent_task params on note/task tools
- Frontend: ProjectListView (stacked milestone bars), ProjectView (milestone-grouped kanban), ProjectSelector, MilestoneSelector, NoteEditorView + TaskEditorView updated

## RAG Auto-injection (Phase B)
- Notes ≥0.60 cosine similarity auto-injected into system prompt (max 3, 800 chars each)
- excluded_note_ids param; ChatView "Auto-included" sidebar section

## Summarisation improvements (Phase C)
- Threshold 20→30, keep-recent 6→8, max_tokens 200→400
- Two-pass summarisation for histories >50 messages

## Browser push notifications (Phase E)
- PushSubscription model + migration; pywebpush dependency
- /api/push routes; VAPID config; fire-and-forget on generation complete
- Frontend: sw.js, push store, Settings toggle

## PWA manifest (Phase F)
- manifest.json, Apple meta tags, service worker registration in main.ts

## Tag normalisation
- All tags lowercased + deduplicated at backend (create_note/update_note) and frontend (TagInput sanitize)
- Note/Task types gain project_id + milestone_id fields; store signatures updated

## CalDAV
- Radicale embedded server reverted; back to user-configured external CalDAV

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/fabledassistant/services/embeddings.py

@@ -78,11 +78,12 @@ async def semantic_search_notes(
     query: str,
     exclude_ids: set[int] | None = None,
     limit: int = 8,
+    threshold: float = _SIMILARITY_THRESHOLD,
 ) -> list[tuple[float, Note]]:
     """Return up to *limit* (score, note) pairs most relevant to *query*.
 
     Scores are cosine similarities in [-1, 1]; only notes at or above
-    _SIMILARITY_THRESHOLD are returned, sorted highest-first.
+    *threshold* are returned, sorted highest-first.
     Returns an empty list if the embedding model is unavailable or on any error.
     """
     try:
@@ -114,7 +115,7 @@ async def semantic_search_notes(
             sim = _cosine_similarity(query_vec, ne.embedding)
         except Exception:
             continue
-        if sim >= _SIMILARITY_THRESHOLD:
+        if sim >= threshold:
             scored.append((sim, note))
 
     scored.sort(key=lambda x: x[0], reverse=True)