Add semantic note search (nomic-embed-text) and per-conversation note cache

- New NoteEmbedding model + migration 0014 stores float embeddings (JSONB)
- services/embeddings.py: get_embedding, upsert_note_embedding,
  semantic_search_notes (cosine similarity), backfill_note_embeddings
- build_context() now tries semantic search first, falls back to keyword search;
  accepts cached_note_ids to reuse last-turn notes and stabilise the system
  prompt prefix for Ollama's KV cache
- generation_buffer.py: per-conversation note ID cache (get/set/clear)
- generation_task.py: passes cached IDs into build_context, updates cache
  after each turn, and invalidates it after create_note/update_note/create_task
- app.py: pulls nomic-embed-text at startup and launches a background backfill
  to embed all existing notes (30 s delay so Ollama has time to load the model)
- routes/notes.py + services/tools.py: fire-and-forget embedding update on
  every note create or update via the API or LLM tool calls

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

src/fabledassistant/models/__init__.py

@@ -18,3 +18,4 @@ from fabledassistant.models.user import User  # noqa: E402, F401
 from fabledassistant.models.app_log import AppLog  # noqa: E402, F401
 from fabledassistant.models.password_reset import PasswordResetToken  # noqa: E402, F401
 from fabledassistant.models.invitation import InvitationToken  # noqa: E402, F401
+from fabledassistant.models.embedding import NoteEmbedding  # noqa: E402, F401