feat: unified_search + ai_proactive plugins with Ollama Cloud DeepSeek V4

- unified_search: Hybride Suche (PostgreSQL FTS + pgvector + RRF Fusion)
  - 5 Search Providers (Contact, Company, Mail, File, Event)
  - KI Query Understanding (Fuzzy, Facetten via LiteLLM)
  - DMS Text-Extraction (PDF, DOCX, XLSX, PPTX)
  - Embedding Pipeline (ollama/nomic-embed-text, 768 Dim)
  - Background Jobs für Indexierung
  - Plugin-basierte Provider Registry

- ai_proactive: Proaktiver KI-Agent
  - Context-Tracking (Frontend → Backend → Event Bus)
  - Proactive Engine mit LLM Suggestion-Generierung
  - SSE Real-time Push an Frontend
  - 6 AI Tools für Tool Registry
  - Rate-Limiting + User Settings
  - Deep Analysis Background Jobs

- Frontend Integration:
  - useAIContext Hook, SuggestionSidebar, SuggestionBadge
  - ProactiveAISettings Page, Search API Client
  - Globale Suche auf neue API umgestellt

- Tests: test_unified_search.py + test_ai_proactive.py (alle bestanden)
- Config: Ollama Cloud DeepSeek V4 als Default, konfigurierbar
- Dependencies: PyMuPDF, python-docx, python-pptx, pgvector
- Bugfixes: notification type_key length, migration IF NOT EXISTS

app/models/notification.py

@@ -53,7 +53,7 @@ class NotificationType(Base):
     id: Mapped[uuid.UUID] = mapped_column(
         PGUUID(as_uuid=True), primary_key=True, default=uuid.uuid4
     )
-    type_key: Mapped[str] = mapped_column(String(20), nullable=False, unique=True)
+    type_key: Mapped[str] = mapped_column(String(100), nullable=False, unique=True)
     plugin_name: Mapped[str] = mapped_column(String(100), nullable=False)
     category: Mapped[str] = mapped_column(String(50), nullable=False, default="general")
     label: Mapped[str] = mapped_column(String(200), nullable=False)