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

.env.example

@@ -54,3 +54,11 @@ RATE_LIMIT_RESET_CONFIRM_MAX=5
 RATE_LIMIT_RESET_CONFIRM_WINDOW=3600
 RATE_LIMIT_GENERAL_MAX=60
 RATE_LIMIT_GENERAL_WINDOW=60
+
+# === AI / Search ===
+# Ollama Cloud API Key (für LiteLLM)
+API_KEY_OLLAMA_CLOUD=
+# Embedding Modell (default: ollama/nomic-embed-text)
+SEARCH_EMBEDDING_MODEL=ollama/nomic-embed-text
+# LLM Modell für Query Understanding (default: ollama/deepseek-v4)
+SEARCH_LLM_MODEL=ollama/deepseek-v4