Implement all core features: Knowledge pipeline, RAG chat, Todos, Habits, Study Cards, Tags, Dashboard

- Google OAuth authentication with callback flow
- Knowledge ingest pipeline (TEXT/WEB/YOUTUBE → chunking → categorization → embedding)
- OCI GenAI integration (chat, embeddings) with multi-model support
- Semantic search via Oracle VECTOR_DISTANCE
- RAG-based AI chat with source attribution
- Todos with subtasks, filters, and priority levels
- Habits with daily check-in, streak tracking, and color customization
- Study Cards with SM-2 spaced repetition and LLM auto-generation
- Tags system with knowledge item mapping
- Dashboard with live data from all modules

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

sundol-backend/src/main/resources/application.yml

@@ -12,12 +12,22 @@ jwt:
   access-token-expiry: ${JWT_ACCESS_TOKEN_EXPIRY:900000}
   refresh-token-expiry: ${JWT_REFRESH_TOKEN_EXPIRY:604800000}
 
+google:
+  client-id: ${GOOGLE_CLIENT_ID}
+  client-secret: ${GOOGLE_CLIENT_SECRET}
+  redirect-uri: ${GOOGLE_REDIRECT_URI:https://sundol.cloud-handson.com/login/callback}
+
 cors:
   origin: ${CORS_ORIGIN:http://localhost:3000}
 
 oci:
   compartment-id: ${OCI_COMPARTMENT_ID:}
   region: ${OCI_REGION:ap-seoul-1}
+  genai:
+    api-key: ${OCI_GENAI_API_KEY:}
+    compartment: ${OCI_GENAI_COMPARTMENT:}
+    model: ${OCI_GENAI_MODEL:google.gemini-2.5-flash}
+    base-url: ${OCI_GENAI_BASE_URL:https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions}
 
 logging:
   level: