Update app.py

app.py

@@ -898,41 +898,6 @@ async def rebuild_custom_ai(
         logger.error(f"Rebuild custom AI error: {e}")
         raise HTTPException(status_code=500, detail=f"Rebuild error: {str(e)}")
 
-@app.post("/chat/custom/{ai_id}", response_model=QuestionResponse)
-async def chat_custom_ai_enhanced(
-        ai_id: str,
-        request: QuestionRequest,
-        current_user: dict = Depends(get_current_user)
-):
-    """Chat with custom AI using enhanced fallback system"""
-    if not lightrag_manager:
-        raise HTTPException(status_code=503, detail="LightRAG system not initialized")
-
-    try:
-        # Generate conversation ID if not provided
-        conversation_id = request.conversation_id or str(uuid.uuid4())
-
-        # Query with enhanced custom AI fallback
-        result = await query_custom_ai_with_fallback(
-            lightrag_manager=lightrag_manager,
-            ai_id=ai_id,
-            question=request.question,
-            conversation_id=conversation_id,
-            user_id=current_user["id"],
-            preferred_mode=request.mode or "hybrid"
-        )
-
-        await update_message_stats(request.question, result["answer"])
-        return QuestionResponse(
-            answer=result["answer"],
-            mode=result["mode"],
-            status=result["status"],
-            conversation_id=conversation_id
-        )
-
-    except Exception as e:
-        logger.error(f"Custom AI chat error: {e}")
-        raise HTTPException(status_code=500, detail=f"Chat error: {str(e)}")
 
 
 @app.post("/admin/stats/recalculate")
@@ -1495,8 +1460,16 @@ async def query_rag_with_fallback(
 ) -> Dict[str, Any]:
     """Query RAG with automatic fallback to working modes"""
 
-    # Try modes in order of preference
-    fallback_modes = ["hybrid", "local", "global", "naive"]
+    # AI-type specific fallback modes
+    fallback_strategies = {
+        "custom": ["hybrid", "naive", "local", "global"],  # Custom AIs: Start with most reliable
+        "fire-safety": ["hybrid", "local", "global", "naive"],  # Fire safety: Complex modes first
+        "general": ["local", "naive", "global", "hybrid"],  # General: Local knowledge first
+        "physics": ["global", "hybrid", "local", "naive"]  # Physics: Global knowledge first
+    }
+
+    # Get fallback modes for this AI type, default to original order
+    fallback_modes = fallback_strategies.get(ai_type, ["hybrid", "naive", "local", "global"])
 
     # Start with user's preferred mode
     if preferred_mode in fallback_modes: