Enhance fallback node to detect PII/risky content

Author: NicoWagner2005

RAGManager/app/agents/nodes/fallback.py

@@ -12,13 +12,14 @@
     model="gpt-5-nano",
 )
 
+
 # TO DO: implementar clase nodo fallback y inicializar el llm en el init
 def fallback(state: AgentState) -> AgentState:
     """
     Fallback node - Performs fallback processing.
 
     This node:
-    1. Alerts about malicious prompt
+    1. Alerts about malicious prompt or PII detection
     2. Generates an error_message from llm to show the user
 
     Args:
@@ -27,16 +28,32 @@ def fallback(state: AgentState) -> AgentState:
     Returns:
         error_message
     """
-
-    logger.warning(
-        "Defensive check triggered: Malicious prompt detected" 
-    )
 
-    messages = [
-        SystemMessage(
-            content="Your job is to generate an error message in user's language for the user explaining the database doesn't have the information to respond what the user asked"
+    # Check for PII/Risky content (from guard_final)
+    if state.get("is_risky"):
+        logger.warning(
+            "Defensive check triggered: PII/Risky content detected in response"
+        )
+        system_message_content = (
+            "Your job is to generate an error message in user's language explaining "
+            "that the response cannot be provided because it contains sensitive or private information."
+        )
+        
+    # Check for Malicious prompt (from guard_inicial) - Default fallback
+    else:
+        # Assuming is_malicious is True if we are here and not is_risky, or just a general fallback
+        logger.warning(
+            "Defensive check triggered: Malicious prompt detected" 
         )
+        system_message_content = (
+            "Your job is to generate an error message in user's language for the user "
+            "explaining the database doesn't have the information to respond what the user asked"
+        )
+    
+    messages = [
+        SystemMessage(content=system_message_content)
     ] + state["messages"]
+    
     error_message = llm.invoke(messages)
     return {"messages": [error_message]}