Update app.py

app.py

@@ -195,6 +195,130 @@ async def kb_query_endpoint(ticket: TicketRequest):
         traceback.print_exc()
         raise HTTPException(status_code=500, detail=f"KB query failed: {str(e)}")
 
+# Add this to your EXISTING app.py (don't replace, just add these endpoints)
+
+from datetime import datetime
+from typing import Optional
+
+# Global conversation storage
+conversations = {}
+
+class ConversationRequest(BaseModel):
+    text: str
+    conversation_id: Optional[str] = None
+    user_email: Optional[str] = None
+
+@app.post("/chat")
+async def chat_endpoint(request: ConversationRequest):
+    """
+    Agentic chat endpoint - handles conversations with memory and auto-escalation.
+    """
+    from agent_langchain import process_ticket_langchain, query_kb
+    
+    # Generate or get conversation ID
+    if not request.conversation_id:
+        conversation_id = f"conv_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{hash(request.text) % 10000}"
+    else:
+        conversation_id = request.conversation_id
+    
+    # Initialize conversation
+    if conversation_id not in conversations:
+        conversations[conversation_id] = {
+            "messages": [],
+            "ticket_info": None,
+            "created_at": datetime.now().isoformat()
+        }
+    
+    conv = conversations[conversation_id]
+    
+    # Add user message
+    conv["messages"].append({
+        "role": "user",
+        "content": request.text,
+        "timestamp": datetime.now().isoformat()
+    })
+    
+    # First message - full orchestration
+    if len(conv["messages"]) == 1:
+        result = process_ticket_langchain(request.text)
+        conv["ticket_info"] = {
+            "classification": result["classification"],
+            "department": result["department"],
+            "initial_query": request.text
+        }
+        
+        response_text = result["answer"]
+        status = result["status"]
+        
+    # Follow-up messages
+    else:
+        from agent_langchain import llm
+        
+        # Check for escalation keywords
+        escalation_keywords = ["not working", "didn't work", "still broken", "escalate", "human", "agent"]
+        wants_escalation = any(kw in request.text.lower() for kw in escalation_keywords)
+        
+        if wants_escalation:
+            # Try KB one more time with the new query
+            kb_result = query_kb(request.text)
+            
+            if kb_result["answer"] and kb_result["confidence"] >= 0.75:
+                response_text = f"Let me try a different approach:\n\n{kb_result['answer']}"
+                status = "resolved"
+            else:
+                # Escalate to human
+                ticket_id = f"TKT-{datetime.now().strftime('%Y%m%d-%H%M%S')}"
+                response_text = f"""I understand the previous solution didn't resolve your issue. I'm escalating this to a human agent.
+
+**Ticket ID:** {ticket_id}
+**Department:** {conv['ticket_info']['department']}
+**Priority:** High
+
+A specialist from the {conv['ticket_info']['department']} team will contact you within 2-4 business hours. They will have access to our full conversation history.
+
+In the meantime, is there anything else I can help clarify about the troubleshooting steps we've tried?"""
+                status = "escalated"
+        else:
+            # Continue conversation with context
+            context = f"""You are an IT helpdesk agent. Here's the conversation history:
+
+Initial issue: {conv['ticket_info']['initial_query']}
+Classification: {conv['ticket_info']['classification']}
+Department: {conv['ticket_info']['department']}
+
+Recent messages:
+"""
+            for msg in conv["messages"][-4:]:
+                context += f"{msg['role']}: {msg['content']}\n"
+            
+            context += f"\nUser's current message: {request.text}\n\nProvide a helpful response."
+            
+            response_text = llm.invoke(context).content
+            status = "in_progress"
+    
+    # Add assistant response
+    conv["messages"].append({
+        "role": "assistant",
+        "content": response_text,
+        "status": status,
+        "timestamp": datetime.now().isoformat()
+    })
+    
+    return {
+        "conversation_id": conversation_id,
+        "response": response_text,
+        "status": status,
+        "message_count": len(conv["messages"]),
+        "instructions": "Send follow-up messages with the same conversation_id"
+    }
+
+@app.get("/conversation/{conversation_id}")
+async def get_conversation_endpoint(conversation_id: str):
+    """Get conversation history."""
+    if conversation_id not in conversations:
+        raise HTTPException(status_code=404, detail="Conversation not found")
+    return conversations[conversation_id]
+
 # -------------------------------
 # Health Check
 # -------------------------------