Update app.py

app.py

@@ -13,7 +13,7 @@ import chromadb
 from chromadb.config import Settings
 from sentence_transformers import SentenceTransformer
 
-# Import from the true agent
+# Import from autonomous agent
 from agent_langchain import (
     process_with_agent,
     get_conversation_history,
@@ -24,7 +24,7 @@ from agent_langchain import (
     conversations
 )
 
-app = FastAPI(title="Smart Helpdesk AI Agent")
+app = FastAPI(title="Smart Helpdesk AI Agent - Autonomous")
 
 # Request Models
 class TicketRequest(BaseModel):
@@ -100,16 +100,16 @@ async def setup_kb(kb_file: UploadFile = File(...)):
         raise HTTPException(status_code=500, detail=f"Setup failed: {str(e)}")
 
 # -------------------------------
-# MAIN ORCHESTRATE ENDPOINT (True Agent)
+# MAIN ORCHESTRATE ENDPOINT (Autonomous Agent)
 # -------------------------------
 @app.post("/orchestrate")
 async def orchestrate_endpoint(ticket: TicketRequest):
     """
-    Main AI Agent endpoint - handles everything:
-    - Initial ticket processing
-    - Follow-up conversations
-    - Automatic escalation decisions
-    - Multi-turn dialogue
+    Main AI Agent endpoint - fully autonomous:
+    - Decides its own workflow
+    - Handles multi-turn conversations
+    - Auto-escalates when needed
+    - Maintains context
     """
     try:
         result = process_with_agent(
@@ -122,10 +122,10 @@ async def orchestrate_endpoint(ticket: TicketRequest):
             "response": result["response"],
             "status": result["status"],
             "message_count": result["message_count"],
-            "can_continue": result.get("can_continue", True),
+            "reasoning_trace": result.get("reasoning_trace", []),
             "instructions": {
-                "continue_conversation": "Send another request with the same conversation_id",
-                "new_ticket": "Send a request without conversation_id"
+                "continue_conversation": "Include the conversation_id in your next request",
+                "new_ticket": "Omit conversation_id to start fresh"
             }
         }
         
@@ -195,130 +195,6 @@ 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
 # -------------------------------
@@ -332,5 +208,6 @@ async def health():
         "status": "ok",
         "kb_status": kb_status,
         "kb_records": kb_count,
-        "active_conversations": len(conversations)
+        "active_conversations": len(conversations),
+        "agent_type": "Autonomous ReAct Agent with Gemini"
     }