""" Hybrid Chat Endpoint: RAG + Scenario FSM Routes between scripted scenarios and knowledge retrieval """ from fastapi import HTTPException from datetime import datetime from typing import Dict, Any import json async def hybrid_chat_endpoint( request, # ChatRequest conversation_service, intent_classifier, scenario_engine, tools_service, advanced_rag, embedding_service, qdrant_service, chat_history_collection, hf_token, lead_storage # NEW: For saving customer leads ): """ Hybrid conversational chatbot: Scenario FSM + RAG Flow: 1. Load session & scenario state 2. Classify intent (scenario vs RAG) 3. Route: - Scenario: Execute FSM flow - RAG: Knowledge retrieval - RAG+Resume: Answer question then resume scenario 4. Save state & history """ try: # ===== SESSION MANAGEMENT ===== session_id = request.session_id if not session_id: session_id = conversation_service.create_session( metadata={"user_agent": "api", "created_via": "hybrid_chat"}, user_id=request.user_id ) print(f"✓ Created session: {session_id} (user: {request.user_id or 'anon'})") else: if not conversation_service.session_exists(session_id): raise HTTPException(404, detail=f"Session {session_id} not found") # ===== LOAD SCENARIO STATE ===== scenario_state = conversation_service.get_scenario_state(session_id) or {} # ===== INTENT CLASSIFICATION ===== intent = intent_classifier.classify(request.message, scenario_state) print(f"🎯 Intent: {intent}") # ===== ROUTING ===== if intent.startswith("scenario:"): # Route to scenario engine response_data = await handle_scenario( intent, request.message, session_id, scenario_state, scenario_engine, conversation_service, advanced_rag, lead_storage # NEW: Pass for action handling ) elif intent == "rag:with_resume": # Answer question but keep scenario active response_data = await handle_rag_with_resume( request, session_id, scenario_state, advanced_rag, embedding_service, qdrant_service, conversation_service ) else: # rag:general # Pure RAG query response_data = await handle_pure_rag( request, session_id, advanced_rag, embedding_service, qdrant_service, tools_service, chat_history_collection, hf_token, conversation_service ) # ===== SAVE HISTORY ===== conversation_service.add_message( session_id, "user", request.message, metadata={"intent": intent} ) conversation_service.add_message( session_id, "assistant", response_data["response"], metadata={ "mode": response_data.get("mode", "unknown"), "context_used": response_data.get("context_used", [])[:3] # Limit size } ) return { "response": response_data["response"], "session_id": session_id, "mode": response_data.get("mode"), "scenario_active": response_data.get("scenario_active", False), "timestamp": datetime.utcnow().isoformat() } except Exception as e: print(f"❌ Error in hybrid_chat: {str(e)}") raise HTTPException(500, detail=f"Chat error: {str(e)}") async def handle_scenario( intent, user_message, session_id, scenario_state, scenario_engine, conversation_service, advanced_rag, lead_storage=None ): """Handle scenario-based conversation""" if intent == "scenario:continue": # Continue existing scenario result = scenario_engine.next_step( scenario_id=scenario_state["active_scenario"], current_step=scenario_state["scenario_step"], user_input=user_message, scenario_data=scenario_state.get("scenario_data", {}), rag_service=advanced_rag ) else: # Start new scenario scenario_type = intent.split(":", 1)[1] result = scenario_engine.start_scenario(scenario_type) # Update scenario state if result.get("end_scenario"): conversation_service.clear_scenario(session_id) scenario_active = False else: conversation_service.set_scenario_state(session_id, result["new_state"]) scenario_active = True # Execute action if any if result.get("action") and lead_storage: action = result['action'] scenario_data = result.get('new_state', {}).get('scenario_data', scenario_state.get('scenario_data', {})) if action == "send_pdf_email": # Save lead with email lead_storage.save_lead( event_name=scenario_data.get('step_1_input', 'Unknown Event'), email=scenario_data.get('step_5_input'), # Email from step 5 interests={ "group": scenario_data.get('group_size'), "wants_pdf": True }, session_id=session_id ) print(f"📧 Lead saved: email sent (saved to DB)") elif action == "save_lead_phone": # Save lead with phone lead_storage.save_lead( event_name=scenario_data.get('step_1_input', 'Unknown Event'), email=scenario_data.get('step_5_input'), phone=scenario_data.get('step_8_input'), # Phone from step 8 interests={ "group": scenario_data.get('group_size'), "wants_reminder": True }, session_id=session_id ) print(f"📱 Lead saved: SMS reminder (saved to DB)") return { "response": result["message"], "mode": "scenario", "scenario_active": scenario_active } async def handle_rag_with_resume( request, session_id, scenario_state, advanced_rag, embedding_service, qdrant_service, conversation_service ): """ Handle RAG query mid-scenario Answer question then remind user to continue scenario """ # Query RAG context_used = [] if request.use_rag: query_embedding = embedding_service.encode_text(request.message) results = qdrant_service.search( query_embedding=query_embedding, limit=request.top_k, score_threshold=request.score_threshold, ef=256 ) context_used = results # Build simple RAG response rag_response = await simple_rag_response( request.message, context_used, request.system_message ) # Add resume hint last_scenario_msg = f"\n\n---\nVậy nha! Quay lại câu hỏi trước, bạn đã quyết định chưa? ^^" return { "response": rag_response + last_scenario_msg, "mode": "rag_with_resume", "scenario_active": True, "context_used": context_used } async def handle_pure_rag( request, session_id, advanced_rag, embedding_service, qdrant_service, tools_service, chat_history_collection, hf_token, conversation_service ): """ Handle pure RAG query (fallback to existing logic) """ # Import existing chat_endpoint logic from chat_endpoint import chat_endpoint # Call existing endpoint result = await chat_endpoint( request, conversation_service, tools_service, advanced_rag, embedding_service, qdrant_service, chat_history_collection, hf_token ) return { "response": result["response"], "mode": "rag", "context_used": result.get("context_used", []) } async def simple_rag_response(message, context, system_message): """Simple RAG response without LLM (for quick answers)""" if context: # Return top context top = context[0] return f"{top['metadata'].get('text', 'Không tìm thấy thông tin.')}" return "Xin lỗi, tôi không tìm thấy thông tin về điều này."