# ============================================================================ # backend/app/main.py # ============================================================================ """ FastAPI Main Application Entry Point (UPDATED) Banking RAG Chatbot API with JWT Authentication CHANGES: - Replaced old chat router with new conversation_routes - Added conversation management features """ from fastapi import FastAPI, Request from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse from contextlib import asynccontextmanager from app.config import settings from app.db.mongodb import connect_to_mongo, close_mongo_connection # ============================================================================ # LIFESPAN MANAGER (Startup & Shutdown) # ============================================================================ from contextlib import asynccontextmanager import asyncio @asynccontextmanager async def lifespan(app: FastAPI): """ Manage application lifespan events. Startup: - Connect to MongoDB Atlas - Create indexes for conversations after DB ready (retry logic) - ML models lazy loaded """ print("\n" + "=" * 80) print("šŸš€ STARTING BANKING RAG CHATBOT API") print("=" * 80) print(f"Environment: {settings.ENVIRONMENT}") print(f"Debug Mode: {settings.DEBUG}") print("=" * 80) # STEP 1 ā€” Connect to MongoDB print("\nšŸ”Œ Connecting to MongoDB Atlas...") await connect_to_mongo() # STEP 2 ā€” Retry index creation until DB is ready from app.db.repositories.conversation_repository import conversation_repository for attempt in range(5): try: await conversation_repository.create_indexes() print("āœ… MongoDB indexes created successfully!") break except Exception as e: print(f"āš ļø Index creation failed (Attempt {attempt + 1}/5): {e}") await asyncio.sleep(2) else: print("āŒ Could not create MongoDB indexes after multiple retries") print("\nšŸ’” ML Models Info:") print(" Policy Network: Loads on first chat request (lazy load)") print(" Retriever Model: Loads on first retrieval (lazy load)") print(" LLM: Groq (ChatGroq) with HuggingFace fallback") print("\nšŸ¤– LLM Configuration:") print(f" Chat Model: {settings.GROQ_CHAT_MODEL} (Llama 3 8B)") print(f" Eval Model: {settings.GROQ_EVAL_MODEL} (Llama 3 70B)") print(f" Groq API Keys: {len(settings.get_groq_api_keys())} configured") print(f" HuggingFace Tokens: {len(settings.get_hf_tokens())} configured") print(f" Fallback: Groq ā†’ HuggingFace") print("\nāœ… Backend startup complete!") print("=" * 80) print(f"šŸ“– API Docs: https://eeshanyaj-questrag-backend.hf.space/docs") print(f"šŸ„ Health Check: https://eeshanyaj-questrag-backend.hf.space/health") print(f"šŸ§  Backend Link: https://eeshanyaj-questrag-backend.hf.space/") print("=" * 80 + "\n") yield # SHUTDOWN print("\n" + "=" * 80) print("šŸ›‘ SHUTTING DOWN API") print("=" * 80) await close_mongo_connection() print("āœ… Shutdown complete") print("=" * 80 + "\n") # ============================================================================ # CREATE FASTAPI APPLICATION # ============================================================================ app = FastAPI( title="Banking RAG Chatbot API", description=""" šŸ¤– AI-powered Banking Assistant with: **Features:** - šŸ” JWT Authentication (Sign up, Login, Protected routes) - šŸ’¬ RAG (Retrieval-Augmented Generation) - šŸ§  RL-based Policy Network (BERT) - šŸ” Custom E5 Retriever - āš” Groq LLM with HuggingFace Fallback (Llama 3 models) - šŸ“ Conversation Management (List, Search, Archive, Delete) **Capabilities:** - Intelligent document retrieval - Context-aware responses - Conversation persistence & history - Auto-generated conversation titles - Real-time chat with RAG pipeline - User authentication & authorization - Multi-provider LLM with automatic fallback """, version="2.0.0", docs_url="/docs", redoc_url="/redoc", lifespan=lifespan ) # ============================================================================ # CORS MIDDLEWARE # ============================================================================ allowed_origins = settings.get_allowed_origins() print("\nšŸŒ CORS Configuration:") print(f" Allowed Origins: {allowed_origins}") app.add_middleware( CORSMiddleware, allow_origins=allowed_origins, allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # ============================================================================ # INCLUDE API ROUTERS (UPDATED) # ============================================================================ from app.api.v1 import auth from app.api.v1 import conversation_routes # āœ… NEW IMPORT from app.api.v1 import file_routes # new file routes # Auth router (public endpoints - register, login) app.include_router( auth.router, prefix="/api/v1/auth", tags=["šŸ” Authentication"] ) # Conversation & Chat router (protected endpoints - requires JWT token) app.include_router( conversation_routes.router, # āœ… NEW ROUTER prefix="/api/v1/chat", tags=["šŸ’¬ Chat & Conversations"] ) # File Upload router (protected endpoints - requires JWT token) app.include_router( file_routes.router, prefix="/api/v1", tags=["šŸ“ File Upload"] ) # ============================================================================ # ROOT ENDPOINTS # ============================================================================ @app.get("/", tags=["šŸ“ Root"]) async def root(): """ Root endpoint - API information and available endpoints """ return { "message": "Banking RAG Chatbot API with Authentication & Conversation Management", "version": "2.0.0", "status": "online", "authentication": "JWT Bearer Token Required for chat endpoints", "llm_provider": "Groq (ChatGroq) with HuggingFace fallback", "models": { "chat": settings.GROQ_CHAT_MODEL, "evaluation": settings.GROQ_EVAL_MODEL }, "documentation": { "swagger_ui": "/docs", "redoc": "/redoc" }, "endpoints": { "auth": { "register": "POST /api/v1/auth/register", "login": "POST /api/v1/auth/login", "me": "GET /api/v1/auth/me (requires token)", "logout": "POST /api/v1/auth/logout (requires token)" }, "chat": { "send_message": "POST /api/v1/chat/ (requires token)", "create_conversation": "POST /api/v1/chat/conversation (requires token)", "list_conversations": "GET /api/v1/chat/conversations (requires token)", "get_conversation": "GET /api/v1/chat/conversation/{id} (requires token)", "update_conversation": "PATCH /api/v1/chat/conversation/{id} (requires token)", "delete_conversation": "DELETE /api/v1/chat/conversation/{id} (requires token)", "search_conversations": "GET /api/v1/chat/conversations/search (requires token)", "conversation_stats": "GET /api/v1/chat/conversations/stats (requires token)" }, "files": { "upload_image": "POST /api/v1/files/upload/image (requires token)", "upload_pdf": "POST /api/v1/files/upload/pdf (requires token)", "upload_document": "POST /api/v1/files/upload/document (requires token)", "upload_audio": "POST /api/v1/files/upload/audio (requires token)", "delete_file": "DELETE /api/v1/files/delete (requires token)", "health": "GET /api/v1/files/health" }, "health": "GET /health" } } @app.get("/health", tags=["šŸ„ Health"]) async def health_check(): """ Comprehensive health check endpoint Checks status of: - API service - MongoDB connection - ML models (lazy loaded) - Authentication system - LLM providers (Groq & HuggingFace) Returns: dict: Health status of all components """ from app.db.mongodb import get_database # Check MongoDB mongodb_status = "connected" if get_database() is not None else "disconnected" # Check ML models (don't load them, just check readiness) ml_models_status = { "policy_network": "ready (lazy load)", "retriever": "ready (lazy load)", "llm": "ready (API-based)" } # Check LLM providers llm_providers = { "groq": { "enabled": settings.is_groq_enabled(), "api_keys_configured": len(settings.get_groq_api_keys()), "chat_model": settings.GROQ_CHAT_MODEL, "eval_model": settings.GROQ_EVAL_MODEL }, "huggingface": { "enabled": settings.is_hf_enabled(), "tokens_configured": len(settings.get_hf_tokens()), "chat_model": settings.HF_CHAT_MODEL, "eval_model": settings.HF_EVAL_MODEL } } # Check authentication auth_status = { "jwt_enabled": bool(settings.SECRET_KEY and settings.SECRET_KEY != "your-secret-key-change-in-production"), "algorithm": settings.ALGORITHM, "token_expiry_minutes": settings.ACCESS_TOKEN_EXPIRE_MINUTES } # Overall health is_healthy = ( mongodb_status == "connected" and auth_status["jwt_enabled"] and (llm_providers["groq"]["enabled"] or llm_providers["huggingface"]["enabled"]) ) return { "status": "healthy" if is_healthy else "degraded", "api": "online", "version": "2.0.0", "mongodb": mongodb_status, "authentication": auth_status, "llm_providers": llm_providers, "ml_models": ml_models_status, "environment": settings.ENVIRONMENT, "debug_mode": settings.DEBUG } # ============================================================================ # GLOBAL EXCEPTION HANDLER # ============================================================================ @app.exception_handler(Exception) async def global_exception_handler(request: Request, exc: Exception): """ Global exception handler for unhandled errors """ print(f"\nāŒ Unhandled Exception:") print(f" Path: {request.url.path}") print(f" Error: {str(exc)}") if settings.DEBUG: import traceback traceback.print_exc() return JSONResponse( status_code=500, content={ "error": "Internal Server Error", "detail": str(exc) if settings.DEBUG else "An unexpected error occurred", "path": str(request.url.path) } ) # ============================================================================ # MAIN ENTRY POINT (for direct execution) # ============================================================================ if __name__ == "__main__": import uvicorn print("\nšŸš€ Starting server directly...") print(" Note: For production, use: uvicorn app.main:app --host 0.0.0.0 --port 8000") uvicorn.run( "app.main:app", host="0.0.0.0", port=8000, reload=settings.DEBUG # Auto-reload only in debug mode ) # ============================================================================================================================ # OLD CODE - KEEP FOR REFERENCE # ============================================================================================================================ # ============================================================================ # LIFESPAN MANAGER (Startup & Shutdown) # ============================================================================ # @asynccontextmanager # async def lifespan(app: FastAPI): # """ # Manage application lifespan events. # Startup: # - Connect to MongoDB Atlas # - Create indexes for conversations # - ML models load lazily on first use # Shutdown: # - Close MongoDB connection # - Cleanup resources # """ # # ======================================================================== # # STARTUP # # ======================================================================== # print("\n" + "=" * 80) # print("šŸš€ STARTING BANKING RAG CHATBOT API") # print("=" * 80) # print(f"Environment: {settings.ENVIRONMENT}") # print(f"Debug Mode: {settings.DEBUG}") # print("=" * 80) # # Connect to MongoDB # # await connect_to_mongo() # # # Create indexes for conversations (async) # # try: # # from app.db.repositories.conversation_repository import conversation_repository # # await conversation_repository.create_indexes() # # except Exception as e: # # print(f"āš ļø Failed to create conversation indexes: {e}") # # Connect to MongoDB first # await connect_to_mongo() # # Create indexes after connection success # from app.db.repositories.conversation_repository import conversation_repository # for attempt in range(5): # try: # await conversation_repository.create_indexes() # print("āœ… Conversation indexes created successfully") # break # except Exception as e: # print(f"āš ļø Index creation failed (Attempt {attempt + 1}/5): {e}") # await asyncio.sleep(2) # else: # print("āŒ Failed to create indexes after retries") # print("\nšŸ’” ML Models Info:") # print(" Policy Network: Loads on first chat request (lazy loading)") # print(" Retriever Model: Loads on first retrieval (lazy loading)") # print(" LLM: Groq (ChatGroq) with HuggingFace fallback") # print("\nšŸ¤– LLM Configuration:") # print(f" Chat Model: {settings.GROQ_CHAT_MODEL} (Llama 3 8B)") # print(f" Eval Model: {settings.GROQ_EVAL_MODEL} (Llama 3 70B)") # print(f" Groq API Keys: {len(settings.get_groq_api_keys())} configured") # print(f" HuggingFace Tokens: {len(settings.get_hf_tokens())} configured") # print(f" Fallback: Groq ā†’ HuggingFace") # print("\nāœ… Backend startup complete!") # print("=" * 80) # print(f"šŸ“– API Docs: https://eeshanyaj-questrag-backend.hf.space/docs") # print(f"šŸ„ Health Check: https://eeshanyaj-questrag-backend.hf.space/health") # print(f"šŸ§  Backend Link: https://eeshanyaj-questrag-backend.hf.space/") # print("=" * 80 + "\n") # yield # Application runs here # # ======================================================================== # # SHUTDOWN # # ======================================================================== # print("\n" + "=" * 80) # print("šŸ›‘ SHUTTING DOWN API") # print("=" * 80) # # Close MongoDB connection # await close_mongo_connection() # print("āœ… Shutdown complete") # print("=" * 80 + "\n")