solved faiss index issue

app/main.py

@@ -414,19 +414,17 @@ logger = logging.getLogger(__name__)
 # Initialize global variables in app state
 @app.on_event("startup")
 async def startup_event():
-
     """Initialize the application on startup."""
     logger = logging.getLogger(__name__)
     logger.info("Starting application initialization...")
 
-       # Check if CUDA is available
+    # Check if CUDA is available
     device = "cuda" if torch.cuda.is_available() else "cpu"
     logger.info(f"Using device: {device}")
 
     if device == "cpu":
         logger.warning("GPU not detected. Model will run slower on CPU.")
-            
-    
+
     # Set NLTK data path
     nltk_data_dir = os.environ.get('NLTK_DATA', os.path.join(os.path.expanduser('~'), 'nltk_data'))
     os.makedirs(nltk_data_dir, exist_ok=True)
@@ -442,8 +440,7 @@ async def startup_event():
 
     # Initialize the model and index
     try:
-        global model, embedding_model, faiss_index, documents
-        model = pipeline(
+        app.state.pipe = pipeline(
             "text-generation",
             model=MODEL_ID,
             trust_remote_code=True,
@@ -451,10 +448,14 @@ async def startup_event():
             device_map="auto",
             torch_dtype=torch.float16 if device == "cuda" else torch.float32
         )
-        embedding_model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
         
-        # Load or create the FAISS index
         faiss_index, documents, embedding_model = await load_or_create_index()
+        
+        # Store these in app.state for access across the application
+        app.state.faiss_index = faiss_index
+        app.state.documents = documents
+        app.state.embedding_model = embedding_model
+        
         logger.info("Application initialization completed successfully")
     except Exception as e:
         logger.error(f"Error initializing application: {str(e)}")
@@ -490,9 +491,9 @@ async def generate_content(request: ContentRequest):
             
         response = generate_response_with_rag(
             request.topic,  # Use topic as the prompt
-                    faiss_index,
-                    embedding_model,
-                    documents,
+            app.state.faiss_index,
+            app.state.embedding_model,
+            app.state.documents,
             settings
                 )