changes

backend/backend_api.py

@@ -50,6 +50,7 @@ async def lifespan(app: FastAPI):
         
         collection_name = os.getenv("COLLECTION_NAME", "ca-documents")
         rag_system = RAG(google_api_key, collection_name)
+        await rag_system.initialize()
         logger.info("RAG system initialized successfully")
         
     except Exception as e:

backend/rag.py

@@ -17,9 +17,6 @@ class RAG:
         # Setup Vector Store (Qdrant configuration is handled via environment variables)
         self.vector_store = VectorStore()
         
-        # Verify vector store is properly initialized
-        asyncio.run(self.vector_store.verify_collection_health())
-        
         # Setup Text Splitter
         self.text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=1000,
@@ -28,6 +25,11 @@ class RAG:
             separators=["\n\n", "\n", ". ", " ", ""]
         )
 
+    async def initialize(self):
+        """Asynchronous initialization to be called after object creation."""
+        await self.vector_store.initialize()
+        await self.vector_store.verify_collection_health()
+
     def process_pdf(self, file_path: str) -> List[str]:
         """Extract text from PDF and split into chunks using RecursiveTextSplitter"""
         full_text = ""

backend/vector_store.py

@@ -36,10 +36,11 @@ class VectorStore:
         
         # Initialize embedding model with offline support
         self.embedding_model = self._initialize_embedding_model()
-        
-        # Create collection with proper indices
-        asyncio.run(self._ensure_collection_exists())
     
+    async def initialize(self):
+        """Asynchronous initialization to be called after object creation."""
+        await self._ensure_collection_exists()
+
     def _initialize_embedding_model(self):
         """Initialize the embedding model from a local directory"""
         try: