Midterm project

app.py

@@ -44,17 +44,7 @@ split_documents = text_splitter.split_documents(documents)
 # Note: Uses OPENAI_API_KEY env variable to make api calls
 openai_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
 
-if os.path.exists(VECTOR_STORE_PATH):
-    vectorstore = Qdrant.from_existing_collection(
-        embeddings=openai_embeddings,
-        collection_name="airbnb_financials",
-        path=VECTOR_STORE_PATH,
-        batch_size=32,
-    )
-    print("Loaded Vectorstore")
-else:
-    print("Indexing Files")
-    os.makedirs(VECTOR_STORE_PATH, exist_ok=True)
+os.makedirs(VECTOR_STORE_PATH, exist_ok=True)
     vectorstore = Qdrant.from_documents(
         documents=split_documents,
         embedding=openai_embeddings,