Midterm project

app.py

@@ -45,13 +45,8 @@ split_documents = text_splitter.split_documents(documents)
 openai_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
 
 os.makedirs(VECTOR_STORE_PATH, exist_ok=True)
-    vectorstore = Qdrant.from_documents(
-        documents=split_documents,
-        embedding=openai_embeddings,
-        path=VECTOR_STORE_PATH,
-        collection_name="airbnb_financials",
-        batch_size=32,
-    )
+vectorstore = Qdrant.from_documents(documents=split_documents,embedding=openai_embeddings,path=VECTOR_STORE_PATH,collection_name="airbnb_financials",batch_size=32,
+)
 
 retriever = vectorstore.as_retriever()