#save index with faiss
index = faiss.IndexFlatL2(len(embeddings.embed_query("hello world")))

#format abstracts as documents
documents = [Document(page_content=ai_search['original_abstract'][i], metadata={"title": ai_search['title'][i], "year": ai_search['publication_year'][i]}) for i in range(len(ai_search))]

#create list of ids as strings
n = len(ai_search)
ids = list(range(1, n + 1))
ids = [str(x) for x in my_list]

#add documents to vector store
vector_store.add_documents(documents=documents, ids=ids)

#save the vector store
vector_store.save_local("Data/faiss_index")