from add_vectordb import GetVectorStore, get_data_list, read_and_split_files, create_ids, get_document, check_existed_data

from dotenv import load_dotenv
import os
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_openai import OpenAIEmbeddings
from supabase.client import Client, create_client
import gdown

load_dotenv("../.env")
supabase_url = os.environ.get("SUPABASE_URL")
supabase_key = os.environ.get("SUPABASE_KEY")
document_table = "documents2"
supabase: Client = create_client(supabase_url, supabase_key)

embeddings = OpenAIEmbeddings()
vector_store = GetVectorStore(embeddings, supabase, document_table)

# a file
url = "https://docs.google.com/document/u/0/export?format=docx&id=1bg1yOYlFd8GkDy_JuASKIWVN4MNbd9moZ4P-3stqaoI&token=AC4w5Vj1CZYNkmPrnJXQrJbcE5VVua5sig%3A1727167683932&ouid=103663058481204095886&includes_info_params=true&usp=drive_web&cros_files=false&inspectorResult=%7B%22pc%22%3A97%2C%22lplc%22%3A9%7D"
path = "/home/ling/systex/file_loader"
output = "new_information.docx"
gdown.download(url, os.path.join(path, output))

vector_store.delete([output])

file_list = [os.path.join(path, output)]
document_ids, documents, document_metadatas = get_document(data_list=file_list, update=True)
vector_store.insert(documents, document_metadatas)