Tutorial on creating a vector database with openml objects

• How would you use the API to create a vector database with openml objects (datasets, flows etc)

from __future__ import annotations
from langchain.globals import set_llm_cache
from langchain_community.cache import SQLiteCache
import os
import sys
import chromadb

from backend.modules.utils import *
from backend.modules.rag_llm import *

config = load_config_and_device("../../../backend/config.json")
config["persist_dir"] = "../../data/doc_examples/chroma_db/"
config["data_dir"] = "../../data/doc_examples/"
config["type_of_data"] = "dataset"
config["training"] = False
config["testing_flag"] = True  # set this to false while training, this is for demo
config["test_subset"] = True  # set this to false while training, this is for demo

# load the persistent database using ChromaDB
client = chromadb.PersistentClient(path=config["persist_dir"])
print(config)

qa_dataset_handler = QASetup(
    config=config,
    data_type="dataset",
    client=client,
)

qa_dataset, _ = qa_dataset_handler.setup_vector_db_and_qa()