Azure OpenAI Service embeddings and document search for multiple items at once

I am new to OpenAI and I am using it for document search after the embedding process. In the given example from the blog, I need to ask questions individually. However, in my use case, I have more than 10 questions and I would like to use a prompt to run the top 4 questions all together. Can someone help me modify the code to accommodate all my 10 questions in “res = search_docs”?

The blog I am using: Azure OpenAI Service embeddings tutorial - Azure OpenAI | Microsoft Learn

The code snippet I need help on :

# search through the reviews for a specific product
def search_docs(df, user_query, top_n=3, to_print=True):
    embedding = get_embedding(
        engine="text-embedding-ada-002" # engine should be set to the deployment name you chose when you deployed the text-embedding-ada-002 (Version 2) model
    df["similarities"] = df.ada_v2.apply(lambda x: cosine_similarity(x, embedding))

    res = (
        df.sort_values("similarities", ascending=False)
    if to_print:
    return res

res = search_docs(df_bills, "Can I get information on cable company tax revenue?", top_n=4)