def rag_classify(context: dict):
    X = context["vars"]["text"]
    rag = vectordb.search(X, 5)
    rag_string = ""
    for example in rag:
        rag_string += textwrap.dedent(f"""
        <example>
            <query>
                "{example["metadata"]["text"]}"
            </query>
            <label>
                {example["metadata"]["label"]}
            </label>
        </example>
        """)
    prompt = (
        textwrap.dedent("""
    You will classify a customer support ticket into one of the following categories:
    <categories>
        {{categories}}
    </categories>

    Here is the customer support ticket:
    <ticket>
        {{ticket}}
    </ticket>

    Use the following examples to help you classify the query:
    <examples>
        {{examples}}
    </examples>

    Respond with just the label of the category between category tags.
    """)
        .replace("{{categories}}", categories)
        .replace("{{ticket}}", X)
        .replace("{{examples}}", rag_string)
    )
    return prompt

Warm-up