def generate_attributes()

in experiments/google/cloud/ml/applied/attributes/attributes.py [0:0]

• 18 lines of code
• 3 McCabe index (conditional complexity)

def generate_attributes(desc: str, candidates: list[dict]) -> m.AttributeValue:
    """Use an LLM to determine attributes given nearest neighbor candidates

    Args:
        desc: product description
        candidates: list of dicts with the following keys:
            attributes: attributes in dict form e.g.
            {'color':'green', 'pattern': 'striped'}
            description: string describing product

    Returns: attributes in dict form e.g. {'color':'green', 'pattern': 'striped'}
    """
    prompt = generate_prompt(desc, candidates)
    llm_parameters = {
        "max_output_tokens": 256,
        "temperature": 0.0,
    }
    response = llm.predict(prompt, **llm_parameters)
    res = response.text
    if not res:
        raise ValueError(
            "ERROR: No LLM response returned. This seems to be an intermittent bug"
        )
    try:
        formatted_res = parse_answer(res)
    except Exception as e:
        logging.error(e)
        raise ValueError(f"LLM Response: {res} is not in the expected format")
    return m.dict_to_attribute_values(formatted_res)