def generate_prompt()

in experiments/legacy/backend/attributes.py [0:0]

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

def generate_prompt(desc: str, candidates: list[dict]) -> str:
    """Populate LLM prompt template.

    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: prompt to feed to LLM
    """
    examples = ''
    for candidate in candidates:
        examples += 'Description: ' + candidate['description']+'\n'
        examples += 'Attributes:\n' +'|'.join([k+':'+v for k,v in candidate['attributes'].items()])+'\n\n'

    prompt = f"""
Here are examples of Product Descriptions followed by Attributes:

{examples}

INSTRUCTIONS:
Generate attributes based on the description below.
Each attribute should be a key:value pair.
Do not write any values that contain "NA" on the list. Examples "Material: NA" or "Type: NA"
Use a pipe separator "|" to separate attributes.

Description: {desc}
Attributes:
    """
    return prompt