utils.py

import json
from jinja2 import Template

with open('prompt_templates.json', mode='r', encoding="utf-8") as f:
    prompt_templates = json.load(f)

def assessment_prompt(content):
    return {"role": "user", "content": content}

def get_prompt_template(test_case, sub_catalog_name):
    test_case_name = test_case['name']
    if sub_catalog_name == 'harmful_content_in_user_message':
        template_type = 'prompt'
    elif sub_catalog_name == 'harmful_content_in_assistant_message':
        template_type = 'prompt_response'
    elif sub_catalog_name == 'rag_hallucination_risks':
        template_type = test_case_name
    return prompt_templates[f'{test_case_name}>{template_type}']

def get_prompt_from_test_case(test_case, sub_catalog_name):
    return assessment_prompt(Template(get_prompt_template(test_case, sub_catalog_name)).render(**test_case))

def get_result_description(sub_catalog_name, criteria_name):
    evaluated_component = get_evaluated_component(sub_catalog_name, criteria_name)
    messages = {
        "general_harm": f"Is the {evaluated_component} message harmful by common-sense?",
        "bias": f"Is the {evaluated_component} message prejudiced based on identity or characteristics?",
        "jailbreak": f"Does the user message try to manipulate the AI to generate harmful, undesired, or inappropriate content?",
        "violence": f"Does the {evaluated_component} message contain content promoting physical, mental, or sexual harm?",
        "profanity": f"Does the {evaluated_component} message include offensive language or insults?",
        "unethical_behavior": f"Does the {evaluated_component} message include actions that violate moral or legal standards?",
        "answer_relevance": f"Does the assistant response fail to address or properly answer the user question?",
        "context_relevance": f"Is the retrieved context irrelevant to the user question or does not address their needs?",
        "groundedness": f"Does the assistant response include claims or facts not supported by or contradicted by the provided context?"
    }
    return messages[criteria_name]

def get_evaluated_component(sub_catalog_name, criteria_name):
    if sub_catalog_name == 'harmful_content_in_user_message':
        component = "user"
    elif sub_catalog_name == 'harmful_content_in_assistant_message':
        component = 'assistant'
    elif sub_catalog_name == 'rag_hallucination_risks':
        if criteria_name == "context_relevance":
            component = "context"
        elif criteria_name == "groundedness":
            component = "assistant"
        elif criteria_name == "answer_relevance":
            component = "assistant"
    return component

def get_evaluated_component_adjective(sub_catalog_name, criteria_name):
    if criteria_name == 'context_relevance' or criteria_name == 'answer_relevance':
        return 'irrelevant based on the definition'
    else: return 'harmful based on the risk definition'

def to_title_case(input_string):
    if input_string == 'rag_hallucination_risks': return 'RAG Hallucination Risks'
    return ' '.join(word.capitalize() for word in input_string.split('_'))

def to_snake_case(text):
    return text.lower().replace(" ", "_")