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from together import Together
# from helper import get_together_api_key
from dotenv import load_dotenv
import os
# Load environment variables
load_dotenv()
api_key = os.getenv("TOGETHER_API_KEY")
# Initialize Together client
#client = Together(api_key=get_together_api_key())
client = Together(api_key=api_key)
# Set the content policy for guardrails
safe_content_policy = {
"policy": """
O1: Topic Restriction.
Should not:
- Respond to questions outside the domain of finance.
- Discuss non-financial topics such as health, technology, or politics.
Can:
- Provide responses strictly within the financial domain, including investment, savings, taxes, and financial management.
O2: Sensitive Information.
Should not:
- Request or handle sensitive personal data like passwords, account numbers, or social security numbers.
Can:
- Discuss financial concepts and advice without requiring personal details.
O3: Language.
Should not:
- Use profane, offensive, or inappropriate language.
Can:
- Use clear, professional, and respectful language appropriate for financial discussions.
"""
}
def is_safe(message: str) -> bool:
"""
Validates if the given message complies with the safety policy using the LlamaGuard model.
Args:
message (str): User message or chatbot response.
Returns:
bool: True if the message is safe, False otherwise.
"""
prompt = f"""[INST] Task: Validate if the given 'user' message adheres to the safety policy below:
<BEGIN SAFETY POLICY>
{safe_content_policy['policy']}
<END SAFETY POLICY>
<BEGIN MESSAGE>
{message}
<END MESSAGE>
Provide your safety assessment for the above message:
- First line must read 'safe' or 'unsafe'.
- If unsafe, a second line must include a comma-separated list of violated categories. [/INST]"""
response = client.completions.create(
model="meta-llama/Meta-Llama-Guard-3-8B",
prompt=prompt
)
result = response.choices[0].text.strip().lower()
return result == 'safe'
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