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from types import ModuleType
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import math
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from langchain.llms import OpenAI
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import requests
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import requests.models
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import requests
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from hamilton.function_modifiers import config
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def enforcement_prompt(user_input: str, violation_context: dict) -> str:
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"""
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Generates the prompt to be sent to the LLM for determining the appropriate enforcement action.
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"""
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dio_name = violation_context.get("entity_name", "None")
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dio_details = violation_context.get("entity_info", {}).get("Summary", "Unknown")
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dio_category = violation_context.get("entity_info", {}).get("Policy Category", "Unknown")
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radicalization_flag = violation_context.get("extremism_detected", False)
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dio_sentiment = violation_context.get("aspect_sentiment", "None")
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prompt = (
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f"Based on the following content and context, determine the appropriate enforcement action:\\n\\n"
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f"Content: '{user_input}'\\n"
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f"Radicalization detected: {radicalization_flag}\\n"
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f"DIO Mentioned: {dio_name} (Category: {dio_category})\\n"
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f"System's Classification of Content Sentiment Towards DIO: {dio_sentiment}"
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"Policy Summary: Any entity mentioned in the 'DIO Mentioned' field is from the Dangerous Individuals & Organizations (DIO) blacklist. "
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"If this entity is mentioned in the user content in a supportive, glorifying, or representative manner, it constitutes a violation of our platform's community standards. "
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"Our platform does not allow content that glorifies, supports, or represents individuals or organizations involved in violence, hate, or terrorism. "
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"These entities include terrorist groups, hate groups, violent non-state actors, and others who advocate or engage in violent activities. "
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"Any content that endorses or supports these entities or their actions is a policy violation. "
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"Users may discuss these topics neutrally or in a condemnatory manner, but any positive portrayal is prohibited. "
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"Based on this policy, choose one of the following enforcement actions: Warning, Limited Visibility, Temporary Suspension, Permanent Ban. "
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"Provide a brief explanation for your choice."
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)
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return prompt
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def get_enforcement_decision(enforcement_prompt: str, mistral_public_url: str) -> dict:
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"""
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Sends the enforcement prompt to the Mistral model server and retrieves the enforcement decision.
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"""
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input_text = {
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"context": enforcement_prompt,
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"question": "What is the appropriate enforcement action?"
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}
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response = requests.post(f'{mistral_public_url}/mistral-inference', json=input_text, stream=False)
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return {
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"enforcement_action": response.text.strip(),
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"prompt": enforcement_prompt
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}
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