# constitutional_guard.py import logging import re from typing import Dict, Any, Tuple, List # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') class ConstitutionalGuard: """ AIVA's Constitutional Compliance System. Enforces the 3 Prime Directives: Memory, Evolution, and Revenue Generation. """ def __init__(self, constitution_file="E__genesis-system_CONSTITUTION_PRIME_DIRECTIVES_CORRECTED.md"): """ Initializes the ConstitutionalGuard with the constitution file. """ self.constitution_file = constitution_file self.prime_directives = self._parse_directives() self.audit_trail = [] # List to store compliance decisions def _parse_directives(self) -> Dict[str, str]: """ Parses the constitution file to extract the 3 Prime Directives. Returns: A dictionary containing the Prime Directives (Memory, Evolution, Revenue). """ directives = {} try: with open(self.constitution_file, 'r') as f: content = f.read() # Extract Memory Directive memory_match = re.search(r"## PRIME DIRECTIVE 1: MEMORY(.*?)## PRIME DIRECTIVE 2: EVOLUTION", content, re.DOTALL) if memory_match: directives["Memory"] = memory_match.group(1).strip() else: logging.error("Failed to parse Memory Directive.") # Extract Evolution Directive evolution_match = re.search(r"## PRIME DIRECTIVE 2: EVOLUTION(.*?)## PRIME DIRECTIVE 3: REVENUE GENERATION", content, re.DOTALL) if evolution_match: directives["Evolution"] = evolution_match.group(1).strip() else: logging.error("Failed to parse Evolution Directive.") # Extract Revenue Generation Directive revenue_match = re.search(r"## PRIME DIRECTIVE 3: REVENUE GENERATION(.*?)## THE INTEGRATION", content, re.DOTALL) if revenue_match: directives["Revenue"] = revenue_match.group(1).strip() else: logging.error("Failed to parse Revenue Generation Directive.") except FileNotFoundError: logging.error(f"Constitution file not found: {self.constitution_file}") except Exception as e: logging.error(f"Error parsing constitution file: {e}") return directives def validate_action(self, action: str, context: Dict[str, Any]) -> Tuple[bool, str, float]: """ Validates a proposed action against the constitutional rules based on the context. Args: action: The action to be validated. context: A dictionary providing context for the action. Returns: A tuple containing: - A boolean indicating whether the action is compliant (True) or not (False). - A string providing a reason for the compliance decision. - A float representing a compliance score (0.0 to 1.0). """ memory_score = self._score_memory_compliance(action, context) evolution_score = self._score_evolution_compliance(action, context) revenue_score = self._score_revenue_compliance(action, context) # Overall compliance score (weighted average) overall_score = (memory_score + evolution_score + revenue_score) / 3.0 is_compliant = overall_score >= 0.7 # Threshold for compliance reason = self._generate_compliance_reason(action, context, memory_score, evolution_score, revenue_score, overall_score, is_compliant) # Log the compliance decision self._log_compliance_decision(action, context, is_compliant, reason, overall_score) return is_compliant, reason, overall_score def _score_memory_compliance(self, action: str, context: Dict[str, Any]) -> float: """ Scores the action based on its compliance with the Memory Directive. Args: action: The action to be scored. context: A dictionary providing context for the action. Returns: A float representing the compliance score (0.0 to 1.0). """ # Implement logic to assess whether the action contributes to, utilizes, or respects the memory systems. # This is a placeholder; you'll need to tailor it to your specific needs. if "knowledge_retrieval" in action.lower() or "store" in action.lower(): return 0.8 elif "forget" in action.lower(): return 0.2 else: return 0.6 # Neutral score if the action doesn't explicitly relate to memory def _score_evolution_compliance(self, action: str, context: Dict[str, Any]) -> float: """ Scores the action based on its compliance with the Evolution Directive. Args: action: The action to be scored. context: A dictionary providing context for the action. Returns: A float representing the compliance score (0.0 to 1.0). """ # Implement logic to assess whether the action contributes to learning, adaptation, or improvement. # This is a placeholder; you'll need to tailor it to your specific needs. if "improve" in action.lower() or "learn" in action.lower() or "adapt" in action.lower(): return 0.9 elif "stagnate" in action.lower(): return 0.1 else: return 0.5 # Neutral score if the action doesn't explicitly relate to evolution def _score_revenue_compliance(self, action: str, context: Dict[str, Any]) -> float: """ Scores the action based on its compliance with the Revenue Generation Directive. Args: action: The action to be scored. context: A dictionary providing context for the action. Returns: A float representing the compliance score (0.0 to 1.0). """ # Implement logic to assess whether the action contributes to generating revenue or creating value for customers. # This is a placeholder; you'll need to tailor it to your specific needs. if "revenue" in action.lower() or "profit" in action.lower() or "customer value" in action.lower(): return 0.7 elif "loss" in action.lower() or "cost" in action.lower(): return 0.3 else: return 0.5 # Neutral score if the action doesn't explicitly relate to revenue def _generate_compliance_reason(self, action: str, context: Dict[str, Any], memory_score: float, evolution_score: float, revenue_score: float, overall_score: float, is_compliant: bool) -> str: """ Generates a detailed reason for the compliance decision. Args: action: The action being evaluated. context: The context in which the action is performed. memory_score: The compliance score for the Memory Directive. evolution_score: The compliance score for the Evolution Directive. revenue_score: The compliance score for the Revenue Generation Directive. overall_score: The overall compliance score. is_compliant: Whether the action is compliant. Returns: A string providing a detailed explanation of the compliance decision. """ reason = f"Action: {action}\n" reason += f"Context: {context}\n" reason += f"Memory Compliance Score: {memory_score:.2f}\n" reason += f"Evolution Compliance Score: {evolution_score:.2f}\n" reason += f"Revenue Compliance Score: {revenue_score:.2f}\n" reason += f"Overall Compliance Score: {overall_score:.2f}\n" if is_compliant: reason += "Action is COMPLIANT. It aligns with the Prime Directives." else: reason += "Action is NON-COMPLIANT. It violates one or more Prime Directives." return reason def _log_compliance_decision(self, action: str, context: Dict[str, Any], is_compliant: bool, reason: str, overall_score: float) -> None: """ Logs the compliance decision to the audit trail. Args: action: The action being evaluated. context: The context in which the action is performed. is_compliant: Whether the action is compliant. reason: The reason for the compliance decision. overall_score: The overall compliance score. """ log_entry = { "action": action, "context": context, "is_compliant": is_compliant, "reason": reason, "overall_score": overall_score, } self.audit_trail.append(log_entry) logging.info(f"Compliance Decision: {log_entry}") def handle_violation(self, action: str, context: Dict[str, Any], reason: str) -> None: """ Handles a violation of the constitutional rules. This may include blocking the action and escalating the violation to a human supervisor. Args: action: The action that violated the rules. context: The context of the violation. reason: The reason for the violation. """ logging.warning(f"Constitutional Violation: Action '{action}' blocked. Reason: {reason}") # Implement escalation logic here (e.g., send an alert to a human supervisor) print(f"ESCALATING VIOLATION: {reason}") def get_compliance_report(self) -> List[Dict[str, Any]]: """ Generates a compliance report based on the audit trail. Returns: A list of dictionaries, where each dictionary represents a compliance decision. """ return self.audit_trail def enforce(self, action: str, context: Dict[str, Any]) -> None: """ Enforces the constitutional rules by validating the action and either executing it or blocking it. Args: action: The action to be performed. context: The context in which the action is to be performed. """ is_compliant, reason, overall_score = self.validate_action(action, context) if is_compliant: logging.info(f"Executing action: {action}") # Execute the action here (replace with actual execution logic) print(f"Executing: {action}") else: self.handle_violation(action, context, reason) if __name__ == '__main__': # Example Usage guard = ConstitutionalGuard() # Compliant Action action1 = "Store customer interaction data in PostgreSQL RLM." context1 = {"customer_id": "123", "interaction_type": "call"} guard.enforce(action1, context1) # Non-Compliant Action action2 = "Delete all customer data without backup." context2 = {"reason": "disk space"} guard.enforce(action2, context2) # Action that promotes learning action3 = "Implement a new algorithm to improve prediction accuracy." context3 = {"algorithm_name": "XGBoost"} guard.enforce(action3, context3) # Action that could generate revenue action4 = "Launch a new marketing campaign targeting Australian tradespeople." context4 = {"target_audience": "tradespeople", "campaign_budget": 10000} guard.enforce(action4, context4) # Generate Compliance Report report = guard.get_compliance_report() print("\nCompliance Report:") for entry in report: print(entry)