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| """ | |
| AetheroOS Reflection Agent Implementation | |
| """ | |
| from typing import Dict, List, Any, Optional | |
| from dataclasses import dataclass | |
| from enum import Enum | |
| import asyncio | |
| import json | |
| class ValidationStatus(Enum): | |
| PASSED = "passed" | |
| FAILED = "failed" | |
| WARNING = "warning" | |
| class ReflectionMetrics: | |
| accuracy: float | |
| consistency: float | |
| ethical_compliance: float | |
| performance_score: float | |
| class ValidationResult: | |
| status: ValidationStatus | |
| metrics: ReflectionMetrics | |
| findings: List[str] | |
| suggestions: List[str] | |
| class ReflectionAgent: | |
| """ | |
| Implementation of the AetheroOS Reflection Agent for introspective evaluation | |
| and continuous improvement of the agent stack. | |
| """ | |
| def __init__(self, config: Dict[str, Any]): | |
| """ | |
| Initialize the reflection agent with configuration. | |
| Args: | |
| config: Configuration dictionary from aetheroos_sovereign_agent_stack_v1.0.yaml | |
| """ | |
| self.config = config | |
| self.aethero_mem = None # Initialize in setup() | |
| self.deep_eval = None # Initialize in setup() | |
| async def setup(self) -> None: | |
| """Initialize connections to Aethero_Mem and DeepEval.""" | |
| # Initialize Aethero_Mem connection | |
| self.aethero_mem = await self._init_aethero_mem() | |
| # Initialize DeepEval | |
| self.deep_eval = await self._init_deep_eval() | |
| async def validate_output(self, | |
| agent_id: str, | |
| output: Any, | |
| context: Dict[str, Any]) -> ValidationResult: | |
| """ | |
| Validate an agent's output using DeepEval. | |
| Args: | |
| agent_id: ID of the agent whose output is being validated | |
| output: The output to validate | |
| context: Contextual information for validation | |
| Returns: | |
| ValidationResult containing metrics and suggestions | |
| """ | |
| # Perform deep evaluation | |
| eval_result = await self.deep_eval.evaluate( | |
| output=output, | |
| criteria={ | |
| "accuracy": self._accuracy_evaluator, | |
| "consistency": self._consistency_evaluator, | |
| "ethical_compliance": self._ethical_evaluator, | |
| "performance": self._performance_evaluator | |
| }, | |
| context=context | |
| ) | |
| # Calculate metrics | |
| metrics = ReflectionMetrics( | |
| accuracy=eval_result["accuracy"], | |
| consistency=eval_result["consistency"], | |
| ethical_compliance=eval_result["ethical_compliance"], | |
| performance_score=eval_result["performance"] | |
| ) | |
| # Determine status | |
| status = self._determine_validation_status(metrics) | |
| # Generate findings and suggestions | |
| findings = self._analyze_evaluation_results(eval_result) | |
| suggestions = self._generate_optimization_suggestions(findings) | |
| # Log to Aethero_Mem | |
| await self._log_reflection(agent_id, metrics, findings, suggestions) | |
| return ValidationResult( | |
| status=status, | |
| metrics=metrics, | |
| findings=findings, | |
| suggestions=suggestions | |
| ) | |
| async def reflect_on_pipeline(self, | |
| pipeline_execution_id: str) -> Dict[str, Any]: | |
| """ | |
| Perform reflection on entire pipeline execution. | |
| Args: | |
| pipeline_execution_id: ID of the pipeline execution to reflect on | |
| Returns: | |
| Dictionary containing reflection results and recommendations | |
| """ | |
| # Retrieve pipeline execution data from Aethero_Mem | |
| pipeline_data = await self.aethero_mem.get_pipeline_execution( | |
| pipeline_execution_id | |
| ) | |
| # Analyze pipeline performance | |
| performance_analysis = await self._analyze_pipeline_performance( | |
| pipeline_data | |
| ) | |
| # Generate optimization recommendations | |
| recommendations = self._generate_pipeline_recommendations( | |
| performance_analysis | |
| ) | |
| # Store reflection results | |
| reflection_id = await self._store_reflection_results( | |
| pipeline_execution_id, | |
| performance_analysis, | |
| recommendations | |
| ) | |
| return { | |
| "reflection_id": reflection_id, | |
| "performance_analysis": performance_analysis, | |
| "recommendations": recommendations | |
| } | |
| async def _init_aethero_mem(self): | |
| """Initialize connection to Aethero_Mem.""" | |
| # Implementation for Aethero_Mem connection | |
| pass | |
| async def _init_deep_eval(self): | |
| """Initialize DeepEval system.""" | |
| # Implementation for DeepEval initialization | |
| pass | |
| def _accuracy_evaluator(self, output: Any, context: Dict[str, Any]) -> float: | |
| """Evaluate output accuracy.""" | |
| # Implementation for accuracy evaluation | |
| pass | |
| def _consistency_evaluator(self, output: Any, context: Dict[str, Any]) -> float: | |
| """Evaluate output consistency.""" | |
| # Implementation for consistency evaluation | |
| pass | |
| def _ethical_evaluator(self, output: Any, context: Dict[str, Any]) -> float: | |
| """Evaluate ethical compliance.""" | |
| # Implementation for ethical evaluation | |
| pass | |
| def _performance_evaluator(self, output: Any, context: Dict[str, Any]) -> float: | |
| """Evaluate performance metrics.""" | |
| # Implementation for performance evaluation | |
| pass | |
| def _determine_validation_status(self, metrics: ReflectionMetrics) -> ValidationStatus: | |
| """Determine overall validation status based on metrics.""" | |
| # Implementation for status determination | |
| pass | |
| def _analyze_evaluation_results(self, eval_result: Dict[str, Any]) -> List[str]: | |
| """Analyze evaluation results to generate findings.""" | |
| # Implementation for results analysis | |
| pass | |
| def _generate_optimization_suggestions(self, findings: List[str]) -> List[str]: | |
| """Generate optimization suggestions based on findings.""" | |
| # Implementation for suggestion generation | |
| pass | |
| async def _log_reflection(self, | |
| agent_id: str, | |
| metrics: ReflectionMetrics, | |
| findings: List[str], | |
| suggestions: List[str]) -> None: | |
| """Log reflection results to Aethero_Mem.""" | |
| # Implementation for reflection logging | |
| pass | |
| async def _analyze_pipeline_performance(self, | |
| pipeline_data: Dict[str, Any]) -> Dict[str, Any]: | |
| """Analyze overall pipeline performance.""" | |
| # Implementation for pipeline analysis | |
| pass | |
| def _generate_pipeline_recommendations(self, | |
| performance_analysis: Dict[str, Any]) -> List[str]: | |
| """Generate recommendations for pipeline optimization.""" | |
| # Implementation for recommendation generation | |
| pass | |
| async def _store_reflection_results(self, | |
| pipeline_execution_id: str, | |
| performance_analysis: Dict[str, Any], | |
| recommendations: List[str]) -> str: | |
| """Store reflection results in Aethero_Mem.""" | |
| # Implementation for results storage | |
| pass | |