Aethero_App/reflection/reflection_agent.py

"""
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"

@dataclass
class ReflectionMetrics:
    accuracy: float
    consistency: float
    ethical_compliance: float
    performance_score: float

@dataclass
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