⚙️ Session 9: Advanced ReAct - Deep Reasoning Patterns¶

async def _execute_advanced_data_reasoning_step(
    self, context: str, step_num: int, execution_history: List[ReActStep]
) -> ReActStep:
    """Execute advanced ReAct reasoning step with historical context"""

    # Analyze historical reasoning patterns for context
    historical_context = await self._analyze_reasoning_context(execution_history)

    # Generate thought with enhanced context awareness
    enhanced_context = {
        'current_data_context': context,
        'reasoning_history': historical_context,
        'identified_patterns': await self._identify_reasoning_patterns(execution_history),
        'quality_trends': await self._analyze_reasoning_quality_trends(execution_history)
    }

    thought = await self._generate_enhanced_data_processing_thought(enhanced_context)

    # Advanced action decision with confidence assessment
    action_decision = await self._decide_next_data_action_with_confidence(
        thought, enhanced_context
    )
    action_type = ActionType(action_decision['action'])
    action_input = action_decision['input']
    confidence_score = action_decision['confidence']

    # Execute data action with advanced monitoring
    execution_start = time.time()
    observation = await self._execute_monitored_data_action(
        action_type, action_input, confidence_score
    )
    execution_duration = time.time() - execution_start

    # Advanced quality assessment with multi-dimensional scoring
    quality_assessment = await self._comprehensive_quality_assessment(
        thought, action_type, observation, execution_history, execution_duration
    )

    # Create enriched ReAct step with advanced metadata
    enriched_step = EnhancedReActStep(
        step_number=step_num,
        thought=thought,
        action=action_type,
        action_input=action_input,
        observation=observation,
        data_quality_score=quality_assessment['overall_score'],
        confidence_score=confidence_score,
        execution_duration=execution_duration,
        quality_breakdown=quality_assessment['detailed_scores'],
        reasoning_metadata=await self._extract_reasoning_metadata(thought),
        timestamp=datetime.now()
    )

    return enriched_step

async def _generate_enhanced_data_processing_thought(
    self, enhanced_context: Dict[str, Any]
) -> str:
    """Generate sophisticated thought with pattern recognition and historical learning"""

    # Extract successful reasoning patterns from history
    successful_patterns = enhanced_context['identified_patterns']['successful']

    # Identify current situation characteristics
    situation_analysis = await self._analyze_current_data_situation(
        enhanced_context['current_data_context']
    )

    # Match current situation to historical success patterns
    pattern_matches = await self._match_situation_to_patterns(
        situation_analysis, successful_patterns
    )

    prompt = f"""
    ADVANCED DATA PROCESSING REASONING FRAMEWORK
    ==========================================

    Current Data Situation Analysis:
    {situation_analysis}

    Historical Success Patterns (Confidence: {pattern_matches['confidence']:.2f}):
    {pattern_matches['applicable_patterns']}

    Quality Trend Analysis:
    {enhanced_context['quality_trends']}

    SYSTEMATIC REASONING PROCESS:
    1. SITUATION ASSESSMENT: What are the key characteristics of this data processing challenge?
    2. PATTERN APPLICATION: Which historical success patterns apply to this situation?
    3. GAP ANALYSIS: What data transformation gaps remain unaddressed?
    4. RISK EVALUATION: What data consistency and quality risks should be considered?
    5. STRATEGIC ACTION: What's the most effective next step given this analysis?

    Reasoning Constraints:
    - Must consider data lineage and downstream dependencies
    - Should optimize for both processing efficiency and data quality
    - Must account for system resource limitations and scalability

    Generate systematic reasoning for the next data processing step:
    """

    return await self.llm.generate(prompt)

async def _comprehensive_quality_assessment(
    self, thought: str, action: ActionType, observation: str,
    execution_history: List[ReActStep], execution_duration: float
) -> Dict[str, Any]:
    """Perform comprehensive multi-dimensional quality assessment"""

    quality_dimensions = {}

    # Reasoning clarity assessment
    quality_dimensions['reasoning_clarity'] = await self._assess_reasoning_clarity(
        thought, execution_history
    )

    # Action appropriateness evaluation
    quality_dimensions['action_appropriateness'] = await self._assess_action_appropriateness(
        action, thought, observation
    )

    # Outcome effectiveness measurement
    quality_dimensions['outcome_effectiveness'] = await self._assess_outcome_effectiveness(
        observation, action, execution_history
    )

    # Progress contribution analysis
    quality_dimensions['progress_contribution'] = await self._assess_progress_contribution(
        observation, execution_history
    )

    # Efficiency evaluation
    quality_dimensions['efficiency'] = await self._assess_reasoning_efficiency(
        execution_duration, action, observation
    )

    # Consistency with goals
    quality_dimensions['goal_consistency'] = await self._assess_goal_consistency(
        thought, action, observation, execution_history
    )

    # Calculate weighted overall score
    weights = {
        'reasoning_clarity': 0.20,
        'action_appropriateness': 0.25,
        'outcome_effectiveness': 0.25,
        'progress_contribution': 0.15,
        'efficiency': 0.10,
        'goal_consistency': 0.05
    }

    overall_score = sum(
        quality_dimensions[dimension] * weights[dimension]
        for dimension in quality_dimensions.keys()
    )

    return {
        'overall_score': overall_score,
        'detailed_scores': quality_dimensions,
        'assessment_metadata': {
            'assessment_timestamp': datetime.now(),
            'execution_duration': execution_duration,
            'historical_context_size': len(execution_history)
        }
    }

class AdvancedMetaDataReActAnalyzer(MetaDataReActAnalyzer):
    """Advanced meta-analysis for sophisticated ReAct reasoning improvement"""

    def __init__(self, llm_client):
        super().__init__(llm_client)
        self.reasoning_pattern_database = {}
        self.performance_trend_tracker = PerformanceTrendTracker()
        self.adaptation_strategy_engine = AdaptationStrategyEngine()

    async def advanced_reasoning_analysis(
        self, reasoning_history: List[EnhancedReActStep]
    ) -> Dict[str, Any]:
        """Perform advanced meta-analysis of reasoning chain performance"""

        if len(reasoning_history) < 3:
            return self._generate_insufficient_data_analysis()

        # Multi-dimensional analysis components
        analysis_components = await asyncio.gather(
            self._analyze_reasoning_trajectory(reasoning_history),
            self._detect_advanced_reasoning_patterns(reasoning_history),
            self._assess_cognitive_load_efficiency(reasoning_history),
            self._evaluate_strategic_coherence(reasoning_history),
            self._measure_adaptive_capability(reasoning_history)
        )

        trajectory_analysis, pattern_analysis, efficiency_analysis, coherence_analysis, adaptability_analysis = analysis_components

        # Synthesis of analysis results
        comprehensive_analysis = {
            'reasoning_trajectory': trajectory_analysis,
            'pattern_recognition': pattern_analysis,
            'cognitive_efficiency': efficiency_analysis,
            'strategic_coherence': coherence_analysis,
            'adaptive_capability': adaptability_analysis,
            'meta_quality_score': self._calculate_meta_quality_score(analysis_components),
            'improvement_recommendations': await self._generate_advanced_improvement_recommendations(
                analysis_components, reasoning_history
            )
        }

        # Update pattern database with new insights
        await self._update_reasoning_pattern_database(comprehensive_analysis, reasoning_history)

        return comprehensive_analysis

async def _analyze_reasoning_trajectory(
    self, history: List[EnhancedReActStep]
) -> Dict[str, Any]:
    """Analyze the trajectory of reasoning quality and effectiveness over time"""

    # Extract quality progression
    quality_progression = [step.data_quality_score for step in history]
    confidence_progression = [step.confidence_score for step in history]

    # Calculate trajectory metrics
    quality_trend = self._calculate_trend_direction(quality_progression)
    confidence_trend = self._calculate_trend_direction(confidence_progression)

    # Identify inflection points
    quality_inflections = self._identify_trend_inflections(quality_progression)
    confidence_inflections = self._identify_trend_inflections(confidence_progression)

    # Assess trajectory stability
    quality_stability = self._assess_trajectory_stability(quality_progression)
    confidence_stability = self._assess_trajectory_stability(confidence_progression)

    # Identify optimal reasoning zones
    optimal_zones = self._identify_optimal_reasoning_zones(history)

    return {
        'quality_trajectory': {
            'trend_direction': quality_trend,
            'stability_score': quality_stability,
            'inflection_points': quality_inflections,
            'average_quality': sum(quality_progression) / len(quality_progression)
        },
        'confidence_trajectory': {
            'trend_direction': confidence_trend,
            'stability_score': confidence_stability,
            'inflection_points': confidence_inflections,
            'average_confidence': sum(confidence_progression) / len(confidence_progression)
        },
        'optimal_zones': optimal_zones,
        'trajectory_summary': self._summarize_reasoning_trajectory(
            quality_trend, confidence_trend, quality_stability, confidence_stability
        )
    }

async def _detect_advanced_reasoning_patterns(
    self, history: List[EnhancedReActStep]
) -> Dict[str, Any]:
    """Detect sophisticated reasoning patterns and strategies"""

    # Extract reasoning pattern features
    action_sequences = [step.action for step in history]
    thought_themes = await self._extract_thought_themes(history)
    quality_patterns = self._analyze_quality_patterns(history)

    # Identify strategic reasoning patterns
    strategic_patterns = await self._identify_strategic_patterns(
        action_sequences, thought_themes, quality_patterns
    )

    # Detect problem-solving methodologies
    methodologies = await self._detect_problem_solving_methodologies(history)

    # Analyze pattern effectiveness
    pattern_effectiveness = await self._evaluate_pattern_effectiveness(
        strategic_patterns, methodologies, history
    )

    # Compare with successful historical patterns
    historical_comparisons = await self._compare_with_historical_patterns(
        strategic_patterns, self.reasoning_pattern_database
    )

    return {
        'identified_patterns': strategic_patterns,
        'problem_solving_methodologies': methodologies,
        'pattern_effectiveness': pattern_effectiveness,
        'historical_comparisons': historical_comparisons,
        'novel_patterns': await self._identify_novel_patterns(
            strategic_patterns, self.reasoning_pattern_database
        ),
        'recommended_patterns': await self._recommend_optimal_patterns(
            pattern_effectiveness, historical_comparisons
        )
    }

class SelfImprovingReActAgent(DataProcessingReActAgent):
    """ReAct agent with self-improvement capabilities based on experience"""

    def __init__(self, llm_client, data_tools: Dict[str, Any], max_steps: int = 12):
        super().__init__(llm_client, data_tools, max_steps)
        self.learning_engine = ReActLearningEngine(llm_client)
        self.adaptation_history = []
        self.performance_baseline = None

    async def process_with_learning(
        self, pipeline_request: str
    ) -> Dict[str, Any]:
        """Process data pipeline with continuous learning and adaptation"""

        # Execute standard ReAct processing
        initial_result = await self.process_data_pipeline(pipeline_request)

        # Perform learning analysis on execution
        learning_analysis = await self.learning_engine.analyze_execution_for_learning(
            self.reasoning_history, initial_result
        )

        # Apply learned adaptations if beneficial
        if learning_analysis['should_adapt']:
            adapted_agent = await self._create_adapted_agent(learning_analysis['adaptations'])
            adapted_result = await adapted_agent.process_data_pipeline(pipeline_request)

            # Compare results and keep better approach
            if self._is_result_better(adapted_result, initial_result):
                self._integrate_adaptations(learning_analysis['adaptations'])
                self.adaptation_history.append({
                    'timestamp': datetime.now(),
                    'adaptations': learning_analysis['adaptations'],
                    'performance_improvement': self._calculate_improvement(
                        adapted_result, initial_result
                    )
                })
                return adapted_result

        return initial_result

class ReActLearningEngine:
    """Learning engine for continuous ReAct agent improvement"""

    def __init__(self, llm_client):
        self.llm = llm_client
        self.successful_pattern_library = SuccessfulPatternLibrary()
        self.adaptation_evaluator = AdaptationEvaluator()

    async def analyze_execution_for_learning(
        self, reasoning_history: List[ReActStep], execution_result: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Analyze execution to identify learning opportunities"""

        # Evaluate execution success and quality
        execution_evaluation = await self._evaluate_execution_quality(
            reasoning_history, execution_result
        )

        # Identify successful reasoning patterns
        successful_patterns = await self._extract_successful_patterns(
            reasoning_history, execution_evaluation
        )

        # Detect improvement opportunities
        improvement_opportunities = await self._identify_improvement_opportunities(
            reasoning_history, execution_evaluation
        )

        # Generate potential adaptations
        potential_adaptations = await self._generate_potential_adaptations(
            improvement_opportunities, successful_patterns
        )

        # Evaluate adaptation benefits
        adaptation_analysis = await self._evaluate_adaptation_benefits(
            potential_adaptations, reasoning_history, execution_result
        )

        return {
            'execution_quality': execution_evaluation,
            'successful_patterns': successful_patterns,
            'improvement_opportunities': improvement_opportunities,
            'potential_adaptations': potential_adaptations,
            'should_adapt': adaptation_analysis['overall_benefit'] > 0.15,
            'adaptations': adaptation_analysis['recommended_adaptations']
        }

async def _generate_potential_adaptations(
    self, improvement_opportunities: List[Dict], successful_patterns: List[Dict]
) -> List[Dict[str, Any]]:
    """Generate specific adaptations based on improvement opportunities"""

    adaptations = []

    for opportunity in improvement_opportunities:
        if opportunity['category'] == 'reasoning_depth':
            adaptations.append({
                'type': 'thought_enhancement',
                'description': 'Increase reasoning depth for complex scenarios',
                'implementation': {
                    'add_analysis_steps': opportunity['recommended_steps'],
                    'enhance_context_awareness': True,
                    'increase_pattern_matching': True
                }
            })

        elif opportunity['category'] == 'action_selection':
            adaptations.append({
                'type': 'action_optimization',
                'description': 'Improve action selection strategy',
                'implementation': {
                    'use_historical_success_weights': True,
                    'apply_situation_specific_preferences': opportunity['preferences'],
                    'enhance_confidence_assessment': True
                }
            })

        elif opportunity['category'] == 'error_recovery':
            adaptations.append({
                'type': 'recovery_enhancement',
                'description': 'Strengthen error recovery mechanisms',
                'implementation': {
                    'add_fallback_strategies': opportunity['fallback_strategies'],
                    'improve_error_pattern_recognition': True,
                    'enhance_recovery_planning': True
                }
            })

    # Apply successful patterns to generate pattern-based adaptations
    for pattern in successful_patterns:
        if pattern['effectiveness'] > 0.8:
            adaptations.append({
                'type': 'pattern_integration',
                'description': f'Integrate successful pattern: {pattern["name"]}',
                'implementation': {
                    'pattern_template': pattern['template'],
                    'application_conditions': pattern['conditions'],
                    'expected_benefit': pattern['effectiveness']
                }
            })

    return adaptations

class EnterpriseReActOrchestrator:
    """Enterprise-scale ReAct system with distributed coordination"""

    def __init__(self, cluster_config: Dict[str, Any]):
        self.cluster_config = cluster_config
        self.react_agents = {}
        self.load_balancer = ReActLoadBalancer()
        self.coordination_hub = DistributedCoordinationHub()
        self.performance_monitor = EnterpriseReActMonitor()

    async def deploy_distributed_react_cluster(
        self, deployment_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Deploy distributed ReAct cluster for enterprise data processing"""

        # Create ReAct agents across cluster nodes
        agent_deployment_tasks = []
        for node_id, node_config in self.cluster_config['nodes'].items():
            task = self._deploy_react_agent_on_node(node_id, node_config, deployment_config)
            agent_deployment_tasks.append(task)

        deployment_results = await asyncio.gather(*agent_deployment_tasks)

        # Setup inter-agent coordination
        coordination_result = await self._setup_distributed_coordination(deployment_results)

        # Initialize load balancing
        load_balancer_result = await self._initialize_load_balancing(deployment_results)

        # Start performance monitoring
        monitoring_result = await self._start_enterprise_monitoring(deployment_results)

        return {
            'cluster_status': 'deployed',
            'total_agents': len(deployment_results),
            'coordination_status': coordination_result['status'],
            'load_balancer_status': load_balancer_result['status'],
            'monitoring_status': monitoring_result['status'],
            'deployment_summary': deployment_results
        }

class FaultTolerantReActAgent(SelfImprovingReActAgent):
    """ReAct agent with advanced fault tolerance and recovery mechanisms"""

    def __init__(self, llm_client, data_tools, max_steps=15):
        super().__init__(llm_client, data_tools, max_steps)
        self.fault_detector = ReActFaultDetector()
        self.recovery_planner = ReActRecoveryPlanner()
        self.checkpoint_manager = ReActCheckpointManager()

    async def process_with_fault_tolerance(
        self, pipeline_request: str
    ) -> Dict[str, Any]:
        """Process data pipeline with comprehensive fault tolerance"""

        # Create processing checkpoint
        checkpoint = await self.checkpoint_manager.create_checkpoint(
            pipeline_request, self.reasoning_history
        )

        try:
            # Execute with fault monitoring
            result = await self._execute_with_fault_monitoring(pipeline_request)

            # Validate result integrity
            integrity_check = await self._validate_result_integrity(result)

            if integrity_check['valid']:
                await self.checkpoint_manager.clear_checkpoint(checkpoint['id'])
                return result
            else:
                # Handle integrity failure
                return await self._handle_integrity_failure(
                    pipeline_request, checkpoint, integrity_check
                )

        except ReActExecutionException as e:
            # Handle ReAct-specific failures
            return await self._handle_react_failure(pipeline_request, checkpoint, e)

        except Exception as e:
            # Handle general failures
            return await self._handle_general_failure(pipeline_request, checkpoint, e)