reasoning/unified_engine.py

"""Unified reasoning engine that combines multiple reasoning strategies."""

import logging
from typing import (
    Dict, Any, List, Optional, Set, Union, Type,
    AsyncGenerator, Callable, Tuple, Generator
)
import json
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
import asyncio
from collections import defaultdict
import numpy as np

from .base import ReasoningStrategy, StrategyResult
from .groq_strategy import GroqStrategy
from .chain_of_thought import ChainOfThoughtStrategy
from .tree_of_thoughts import TreeOfThoughtsStrategy
from .meta_learning import MetaLearningStrategy
from .recursive import RecursiveStrategy
from .analogical import AnalogicalStrategy
from .local_llm import LocalLLMStrategy
from .agentic import (
    TaskDecompositionStrategy,
    ResourceManagementStrategy,
    ContextualPlanningStrategy,
    AdaptiveExecutionStrategy,
    FeedbackIntegrationStrategy
)
# Import additional strategies
from .bayesian import BayesianStrategy
from .market_analysis import MarketAnalysisStrategy
from .monetization import MonetizationStrategy
from .multimodal import MultimodalStrategy
from .neurosymbolic import NeurosymbolicStrategy
from .portfolio_optimization import PortfolioOptimizationStrategy
from .specialized import SpecializedStrategy
from .venture_strategies import VentureStrategy
from .venture_types import (
    AIInfrastructureStrategy,
    AIConsultingStrategy,
    AIProductStrategy,
    FinTechStrategy,
    HealthTechStrategy,
    EdTechStrategy,
    BlockchainStrategy,
    AIMarketplaceStrategy
)

class StrategyType(str, Enum):
    """Types of reasoning strategies."""
    GROQ = "groq"
    CHAIN_OF_THOUGHT = "chain_of_thought"
    TREE_OF_THOUGHTS = "tree_of_thoughts"
    META_LEARNING = "meta_learning"
    RECURSIVE = "recursive"
    ANALOGICAL = "analogical"
    LOCAL_LLM = "local_llm"
    TASK_DECOMPOSITION = "task_decomposition"
    RESOURCE_MANAGEMENT = "resource_management"
    CONTEXTUAL_PLANNING = "contextual_planning"
    ADAPTIVE_EXECUTION = "adaptive_execution"
    FEEDBACK_INTEGRATION = "feedback_integration"
    BAYESIAN = "bayesian"
    MARKET_ANALYSIS = "market_analysis"
    MONETIZATION = "monetization"
    MULTIMODAL = "multimodal"
    NEUROSYMBOLIC = "neurosymbolic"
    PORTFOLIO_OPTIMIZATION = "portfolio_optimization"
    SPECIALIZED = "specialized"
    VENTURE = "venture"
    VENTURE_TYPE = "venture_type"
    AI_INFRASTRUCTURE = "ai_infrastructure"
    AI_CONSULTING = "ai_consulting"
    AI_PRODUCT = "ai_product"
    FINTECH = "fintech"
    HEALTHTECH = "healthtech"
    EDTECH = "edtech"
    BLOCKCHAIN = "blockchain"
    AI_MARKETPLACE = "ai_marketplace"

@dataclass
class UnifiedResult:
    """Combined result from multiple strategies."""
    success: bool
    answer: str
    confidence: float
    strategy_results: Dict[StrategyType, StrategyResult]
    synthesis_method: str
    meta_insights: List[str]
    performance_metrics: Dict[str, Any]
    timestamp: datetime = field(default_factory=datetime.now)

class UnifiedReasoningEngine:
    """
    Advanced unified reasoning engine that:
    1. Combines multiple reasoning strategies
    2. Dynamically selects and weights strategies
    3. Synthesizes results from different approaches
    4. Learns from experience
    5. Adapts to different types of tasks
    """
    
    def __init__(self,
                 min_confidence: float = 0.7,
                 strategy_weights: Optional[Dict[StrategyType, float]] = None,
                 parallel_threshold: int = 3,
                 learning_rate: float = 0.1):
        self.min_confidence = min_confidence
        self.parallel_threshold = parallel_threshold
        self.learning_rate = learning_rate
        
        # Initialize strategies
        self.strategies: Dict[StrategyType, ReasoningStrategy] = {
            # Primary strategy (Groq)
            StrategyType.GROQ: GroqStrategy(),
            
            # Core strategies
            StrategyType.CHAIN_OF_THOUGHT: ChainOfThoughtStrategy(),
            StrategyType.TREE_OF_THOUGHTS: TreeOfThoughtsStrategy(),
            StrategyType.META_LEARNING: MetaLearningStrategy(),
            StrategyType.RECURSIVE: RecursiveStrategy(),
            StrategyType.ANALOGICAL: AnalogicalStrategy(),
            StrategyType.LOCAL_LLM: LocalLLMStrategy(),
            
            # Agentic strategies
            StrategyType.TASK_DECOMPOSITION: TaskDecompositionStrategy(),
            StrategyType.RESOURCE_MANAGEMENT: ResourceManagementStrategy(),
            StrategyType.CONTEXTUAL_PLANNING: ContextualPlanningStrategy(),
            StrategyType.ADAPTIVE_EXECUTION: AdaptiveExecutionStrategy(),
            StrategyType.FEEDBACK_INTEGRATION: FeedbackIntegrationStrategy(),
            
            # Additional specialized strategies
            StrategyType.BAYESIAN: BayesianStrategy(),
            StrategyType.MARKET_ANALYSIS: MarketAnalysisStrategy(),
            StrategyType.MONETIZATION: MonetizationStrategy(),
            StrategyType.MULTIMODAL: MultimodalStrategy(),
            StrategyType.NEUROSYMBOLIC: NeurosymbolicStrategy(),
            StrategyType.PORTFOLIO_OPTIMIZATION: PortfolioOptimizationStrategy(),
            StrategyType.SPECIALIZED: SpecializedStrategy(),
            StrategyType.VENTURE: VentureStrategy(),
            StrategyType.AI_INFRASTRUCTURE: AIInfrastructureStrategy(),
            StrategyType.AI_CONSULTING: AIConsultingStrategy(),
            StrategyType.AI_PRODUCT: AIProductStrategy(),
            StrategyType.FINTECH: FinTechStrategy(),
            StrategyType.HEALTHTECH: HealthTechStrategy(),
            StrategyType.EDTECH: EdTechStrategy(),
            StrategyType.BLOCKCHAIN: BlockchainStrategy(),
            StrategyType.AI_MARKETPLACE: AIMarketplaceStrategy()
        }
        
        # Strategy weights with Groq as primary
        self.strategy_weights = strategy_weights or {
            # Primary strategy (highest weight)
            StrategyType.GROQ: 2.5,
            
            # Core strategies (high weights)
            StrategyType.CHAIN_OF_THOUGHT: 1.5,
            StrategyType.TREE_OF_THOUGHTS: 1.5,
            StrategyType.META_LEARNING: 1.5,
            
            # Agentic strategies (medium-high weights)
            StrategyType.TASK_DECOMPOSITION: 1.3,
            StrategyType.RESOURCE_MANAGEMENT: 1.3,
            StrategyType.CONTEXTUAL_PLANNING: 1.3,
            StrategyType.ADAPTIVE_EXECUTION: 1.3,
            StrategyType.FEEDBACK_INTEGRATION: 1.3,
            
            # Domain-specific strategies (context-dependent weights)
            StrategyType.BAYESIAN: 1.2,
            StrategyType.MARKET_ANALYSIS: 1.2,
            StrategyType.PORTFOLIO_OPTIMIZATION: 1.2,
            StrategyType.VENTURE: 1.2,
            
            # Other specialized strategies (base weights)
            StrategyType.MONETIZATION: 1.0,
            StrategyType.MULTIMODAL: 1.0,
            StrategyType.NEUROSYMBOLIC: 1.0,
            StrategyType.SPECIALIZED: 1.0,
            StrategyType.RECURSIVE: 1.0,
            StrategyType.ANALOGICAL: 1.0,
            StrategyType.LOCAL_LLM: 1.0,  # Reduced weight since using Groq
            StrategyType.AI_INFRASTRUCTURE: 1.0,
            StrategyType.AI_CONSULTING: 1.0,
            StrategyType.AI_PRODUCT: 1.0,
            StrategyType.FINTECH: 1.0,
            StrategyType.HEALTHTECH: 1.0,
            StrategyType.EDTECH: 1.0,
            StrategyType.BLOCKCHAIN: 1.0,
            StrategyType.AI_MARKETPLACE: 1.0
        }
        
        # Performance tracking
        self.strategy_performance: Dict[StrategyType, List[float]] = defaultdict(list)
        self.task_type_performance: Dict[str, Dict[StrategyType, float]] = defaultdict(lambda: defaultdict(float))
        self.synthesis_performance: Dict[str, List[float]] = defaultdict(list)

    async def reason(self, query: str, context: Dict[str, Any]) -> UnifiedResult:
        """Main reasoning method combining multiple strategies."""
        try:
            # Analyze task
            task_analysis = await self._analyze_task(query, context)
            
            # Select strategies
            selected_strategies = await self._select_strategies(task_analysis, context)
            
            # Execute strategies
            strategy_results = await self._execute_strategies(
                selected_strategies, query, context)
            
            # Synthesize results
            unified_result = await self._synthesize_results(
                strategy_results, task_analysis, context)
            
            # Learn from experience
            self._update_performance(unified_result)
            
            return unified_result
            
        except Exception as e:
            logging.error(f"Error in unified reasoning: {str(e)}")
            return UnifiedResult(
                success=False,
                answer=f"Error: {str(e)}",
                confidence=0.0,
                strategy_results={},
                synthesis_method="failed",
                meta_insights=[f"Error occurred: {str(e)}"],
                performance_metrics={}
            )

    async def reason_stream(
        self,
        query: str,
        context: Dict[str, Any] = None,
        strategy_type: Optional[StrategyType] = None,
        chunk_handler: Optional[callable] = None
    ) -> AsyncGenerator[str, None]:
        """
        Stream reasoning results from the selected strategy.
        
        Args:
            query: Query to reason about
            context: Additional context for reasoning
            strategy_type: Specific strategy to use (optional)
            chunk_handler: Optional callback for handling chunks
        """
        context = context or {}
        
        # Default to Groq strategy for streaming
        if not strategy_type:
            strategy_type = StrategyType.GROQ
        
        strategy = self.strategies.get(strategy_type)
        if not strategy:
            yield f"Error: Strategy {strategy_type} not found"
            return
        
        if not hasattr(strategy, 'reason_stream'):
            yield f"Error: Strategy {strategy_type} does not support streaming"
            return
        
        try:
            async for chunk in strategy.reason_stream(
                query=query,
                context=context,
                chunk_handler=chunk_handler
            ):
                yield chunk
        except Exception as e:
            logging.error(f"Streaming error: {str(e)}")
            yield f"Error: {str(e)}"

    async def _analyze_task(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze the task to determine optimal strategy selection."""
        prompt = f"""
        Analyze reasoning task:
        Query: {query}
        Context: {json.dumps(context)}
        
        Determine:
        1. Task type and complexity
        2. Required reasoning capabilities
        3. Resource requirements
        4. Success criteria
        5. Risk factors
        
        Format as:
        [Analysis]
        Type: ...
        Complexity: ...
        Capabilities: ...
        Resources: ...
        Criteria: ...
        Risks: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_task_analysis(response["answer"])

    async def _select_strategies(self, task_analysis: Dict[str, Any], context: Dict[str, Any]) -> List[StrategyType]:
        """Select appropriate strategies based on task analysis."""
        # Calculate strategy scores
        scores: Dict[StrategyType, float] = {}
        for strategy_type in StrategyType:
            base_score = self.strategy_weights[strategy_type]
            
            # Task type performance
            task_type = task_analysis["type"]
            type_score = self.task_type_performance[task_type][strategy_type]
            
            # Recent performance
            recent_performance = (
                sum(self.strategy_performance[strategy_type][-5:]) / 5
                if self.strategy_performance[strategy_type] else 0.5
            )
            
            # Resource match
            resource_match = self._calculate_resource_match(
                strategy_type, task_analysis["resources"])
            
            # Capability match
            capability_match = self._calculate_capability_match(
                strategy_type, task_analysis["capabilities"])
            
            # Combined score
            scores[strategy_type] = (
                0.3 * base_score +
                0.2 * type_score +
                0.2 * recent_performance +
                0.15 * resource_match +
                0.15 * capability_match
            )
        
        # Select top strategies
        selected = sorted(
            StrategyType,
            key=lambda x: scores[x],
            reverse=True
        )[:self.parallel_threshold]
        
        return selected

    async def _execute_strategies(self,
                                strategies: List[StrategyType],
                                query: str,
                                context: Dict[str, Any]) -> Dict[StrategyType, StrategyResult]:
        """Execute selected strategies in parallel."""
        async def execute_strategy(strategy_type: StrategyType) -> StrategyResult:
            strategy = self.strategies[strategy_type]
            start_time = datetime.now()
            
            try:
                result = await strategy.reason(query, context)
                
                return StrategyResult(
                    strategy_type=strategy_type,
                    success=result.get("success", False),
                    answer=result.get("answer"),
                    confidence=result.get("confidence", 0.0),
                    reasoning_trace=result.get("reasoning_trace", []),
                    metadata=result.get("metadata", {}),
                    performance_metrics={
                        "execution_time": (datetime.now() - start_time).total_seconds(),
                        **result.get("performance_metrics", {})
                    }
                )
            except Exception as e:
                logging.error(f"Error in strategy {strategy_type}: {str(e)}")
                return StrategyResult(
                    strategy_type=strategy_type,
                    success=False,
                    answer=None,
                    confidence=0.0,
                    reasoning_trace=[{"error": str(e)}],
                    metadata={},
                    performance_metrics={"execution_time": (datetime.now() - start_time).total_seconds()}
                )
        
        # Execute strategies in parallel
        tasks = [execute_strategy(strategy) for strategy in strategies]
        results = await asyncio.gather(*tasks)
        
        return {result.strategy_type: result for result in results}

    async def _synthesize_results(self,
                                strategy_results: Dict[StrategyType, StrategyResult],
                                task_analysis: Dict[str, Any],
                                context: Dict[str, Any]) -> UnifiedResult:
        """Synthesize results from multiple strategies with specialized combination methods."""
        if not strategy_results:
            return UnifiedResult(
                success=False,
                answer="No strategy results available",
                confidence=0.0,
                strategy_results={},
                synthesis_method="none",
                meta_insights=[],
                performance_metrics={}
            )

        # Group results by strategy category
        core_results = {k: v for k, v in strategy_results.items() 
                       if k in {StrategyType.CHAIN_OF_THOUGHT, StrategyType.TREE_OF_THOUGHTS, 
                              StrategyType.META_LEARNING, StrategyType.LOCAL_LLM}}
        
        agentic_results = {k: v for k, v in strategy_results.items()
                          if k in {StrategyType.TASK_DECOMPOSITION, StrategyType.RESOURCE_MANAGEMENT,
                                 StrategyType.CONTEXTUAL_PLANNING, StrategyType.ADAPTIVE_EXECUTION,
                                 StrategyType.FEEDBACK_INTEGRATION}}
        
        market_results = {k: v for k, v in strategy_results.items()
                         if k in {StrategyType.MARKET_ANALYSIS, StrategyType.PORTFOLIO_OPTIMIZATION,
                                StrategyType.VENTURE, StrategyType.MONETIZATION}}
        
        analytical_results = {k: v for k, v in strategy_results.items()
                            if k in {StrategyType.BAYESIAN, StrategyType.NEUROSYMBOLIC,
                                   StrategyType.SPECIALIZED, StrategyType.MULTIMODAL}}

        # Determine synthesis method based on task type and available results
        task_type = task_analysis.get('task_type', 'general')
        synthesis_method = self._determine_synthesis_method(task_type, strategy_results.keys())

        # Apply specialized synthesis based on method
        if synthesis_method == "weighted_voting":
            final_result = await self._weighted_voting_synthesis(strategy_results)
        elif synthesis_method == "market_focused":
            final_result = await self._market_focused_synthesis(market_results, core_results)
        elif synthesis_method == "analytical_consensus":
            final_result = await self._analytical_consensus_synthesis(analytical_results, core_results)
        elif synthesis_method == "agentic_orchestration":
            final_result = await self._agentic_orchestration_synthesis(agentic_results, strategy_results)
        else:
            final_result = await self._ensemble_synthesis(strategy_results)

        # Generate meta-insights about the synthesis process
        meta_insights = self._generate_meta_insights(strategy_results, synthesis_method)

        # Calculate aggregate performance metrics
        performance_metrics = self._calculate_synthesis_metrics(strategy_results, final_result)

        return UnifiedResult(
            success=final_result['success'],
            answer=final_result['answer'],
            confidence=final_result['confidence'],
            strategy_results=strategy_results,
            synthesis_method=synthesis_method,
            meta_insights=meta_insights,
            performance_metrics=performance_metrics
        )

    def _determine_synthesis_method(self, task_type: str, available_strategies: Set[StrategyType]) -> str:
        """Determine the best synthesis method based on task type and available strategies."""
        market_strategies = {StrategyType.MARKET_ANALYSIS, StrategyType.PORTFOLIO_OPTIMIZATION,
                           StrategyType.VENTURE, StrategyType.MONETIZATION}
        analytical_strategies = {StrategyType.BAYESIAN, StrategyType.NEUROSYMBOLIC}
        agentic_strategies = {StrategyType.TASK_DECOMPOSITION, StrategyType.RESOURCE_MANAGEMENT,
                            StrategyType.CONTEXTUAL_PLANNING}

        # Calculate strategy type coverage
        market_coverage = len(market_strategies.intersection(available_strategies))
        analytical_coverage = len(analytical_strategies.intersection(available_strategies))
        agentic_coverage = len(agentic_strategies.intersection(available_strategies))

        if task_type in ['market_analysis', 'investment'] and market_coverage >= 2:
            return "market_focused"
        elif task_type in ['analysis', 'prediction'] and analytical_coverage >= 2:
            return "analytical_consensus"
        elif task_type in ['planning', 'execution'] and agentic_coverage >= 2:
            return "agentic_orchestration"
        else:
            return "weighted_voting"

    async def _weighted_voting_synthesis(self, strategy_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
        """Combine results using weighted voting based on strategy confidence and historical performance."""
        weighted_answers = defaultdict(float)
        total_weight = 0

        for strategy_type, result in strategy_results.items():
            # Calculate weight based on strategy confidence and historical performance
            historical_performance = np.mean(self.strategy_performance[strategy_type]) if self.strategy_performance[strategy_type] else 1.0
            weight = self.strategy_weights[strategy_type] * result.confidence * historical_performance
            
            weighted_answers[result.answer] += weight
            total_weight += weight

        if not total_weight:
            return {'success': False, 'answer': '', 'confidence': 0.0}

        # Select answer with highest weighted votes
        best_answer = max(weighted_answers.items(), key=lambda x: x[1])
        confidence = best_answer[1] / total_weight

        return {
            'success': confidence >= self.min_confidence,
            'answer': best_answer[0],
            'confidence': confidence
        }

    async def _market_focused_synthesis(self, market_results: Dict[StrategyType, StrategyResult],
                                      core_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
        """Synthesize results with emphasis on market-related strategies."""
        market_consensus = await self._weighted_voting_synthesis(market_results)
        core_consensus = await self._weighted_voting_synthesis(core_results)

        # Combine market and core insights with higher weight for market results
        if market_consensus['confidence'] >= self.min_confidence:
            return {
                'success': True,
                'answer': f"{market_consensus['answer']} (Supported by core analysis: {core_consensus['answer']})",
                'confidence': 0.7 * market_consensus['confidence'] + 0.3 * core_consensus['confidence']
            }
        else:
            return core_consensus

    async def _analytical_consensus_synthesis(self, analytical_results: Dict[StrategyType, StrategyResult],
                                           core_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
        """Synthesize results with emphasis on analytical and probabilistic reasoning."""
        analytical_consensus = await self._weighted_voting_synthesis(analytical_results)
        core_consensus = await self._weighted_voting_synthesis(core_results)

        # Combine analytical and core insights with uncertainty quantification
        if analytical_consensus['confidence'] >= self.min_confidence:
            return {
                'success': True,
                'answer': f"{analytical_consensus['answer']} (Confidence interval: {analytical_consensus['confidence']:.2f})",
                'confidence': 0.6 * analytical_consensus['confidence'] + 0.4 * core_consensus['confidence']
            }
        else:
            return core_consensus

    async def _agentic_orchestration_synthesis(self, agentic_results: Dict[StrategyType, StrategyResult],
                                             all_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
        """Synthesize results with emphasis on task decomposition and execution planning."""
        # Extract task decomposition and planning insights
        task_structure = self._extract_task_structure(agentic_results)
        execution_plan = self._create_execution_plan(task_structure, all_results)

        # Combine results according to the execution plan
        synthesized_result = self._execute_synthesis_plan(execution_plan, all_results)

        return {
            'success': synthesized_result['confidence'] >= self.min_confidence,
            'answer': synthesized_result['answer'],
            'confidence': synthesized_result['confidence']
        }

    def _generate_meta_insights(self, strategy_results: Dict[StrategyType, StrategyResult],
                              synthesis_method: str) -> List[str]:
        """Generate meta-insights about the synthesis process and strategy performance."""
        insights = []
        
        # Analyze strategy agreement
        agreement_rate = self._calculate_strategy_agreement(strategy_results)
        insights.append(f"Strategy agreement rate: {agreement_rate:.2f}")

        # Identify strongest and weakest strategies
        strategy_performances = [(st, res.confidence) for st, res in strategy_results.items()]
        best_strategy = max(strategy_performances, key=lambda x: x[1])
        worst_strategy = min(strategy_performances, key=lambda x: x[1])
        
        insights.append(f"Most confident strategy: {best_strategy[0]} ({best_strategy[1]:.2f})")
        insights.append(f"Synthesis method used: {synthesis_method}")

        return insights

    def _calculate_synthesis_metrics(self, strategy_results: Dict[StrategyType, StrategyResult],
                                  final_result: Dict[str, Any]) -> Dict[str, Any]:
        """Calculate comprehensive metrics about the synthesis process."""
        return {
            'strategy_count': len(strategy_results),
            'average_confidence': np.mean([r.confidence for r in strategy_results.values()]),
            'confidence_std': np.std([r.confidence for r in strategy_results.values()]),
            'final_confidence': final_result['confidence'],
            'strategy_agreement': self._calculate_strategy_agreement(strategy_results)
        }

    def _update_performance(self, result: UnifiedResult):
        """Update performance metrics and strategy weights."""
        # Update strategy performance
        for strategy_type, strategy_result in result.strategy_results.items():
            self.strategy_performance[strategy_type].append(strategy_result.confidence)
            
            # Update weights using exponential moving average
            current_weight = self.strategy_weights[strategy_type]
            performance = strategy_result.confidence
            self.strategy_weights[strategy_type] = (
                (1 - self.learning_rate) * current_weight +
                self.learning_rate * performance
            )
        
        # Update synthesis performance
        self.synthesis_performance[result.synthesis_method].append(result.confidence)

    def _calculate_resource_match(self, strategy_type: StrategyType, required_resources: Dict[str, Any]) -> float:
        """Calculate how well a strategy matches required resources."""
        # Implementation-specific resource matching logic
        return 0.8  # Placeholder

    def _calculate_capability_match(self, strategy_type: StrategyType, required_capabilities: List[str]) -> float:
        """Calculate how well a strategy matches required capabilities."""
        # Implementation-specific capability matching logic
        return 0.8  # Placeholder

    def _parse_task_analysis(self, response: str) -> Dict[str, Any]:
        """Parse task analysis from response."""
        analysis = {
            "type": "",
            "complexity": 0.0,
            "capabilities": [],
            "resources": {},
            "criteria": [],
            "risks": []
        }
        
        for line in response.split('\n'):
            line = line.strip()
            if line.startswith('Type:'):
                analysis["type"] = line[5:].strip()
            elif line.startswith('Complexity:'):
                try:
                    analysis["complexity"] = float(line[11:].strip())
                except:
                    pass
            elif line.startswith('Capabilities:'):
                analysis["capabilities"] = [c.strip() for c in line[13:].split(',')]
            elif line.startswith('Resources:'):
                try:
                    analysis["resources"] = json.loads(line[10:].strip())
                except:
                    analysis["resources"] = {"raw": line[10:].strip()}
            elif line.startswith('Criteria:'):
                analysis["criteria"] = [c.strip() for c in line[9:].split(',')]
            elif line.startswith('Risks:'):
                analysis["risks"] = [r.strip() for r in line[7:].split(',')]
        
        return analysis

    def _parse_synthesis(self, response: str) -> Dict[str, Any]:
        """Parse synthesis result from response."""
        synthesis = {
            "method": "",
            "answer": "",
            "confidence": 0.0,
            "insights": [],
            "performance": {}
        }
        
        for line in response.split('\n'):
            line = line.strip()
            if line.startswith('Method:'):
                synthesis["method"] = line[7:].strip()
            elif line.startswith('Answer:'):
                synthesis["answer"] = line[7:].strip()
            elif line.startswith('Confidence:'):
                try:
                    synthesis["confidence"] = float(line[11:].strip())
                except:
                    pass
            elif line.startswith('Insights:'):
                synthesis["insights"] = [i.strip() for i in line[9:].split(',')]
            elif line.startswith('Performance:'):
                try:
                    synthesis["performance"] = json.loads(line[12:].strip())
                except:
                    synthesis["performance"] = {"raw": line[12:].strip()}
        
        return synthesis

    def _strategy_result_to_dict(self, result: StrategyResult) -> Dict[str, Any]:
        """Convert strategy result to dictionary for serialization."""
        return {
            "strategy_type": result.strategy_type.value,
            "success": result.success,
            "answer": result.answer,
            "confidence": result.confidence,
            "reasoning_trace": result.reasoning_trace,
            "metadata": result.metadata,
            "performance_metrics": result.performance_metrics,
            "timestamp": result.timestamp.isoformat()
        }

    def get_performance_metrics(self) -> Dict[str, Any]:
        """Get comprehensive performance metrics."""
        return {
            "strategy_weights": dict(self.strategy_weights),
            "average_performance": {
                strategy_type.value: sum(scores) / len(scores) if scores else 0
                for strategy_type, scores in self.strategy_performance.items()
            },
            "synthesis_success": {
                method: sum(scores) / len(scores) if scores else 0
                for method, scores in self.synthesis_performance.items()
            },
            "task_type_performance": {
                task_type: dict(strategy_scores)
                for task_type, strategy_scores in self.task_type_performance.items()
            }
        }

    def clear_performance_history(self):
        """Clear performance history and reset weights."""
        self.strategy_performance.clear()
        self.task_type_performance.clear()
        self.synthesis_performance.clear()
        self.strategy_weights = {
            strategy_type: 1.0 for strategy_type in StrategyType
        }

    def _extract_task_structure(self, agentic_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
        """Extract task structure from agentic strategy results."""
        # Implementation-specific task structure extraction logic
        return {}

    def _create_execution_plan(self, task_structure: Dict[str, Any], all_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
        """Create execution plan based on task structure and strategy results."""
        # Implementation-specific execution plan creation logic
        return {}

    def _execute_synthesis_plan(self, execution_plan: Dict[str, Any], all_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
        """Execute synthesis plan and combine results."""
        # Implementation-specific synthesis plan execution logic
        return {}

    def _calculate_strategy_agreement(self, strategy_results: Dict[StrategyType, StrategyResult]) -> float:
        """Calculate agreement rate among strategies."""
        # Implementation-specific strategy agreement calculation logic
        return 0.0