"""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