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| """ | |
| Emergent Reasoning Module | |
| ------------------------ | |
| Implements emergent reasoning capabilities that arise from the interaction | |
| of multiple reasoning strategies and patterns. | |
| """ | |
| from typing import Dict, Any, List, Optional | |
| from .base import ReasoningStrategy | |
| from .meta_learning import MetaLearningStrategy | |
| from .chain_of_thought import ChainOfThoughtStrategy | |
| from .tree_of_thoughts import TreeOfThoughtsStrategy | |
| class EmergentReasoning(ReasoningStrategy): | |
| """ | |
| A reasoning strategy that combines multiple approaches to discover | |
| emergent patterns and solutions. | |
| """ | |
| def __init__(self, config: Optional[Dict[str, Any]] = None): | |
| """Initialize emergent reasoning with component strategies.""" | |
| super().__init__() | |
| self.config = config or {} | |
| # Standard reasoning parameters | |
| self.min_confidence = self.config.get('min_confidence', 0.7) | |
| self.parallel_threshold = self.config.get('parallel_threshold', 3) | |
| self.learning_rate = self.config.get('learning_rate', 0.1) | |
| self.strategy_weights = self.config.get('strategy_weights', { | |
| "LOCAL_LLM": 0.8, | |
| "CHAIN_OF_THOUGHT": 0.6, | |
| "TREE_OF_THOUGHTS": 0.5, | |
| "META_LEARNING": 0.4 | |
| }) | |
| # Initialize component strategies with shared config | |
| strategy_config = { | |
| 'min_confidence': self.min_confidence, | |
| 'parallel_threshold': self.parallel_threshold, | |
| 'learning_rate': self.learning_rate, | |
| 'strategy_weights': self.strategy_weights | |
| } | |
| self.meta_learner = MetaLearningStrategy(strategy_config) | |
| self.chain_of_thought = ChainOfThoughtStrategy(strategy_config) | |
| self.tree_of_thoughts = TreeOfThoughtsStrategy(strategy_config) | |
| # Configure weights for strategy combination | |
| self.weights = self.config.get('combination_weights', { | |
| 'meta': 0.4, | |
| 'chain': 0.3, | |
| 'tree': 0.3 | |
| }) | |
| async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: | |
| """ | |
| Apply emergent reasoning by combining multiple strategies and | |
| identifying patterns that emerge from their interaction. | |
| Args: | |
| query: The input query to reason about | |
| context: Additional context and parameters | |
| Returns: | |
| Dict containing reasoning results and confidence scores | |
| """ | |
| try: | |
| # Get results from each strategy | |
| meta_result = await self.meta_learner.reason(query, context) | |
| chain_result = await self.chain_of_thought.reason(query, context) | |
| tree_result = await self.tree_of_thoughts.reason(query, context) | |
| # Combine results with weighted averaging | |
| combined_answer = self._combine_results([ | |
| (meta_result.get('answer', ''), self.weights['meta']), | |
| (chain_result.get('answer', ''), self.weights['chain']), | |
| (tree_result.get('answer', ''), self.weights['tree']) | |
| ]) | |
| # Calculate overall confidence | |
| confidence = ( | |
| meta_result.get('confidence', 0) * self.weights['meta'] + | |
| chain_result.get('confidence', 0) * self.weights['chain'] + | |
| tree_result.get('confidence', 0) * self.weights['tree'] | |
| ) | |
| return { | |
| 'answer': combined_answer, | |
| 'confidence': confidence, | |
| 'reasoning_path': { | |
| 'meta': meta_result.get('reasoning_path'), | |
| 'chain': chain_result.get('reasoning_path'), | |
| 'tree': tree_result.get('reasoning_path') | |
| }, | |
| 'emergent_patterns': self._identify_patterns([ | |
| meta_result, chain_result, tree_result | |
| ]) | |
| } | |
| except Exception as e: | |
| return { | |
| 'error': f"Emergent reasoning failed: {str(e)}", | |
| 'confidence': 0.0 | |
| } | |
| def _combine_results(self, weighted_results: List[tuple[str, float]]) -> str: | |
| """Combine multiple reasoning results with weights.""" | |
| if not weighted_results: | |
| return "" | |
| # For now, use the highest weighted result | |
| return max(weighted_results, key=lambda x: x[1])[0] | |
| def _identify_patterns(self, results: List[Dict[str, Any]]) -> List[str]: | |
| """Identify common patterns across different reasoning strategies.""" | |
| patterns = [] | |
| # Extract common themes or conclusions | |
| answers = [r.get('answer', '') for r in results if r.get('answer')] | |
| if len(set(answers)) == 1: | |
| patterns.append("All strategies reached the same conclusion") | |
| elif len(set(answers)) < len(answers): | |
| patterns.append("Some strategies converged on similar conclusions") | |
| # Look for common confidence patterns | |
| confidences = [r.get('confidence', 0) for r in results] | |
| avg_confidence = sum(confidences) / len(confidences) if confidences else 0 | |
| if avg_confidence > 0.8: | |
| patterns.append("High confidence across all strategies") | |
| elif avg_confidence < 0.3: | |
| patterns.append("Low confidence across strategies") | |
| return patterns | |