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"""
Emergent reasoning strategy implementation.
"""
import logging
from typing import Dict, Any, List, Optional
from datetime import datetime
from .base import ReasoningStrategy
from .meta_learning import MetaLearningStrategy
from .chain_of_thought import ChainOfThoughtStrategy
from .tree_of_thoughts import TreeOfThoughtsStrategy
class EmergentStrategy(ReasoningStrategy):
"""Advanced emergent reasoning that:
1. Identifies patterns
2. Discovers relationships
3. Generates insights
4. Adapts to complexity
5. Evolves understanding
"""
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
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