Spaces:
Runtime error
Runtime error
File size: 5,470 Bytes
1d75522 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
"""
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
|