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# Reasoning tool for scientific analysis
from typing import Dict, Any, List, Optional
from .base_tool import BaseTool
class ReasoningTool(BaseTool):
"""Tool for structured scientific reasoning and analysis.
This tool helps agents perform structured reasoning about scientific hypotheses,
including causal analysis, counter-factual reasoning, and identification of
potential confounding variables.
"""
def __init__(self):
"""Initialize the reasoning tool."""
super().__init__(
name="reasoning",
description="Perform structured scientific reasoning and analysis"
)
def execute(
self,
reasoning_type: str,
hypothesis: str,
context: Optional[str] = None,
depth: int = 2
) -> Dict[str, Any]:
"""Execute structured scientific reasoning on a hypothesis.
Args:
reasoning_type: Type of reasoning to perform (causal, counterfactual, confounders)
hypothesis: The scientific hypothesis to analyze
context: Optional additional context to consider
depth: Depth of reasoning (1-3, with 3 being the deepest)
Returns:
A dictionary containing the reasoning results
"""
self.logger.info(f"Performing {reasoning_type} reasoning on hypothesis: {hypothesis[:50]}...")
# TODO: Implement actual reasoning functionality
# This would use structured reasoning frameworks and potentially specialized models
# Mock implementation for now
result = {
"reasoning_type": reasoning_type,
"hypothesis": hypothesis,
"analysis": f"Structured {reasoning_type} analysis of the hypothesis.",
"insights": [
f"Key insight 1 about {hypothesis[:30]}...",
f"Key insight 2 about {hypothesis[:30]}...",
f"Key insight 3 about {hypothesis[:30]}..."
],
"confidence": 0.85,
"recommendations": [
"Consider examining related factor X",
"The hypothesis may be strengthened by addressing Y"
]
}
if reasoning_type == "causal":
result["causal_factors"] = [
{"factor": "Factor A", "effect_direction": "positive", "confidence": 0.9},
{"factor": "Factor B", "effect_direction": "negative", "confidence": 0.7}
]
elif reasoning_type == "counterfactual":
result["counterfactual_scenarios"] = [
{"scenario": "If X were not present", "outcome": "Y would likely decrease"},
{"scenario": "If Z were increased", "outcome": "Y would likely increase"}
]
elif reasoning_type == "confounders":
result["potential_confounders"] = [
{"confounder": "Confounder A", "impact": "high", "explanation": "Explains both X and Y"},
{"confounder": "Confounder B", "impact": "medium", "explanation": "Partially explains Z"}
]
return result
def get_parameters_schema(self) -> Dict[str, Any]:
"""Get the JSON schema for the tool's parameters.
Returns:
A dictionary containing the JSON schema
"""
return {
"type": "object",
"properties": {
"reasoning_type": {
"type": "string",
"description": "Type of reasoning to perform",
"enum": ["causal", "counterfactual", "confounders", "general"]
},
"hypothesis": {
"type": "string",
"description": "The scientific hypothesis to analyze"
},
"context": {
"type": "string",
"description": "Optional additional context to consider"
},
"depth": {
"type": "integer",
"description": "Depth of reasoning (1-3, with 3 being the deepest)",
"minimum": 1,
"maximum": 3,
"default": 2
}
},
"required": ["reasoning_type", "hypothesis"]
}
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