| |
|
|
| 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]}...") |
| |
| |
| |
| |
| |
| 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"] |
| } |
|
|
