Method execute

internagent/mas/agents/ranking_agent.py:98–314

Rank hypotheses using weighted multi-criteria scoring. Evaluates each hypothesis across configured criteria (novelty, plausibility, testability, alignment), computes weighted overall scores, and selects top candidates. Processes hypotheses in batches for efficiency.

(self, context: Dict[str, Any], params: Dict[str, Any])

        return descriptions.get(criterion, f"Evaluation of {criterion}")
        
    async def execute(self, context: Dict[str, Any], params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Rank hypotheses using weighted multi-criteria scoring.

        Evaluates each hypothesis across configured criteria (novelty, plausibility,
        testability, alignment), computes weighted overall scores, and selects top
        candidates. Processes hypotheses in batches for efficiency. Supports distinct
        selection strategy to ensure diversity across hypothesis families.

        Args:
            context (Dict[str, Any]): Execution context with keys:
                - goal (Dict): Research goal and constraints
                - hypotheses (List[Dict]): Hypotheses to rank with id/text/rationale
                - iteration (int): Current iteration number
                - feedback (List[Dict]): Scientist feedback (optional)
            params (Dict[str, Any]): Runtime parameters (currently unused)

        Returns:
            Dict[str, Any]: Ranking results containing:
                - ranked_hypotheses (List[Dict]): Scored hypotheses sorted by score
                - scoring_explanation (str): Overall scoring rationale
                - top_hypotheses (List[str]): Top N hypothesis IDs
                - metadata (Dict): Ranking context

        Raises:
            AgentExecutionError: If goal/hypotheses missing or ranking fails
        """
        # Extract parameters
        goal = context.get("goal", {})
        hypotheses = context.get("hypotheses", [])
        
        if not goal or not hypotheses:
            raise AgentExecutionError("Research goal and hypotheses are required for ranking")
        
        if len(hypotheses) == 0:
            raise AgentExecutionError("At least one hypothesis is required for ranking")
            
        # Extract optional parameters
        iteration = context.get("iteration", 0)
        feedback = context.get("feedback", [])
        
        # Create a JSON schema for the expected output
        output_schema = {
            "type": "object",
            "properties": {
                "scored_hypotheses": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "id": {
                                "type": "string",
                                "description": "ID of the hypothesis"
                            },
                            "overall_score": {
                                "type": "number",
                                "description": "Overall score (0.0-10.0)"
                            },