examples/aflow/pubmedqa/optimized/round_13/graph.py

import evoagentx.workflow.operators as operator
import examples.aflow.pubmedqa.optimized.round_13.prompt as prompt_custom
from evoagentx.models.model_configs import LLMConfig
from evoagentx.benchmark.benchmark import Benchmark
from evoagentx.models.model_utils import create_llm_instance

class Workflow:
    
    def __init__(
        self,
        name: str,
        llm_config: LLMConfig,
        benchmark: Benchmark
    ):
        self.name = name
        self.llm = create_llm_instance(llm_config)
        self.benchmark = benchmark
        self.custom = operator.Custom(self.llm)
        self.answer_generate = operator.AnswerGenerate(self.llm)
        self.qas_ensemble = operator.QAScEnsemble(self.llm)
        self.summary = operator.Custom(self.llm)  # Added summary operator for better context
    
    async def __call__(self, problem: str):
        """
        Implementation of the workflow
        """
        # Generate initial answer
        solution = await self.answer_generate(input=problem)
        solutions = [solution['answer']]
        
        # Generate additional answers
        for _ in range(2):
            additional_solution = await self.answer_generate(input=problem)
            solutions.append(additional_solution['answer'])
        
        # Generate a summary of the answers for better context
        summary_response = await self.summary(input=" ".join(solutions), instruction="Summarize these answers.")
        
        # Use QAScEnsemble to select the best solution
        ensemble_result = await self.qas_ensemble(solutions=solutions + [summary_response['response']])
        return ensemble_result['response']  # Return the best selected answer