examples/aflow/pubmedqa/optimized/round_5/graph.py

import evoagentx.workflow.operators as operator
import examples.aflow.pubmedqa.optimized.round_5.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)
    
    async def __call__(self, problem: str):
        """
        Implementation of the workflow
        """
        initial_solution = await self.answer_generate(input=problem)  # Generate an initial solution
        ensemble_response = await self.qas_ensemble(solutions=[initial_solution['answer']])  # Use QAScEnsemble
        return ensemble_response['response']  # Return the ensemble response