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import evoagentx.workflow.operators as operator
import examples.aflow.pertqa.optimized_reploge.round_7.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.ensemble = operator.QAScEnsemble(self.llm)
async def __call__(self, problem: str):
"""
Implementation of the workflow
"""
refined_problem = await self.custom(input=problem, instruction="Refine the problem statement for clarity.")
solution = await self.answer_generate(input=refined_problem['response'])
solutions = [solution['answer']]
ensemble_result = await self.ensemble(solutions=solutions)
return ensemble_result['response']
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