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import evoagentx.workflow.operators as operator
import examples.aflow.hotpotqa.optimized.round_9.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.additional_step = operator.AnswerGenerate(self.llm)
self.review_step = operator.AnswerGenerate(self.llm)
self.refinement_step = operator.AnswerGenerate(self.llm) # Added a refinement step for further enhancement
async def __call__(self, problem: str):
"""
Implementation of the workflow
"""
solution = await self.answer_generate(input=problem)
additional_solution = await self.additional_step(input=problem)
reviewed_solution = await self.review_step(input=solution['answer'] + " " + additional_solution['answer'])
refined_solution = await self.refinement_step(input=reviewed_solution['answer']) # Refine the reviewed solution
ensemble_response = await self.qas_ensemble(solutions=[refined_solution['answer'], additional_solution['answer']])
return ensemble_response['response']
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