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import evoagentx.workflow.operators as operator
import examples.aflow.hotpotqa.optimized.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.qas_ensemble = operator.QAScEnsemble(self.llm)
self.additional_step = operator.AnswerGenerate(self.llm) # Added another AnswerGenerate operator for additional processing
self.review_step = operator.AnswerGenerate(self.llm) # Added a review step for better solution structuring
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) # Generate an additional solution
review_response = await self.review_step(input=f"Review these solutions: {solution['answer']}, {additional_solution['answer']}") # Review the generated solutions
ensemble_response = await self.qas_ensemble(solutions=[review_response['answer']]) # Use QAScEnsemble to select the best solution
return ensemble_response['response'] # Return the selected response
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