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import evoagentx.workflow.operators as operator
import examples.aflow.pertqa.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.sc_ensemble = operator.QAScEnsemble(self.llm)
self.review = operator.Custom(self.llm) # Added review operator for enhanced reasoning
async def __call__(self, problem: str):
"""
Implementation of the workflow
"""
solution = await self.answer_generate(input=problem)
# Generate a review of the solution for better reasoning
review_response = await self.review(input=solution['answer'], instruction="Review the following solution for accuracy and completeness.")
# Generate multiple answers for self-consistency
ensemble_response = await self.sc_ensemble(solutions=[solution['answer'], review_response['response']])
# Check for self-consistency before returning the final answer
final_response = await self.sc_ensemble(solutions=[ensemble_response['response'], solution['answer']])
return final_response['response'] # Return the best solution from ensemble
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