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import evoagentx.workflow.operators as operator
import examples.aflow.pertqa.optimized_adamson_update.round_18.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
"""
solutions_list = []
for _ in range(3): # Collecting multiple answers
solution = await self.answer_generate(input=problem)
solutions_list.append(solution['answer'])
review_responses = []
for answer in solutions_list: # Review each generated answer
review_response = await self.custom(input=problem + " Review the answer: " + answer, instruction=prompt_custom.Review_PROMPT)
review_responses.append(review_response['response'])
ensemble_response = await self.qas_ensemble(solutions=review_responses)
# Added self-consistency check before final review
if ensemble_response['response'] not in solutions_list:
consistency_check = await self.custom(input=problem + " Check consistency of the ensemble answer: " + ensemble_response['response'], instruction=prompt_custom.Review_PROMPT)
solutions_list.append(consistency_check['response'])
final_review_response = await self.custom(input=problem + " Final review of the ensemble answer: " + ensemble_response['response'], instruction=prompt_custom.Review_PROMPT)
return final_review_response['response'] # Return the final reviewed response