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import evoagentx.workflow.operators as operator |
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import examples.aflow.pertqa.optimized_adamson_update.round_18.prompt as prompt_custom |
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from evoagentx.models.model_configs import LLMConfig |
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from evoagentx.benchmark.benchmark import Benchmark |
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from evoagentx.models.model_utils import create_llm_instance |
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class Workflow: |
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def __init__( |
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self, |
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name: str, |
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llm_config: LLMConfig, |
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benchmark: Benchmark |
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): |
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self.name = name |
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self.llm = create_llm_instance(llm_config) |
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self.benchmark = benchmark |
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self.custom = operator.Custom(self.llm) |
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self.answer_generate = operator.AnswerGenerate(self.llm) |
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self.qas_ensemble = operator.QAScEnsemble(self.llm) |
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async def __call__(self, problem: str): |
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""" |
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Implementation of the workflow |
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""" |
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solutions_list = [] |
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for _ in range(3): |
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solution = await self.answer_generate(input=problem) |
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solutions_list.append(solution['answer']) |
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review_responses = [] |
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for answer in solutions_list: |
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review_response = await self.custom(input=problem + " Review the answer: " + answer, instruction=prompt_custom.Review_PROMPT) |
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review_responses.append(review_response['response']) |
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ensemble_response = await self.qas_ensemble(solutions=review_responses) |
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if ensemble_response['response'] not in solutions_list: |
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consistency_check = await self.custom(input=problem + " Check consistency of the ensemble answer: " + ensemble_response['response'], instruction=prompt_custom.Review_PROMPT) |
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solutions_list.append(consistency_check['response']) |
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final_review_response = await self.custom(input=problem + " Final review of the ensemble answer: " + ensemble_response['response'], instruction=prompt_custom.Review_PROMPT) |
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return final_review_response['response'] |
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