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import evoagentx.workflow.operators as operator |
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import examples.aflow.scicode.optimized.round_2.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.custom_code_generate = operator.CustomCodeGenerate(self.llm) |
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self.test = operator.Test(self.llm) |
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self.ensemble = operator.ScEnsemble(self.llm) |
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async def __call__(self, problem: str, entry_point: str): |
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""" |
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Implementation of the workflow |
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Custom operator to generate anything you want. |
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But when you want to get standard code, you should use custom_code_generate operator. |
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""" |
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solutions = [] |
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for _ in range(3): |
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response = await self.custom(input=problem + " Generate a solution, ensure it is functional.", instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT) |
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solutions.append(response['response']) |
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test_results = [] |
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for solution in solutions: |
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test_result = await self.test(problem=problem, solution=solution, entry_point=entry_point, benchmark=self.benchmark) |
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test_results.append(test_result) |
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successful_solutions = [result['solution'] for result in test_results if result['result']] |
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if successful_solutions: |
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return await self.ensemble(solutions=successful_solutions, problem=problem) |
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return "No valid solutions found." |
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