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import evoagentx.workflow.operators as operator |
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import examples.aflow.livecodebench.optimized.round_1.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.sc_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 initial insights about the problem. |
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""" |
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insights = await self.custom(input=f"The following coding problem is provided: {problem}. Please provide detailed insights, including potential pitfalls, testing strategies, and relevant examples to clarify the approach. ", instruction="Provide enhanced insights for the problem.") |
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solutions = [] |
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for _ in range(5): |
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solution = await self.custom_code_generate(problem=problem+f" Insights:{insights['response']}", entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT) |
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solutions.append(solution['response']) |
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best_solution = await self.sc_ensemble(solutions=solutions, problem=problem) |
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test_result = await self.test(problem=problem, solution=best_solution['response'], entry_point=entry_point, benchmark=self.benchmark) |
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if not test_result['result']: |
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specific_feedback = f"Solution failed for the problem: {problem}. Errors encountered: {test_result['solution']}" |
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return specific_feedback |
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return best_solution['response'] |
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