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import evoagentx.workflow.operators as operator
import examples.aflow.livecodebench.optimized.round_1.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.custom_code_generate = operator.CustomCodeGenerate(self.llm)
self.test = operator.Test(self.llm)
self.sc_ensemble = operator.ScEnsemble(self.llm)
async def __call__(self, problem: str, entry_point: str):
"""
Implementation of the workflow
Custom operator to generate initial insights about the problem.
"""
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.")
solutions = []
for _ in range(5):
solution = await self.custom_code_generate(problem=problem+f" Insights:{insights['response']}", entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
solutions.append(solution['response'])
best_solution = await self.sc_ensemble(solutions=solutions, problem=problem)
test_result = await self.test(problem=problem, solution=best_solution['response'], entry_point=entry_point, benchmark=self.benchmark)
if not test_result['result']:
specific_feedback = f"Solution failed for the problem: {problem}. Errors encountered: {test_result['solution']}" # Enhanced feedback
return specific_feedback # Provide detailed error feedback
return best_solution['response']
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