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import evoagentx.workflow.operators as operator
import examples.aflow.humaneval.optimized.round_7.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)
self.validate_solution = operator.Custom(self.llm) # New operator to validate the solution
async def __call__(self, problem: str, entry_point: str):
"""
Workflow implementation to generate, validate, and test solutions.
"""
solutions = []
for _ in range(3):
solution = await self.custom_code_generate(problem=problem, 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)
# Validate the best solution before testing
validation_result = await self.validate_solution(input=best_solution['response'], instruction="Validate this Python code.")
if validation_result['response'] == "Valid":
test_result = await self.test(problem=problem, solution=best_solution['response'], entry_point=entry_point, benchmark=self.benchmark)
return test_result['solution']
else:
return "Best solution failed validation." # Handle case where validation fails
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