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import evoagentx.workflow.operators as operator
import examples.aflow.humanevalplus_final.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) # Added test operator for validation
self.sc_ensemble = operator.ScEnsemble(self.llm) # Added ensemble operator for solution selection
async def __call__(self, problem: str, entry_point: str):
"""
Implementation of the workflow
Custom operator to generate anything you want.
But when you want to get standard code, you should use custom_code_generate operator.
"""
solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
# Validate the solution using the test operator
test_result = await self.test(problem=problem, solution=solution['response'], entry_point=entry_point, benchmark=self.benchmark)
if not test_result['result']:
# If the solution fails, use self-consistency ensemble to improve the solution
solutions_list = [solution['response']] # Collecting solutions for ensemble
ensemble_result = await self.sc_ensemble(solutions=solutions_list, problem=problem)
return ensemble_result['response'] # Return the best solution from ensemble
return solution['response']
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