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import evoagentx.workflow.operators as operator
import examples.aflow.humaneval.optimized.round_10.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.ensemble = operator.ScEnsemble(self.llm) # Added ScEnsemble operator for improved solution selection
async def __call__(self, problem: str, entry_point: str):
"""
Implementation of the workflow
Custom operator to generate code and validate it with tests.
"""
solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
validation = await self.test(problem=problem, solution=solution['response'], entry_point=entry_point, benchmark=self.benchmark) # Testing the solution
if validation['result']:
return solution['response']
else:
# If tests fail, modify solution by using context from validation errors and generate additional solutions
modified_solution = await self.custom(input=problem + f" with problems: {validation['solution']}", instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
# Collect potential solutions for ensemble decision
solutions_list = [solution['response'], modified_solution['response']]
# Use ScEnsemble to determine the best solution from collected options
ensemble_result = await self.ensemble(solutions=solutions_list, problem=problem)
return ensemble_result['response'] # Return the ensemble-selected solution if tests fail
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