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5374a2d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | import evoagentx.workflow.operators as operator
import examples.aflow.mbpp_new_full.optimized.round_3.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
"""
solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
test_result = await self.test(problem=problem, solution=solution['response'], entry_point=entry_point, benchmark=self.benchmark)
if not test_result['result']:
alternate_solutions = await self.custom(input=problem, instruction=prompt_custom.ALTERNATE_SOLUTIONS_PROMPT)
selected_solution = await self.sc_ensemble(solutions=alternate_solutions['response'], problem=problem)
revision_response = await self.custom(input=problem+f" Current Solution: {selected_solution['response']}", instruction=prompt_custom.REVISE_PROMPT)
# Retest the revised solution to ensure correctness
test_result_revised = await self.test(problem=problem, solution=revision_response['response'], entry_point=entry_point, benchmark=self.benchmark)
if test_result_revised['result']:
return revision_response['response'] # Return the revised and validated solution
else:
return "Revised solution still failed the tests." # Inform about failure in testing of the revised solution
return solution['response']
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