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import evoagentx.workflow.operators as operator
import examples.aflow.humaneval.optimized.round_20.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):
solutions = [] # Initialize a list to collect multiple solutions
for _ in range(3): # Generate three variations of the solution
try: # Error Handling
solution = await self.custom_code_generate(problem=problem, entry_point=entry_point,
instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
# Validate if the solution is not empty and is a valid Python code
if 'response' in solution and solution['response'].strip():
solutions.append(solution['response']) # Collect each valid solution
else:
solutions.append("Generated code is empty or invalid.") # Append error message
except Exception as e: # Catch any exceptions
solutions.append(f"Error generating code: {str(e)}") # Add error message
selected_solution = await self.sc_ensemble(solutions=solutions, problem=problem) # Select best solution
validation = await self.test(problem=problem, solution=selected_solution['response'],
entry_point=entry_point, benchmark=self.benchmark)
if validation['result']:
return selected_solution['response']
else:
modified_solution = await self.custom(input=problem + f" with issues: {validation['solution']}",
instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
return modified_solution['response'] # Return the modified solution if tests fail
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