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import evoagentx.workflow.operators as operator
import examples.aflow.mbpp.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) # Initialize the Test operator
self.ensemble = operator.ScEnsemble(self.llm) # Initialize the self-consistency operator
self.alternative_fallback = operator.Custom(self.llm) # Operator for alternative fallback
async def __call__(self, problem: str, entry_point: str):
"""
Implementation of the workflow
Custom operator to generate solutions.
"""
# Generate the first solution
solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
# Testing the generated solution
test_result = await self.test(problem=problem, solution=solution['response'], entry_point=entry_point, benchmark=self.benchmark)
if not test_result['result']:
unique_solutions = set() # Unique fallback solutions
while len(unique_solutions) < 3: # Generate three unique solutions
feedback = f"Last solution failed: {test_result['solution']}.\nPrevious errors: {test_result['error_logs']}."
fallback_solution = await self.custom_code_generate(problem=problem, entry_point=entry_point, instruction=prompt_custom.GENERATE_PYTHON_CODE_WITH_FEEDBACK_PROMPT + feedback)
# Check to avoid duplicates
if fallback_solution['response'] not in unique_solutions:
unique_solutions.add(fallback_solution['response'])
# Test all unique fallback solutions
fallback_results = await asyncio.gather(*(self.test(problem=problem, solution=fallback, entry_point=entry_point, benchmark=self.benchmark) for fallback in unique_solutions))
valid_fallbacks = [res['solution'] for res in fallback_results if res['result']]
if valid_fallbacks:
# Prioritize quality by selecting the best valid fallback
best_fallback = valid_fallbacks[0] # Assuming results are sorted by effectiveness
final_fallback = await self.custom(input=problem + f" Verify this solution: {best_fallback}.", instruction=prompt_custom.VERIFY_SOLUTION_PROMPT)
return final_fallback['response'] # Return the validated fallback solution
# Generate an alternative solution for another approach
alternative_solution = await self.alternative_fallback(input=problem, instruction=prompt_custom.GENERATE_PYTHON_CODE_PROMPT)
ensemble_result = await self.ensemble(solutions=[solution['response']] + list(unique_solutions) + [alternative_solution['response']], problem=problem)
return ensemble_result['response'] # Return the ensemble decision
return test_result['solution'] # Return the verified solution
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