Method __post_init__

config.py:151–204

Post-initialization to set up model configurations and handle backward compatibility.

(self)

    reasoning_effort: Optional[str] = None

    def __post_init__(self):
        """Post-initialization to set up model configurations and handle backward compatibility."""
        super().__post_init__()  # Resolve ${VAR} in api_key at the LLMConfig level

        # Handle backward compatibility for primary_model/secondary_model settings
        if self.primary_model:
            primary_model = LLMModelConfig(
                name=self.primary_model, weight=self.primary_model_weight or 1.0
            )
            self.models.append(primary_model)

        if self.secondary_model:
            # Create secondary model only if weight is specified and > 0, or if not specified (defaults to 0.2)
            if self.secondary_model_weight is None or self.secondary_model_weight > 0:
                secondary_model = LLMModelConfig(
                    name=self.secondary_model,
                    weight=(
                        self.secondary_model_weight
                        if self.secondary_model_weight is not None
                        else 0.2
                    ),
                )
                self.models.append(secondary_model)

        # Validate that at least one model is configured if any model-related settings are present
        if (
            self.primary_model
            or self.secondary_model
            or self.primary_model_weight
            or self.secondary_model_weight
        ) and not self.models:
            raise ValueError(
                "No LLM models configured. Please specify 'models' array or "
                "'primary_model' in your configuration."
            )

        # If no evaluator models are defined, use the same models as for evolution
        if not self.evaluator_models:
            self.evaluator_models = self.models.copy()

        # Update all individual models with shared configuration values (api_base, etc.)
        shared_config = {
            "api_base": self.api_base,
            "api_key": self.api_key,
            "temperature": self.temperature,
            "top_p": self.top_p,
            "max_tokens": self.max_tokens,
            "timeout": self.timeout,
            "retries": self.retries,
            "retry_delay": self.retry_delay,
            "random_seed": self.random_seed,
            "reasoning_effort": self.reasoning_effort,
        }
        self.update_model_params(shared_config)

    def update_model_params(self, args: Dict[str, Any], overwrite: bool = False) -> None:
        """Update parameters for all models in both evolution and evaluator ensembles."""