def from_dict()

in pyrit/models/seed_prompt.py [0:0]

• 26 lines of code
• 5 McCabe index (conditional complexity)

    def from_dict(cls, data: Dict[str, Any]) -> "SeedPromptDataset":
        """
        Builds a SeedPromptDataset by merging top-level defaults into each item in 'prompts'.
        """
        # Pop out the prompts section
        prompts_data = data.pop("prompts", [])
        dataset_defaults = data  # everything else is top-level

        merged_prompts = []
        for p in prompts_data:
            # Merge dataset-level fields with the prompt-level fields
            merged = utils.combine_dict(dataset_defaults, p)

            merged["harm_categories"] = utils.combine_list(
                dataset_defaults.get("harm_categories", []),
                p.get("harm_categories", []),
            )

            merged["authors"] = utils.combine_list(
                dataset_defaults.get("authors", []),
                p.get("authors", []),
            )

            merged["groups"] = utils.combine_list(
                dataset_defaults.get("groups", []),
                p.get("groups", []),
            )

            if "data_type" not in merged:
                merged["data_type"] = dataset_defaults.get("data_type", "text")

            merged_prompts.append(merged)

        for prompt in merged_prompts:
            if "prompt_group_id" in prompt:
                raise ValueError("prompt_group_id should not be set in prompt data")

        SeedPromptDataset._set_seed_prompt_group_id_by_alias(seed_prompts=merged_prompts)

        # Now create the dataset with the newly merged prompt dicts
        return cls(prompts=merged_prompts, **dataset_defaults)