data_juicer_agents.tools.dev.develop_operator

develop_operator tool package.

class data_juicer_agents.tools.dev.develop_operator.DevelopOperatorInput(*, intent: str, operator_name: str = '', output_dir: str = '', operator_type: str = '', from_retrieve: str = '', smoke_check: bool = False)

Bases: BaseModel

intent: str

operator_name: str

output_dir: str

operator_type: str

from_retrieve: str

smoke_check: bool

model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class data_juicer_agents.tools.dev.develop_operator.DevUseCase

Bases: object

Generate and optionally smoke-check custom operator scaffolds.

static execute(*, intent: str, operator_name: str, output_dir: str, operator_type: str | None = None, from_retrieve: str | None = None, smoke_check: bool = False) → Dict[str, Any]

class data_juicer_agents.tools.dev.develop_operator.GenericOutput(*, ok: bool = True)

Bases: BaseModel

ok: bool

model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class data_juicer_agents.tools.dev.develop_operator.ScaffoldResult(operator_name: 'str', operator_type: 'str', class_name: 'str', output_dir: 'Path', generated_files: 'List[str]', summary_path: 'Path', notes: 'List[str]')

Bases: object

operator_name: str

operator_type: str

class_name: str

output_dir: Path

generated_files: List[str]

summary_path: Path

notes: List[str]

__init__(operator_name: str, operator_type: str, class_name: str, output_dir: Path, generated_files: List[str], summary_path: Path, notes: List[str]) → None

data_juicer_agents.tools.dev.develop_operator.generate_operator_scaffold(intent: str, operator_name: str, output_dir: str, operator_type: str | None = None, from_retrieve_path: str | None = None) → ScaffoldResult

data_juicer_agents.tools.dev.develop_operator.run_smoke_check(scaffold: ScaffoldResult) → Tuple[bool, str]