metric / llm_as_judge_operators.py
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from typing import Any
from .artifact import fetch_artifact
from .llm_as_judge_constants import Criteria, CriteriaOption, CriteriaWithOptions
from .operators import FieldOperator
class LoadCriteriaWithOptions(FieldOperator):
def process_value(self, text: Any) -> CriteriaWithOptions:
return fetch_artifact(text)[0]
class CreateCriteriaWithOptionsFromDict(FieldOperator):
def process_value(self, criteria_dict: dict) -> Any:
return CriteriaWithOptions.from_obj(criteria_dict)
class CreateCriteriaWithOptionsFromJson(FieldOperator):
def process_value(self, text: str) -> Any:
return CriteriaWithOptions.from_jsons(text)
class CreateYesNoCriteriaFromString(FieldOperator):
def process_value(self, text: Any) -> Any:
return CriteriaWithOptions(
name=f"Unknown ({text[:20]}...)",
description=text,
options=[
CriteriaOption(name="Yes", description=""),
CriteriaOption(name="No", description=""),
],
option_map={
"Yes": 1.0,
"No": 0.0,
},
)
class CreateYesNoPartiallyCriteriaFromString(FieldOperator):
def process_value(self, text: str) -> Any:
return CriteriaWithOptions(
name=f"Unknown ({text[:20]}...)",
description=text,
options=[
CriteriaOption(name="Yes", description=""),
CriteriaOption(name="Partially", description=""),
CriteriaOption(name="No", description=""),
],
option_map={
"Yes": 1.0,
"Partially": 0.5,
"No": 0.0,
},
)
class LoadCriteria(FieldOperator):
def process_value(self, text: Any) -> Criteria:
return fetch_artifact(text)[0]
class CreateCriteriaFromDict(FieldOperator):
def process_value(self, criteria_dict: dict) -> Any:
return Criteria.from_obj(criteria_dict)
class CreateCriteriaFromJson(FieldOperator):
def process_value(self, text: str) -> Any:
return Criteria.from_jsons(text)
class CreateCriteriaFromString(FieldOperator):
def process_value(self, text: str) -> Any:
return Criteria(
name=f"Unknown ({text[:20]}...)",
description=text,
)