Class LLMMetric (2.4.0) 

LLMMetric(**data: typing.Any)

A metric that uses LLM-as-a-judge for evaluation.

Methods

LLMMetric

LLMMetric(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

load

load(
    config_path: str, client: typing.Optional[typing.Any] = None
) -> vertexai._genai.types.common.LLMMetric

Loads a metric configuration from a YAML or JSON file.

This method allows for the creation of an LLMMetric instance from a local file path or a Google Cloud Storage (GCS) URI. It will automatically detect the file type (.yaml, .yml, or .json) and parse it accordingly.

Exceptions
Type Description
ValueError If the file path is invalid or the file content cannot be parsed.
ImportError If a required library like 'PyYAML' or 'google-cloud-storage' is not installed.
IOError If the file cannot be read from the specified path.

model_post_init

model_post_init(context: Any, /) -> None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that's what pydantic-core passes when calling it.

validate_judge_model_sampling_count

validate_judge_model_sampling_count(
    value: typing.Optional[int],
) -> typing.Optional[int]

Validates judge_model_sampling_count to be between 1 and 32.

validate_prompt_template

validate_prompt_template(
    value: typing.Union[str, vertexai._genai.types.common.MetricPromptBuilder],
) -> str

Validates prompt template to be a non-empty string.