Support Flexible Inputs to vertexai.types.Metric

[annedranowski]

Many evaluation metrics do not require extra context provided by prompts. In fact, the prompt may confuse the metric. For example, a non_advice metric might start checking response on instruction-following confusing instructions for extensions to non_advice criteria.

My use case for evaluating responses without relying on a prompt is currently not supported.

Hardcoding an LLMMetric with only a {response} field, and

• supplying an eval_dataset with only a response column raises EvalDatasetSchema.UNKNOWN
• while supplying an eval_dataset with prompt and response columns raises INVALID_ARGUMENT

The current workarounds (supplying dummy prompt or extra 'ignore prompt' instructions to the judge) are cumbersome.

I propose adding infrastructure to support the bare minimum 'response' input, in the form of a more flexible evaluate -- so addressing the INVALID_ARGUMENT error.

This would be a welcome addition to the existing infrastructure supporting extra inputs.