Settings Input / Output Advanced History

Input dataset

*required fields

Input Dataset Format*

If empty, defaults to a random value. Limited to a-z, 0-9 and _. Specify same id as other Model Evaluation to overwrite.

Evaluation name

If empty, defaults to the date and time of evaluation

Evaluation labels

Can contain variables in ${} notation, which are expanded when the recipe is run.
A Model Evaluation will include dynamically generated evaluation:date and evaluationDataset:dataset-name labels, that you may override there.

Metrics

LLM-as-a-judge metrics computation

Define LLM connection to use for metrics computation (see metrics help for when each is required)

Embedding LLM

Completion LLM

Select All

{{ metric.label }}

Input	Output	Ground Truth	Actual tool calls	Reference tool calls

Custom Metrics

Code Metrics

Add custom evaluation metrics to score the agent

Warning: You do not have permission to run arbitrary code. The recipe will fail if it includes custom metrics and is run by a user not having this permission.

Traits

Traits are a list of assertions submitted to an LLM to validate the agent actions or answers

Input dataset

Metrics

LLM-as-a-judge metrics computation

{{group.label}}

Custom Metrics

Code Metrics

Traits

Python environment

Container configuration

Execution configuration

Completion LLM parameters

Metric configuration

Input dataset

Metrics

LLM-as-a-judge metrics computation

{{group.label}}

Custom Metrics

Code Metrics

Traits

{{it.name}}

Python environment

Container configuration

Execution configuration

Completion LLM parameters

Metric configuration