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Use this tutorial when the metric depends on the full multi-turn session, not just one span or a simplified trace view.

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Current support

In the current SDK, full sessions are advanced workflows:
  • use metric.input_format: "session"
  • typically run with label_only_mode
  • are not part of the normal synthetic data generation path

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Minimal config

run_steps:
  - data_generation
  - training

pipeline_provider: "local"
metric_name: "custom"

data_generation:
  metric:
    name: "Action Completion"
    type: "binary"
    input_format: "session"
    llmaj_source_prompt: "Determine whether the action is advancement or not."
  source_data:
    dataset:
      source_type: "huggingface"
      huggingface:
        name: "action-completion-dataset"
  labelling:
    label_only_mode: true
  output:
    dataset:
      repo_name: "action-completion-labelled"
  training:
    dataset:
      name: "action-completion-labelled"
    prompt_template: |
      Determine whether the action is advancement or not.
      Session:
      {input}

      Respond with "true" or "false".
    output:
      model_name: "action-completion-model"