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Use this tutorial when the metric is about the retrieved material itself, rather than about a final model answer. Context relevance is the main example of this pattern.

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Dataset schema

Typical columns:
  • documents: the retrieved documents
  • input: the user question or retrieval query
  • label: the ground-truth class for the metric

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Config shape

Retriever span tutorials normally use:
  • data_generation.metric.input_format: "rag"
  • data_generation.source_data.dataset.columns.features: ["documents", "input"]
  • training.metric.type: "boolean"

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Minimal end-to-end config

run_steps:
  - data_generation
  - training

pipeline_provider: "local"
metric_name: "custom"

data_generation:
  metric:
    name: "Context Relevance"
    type: "binary"
    input_format: "rag"
    llmaj_source_prompt: "Determine whether the retrieved documents are sufficient to answer the question."
  source_data:
    dataset:
      source_type: "huggingface"
      huggingface:
        name: "context_relevance_dataset"
  generation:
    context_examples: 1
  output:
    dataset:
      repo_name: "context-relevance-training-dataset"

training:
  dataset:
    name: "context-relevance-training-dataset"
  prompt_template: |
    Determine whether the retrieved documents are sufficient to answer the question.
    Question:
    {input}

    Documents:
    {documents}

    Respond with "true" or "false".
  output:
    model_name: "context-relevance-model"