Retriever spans

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"