> ## Documentation Index
> Fetch the complete documentation index at: https://docs.galileo.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Retriever spans

> Train a Luna metric that evaluates retrieval quality or document sufficiency.

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.

## Dataset schema

Typical columns:

* `documents`: the retrieved documents
* `input`: the user question or retrieval query
* `label`: the ground-truth class for the metric

## 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"`

## Minimal end-to-end config

```yaml theme={null}
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"
```