> ## 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.

# Metric Input Types

> Understanding the different input types of metrics supported by Luna Studio

Luna metric inputs are grouped into three high-level categories: **Spans**, **Traces**, and **Sessions**.

In the SDK, you set these using `metric.input_format` and the matching dataset columns.

## Spans

### LLM spans without RAG

Use `input_format: tuple` when your metric is built for a span level, without retrieved document context. For example: `instruction_adherence`

Required dataset columns:

* `source_data.dataset.columns.features`: a list with **2+** column names (for example `["input", "output"]`)
* `source_data.dataset.columns.label`: your label column name (for example `"label"`)

Example:

```yaml theme={null}
metric:
  input_format: "tuple"

source_data:
  dataset:
    columns:
      features: ["input", "output"]
      label: "label"
```

Generated examples format:

Generated data items are objects (`dicts`) with exactly the requested fields, for example:

```json theme={null}
{ "input": "...", "output": "..." }
```

Detailed Tutorial: [LLM spans without RAG](/luna-studio/sdk/tutorials/llm-spans-without-rag)

### LLM spans with RAG

Use `input_format: rag` when your metric depends on retrieved **documents** and optionally the user **input** and/or model **output**. For example: `context_adherence`

Required dataset columns:

* `source_data.dataset.columns.features` must be a list that:
  * includes `documents`
  * includes at least one of `input` or `output`
* `source_data.dataset.columns.label`: your label column name (for example `"label"`)

Example:

```yaml theme={null}
metric:
  input_format: "rag"

source_data:
  dataset:
    columns:
      features: ["documents", "input", "output"]
      label: "label"
```

RAG-specific constraints:

* `generation.context_examples` must be **exactly 1**
* `features` can only contain: `documents`, `input`, `output`

Generated examples format:

Generated items include only the generation targets (a subset of `input`, `output`). `documents` is reused from the context example and is not regenerated.

Detailed Tutorial: [LLM spans with RAG](/luna-studio/sdk/tutorials/llm-spans-with-rag)

### LLM spans with tools (Agentic)

Use `input_format: span_with_tools` when the metric depends on tool context in addition to the user input and model output. For example: `tool_selection_quality`

Required dataset columns:

* `source_data.dataset.columns.features` must be exactly `["tools", "input", "output"]`
* `source_data.dataset.columns.label`: your label column name (for example `"label"`)

Additional constraint:

* `generation.context_examples` must be **exactly 1**

Detailed Tutorial: [LLM spans with tools (Agentic)](/luna-studio/sdk/tutorials/llm-spans-with-tools-agentic)

### Retriever spans

Use `input_format: rag` for retriever spans. For example: `context_relevance`

* `features` must contain: `documents` column and optionally `input` / `output`

Detailed Tutorial: [Retriever spans](/luna-studio/sdk/tutorials/retriever-spans)

## Traces

Trace based metrics are split into 2 categories:

### Trace input / output only

Use `input_format: single` for this type of input. Most of the security metrics fall under this category. For example: `toxicity`, `sexism`, `prompt_injection`

Required dataset columns:

* `source_data.dataset.columns.features`: a list with **1+** column names (for example `["input"]`)
* `source_data.dataset.columns.label`: your label column name (for example `"label"`)

Example:

```yaml theme={null}
metric:
  input_format: "single"

source_data:
  dataset:
    columns:
      features: ["input"]
      label: "label"
```

Generated examples format:

Generated data items are objects (`dicts`) with exactly the requested fields, for example:

```json theme={null}
{ "input": "..." }
```

Detailed Tutorials: [Using a preset metric](/luna-studio/sdk/tutorials/preset-metric) and [Custom example](/luna-studio/sdk/tutorials/trace-input-output-only)

### Full traces

Today, full trace inputs are intended for `label_only_mode` workflows or for cases where you skip synthetic data generation and proceed directly to training. They are not supported for normal synthetic data generation.
You can use `input_format: trace` for this type of input.

Detailed Tutorial: [Full traces](/luna-studio/sdk/tutorials/full-traces)

## Sessions

Like Trace based metrics, Session level metrics are also split into 2 categories:

### List of Trace inputs / outputs only

Use `input_format: tuple` for this type of input. For example: `conversation_quality`

Required dataset columns:

* `source_data.dataset.columns.features`: a list with **2+** column names (for example `["input", "output"]`)
* `source_data.dataset.columns.label`: your label column name (for example `"label"`)

Example:

```yaml theme={null}
metric:
  input_format: "tuple"

source_data:
  dataset:
    columns:
      features: ["input", "output"]
      label: "label"
```

Generated examples format:

Generated data items are objects (`dicts`) with exactly the requested fields, for example:

```json theme={null}
{ "input": "...", "output": "..." }
```

Detailed Tutorial: [List of Trace inputs / outputs only](/luna-studio/sdk/tutorials/list-of-trace-inputs-outputs-only)

### Full Sessions

Full Session-based metrics are supported in the SDK as `metric.input_format: session`.
Like trace inputs, session inputs are currently intended for `label_only_mode` workflows or for cases where you skip synthetic data generation and proceed directly to training. They are not supported for normal synthetic data generation.

Detailed Tutorial: [Full Sessions](/luna-studio/sdk/tutorials/full-sessions)