AutoRAG-Research: Skill for Claude Code

Create Metric Plugin

Workflow

1. Scaffold

# For retrieval metric:
autorag-research plugin create my_metric --type=metric_retrieval

# For generation metric:
autorag-research plugin create my_metric --type=metric_generation

Read the generated metric.py, pyproject.toml, YAML config, and test file to understand the structure.

2. Implement the metric function

Use the @metric decorator (per-input) or @metric_loop decorator (batch) from autorag_research.evaluation.metrics.util. Both validate that required fields are non-None before calling.

• @metric(fields_to_check=[...]) — function receives a single MetricInput, returns float
• @metric_loop(fields_to_check=[...]) — function receives list[MetricInput], returns list[float]

See autorag_research/schema.py for the full MetricInput dataclass definition.

3. Understanding retrieval_gt (AND/OR group structure)

For retrieval metrics, metric_input.retrieval_gt uses a nested list structure with AND/OR semantics:

retrieval_gt: list[list[str]]

Example: [["A", "B"], ["C"]]
  → Means: (A OR B) AND C
  → Each inner list is an OR group (any item satisfies the group)
  → Outer list is AND (ALL groups must be satisfied for complete retrieval)

This is critical for multi-hop queries where multiple evidence pieces are needed. Your metric must handle this structure correctly — don't just flatten it into a single set unless your metric semantics allow it.

Examples:

• [["doc1"]] — single required document
• [["doc1", "doc2"], ["doc3"]] — need (doc1 OR doc2) AND doc3
• [["doc1"], ["doc2"], ["doc3"]] — need doc1 AND doc2 AND doc3

See retrieval_ndcg in autorag_research/evaluation/metrics/retrieval.py for a real implementation that handles AND/OR groups with graded relevance.

4. Wire up config and install

The generated config class just needs get_metric_func() to return your metric function. If your metric takes extra kwargs, override get_metric_kwargs().