# Metric Aggregation

For cases where a selector names more than one value as an input, aggregation can be used.

## Example

```
import numpy as np
from trulens.core import Metric, Selector

# Context relevance between question and each context chunk.
f_context_relevance = Metric(
    implementation=provider.context_relevance_with_cot_reasons,
    name="Context Relevance",
    selectors={
        "question": Selector.select_record_input(),
        "context": Selector.select_context(collect_list=False),
    },
    agg=np.mean,
)
```

The `agg` parameter specifies how metric outputs are to be aggregated. This only applies to cases where the selector names more than one value for an input. The `context` selector with `collect_list=False` is of this type, meaning the metric will be evaluated for each context individually.

The input to `agg` must be a method which can be imported globally. This function is called on the `float` results of metric evaluations to produce a single float.

The default is `numpy.mean`.