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

# Add tags and metadata

> How to add metadata to evaluations.

This is relevant when you logging Reports to the Platform. Tags help you associate each Report with a specific model / prompt version, time period, or other context.

## Add timestamp

Each Report run has a single timestamp. By default, Evidently assigns `datetime.now()` as the run time based on the user's time zone.

You can also specify a custom timestamp by passing it to the `run()` method:

```python theme={null}
from datetime import datetime

my_eval_4 = report.run(eval_data_1,
                       eval_data_2,
                       timestamp=datetime(2024, 1, 29))
```

Because timestamps are fully customizable, you can log Reports asynchronously or with a delay. For example, make an evaluation after receiving ground truth and backdate Reports to the relevant time period.

## Add tags and metadata

You can add `tags` and `metadata` to Reports to support search and ease of filtering. Tags also let you visualize data from specific subsets of Reports on monitoring Panels.

Use tags in the following scenarios:

* Mark evaluation runs by model version, prompt version, or test scenario.
* Indicate status: production, shadow, champion/challenger, A/B versions.
* Identify Reports by geography, use case, user segment, or role.
* Tag based on reference dataset windows (for example, weekly vs. monthly drift comparisons)
* Highlight Reports with a specific role, such as datasheet or model card.

**Custom tags**. You can add tags to the Report. Pass any custom Tags as a list:

```python theme={null}
report = Report([
    ClassificationPreset()
],
tags=["classification", "production"])
```

**Custom metadata**. Pass metadata as a Python dictionary in key:value pairs:

```python theme={null}
report = Report([
    ClassificationPreset()
],
metadata = {
	"deployment": "shadow",
	"status": "production",
	})
```

**Default metadata**. Use built-in metadata fields `model_id`, `reference_id`, `batch_size`, `dataset_id`:

```python theme={null}
report = Report([
    ClassificationPreset()
],
  model_id="model_id",
	reference_id="reference_id",
	batch_size="batch_size",
	dataset_id="dataset_id"
)
```

**Add tags to run**: You can also tag individual Report runs. This is useful for experiments where you re-run the same Report with different prompts or hyperparameter settings.

```python theme={null}
my_eval = report.run(eval_data_1, eval_data_2, tags=["prompt_v1", "claude"])
```