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.

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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:

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.

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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:

report = Report([
    ClassificationPreset()
],
tags=["classification", "production"])

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

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

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

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.

my_eval = report.run(eval_data_1, eval_data_2, tags=["prompt_v1", "claude"])