This relies on the core evaluation API of the Evidently Python library. Check the detailed guide.

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Simple Example

You must first connect to Evidently Cloud and create a Project.

To run a single eval with text evaluation results uploaded to a workspace:

eval_data = Dataset.from_pandas(
    pd.DataFrame(source_df),
    data_definition=DataDefinition()
)

report = Report([
    TextEvals()
])

my_eval = report.run(eval_data, None)
ws.add_run(project.id, my_eval, include_data=True)

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Workflow

The complete workflow looks as the following.

1

Run a Report

Configure the evals and run the Evidently Report with optional Test conditions.

2

Upload to the platform

Upload the raw data or only the evaluation results.

3

Explore the results

Go to the Explore view inside your Project to debug the results and compare the outcomes between runs. Understand the Explore view.

4

(Optional) Set up a Dashboard

Set a Dashboard to track results over time. This helps you monitor metric changes across experiments or results of ongoing safety Tests. Check the docs on Dashboard.

5

(Optional) Configure alerts

Optionally, configure alerts on failed Tests. Check the section on Alerts.

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Uploading data

Raw data upload is available only for Evidently Cloud and Enterprise.

When you upload a Report, you can decide to:

  • include only the resulting Metrics and a summary Report (with distribution summaries, etc.), or

  • also upload the raw Dataset you evaluated, together with added Descriptors if any. This helps with row-level debugging and analysis.

Useinclude_data (default False) to specify whether to include the data.

ws.add_run(project.id, my_eval, include_data=False)
OverviewNo code evals