Atoti Intelligence Essentials
This is part of the Atoti Intelligence Essentials offer.Auto-Explain does not require an LLM to function. The root-cause analysis, contribution percentages, and contribution tables are produced by a deterministic algorithm and are always available without any AI provider configured.An LLM is only required if the optional AI summary is needed. To enable AI summaries, configure an LLM provider. See Set up an LLM.
Prerequisites
Before setting up Auto-Explain, ensure the following requirements are met:- An Atoti Python project
- A license with the AI flag enabled
Install the package
Install the Atoti AI package:Enable Auto-Explain
Pass anAiConfig to SessionConfig when starting the session. Auto-Explain works with an empty AiConfig(), so no LLM provider is required:
Cube.auto_explain.
Configure Auto-Explain
Auto-Explain constants are read and set as attributes ofCube.auto_explain. For the meaning, defaults, and tuning guidance of each constant, see Configure Auto-Explain.
excluded_hierarchies_per_measure and included_hierarchies_per_measure, keyed by measure name — the Python equivalent of the Java excluded-measure-hierarchies / included-measure-hierarchies configuration.
Verify the setup
After enabling Auto-Explain, verify that it is available:- Start the Atoti session.
- Open the Atoti UI.
- Right-click two cells in a pivot table.
- Check that the Auto-Explain option appears in the context menu.
Related reading
- How Auto-Explain works — the algorithm and a worked example
- Configure Auto-Explain — constants reference and tuning guidance
- How to use Auto-Explain in the Atoti UI
atoti_ai.AutoExplainAPI reference