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Release Notes

0.12.8


December 10, 2025 10:29 AM UTC

Version 0.12.8 of the relationalai Python package is now available!

To upgrade, activate your virtual environment and run the following command:

Terminal window
pip install --upgrade relationalai
  • The client now tells you when results use cached index data. You see how many data streams are cached and when the cache was last updated. This helps you know if your results are fresh or from cache. No code changes are needed. If you use tools that check the exact output (such as log parsers or tests), update them for this new message.

  • When you run rai init, you can now choose how often your Snowflake data refreshes. The default is 30 minutes. Set it to 0 to refresh every time. This helps you keep data up to date without editing config files. Existing settings are kept, and scripted setups can pre-set the data_freshness_mins configuration option to skip the prompt.

0.12.7


December 5, 2025 6:58 PM UTC

Version 0.12.7 of the relationalai Python package is now available!

To upgrade, activate your virtual environment and run the following command:

Terminal window
pip install --upgrade relationalai
  • Updated the lqp library dependency from 0.1.17 to 0.1.18.

0.12.6


November 27, 2025 12:12 PM UTC

Version 0.12.6 of the relationalai Python package is now available!

To upgrade, activate your virtual environment and run the following command:

Terminal window
pip install --upgrade relationalai
  • Updated Storybook-related dependencies for the RelationalAI debugger from 8.0.4 to 8.6.14. This addresses CVE-2025-64756 / GHSA-5j98-mcp5-4vw2 by updating Storybook’s glob dependency to 10.5.0.

2025.11.22-96885d5


November 26, 2025 10:00 PM UTC

Version 2025.11.22-96885d5 of the RelationalAI Native App is now available!

Note that RelationalAI Native App upgrades are applied automatically and require no action on your part, unless you have opted-in to manual upgrades.

  • Data Streams now automatically detect recreated source tables. If a table is recreated, its Data Stream is quarantined and the Python API will rebuild it the next time you query the data. You no longer need to manually fix Data Streams after a table is recreated. Your queries will always return up-to-date results.