From 9694b26ed8bf13df317053393c1b21a7a9761393 Mon Sep 17 00:00:00 2001 From: Brendan Clement Date: Tue, 6 Oct 2026 10:16:47 -0700 Subject: [PATCH] docs: say the LeRobot feature-engineering example uses Geneva The example calls Geneva's @udf, add_columns and backfill, but the sentence before it described lancedb computed columns and Functions, which take none of those parameters and have no backfill. This restores the production sentence, which names Geneva and links its UDF guide; the code and its timings are unchanged. --- docs/web/integrations/lerobotdataset.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/web/integrations/lerobotdataset.mdx b/docs/web/integrations/lerobotdataset.mdx index e1a88a30e..9c87575f9 100644 --- a/docs/web/integrations/lerobotdataset.mdx +++ b/docs/web/integrations/lerobotdataset.mdx @@ -115,7 +115,7 @@ Filter with SQL over any feature, using the indexes built at conversion: From here it is standard LanceDB. The examples below are from our DROID walkthrough, all on the table the trainer reads. -**Feature engineering costs nothing until you use it.** A new column is a [declaration](/feature-engineering/add-computed-column): `add_columns` registers the expression or [function](/management/function) and computes nothing, and a [refresh](/feature-engineering/refresh-column) materializes it in parallel when you want it. No copy of the dataset, and the videos are never touched: +**Derived columns are added without rewriting existing data.** With [Geneva](/geneva/udfs/udfs), `add_columns` registers a Python function and its input columns as a new derived column. No values are computed at that point. `backfill` computes and stores the derived values later, in parallel, without rewriting the existing columns or video blobs: ```python @udf(data_type=pa.float32(), input_columns=["action_joint_velocity"])