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docs: say the LeRobot feature-engineering example uses Geneva

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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.
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Brendan Clement
2026-10-06 10:16:47 -07:00
parent 0399d2f4da
commit 9694b26ed8
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docs/web/integrations/lerobotdataset.mdx
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@@ -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"])