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Jack Ye
c0fe2eacda
The feature engineering documentation describes the geneva package, a separate SDK. The capability it covers now lives in this client — materialized views, computed columns, and registered functions are all shipped API with no pages describing them. Four pages cover the three mechanisms and, more usefully, when each applies. The overview leads with that choice rather than with concepts, because the mechanisms overlap and picking the wrong one is the expensive mistake. Two things they say plainly that the API reference cannot: Declaring and filling are separate everywhere, and filling never revisits a row it has already done. That makes refreshes idempotent and appends cheap, and it means a filled value does not track its inputs — surprising if you expect a spreadsheet, so it is stated rather than left to be discovered. The availability split runs the opposite way to expectation. Materialized views are local only and raise on a remote connection; functions are Cloud and Enterprise only and raise on a local one. Someone reading about both in one sitting will otherwise assume the usual direction and be wrong in one of them. Examples are the tested ones from the docstrings, so they cannot drift from the behaviour they describe. The training and tutorial pages that pointed into the geneva documentation now point here.
23 lines
1.7 KiB
Plaintext
23 lines
1.7 KiB
Plaintext
---
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title: "Feature Engineering"
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sidebarTitle: "Feature Engineering"
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description: "Learn how to build features for your data in LanceDB."
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layout: wide
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---
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| Example | Description |
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|--------|:------------|
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| **Feature Engineering 101** <br /> <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/feature-engineering/feature-engineering-101.ipynb" rel="noreferer, ,noopener" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a> [View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/feature-engineering/feature-engineering-101.ipynb) | This example demonstrates how to use LanceDB's feature engineering platform to add new derived features.
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| **Materialized Views** <br /> <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/feature-engineering/materialized-views.ipynb" rel="noreferer, ,noopener" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a> [View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/feature-engineering/materialized-views.ipynb) | This example shows how to create materialized views: query results persisted as physical tables.
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## Read the docs {#read-the-docs}
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The relevant section of the documentation are listed below.
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| Feature | Description |
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|:--------|:------------|
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| [Feature engineering](/feature-engineering) | The three ways to derive columns and tables. |
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| [Materialized views](/feature-engineering/materialized-views) | Derive a table from a query and refresh it incrementally. |
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| [Computed columns](/feature-engineering/computed-columns) | Declare a column from an expression and fill it. |
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