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### Changes 🏗️
Pull changes from gitbook into dev
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---
> [!NOTE]
> Migrates documentation to GitBook and removes the old MkDocs setup.
>
> - Removes MkDocs configuration and infra: `docs/mkdocs.yml`,
`docs/netlify.toml`, `docs/overrides/main.html`,
`docs/requirements.txt`, and JS assets (`_javascript/mathjax.js`,
`_javascript/tablesort.js`)
> - Updates `docs/content/contribute/index.md` to describe GitBook
workflow (gitbook branch, editing, previews, and `SUMMARY.md`)
> - Adds GitBook navigation file `docs/platform/SUMMARY.md` and a new
platform overview page `docs/platform/what-is-autogpt-platform.md`
>
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
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[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->
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## Summary by CodeRabbit
* **Documentation**
* Updated contribution guide for new documentation platform and workflow
* Added new platform overview and navigation documentation
* **Chores**
* Removed MkDocs configuration and related dependencies
* Removed deprecated JavaScript integrations and deployment overrides
<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
31 lines
2.1 KiB
Markdown
31 lines
2.1 KiB
Markdown
# Data Sampling
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## What it is
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The Data Sampling block is a tool for selecting a subset of data from a larger dataset using various sampling methods.
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## What it does
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This block takes a dataset as input and returns a smaller sample of that data based on specified criteria. It supports multiple sampling methods, allowing users to choose the most appropriate technique for their needs.
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## How it works
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The block processes the input data and applies the chosen sampling method to select a subset of items. It can work with different data structures and supports data accumulation for scenarios where data is received in batches.
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## Inputs
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| Input | Description |
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| Data | The dataset to sample from. This can be a single dictionary, a list of dictionaries, or a list of lists. |
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| Sample Size | The number of items to select from the dataset. |
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| Sampling Method | The technique used to select the sample. Options include random, systematic, top, bottom, stratified, weighted, reservoir, and cluster sampling. |
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| Accumulate | A flag indicating whether to accumulate data before sampling. This is useful for scenarios where data is received in batches. |
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| Random Seed | An optional value to ensure reproducible random sampling. |
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| Stratify Key | The key to use for stratified sampling (required when using the stratified sampling method). |
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| Weight Key | The key to use for weighted sampling (required when using the weighted sampling method). |
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| Cluster Key | The key to use for cluster sampling (required when using the cluster sampling method). |
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## Outputs
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| Output | Description |
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| Sampled Data | The selected subset of the input data. |
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| Sample Indices | The indices of the sampled items in the original dataset. |
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## Possible use case
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A data scientist working with a large customer dataset wants to create a representative sample for analysis. They could use this Data Sampling block to select a smaller subset of customers using stratified sampling, ensuring that the sample maintains the same proportions of different customer segments as the full dataset. |