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we met some reality when merging into the docs site but this fixes it
### Changes 🏗️
updates paths, adds some guides
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update to match reality
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---
> [!NOTE]
> Aligns block integrations documentation with GitBook.
>
> - Changes generator default output to
`docs/integrations/block-integrations` and writes overview `README.md`
and `SUMMARY.md` at `docs/integrations/`
> - Adds GitBook frontmatter and hint syntax to overview; prefixes block
links with `block-integrations/`
> - Introduces `generate_summary_md` to build GitBook navigation
(including optional `guides/`)
> - Preserves per-block manual sections and adds optional `extras` +
file-level `additional_content`
> - Updates sync checker to validate parent `README.md` and `SUMMARY.md`
> - Rewrites `docs/integrations/README.md` with GitBook frontmatter and
updated links; adds `docs/integrations/SUMMARY.md`
> - Adds new guides: `guides/llm-providers.md`,
`guides/voice-providers.md`
>
> <sup>Written by [Cursor
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---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: bobby.gaffin <bobby.gaffin@agpt.co>
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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|-------|-------------|
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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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|--------|-------------|
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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. |