> ## Documentation Index
> Fetch the complete documentation index at: https://docs.maia.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Summarize

export const ComponentMetadata = ({warehouses, unsupportedWarehouses = [], componentType, connectionInputs, connectionOutputs}) => {
  const allWarehouses = [...warehouses.map(w => ({
    name: w,
    supported: true
  })), ...unsupportedWarehouses.map(w => ({
    name: w,
    supported: false
  }))];
  return <div style={{
    background: 'var(--colors-background-light, #f9fafb)',
    border: '1px solid var(--colors-border-default, #e5e7eb)',
    borderRadius: '12px',
    padding: '20px 28px',
    marginBottom: '28px',
    boxShadow: '0 1px 4px rgba(0,0,0,0.10)'
  }}>
      <table style={{
    width: '100%',
    borderCollapse: 'collapse'
  }}>
        <tbody>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle',
    width: '180px'
  }}>Project Availability</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>
              <div style={{
    display: 'flex',
    flexWrap: 'wrap',
    gap: '8px'
  }}>
                {allWarehouses.map((w, i) => <span key={i} style={{
    background: w.supported ? '#dcfce7' : '#fee2e2',
    color: w.supported ? '#15803d' : '#b91c1c',
    border: `1px solid ${w.supported ? '#bbf7d0' : '#fca5a5'}`,
    borderRadius: '9999px',
    padding: '3px 12px',
    fontSize: '0.85rem',
    fontWeight: '500',
    whiteSpace: 'nowrap'
  }}>
                    {w.name} {w.supported ? '✅' : '❌'}
                  </span>)}
              </div>
            </td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Component Type</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{componentType}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Inputs</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{connectionInputs}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Outputs</td>
            <td style={{
    verticalAlign: 'middle'
  }}>{connectionOutputs}</td>
          </tr>
        </tbody>
      </table>
    </div>;
};

<ComponentMetadata warehouses={["Databricks"]} unsupportedWarehouses={["Snowflake", "Amazon Redshift", "BigQuery"]} componentType="Transformation" connectionInputs="One" connectionOutputs="Unlimited" />

<Info>
  Production use of this feature is available for specific editions only. [Contact our sales team](https://www.matillion.com/contact) for more information.
</Info>

The **AI Summarize** transformation component uses the Databricks [ai\_summarize()](https://docs.databricks.com/en/sql/language-manual/functions/ai_summarize.html) function to generate a summary of a given input text. This function uses a Databricks chat model serving endpoint made available by [Databricks Foundation Model APIs](https://docs.databricks.com/en/machine-learning/foundation-models/index.html).

The input is a column of text data that is to be summarized. The output is a column of text data that summarizes the input text. You can select multiple columns to summarize from a single input table, and you can select the same input column multiple times to generate alternative summaries.

All rows in the selected input column will be summarized. If the content of any input row is `NULL`, the output for that row will be `NULL`.

<Note>
  Make sure you have read and understand the [Requirements](https://docs.databricks.com/en/sql/language-manual/functions/ai_summarize.html#requirements) set out by Databricks before using this component.
</Note>

### Use case

Some typical use cases for this component include:

* Summarizing long text documents, such as articles, reports, or customer feedback, to extract key points and insights.
* Generating concise summaries of product descriptions, reviews, or user comments to provide quick overviews of content.
* Creating summaries of meeting notes, emails, or chat conversations to highlight important information and action items.

***

## Properties

<ResponseField name="Name" type="string" required>
  A human-readable name for the component.
</ResponseField>

{/* <!-- param-start:[columns] | warehouses: [databricks] --> */}

<ResponseField name="Columns" type="column editor" required>
  * **Input Column:** The drop-down lists each column in the input stream. Choose the column to summarize. All input columns are available to select, but only text columns will produce meaningful summaries.
  * **Alias:** The name used for the output column that corresponds to this input column. Aliases must be unique.
  * **Max number of words in summary:** Optionally, specify the maximum number of words to allow in the summary text. If no number is specified, the default value is 50. If set to `0`, there is no maximum word limit.

  You can select multiple columns to summarize. You can also select the same column multiple times to generate alternative summaries, for example with different numbers of max words. In this case, the **Alias** allows you to differentiate the output columns.
</ResponseField>

{/* <!-- param-start:[includeInputColumns] | warehouses: [databricks] --> */}

<ResponseField name="Include Input Columns" type="boolean" required>
  * **Yes:** Outputs both your source input columns *and* the new summary columns. This will also include those input columns *not* selected in **Data Columns**.
  * **No:** Only outputs the new summary columns.
</ResponseField>